diff --git a/README.md b/README.md index c969bfe2b..571a4fafa 100644 --- a/README.md +++ b/README.md @@ -70,6 +70,9 @@ This section explain how to install some python packages to Brightics Studio pyt /brightics-studio/lib/python/python get-pip.py : This step is only needed once. /brightics-studio/lib/python/Scripts/pip install +### Notes +Tokenizer (Korean) function will not work properly if the installation path contains Korean characters. You need to install Brightics Studio in a folder whose full path does not contain Korean characters in order to make use of this function. + ## Development ### Prerequisite * JDK 1.8, 1.9 (64bit) diff --git a/api-server/brightics-server/bin/start-server.cmd b/api-server/brightics-server/bin/start-server.cmd index cb6d0e368..4e3537cb9 100644 --- a/api-server/brightics-server/bin/start-server.cmd +++ b/api-server/brightics-server/bin/start-server.cmd @@ -21,6 +21,7 @@ if "%USER_ID%"=="" ( EXIT /b 1 ) +SET _JAVA_OPTIONS= SET GC_OPTS=-XX:+UseConcMarkSweepGC -verbose:gc --Xloggc:"%BRIGHTICS_SERVER_HOME%gc.out" -XX:+CMSClassUnloadingEnabled SET JAVA_OPTS=-Xms1g -Xmx2g -XX:MaxMetaspaceSize=512m -XX:+HeapDumpOnOutOfMemoryError -Djava.net.preferIPv4Stack=true -Djava.io.tmpdir="%BRIGHTICS_SERVER_HOME%tmp" -Dbrightics.local.token=%ACCESS_TOKEN% -Dbrightics.local.user=%USER_ID% -Dfile.encoding=utf-8 SET MAIN=org.springframework.boot.loader.JarLauncher -Dspring.config.location="%BRIGHTICS_SERVER_HOME%BOOT-INF\classes\" diff --git a/api-server/brightics-server/data/brightics@samsung.com/upload/sample_ALSRecommend.csv/_SUCCESS b/api-server/brightics-server/data/brightics@samsung.com/upload/sample_ALSRecommend.csv/_SUCCESS new file mode 100644 index 000000000..e69de29bb diff --git a/api-server/brightics-server/data/brightics@samsung.com/upload/sample_ALSRecommend.csv/part-00000-41d44bb8-3671-405e-befb-115f0e2eb1f0-c000.snappy.parquet b/api-server/brightics-server/data/brightics@samsung.com/upload/sample_ALSRecommend.csv/part-00000-41d44bb8-3671-405e-befb-115f0e2eb1f0-c000.snappy.parquet new file mode 100644 index 000000000..28a095baa Binary files /dev/null and b/api-server/brightics-server/data/brightics@samsung.com/upload/sample_ALSRecommend.csv/part-00000-41d44bb8-3671-405e-befb-115f0e2eb1f0-c000.snappy.parquet differ diff --git a/api-server/brightics-server/data/brightics@samsung.com/upload/sample_ANOVA.csv/_SUCCESS 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b/api-server/brightics-server/data/brightics@samsung.com/upload/sample_ts_user_dict.csv/part-00001-6be200ad-5a0a-4066-a886-7d594c44c246.snappy.parquet new file mode 100644 index 000000000..cb7ca59f5 Binary files /dev/null and b/api-server/brightics-server/data/brightics@samsung.com/upload/sample_ts_user_dict.csv/part-00001-6be200ad-5a0a-4066-a886-7d594c44c246.snappy.parquet differ diff --git a/common/network/pom.xml b/common/network/pom.xml index 096bf7b5b..a765e1ec1 100644 --- a/common/network/pom.xml +++ b/common/network/pom.xml @@ -88,7 +88,7 @@ io.netty netty-all - 4.1.17.Final + 4.1.42.Final diff --git a/docker/Dockerfile b/docker/Dockerfile index 4ff14521c..24dfc6589 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -28,8 +28,8 @@ WORKDIR /brightics-studio RUN mv /git/studio/build/target/dist/brightics-studio / RUN sed -i "s/\"127.0.0.1\",/\"0.0.0.0\",/g" /brightics-studio/visual-analytics/conf.json RUN sed -i "s/\%\*\ //g" /brightics-studio/setup.sh -RUN sed -i '24d' /brightics-studio/start-brightics.sh RUN ./setup.sh +RUN mv /brightics-studio/lib/nltk_data /brightics-studio/lib/brightics_python_env/ RUN rm -rf /brightics-studio/lib/etc /brightics-studio/lib/graphviz /brightics-studio/lib/hadoop /brightics-studio/lib/shortcut /brightics-studio/lib/node/node_modules/npm/changelogs /brightics-studio/lib/node/node_modules/npm/doc /brightics-studio/lib/node/node_modules/npm/html /brightics-studio/lib/node/node_modules/npm/man /brightics-studio/lib/node/node_modules/npm/scripts /brightics-studio/lib/node/node_modules/npm/*.md /brightics-studio/lib/node/node_modules/npm/AUTHORS /brightics-studio/lib/node/node_modules/npm/TODO.org /brightics-studio/lib/node/node_modules/npm/.github /opt/zulu8.46.0.19-ca-jre8.0.252-linux_x64/man diff --git a/function/python/brightics/brightics_data_api.py b/function/python/brightics/brightics_data_api.py index 0f3e2c236..fcb6ff57b 100644 --- a/function/python/brightics/brightics_data_api.py +++ b/function/python/brightics/brightics_data_api.py @@ -30,7 +30,7 @@ import brightics.common.data.table_data_reader as table_reader import brightics.common.data.utils as data_util -import brightics.common.json as data_json +import brightics.common.datajson as data_json from brightics.brightics_java_gateway import brtc_java_gateway as gateway from brightics.brightics_kv_store_client import KVStoreClient @@ -126,7 +126,8 @@ def col_dtype(col): def ensure_none(df): val = df.values - val[isna(df.values)] = None + if isna(val).any(): + val[isna(df.values)] = None return val return data_json.to_json({ diff --git a/function/python/brightics/brightics_kv_store_client.py b/function/python/brightics/brightics_kv_store_client.py index c584958e0..b0eec2731 100644 --- a/function/python/brightics/brightics_kv_store_client.py +++ b/function/python/brightics/brightics_kv_store_client.py @@ -14,7 +14,7 @@ limitations under the License. """ -import brightics.common.json as data_json +import brightics.common.datajson as data_json from brightics.brightics_java_gateway import brtc_java_gateway as gateway diff --git a/function/python/brightics/brightics_python_runner.py b/function/python/brightics/brightics_python_runner.py index 3605a3514..e62ef62fd 100644 --- a/function/python/brightics/brightics_python_runner.py +++ b/function/python/brightics/brightics_python_runner.py @@ -27,7 +27,8 @@ import json import matplotlib matplotlib.use("agg") - +import multiprocessing +multiprocessing.set_start_method('spawn', True) try: from StringIO import StringIO except ImportError: @@ -125,6 +126,7 @@ def _executer(self): interactive_code_object = compile(ast.Interactive(single_code), '', 'single') with redirect_stderr(): +# exec("print('　')") exec(def_object, globals()) try: exec(exec_code_object) diff --git a/function/python/brightics/common/datajson/__init__.py b/function/python/brightics/common/datajson/__init__.py new file mode 100644 index 000000000..18e21c025 --- /dev/null +++ b/function/python/brightics/common/datajson/__init__.py @@ -0,0 +1,24 @@ +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +def to_json(data, for_redis=False): + from .encoder import encode + return encode(data, for_redis) + + +def from_json(json_str): + from .decoder import decode + return decode(json_str) diff --git a/function/python/brightics/common/datajson/decoder.py b/function/python/brightics/common/datajson/decoder.py new file mode 100644 index 000000000..2804de61c --- /dev/null +++ b/function/python/brightics/common/datajson/decoder.py @@ -0,0 +1,38 @@ +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +import json +import pickle +import numpy + + +def decode(obj): + def redis_read_hook(o): + if '__inf__' in o: + return float(o['__inf__']) + if '__set__' in o: + return set(o['__set__']) + if '__tuple__' in o: + return tuple(o['__tuple__']) + if '__numpy__' in o: + return numpy.array(o['__numpy__']) + # TODO add more support types + if '__pickled__' in o: + import array + return pickle.loads(array.array('B', o['__pickled__']).tobytes()) + return o + + return json.loads(obj, object_hook=redis_read_hook) diff --git a/function/python/brightics/common/datajson/encoder.py b/function/python/brightics/common/datajson/encoder.py new file mode 100644 index 000000000..cbcec0bb5 --- /dev/null +++ b/function/python/brightics/common/datajson/encoder.py @@ -0,0 +1,126 @@ +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +import json +import pickle +import numpy +import pandas as pd +from brightics.common.repr import BrtcReprBuilder + +def _to_default_list(np_arr): + return numpy.where(pd.isnull(np_arr), None, np_arr).tolist() + +class DefaultEncoder(json.JSONEncoder): + """ + DefaultEncoder is used for building viewable json string for in browser + """ + + def default(self, obj): + # TODO add more support types + if isinstance(obj, set): + return list(obj) + elif isinstance(obj, numpy.ndarray): + return _to_default_list(obj) + else: + rb = BrtcReprBuilder() + rb.addRawTextMD(str(obj)) + return {'type':'python object', '_repr_brtc_':rb.get()} + + +class PickleEncoder(DefaultEncoder): + """ + PickleEncoder is used for building json string saved in redis + """ + + def encode(self, obj): + + def hint_tuples(item): + if isinstance(item, tuple): + new_tuple = [] + for i in item: + if (isinstance(i,numpy.floating) or isinstance(i,float)) and i == numpy.inf: + new_tuple.append({'__inf__':'inf'}) + elif (isinstance(i,numpy.floating) or isinstance(i,float)) and i == -numpy.inf: + new_tuple.append({'__inf__':'-inf'}) + elif (isinstance(i,numpy.floating) or isinstance(i,float)) and pd.isnull(i): + new_tuple.append(None) + else: + new_tuple.append(hint_tuples(i)) + return {'__tuple__': new_tuple} + if isinstance(item, list): + new_list = [] + for i in item: + if (isinstance(i,numpy.floating) or isinstance(i,float)) and i == numpy.inf: + new_list.append({'__inf__':'inf'}) + elif (isinstance(i,numpy.floating) or isinstance(i,float)) and i == -numpy.inf: + new_list.append({'__inf__':'-inf'}) + elif (isinstance(i,numpy.floating) or isinstance(i,float)) and pd.isnull(i): + new_list.append(None) + else: + new_list.append(hint_tuples(i)) + return new_list + if isinstance(item, dict): + new_dict = {} + for key in item: + if (isinstance(item[key],numpy.floating) or isinstance(item[key],float)) and item[key] == numpy.inf: + new_dict[key] = {'__inf__':'inf'} + elif (isinstance(item[key],numpy.floating) or isinstance(item[key],float)) and item[key] == -numpy.inf: + new_dict[key] = ({'__inf__':'-inf'}) + elif (isinstance(item[key],numpy.floating) or isinstance(item[key],float)) and pd.isnull(item[key]): + new_dict[key] = None + else: + new_dict[key] = hint_tuples(item[key]) + return new_dict + else: + return item + + return super(DefaultEncoder, self).encode(hint_tuples(obj)) + + def default(self, o): + # TODO add more support types + if isinstance(o, set): + return {'__set__': _to_default_list(list(o))} + elif isinstance(o, numpy.ndarray): + return {'__numpy__': _to_default_list(o)} + elif hasattr(o, '_repr_html_'): + rb = BrtcReprBuilder() + rb.addHTML(o._repr_html_()) + if isinstance(o, pd.DataFrame): + return {'_repr_brtc_':rb.get(), '__pickled__': list(pickle.dumps(o)), 'type':'table'} + else: + return {'_repr_brtc_':rb.get(), '__pickled__': list(pickle.dumps(o))} + elif hasattr(o, 'savefig'): + rb = BrtcReprBuilder() + rb.addPlt(o) + if isinstance(o, pd.DataFrame): + return {'_repr_brtc_':rb.get(), '__pickled__': list(pickle.dumps(o)), 'type':'table'} + else: + return {'_repr_brtc_':rb.get(), '__pickled__': list(pickle.dumps(o))} + else: + rb = BrtcReprBuilder() + rb.addRawTextMD(str(o)) + if isinstance(o, pd.DataFrame): + return {'_repr_brtc_':rb.get(), '__pickled__': list(pickle.dumps(o)), 'type':'table'} + else: + return {'_repr_brtc_':rb.get(), '__pickled__': list(pickle.dumps(o))} + + + +def encode(obj, for_redis): + if for_redis: + return json.dumps(obj, cls=PickleEncoder) + else: + return json.dumps(obj, cls=DefaultEncoder) diff --git a/function/python/brightics/function/classification/ada_boost_classification.py b/function/python/brightics/function/classification/ada_boost_classification.py index ffdfa5ca6..9f84096d8 100644 --- a/function/python/brightics/function/classification/ada_boost_classification.py +++ b/function/python/brightics/function/classification/ada_boost_classification.py @@ -123,6 +123,16 @@ def ada_boost_classification_predict(table, model, **params): def _ada_boost_classification_predict(table, model, pred_col_name='prediction', prob_col_prefix='probability', suffix='index'): + if (table.shape[0] == 0): + new_cols = table.columns.tolist() + [pred_col_name] + classes = model['classifier'].classes_ + if suffix == 'index': + prob_cols = [prob_col_prefix + '_{}'.format(i) for i in range(len(classes))] + else: + prob_cols = [prob_col_prefix + '_{}'.format(i) for i in classes] + new_cols += prob_cols + out_table = pd.DataFrame(columns=new_cols) + return {'out_table': out_table} out_table = table.copy() classifier = model['classifier'] _, test_data = check_col_type(table, model['params']['feature_cols']) diff --git a/function/python/brightics/function/classification/classification_predict.py b/function/python/brightics/function/classification/classification_predict.py index 0ee3bd60b..0438e9847 100644 --- a/function/python/brightics/function/classification/classification_predict.py +++ b/function/python/brightics/function/classification/classification_predict.py @@ -20,6 +20,9 @@ from .decision_tree_classification import decision_tree_classification_predict from .random_forest_classification import random_forest_classification_predict from .naive_bayes_classification import naive_bayes_predict +from .ada_boost_classification import ada_boost_classification_predict +from .mlp_classification import mlp_classification_predict +from .xgb_classification import xgb_classification_predict import numpy as np @@ -35,7 +38,7 @@ def classification_predict(table, model, prediction_col='prediction', prob_prefi return logistic_regression_predict(table=table, model=model, prediction_col=prediction_col, prob_prefix=prob_prefix, output_log_prob=output_log_prob, log_prob_prefix=log_prob_prefix, thresholds=thresholds, suffix=suffix) - if tmp_model['_type'] == 'svm_model': + if tmp_model['_type'] == 'svc_model': return svm_classification_predict(table=table, model=model, prediction_col=prediction_col, prob_prefix=prob_prefix, display_log_prob=output_log_prob, log_prob_prefix=log_prob_prefix, thresholds=thresholds, suffix=suffix) @@ -52,4 +55,26 @@ def classification_predict(table, model, prediction_col='prediction', prob_prefi if tmp_model['_type'] == 'naive_bayes_model': return naive_bayes_predict(table=table, model=model, prediction_col=prediction_col, prob_prefix=prob_prefix, display_log_prob=output_log_prob, log_prob_prefix=log_prob_prefix, suffix=suffix) + if tmp_model['_type'] == 'ada_boost_classification_model': + return ada_boost_classification_predict( + table=table, model=model, + pred_col_name=prediction_col, + prob_col_prefix=prob_prefix, suffix=suffix + ) + if tmp_model['_type'] == 'mlp_classification_model': + return mlp_classification_predict( + table=table, model=model, + prediction_col=prediction_col, + prob_prefix=prob_prefix, + output_log_prob=output_log_prob, + log_prob_prefix=log_prob_prefix, + suffix=suffix, thresholds=thresholds + ) + if tmp_model['_type'] == 'xgb_classification_model': + return xgb_classification_predict( + table=table, model=model, + prediction_col=prediction_col, + probability_col=prob_prefix, + suffix=suffix, thresholds=thresholds + ) raise_runtime_error('''It is not supported yet.''') diff --git a/function/python/brightics/function/classification/knn_classification.py b/function/python/brightics/function/classification/knn_classification.py index c15a472a8..a8296f1a6 100644 --- a/function/python/brightics/function/classification/knn_classification.py +++ b/function/python/brightics/function/classification/knn_classification.py @@ -51,10 +51,19 @@ def _knn_classification(train_table, test_table, feature_cols, label_col, k=5, a # Predict the class labels for the provided data knn.fit(X_train, y_train) + classes = knn.classes_ + if (test_table.shape[0] == 0): + new_cols = test_table.columns.tolist() + [pred_col_name] + if suffix == 'index': + prob_cols = [prob_col_prefix + '_{}'.format(i) for i in range(len(classes))] + else: + prob_cols = [prob_col_prefix + '_{}'.format(i) for i in classes] + new_cols += prob_cols + out_table = pd.DataFrame(columns=new_cols) + return {'out_table': out_table} pred = knn.predict(X_test) out_col_pred = pd.DataFrame(pred, columns=[pred_col_name]) - classes = knn.classes_ if suffix == 'index': suffixes = [i for i, _ in enumerate(classes)] else: diff --git a/function/python/brightics/function/classification/logistic_regression.py b/function/python/brightics/function/classification/logistic_regression.py index 393c68982..beae6f9d7 100644 --- a/function/python/brightics/function/classification/logistic_regression.py +++ b/function/python/brightics/function/classification/logistic_regression.py @@ -206,6 +206,23 @@ def logistic_regression_predict(table, model, **params): def _logistic_regression_predict(table, model, prediction_col='prediction', prob_prefix='probability', output_log_prob=False, log_prob_prefix='log_probability', thresholds=None, suffix='index'): + if (table.shape[0] == 0): + new_cols = table.columns.tolist() + [prediction_col] + classes = model['lr_model'].classes_ + if suffix == 'index': + prob_cols = [prob_prefix + '_{}'.format(i) for i in range(len(classes))] + else: + prob_cols = [prob_prefix + '_{}'.format(i) for i in classes] + if output_log_prob: + if suffix == 'index': + log_cols = [log_prob_prefix + '_{}'.format(i) for i in range(len(classes))] + else: + log_cols = [log_prob_prefix + '_{}'.format(i) for i in classes] + else: + log_cols = [] + new_cols += prob_cols + log_cols + out_table = pd.DataFrame(columns=new_cols) + return {'out_table': out_table} if 'features' in model: feature_cols = model['features'] else: diff --git a/function/python/brightics/function/classification/meta/ada_boost_classification_train.json b/function/python/brightics/function/classification/meta/ada_boost_classification_train.json index 58bf96491..7153c8d6a 100644 --- a/function/python/brightics/function/classification/meta/ada_boost_classification_train.json +++ b/function/python/brightics/function/classification/meta/ada_boost_classification_train.json @@ -75,7 +75,7 @@ }, { "id": "max_depth", - "label": "Maximum Depth", + "label": "Max Depth", "description": "The maximum depth of the base estimator. In this Brightics Studio, the base estimator for Adaboost Regression model is Decision Tree Regression model. Note that it is one of the main parameters to tune to obtain good results by controlling the complexity of the base estimmators.", "visibleOption": [], "control": "InputBox", diff --git a/function/python/brightics/function/classification/meta/classification_predict.json b/function/python/brightics/function/classification/meta/classification_predict.json index 191b0abe5..24922d246 100644 --- a/function/python/brightics/function/classification/meta/classification_predict.json +++ b/function/python/brightics/function/classification/meta/classification_predict.json @@ -1641,4 +1641,4 @@ "base64": 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" } ] -} +} \ No newline at end of file diff --git a/function/python/brightics/function/classification/meta/logistic_regression_predict.json b/function/python/brightics/function/classification/meta/logistic_regression_predict.json index 85984541d..dad4a10ad 100644 --- a/function/python/brightics/function/classification/meta/logistic_regression_predict.json +++ b/function/python/brightics/function/classification/meta/logistic_regression_predict.json @@ -1625,4 +1625,4 @@ "base64": 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} ] -} +} \ No newline at end of file diff --git a/function/python/brightics/function/classification/meta/naive_bayes_predict.json b/function/python/brightics/function/classification/meta/naive_bayes_predict.json index ce01769ac..0ab9fb25e 100644 --- a/function/python/brightics/function/classification/meta/naive_bayes_predict.json +++ b/function/python/brightics/function/classification/meta/naive_bayes_predict.json @@ -85,6 +85,31 @@ "type": "String", "placeHolder": "log_probability" }, + { + "id": "display_joint_log_likelihood", + "label": "Display Joint Log Likelihood", + "description": "", + "mandatory": false, + "items": [], + "visibleOption": [], + "control": "BooleanRadio", + "columnType": [], + "validation": [], + "defaultValue": false + }, + { + "id": "joint_log_likelihood_prefix", + "label": "Joint Log Likelihood Column Prefix", + "description": "", + "mandatory": false, + "items": [], + "visibleOption": [], + "control": "InputBox", + "columnType": [], + "validation": [], + "type": "String", + "placeHolder": "joint_log_likelihood" + }, { "id": "suffix", "label": "Suffix Type", @@ -814,7 +839,7 @@ }, { "persist-mode": "auto", - "func": "brightics.function.classification$naive_bayes_train", + "func": "brightics.function.classification$naive_bayes_traintrain", "name": "brightics.function.classification$naive_bayes_train", "param": { "fit_prior": true, @@ -1590,4 +1615,4 @@ "base64": 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p5zX6A8v8y8focJ+bxr5V0reQ8WTciHXFosKdnV0ivuvLnospbpfdpq/urf7CO/kdR1XXUvKW81gEMoLbw4T/73HXOl8kytOeoe7MRcj3bf+x3n0mpnusV93/HY0OoOtVv+e7rYRiM9oq4b7ta0TO9TyTFyPrOvnfZwxrk3mpgr7eZ248Xj5OG/u9Tc9ktpiZRYV8Wbtlgx7O6YOjuhTrwu+f3zzXHrK3HWlnLr/qe2a4uTtspZtq70/fgWpsh9uiNsJeYrttslxe886fx+6OVJedO0YDUn+8Wr/sv6DbGb23z+3qDLiPPIi/NAKF71ihNLbXmPI41n67aJY89lm/0c97Ha+u4+X38LTKFfsnN0h7RSip335jP+9bYT98rf9bd8vS78Rri/b9ZvoXkN8x5arq/pfbm/m84BEPc9vtn0HtzPE349Kc6VZ3Ler689sFtZld1yGEvaSzC1BVt74NgXN0HWFl7r9uPXyp0tlm32c9zHakuz+/y75cU7TKFfiqp0DuwUGeK8tzu/9eWm19nwyy8779l5/Yeb92P8XX/L1/M/+X/0g2I5JYkyrqDATpCt57/78E3m2v0eWry2x3fT4v08nict2pi1pdwqb3qPzmu7r2OXW89flCPaJyt17KwWnztQtIVcE/HWtEzvU+t2HjbX06f41f0qZLo1HF3v3qE5+WP1liL51YqPnFtt6Sjr3//Ne/KH7y/02pW9u3Qj89RWt0uXbphWWjvu2XY3OGvj9VTxDFnyi6slx+nG9ZS1sXr6a9ny5m91h9p5nm7kvHRhQ2Rzu7h5496n52G+s/p1sxwLdKddL1sLP5Exo/JkakHwT7dor/v5oPQUaTx3XjZ/Viwvr9st+4srJMuK8+tnXSBXTMmVJOcctU7RlurvlHvtpllcnGnF8x1OF/WN8vQ7U2XJVc6dbWhif50c7OypKYMzJUeKpKTY2pEdvEWeXpVuvZf7m35fVvz2bZm2+Gq5b3FJy66hPn9vgv83imXEeWji3B/W12wuDY1OgUVDvNPnmg5Kt2KkUla8tVUWfM0pc7Wz3W42WKZNT5cVq8rNgbwcKzY36DZ8ykQrTp2Dcc5pKGIloEuetZLPxffLgkHmye04Jps36Y5hruToY631+4r/2VOsdWzdYSXVU8x9fq1f9PUqTcv3kqtKzOt19Tdis0w0By3NsrUfVKKt6dbvkCb9S1ZZr7t9itzntYG8SA7m6AFP5/OYv0H461dPjHOlyfmadQ0SF7dHqmvbjucQDjoKuw765p5jrk6eqTPb+k6pqZdTyYky8xIruWs9y5uVJN6QViy/evAtWaoJopUM3mAlgY8+ZidmtkL54PBUmTk22enifalM1jp9rFQeNQl7nuTW2N2zxUpUvz3bSqaP/lEe6HBGuQK5ZJge4KmUo/pYa/13ja2W1x78o/W6mtjmycxrrGT3/bdk2zFrHYMHmaRa7/Nr/aKvl+h0Q083r2d/tva67fuWmlZjfU/L7J4Dmlxbr213Q7ff67et115qvfYPHtau8PXyyXL7Pby7KN36G5iXaMVOvsV6f7P/o9Du3q+9Bp5fJrcvTzV/C70vGN3X3S7r3rj3HSitMtdvrz4jV88PfN7YVbF3aDzgPDeEaoosvMaqhFvX20d/tRurtVEs0aPDTquWUbpFPrLuz7nmSvt5k9znA2hNzxn3ddHheP70153yT0//WdZsLZKi0pPyyd6j8uPn15mDdaetjbLfBl0t92r8GkXWDmNzC48rZ/pUu+vqpCtN93a7VT2ITPfPOyTbtCw5re6++Pq9AWLEkCyR6y8RuXFu82XBxdbOVGd7FefMkYWaB7rbak9+xlHO1DHm4NkGbYXersmxWIm69aLu9t35rbAfV2nOh/fO/q2xW47tc7mn3eUcEGwn/qdN1zSha+vX3jVL3sm2kn0rOe5iQmzW5Zj2Ne2irkm+9ZtpJf3tS5cRJlnPlhF64KG4hN+qMDt8tDOneQWfToumo7C7Xar1ctXDS81YMZ1SVSRrtN4Pzm3bWq7dqa1kNldbnp3WWW+WFpbLKavOTtbjT3cMMsl5UZGV3M4bLuOSRU4dPmSSVftxiZLl86tMlxuaWoZnyUzruUUbnPPdreT7C1bSW2RaoFue4/1okcZT19avrc3vfqtSbtfv0M+E/AeX6HdRL3sKmxNidx3qB7l2T55tphv6Otmm329aqtzZdMCgyD4o8f4h2eMr/zWfo3kdjz6mf2P33P/Ise5vkdVzpFtJ+bnOHtHqovlTc01LWuvLtxfOMvcPy0qTn37jyqC0krvdXXNyPI+gN3O7iT0l14k5V9QpR2zQQWB8ce/TKVWumv+lqLpoy5gvOiBU376pMqVgpvW4cSGLc182fVYsT7z+iRmEx5OO4rziw71Won7AKfFPzlX3WzvkM8zOrGqvi2tIaIu87uhuGmPtnNvnufvC701wBDrO5192jVw863KZNvlCGZ9XICNHjDGDqyUnWXtYjuTkFBmdmydzLrrC62sE4hJNce4qPSmyxtqPe3dr8+UvO0TqzjoP6IRpX9MYqZQVv11vtuMuv+No0FSZZcXj5k1brYuO7ZArs3UciWPlJsk0p5VYr+Mm9qbni1ctzylfck26bH7WOSDYXvw7B/P1df1ev5Xkm4MRHve3PvDYeU5X/u+sFLnL+gzOeei+pdu9ACJMtGzP9Tdh0MC2+5wZ6ZkydvR4mTh+ivXbMktmz7xU5l58paSnD3Ae4Z0O/KY0zlMipHu+zlOu06LpgG/u5YvTR3ep59ujj+2VIitZnXlNrmQ6Zcrtgn2D6PnU+hgfnOQyN/fSlgnpkOTmbtaaaDuJfeYAX+ewtzyn/Fef1kvu7LutpNl6vNO1+9vDyuVXD34kn3jmgs+Xmvemr+v3+q0k3xyM8LjfrMeXwXn2a1gXHZCuaIOv1v4CGZKm180HGMwAdsluTwL/3DkgxVkKPR3UzRf3Pp0iLRJ1KynXneJw0Z3xf31+raT3TTIjOOrAb0ExKFv0p7G42HOT7nK6oWkXMh9Hor0/D9FCR2VN7N12I6Flep/SOU5jgbuTrhv8ObPnNY3QHIo4f+yFdfLqh3uapj3asOeIvWB5ee0uqW+VkLt0qouX1+1ybnWCOziRaTVvr4UrSJyd6uycwU072/ct7mhwqY5/b9A1gY5zjaXUfmkyMCvbjII87oIJJvm9xIorja/MAQOlpqZa9hXtlQ8/+ovs+TR0Y46EM879seOQyC9esy6vN1/+3bqcrHYe0ClOz7bSShNvts7Ekd2FXVvbV+gLuD3mzOkn4jE4mpNs+znYmd2ybWs//qfIbOstFm96TzZ0Yv3aqm3KnOS52wcem3r+fT2MI8Z3XzRsz4tLDpvR1EuPl0iCFasan/njJsm8uVfLhdMvkVG5Y2XokBHWb8tg6Z+WYQ7u9TKTu7WlyXju8DHyhXnXNXXRzUrznbCE0lRrn13nKdf996aLdVv36TtvnbxmJcCSnGiSVpvTfVy7fnfYgqxd2K3nD86VKzRXM13HLUdrRMfi1y7ini36/na5tlu2bW6y/ZrXweXs1ujUYXkyuRPrt1ufrcsGu+dKu4O3eQz0phffg7EVylHTs7vlAYYWrdym1bx9S0/oD3Z7vQqCR0dZP9/YNia0TO9TOme5iqSu66p7SbmXuUZDRQd2q6k7a84jD1pCbtgbRTHndamtskK7bk2ZIwsGDZYc3TKW2uecFb+zvrnrmXOEvel5TtczRBedJmXJjHtkRtZ4s+HWiy5rmTuFyunqOufR0cnXTrorFHH+0Z6j8tftB6S8yt7z/rcX1jcl5vtL7A2OL/uKK5yljjW1kLUYlMntamkrXvWeE7PvmW6mTd3ZA+aYvLnS2hG3koKF1g5ui0GfnHV6187vDbolVHEeFxdvdqhnTL3I2sGeYxJ2jb8Dh/bJ3z5eK5UnTziPDLxIiHN/NDbqAQLnhqO+oevzlOdcNadVS3jn4sjtGq7x2dSd29m+u6e22L8r/g9oVrzl86aDch3Fv+nCXlokm/1dv9Pybn7jJt1hRnrvdst1i8YJ5/crCkXy9lzP+961Z5ts37lZ6uvrZEjOcLlo1mXmYN7wobmSmOB/C7dJxkfYyXjeWHtatEsvspPxnIzISEICPU/50v8oatUS7iSXTgvvnd/Kbbel1+0anmp9TabruHJa0FOHDfcY/MwdoK1jdxZkmoME5ScKWw76dkee6druyXRhT06XXH/X7zmo2vPLzIjqItX2+evd1KI7vTn//W5nQDanK3typlyi78HpXu+V0/qvvQ9UZ7+77tBpz46uy5HqkhSTiOtFl7XMnRLtxovsXmRuXESKbiXl2koVLnpUTStkQLus6+jJ2hXMvTijrupR5/umuOeErWwxoNO0a7UbrHNu6irrB083fOb8KXsAqGnOffdush5rnoFooxvrhybfKb+f94i56LK7Aa84GZ3Ti1TXVJsNd3s76a5QxLl2XSvcf1y+9MiL5qjs8ZPV8uL7O2XvkROS1b/9blADO7jfk3Zdt6cjsmNcz42cdlfLwZlyrsmUDRqzOhWZFesdt365B+7098PXSMetzin1mAfZThoqZYV2D3220hn8yU4Ymna+tRuq9XuU7fP3Bt0V6jjPSB9gurbPspLzARlZUnWqUj7auN60ngdKpMV5eDizHXjwvd32wj3Abu3Sm67jhrN9NwNGev8dackz/u3Hu63O7ca/ahqPxs/1W4m42z1e16VzpDedv95l7lg6+hunv19Wkm+V+u6uH7kicXt+7lyjFfvr5NAR+1Ssgvyp5pKc5N+2rbbW7gvdIhn3mKNcuefOZmdE1hRQgbNObndajF32KON2N+xvjxU5pV+Br1bepvOj3XOplU6dtleKknPk2+Y1dPCz9gZ58zyn3H68tnJrq7R90CBRZi6y7pudYr8Xzy7hThLr9/qtRNztHu92MW86f727Wry2M9Cc09vg0ce0673zOa5JlnK7Wnlh/T2WW9+/022+xXt3DzZot/sWI7sHjibfJRsGy/43cs1Fl92E/KFFs51HNcdFpOh15FhFF48/62iovWSgFeDxOlRqhNFp0lZ+9KmpDECgaLDoOWfaxS3aW8h1KiRfO+ieQhHnp63v87UP95pWcR199d3N+2XRnHHy9/MmyqbPSuQnz6/z2r1WZ4D4xjXT5OtXT3VKusEZhblppHP0WOGI88KdW+RoySGzrEn6zGkXm+XuiqQ4Vxrbun2uO9tyaqqMfkmy6tE7zPnk2n3d8xiBnmb6629a+XF/p6BHsUcw79TsDvBLpGzP9UDcZ/v2mOW5l1zpdzLuSecjb52Ie/rhY2Xy44ezzIjn3392jRwucztWB4eeL67d01u3huv6dYA3b/fXn22UWx99RUoqTjslPYk9ynmudjPv4gjqaEs7XsRZdUzPIdcu624LucuNi0jR7aQ8zdqQ9u30sKj+0413V2j3dp0qjaQcgXKmpk6qTtfK+a72o4xSoYjz1pb88a9y33XTzVH9WmsjrqOs6zgSnq15mpDPmTBcvn/rJWbatG4jKYclnHGucwrrzrVLB30KlVDFufaC0e2zt6T8EiuedaA3Pa9cu7G7EqykXPel+iQ4BT2EPR2rLulAcd1t7YanSNmenz5zyrSSNzQ0mNNadLyJYHCTj1or7v6wulDe/MSetzxYdCA3PW9cu6l70qnQdOR1b/fr2DFrCw+abX5PYqYLM7sclfJaBI5QHs0WzBwtX5lfIEm6EfEippJylWh90KwgdofRrqzdQVKOQCmrOC31rXYke4pgx3lrZ2rrpa/HKLE67ZmOsv6ntTtNa7p2af/S7LHWJc8sW/kEEBDhjvMz1adl/d/WmOX0/gNk1ow5ZjkUQh3nQLhEyva8cNcWOVp8yIy2PnXShU5p4HnOx7znyAlZ/LsAnHwMRLjH/2GejBvqe3aCSJunvNtJue4MDxqQZroHALFKB0EqPVHV5cGGoh1xjp4gUuL87Nl6WbP2bbM8auQFMnZMvlkONuIcPUGkxHnZiVLZtGWD9OmTJBdfeJkkJoamN5r2OLv/P98xvVaAWKXjDT35zavanEIRyQLyTqtrwzuFBBBs1HG+A8S+SKnjCQmJMmuGPWrt/gOfyZGjB81yKBDniHWRUscrK+3ZFnRwtmAn5Ju21jpLOpPBefnC1JHOLSA2aR3v6PQUz7iIBN1OyvXz6gAZPe08W/QcWrftOu4U9EDEOWJdpMV5ev8MMwKz2rF7qxwvC/5I18Q5Yl0kxfmJijJznZGeaa6DafnK5sHTEuLjZOHsPEmMwEGagUDQuq11XOt6ezzjIhIErE1ff+SAWETdbsZ3gVgViXVb5yoelTvWLG/bsUlOVvk/J393EOeIVZFSt3Uas8qTFWak9bTU0E8rcN76d/OcliNRA7FC67bW8WgTkKRcjzyeOlNrppYAYonWaa3btBwR54hdkRznY0ePN1OaNTY2yI7d26S2Lrjd7YhzxKpIivOmVvKM4LeSq+lTkpwlm45GfeucfBmWleqUALFB67TWbV8jrntqHRfhFtCz3yvNbPhA7KBOt8V3glgT6XV6/NgCSe2XJqdPV8nuvdud0uAizhFrIqlOu6ejDAhB13W16Lq2syrowI7fun6GcwuIDVqn/Z2Rx1tchFNAk3L3KCQQC2gt8o44RyyJhjjX0Znzx02ShPgEKT1eIns85jIPFuIcsSTS4ry23o6tlJTQTMe0e6/3we2GZabK318emtkdgGDTuqx12l++4iJcApqUnztvD6DB6K2IdlqHtS5rnUZLxDliRTTFuc5Znj9uslk+cPBzOXSkyCwHC3GOWBGJcX6u0T5AEBfXcRfbQFj6cpWz1FLfpAT50qyxMn8yo7Ejumkd1rqsddpfvuIiXAKalCs9V+fkqWqpqTvrlADRReuu1uFIPL80UhDniHbRGOfZg4dI3gUTzPLuvYVmnuNgIs4R7SI1zhvdpLx3+EdAT06Ml28umCZz8oc6JUB00bqrdVjrcjQLeFKu9LevsuoMR9gRdbTOat2Nov30sCHOEa2iOc51TuMRw0ebJEMT8/qzwY0/4hzRKpLjvPGc21IemqR8fF6is+RdfFxv+aeFF9JijqijdVbrrtbhzuooLkItKEm50h/Bk6dqOCcNUUPrqtbZaNxRDxfiHNEmFuJ8/NiJkpU5SKqrz8ix0qNOafAQ54g2kR7nTS3lIUrK77wlzVnyTZOab1w9hXPMETW0rmqd7UpCrvyJi1AKWlKu9Ej+meo6Kas4zYBZiFhaN7WOal2Npq6skYI4RzSItTgfPGiIuT5WWmyug404RzSIljgPdVJ+8LB/p6Bo99/rZl4gj//DPKZLQ8TSuql1VOtqd7qs+xsXoRLUpFzpwBr1Zxuk9MQpc+SSpAeRQuui1kmtm1pHo2Gwp0hFnCNSxWqcD80ZLkl9ksx8x6fPnHJKg4s4R6SKpjivr9cDBuckMbGP9OoV9N1w45nnTjpLHdOBssYNHSC/+scvmpbIxPjwn/cOKK2LWie1bmod7cygbt50Ji5CITS/Bg4d/bKkrMr8cDY2nnNKgdDSuqd1UOui1kkEFnGOSNAT4txtLS8uOWyuQ4k4RySIxjh3D6Kl9ousrrOt6VzPN8weK89953qTCA3sn+LcA4SW1j2tg1oXtU76Ow95tOl15FhFyA8nul9mQny8JCclSJ+EeInnSByCSLu01Z1tkJras3K2ocGU0cgTXMQ5Qq2nxXl1zRlZ9+FqSUpKlssu+YJTGlrEOUIt2uP84KH9svvTQhk5fLSMGzvRKQ0ubRG856v9nVudd9b6zntZwf55SaX8tfCgbCs6LofLQtNDBz2TdlGfnDtQLisYIWOy001vmIQAb1u6GxeBFpak3JMGuUtP1I/TS+/e0rt3jB4GQUicO3deGs+dM0fRGzxacehuGR7EOYKBOBfZtHWDlJWXyoXTL5GM9EynNDyIcwRDrMX5rj3b5NCRA1KQP1WG5Ax3SqNH7dkGcxCktxXvJRWnpbjijJRV1Uh1Xb31t3IeBHSCtZmQlD6JkpWWLDkZfSU7o585BUU3KUkJ0T3NWWeEPSkHAABdU3Twc9n72U4pmDBNhmQPc0oBRKpPNn9oxoK46MLLJC01NK10ZeWNkpVJDxbAU6TFRUjPKQcAAIGTmGDPs1pbW2OuAUQ2ncowPi4+pOeU//LpCmcJgCvS4oKkHACAKKUjOKvaOpJyINIdLztmYrWflZB7nu4BAHRfR8fK/ypPLBO5/Z7LJEtv710hP1p2xNyl8m+6X27Lc24YZbL2mZdkdblzM7NAHnCf24I+bpXITV+WueZUyF3y0uNrrP8d46+QR27MN4u7Xv2jlM3Vx3m+9lC5bfFCsR/RSuv33Ia+znrJusfH84GeJmhxbj1y/R/lieMzmuLZ0NffmdeijDjvvJNVlbLhk7WSlTlIpk+Z7ZQCPnQ6zjUun5SXdtvL3u438b22yixnzb1NHpjjGY26Xd8oOfe42/kuxLnzW1N8edt126Inzjdv+8gk5vnjJsnwoblOafBpi+CD92Y4txDzOhnnnjFsS5P5HjGrvMd5dO+3R1pcxH33e4uXOMtAWxrISz+X6pRBMmvGSNEJMcp2fyL7Ry6U791xucy79EKZ2GZsoQOy4c9nZf7ir8gt1v3znOe1pIG8Uj6u6SOjZkyWkfqA8l3y/oFBcteDt8gCfd74gfZDrffwksyXW8alWD8Ky2T1QGfdg/bIk2vPNz/O5eU9t5UiI2eclzWvlsnE1s8HepqgxbnFeu2fvV1mbcVHNcWqbtxblxHnXaPzHetozr1795bhw0K3k48o1JU4N3F5rXN/P9mxbJcMbhFvu+SNtxPkdrPd7idFz2+R3peOFyfSrZ3oVtv5zsa5Zderv5M3DokMzPf2O6SiI841Gf98/17p1zdVJoybHNKW8otmJjtLiHldiPOUEZNNubnk10hp34tlgRWjzXzEeZTvt0daXNB9He2wNqjH8+SRewpaHLU6frxKyta+JD96/En50TN/tR5l0SNcTcvH5bgcsZJu637r8tJeLdQNa/Ny2frjMmnxbTLf84ehvELKygvlCfO8P8papwVu185TMnm8/Q6OH5emZcnLk3yrwKyziff3rOs271cvr7rH9PJlkmxsWg/QMwUvznVD/tIykdtuGurcVrusf9fIIy3KiPOuSkyg+zr80bU4N3Ep6524WiNyud2K1Rzn+XKbzx4yq2TbhCtabOc7F+cWs3N/hdw23rltidY4P3z0gLkemjPCHEQLpeoahkXvGbq4PW9ib7PnO71dOozzKN9vj7S4IClHO7Jk7pzWnUTKpOy4dmu5Xx5ZfL88MOGgvLjeCq/My5q7rmqQahcW6/5HFl8hsmyFFeZWKN3Y3F0ma85lVmi1VHb8lOkSY553zwjZtkx/LHbJ9uMjJN/r0XGLtS4r3j14e8/6GgXygHk/1sWju+zAgSLFJOXo0YIV59aG9hntttq6q1m+tT7PTa8izrsqLi5O4uPjpaGhQc42nHVKgda6GOdSJat1h9lnnNu094sm7cfnzrHj3Uqm9ZSVB+Z4tmh1Ms41afhLhjzgedpLlMa5tpLrJTk5RYYOCf00aP/27yecJcS2rsa5rWz9xqYDb6qjOI/2/fZIiwuScnSSFTwe55lkDUy1grJleEneQo8AGig5maekzI8Ayprz5eZz0TIHysA2getFZobTTa49+TLf+hGyj+R5HnGzHT/e8pgdgADEeflO2VZu7dA/Y8Wcnsu2e408oTsCXUGctyupj90Fr6am2lwD/vEjzvXc0rkdb891+607z7fLKtOytmunHfM/elzPJdXfATuZb1erOC/bfbCpFU7Pad+1TFviojPOPVvJ4+MTzDIQGv7EubLS6Z2pMskjCW+tdZyz3x5YJOXoJD0XvHnjqhve/Aktj3CZwSDcnW/dMZd2jph5aO4mY9m7V3aNz7OPuHvQI2TbdjuvrY+xCjyP8vni/pA8ol3mj7fs+jJwoD+vAPQkAYhzPQrvHuXWrurjr2g1AJRvxHnnJCWRlKMrOo7z/Ampsnqt8whzoC1VsjzjvFUXWO0mq7SFrSkWM7WVru3gTB3FeXM832+6r+ffZCcW0Rbnuz/dYVrJU1L6ytAhI5zS0EpJZne/5+o4zg2N74Ft97sNH3Ee7fvtkRYXRCk6KV9uu0mazyPVc730qJpHwGbNuUYm73TPXTkok2+yu8K0CF4v8m/UrnHOUTE9D9W0wuXLpIEHZZcTjC1eu+kx2k22+VyWtvQHyXldPWo/cEbTEUM9WJjjxwEDoGcJXpx7R5x3R1NSXktSjs7oOM61R8xtoi3eGueFMvAmO7luivPMy+R2jxatptfwqufFedHBz+XgoX1mULfxeQXSx5nCMNT++X8OcJbQ8/gR56q8ws6gPXQU59G+3x5pccGUaIh85ty0OX63svlNf5DWDmx1vhqAsCDOu2xf0afy2b7dZoolnWoJiFg9KM5Ljh2VbTs2muVQT4EGhBXb8y6hpRyRL2+hzD++qp0jal1RJmuXVch8EnIgMhDnXUZLOaJGD4nzypMnZNeebWZ55IgxYU/If/hYU3soEHxREueRFhe0lAMAEMUqKsvl400fSFzvOLly3gKnFEA41NXVyqatG+TU6SoZNDBbpk660LknfDT5+PHDAW61BKJcpMUFSTkAAFHurx+8K7W1NTJz2sUyIIOdbyBcthZ+IsdKi6VfvzSZPmW2JPVJcu4JH88WQTcJWfJ/yqWx0U4BlvyvTImL6yU/+Xm51NXbZf/yUKb0Sexlpo1y53PWc3B1cKyf/r8TcuqUXfb9/zFAUlN7yy+erJCKykZT9p37MyQjPU5++bTOY22XPXhvhmRlxskTv6mUktIGU/bAN9Ile1C8PPVspRwptsvuuytdhubEyzPPnZSDh+1pHu/5an8ZMSxBnl16UvYdsMvuurO/jB6ZIM+9UCWf7qs3ZV/9uzQZOzpRlr5cJbv32mV33pIm4/MS5cXlp6RwV50pu31RqhTk95GXXztl/b3ssltuSJUpBX1k+crTsmlrrSlD7CMpBwAAAaPnlOu55aNGXiBjx3BaDhAOn1pxuN+Kw7i4eJk57SLpn5bh3INoowcynvr5KOcWYtF9D+2PqKScc8oBAIhyA7MGm+uy8lJzDSC0jhYfMgm5mjxxOgk5gE4hKQcAIMppAtA/Ld2cx3qyqsIpBRAKlScrpHDXFrM8cfyUpoNkAOAvknIAAGJAVuYgc/3p57vNNYDgO3u2Xj7auM4s6+kjQ4eMMMsA0Bkk5QAAxIAxo8ZJar80OVFRJrv2bHdKAQTLmerTsmbt22Y5vf8AxnMA0GUk5QAAxIgxo8eZ60NHiujGDgRR0cHPZf3f1ji3RGbNmOMsAUDnMfo6AAAxZNuOTVJy7IhZnnvJlZKclGKWAQRG4c4tcrTkkFnWsRxmz5xrlhE7Ojv6+oEDRbJjx04pKtovJ0+eNGX9+/eX3NxRMmFCvrkOhnCtd//+fVK4vVAOHT4kVVVVpiwtLU2GDxsuEwsmyqhRo6VXr16mPJD08xYW7jCfV9fbu3dvs94RI0bKxIkTOvV5I230dZJyAABizDurX3eWRAryp8qQnOHOLQBdVV19Rnbu2WZOEVHDh+VKft4ks4zY4m9SXlZWJqveekvOWHVj2rTpMnr0aElPTzf3VVZWyr59+2Tz5k3SN6WvXHPttZKVFZgkMJzrXbnyDamvr5fp02fIyJEjW6z3wIEDZr06P/+1CxZIZmamua+7ysvL5a033zSf98ILZ5n1ZmRkSGNjozkYcfDgQdm48RNJTkr2+/OSlAMAgKDbvbdQDh7eb5Y1KR8zKo9Wc6ALKipPyLHSo1JiXerr66SPlXDo+eNDsoc5j0Cs8ScpP2gloK8se0Uuu+xyK0Gd7rNl+Pz581bCuFH+8pf35dZbbpURVkLZHeFe7+WXz5Np06a1u96tW7fK+++vkVtuvkWGDe/eQWFd77Lly8zn7Wi9mzdvNp/35ptu7vDzRlpSHvfd7y1e4iwDAIAYoaOxp6WmS/mJUjl5skKKiw9L1alKM1p0cnKKxMXFOY8E0FpjY4MUlxyWTz/fJZ/t2y0nqypNq9yggdkyacI0ycyInJ15BN6atdVy/VW+55rXFuMXXnxBbrzxRpkwYWK7XbX1viFDhkhOTrYsX75cxo4dKykpXTtAGq71akv1Cy/8t7PeCR2uNzs721w0iQ/EehctWiT5+c3rfenFF2XHjh1Nl4kFBea+nJwc6zPnyLJlyzpc7xvvVMr8uZFzoJqkHACAGNU3pZ9kDx4itbW1cur0STlz5rSUlZeaQaoqKsulrq7WdMnV64aGs3L+3Hlzjp5egJ5A6712ia2qqpDyE8elpLRYjhw9YBLxo1ZSXlNTLYmJfWRozggZN7ZAckeMkcSEROfZiFXtJeXaIvunP71kulFPnDjRKe2YdrdOSEiQdevWypQpU9tNbL0J53qXvfKK6a4+cWKBU9qx9PQMiY+Pl/Xr18nUqdOc0s7Rzztz5oXmAITSZHzv3r1y7tw5ue32200y3jo51+70iYmJZr1Tpkzx+XkjLSmn+zoAAD2AJt+lZSVyvOyYScgBtC+9f4ZkDx4q2YOGmMQcPUd73dd1kLN33nlH/vEf721K+H7ykx/Lv/zLD82yN+79muD++tdPy1VXXWUGQ+sMd70XXXSxdf221NXpqRR95Oqrr5HJkyc7jxLZtm2bvP32qhb3T5o0qcvr1e7jq6zXu+eef2yR4D777G/NAd/58+fLuHHjndKW9PP+5jfPyJVXXimjR49xSv3j7XvWpFyTcb325JbptdL1PvPMr+WLX/yiz88bad3XORQOAEAPkJLS17TyXTj9Erl8zhfNAFUjh4+WnOxhkjVAu7r3l6SkZLq1o0eJj08w8/sPzBpsBm7Tc8ULJkyTmdMullkzLpURw0aRkKOFnTt3mcHVPBNUTbg18fbGM2HX5+hz9TU6y12vJuR33/1185p6rQm4J73d+v7urHf79u2mpdvz86ojR46YJH/16tXy/NKlpmt9a/ocbWHfvm27U+K/Xbt2t/meXZp8e15aa/q8O3Y6JZGPlnIAAAAAcLTXUv7kk0/Ibbfd7nWE79Yt5t5a0DV5femlF+X++x9wSvzjrldbqDXZ1pHN9Zxrt8X60MFDMnzEcDPy+s9+9vMW9z/00Pe6vN7//M8n5dZbb2vzed3PpmMtfPzxx/LBB+tN9/bLL79ckpKSnEfZ54W/+OILnV7v008/JYsW3SSDBg1ySppbyr1pfZ9+Xl3vAw98yylpiZZyAAAAAIhCOgWXOw1Ya5qkui3m3hJyped4u3OKd4a7Xu2O/rvfPWteX6+vuupqsx5NyPV60Y2L2tyvurpenQ/c1+dV2rvqoosukvvu+6Y511uTeG1dd+m86e5c5p3R3no1Afe8eKPzl3dlveFCUg4AAAAAftDBy7x1qXa5ibm3hFxpy3JXuOvV88e/+92HzOvrtef55OpLN9zg9f6urtdfOtL5tddeK3PmXCpvvrnSKe06TfD13HBvPLuu+2o5j7ZTsUjKAQAAAMAP/fr1k4qKCudWW25CrtfeaGu1tuJ2VkfrdelAbz/72U/N+vVab6uurlefU1lZ6dzyTVulX12+XD788AO5/vovOaVintvV9fpq2fenpVy/K22ljxYk5QAAAADgh6FDhprztr3xbCH3lZjv379fhg8b7tzyX3vr9eRtoDfV1fXqc/S5vmgL/Pr1681o52lWEvzNb97fYsq2oqKiLq135Mhc81xv/Gkp1+9KXyNakJQDAAAAgB8KJhXIli2b23St9kzIXa0Tc32OPnfS5ElOif98rdeTrmvXrpYjrGuX92Ct99NP95oB2Y4eOSJ33XW3mR5N5wh3ueudWOD/vOquCRPyZfPmTe1+Xl/c9eprRIu4735v8RJnGQAAAAB6tDVrq+X6qzKcWy2lp2dIYeF200I8ZMgQU+YtIXdddtnl5n693rRpk5SXl8m8eVe0e166N97W60lfXy86L/eyZa/I6tXvyY4dhWagN52+rDvr1depr6+XoUOHOqV2y/tnn31m5gK/dO5cSU5Odu5ptnnzZqmsqDTvqyvr3blzhzQ0NDR93h07dvi8qIkFBeZav+cTJ8rl8svn+VzvG+9Uyvy5Kc6t8GNKNAAAAABwtDclmtLptv7wh+dk0aJFkpvr+3Ge9u/fJ6+++qp85Stf9Tqdmj/CtV6d1uy5534vN954o0n6/XHo4EFZtnyZfPnLXzHTs3WFrlc/78KFC/1e74EDRebzdrTeSJsSjZZyAAAAAHC011KudKTxoUOGmKSzT58+kp2d7bNFVrtSb9y4UVatektuufkWyfHSyu2vcK535IgRsmzZMklM7Hi9Om+5nst+2623yeDBg517Os/+vENl+avLJT4+ocP1agv5n//8Z7n5ppvNY9tDSzkAAAAARKiOWspd2nL9xhuvS11dnUybNl3GjBlj5tbWLuY6crh28dbzojWB1hHJu9pS3Vq41qst16+//lrTekeNGmXWq3S9OjCbrlfPK7/uuusDtt4TJ07IW2++KafPnJYpU6bKBRdc0LReHd1dB3XT9fbr20+uXbBABgwYYO5rT6S1lJOUAwAAAIDD36RcaQttUdF+2blzl7nWqcF0jmyd0mv48BFmsDHtau6rhberwrle7Zq+fft2OXjooFmv0vUOGzrMDAznb1fzztKu6Tt27JRDrdarn3fixAmdGm2dpBwAAAAAIlRnknJEp0hLypkSDQAAAACAMCEpBwAAAAAgTEjKAQAAAAAIE5JyAAAAAADChKQcAAAAAIAwYfR1AAAAAHDo6OuIfUyJBgAAAAAA6L4OAAAAAEC4kJQDAAAAABAmJOUAAAAAAIQJSTkAAAAAAGFCUg4AAAAAQJiQlAMAAAAAECYk5QAAAAAAhAlJOQAAAAAAYUJSDgAAAABAmJCUAwAAAAAQJiTlAAAAAACECUk5AAAAAABhQlIOAAAAAECYkJQDAAAAABAmJOUAAAAAAIQJSTkAAAAAAGFCUg4AAAAAQJiQlAMAAAAAECYk5QAAAAAAhAlJOQAAAAAAYUJSDgAAAABAmJCUAwAAAAAQJiTlAAAAAACECUk5AAAAAABhQlIOAAAAAECYkJQDAAAAABAmJOUAAAAAAIQJSTkAAAAAAGFCUg4AAAAAQFiI/H93vsj1MQOypwAAAABJRU5ErkJggg==" } ] -} +} \ No newline at end of file diff --git a/function/python/brightics/function/classification/meta/naive_bayes_train.json b/function/python/brightics/function/classification/meta/naive_bayes_train.json index b998f6716..05ccd70fc 100644 --- a/function/python/brightics/function/classification/meta/naive_bayes_train.json +++ b/function/python/brightics/function/classification/meta/naive_bayes_train.json @@ -1076,7 +1076,7 @@ }, { "persist-mode": "auto", - "func": "brightics.function.classification$naive_bayes_predict", + "func": "brightics.function.classification$naive_bayes_predictedict", "name": "brightics.function.classification$naive_bayes_predict", "param": { "display_log_prob": false, @@ -1587,4 +1587,4 @@ "base64": 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" } ] -} +} \ No newline at end of file diff --git a/function/python/brightics/function/classification/meta/random_forest_classification_train.json b/function/python/brightics/function/classification/meta/random_forest_classification_train.json index 944dbf740..c0611c47f 100644 --- a/function/python/brightics/function/classification/meta/random_forest_classification_train.json +++ b/function/python/brightics/function/classification/meta/random_forest_classification_train.json @@ -102,7 +102,7 @@ }, { "id": "max_depth", - "label": "Maximum Depth", + "label": "Max Depth", "description": "The maximum depth of the tree. It restricts the depth of each individual tree to prevent overfitting. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than `Minimum Number of Samples to Split`.\n", "visibleOption": [], "control": "InputBox", @@ -116,7 +116,7 @@ }, { "id": "min_samples_split", - "label": "Minimum Number of Samples to Split", + "label": "Min Sample Split", "description": "The minimum number of samples required to split an internal node. A very small number will usually mean the tree will overfit, whereas a large number will prevent the tree from learning the data.", "visibleOption": [], "control": "InputBox", @@ -130,7 +130,7 @@ }, { "id": "min_samples_leaf", - "label": "Minimum Number of Samples in a Leaf", + "label": "Min Sample Leaf", "description": "The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least `Minimum Number of Samples in a Leaf` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression. While `Minimum Number of Samples to Split` can create arbitrarily small leaves, `Minimum Number of Samples in a Leaf` guarantees that each leaf has a minimum size, avoiding low-variance, over-fit leaf nodes in regression problems.\n", "visibleOption": [], "control": "InputBox", diff --git a/function/python/brightics/function/classification/meta/svm_classification_train.json b/function/python/brightics/function/classification/meta/svm_classification_train.json index 8f0e6570d..dd6f9309c 100644 --- a/function/python/brightics/function/classification/meta/svm_classification_train.json +++ b/function/python/brightics/function/classification/meta/svm_classification_train.json @@ -33,7 +33,7 @@ "params": [ { "id": "feature_cols", - "label": "Feature Column", + "label": "Feature Columns", "description": "Feature column names. Only values in numeric type are possible.", "visibleOption": [], "control": "ColumnSelector", @@ -114,6 +114,51 @@ ], "columnType": [] }, + { + "id": "gamma", + "label": "Gamma", + "description": "Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’.", + "mandatory": false, + "items": [ + { + "label": "Scale", + "value": "scale", + "default": false + }, + { + "label": "Auto", + "value": "auto", + "default": true + }, + { + "label": "Other", + "value": "other", + "default": false + } + ], + "visibleOption": [], + "control": "RadioButton", + "columnType": [], + "validation": [] + }, + { + "id": "gamma_val", + "label": "Gamma Value", + "description": "Gamma value when gamma mode is 'Other'.", + "mandatory": true, + "items": [], + "visibleOption": [ + { + "id": "gamma", + "value": "other" + } + ], + "control": "InputBox", + "columnType": [], + "validation": [], + "type": "Double", + "placeHolder": "(value > 0)" + }, { "id": "degree", "label": "Degree", diff --git a/function/python/brightics/function/classification/meta/xgb_classification_train.json b/function/python/brightics/function/classification/meta/xgb_classification_train.json index b92c513d2..90a859113 100644 --- a/function/python/brightics/function/classification/meta/xgb_classification_train.json +++ b/function/python/brightics/function/classification/meta/xgb_classification_train.json @@ -75,7 +75,7 @@ { "id": "objective", "label": "Objective for Binary Classification", - "description": "The learning task and the corresponding learning objective. For multi-class classification (the number of classes is greater than or equal to 3), the learning task uses softmax objective.", + "description": "Specify the learning task and the corresponding learning objective or a custom objective function to be used. The objective options are below:\n\tbinary:logistic - logistic regression for binary classification, output probability.\n\tbinary:hinge - hinge loss for binary classification. This makes predictions of 0 or 1, rather than producing probabilities.\n\tcount:poisson - Poisson regression for count data, output mean of poisson distribution\n\tsurvival:cox - Cox regression for right censored survival time data (negative values are considered right censored)\n\trank:pairwise - Use LambdaMART to perform pairwise ranking where the pairwise loss is minimized\n\treg:gamma - gamma regression with log-link. Output is a mean of gamma distribution. It might be useful, e.g., for modeling insurance claims severity, or for any outcome that might be gamma-distributed\n\treg:tweedie - Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any outcome that might be Tweedie-distributed.", "visibleOption": [], "control": "DropDownList", "validation": [], @@ -125,7 +125,7 @@ { "id": "max_depth", "label": "Max Depth", - "description": "Maximum depth of the tree. For example, depth 0 means 1 leaf node and depth 1 means 1 internal node + 2 leaf nodes. Increasing this value will make the model more complex and more likely to overfit. Beware that XGBoost aggressively consumes memory when training a deep tree.", + "description": "Maximum depth of the tree. For example, depth 0 means 1 leaf node and depth 1 means 1 internal node + 2 leaf nodes. Increasing this value will make the model more complex and more likely to overfit. 0 is only accepted in loss guided growing policy when tree_method is set as hist and it indicates no limit on depth. Beware that XGBoost aggressively consumes memory when training a deep tree.", "visibleOption": [], "control": "InputBox", "validation": [], @@ -271,7 +271,7 @@ ], "summary": "This function trains a XGBoost Classification model which is highly efficient, flexible and portable." }, - "md": "# XGB Classification Train\nThis function trains a XGBoost Classification model which is highly efficient, flexible and portable.\n\n## Description\nXGBoost stands for 'Extreme Gradient Boosting', where the term 'Gradient Boosting' originates from the paper Greedy Function Approximation: A Gradient Boosting Machine, by Friedman. \n\nXGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples. To sum up, XGBoost is an optimized distributed gradient boosting.\n\nReference\n- \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table)\nTraining data in a Table. It must contain at least one column for labels. \n\n#### OUTPUT\n1. **model**: (Model) Trained XGBoost Classification model. \n\n#### PARAMETER\n1. **Feature Columns***: Feature Column names. Only values in numeric type are possible.\n\n2. **Label Column***: The column designated for the labels of data. The column should contain categorical type values as it is for classification.\n\n3. **Objective for Binary Classification**: The learning task and the corresponding learning objective. For multi-class classification (the number of classes is greater than or equal to 3), the learning task uses softmax objective.\nequal to 3), the learning task uses softmax objective.\n - Available Options : `Logistic regression`, `Hinge loss for binary classification`,\n `Poisson regression for count data`, `Cox regression for right censored survival time`, `Pairwise ranking`, `Gamma regression with log-link`, `Tweedie regression with log-link`\n\n4. **Max Depth**: Maximum depth of the tree. For example, depth 0 means 1 leaf node and depth 1 means 1 internal node + 2 leaf nodes. Increasing this value will make the model more complex and more likely to overfit. Beware that XGBoost aggressively consumes memory when training a deep tree.\n\n5. **Learning Rate**: Step size shrinkage used in update to prevents overfitting. After each boosting step, we can directly get the weights of new features, and the `learning rate` shrinks the feature weights to make the boosting process more conservative. \n\n6. **Number of Trees**: Number of generated trees. If 1, then no bootstrapping is used. If > 1, then bootstrapping will be\n performed.\n\n7. **Class Weights**: Weights associated with classes. If weights are not given, all classes are supposed to have weight one. Note that the class labels are considered in lexicographical order.\n\n8. **Importance Type**: `Importance Type` is a feature importance type property.\n - Available Options : `Gain`, `Weight`, `Cover`, `Total Gain`, `Total Cover`\n \n9. **Fraction of Samples**: The `fraction of samples` is to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. `Fraction of samples` interacts with the parameter `Number of Trees`. Choosing `Fraction of samples` < 1.0 leads to a reduction of variance and an increase in bias.\n\n10. **Seed**: Random seed. Use the same seed in order to reproduce the same results.\n\n11. **Group By**: Columns to group by.\n\n\n### Python\n#### USAGE\n\n```\nxgb_classification_train(table, feature_cols, label_col, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True objective='binary:logistic', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, random_state=None, seed=None, missing=None, importance_type='gain', class_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None, sample_weight_eval_set=None)\n```\n\n#### INPUT\n1. **table***: (Table) Training data in a Table. It must contain at least one column for labels.\n\n#### OUTPUT\n1. **model**: (Model) Trained XGBoost classification model.\n\n#### PARAMETER\n1. **feature_cols***: Feature Column names. Only values in numeric type are possible.\n\t* Type: *list[str]*\n2. **label_col***: The column designated for the labels of data. The column should contain categorical type values as it is for classification.\n\t* Type: *str*\n3. **objective**: The learning task and the corresponding learning objective. For multi-class classification (the number of classes is greater than or equal to 3), the learning task uses softmax objective.\n\t* Type: *str*\n\t* Default / Range: binary:logistic ( binary:logistic | binary:hinge | count:poisson | survival:cox | rank:pairwise | reg:gamma | reg:tweedie )\n4. **max_depth**: Maximum depth of the tree. For example, depth 0 means 1 leaf node and depth 1 means 1 internal node + 2 leaf nodes. Increasing this value will make the model more complex and more likely to overfit. Beware that XGBoost aggressively consumes memory when training a deep tree.\n\t* Type: *int*\n\t* Default / Range: 3 (1 <= value)\n5. **learning_rate**: Step size shrinkage used in update to prevents overfitting. After each boosting step, we can directly get the weights of new features, and the `learning rate` shrinks the feature weights to make the boosting process more conservative. \n\t* Type: *float*\n\t* Default / Range: 0.1 (0 <= value)\n6. **n_estimators**: Number of generated trees. If 1, then no bootstrapping is used. If > 1, then bootstrapping will be\n performed.\n\t* Type: *int*\n\t* Default / Range: 100 (1 <= value)\n7. **class_weight**: Weights associated with classes. If weights are not given, all classes are supposed to have weight one. Note that the class labels are considered in lexicographical order.\n\t* Type: *int*\n\t* Default / Range: None (1 <= value)\n8. **importance_type**: The feature importance type for the `feature_importances_` property\n\t* Type: *str*\n\t* Default / Range: gain ( gain | weight | cover | total_gain | total_cover )\n9. **subsample**: The subsample to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. `subsample` interacts with the parameter `n_estimators`. Choosing Subsample < 1.0 leads to a reduction of variance and an increase in bias.\n\t* Type: *float*\n\t* Default / Range: 1.0 (0 < value <= 1.0)\n10. **random_state**: The seed used by the random number generator.\n\t* Type: *int*\n11. **silent** : Whether print messages during construction.\n - Type : _bool_ \n - Default value: True\n12. **booster** : Specify which booster to use.\n - Type : _str_ \n - Default / Range : gbtree ( gbtree | gblinear | dart )\n13. **n_jobs** : Number of parallel threads used to run xgboost.\n - Type : _int_ \n - Default / Range: 1 (1 <= value)\n14. **nthread** : Number of threads to use for loading data from numpy array. If -1, uses maximum threads available on the system.\n - Type : _int_ \n - Default / Range: None (-1 <= value)\n15. **gamma** : Minimum loss reduction required to make a further partition on a leaf node of the tree.\n - Type : _float_ \n - Default / Range: 0 (0 <= value)\n16. **min_child_weight** : Minimum sum of instance weight(hessian) needed in a child.\n - Type : _int_ \n - Default / Range: 1 (0 <= value)\n17. **max_delta_step** : Maximum delta step we allow each tree’s weight estimation to be.\n - Type : _int_ \n - Default / Range: 0 (0 <= value)\n18. **colsample_bytree** : `subsample` ratio of columns when constructing each tree.\n - Type : _float_ \n - Default / Range: 1 (0 <= value)\n19. **colsample_bylevel** : `subsample` ratio of columns for each level.\n - Type : _float_ \n - Default / Range: 1 (0 <= value)\n20. **reg_alpha** : L1 regularization term on weights.\n - Type : _float_ \n - Default value: 0\n21. **reg_lambda** : L2 regularization term on weights.\n - Type : _float_ \n - Default value: 1\n22. **scale_pos_weight** : Balancing of positive and negative weights.\n - Type : _float_ \n - Default / Range: 1 (0 <= value)\n23. **base_score** : The initial prediction score of all instances, global bias.\n - Type : _float_ \n - Default / Range: 0.5 (0 <= value)\n24. **seed** : Random seed. Use the same seed in order to reproduce the same results.\n - Type : _int_ \n - Default / Range: 0 (0 <= value)\n25. **missing** : Value in the data which needs to be present as a missing value.\n - Type : _float_ \n - Default / Range: None (0 <= value)\n26. **group_by**: Columns to group by.\n\t* Type: *list[str]*\n27. **eval_set** : A list of (X, y) tuple pairs to use as validation sets, for which metrics will be computed. Validation metrics will help us track the performance of the model.\n - Type : _list_[_tuple_] \n - Default value: None\n28. **eval_metric** : If a str, should be a built-in evaluation metric to use. If a list of str, should be the list of multiple built-in evaluation metrics to use. If callable, a custom evaluation metric. The call signature is func(y_predicted, y_true) where y_true will be a DMatrix object such that you may need to call the get_label method. It must return a str, value pair where the str is a name for the evaluation and value is the value of the evaluation function. The callable custom objective is always minimized.\n - Type : _str_ or _list[_str_]\n - Default / Range: None ( logloss | error | auc | aucpr | mlogloss | merror )\n29. **early_stopping_rounds** : Activates early stopping. Cross-Validation metric (average of validation metric computed over CV folds) needs to improve at least once in every `early_stopping_rounds` round(s) to continue training. The last entry in the evaluation history will represent the best iteration. If there’s more than one metric in the eval_metric parameter given in params, the last metric will be used for early stopping.\n - Type : _int_ \n - Default / Range: None (0 <= value)\n30. **verbose** : If `verbose` and an `eval_set` is used, writes the evaluation metric measured on the validation set to stderr.\n - Type : _bool_ \n - Default value : True\n31. **xgb_model** : file name of stored XGBoost model or `booster` instance XGBoost model to be loaded before training (allows training continuation).\n - Type : _str_ \n - Default value: None\n32. **sample_weight_eval_set** : A list of the form [L\\_1, L\\_2, …, L\\_n], where each L\\_i is a list of instance weights on the i-th validation set.\n - Type : _list_[_str_] \n - Default value: None\n\n\t\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\n\nIn this tutorial workflow, sample_iris data is used to train a XGB classification. The generated model is afterward applied to a test dataset to predict the class of _species_ by using XGB Classification Predict function. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Feature Columns***: sepal\\_length, sepal\\_width\n2. **Label Column***: species\n3. **Objective for Binary Classification**: Logistic regression\n4. **Max Depth**: 3\n5. **Learning Rate**: 0.1\n6. **Number of Trees**: 100\n7. **Class Weights**: None\n8. **Importance Type**: Gain\n9. **Fraction of Samples**: 1.0\n10. **Seed**: 0\n11. **Group By**: None\n\n\n### Python\n\n```\nfrom brightics.function.classification import xgb_classification_train\ninput_table = inputs[0]\nresult = xgb_classification_train(table=input_table,\n\t\t\t\tfeature_cols=['sepal_length', 'sepal_width'],\n\t\t\t\tlabel_col='species',\n\t\t\t\tmax_depth=3,\n\t\t\t\tlearning_rate=0.1, \n\t\t\t\tn_estimators=100, \t\n\t\t\t\tsilent=True, \n\t\t\t\tobjective='binary:logistic', \n\t\t\t\tbooster='gbtree', \n\t\t\t\tn_jobs=1,\n\t\t\t\tnthread=None,\n\t\t\t\tgamma=0,\n\t\t\t\tmin_child_weight=1,\n\t\t\t\tmax_delta_step=0,\n\t\t\t\tsubsample=1,\n\t\t\t\tcolsample_bytree=1,\n\t\t\t\tcolsample_bylevel=1,\n\t\t\t\treg_alpha=0,\n\t\t\t\treg_lambda=1, \n \t\t\t\tscale_pos_weight=1, \n\t\t\t\tbase_score=0.5, \n\t\t\t\trandom_state=None, \n\t\t\t\tseed=None, \n\t\t\t\tmissing=None, \n\t\t\t\timportance_type='gain', \n\t\t\t\tclass_weight=None, \n\t\t\t\teval_set=None, \n\t\t\t\teval_metric=None, \n\t\t\t\tearly_stopping_rounds=None, \n\t\t\t\tverbose=True, \n\t\t\t\txgb_model=None, \n\t\t\t\tsample_weight_eval_set=None)\noutput = result['model']\n```\nIn this python script, sample_iris data is used to train a XGB classification. The generated model can be applied to a test dataset to predict the class of _species_ by using XGB Classification Predict function.\n", + "md": "# XGB Classification Train\nThis function trains a XGBoost Classification model which is highly efficient, flexible and portable.\n\n## Description\nXGBoost stands for 'Extreme Gradient Boosting', where the term 'Gradient Boosting' originates from the paper Greedy Function Approximation: A Gradient Boosting Machine, by Friedman. \n\nXGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples. To sum up, XGBoost is an optimized distributed gradient boosting.\n\nReference\n- \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table)\nTraining data in a Table. It must contain at least one column for labels. \n\n#### OUTPUT\n1. **model**: (Model) Trained XGBoost Classification model. \n\n#### PARAMETER\n1. **Feature Columns***: Feature Column names. Only values in numeric type are possible.\n\n2. **Label Column***: The column designated for the labels of data. The column should contain categorical type values as it is for classification.\n\n3. **Objective for Binary Classification**: Specify the learning task and the corresponding learning objective or a custom objective function to be used. The objective options are below:\n\tbinary:logistic - logistic regression for binary classification, output probability.\n\tbinary:hinge - hinge loss for binary classification. This makes predictions of 0 or 1, rather than producing probabilities.\n\tcount:poisson - Poisson regression for count data, output mean of poisson distribution\n\tsurvival:cox - Cox regression for right censored survival time data (negative values are considered right censored)\n\trank:pairwise - Use LambdaMART to perform pairwise ranking where the pairwise loss is minimized\n\treg:gamma - gamma regression with log-link. Output is a mean of gamma distribution. It might be useful, e.g., for modeling insurance claims severity, or for any outcome that might be gamma-distributed\n\treg:tweedie - Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any outcome that might be Tweedie-distributed.\n - Available Options : `Logistic regression`, `Hinge loss for binary classification`,\n `Poisson regression for count data`, `Cox regression for right censored survival time`, `Pairwise ranking`, `Gamma regression with log-link`, `Tweedie regression with log-link`\n\n4. **Max Depth**: Maximum depth of the tree. For example, depth 0 means 1 leaf node and depth 1 means 1 internal node + 2 leaf nodes. Increasing this value will make the model more complex and more likely to overfit. 0 is only accepted in loss guided growing policy when tree_method is set as hist and it indicates no limit on depth. Beware that XGBoost aggressively consumes memory when training a deep tree.\n\n5. **Learning Rate**: Step size shrinkage used in update to prevents overfitting. After each boosting step, we can directly get the weights of new features, and the `learning rate` shrinks the feature weights to make the boosting process more conservative. \n\n6. **Number of Trees**: Number of generated trees. If 1, then no bootstrapping is used. If > 1, then bootstrapping will be\n performed.\n\n7. **Class Weights**: Weights associated with classes. If weights are not given, all classes are supposed to have weight one. Note that the class labels are considered in lexicographical order.\n\n8. **Importance Type**: `Importance Type` is a feature importance type property.\n - Available Options : `Gain`, `Weight`, `Cover`, `Total Gain`, `Total Cover`\n \n9. **Fraction of Samples**: The `fraction of samples` is to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. `Fraction of samples` interacts with the parameter `Number of Trees`. Choosing `Fraction of samples` < 1.0 leads to a reduction of variance and an increase in bias.\n\n10. **Seed**: Random seed. Use the same seed in order to reproduce the same results.\n\n11. **Group By**: Columns to group by.\n\n\n### Python\n#### USAGE\n\n```\nxgb_classification_train(table, feature_cols, label_col, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True objective='binary:logistic', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, random_state=None, seed=None, missing=None, importance_type='gain', class_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None, sample_weight_eval_set=None)\n```\n\n#### INPUT\n1. **table***: (Table) Training data in a Table. It must contain at least one column for labels.\n\n#### OUTPUT\n1. **model**: (Model) Trained XGBoost classification model.\n\n#### PARAMETER\n1. **feature_cols***: Feature Column names. Only values in numeric type are possible.\n\t* Type: *list[str]*\n2. **label_col***: The column designated for the labels of data. The column should contain categorical type values as it is for classification.\n\t* Type: *str*\n3. **objective**: Specify the learning task and the corresponding learning objective or a custom objective function to be used. The objective options are below:\n\tbinary:logistic - logistic regression for binary classification, output probability.\n\tbinary:hinge - hinge loss for binary classification. This makes predictions of 0 or 1, rather than producing probabilities.\n\tcount:poisson - Poisson regression for count data, output mean of poisson distribution\n\tsurvival:cox - Cox regression for right censored survival time data (negative values are considered right censored)\n\trank:pairwise - Use LambdaMART to perform pairwise ranking where the pairwise loss is minimized\n\treg:gamma - gamma regression with log-link. Output is a mean of gamma distribution. It might be useful, e.g., for modeling insurance claims severity, or for any outcome that might be gamma-distributed\n\treg:tweedie - Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any outcome that might be Tweedie-distributed.\n\t* Type: *str*\n\t* Default / Range: binary:logistic ( binary:logistic | binary:hinge | count:poisson | survival:cox | rank:pairwise | reg:gamma | reg:tweedie )\n4. **max_depth**: Maximum depth of the tree. For example, depth 0 means 1 leaf node and depth 1 means 1 internal node + 2 leaf nodes. Increasing this value will make the model more complex and more likely to overfit. 0 is only accepted in loss guided growing policy when tree_method is set as hist and it indicates no limit on depth. Beware that XGBoost aggressively consumes memory when training a deep tree.\n\t* Type: *int*\n\t* Default / Range: 3 (1 <= value)\n5. **learning_rate**: Step size shrinkage used in update to prevents overfitting. After each boosting step, we can directly get the weights of new features, and the `learning rate` shrinks the feature weights to make the boosting process more conservative. \n\t* Type: *float*\n\t* Default / Range: 0.1 (0 <= value)\n6. **n_estimators**: Number of generated trees. If 1, then no bootstrapping is used. If > 1, then bootstrapping will be\n performed.\n\t* Type: *int*\n\t* Default / Range: 100 (1 <= value)\n7. **class_weight**: Weights associated with classes. If weights are not given, all classes are supposed to have weight one. Note that the class labels are considered in lexicographical order.\n\t* Type: *int*\n\t* Default / Range: None (1 <= value)\n8. **importance_type**: The feature importance type for the `feature_importances_` property\n\t* Type: *str*\n\t* Default / Range: gain ( gain | weight | cover | total_gain | total_cover )\n9. **subsample**: The subsample to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. `subsample` interacts with the parameter `n_estimators`. Choosing Subsample < 1.0 leads to a reduction of variance and an increase in bias.\n\t* Type: *float*\n\t* Default / Range: 1.0 (0 < value <= 1.0)\n10. **random_state**: The seed used by the random number generator.\n\t* Type: *int*\n11. **silent** : Whether print messages during construction.\n - Type : _bool_ \n - Default value: True\n12. **booster** : Specify which booster to use.\n - Type : _str_ \n - Default / Range : gbtree ( gbtree | gblinear | dart )\n13. **n_jobs** : Number of parallel threads used to run xgboost.\n - Type : _int_ \n - Default / Range: 1 (1 <= value)\n14. **nthread** : Number of threads to use for loading data from numpy array. If -1, uses maximum threads available on the system.\n - Type : _int_ \n - Default / Range: None (-1 <= value)\n15. **gamma** : Minimum loss reduction required to make a further partition on a leaf node of the tree.\n - Type : _float_ \n - Default / Range: 0 (0 <= value)\n16. **min_child_weight** : Minimum sum of instance weight(hessian) needed in a child.\n - Type : _int_ \n - Default / Range: 1 (0 <= value)\n17. **max_delta_step** : Maximum delta step we allow each tree’s weight estimation to be.\n - Type : _int_ \n - Default / Range: 0 (0 <= value)\n18. **colsample_bytree** : `subsample` ratio of columns when constructing each tree.\n - Type : _float_ \n - Default / Range: 1 (0 <= value)\n19. **colsample_bylevel** : `subsample` ratio of columns for each level.\n - Type : _float_ \n - Default / Range: 1 (0 <= value)\n20. **reg_alpha** : L1 regularization term on weights.\n - Type : _float_ \n - Default value: 0\n21. **reg_lambda** : L2 regularization term on weights.\n - Type : _float_ \n - Default value: 1\n22. **scale_pos_weight** : Balancing of positive and negative weights.\n - Type : _float_ \n - Default / Range: 1 (0 <= value)\n23. **base_score** : The initial prediction score of all instances, global bias.\n - Type : _float_ \n - Default / Range: 0.5 (0 <= value)\n24. **seed** : Random seed. Use the same seed in order to reproduce the same results.\n - Type : _int_ \n - Default / Range: 0 (0 <= value)\n25. **missing** : Value in the data which needs to be present as a missing value.\n - Type : _float_ \n - Default / Range: None (0 <= value)\n26. **group_by**: Columns to group by.\n\t* Type: *list[str]*\n27. **eval_set** : A list of (X, y) tuple pairs to use as validation sets, for which metrics will be computed. Validation metrics will help us track the performance of the model.\n - Type : _list_[_tuple_] \n - Default value: None\n28. **eval_metric** : If a str, should be a built-in evaluation metric to use. If a list of str, should be the list of multiple built-in evaluation metrics to use. If callable, a custom evaluation metric. The call signature is func(y_predicted, y_true) where y_true will be a DMatrix object such that you may need to call the get_label method. It must return a str, value pair where the str is a name for the evaluation and value is the value of the evaluation function. The callable custom objective is always minimized.\n - Type : _str_ or _list[_str_]\n - Default / Range: None ( logloss | error | auc | aucpr | mlogloss | merror )\n29. **early_stopping_rounds** : Activates early stopping. Cross-Validation metric (average of validation metric computed over CV folds) needs to improve at least once in every `early_stopping_rounds` round(s) to continue training. The last entry in the evaluation history will represent the best iteration. If there’s more than one metric in the eval_metric parameter given in params, the last metric will be used for early stopping.\n - Type : _int_ \n - Default / Range: None (0 <= value)\n30. **verbose** : If `verbose` and an `eval_set` is used, writes the evaluation metric measured on the validation set to stderr.\n - Type : _bool_ \n - Default value : True\n31. **xgb_model** : file name of stored XGBoost model or `booster` instance XGBoost model to be loaded before training (allows training continuation).\n - Type : _str_ \n - Default value: None\n32. **sample_weight_eval_set** : A list of the form [L\\_1, L\\_2, …, L\\_n], where each L\\_i is a list of instance weights on the i-th validation set.\n - Type : _list_[_str_] \n - Default value: None\n\n\t\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\n\nIn this tutorial workflow, sample_iris data is used to train a XGB classification. The generated model is afterward applied to a test dataset to predict the class of _species_ by using XGB Classification Predict function. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Feature Columns***: sepal\\_length, sepal\\_width\n2. **Label Column***: species\n3. **Objective for Binary Classification**: Logistic regression\n4. **Max Depth**: 3\n5. **Learning Rate**: 0.1\n6. **Number of Trees**: 100\n7. **Class Weights**: None\n8. **Importance Type**: Gain\n9. **Fraction of Samples**: 1.0\n10. **Seed**: 0\n11. **Group By**: None\n\n\n### Python\n\n```\nfrom brightics.function.classification import xgb_classification_train\ninput_table = inputs[0]\nresult = xgb_classification_train(table=input_table,\n\t\t\t\tfeature_cols=['sepal_length', 'sepal_width'],\n\t\t\t\tlabel_col='species',\n\t\t\t\tmax_depth=3,\n\t\t\t\tlearning_rate=0.1, \n\t\t\t\tn_estimators=100, \t\n\t\t\t\tsilent=True, \n\t\t\t\tobjective='binary:logistic', \n\t\t\t\tbooster='gbtree', \n\t\t\t\tn_jobs=1,\n\t\t\t\tnthread=None,\n\t\t\t\tgamma=0,\n\t\t\t\tmin_child_weight=1,\n\t\t\t\tmax_delta_step=0,\n\t\t\t\tsubsample=1,\n\t\t\t\tcolsample_bytree=1,\n\t\t\t\tcolsample_bylevel=1,\n\t\t\t\treg_alpha=0,\n\t\t\t\treg_lambda=1, \n \t\t\t\tscale_pos_weight=1, \n\t\t\t\tbase_score=0.5, \n\t\t\t\trandom_state=None, \n\t\t\t\tseed=None, \n\t\t\t\tmissing=None, \n\t\t\t\timportance_type='gain', \n\t\t\t\tclass_weight=None, \n\t\t\t\teval_set=None, \n\t\t\t\teval_metric=None, \n\t\t\t\tearly_stopping_rounds=None, \n\t\t\t\tverbose=True, \n\t\t\t\txgb_model=None, \n\t\t\t\tsample_weight_eval_set=None)\noutput = result['model']\n```\nIn this python script, sample_iris data is used to train a XGB classification. The generated model can be applied to a test dataset to predict the class of _species_ by using XGB Classification Predict function.\n", "exampleModels": [ { "fileName": "brightics.function.classification$xgb_classification.json", diff --git a/function/python/brightics/function/classification/naive_bayes_classification.py b/function/python/brightics/function/classification/naive_bayes_classification.py index 62db5d783..12fabc494 100644 --- a/function/python/brightics/function/classification/naive_bayes_classification.py +++ b/function/python/brightics/function/classification/naive_bayes_classification.py @@ -140,7 +140,9 @@ def naive_bayes_predict(table, model, **params): return _naive_bayes_predict(table, model, **params) -def _naive_bayes_predict(table, model, suffix, display_log_prob=False, prediction_col='prediction', prob_prefix='probability', log_prob_prefix='log_probability'): +def _naive_bayes_predict(table, model, suffix, display_log_prob=False, prediction_col='prediction', + prob_prefix='probability', log_prob_prefix='log_probability', + display_joint_log_likelihood=False, joint_log_likelihood_prefix='joint_log_likelihood'): if 'features' in model: feature_cols = model['features'] else: @@ -169,7 +171,8 @@ def _naive_bayes_predict(table, model, suffix, display_log_prob=False, predictio else: suffixes = [0, 1] - prob = nb_model.predict_proba(features) + prob = nb_model.predict_proba(features) + likelihood = nb_model._joint_log_likelihood(features) prob_cols = ['{prefix}_{suffix}'.format(prefix=prob_prefix, suffix=suffix) for suffix in suffixes] prob_df = pd.DataFrame(data=prob, columns=prob_cols) @@ -184,5 +187,10 @@ def _naive_bayes_predict(table, model, suffix, display_log_prob=False, predictio else: result = pd.concat([result, prob_df], axis=1) + if display_joint_log_likelihood: + likelihood_cols = ['{prefix}_{suffix}'.format(prefix=joint_log_likelihood_prefix, suffix=suffix) for suffix in suffixes] + likelihood_df = pd.DataFrame(likelihood, columns=likelihood_cols) + result = pd.concat([result, likelihood_df], axis=1) + return {'out_table' : result} diff --git a/function/python/brightics/function/classification/svm_classification.py b/function/python/brightics/function/classification/svm_classification.py index 22d9b6201..fc8f88817 100644 --- a/function/python/brightics/function/classification/svm_classification.py +++ b/function/python/brightics/function/classification/svm_classification.py @@ -26,11 +26,21 @@ over_to, less_than_or_equal_to, raise_runtime_error, greater_than_or_equal_to_or_equal_to import sklearn.utils as sklearn_utils from brightics.common.classify_input_type import check_col_type +from brightics.common.exception import BrighticsFunctionException as BFE def svm_classification_train(table, group_by=None, **params): - check_required_parameters(_svm_classification_train, params, ['table']) + check_required_parameters(_svm_classification_train, params, ['table','gamma_val']) params = get_default_from_parameters_if_required(params, _svm_classification_train) + + if params['gamma'] == 'other': + if 'gamma_val' not in params: + raise BFE.from_errors([{'0100': 'Gamma value is mandatory when gamma is other'}]) + if params['gamma_val'] <= 0: + raise BFE.from_errors([{'0100': 'Gamma value must be greater than 0'}]) + else: + params['gamma_val'] = None + param_validation_check = [over_to(params, 0.0, 1.0, 'c'), greater_than_or_equal_to(params, 0, 'degree'), greater_than(params, 0.0, 'tol'), @@ -44,7 +54,7 @@ def svm_classification_train(table, group_by=None, **params): return _svm_classification_train(table, **params) -def _svm_classification_train(table, feature_cols, label_col, c=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, +def _svm_classification_train(table, feature_cols, label_col, gamma_val, c=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=True, tol=1e-3, max_iter=-1, random_state=None, class_weight=None): _table = table.copy() @@ -61,7 +71,11 @@ def _svm_classification_train(table, feature_cols, label_col, c=1.0, kernel='rbf else: class_weight = {class_labels[i] : class_weight[i] for i in range(len(class_labels))} - _svc = svm.SVC(C=c, kernel=kernel, degree=degree, gamma=gamma, coef0=coef0, shrinking=shrinking, + if gamma == 'other': + _gamma = gamma_val + else: + _gamma = gamma + _svc = svm.SVC(C=c, kernel=kernel, degree=degree, gamma=_gamma, coef0=coef0, shrinking=shrinking, probability=probability, tol=tol, max_iter=max_iter, random_state=random_state, class_weight=class_weight) _svc_model = _svc.fit(features, _label_col) diff --git a/function/python/brightics/function/classification/xgb_classification.py b/function/python/brightics/function/classification/xgb_classification.py index 7c9ef3155..407ddfb56 100644 --- a/function/python/brightics/function/classification/xgb_classification.py +++ b/function/python/brightics/function/classification/xgb_classification.py @@ -58,7 +58,7 @@ def _xgb_classification_train(table, feature_cols, label_col, max_depth=3, learn class_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None, sample_weight_eval_set=None): feature_names, features = check_col_type(table, feature_cols) - + if isinstance(features, list): features = np.array(features) @@ -106,10 +106,11 @@ def _xgb_classification_train(table, feature_cols, label_col, max_depth=3, learn get_param = classifier.get_params() feature_importance = classifier.feature_importances_ # plt.rcdefaults() - plot_importance(classifier) - plt.tight_layout() - fig_plot_importance = plt2MD(plt) - plt.clf() + # plot_importance(classifier) + # plt.tight_layout() + # fig_plot_importance = plt2MD(plt) + fig_plot_importance = _plot_feature_importances(feature_names, classifier) + # plt.clf() # plt.rcParams['figure.dpi'] = figure_dpi # plot_tree(classifier) # fig_plot_tree_UT = plt2MD(plt) @@ -206,3 +207,22 @@ def _xgb_classification_predict(table, model, prediction_col='prediction', proba result = pd.concat([result, prob_df], axis=1) return {'out_table': result} + + +def _plot_feature_importances(feature_names, classifier): + + feature_importance = classifier.feature_importances_ + indices = np.argsort(feature_importance) + sorted_feature_names = np.array(feature_names)[indices] + + plt.barh(range(len(indices)), feature_importance[indices], color='b', align='center') + for i, v in enumerate(feature_importance[indices]): + plt.text(v, i, " {:.2f}".format(v), color='b', va='center', fontweight='bold') + + plt.yticks(range(len(indices)), sorted_feature_names) + plt.xlabel("Feature importance") + plt.ylabel("Feature") + plt.tight_layout() + fig_feature_importances = plt2MD(plt) + plt.close() + return fig_feature_importances \ No newline at end of file diff --git a/function/python/brightics/function/clustering/__init__.py b/function/python/brightics/function/clustering/__init__.py index fc27adcbb..07b116e94 100644 --- a/function/python/brightics/function/clustering/__init__.py +++ b/function/python/brightics/function/clustering/__init__.py @@ -21,6 +21,7 @@ from .hierarchical_clustering import hierarchical_clustering from .hierarchical_clustering import hierarchical_clustering_post from .gaussian_mixture import gaussian_mixture_train +from .gaussian_mixture import gaussian_mixture_train2 from .gaussian_mixture import gaussian_mixture_predict from .mean_shift import mean_shift from .mean_shift import mean_shift_predict diff --git a/function/python/brightics/function/clustering/gaussian_mixture.py b/function/python/brightics/function/clustering/gaussian_mixture.py index c7935b459..d253434b2 100644 --- a/function/python/brightics/function/clustering/gaussian_mixture.py +++ b/function/python/brightics/function/clustering/gaussian_mixture.py @@ -118,12 +118,97 @@ def gaussian_mixture_predict(table, model, **params): return _gaussian_mixture_predict(table, model, **params) -def _gaussian_mixture_predict(table, model, display_probability, prediction_col_name='prediction'): +def _gaussian_mixture_predict(table, model, display_probability=False, prediction_col_name='prediction'): out_table = table.copy() _, inputarr = check_col_type(table, model['input_cols']) out_table[prediction_col_name] = model['gmm'].predict(inputarr) - if display_probability == True: + if display_probability is True: for i in range (0, model['number_of_components']): out_table['probability_' + str(i)] = pd.DataFrame(model['gmm'].predict_proba(table[model['input_cols']]))[i] return {'out_table':out_table} + +def gaussian_mixture_train2(table, group_by=None, **params): + check_required_parameters(_gaussian_mixture_train2, params, ['table']) + params = get_default_from_parameters_if_required(params, _gaussian_mixture_train2) + param_validation_check = [greater_than_or_equal_to(params, 1, 'number_of_components'), + greater_than(params, 0, 'tolerance'), + greater_than(params, 0, 'regularize_covariance'), + greater_than_or_equal_to(params, 1, 'max_iteration')] + validate(*param_validation_check) + if group_by is not None: + return _function_by_group(_gaussian_mixture_train2, table, group_by=group_by, **params) + else: + return _gaussian_mixture_train2(table, **params) + + +def _gaussian_mixture_train2(table, input_cols, number_of_components=1, prediction_col='prediction', covariance_type='full', tolerance=0.001, \ + regularize_covariance=1e-06, max_iteration=100, initial_params='kmeans', seed=None): + + gmm = GaussianMixture(n_components=number_of_components, covariance_type=covariance_type, tol=tolerance, \ + reg_covar=regularize_covariance, max_iter=max_iteration, init_params=initial_params, random_state=seed) + feature_names, X_train = check_col_type(table, input_cols) + gmm.fit(X_train) + gmm_prediction = gmm.predict(X_train) + + out_table = pd.DataFrame() + + comp_num_arr = [] + for i in range(0, number_of_components): + comp_num_arr.append(i) + + mean_arr = [] + for i in range(0, number_of_components): + mean_arr.append(gmm.means_[i].tolist()) + + covar_arr = [] + for i in range(0, number_of_components): + covar_arr.append(gmm.covariances_[i].tolist()) + + out_table['component_number'] = comp_num_arr + out_table['weight'] = gmm.weights_ + out_table['mean_coordinate'] = mean_arr + # temporary string type to covar_arr because it is array(array(array(double))) which is not supported so it causes problems + out_table['covariance_matrix'] = str(covar_arr) + + rb = BrtcReprBuilder() + params = { + 'Input Columns': feature_names, + 'Number of Components': number_of_components, + 'Covariance Type': covariance_type, + 'Tolerance': tolerance, + 'Regularization of Covariance': regularize_covariance, + 'Number of Iteration': max_iteration, + 'Method to Initialize': initial_params + } + + rb.addMD(strip_margin(""" + |## Gaussian Mixture Train Result + | + |### Parameters + | + | {params} + | + |### Summary + | + |{result_table} + | + """.format(params=dict2MD(params), result_table=pandasDF2MD(out_table)))) + + model = _model_dict('gaussian_mixture_train') + model['input_cols'] = input_cols + model['number_of_components'] = number_of_components + model['covariance_type'] = covariance_type + model['tolerance'] = tolerance + model['regularize_covariance'] = regularize_covariance + model['max_iteration'] = max_iteration + model['initial_params'] = initial_params + model['seed'] = seed + model['summary'] = out_table + model['gmm'] = gmm + model['_repr_brtc_'] = rb.get() + + tmp_out_table = table.copy() + tmp_out_table[prediction_col] = gmm_prediction + + return {'out_table':tmp_out_table, 'model':model} \ No newline at end of file diff --git a/function/python/brightics/function/clustering/hierarchical_clustering.py b/function/python/brightics/function/clustering/hierarchical_clustering.py index 882aa4a8f..bc2757e29 100644 --- a/function/python/brightics/function/clustering/hierarchical_clustering.py +++ b/function/python/brightics/function/clustering/hierarchical_clustering.py @@ -48,9 +48,8 @@ def hierarchical_clustering(table, group_by=None, **params): def _hierarchical_clustering(table, input_cols, input_mode='original', key_col=None, link='complete', met='euclidean', num_rows=20, figure_height=6.4, orient='right'): out_table = table.copy() feature_names, features = check_col_type(out_table, input_cols) - + len_features = len(features) if input_mode == 'original': - len_features = len(features) if key_col != None: data_names = list(out_table[key_col]) elif key_col == None: @@ -58,20 +57,15 @@ def _hierarchical_clustering(table, input_cols, input_mode='original', key_col=N out_table['name'] = data_names Z = linkage(ssd.pdist(features, metric=met), method=link, metric=met) elif input_mode == 'matrix': - len_features = len(input_cols) - if key_col != None: - data_names = [] - for column in input_cols: - data_names.append(out_table[key_col][out_table.columns.get_loc(column)]) - elif key_col == None: - data_names = [] - for column in input_cols: - data_names.append(out_table.columns[out_table.columns.get_loc(column)]) + data_names = input_cols col_index = [] + if key_col is None: + col_list = out_table.columns.tolist() + else: + col_list = out_table[key_col].tolist() for column in input_cols: - col_index.append(out_table.columns.get_loc(column)) + col_index.append(col_list.index(column)) dist_matrix = features.iloc[col_index] - Z = linkage(ssd.squareform(dist_matrix), method=link, metric=met) dist_matrix['name'] = data_names else: diff --git a/function/python/brightics/function/clustering/meta/gaussian_mixture_predict.json b/function/python/brightics/function/clustering/meta/gaussian_mixture_predict.json index fb96b128f..6051fe29f 100644 --- a/function/python/brightics/function/clustering/meta/gaussian_mixture_predict.json +++ b/function/python/brightics/function/clustering/meta/gaussian_mixture_predict.json @@ -77,42 +77,38 @@ { "type": "file", "data": { - "id": "m2yf2r32xaermewx", - "project_id": "pbdkuznb3y7rhnr9", + "id": "m4pz36vy83772shv", + "project_id": "p5dmzu78qb564crk", "label": "gaussian_mixture", "contents": { - "mid": "m2yf2r32xaermewx", + "mid": "m4pz36vy83772shv", "type": "data", "title": "gaussian_mixture", "functions": [ { "persist-mode": "auto", - "func": "brightics.function.clustering$gaussian_mixture_train06900", - "name": "brightics.function.clustering$gaussian_mixture_train", + "func": "brightics.function.io$load13889", + "name": "brightics.function.io$load", "param": { - "covariance_type": "full", - "initial_params": "random", - "input_cols": [ - "sepal_length", - "sepal_width" - ], - "number_of_components": 3 + "partial_path": [ + "/brightics@samsung.com/upload/sample_iris.csv" + ] }, "display": { - "label": "Gaussian Mixture Train", + "label": "Load", "diagram": { "position": { - "x": 770, + "x": 270, "y": 220 } }, "sheet": { - "in": { + "out": { "partial": [ { "panel": [ { - "id": "default-1583817330225", + "id": "default-1583819857307", "chartOption": { "colorSet": [ "#FD026C", @@ -301,12 +297,12 @@ "selectable": true }, "style": {}, - "group": "xd9kvyjfg24iuvfxem3hr7r5" + "group": "xp56uzsy2bsxb7wnvdpzfeqp" } ], "layout": { "type": "panel", - "id": "default-1583817330225" + "id": "default-1583819857307" } } ], @@ -316,65 +312,41 @@ "layout": {} } ] - }, - "out": { - "partial": [ - { - "panel": [], - "layout": {} - } - ], - "full": [ - { - "panel": [], - "layout": {} - } - ] } } }, "meta": { "table": { "type": "table" - }, - "model": { - "type": "model" } }, "context": "python", "version": "3.6", - "inputs": { - "table": "tjvq7msjmhx3brdg" - }, "outputs": { - "model": "tz6qz7tmz5z5cubb" + "table": "tcn7c8sauan95feh" }, - "fid": "fjmwfv7u2d9utem9" + "fid": "fk4fx2ws8rf2uygm" }, { "persist-mode": "auto", - "func": "brightics.function.io$load13889", - "name": "brightics.function.io$load", - "param": { - "partial_path": [ - "/brightics@samsung.com/upload/sample_iris.csv" - ] - }, + "func": "brightics.function.transform$split_data", + "name": "brightics.function.transform$split_data", + "param": {}, "display": { - "label": "Load", + "label": "Split Data", "diagram": { "position": { - "x": 270, + "x": 520, "y": 220 } }, "sheet": { - "out": { + "in": { "partial": [ { "panel": [ { - "id": "default-1583819857307", + "id": "default-1583819876807", "chartOption": { "colorSet": [ "#FD026C", @@ -563,12 +535,12 @@ "selectable": true }, "style": {}, - "group": "xp56uzsy2bsxb7wnvdpzfeqp" + "group": "8kdasghsptgwdun95p4ukenq" } ], "layout": { "type": "panel", - "id": "default-1583819857307" + "id": "default-1583819876807" } } ], @@ -578,41 +550,13 @@ "layout": {} } ] - } - } - }, - "meta": { - "table": { - "type": "table" - } - }, - "context": "python", - "version": "3.6", - "outputs": { - "table": "tnazfdvy9xarr58k" - }, - "fid": "fm2mfmdrujyzaref" - }, - { - "persist-mode": "auto", - "func": "brightics.function.transform$split_data", - "name": "brightics.function.transform$split_data", - "param": {}, - "display": { - "label": "Split Data", - "diagram": { - "position": { - "x": 520, - "y": 220 - } - }, - "sheet": { - "in": { + }, + "out": { "partial": [ { "panel": [ { - "id": "default-1583819876807", + "id": "default-1583819876867", "chartOption": { "colorSet": [ "#FD026C", @@ -801,12 +745,12 @@ "selectable": true }, "style": {}, - "group": "8kdasghsptgwdun95p4ukenq" + "group": "ep6979rkqp6kk5jnvw65pfsw" } ], "layout": { "type": "panel", - "id": "default-1583819876807" + "id": "default-1583819876867" } } ], @@ -816,13 +760,53 @@ "layout": {} } ] - }, - "out": { + } + } + }, + "meta": { + "table": { + "type": "table" + }, + "train_table": { + "type": "table" + }, + "test_table": { + "type": "table" + } + }, + "context": "python", + "version": "3.6", + "inputs": { + "table": "tcn7c8sauan95feh" + }, + "outputs": { + "train_table": "twrc64q2c3aw86px", + "test_table": "tu6vqbprjzxfktjh" + }, + "fid": "fzrbh8avzwp9hygh" + }, + { + "persist-mode": "auto", + "func": "brightics.function.clustering$gaussian_mixture_predict65689", + "name": "brightics.function.clustering$gaussian_mixture_predict", + "param": { + "display_probability": false + }, + "display": { + "label": "Gaussian Mixture Predict", + "diagram": { + "position": { + "x": 1020, + "y": 220 + } + }, + "sheet": { + "in": { "partial": [ { "panel": [ { - "id": "default-1583819876867", + "id": "default-1583819927689", "chartOption": { "colorSet": [ "#FD026C", @@ -1011,12 +995,12 @@ "selectable": true }, "style": {}, - "group": "ep6979rkqp6kk5jnvw65pfsw" + "group": "22ze4re4xmfajxsvqfrwbnus" } ], "layout": { "type": "panel", - "id": "default-1583819876867" + "id": "default-1583819927689" } } ], @@ -1026,53 +1010,13 @@ "layout": {} } ] - } - } - }, - "meta": { - "table": { - "type": "table" - }, - "train_table": { - "type": "table" - }, - "test_table": { - "type": "table" - } - }, - "context": "python", - "version": "3.6", - "inputs": { - "table": "tnazfdvy9xarr58k" - }, - "outputs": { - "train_table": "tjvq7msjmhx3brdg", - "test_table": "t67jbpw7b7j53xpd" - }, - "fid": "fvu2z8ta8a9ds2px" - }, - { - "persist-mode": "auto", - "func": "brightics.function.clustering$gaussian_mixture_predict65689", - "name": "brightics.function.clustering$gaussian_mixture_predict", - "param": { - "display_probability": false - }, - "display": { - "label": "Gaussian Mixture Predict", - "diagram": { - "position": { - "x": 1020, - "y": 220 - } - }, - "sheet": { - "in": { + }, + "out": { "partial": [ { "panel": [ { - "id": "default-1583819927689", + "id": "default-1583819927750", "chartOption": { "colorSet": [ "#FD026C", @@ -1120,7 +1064,11 @@ "right": "8px", "top": "8px", "orientation": "horizontal", - "menu": {} + "menu": { + "zoom": { + "zoomAxis": "all" + } + } }, "chart": { "border": "1px #000000 none", @@ -1137,11 +1085,20 @@ "top": "60px", "bottom": "60px", "width": "auto", - "height": "auto" + "height": "auto", + "row": 5, + "column": 5, + "cellWidth": 50, + "cellHeight": 50, + "showImageGrid": true }, "xAxis": [ { - "selected": [], + "selected": [ + { + "name": "species" + } + ], "title": { "show": true, "text": "", @@ -1181,7 +1138,12 @@ ], "yAxis": [ { - "selected": [], + "selected": [ + { + "name": "sepal_length", + "aggregation": "sum" + } + ], "title": { "show": true, "text": "", @@ -1245,28 +1207,135 @@ "table": { "formatter": [] }, + "bubble": { + "marker": { + "symbolSize": 1, + "itemStyle": { + "normal": { + "opacity": 1 + } + } + }, + "stripLine": { + "lineStyle": { + "normal": { + "color": "#000000", + "type": "solid", + "width": 1 + } + }, + "data": [ + {} + ] + }, + "trendLine": { + "lineStyle": { + "normal": { + "color": "#000000", + "type": "solid", + "width": 1 + } + }, + "data": [ + {} + ] + }, + "sizeBy": [ + { + "selected": [] + } + ] + }, "map": { "geoData": {} - } - }, - "source": { - "dataType": "lazy", - "lazyData": [] - }, + }, + "area": { + "smooth": true, + "marker": { + "symbolSize": 1, + "itemStyle": { + "normal": { + "opacity": 1 + } + } + }, + "stripLine": { + "lineStyle": { + "normal": { + "color": "#000000", + "type": "solid", + "width": 1 + } + }, + "data": [ + {} + ] + }, + "trendLine": { + "lineStyle": { + "normal": { + "color": "#000000", + "type": "solid", + "width": 1 + } + }, + "data": [ + {} + ] + }, + "tooltip": { + "trigger": "axis" + }, + "showSymbol": { + "all": "auto" + } + }, + "pie": { + "radius": "75%", + "center": [ + "50%", + "50%" + ], + "sizeBy": [ + { + "selected": [] + } + ] + } + }, + "source": { + "dataType": "lazy", + "lazyData": [] + }, "guide": { "message": "Set data options using Chart Setting Button." - } + }, + "sortBy": [ + { + "selected": [] + } + ], + "imageColumn": [ + { + "selected": [] + } + ], + "labelColumn": [ + { + "selected": [] + } + ] }, "dataSource": { "selectable": true }, "style": {}, - "group": "22ze4re4xmfajxsvqfrwbnus" + "group": "dg2nwyqi2hcyquwi6nin9bfh" } ], "layout": { "type": "panel", - "id": "default-1583819927689" + "id": "default-1583819927750" } } ], @@ -1276,13 +1345,59 @@ "layout": {} } ] - }, - "out": { + } + } + }, + "meta": { + "table": { + "type": "table" + }, + "model": { + "type": "model" + }, + "out_table": { + "type": "table" + } + }, + "context": "python", + "version": "3.6", + "inputs": { + "table": "tu6vqbprjzxfktjh", + "model": "th6de2wyq7ujhxsx" + }, + "outputs": { + "out_table": "tzps8z8denr8ug6d" + }, + "fid": "f7xtm78jdqxhtrgd" + }, + { + "persist-mode": "auto", + "func": "brightics.function.clustering$gaussian_mixture_train2", + "name": "brightics.function.clustering$gaussian_mixture_train2", + "param": { + "covariance_type": "full", + "initial_params": "random", + "input_cols": [ + "sepal_length", + "sepal_width" + ], + "number_of_components": 3 + }, + "display": { + "label": "Gaussian Mixture", + "diagram": { + "position": { + "x": 770, + "y": 220 + } + }, + "sheet": { + "in": { "partial": [ { "panel": [ { - "id": "default-1583819927750", + "id": "default-1593657713685", "chartOption": { "colorSet": [ "#FD026C", @@ -1330,11 +1445,7 @@ "right": "8px", "top": "8px", "orientation": "horizontal", - "menu": { - "zoom": { - "zoomAxis": "all" - } - } + "menu": {} }, "chart": { "border": "1px #000000 none", @@ -1351,20 +1462,11 @@ "top": "60px", "bottom": "60px", "width": "auto", - "height": "auto", - "row": 5, - "column": 5, - "cellWidth": 50, - "cellHeight": 50, - "showImageGrid": true + "height": "auto" }, "xAxis": [ { - "selected": [ - { - "name": "species" - } - ], + "selected": [], "title": { "show": true, "text": "", @@ -1404,12 +1506,7 @@ ], "yAxis": [ { - "selected": [ - { - "name": "sepal_length", - "aggregation": "sum" - } - ], + "selected": [], "title": { "show": true, "text": "", @@ -1470,103 +1567,11 @@ }, "noDataMessage": "", "plotOptions": { - "bubble": { - "marker": { - "symbolSize": 1, - "itemStyle": { - "normal": { - "opacity": 1 - } - } - }, - "stripLine": { - "lineStyle": { - "normal": { - "color": "#000000", - "type": "solid", - "width": 1 - } - }, - "data": [ - {} - ] - }, - "trendLine": { - "lineStyle": { - "normal": { - "color": "#000000", - "type": "solid", - "width": 1 - } - }, - "data": [ - {} - ] - }, - "sizeBy": [ - { - "selected": [] - } - ] - }, "table": { "formatter": [] }, "map": { "geoData": {} - }, - "area": { - "smooth": true, - "marker": { - "symbolSize": 1, - "itemStyle": { - "normal": { - "opacity": 1 - } - } - }, - "stripLine": { - "lineStyle": { - "normal": { - "color": "#000000", - "type": "solid", - "width": 1 - } - }, - "data": [ - {} - ] - }, - "trendLine": { - "lineStyle": { - "normal": { - "color": "#000000", - "type": "solid", - "width": 1 - } - }, - "data": [ - {} - ] - }, - "tooltip": { - "trigger": "axis" - }, - "showSymbol": { - "all": "auto" - } - }, - "pie": { - "radius": "75%", - "center": [ - "50%", - "50%" - ], - "sizeBy": [ - { - "selected": [] - } - ] } }, "source": { @@ -1575,33 +1580,228 @@ }, "guide": { "message": "Set data options using Chart Setting Button." + } + }, + "dataSource": { + "selectable": true + }, + "style": {}, + "group": "i685eyhg58n4h5fdr578meun" + } + ], + "layout": { + "type": "panel", + "id": "default-1593657713685" + } + } + ], + "full": [ + { + "panel": [], + "layout": {} + } + ] + }, + "out": { + "partial": [ + { + "panel": [ + { + "id": "default-1593657713775", + "chartOption": { + "colorSet": [ + "#FD026C", + "#4682B8", + "#A5D22D", + "#F5CC0A", + "#FE8C01", + "#6B9494", + "#B97C46", + "#84ACD0", + "#C2E173", + "#F9DD5B", + "#FE569D", + "#FEB356", + "#9CB8B8", + "#D0A884", + "#2E6072", + "#6D8C1E", + "#A48806", + "#A90148", + "#A95E01", + "#476363", + "#7B532F" + ], + "title": { + "show": true, + "text": "", + "subtext": "", + "rotate": 0, + "textStyle": { + "color": "#000000", + "fontSize": 12, + "fontFamily": "" + }, + "subtextStyle": { + "color": "#000000", + "fontSize": 12, + "fontFamily": "" + }, + "left": "50%", + "top": "0px" }, - "sortBy": [ + "toolbar": { + "show": false, + "right": "8px", + "top": "8px", + "orientation": "horizontal", + "menu": {} + }, + "chart": { + "border": "1px #000000 none", + "background": "rgba(255, 255, 255,1)", + "animationDuration": 1000, + "type": "table" + }, + "tooltip": { + "triggerOn": "mousemove" + }, + "grid": { + "left": "10%", + "right": "10%", + "top": "60px", + "bottom": "60px", + "width": "auto", + "height": "auto" + }, + "xAxis": [ { - "selected": [] + "selected": [], + "title": { + "show": true, + "text": "", + "subtext": "", + "rotate": 0, + "textStyle": { + "color": "#000000", + "fontSize": 12, + "fontFamily": "" + }, + "subtextStyle": { + "color": "#000000", + "fontSize": 12, + "fontFamily": "" + }, + "left": "50%", + "bottom": "5px" + }, + "axisTick": { + "show": true + }, + "axisLine": { + "onZero": false + }, + "scale": true, + "axisLabel": { + "show": true, + "rotate": 0, + "textStyle": { + "color": "#000000", + "fontSize": 12, + "fontFamily": "" + } + }, + "zlevel": 1 } ], - "imageColumn": [ + "yAxis": [ { - "selected": [] + "selected": [], + "title": { + "show": true, + "text": "", + "subtext": "", + "rotate": -90, + "textStyle": { + "color": "#000000", + "fontSize": 12, + "fontFamily": "" + }, + "subtextStyle": { + "color": "#000000", + "fontSize": 12, + "fontFamily": "" + }, + "left": "8px", + "top": "50%" + }, + "axisTick": { + "show": true + }, + "axisLine": { + "onZero": false + }, + "scale": true, + "axisLabel": { + "show": true, + "rotate": 0, + "textStyle": { + "color": "#000000", + "fontSize": 12, + "fontFamily": "" + } + }, + "zlevel": 1 } ], - "labelColumn": [ + "colorBy": [ { "selected": [] } - ] + ], + "legend": { + "show": true, + "orientation": "horizontal", + "left": "50%", + "right": "auto", + "top": "10px", + "bottom": "auto", + "textStyle": { + "color": "#000000", + "fontSize": 12, + "fontFamily": "", + "fontStyle": "normal", + "fontWeight": "normal", + "fontDecoration": "none" + } + }, + "noDataMessage": "", + "plotOptions": { + "table": { + "formatter": [] + }, + "map": { + "geoData": {} + } + }, + "source": { + "dataType": "lazy", + "lazyData": [] + }, + "guide": { + "message": "Set data options using Chart Setting Button." + } }, "dataSource": { "selectable": true }, "style": {}, - "group": "dg2nwyqi2hcyquwi6nin9bfh" + "group": "cntjrabet3zxa86g5hcmtdng" } ], "layout": { "type": "panel", - "id": "default-1583819927750" + "id": "default-1593657713775" } } ], @@ -1618,45 +1818,45 @@ "table": { "type": "table" }, - "model": { - "type": "model" - }, "out_table": { "type": "table" + }, + "model": { + "type": "model" } }, "context": "python", "version": "3.6", "inputs": { - "table": "t67jbpw7b7j53xpd", - "model": "tz6qz7tmz5z5cubb" + "table": "twrc64q2c3aw86px" }, "outputs": { - "out_table": "tyygzvy2heuvcn26" + "out_table": "t5pj9pycshzv26bu", + "model": "th6de2wyq7ujhxsx" }, - "fid": "ft8kdujhx5zwf9x8" + "fid": "faqzwrj2scnmtbge" } ], "links": [ { - "kid": "kt5f72y8bc6pasjf", - "sourceFid": "fm2mfmdrujyzaref", - "targetFid": "fvu2z8ta8a9ds2px" + "kid": "kk9hehpasrtx3999", + "sourceFid": "fk4fx2ws8rf2uygm", + "targetFid": "fzrbh8avzwp9hygh" }, { - "kid": "khatkb4uvbqyyhct", - "sourceFid": "fvu2z8ta8a9ds2px", - "targetFid": "fjmwfv7u2d9utem9" + "kid": "ku284czm28g37jpn", + "sourceFid": "fzrbh8avzwp9hygh", + "targetFid": "f7xtm78jdqxhtrgd" }, { - "kid": "k3frqb9u9sdkxazp", - "sourceFid": "fjmwfv7u2d9utem9", - "targetFid": "ft8kdujhx5zwf9x8" + "kid": "kdqbkdv9w76vc4rw", + "sourceFid": "fzrbh8avzwp9hygh", + "targetFid": "faqzwrj2scnmtbge" }, { - "kid": "kxjmwypk4cnwfz9v", - "sourceFid": "fvu2z8ta8a9ds2px", - "targetFid": "ft8kdujhx5zwf9x8" + "kid": "keng54em2vkdnzqc", + "sourceFid": "faqzwrj2scnmtbge", + "targetFid": "f7xtm78jdqxhtrgd" } ], "preferences": {}, @@ -1685,10 +1885,10 @@ "description": "


", "model_image": null, "creator": "brightics@samsung.com", - "create_time": "2020-03-10T05:15:22.000Z", + "create_time": "2020-07-02T02:41:19.000Z", "updater": "brightics@samsung.com", - "update_time": "2020-03-10T06:01:15.000Z", - "event_key": "2020_060115.000", + "update_time": "2020-07-02T02:42:55.000Z", + "event_key": "2020_024255.000", "type": "data", "tag": "null", "from_version": "0.0" diff --git a/function/python/brightics/function/clustering/meta/gaussian_mixture_train.json b/function/python/brightics/function/clustering/meta/gaussian_mixture_train.json index 4da03c650..3b99c9f29 100644 --- a/function/python/brightics/function/clustering/meta/gaussian_mixture_train.json +++ b/function/python/brightics/function/clustering/meta/gaussian_mixture_train.json @@ -4,11 +4,12 @@ "content": "" }, "specJson": { + "deprecated": true, "category": "clustering", "func": "brightics.function.clustering$gaussian_mixture_train06900", "name": "brightics.function.clustering$gaussian_mixture_train", "context": "python", - "label": "Gaussian Mixture Train", + "label": "Gaussian Mixture", "description": "A Gaussian mixture model is a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters. One can think of mixture models as generalizing k-means clustering to incorporate information about the covariance structure of the data as well as the centers of the latent Gaussians.\n\n- Pros : It is the fastest algorithm for learning mixture models. As this algorithm maximizes only the likelihood, it will not bias the means towards zero, or bias the cluster sizes to have specific structures that might or might not apply.\n\n- Cons : When one has insufficiently many points per mixture, estimating the covariance matrices becomes difficult, and the algorithm is known to diverge and find solutions with infinite likelihood unless one regularizes the covariances artificially. This algorithm will always use all the components it has access to, needing held-out data or information theoretical criteria to decide how many components to use in the absence of external cues.\n\nReference:\n+ \n+ ", "tags": [ "gaussian_mixture_train", diff --git a/function/python/brightics/function/clustering/meta/gaussian_mixture_train2.json b/function/python/brightics/function/clustering/meta/gaussian_mixture_train2.json new file mode 100644 index 000000000..4422a4911 --- /dev/null +++ b/function/python/brightics/function/clustering/meta/gaussian_mixture_train2.json @@ -0,0 +1,2065 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "clustering", + "func": "brightics.function.clustering$gaussian_mixture_train2", + "name": "brightics.function.clustering$gaussian_mixture_train2", + "context": "python", + "label": "Gaussian Mixture", + "description": "A Gaussian mixture model is a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters. One can think of mixture models as generalizing k-means clustering to incorporate information about the covariance structure of the data as well as the centers of the latent Gaussians.\n\n- Pros : It is the fastest algorithm for learning mixture models. As this algorithm maximizes only the likelihood, it will not bias the means towards zero, or bias the cluster sizes to have specific structures that might or might not apply.\n\n- Cons : When one has insufficiently many points per mixture, estimating the covariance matrices becomes difficult, and the algorithm is known to diverge and find solutions with infinite likelihood unless one regularizes the covariances artificially. This algorithm will always use all the components it has access to, needing held-out data or information theoretical criteria to decide how many components to use in the absence of external cues.\n\nReference:\n+ \n+ ", + "tags": [ + "gaussian_mixture_train", + "gmm" + ], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "out_table": "", + "model": "" + }, + "meta": { + "table": { + "type": "table", + "desc": "Training data in a Table. It must contain at least one column for labels.", + "optional": false + }, + "out_table": { + "type": "table", + "desc": "Data table with the prediction column." + }, + "model": { + "type": "model", + "desc": "Trained Gaussian Mixture model." + } + }, + "params": [ + { + "id": "input_cols", + "label": "Input Columns", + "description": "Input column names. Only values in numeric type are possible.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "columnType": [ + "Integer", + "Long", + "Float", + "Double", + "Double[]" + ], + "multiple": true + }, + { + "id": "number_of_components", + "label": "Number of Components", + "description": "The number of mixture components.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "placeHolder": "1 (value >= 1)", + "type": "Integer", + "min": 1 + }, + { + "id": "prediction_col", + "label": "Prediction Column Name", + "description": "Column name for prediction", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "type": "String", + "placeHolder": "prediction" + }, + { + "id": "covariance_type", + "label": "Covariance Type", + "description": "String describing the type of covariance parameters to use. Must be one of:\n\n Full : each component has its own general covariance matrix\n Tied : all components share the same general covariance matrix\n Diagonal : each component has its own diagonal covariance matrix\n Spherical : each component has its own single variance", + "visibleOption": [], + "control": "RadioButton", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "Full", + "value": "full", + "default": true + }, + { + "label": "Tied", + "value": "tied", + "default": false + }, + { + "label": "Diagonal", + "value": "diag", + "default": false + }, + { + "label": "Spherical", + "value": "spherical", + "default": false + } + ], + "columnType": [] + }, + { + "id": "tolerance", + "label": "Tolerance", + "description": "The convergence threshold. EM iterations will stop when the lower bound average gain is below this threshold.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "type": "Double", + "min": 0, + "placeHolder": "0.001 (value > 0)" + }, + { + "id": "regularize_covariance", + "label": "Regularization of Covariance", + "description": "Non-negative regularization added to the diagonal of covariance. 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Must be one of:\n\nK-means : responsibilities are initialized using kmeans.\nRandom : responsibilities are initialized randomly.", + "visibleOption": [], + "control": "RadioButton", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "K-means", + "value": "kmeans", + "default": true + }, + { + "label": "Random", + "value": "random", + "default": false + } + ], + "columnType": [] + }, + { + "id": "seed", + "label": "Seed", + "description": "Random seed. Use the same seed in order to reproduce the same results.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "type": "Integer" + }, + { + "id": "group_by", + "label": "Group By", + "description": "Columns to group by.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "type": "Integer", + "multiple": true + } + ], + "summary": "This function trains an gaussian mixture clustering model.", + "tutorialLink": "https://www.brightics.ai/kr/docs/ai/v3.7/tutorials/66_1_travel_locationdata_clustering?type=insight" + }, + "md": { + "en": "# Gaussian Mixture\nThis function trains an gaussian mixture clustering model.\n\n## Description\nA Gaussian mixture model is a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters. One can think of mixture models as generalizing k-means clustering to incorporate information about the covariance structure of the data as well as the centers of the latent Gaussians.\n\n- Pros : It is the fastest algorithm for learning mixture models. As this algorithm maximizes only the likelihood, it will not bias the means towards zero, or bias the cluster sizes to have specific structures that might or might not apply.\n\n- Cons : When one has insufficiently many points per mixture, estimating the covariance matrices becomes difficult, and the algorithm is known to diverge and find solutions with infinite likelihood unless one regularizes the covariances artificially. This algorithm will always use all the components it has access to, needing held-out data or information theoretical criteria to decide how many components to use in the absence of external cues.\n\nReference:\n+ \n+ \n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Training data in a Table. It must contain at least one column for labels.\n#### OUTPUT\n1. **out_table**: (Table) Data table with the prediction column.\n2. **model**: (Model) Trained Gaussian Mixture model.\n#### PARAMETER\n1. **Input Columns***: Input column names. Only values in numeric type are possible.\n\n2. **Number of Components**: The number of mixture components.\n\n3. **Prediction Column Name**: Column name for prediction\n\n4. **Covariance Type**: String describing the type of covariance parameters to use. Must be one of:\n * `Full` : each component has its own general covariance matrix\n * `Tied` : all components share the same general covariance matrix\n * `Diagonal` : each component has its own diagonal covariance matrix\n * `Spherical` : each component has its own single variance\n\n5. **Tolerance**: The convergence threshold. EM iterations will stop when the lower bound average gain is below this threshold.\n\n6. **Regularization of Covariance**: Non-negative regularization added to the diagonal of covariance. Allows to assure that the covariance matrices are all positive.\n\n7. **Number of Iteration**: The number of EM iterations to perform.\n\n8. **Method to Initialize**: The method used to initialize the weights, the means and the precisions. Must be one of:\n * `K-means` : responsibilities are initialized using kmeans.\n * `Random` : responsibilities are initialized randomly.\n\n9. **Seed**: Random seed. Use the same seed in order to reproduce the same results.\n\n10. **Group By**: Columns to group by.\n\n\n### Python\n#### USAGE\n\n```\ngaussian_mixture_train2(table, input_cols, number_of_components=1, prediction_col='prediction', covariance_type='full', tolerance=0.001, regularize_covariance=1e-06, max_iteration=100, initial_params='kmeans', seed=None, group_by=None)\n```\n\n#### INPUT\n1. **table***: (Table) Training data in a Table. It must contain at least one column for labels.\n#### OUTPUT\n1. **out_table**: (Table) Data table with the prediction column.\n2. **model**: (Model) Trained Gaussian Mixture model.\n#### PARAMETER\n1. **input_cols***: Input column names. Only values in numeric type are possible.\n\t* Type: *list[str]*\n2. **number_of_components**: The number of mixture components.\n\t* Type: *int*\n\t* Default / Range: 1 (value >= 1)\n3. **prediction_col**: Column name for prediction\n\t* Type: *str*\n4. **covariance_type**: String describing the type of covariance parameters to use. Must be one of:\n * `full` : each component has its own general covariance matrix\n * `tied` : all components share the same general covariance matrix\n * `diagonal` : each component has its own diagonal covariance matrix\n * `spherical` : each component has its own single variance\n\t* Type: *str*\n\t* Default / Range: full ( full | tied | diag | spherical )\n5. **tolerance**: The convergence threshold. EM iterations will stop when the lower bound average gain is below this threshold.\n\t* Type: *double*\n\t* Default / Range: 0.001 (value > 0)\n6. **regularize_covariance**: Non-negative regularization added to the diagonal of covariance. Allows to assure that the covariance matrices are all positive.\n\t* Type: *double*\n\t* Default / Range: 0.000001 (value > 0)\n7. **max_iteration**: The number of EM iterations to perform.\n\t* Type: *int*\n\t* Default / Range: 100 (value >= 1)\n8. **initial_params**: The method used to initialize the weights, the means and the precisions. Must be one of:\n * `kmeans` : responsibilities are initialized using kmeans.\n * `random` : responsibilities are initialized randomly.\n\t* Type: *str*\n\t* Default / Range: kmeans ( kmeans | random )\n9. **seed**: Random seed. Use the same seed in order to reproduce the same results.\n\t* Type: *int*\n * Default value: None\n10. **group_by**: Columns to group by.\n\t* Type: *list[str]*\n\t* Default value: None\n\n## Example\n### VA\n**[Related Tutorial]**\n\n\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, sample_iris data is used to train a gaussian mixture. The generated model is afterward applied to a test data set to predict the cluster of data, by using Gaussian Mixture Predict function. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Input Columns***: sepal_length, sepal_width\n2. **Number of Components**: 3\n3. **Prediction Column Name**: prediction\n4. **Covariance Type**: Full\n5. **Tolerance**: 0.001\n6. **Regularization of Covariance**: 0.000001\n7. **Number of Iteration**: 100\n8. **Method to Initialize**: Random\n9. **Seed**: None\n10. **Group By**: None\n\n\n\n### Python\n\n```\nfrom brightics.function.clustering import gaussian_mixture_train2\ninput_table = inputs[0]\nresult = gaussian_mixture_train2(table=input_table,\n\t\t\t\t\t\t\t\t input_cols=['sepal_width', 'sepal_length'],\n number_of_components=3,\n\t\t\t\t\t\t\t\t prediction_col='prediction',\n covariance_type='full',\n\t\t\t\t\t\t\t\t tolerance=0.001,\n\t\t\t\t\t\t\t\t regularize_covariance=0.000001,\n\t\t\t\t\t\t\t\t max_iteration=100,\n\t\t\t\t\t\t\t\t initial_params='kmeans',\n\t\t\t\t\t\t\t\t seed=None,\n\t\t\t\t\t\t\t\t group_by=None)\noutput_table = result['out_table']\noutput_model = result['model']\n\n```\nIn this tutorial workflow, sample_iris data is used to train a gaussian mixture. 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+ "description": "In this tutorial workflow, sample_iris data is used to train a gaussian mixture. The generated model is afterward applied to a test data set to predict the cluster of data, by using Gaussian Mixture Predict function. The parameter settings used in the function are shown below.", + "parameter": "1. **Input Columns***: sepal_length, sepal_width\n2. **Number of Components**: 3\n3. **Prediction Column Name**: prediction\n4. **Covariance Type**: Full\n5. **Tolerance**: 0.001\n6. **Regularization of Covariance**: 0.000001\n7. **Number of Iteration**: 100\n8. **Method to Initialize**: Random\n9. **Seed**: None\n10. **Group By**: None\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.clustering import gaussian_mixture_train2\r\ninput_table = inputs[0]\r\nresult = gaussian_mixture_train2(table=input_table,\r\n\t\t\t\t\t\t\t\t input_cols=['sepal_width', 'sepal_length'],\r\n number_of_components=3,\r\n\t\t\t\t\t\t\t\t prediction_col='prediction',\r\n covariance_type='full',\r\n\t\t\t\t\t\t\t\t tolerance=0.001,\r\n\t\t\t\t\t\t\t\t regularize_covariance=0.000001,\r\n\t\t\t\t\t\t\t\t max_iteration=100,\r\n\t\t\t\t\t\t\t\t initial_params='kmeans',\r\n\t\t\t\t\t\t\t\t seed=None,\r\n\t\t\t\t\t\t\t\t group_by=None)\r\noutput_table = result['out_table']\r\noutput_model = result['model']\r\n", + "description": "In this tutorial workflow, sample_iris data is used to train a gaussian mixture. The generated model is afterward applied to a test data set to predict the cluster of data, by using Gaussian Mixture Predict function.", + "context": "python" + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/clustering/meta/kmeans_predict.json b/function/python/brightics/function/clustering/meta/kmeans_predict.json index 2a33db438..7cbb736d3 100644 --- a/function/python/brightics/function/clustering/meta/kmeans_predict.json +++ b/function/python/brightics/function/clustering/meta/kmeans_predict.json @@ -53,7 +53,7 @@ "md": "# K-Means Predict\r\nThis function predicts cluster labels from trained model.\r\n\r\n## Description\r\nKmeans algorithm is an iterative algorithm that tries to partition the dataset into Kpre-defined distinct non-overlapping subgroups (clusters) where each data point belongs to only one group. It tries to make the inter-cluster data points as similar as possible while also keeping the clusters as different (far) as possible. It assigns data points to a cluster such that the sum of the squared distance between the data points and the cluster’s centroid (arithmetic mean of all the data points that belong to that cluster) is at the minimum. The less variation we have within clusters, the more homogeneous (similar) the data points are within the same cluster.\r\n\r\nReference: \r\n+ \r\n+ \r\n+ \r\n\r\n\r\n## Properties\r\n### VA\r\n#### INPUT\r\n1. **table***: (Table) Data in a Table. It must contain columns that was used in model training.\r\n2. **model***: (Model) Trained K-Means model.\r\n#### OUTPUT\r\n1. **table**: (Table) A table that contains input `table`'s columns and prediction column as `Prediction Column Name`.\r\n#### PARAMETER\r\n1. **Prediction Column Name**: A name of prediction column.\r\n\r\n\r\n### Python\r\n#### USAGE\r\n\r\n```\r\nkmeans_predict(table, model, prediction_col='prediction')\r\n```\r\n\r\n#### INPUT\r\n1. **table***: (Table) Data in a Table. It must contain columns that was used in model training.\r\n2. **model***: (Model) Trained K-Means model.\r\n#### OUTPUT\r\n1. **table**: (Table) A table that contains input `table`'s columns and prediction column as `prediction_col`.\r\n#### PARAMETER\r\n1. **prediction_col**: A name of prediction column.\r\n\t* Type: *str*\r\n\t* Default value: 'prediction'\r\n\r\n## Example\r\n### VA\r\n**[Related Tutorial]**\r\n\r\n\r\n**[Sample Model]**\r\n\r\n\r\n\r\n\r\n\r\n
In this tutorial workflow, sample_iris data is used to train a K-Means model. The generated model is afterward applied to a test data set to predict the clustering label, by using K-Means Predict function. The parameter settings used in the function are shown below.\r\n\r\n##### ++Parameters++\r\n1. **Prediction Column Name**: prediction\r\n\r\n\r\n### Python\r\n```\r\nfrom brightics.function.clustering import kmeans_predict \r\ninput_table = inputs[1]\r\ninput_model = models[0]\r\nresult = kmeans_predict(table=input_table,\r\n model=input_model,\r\n prediction_col='prediction')\r\noutput = result['out_table']\r\n```\r\n
In this python script, a trained K-Means model is applied to a test data set to predict clustering label, by using kmeans_predict function.\r\n", "exampleModels": [ { - "fileName": "brightics.function.clustering$kmeans_train_predict.json", + "fileName": "brightics.function.clustering$kmeans_predict.json", "label": "K-Means", "contents": { "type": "files", diff --git a/function/python/brightics/function/clustering/meta/kmeans_train_predict.json b/function/python/brightics/function/clustering/meta/kmeans_train_predict.json index 7369b62d1..f0a0d111f 100644 --- a/function/python/brightics/function/clustering/meta/kmeans_train_predict.json +++ b/function/python/brightics/function/clustering/meta/kmeans_train_predict.json @@ -1424,7 +1424,7 @@ }, { "persist-mode": "auto", - "func": "brightics.function.clustering$kmeans_predict", + "func": "brightics.function.clustering$kmeans_predict54809", "name": "brightics.function.clustering$kmeans_predict", "param": {}, "display": { diff --git a/function/python/brightics/function/clustering/meta/mean_shift.json b/function/python/brightics/function/clustering/meta/mean_shift.json index 0f81d7d25..84e58aa9c 100644 --- a/function/python/brightics/function/clustering/meta/mean_shift.json +++ b/function/python/brightics/function/clustering/meta/mean_shift.json @@ -66,7 +66,7 @@ { "id": "bandwidth", "label": "Bandwidth", - "description": "Window size used in the RBF kernel. It has a physical meaning unlike 'k' of K-Means. ", + "description": "Window size used in the RBF kernel. It has a physical meaning unlike 'k' of K-Means. If None, the bandwidth is estimated using a heuristic.", "visibleOption": [], "control": "InputBox", "validation": [], @@ -134,7 +134,7 @@ ], "summary": "This function is a Mean Shift Clustering model which is a nonparametric clustering algorithm." }, - "md": "# Mean Shift\nThis function is a Mean Shift Clustering model which is a nonparametric clustering algorithm.\n\n## Description\nMean shift clustering aims to discover 'blobs' in a smooth density of samples. It is a centroid-based algorithm, which works by updating candidates for centroids to be the mean of the points within a given region. These candidates are then filtered in a post-processing stage to eliminate near-duplicates to form the final set of centroids.\n\nDeciding appropriate value of `bandwidth` is important. Inappropriate `bandwidth` can cause modes to be merged, or generate additional “shallow” modes. The best cases to use mean shift clustering are 1) when there are not too many data because it would take lots of calculating time, 2) when we cannot predict the distribution of data so that we do not know how many clusters would be created. Application domains include cluster analysis in computer vision and image processing.\n\nReference\n- \n- \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Training data in a Table. \n#### OUTPUT\n1. **out_table**: (Table) A table that contains input `table`'s column and prediction column as `Prediction Column Name`.\n2. **model**: (Model) Trained Mean Shift Clustering model.\n#### PARAMETER\n1. **Input Columns***: Columns to select as input. Only values in numeric type are possible.\n\n2. **Prediction Column Name**: Column name for prediction.\n\n3. **Bandwidth**: Window size used in the RBF kernel. It has a physical meaning unlike 'k' of K-Means. \n\n4. **Bin Seeding**: If true, initial kernel locations are the location of the discretized version of points (by binding points into grid), therefore algorithm will be speeded up.\n\n\t* Default value: False \n5. **Minimum Bin Frequency**: To speed up the algorithm, accept only those bins with at least minimum bin frequency points as seed.\n\n6. **Cluster All**: If true, then all points are clustered, even those orphans that are not within any kernel. Orphans are assigned to the nearest kernel. If false, then orphans are given cluster label -1.\n\n\t* Default value: True \n7. **Group By**: Columns to group by.\n\n\n### Python\n#### USAGE\n\n```\nmean_shift(table, input_cols, prediction_col='prediction', bandwidth=None, bin_seeding=False, min_bin_freq=1, cluster_all=True, group_by=None)\n```\n\n#### INPUT\n1. **table***: (Table) Training data in a Table.\n#### OUTPUT\n2. **model**: (Model) Trained Mean Shift Clustering model.\n#### PARAMETER\n1. **input_cols***: Columns to select as input. Only values in numeric type are possible.\n\t* Type: *list[str]*\n2. **prediction_col**: Column name for prediction.\n\t* Type: *str*\n\t* Default value: prediction\n3. **bandwidth**: Window size used in the RBF kernel. It has a physical meaning unlike 'k' of K-Means. \n\t* Type: *float*\n\t* Default / Range: None (value > 0)\n4. **bin_seeding**: If true, initial kernel locations are the location of the discretized version of points (by binding points into grid), therefore algorithm will be speeded up.\n\t* Type: *bool*\n\t* Default value: False \n5. **min_bin_freq**: To speed up the algorithm, accept only those bins with at least minimum bin frequency points as seed.\n\t* Type: *int*\n\t* Default / Range: 1 (value >= 1)\n6. **cluster_all**: If true, then all points are clustered, even those orphans that are not within any kernel. Orphans are assigned to the nearest kernel. If false, then orphans are given cluster label -1.\n\t* Type: *bool*\n\t* Default value: True \n7. **group_by**: Columns to group by.\n\t* Type: *list[str]*\n\t* Default value: None\n\n## Example\n### VA\n**[Sample Model]**\n\n\n\n\nIn this tutorial workflow, sample_iris data is used to Mean Shift Clustering. The generated model is afterward applied to a test dataset to predict the cluster by using Mean Shift Predict function. The parameter settings used in the function are shown below. \n\n++Parameters++\n1. **Input Columns***: sepal_length, sepal_width \n2. **Prediction Column Name**: prediction\n3. **Bandwidth**: 1\n4. **Bin Seeding**: False\n5. **Minimum Bin Frequency**: 1\n6. **Cluster All**: True\n7. **Group By**: None\n\n\n### Python\n\n```\nfrom brightics.function.clustering import mean_shift\ninput_table = inputs[0]\nresult = mean_shift(table=input_table,\n input_cols=['sepal_length', 'sepal_width'],\n prediction_col='prediction',\n bandwidth=None,\n bin_seeding=False,\n min_bin_freq=1, \n cluster_all=True,\n group_by=None)\noutput = result['model']\n```\nIn this python script, sample_iris data is used to train an Mean Shift Clustering. The generated model is afterward applied to a test dataset to predict the cluster by using Mean Shift Predict function.\n", + "md": "# Mean Shift\nThis function is a Mean Shift Clustering model which is a nonparametric clustering algorithm.\n\n## Description\nMean shift clustering aims to discover 'blobs' in a smooth density of samples. It is a centroid-based algorithm, which works by updating candidates for centroids to be the mean of the points within a given region. These candidates are then filtered in a post-processing stage to eliminate near-duplicates to form the final set of centroids.\n\nDeciding appropriate value of `bandwidth` is important. Inappropriate `bandwidth` can cause modes to be merged, or generate additional “shallow” modes. The best cases to use mean shift clustering are 1) when there are not too many data because it would take lots of calculating time, 2) when we cannot predict the distribution of data so that we do not know how many clusters would be created. Application domains include cluster analysis in computer vision and image processing.\n\nReference\n- \n- \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Training data in a Table. \n#### OUTPUT\n1. **out_table**: (Table) A table that contains input `table`'s column and prediction column as `Prediction Column Name`.\n2. **model**: (Model) Trained Mean Shift Clustering model.\n#### PARAMETER\n1. **Input Columns***: Columns to select as input. Only values in numeric type are possible.\n\n2. **Prediction Column Name**: Column name for prediction.\n\n3. **Bandwidth**: Window size used in the RBF kernel. It has a physical meaning unlike 'k' of K-Means. If None, the bandwidth is estimated using a heuristic. \n\n4. **Bin Seeding**: If true, initial kernel locations are the location of the discretized version of points (by binding points into grid), therefore algorithm will be speeded up.\n\n\t* Default value: False \n5. **Minimum Bin Frequency**: To speed up the algorithm, accept only those bins with at least minimum bin frequency points as seed.\n\n6. **Cluster All**: If true, then all points are clustered, even those orphans that are not within any kernel. Orphans are assigned to the nearest kernel. If false, then orphans are given cluster label -1.\n\n\t* Default value: True \n7. **Group By**: Columns to group by.\n\n\n### Python\n#### USAGE\n\n```\nmean_shift(table, input_cols, prediction_col='prediction', bandwidth=None, bin_seeding=False, min_bin_freq=1, cluster_all=True, group_by=None)\n```\n\n#### INPUT\n1. **table***: (Table) Training data in a Table.\n#### OUTPUT\n2. **model**: (Model) Trained Mean Shift Clustering model.\n#### PARAMETER\n1. **input_cols***: Columns to select as input. Only values in numeric type are possible.\n\t* Type: *list[str]*\n2. **prediction_col**: Column name for prediction.\n\t* Type: *str*\n\t* Default value: prediction\n3. **bandwidth**: Window size used in the RBF kernel. It has a physical meaning unlike 'k' of K-Means. If None, the bandwidth is estimated using a heuristic.\n\t* Type: *float*\n\t* Default / Range: None (value > 0)\n4. **bin_seeding**: If true, initial kernel locations are the location of the discretized version of points (by binding points into grid), therefore algorithm will be speeded up.\n\t* Type: *bool*\n\t* Default value: False \n5. **min_bin_freq**: To speed up the algorithm, accept only those bins with at least minimum bin frequency points as seed.\n\t* Type: *int*\n\t* Default / Range: 1 (value >= 1)\n6. **cluster_all**: If true, then all points are clustered, even those orphans that are not within any kernel. Orphans are assigned to the nearest kernel. If false, then orphans are given cluster label -1.\n\t* Type: *bool*\n\t* Default value: True \n7. **group_by**: Columns to group by.\n\t* Type: *list[str]*\n\t* Default value: None\n\n## Example\n### VA\n**[Sample Model]**\n\n\n\n\nIn this tutorial workflow, sample_iris data is used to Mean Shift Clustering. The generated model is afterward applied to a test dataset to predict the cluster by using Mean Shift Predict function. The parameter settings used in the function are shown below. \n\n++Parameters++\n1. **Input Columns***: sepal_length, sepal_width \n2. **Prediction Column Name**: prediction\n3. **Bandwidth**: 1\n4. **Bin Seeding**: False\n5. **Minimum Bin Frequency**: 1\n6. **Cluster All**: True\n7. **Group By**: None\n\n\n### Python\n\n```\nfrom brightics.function.clustering import mean_shift\ninput_table = inputs[0]\nresult = mean_shift(table=input_table,\n input_cols=['sepal_length', 'sepal_width'],\n prediction_col='prediction',\n bandwidth=None,\n bin_seeding=False,\n min_bin_freq=1, \n cluster_all=True,\n group_by=None)\noutput = result['model']\n```\nIn this python script, sample_iris data is used to train an Mean Shift Clustering. The generated model is afterward applied to a test dataset to predict the cluster by using Mean Shift Predict function.\n", "exampleModels": [ { "fileName": "brightics.function.clustering$mean_shift.json", diff --git a/function/python/brightics/function/evaluation/__init__.py b/function/python/brightics/function/evaluation/__init__.py index 5bb214337..ab44a3416 100644 --- a/function/python/brightics/function/evaluation/__init__.py +++ b/function/python/brightics/function/evaluation/__init__.py @@ -140,7 +140,7 @@ def _evaluate_classification(table, label_col, prediction_col, average="weighted all_df = pd.DataFrame(all_dict_list) all_df = all_df.transpose() all_df.columns = ['f1', 'precision', 'recall'] - all_df['label'] = set(label) + all_df['label'] = class_names all_df = all_df[['label'] + all_df.columns[:-1].tolist()] summary['metrics'] = all_df @@ -369,8 +369,10 @@ def _ndcg_k(k_value, num_users, documents, recommend_table): def evaluate_ranking_algorithm(table1, table2, user_col, item_col, evaluation_measure, rating_col=None, rating_edge=None, k_values=None): + none_str = 'None' item_encoder = preprocessing.LabelEncoder() - tmp_table_item_col = table1[item_col] + tmp_table_item_col = table1[item_col].values.tolist() + tmp_table_item_col.append(none_str) item_encoder.fit(tmp_table_item_col) if table2.columns[0] != 'user_name' and table2.columns[0] != 'user': raise_runtime_error("topN-list data schema should consist of [user_name, item_top1, rating_top1, .... item_topN, rating_topN]") @@ -395,7 +397,7 @@ def evaluate_ranking_algorithm(table1, table2, user_col, item_col, evaluation_me if table2.columns[2 * i + 1] != 'item_%d' % (i + 1) and table2.columns[2 * i + 2] != 'rating_%d' % (i + 1) and table2.columns[2 * i + 1] != 'item_top%d' % (i + 1) and table2.columns[2 * i + 2] != 'rating_top%d' % (i + 1): raise_runtime_error("topN-list data schema should consist of [user_name, item_top1, rating_top1, .... item_topN, rating_topN]") columns.append(table2.columns[2 * i + 1]) - recommend_table = table2[columns].values + recommend_table = table2[columns].replace('', none_str).fillna(none_str).values for i in range(len(recommend_table)): recommend_table[i] = item_encoder.transform(recommend_table[i]) result = [] diff --git a/function/python/brightics/function/extraction/__init__.py b/function/python/brightics/function/extraction/__init__.py index 1e2678d88..91bf46b84 100644 --- a/function/python/brightics/function/extraction/__init__.py +++ b/function/python/brightics/function/extraction/__init__.py @@ -20,7 +20,10 @@ from .scale import scale_model from .encoder import label_encoder from .encoder import label_encoder_model +from .encoder import label_encoder2 +from .encoder import label_encoder_model2 from .encoder import one_hot_encoder +from .encoder import one_hot_encoder2 from .encoder import one_hot_encoder_model from .pca import pca from .pca import pca_model @@ -41,3 +44,5 @@ from .decompose_datetime import decompose_datetime from .shift_datetime import shift_datetime from .datetime_formatter import datetime_formatter +from .variance_filter import variance_filter +from .correlation_filter import correlation_filter diff --git a/function/python/brightics/function/extraction/array_column_conversion.py b/function/python/brightics/function/extraction/array_column_conversion.py index 395b540a8..0c040c5b1 100644 --- a/function/python/brightics/function/extraction/array_column_conversion.py +++ b/function/python/brightics/function/extraction/array_column_conversion.py @@ -41,12 +41,20 @@ def _columns_to_array(table, input_cols, remain_cols=False, output_col_name='arr if remain_cols: out_table = table.copy() npa = np.array(table[input_cols]) - out_table[_output_col_name] = list(npa) + tmp_col = table[input_cols].dtypes == 'object' + if tmp_col.any(): + out_table[_output_col_name] = list(npa.astype(str)) + else: + out_table[_output_col_name] = list(npa) return {'out_table' : out_table} else: out_table = table[list(set(table.axes[1]) - set(input_cols))].copy() npa = np.array(table[input_cols]) - out_table[_output_col_name] = list(npa) + tmp_col = table[input_cols].dtypes == 'object' + if tmp_col.any(): + out_table[_output_col_name] = list(npa.astype(str)) + else: + out_table[_output_col_name] = list(npa) return {'out_table' : out_table} diff --git a/function/python/brightics/function/extraction/correlation_filter.py b/function/python/brightics/function/extraction/correlation_filter.py new file mode 100644 index 000000000..46dff2d58 --- /dev/null +++ b/function/python/brightics/function/extraction/correlation_filter.py @@ -0,0 +1,193 @@ +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +from brightics.common.repr import BrtcReprBuilder, strip_margin, plt2MD, pandasDF2MD +from brightics.common.groupby import _function_by_group +from brightics.common.utils import check_required_parameters +from brightics.common.utils import get_default_from_parameters_if_required +from brightics.common.validation import validate, greater_than, greater_than_or_equal_to +from brightics.common.exception import BrighticsFunctionException as BFE + +import matplotlib.pyplot as plt +import seaborn as sns +from scipy import stats +import pandas as pd +import numpy as np +from sklearn.preprocessing import LabelEncoder + +from scipy.sparse import issparse +from scipy.stats import spearmanr + +from sklearn.base import BaseEstimator +from sklearn.feature_selection.base import SelectorMixin +from sklearn.utils import check_array +from sklearn.utils.validation import check_is_fitted +from sklearn.utils.extmath import safe_sparse_dot +from sklearn.utils.sparsefuncs import min_max_axis, mean_variance_axis + +def correlation_filter(table, group_by=None, **params): + check_required_parameters(_correlation_filter, params, ['table']) + + if group_by is not None: + return _function_by_group(_correlation_filter, table, group_by=group_by, **params) + else: + return _correlation_filter(table, **params) + + +def _correlation_filter(table, feature_cols, correlation_threshold=0.9, method='pearson'): + X = table[feature_cols] + + keep_features = CorrelationThreshold(X, correlation_threshold, method) + + out_table = table[X.columns[keep_features]] + + return {'out_table': out_table} + +def CorrelationThreshold(X, threshold, kind): + """Learn empirical variances from X. + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + Training set to compute correlations. + y : ignored + Not used, present here for API consistency by convention. + Returns + ------- + support_mask : Boolean array for feature selection + """ + + if not (0.0 <= threshold <= 1.0): + raise BFE.from_errors([{'0100': 'Threshold value must in [0.0, 1.0]'}]) + + if kind not in ('pearson', 'spearmanr'): + raise BFE.from_errors([{'0100': "Kind must be 'pearson' or 'spearmanr"}]) + + if issparse(X) and kind != 'pearson': + raise BFE.from_errors([{'0100': "Only pearson correlation is supported with 'sparse matrices'"}]) + + X = check_array(X, accept_sparse=['csc', 'csr'], dtype=[np.float64, np.float32]) + + n_features = X.shape[1] + if threshold == 1 or (1 in X.shape): + support_mask = np.ones(n_features, dtype=np.bool) + return support_mask + + # get constant features + if issparse(X): + mins, maxes = min_max_axis(X, axis=0) + peak_to_peaks = maxes - mins + constant_mask = np.isclose(peak_to_peaks, 0.0) + + # sparse correlation + mu, sparse_var = mean_variance_axis(X, 0) + X_corr = sparse_correlation(X, mu, ~constant_mask) + else: + peak_to_peaks = np.ptp(X, axis=0) + constant_mask = np.isclose(peak_to_peaks, 0.0) + + if kind == 'pearson': + X_corr = np.corrcoef(X, rowvar=False) + else: # spearmanr + X_corr, _ = spearmanr(X) + # spearmanr returns scaler when comparing two columns + if isinstance(X_corr, float): + X_corr = np.array([[1, X_corr], [X_corr, 1]]) + + np.fabs(X_corr, out=X_corr) + + # Removes constant features from support_mask + support_mask = np.ones(n_features, dtype=np.bool) + upper_idx = np.triu_indices(n_features, 1) + + non_constant_features = n_features + for i in np.flatnonzero(constant_mask): + feat_remove_mask = np.logical_and(upper_idx[0] != i, + upper_idx[1] != i) + upper_idx = (upper_idx[0][feat_remove_mask], + upper_idx[1][feat_remove_mask]) + support_mask[i] = False + non_constant_features -= 1 + + for _ in range(non_constant_features -1): + max_idx = np.argmax(X_corr[upper_idx]) + feat1, feat2 = upper_idx[0][max_idx], upper_idx[1][max_idx] + cur_corr = X_corr[feat1, feat2] + + # max correlation is lower than threshold + if cur_corr < threshold: + break + + # Temporary remove both features to calculate the mean with other + # features. One of the featuers will be selected. + support_mask[[feat1, feat2]] = False + + # if there are no other features to compare, keep the feature with the most + # variance + if np.all(~support_mask): + if issparse(X): + # sparse precalculates variance for all features + var = sparse_var[[feat1, feat2]] + else: + var = np.var(X[:, [feat1, feat2]], axis=0) + + print(feat1, feat2) + if var[0] < var[1]: + support_mask[feat2] = True + else: + support_mask[feat1] = True + break + + # mean with other features + feat1_mean = np.mean(X_corr[feat1, support_mask]) + feat2_mean = np.mean(X_corr[feat2, support_mask]) + + # feature with lower mean is kept + if feat1_mean < feat2_mean: + support_mask[feat1] = True + feat_to_remove = feat2 + else: + support_mask[feat2] = True + feat_to_remove = feat1 + + # remove the removed feature from consideration + upper_idx_to_keep = np.logical_and(upper_idx[0] != feat_to_remove, + upper_idx[1] != feat_to_remove) + upper_idx = (upper_idx[0][upper_idx_to_keep], + upper_idx[1][upper_idx_to_keep]) + + return support_mask + +def sparse_correlation(X, mu, non_constant_mask): + """Calcuate Pearson correlation for sparse matrices + Parameters + ---------- + X : sparse matrix of shape (n_samples, n_features) + Matrix to find correlation on. + mu : ndarray of shape (n_features,) + Mean of feature columns. + non_constant_mask : ndarray of shape (n_features,) + Boolean mask for non constant features. + Returns + ------- + correlation matrix : ndarray of shape (n_features, n_features) + """ + X_diff = X - mu[None, :] + X_corr = safe_sparse_dot(X_diff.T, X_diff, dense_output=True) + stddev = np.sqrt(np.diag(X_corr)) + + X_corr[non_constant_mask, :] /= stddev[non_constant_mask][:, None] + X_corr[:, non_constant_mask] /= stddev[non_constant_mask][None, :] + return X_corr \ No newline at end of file diff --git a/function/python/brightics/function/extraction/encoder.py b/function/python/brightics/function/extraction/encoder.py index 9fccbba30..fb3205b0b 100644 --- a/function/python/brightics/function/extraction/encoder.py +++ b/function/python/brightics/function/extraction/encoder.py @@ -22,12 +22,14 @@ from brightics.function.utils import _model_dict from brightics.common.utils import check_required_parameters from brightics.common.validation import raise_runtime_error +from brightics.common.repr import BrtcReprBuilder, strip_margin, pandasDF2MD, dict2MD, plt2MD, keyValues2MD +import re def label_encoder(table, group_by=None, **params): check_required_parameters(_label_encoder, params, ['table']) if group_by is not None: - grouped_model = _function_by_group(_label_encoder, table, group_by=group_by, **params) + grouped_model = _function_by_group(_label_encoder, table, group_by=group_by, **params) return grouped_model else: return _label_encoder(table, **params) @@ -40,8 +42,20 @@ def _label_encoder(table, input_col, new_column_name='encoded_column'): out_model['label_encoder'] = le out_model['input_col'] = input_col out_model['classes'] = le.classes_ + rb = BrtcReprBuilder() + params = { + 'Input Column': input_col, + "No. distinct classes": len(le.classes_), + "New column name": new_column_name + } + rb.addMD(strip_margin(""" + | ## Label Encoder Model + | ### Parameters + | {params} + | + """.format(params=dict2MD(params)))) + out_model['_repr_brtc_'] = rb.get() out_table[new_column_name] = le.transform(table[input_col]) - return {'out_table' : out_table, 'model' : out_model} @@ -56,10 +70,70 @@ def label_encoder_model(table, model, **params): def _label_encoder_model(table, model, new_column_name='encoded_column'): out_table = table.copy() out_table[new_column_name] = model['label_encoder'].transform(out_table[model['input_col']]) - return {'out_table' : out_table} +def label_encoder2(table, group_by=None, **params): + check_required_parameters(_label_encoder2, params, ['table']) + if group_by is not None: + grouped_model = _function_by_group( + _label_encoder2, table, group_by=group_by, **params) + return grouped_model + else: + return _label_encoder2(table, **params) + + +def _label_encoder2(table, input_cols, suffix='_index'): + out_table = table.copy() + out_model_list = [None] * len(input_cols) + new_col_list = [] + number_distinct_classes = [] + for ind, col in enumerate(input_cols): + le = LabelEncoder().fit(table[col]) + out_model_list[ind] = le + new_col_name = col + suffix + new_col_list.append(new_col_name) + number_distinct_classes.append(len(le.classes_)) + out_table[new_col_name] = le.transform(table[col]) + out_model = _model_dict('label_encoders') + out_model['label_encoders'] = out_model_list + out_model['input_cols'] = input_cols + rb = BrtcReprBuilder() + params = { + "Input columns": input_cols, + "Suffix": suffix + } + summary_table = pd.DataFrame() + summary_table['Input columns'] = input_cols + summary_table['No. distinct classes'] = number_distinct_classes + summary_table['New column names'] = new_col_list + rb.addMD(strip_margin(""" + | ## Label Encoder Model + | ### Parameters + | {params} + | ### Summary + | {summary_table} + """.format(params=dict2MD(params), summary_table=pandasDF2MD(summary_table)))) + out_model['_repr_brtc_'] = rb.get() + return {'out_table': out_table, 'model': out_model} + + +def label_encoder_model2(table, model, **params): + check_required_parameters(_label_encoder_model2, params, ['table', 'model']) + if '_grouped_data' in model: + return _function_by_group(_label_encoder_model2, table, model, **params) + else: + return _label_encoder_model2(table, model, **params) + + +def _label_encoder_model2(table, model, suffix='_index'): + out_table = table.copy() + for ind, col in enumerate(model['input_cols']): + new_col_name = col + suffix + out_table[new_col_name] = model['label_encoders'][ind].transform(out_table[col]) + return {'out_table': out_table} + + def one_hot_encoder(table, group_by=None, **params): check_required_parameters(_one_hot_encoder, params, ['table']) if group_by is not None: @@ -133,6 +207,103 @@ def _one_hot_encoder(table, input_cols, prefix='list', prefix_list=None, suffix= return {'out_table' : out_table, 'model' : out_model} +def one_hot_encoder2(table, group_by=None, **params): + check_required_parameters(_one_hot_encoder2, params, ['table']) + if group_by is not None: + grouped_model = _function_by_group(_one_hot_encoder2, table, group_by=group_by, **params) + return grouped_model + else: + return _one_hot_encoder2(table, **params) + + +def _one_hot_encoder2(table, input_cols, prefix='list', prefix_list=None, suffix='index', n_values='auto', categorical_features='all', sparse=True, handle_unknown='error', drop_last=False): + out_table = table.copy() + sparse = False + enc_list = [] + le_list = [] + if drop_last: + new_col_names_list_with_true_drop_last = [] + new_col_names_list = [] + prefix_list_index = 0 + if prefix == 'list': + len_prefix_list = 0 if prefix_list is None else len(prefix_list) + if len(input_cols) != len_prefix_list: + # TODO: make the error message code + raise_runtime_error('The number of Input Columns and the number of Prefixes should be equal.') + number_distinct_classes = [] + for col_name in input_cols: + enc = OneHotEncoder(n_values=n_values, categorical_features=categorical_features, sparse=sparse, handle_unknown=handle_unknown) + le = LabelEncoder() + distinct_classes = np.unique(out_table[col_name].values) + number_distinct_classes.append(len(distinct_classes)) + new_col_names = [] + if suffix == 'index': + if prefix == 'list': + for i in range(0, len(distinct_classes)): + new_col_names.append(prefix_list[prefix_list_index] + '_' + str(i)) + else: + for i in range(0, len(distinct_classes)): + new_col_names.append(col_name + '_' + str(i)) + else: + pattern = re.compile("\W") + for i in distinct_classes: + i = re.sub(pattern, "_", str(i)) + if prefix == 'list': + new_col_names.append(prefix_list[prefix_list_index] + '_' + i) + else: + new_col_names.append(col_name + '_' + i) + + transformed_table = pd.DataFrame(enc.fit_transform(le.fit_transform(out_table[col_name]).reshape(-1, 1)), columns=new_col_names) + new_col_names_list.append(new_col_names) + if drop_last: + new_col_names = new_col_names[:-1] + new_col_names_list_with_true_drop_last.append(new_col_names) + for new_col_name in new_col_names: + out_table[new_col_name] = transformed_table[new_col_name] + + enc_list.append(enc) + le_list.append(le) + prefix_list_index = prefix_list_index + 1 + rb = BrtcReprBuilder() + params = { + 'Input Columns': input_cols, + "Prefix Type": prefix, + "Suffix Type": suffix, + "Drop Last": drop_last, + "Number of values per feature": n_values, + "Categorical features": categorical_features, + "Error handling": handle_unknown + } + summary_table = pd.DataFrame() + summary_table['Input Columns'] = input_cols + summary_table['No. distinct classes'] = number_distinct_classes + if drop_last: + summary_table['New encoded columns'] = new_col_names_list_with_true_drop_last + else: + summary_table['New encoded columns'] = new_col_names_list + rb.addMD(strip_margin(""" + | ## One Hot Encoder Model + | ### Parameters + | {params} + | + | ### Summary + | {summary_table} + """.format(params=dict2MD(params), summary_table=pandasDF2MD(summary_table)))) + out_model = _model_dict('one_hot_encoder') + out_model['one_hot_encoder_list'] = enc_list + out_model['label_encoder_list'] = le_list + out_model['input_cols'] = input_cols + out_model['prefix'] = prefix + out_model['prefix_list'] = prefix_list + out_model['suffix'] = suffix + out_model['drop_last'] = drop_last + out_model['_repr_brtc_'] = rb.get() + if drop_last: + out_model['new_col_names_list_with_true_drop_last'] = new_col_names_list_with_true_drop_last + out_model['new_col_names_list'] = new_col_names_list + return {'out_table' : out_table, 'model' : out_model} + + def one_hot_encoder_model(table, model, **params): check_required_parameters(_one_hot_encoder_model, params, ['table', 'model']) if '_grouped_data' in model: @@ -152,5 +323,4 @@ def _one_hot_encoder_model(table, model): else: for new_col_name in model['new_col_names_list'][i]: out_table[new_col_name] = transformed_table[new_col_name] - return {'out_table' : out_table} diff --git a/function/python/brightics/function/extraction/lda.py b/function/python/brightics/function/extraction/lda.py index c8e56f80d..9dc0dcb59 100644 --- a/function/python/brightics/function/extraction/lda.py +++ b/function/python/brightics/function/extraction/lda.py @@ -25,7 +25,7 @@ from brightics.common.validation import validate, greater_than_or_equal_to, less_than_or_equal_to from brightics.common.utils import get_default_from_parameters_if_required from brightics.common.exception import BrighticsFunctionException - +from sklearn.utils.multiclass import unique_labels def lda(table, group_by=None, **params): check_required_parameters(_lda, params, ['table']) @@ -71,10 +71,11 @@ def _lda(table, feature_cols, label_col, new_column_name='projected_', solver='s tol=1.0e-4): num_feature_cols = len(feature_cols) - if n_components is not None and n_components >= num_feature_cols - 1: - n_components = num_feature_cols - 2 - if n_components is None or n_components < 1: - n_components = 1 + num_classes = unique_labels(table[label_col]) + tmp_components = min(len(num_classes) - 1, num_feature_cols) + + if n_components is None or n_components > tmp_components: + n_components = tmp_components #validate(greater_than_or_equal_to(n_components, 1, 'n_components')) @@ -121,7 +122,11 @@ def _lda(table, feature_cols, label_col, new_column_name='projected_', solver='s res_xbar = lda_model.xbar_ if hasattr(lda_model, 'xbar_') else None res_classes = lda_model.classes_ if hasattr(lda_model, 'classes_') else None res_get_param = lda_model.get_params() - + res_get_param['covariance'] = res_covariance + res_get_param['means'] = res_mean + res_get_param['coef'] = res_coef + res_get_param['xbar'] = res_xbar + # visualization plt.figure() fig_scree = _screeplot(res_explained_variance_ratio, n_components) diff --git a/function/python/brightics/function/extraction/meta/array_to_columns.json b/function/python/brightics/function/extraction/meta/array_to_columns.json index cf273abf8..bd15d94a2 100644 --- a/function/python/brightics/function/extraction/meta/array_to_columns.json +++ b/function/python/brightics/function/extraction/meta/array_to_columns.json @@ -9,7 +9,7 @@ "name": "brightics.function.extraction$array_to_columns", "context": "python", "label": "Array To Columns", - "description": "Expands columns of Numeric Array to DoubleType columns.", + "description": "Expands columns of array type to columns.", "tags": [ "array", "columns" @@ -24,7 +24,7 @@ "meta": { "table": { "type": "table", - "desc": "Input table should contains at least one column of numeric array. ", + "desc": "Input table should contains at least one column of array type. ", "optional": false }, "out_table": { @@ -36,7 +36,7 @@ { "id": "input_cols", "label": "Input Columns", - "description": "The name of the input columns. The columns should be should be numeric array type. ", + "description": "The name of the input columns. The columns should be should be array type.", "visibleOption": [], "control": "ColumnSelector", "validation": [], @@ -46,7 +46,8 @@ "Integer[]", "Long[]", "Float[]", - "Double[]" + "Double[]", + "String[]" ], "multiple": true, "rowCount": 5 @@ -65,9 +66,9 @@ "defaultValue": false } ], - "summary": "Expands columns of Numeric Array to DoubleType columns." + "summary": "Expands columns of array type to columns." }, - "md": "# Array To Columns\nExpands columns of Numeric Array to DoubleType columns.\n\n## Description\nExpands columns of Numeric Array to DoubleType columns.\n\nReference\n- \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Input table should contains at least one column of numeric array. \n#### OUTPUT\n1. **out_table**: (Table) Result table contains one or more new columns decomposing a single array type column of the input table. \n#### PARAMETER\n1. **Input Columns***: The name of the input columns. The columns should be should be numeric array type. \n\n2. **Remain Columns**: Keep all input columns in output table\n\n\t* Default value: false \n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\n\nThis model is a simple example for Array To Columns function. The function decomposes the array in the input column to multiple columns\n\n++Parameters++\n1. **Input Columns***: \n2. **Remain Columns**: false\n\n\n", + "md": "# Array To Columns\nExpands columns of array type to columns.\n\n## Description\nExpands columns of array type to columns.\n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Input table should contains at least one column of array type. \n#### OUTPUT\n1. **out_table**: (Table) Result table contains one or more new columns decomposing a single array type column of the input table. \n#### PARAMETER\n1. **Input Columns***: The name of the input columns. The columns should be should be array type. \n\n2. **Remain Columns**: Keep all input columns in output table\n\n\t* Default value: false \n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\n\nThis model is a simple example for Array To Columns function. The function decomposes the array in the input column to multiple columns\n\n++Parameters++\n1. **Input Columns***: \n2. **Remain Columns**: false\n\n\n", "exampleModels": [ { "fileName": "array_to_column.json", diff --git a/function/python/brightics/function/extraction/meta/columns_to_array.json b/function/python/brightics/function/extraction/meta/columns_to_array.json index 328703f90..b243c0070 100644 --- a/function/python/brightics/function/extraction/meta/columns_to_array.json +++ b/function/python/brightics/function/extraction/meta/columns_to_array.json @@ -9,7 +9,7 @@ "name": "brightics.function.extraction$columns_to_array", "context": "python", "label": "Columns To Array", - "description": "Create a new ArrayType[DoubleType] column by zipping DoubleType columns.", + "description": "Create a new array type column by zipping columns.", "tags": [ "array", "columns" @@ -46,7 +46,8 @@ "Integer", "Long", "Float", - "Double" + "Double", + "String" ], "multiple": true, "rowCount": 5 @@ -78,9 +79,9 @@ "type": "String" } ], - "summary": "Create a new ArrayType[DoubleType] column by zipping DoubleType columns." + "summary": "Create a new array type column by zipping columns." }, - "md": "# Columns To Array\nCreate a new ArrayType[DoubleType] column by zipping DoubleType columns.\n\n## Description\nCreate a new ArrayType[DoubleType] column by zipping DoubleType columns.\n\n\n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Input table.\n#### OUTPUT\n1. **out_table**: (Table) Output table with the zipping result.\n#### PARAMETER\n1. **Input Columns***: The name of the input columns.\n\n2. **Remain Columns**: Whether or not to preserve input columns in the output table.\n\n\t* Default value: false \n3. **Output Column Name**: Name of the output column.\n\n\n### Python\n#### USAGE\n\n```\nfrom brightics.function.extraction import columns_to_array\nres = columns_to_array(table, input_cols, remain_cols=False, output_col_name='array')\nres['out_table']\n```\n\n#### INPUT\n1. **table***: (Table) Input table.\n#### OUTPUT\n1. **out_table**: (Table) Output table with the zipping result.\n#### PARAMETER\n1. **input_cols***: The name of the input columns.\n\t* Type: *list[str]*\n2. **remain_cols**: Whether or not to preserve input columns in the output table.\n\t* Type: *\n\t* Default value: false \n3. **output_col_name**: Name of the output column.\n\t* Type: *\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\n\n
In this tutorial workflow, four DoubleType columns 'sepal_length','sepal_width', 'petal_length', 'petal_width' in sample_iris are collected and zipped into a single Array[DoubleType] column.\n\n++Parameters++\n1. **Input Columns***: \n2. **Remain Columns**: false\n3. **Output Column Name**: \n\n\n### Python\n\n```\nfrom brightics.function.extraction import columns_to_array\nres = columns_to_array(table=inputs[0],\n input_cols=['sepal_length','sepal_width', 'petal_length', 'petal_width'],\n remain_cols=False, output_col_name='output_col')\ntable = res['out_table']\n```\n\n
In this python script, four DoubleType columns 'sepal_length','sepal_width', 'petal_length', 'petal_width' in sample_iris are collected and zipped into a single Array[DoubleType] column.", + "md": "# Columns To Array\nCreate a new array type column by zipping columns.\n\n## Description\nCreate a new array type column by zipping columns.\n\n\n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Input table.\n#### OUTPUT\n1. **out_table**: (Table) Output table with the zipping result.\n#### PARAMETER\n1. **Input Columns***: The name of the input columns.\n\n2. **Remain Columns**: Whether or not to preserve input columns in the output table.\n\n\t* Default value: false \n3. **Output Column Name**: Name of the output column.\n\n\n### Python\n#### USAGE\n\n```\nfrom brightics.function.extraction import columns_to_array\nres = columns_to_array(table, input_cols, remain_cols=False, output_col_name='array')\nres['out_table']\n```\n\n#### INPUT\n1. **table***: (Table) Input table.\n#### OUTPUT\n1. **out_table**: (Table) Output table with the zipping result.\n#### PARAMETER\n1. **input_cols***: The name of the input columns.\n\t* Type: *list[str]*\n2. **remain_cols**: Whether or not to preserve input columns in the output table.\n\t* Type: *\n\t* Default value: false \n3. **output_col_name**: Name of the output column.\n\t* Type: *\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\n\n
In this tutorial workflow, four DoubleType columns 'sepal_length','sepal_width', 'petal_length', 'petal_width' in sample_iris are collected and zipped into a single Array[DoubleType] column.\n\n++Parameters++\n1. **Input Columns***: \n2. **Remain Columns**: false\n3. **Output Column Name**: \n\n\n### Python\n\n```\nfrom brightics.function.extraction import columns_to_array\nres = columns_to_array(table=inputs[0],\n input_cols=['sepal_length','sepal_width', 'petal_length', 'petal_width'],\n remain_cols=False, output_col_name='output_col')\ntable = res['out_table']\n```\n\n
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+ "description": "In this tutorial workflow, sample_iris data is used as input of the Correlation Filter function. The output of this function is a table that contains the features filtered considering their correlations.", + "parameter": "1. **Feature Columns***: ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\n2. **Correlation Threshold**: 0.9\n3. **Method**: pearson\n4. **Group By**: None\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.extraction import correlation_filter\r\nres = correlation_filter(table = inputs[0], feature_cols = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'])\r\noutput = res['out_table']", + "description": "In this script, sample_iris data is used as input of the Correlation Filter function. The output of this function is a table that contains the features filtered considering their correlations.", + "context": "python" + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/extraction/meta/ewma.json b/function/python/brightics/function/extraction/meta/ewma.json index ad8d66488..d5809dafc 100644 --- a/function/python/brightics/function/extraction/meta/ewma.json +++ b/function/python/brightics/function/extraction/meta/ewma.json @@ -83,11 +83,7 @@ "default": false } ], - "columnType": [], - "type": "Double", - "min": 0, - "max": 1, - "placeHolder": "0.5 (0.0<=value<=1.0)" + "columnType": [] }, { "id": "custom_ratio", diff --git a/function/python/brightics/function/extraction/meta/label_encoder.json b/function/python/brightics/function/extraction/meta/label_encoder.json index 0cfd712c3..92b22496a 100644 --- a/function/python/brightics/function/extraction/meta/label_encoder.json +++ b/function/python/brightics/function/extraction/meta/label_encoder.json @@ -4,6 +4,7 @@ "content": "" }, "specJson": { + "deprecated": true, "category": "extraction", "func": "brightics.function.extraction$label_encoder", "name": "brightics.function.extraction$label_encoder", diff --git a/function/python/brightics/function/extraction/meta/label_encoder2.json b/function/python/brightics/function/extraction/meta/label_encoder2.json new file mode 100644 index 000000000..e2258849d --- /dev/null +++ b/function/python/brightics/function/extraction/meta/label_encoder2.json @@ -0,0 +1,883 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "extraction", + "func": "brightics.function.extraction$label_encoder2", + "name": "brightics.function.extraction$label_encoder2", + "context": "python", + "label": "Label Encoder", + "description": "Encode categorical data into integer labels with values between 0 and n_classes-1.\n\nReference\n- ", + "tags": [ + "indexer", + "encoder", + "preprocessing", + "add columns", + "add", + "column" + ], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "out_table": "", + "model": "" + }, + "meta": { + "out_table": { + "type": "table", + "desc": "Encoded data." + }, + "model": { + "type": "model", + "desc": "Encoded data with configurations which can be used in 'Label Encoder Model' function." + }, + "table": { + "type": "table", + "desc": "Input data.", + "optional": false + } + }, + "params": [ + { + "id": "input_cols", + "label": "Input Columns", + "description": "Column to be encoded with integer labels.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "columnType": [ + "Integer", + "Float", + "Double", + "Long", + "String" + ], + "multiple": true + }, + { + "id": "suffix", + "label": "Suffix", + "description": "Suffix for new column names. Default = _index", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "type": "String", + "placeHolder": "_index" + }, + { + "id": "group_by", + "label": "Group By", + "description": "Group by column.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "multiple": true + } + ], + "summary": "Encode categorical data into integer labels." + }, + "md": { + "en": "# Label Encoder\nEncode categorical data into integer labels.\n\n## Description\nEncode categorical data into integer labels with values between 0 and n_classes-1.\n\nReference\n- \n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Input data.\n#### OUTPUT\n1. **out_table**: (Table) Encoded data.\n2. **model**: (Model) Encoded data with configurations which can be used in 'Label Encoder Model' function.\n#### PARAMETER\n1. **Input Columns***: Column to be encoded with integer labels.\n\n2. **Suffix**: Suffix for new column names. Default = _index\n\n3. **Group By**: Group by column.\n\n\n### Python\n#### USAGE\n\n```\nlabel_encoder2(table = , input_cols = , suffix = , group_by = )\n```\n\n#### INPUT\n1. **table***: (Table) Input data.\n#### OUTPUT\n1. **out_table**: (Table) Encoded data.\n2. **model**: (Model) Encoded data with configurations which can be used in 'Label Encoder Model' function.\n#### PARAMETER\n1. **input_cols***: Column to be encoded with integer labels.\n\t* Type: *list[str]*\n2. **suffix**: Suffix for new column names. Default = _index\n\t* Type: *str*\n3. **group_by**: Group by column.\n\t* Type: *list[str]*\n\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, sample_iris data is loaded and the column named 'species' are encoded into integer values.\n\n++Parameters++\n1. **Input Columns***: species\n2. **Suffix**: _index\n3. **Group By**: None\n\n\n\n### Python\n\n```\nfrom brightics.function.extraction import label_encoder2\nres = label_encoder2(table=inputs[0], input_cols=['species'], suffix='_index')\ntable=res['out_table']\nmodel=res['model']\n```\nIn this tutorial workflow, sample_iris data is loaded and the column named 'species' are encoded into integer values.\n\n\n", + "kr": "" + }, + "exampleModels": [ + { + "fileName": "label_encoder (1).json", + "label": "label_encoder", + "contents": { + "type": "files", + "data": [ + { + "type": "file", + "data": { + "id": "mtyw2vzgytw85upw", + "project_id": "p5dmzu78qb564crk", + "label": "label_encoder", + "contents": { + 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", + "description": "In this tutorial workflow, sample_iris data is loaded and the column named 'species' are encoded into integer values.", + "parameter": "1. **Input Columns***: species\n2. **Suffix**: _index\n3. **Group By**: None\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.extraction import label_encoder2\r\nres = label_encoder2(table=inputs[0], input_cols=['species'], suffix='_index')\r\ntable=res['out_table']\r\nmodel=res['model']", + "description": "In this tutorial workflow, sample_iris data is loaded and the column named 'species' are encoded into integer values.", + "context": "python" + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/extraction/meta/label_encoder_model.json b/function/python/brightics/function/extraction/meta/label_encoder_model.json index 3dcd9dfb1..2a52b7e90 100644 --- a/function/python/brightics/function/extraction/meta/label_encoder_model.json +++ b/function/python/brightics/function/extraction/meta/label_encoder_model.json @@ -4,6 +4,7 @@ "content": "" }, "specJson": { + "deprecated": true, "category": "extraction", "func": "brightics.function.extraction$label_encoder_model", "name": "brightics.function.extraction$label_encoder_model", diff --git a/function/python/brightics/function/extraction/meta/label_encoder_model2.json b/function/python/brightics/function/extraction/meta/label_encoder_model2.json new file mode 100644 index 000000000..69f3911df --- /dev/null +++ b/function/python/brightics/function/extraction/meta/label_encoder_model2.json @@ -0,0 +1,1792 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "extraction", + "func": "brightics.function.extraction$label_encoder_model2", + "name": "brightics.function.extraction$label_encoder_model2", + "context": "python", + "label": "Label Encoder Model", + "description": "This function transforms the features using the result of Label Encoder.", + "tags": [ + "model", + "label", + "preprocessing" + ], + "version": "3.6", + "inputs": { + "table": "", + "model": "" + }, + "outputs": { + "out_table": "" + }, + "meta": { + "table": { + "type": "table", + "desc": "Input Data with label column.", + "optional": false + }, + "out_table": { + "type": "table", + "desc": "Output table." + }, + "model": { + "type": "model", + "desc": "Label encoder Model from Label encoder function.", + "optional": false + } + }, + "params": [ + { + "id": "suffix", + "label": "Suffix", + "description": "Suffix for new column names. 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+ "description": "In this tutorial workflow, sample_iris data loaded and split into train data set and test data set. Then the 'species' column in the train data set is labeled with integer values. The trained result is finally applied to the test dataset to give integer label values corresponds to 'species' values.", + "parameter": "1. **Suffix**: _index", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.extraction.encoder import label_encoder_model2 as lemodel\r\ninput_table = inputs[2]\r\ninput_model = models[0]\r\nres = lemodel(table = input_table, model = input_model)\r\noutput = res['out_table']", + "description": "In this script, the result of the Label encoder function(models[0]) is applied to the input dataset(inputs[2]) to encode labels with integral values.", + "context": "python" + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/extraction/meta/lda.json b/function/python/brightics/function/extraction/meta/lda.json index ee2ecc7e3..6e284cb91 100644 --- a/function/python/brightics/function/extraction/meta/lda.json +++ b/function/python/brightics/function/extraction/meta/lda.json @@ -226,7 +226,7 @@ { "id": "group_by", "label": "Group By", - "description": "", + "description": "Columns to group by.", "visibleOption": [], "control": "ColumnSelector", "validation": [], @@ -238,7 +238,7 @@ ], "summary": "Linear Discriminant Analysis for finding a linear combination of features that separates two or more classes. " }, - "md": "# LDA\nLinear Discriminant Analysis for finding a linear combination of features that separates two or more classes. \n\n## Description\nLinear Discriminant Analysis (LDA), Normal Discriminant Analysis (NDA), or Discriminant Function Analysis is generalization of Fisher's linear discriminant, a method used in statistics and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or for dimensionality reduction before classification.\n\nReference:\n+ \n\nReference\n- \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Input table to be analyzed. \n#### OUTPUT\n1. **out_table**: (Table) \n2. **model**: (Model) \n#### PARAMETER\n1. **Feature Columns***: Feature columns.\n\n2. **Label Column***: Label column\n\n3. **Solver**: Solver to use\n\n4. **Store Covariance**: Option for storing covariance. \n\n5. **Tolerance**: Threshold of the convergence during the learning \n\n6. **Number of components**: Number of components (< n_classes - 1) for dimensionality reduction\n\n7. **Shrinkage**: Shrinkage works only with eigen solver.\n\n8. **Shrinkage value**: Value for float Shrinkage\n\n9. **New Column Name**: Name of new column(s)\n\n10. **Group By**: \n\n\n### Python\n#### USAGE\n\n```\nfrom brightics.function.extraction import lda\nres = lda(table = , feature_cols = , label_col = , solver = , store_covariance = , tol = , n_components = , shrinkage = , shrinkage_value = , new_column_name = , group_by = )\nres['out_table'], res['model']\n```\n\n#### INPUT\n1. **table***: (Table) Input table to be analyzed. \n#### OUTPUT\n1. **out_table**: (Table) \n2. **model**: (Model) \n#### PARAMETER\n1. **feature_cols***: Feature columns.\n\t* Type: *list[str]*\n2. **label_col***: Label column\n\t* Type: *str*\n3. **solver**: Solver to use\n\t* Type: *str*\n\t* Default / Range: svd ( svd | eigen )\n4. **store_covariance**: Option for storing covariance. \n\t* Type: *str*\n\t* Default / Range: ( TrueFalse | False )\n5. **tol**: Threshold of the convergence during the learning \n\t* Type: *double*\n\t* Default / Range: 0.0001 (value >= 0)\n6. **n_components**: Number of components (< n_classes - 1) for dimensionality reduction\n\t* Type: *int*\n\t* Default / Range: 1 (value >= 1)\n7. **shrinkage**: Shrinkage works only with eigen solver.\n\t* Type: *str*\n\t* Default / Range: None ( None | auto | float )\n8. **shrinkage_value**: Value for float Shrinkage\n\t* Type: *double*\n\t* Default / Range: (1 >= value >= 0)\n9. **new_column_name**: Name of new column(s)\n\t* Type: *str*\n10. **group_by**: \n\t* Type: *list[str]*\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\n\n
This model is a simple example of LDA function. The model trained from the LDA is used for LDA Model function for the prediction of the class of the new data. \n\n++Parameters++\n1. **Feature Columns***: ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\n2. **Label Column***: species\n\n\n### Python\n\n```\nfrom brightics.function.extraction import lda\nres = lda(table = inputs[0] , feature_cols = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\n\t\t , label_col = 'species' )\nout_table = res['out_table']\nout_model = res['model']\n```\n\nThis script yields the same result of the VA sample model, if the LDA function is altered by Python Script function with the script. \n\n\n\n", + "md": "# LDA\nLinear Discriminant Analysis for finding a linear combination of features that separates two or more classes. \n\n## Description\nLinear Discriminant Analysis (LDA), Normal Discriminant Analysis (NDA), or Discriminant Function Analysis is generalization of Fisher's linear discriminant, a method used in statistics and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or for dimensionality reduction before classification.\n\nReference:\n+ \n\nReference\n- \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Input table to be analyzed. \n#### OUTPUT\n1. **out_table**: (Table) \n2. **model**: (Model) \n#### PARAMETER\n1. **Feature Columns***: Feature columns.\n\n2. **Label Column***: Label column\n\n3. **Solver**: Solver to use\n\n4. **Store Covariance**: Option for storing covariance. \n\n5. **Tolerance**: Threshold of the convergence during the learning \n\n6. **Number of components**: Number of components (< n_classes - 1) for dimensionality reduction\n\n7. **Shrinkage**: Shrinkage works only with eigen solver.\n\n8. **Shrinkage value**: Value for float Shrinkage\n\n9. **New Column Name**: Name of new column(s)\n\n10. **Group By**: Columns to group by. \n\n\n### Python\n#### USAGE\n\n```\nfrom brightics.function.extraction import lda\nres = lda(table = , feature_cols = , label_col = , solver = , store_covariance = , tol = , n_components = , shrinkage = , shrinkage_value = , new_column_name = , group_by = )\nres['out_table'], res['model']\n```\n\n#### INPUT\n1. **table***: (Table) Input table to be analyzed. \n#### OUTPUT\n1. **out_table**: (Table) \n2. **model**: (Model) \n#### PARAMETER\n1. **feature_cols***: Feature columns.\n\t* Type: *list[str]*\n2. **label_col***: Label column\n\t* Type: *str*\n3. **solver**: Solver to use\n\t* Type: *str*\n\t* Default / Range: svd ( svd | eigen )\n4. **store_covariance**: Option for storing covariance. \n\t* Type: *str*\n\t* Default / Range: ( TrueFalse | False )\n5. **tol**: Threshold of the convergence during the learning \n\t* Type: *double*\n\t* Default / Range: 0.0001 (value >= 0)\n6. **n_components**: Number of components (< n_classes - 1) for dimensionality reduction\n\t* Type: *int*\n\t* Default / Range: 1 (value >= 1)\n7. **shrinkage**: Shrinkage works only with eigen solver.\n\t* Type: *str*\n\t* Default / Range: None ( None | auto | float )\n8. **shrinkage_value**: Value for float Shrinkage\n\t* Type: *double*\n\t* Default / Range: (1 >= value >= 0)\n9. **new_column_name**: Name of new column(s)\n\t* Type: *str*\n10. **group_by**: Columns to group by. \n\t* Type: *list[str]*\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\n\n
This model is a simple example of LDA function. The model trained from the LDA is used for LDA Model function for the prediction of the class of the new data. \n\n++Parameters++\n1. **Feature Columns***: ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\n2. **Label Column***: species\n\n\n### Python\n\n```\nfrom brightics.function.extraction import lda\nres = lda(table = inputs[0] , feature_cols = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\n\t\t , label_col = 'species' )\nout_table = res['out_table']\nout_model = res['model']\n```\n\nThis script yields the same result of the VA sample model, if the LDA function is altered by Python Script function with the script. \n\n\n\n", "exampleModels": [ { "fileName": "lda.json", diff --git a/function/python/brightics/function/extraction/meta/one_hot_encoder.json b/function/python/brightics/function/extraction/meta/one_hot_encoder.json index 4915f016d..cd2c4f3fa 100644 --- a/function/python/brightics/function/extraction/meta/one_hot_encoder.json +++ b/function/python/brightics/function/extraction/meta/one_hot_encoder.json @@ -4,6 +4,7 @@ "content": "" }, "specJson": { + "deprecated": true, "category": "extraction", "func": "brightics.function.extraction$one_hot_encodercoder", "name": "brightics.function.extraction$one_hot_encoder", diff --git a/function/python/brightics/function/extraction/meta/one_hot_encoder2.json b/function/python/brightics/function/extraction/meta/one_hot_encoder2.json new file mode 100644 index 000000000..b201d5f47 --- /dev/null +++ b/function/python/brightics/function/extraction/meta/one_hot_encoder2.json @@ -0,0 +1,916 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "extraction", + "func": "brightics.function.extraction$one_hot_encoder2", + "name": "brightics.function.extraction$one_hot_encoder2", + "context": "python", + "label": "One Hot Encoder", + "description": "In machine learning, one-hot is a group of bits among which the legal combinations of values are only those with a single high (1) bit and all the others low (0). A similar implementation in which all bits are '1' except one '0' is sometimes called one-cold. In statistics, dummy variables represent a similar technique for representing categorical data.", + "tags": [ + "one", + "hot", + "encoder", + "preprocessing", + "add columns", + "add", + "column" + ], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "out_table": "", + "model": "" + }, + "meta": { + "table": { + "type": "table", + "desc": "Data table with at least one categorical column.", + "optional": false + }, + "out_table": { + "type": "table", + "desc": "Data table with one-hot encoded columns appended." + }, + "model": { + "type": "model", + "desc": "Result one hot encoder model. This model is used as an input of One Hot Encoder Model function." + } + }, + "params": [ + { + "id": "input_cols", + "label": "Input Columns", + "description": "Columns to select as an input. Available are Integer, Long, Float, Double, and String type columns.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "columnType": [ + "Integer", + "Long", + "Float", + "Double", + "String" + ], + "type": "String", + "multiple": true + }, + { + "id": "prefix", + "label": "Prefix Type", + "description": "Prefix type for newly added columns.\n - Available Options: `Use existing column name(s)`, `Enter new column name(s)`", + "visibleOption": [], + "control": "RadioButton", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "Use existing column name(s)", + "value": "col_name", + "default": true + }, + { + "label": "Enter new column name(s)", + "value": "list", + "default": false + } + ], + "columnType": [] + }, + { + "id": "prefix_list", + "label": "Prefix(es)", + "description": "List of prefix name(s). The length of this list should be equal to the length of input columns.", + "visibleOption": [ + { + "id": "prefix", + "value": "list" + } + ], + "control": "ArrayInput", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "type": "String" + }, + { + "id": "suffix", + "label": "Suffix Type", + "description": "Suffix type for newly added columns. Available are index, which yields integer indices starting with 0, and label, which yields label suffix same as the category values.\n - Available Options: `Index`, `Label`", + "visibleOption": [], + "control": "RadioButton", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "Index", + "value": "index", + "default": true + }, + { + "label": "Label", + "value": "label", + "default": false + } + ], + "columnType": [] + }, + { + "id": "drop_last", + "label": "Drop Last", + "description": "Whether to drop the last column.\n - Available Option: `True`, `False`", + "visibleOption": [], + "control": "BooleanRadio", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "defaultValue": false + }, + { + "id": "group_by", + "label": "Group By", + "description": "Columns to group by.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "multiple": true + } + ], + "summary": "This function encodes categorical integer features using a one-hot, also known as one-of-K, scheme." + }, + "md": "# One Hot Encoder\nThis function encodes categorical integer features using a one-hot, also known as one-of-K, scheme.\n\n## Description\nIn machine learning, one-hot is a group of bits among which the legal combinations of values are only those with a single high (1) bit and all the others low (0). A similar implementation in which all bits are '1' except one '0' is sometimes called one-cold. In statistics, dummy variables represent a similar technique for representing categorical data.\n\nReference\n- \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data table with at least one categorical column.\n#### OUTPUT\n1. **out_table**: (Table) Data table with one-hot encoded columns appended.\n2. **model**: (Model) Result one hot encoder model. This model is used as an input of One Hot Encoder Model function.\n#### PARAMETER\n1. **Input Columns***: Columns to select as an input. Available are Integer, Long, Float, Double, and String type columns.\n\n2. **Prefix Type**: Prefix type for newly added columns.\n - Available Options: `Use existing column name(s)`, `Enter new column name(s)`\n\n3. **Prefix(es)**: List of prefix name(s). The length of this list should be equal to the length of input columns. This option is available when Prefix Type is set to `Enter new column name(s)`.\n\n4. **Suffix Type**: Suffix type for newly added columns. Available are index, which yields integer indices starting with 0, and label, which yields label suffix same as the category values.\n - Available Options: `Index`, `Label`\n\n5. **Drop Last**: Whether to drop the last column.\n - Available Option: `True`, `False`\n\n\t* Default value: false \n6. **Group By**: Columns to group by.\n\n\n### Python\n#### USAGE\n\n```\none_hot_encoder2(table, input_cols, prefix='col_name', prefix_list=None, suffix='index', n_values='auto', categorical_features='all', handle_unknown='error', drop_last=False, group_by=None)\n```\n\n#### INPUT\n1. **table***: (Table) Data table with at least one categorical column.\n#### OUTPUT\n1. **out_table**: (Table) Data table with one-hot encoded columns appended.\n2. **model**: (Model) Result one hot encoder model. This model is used as an input of One Hot Encoder Model function.\n#### PARAMETER\n1. **input_cols***: Columns to select as an input. Available are Integer, Long, Float, Double, and String type columns.\n\t* Type: *list[str]*\n2. **prefix**: Prefix type for newly added columns.\n\t* Type: *str*\n\t* Default / Range: col_name ( col_name | list )\n3. **prefix_list**: List of prefix name(s). The length of this list should be equal to the length of input columns.\n\t* Type: *list[str]*\n4. **suffix**: Suffix type for newly added columns. Available are index, which yields integer indices starting with 0, and label, which yields label suffix same as the category values.\n\t* Type: *str*\n\t* Default / Range: index ( index | label )\n5. **n_values**: Number of values per feature.\n\t* Type: *int, list[int]*\n\t* Default / Range: auto (1 <= value)\n6. **catorical_features**: Specify what features are treated as categorical.\n\t* Type: *list[int], list[bool]*\n\t* Default / Range: all\n7. **handle_unknown**: Whether to raise an error or ignore if an unknown categorical feature is present during transform.\n\t* Type: *str*\n\t* Default / Range: error ( error | ignoew )\n8. **drop_last**: Whether to drop the last column.\n\t* Type: *bool*\n\t* Default value: false \n9. **group_by**: Columns to group by.\n\t* Type: *list[str]*\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\n\nIn this tutorial workflow, sample_iris data is used to apply one hot eoncoder. The data table with one-hot encoded vectors appended as columns is returned as the output table. The generated model is afterward applied to another data set via One Hot Encoder Model function. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Input Columns***: species\n2. **Prefix Type**: Use existing column name(s)\n3. **Prefix(es)**: None\n4. **Suffix Type**: Index\n5. **Drop Last**: False\n6. **Group By**: None\n\n\n### Python\n\n```\nfrom brightics.function.extraction import one_hot_encoder2\nres = one_hot_encoder2(table=inputs[0],\n input_cols=['species'],\n prefix='col_name',\n prefix_list=None,\n suffix='index',\n n_values='auto',\n categorical_features='all',\n handle_unknown='error',\n drop_last=False,\n group_by=None)\ndf, model = res['out_table'], res['model']\n```\nIn this python script, sample_iris data is used to apply one hot encoder. The data table with one-hot encoded vectors appended as columns is returned as the output table. 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" + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/extraction/meta/one_hot_encoder_model.json b/function/python/brightics/function/extraction/meta/one_hot_encoder_model.json index aec38291e..33b0641be 100644 --- a/function/python/brightics/function/extraction/meta/one_hot_encoder_model.json +++ b/function/python/brightics/function/extraction/meta/one_hot_encoder_model.json @@ -302,8 +302,8 @@ }, { "persist-mode": "auto", - "func": "brightics.function.extraction$one_hot_encodercoder", - "name": "brightics.function.extraction$one_hot_encoder", + "func": "brightics.function.extraction$one_hot_encoder2", + "name": "brightics.function.extraction$one_hot_encoder2", "param": { "prefix": "col_name", "suffix": "index", diff --git a/function/python/brightics/function/extraction/meta/pca.json b/function/python/brightics/function/extraction/meta/pca.json index 6b278d6b0..ec0313a75 100644 --- a/function/python/brightics/function/extraction/meta/pca.json +++ b/function/python/brightics/function/extraction/meta/pca.json @@ -174,7 +174,7 @@ { "id": "group_by", "label": "Group By", - "description": "", + "description": "Columns to group by.", "visibleOption": [], "control": "ColumnSelector", "validation": [], @@ -186,7 +186,7 @@ ], "summary": "Principal component analysis." }, - "md": "# PCA\nPrincipal component analysis.\n\n## Description\nPrincipal component analysis. Linear dimensionality reduction using Singular Value Decomposition of the high dimensional data to project it to a lower dimensional space.\n\nReference\n- \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Input table that contains at least one numeric column for PCA\n#### OUTPUT\n1. **out_table**: (Table) Result table with PCA result columns added.\n2. **model**: (Model) PCA model trained from input table and its summary. \n#### PARAMETER\n1. **Input Columns***: A set of numeric columns for PCA\n\n2. **Number of Components**: Number of components or dimensions for the PCA result. It should be less than or equal to the number of input columns since PCA project the original data to lower dimension. \n\n3. **SVD Solver**: Singular Value Decomposition method to apply. \n\n4. **Tolerance**: Tolerance for singular values computed by 'SVD Solver' == 'ARPACK'.\n\n5. **Number of Iterations**: Number of iterations for the power method computed by 'SVD Solver' == 'Randomized'.\n\n6. **Seed**: The seed used by the random number generator.\n\n7. **New Column Name**: The prefix for the name of new column(s). \n\n8. **Group By**: \n\n\n\n### Python\n#### USAGE\n\n```\nfrom brightics.function.extraction import pca\nres = pca(table = , input_cols = , n_components = , svd_solver = , tol = , iterated_power = , seed = , new_column_name = , group_by = )\nres['out_table'], res['model']\n```\n\n#### INPUT\n1. **table***: (Table) Input table that contains at least one numeric column for PCA\n#### OUTPUT\n1. **out_table**: (Table) \n2. **model**: (Model) The result table with PCA result columns added.\n#### PARAMETER\n1. **input_cols***: A set of numeric columns for PCA\n\t* Type: *list[str]*\n2. **n_components**: Number of components or dimensions for the PCA result. It should be less than or equal to the number of input columns since PCA project the original data to lower dimension. \n\t* Type: *int*\n\t* Default / Range: 1 (value >= 1)\n3. **svd_solver**: Singular Value Decomposition method to apply. \n\t* Type: *str*\n\t* Default / Range: auto ( auto | full | arpack | randomized )\n4. **tol**: Tolerance for singular values computed by 'SVD Solver' == 'ARPACK'.\n\t* Type: *double*\n\t* Default / Range: 0.0 (value >= 0)\n5. **iterated_power**: Number of iterations for the power method computed by 'SVD Solver' == 'Randomized'.\n\t* Type: *int*\n\t* Default / Range: 'auto' (value >= 0)\n6. **seed**: The seed used by the random number generator.\n\t* Type: *int*\n\n7. **new_column_name**: The prefix for the name of new column(s). \n\t* Type: *str*\n8. **group_by**: \n\t* Type: *list[str]*\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\n\nThis example shows a simple application of PCA function. \n\n++Parameters++\n1. **Input Columns***: ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'] \n2. **Number of Components**: 3\n3. **SVD Solver**: auto\n\n### Python\n\n```\nfrom brightics.function.extraction import pca\nres = pca(table = inputs[0] ,input_cols = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'] \n ,n_components = 3 ,svd_solver = 'auto')\nout_table = res['out_table']\nout_model = res['model']\n```\nThis script yields the same result of the VA sample model, if the PCA function is altered by Python Script function with the script. \n\n", + "md": "# PCA\nPrincipal component analysis.\n\n## Description\nPrincipal component analysis. Linear dimensionality reduction using Singular Value Decomposition of the high dimensional data to project it to a lower dimensional space.\n\nReference\n- \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Input table that contains at least one numeric column for PCA\n#### OUTPUT\n1. **out_table**: (Table) Result table with PCA result columns added.\n2. **model**: (Model) PCA model trained from input table and its summary. \n#### PARAMETER\n1. **Input Columns***: A set of numeric columns for PCA\n\n2. **Number of Components**: Number of components or dimensions for the PCA result. It should be less than or equal to the number of input columns since PCA project the original data to lower dimension. \n\n3. **SVD Solver**: Singular Value Decomposition method to apply. \n\n4. **Tolerance**: Tolerance for singular values computed by 'SVD Solver' == 'ARPACK'.\n\n5. **Number of Iterations**: Number of iterations for the power method computed by 'SVD Solver' == 'Randomized'.\n\n6. **Seed**: The seed used by the random number generator.\n\n7. **New Column Name**: The prefix for the name of new column(s). \n\n8. **Group By**: Columns to group by.\n\n\n\n### Python\n#### USAGE\n\n```\nfrom brightics.function.extraction import pca\nres = pca(table = , input_cols = , n_components = , svd_solver = , tol = , iterated_power = , seed = , new_column_name = , group_by = )\nres['out_table'], res['model']\n```\n\n#### INPUT\n1. **table***: (Table) Input table that contains at least one numeric column for PCA\n#### OUTPUT\n1. **out_table**: (Table) \n2. **model**: (Model) The result table with PCA result columns added.\n#### PARAMETER\n1. **input_cols***: A set of numeric columns for PCA\n\t* Type: *list[str]*\n2. **n_components**: Number of components or dimensions for the PCA result. It should be less than or equal to the number of input columns since PCA project the original data to lower dimension. \n\t* Type: *int*\n\t* Default / Range: 1 (value >= 1)\n3. **svd_solver**: Singular Value Decomposition method to apply. \n\t* Type: *str*\n\t* Default / Range: auto ( auto | full | arpack | randomized )\n4. **tol**: Tolerance for singular values computed by 'SVD Solver' == 'ARPACK'.\n\t* Type: *double*\n\t* Default / Range: 0.0 (value >= 0)\n5. **iterated_power**: Number of iterations for the power method computed by 'SVD Solver' == 'Randomized'.\n\t* Type: *int*\n\t* Default / Range: 'auto' (value >= 0)\n6. **seed**: The seed used by the random number generator.\n\t* Type: *int*\n\n7. **new_column_name**: The prefix for the name of new column(s). \n\t* Type: *str*\n8. **group_by**: Columns to group by.\n\t* Type: *list[str]*\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\n\nThis example shows a simple application of PCA function. \n\n++Parameters++\n1. **Input Columns***: ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'] \n2. **Number of Components**: 3\n3. **SVD Solver**: auto\n\n### Python\n\n```\nfrom brightics.function.extraction import pca\nres = pca(table = inputs[0] ,input_cols = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'] \n ,n_components = 3 ,svd_solver = 'auto')\nout_table = res['out_table']\nout_model = res['model']\n```\nThis script yields the same result of the VA sample model, if the PCA function is altered by Python Script function with the script. \n\n", "exampleModels": [ { "fileName": "pca.json", diff --git a/function/python/brightics/function/extraction/meta/polynomial_expansion.json b/function/python/brightics/function/extraction/meta/polynomial_expansion.json index 59f52fff7..f7e8e19f4 100644 --- a/function/python/brightics/function/extraction/meta/polynomial_expansion.json +++ b/function/python/brightics/function/extraction/meta/polynomial_expansion.json @@ -51,25 +51,6 @@ ], "multiple": true, "rowCount": 5 - }, - { - "id": "hold_cols", - "label": "Hold Columns", - "description": "The name of the columns to be kept in output table.", - "visibleOption": [], - "control": "ColumnSelector", - "validation": [], - "mandatory": false, - "items": [], - "columnType": [ - "Integer", - "Long", - "Float", - "Double", - "String" - ], - "multiple": true, - "rowCount": 5 } ], "summary": "This function performs polynomial expansion of degree 2 in a polynomial space. For instance, if one wants to expand two values x and y with degree 2, then (x, y, x * x, x * y, y * y) will be returned. The number of variables may be larger than 2.", diff --git a/function/python/brightics/function/extraction/meta/scale.json b/function/python/brightics/function/extraction/meta/scale.json index 9e9cb4b69..086660df5 100644 --- a/function/python/brightics/function/extraction/meta/scale.json +++ b/function/python/brightics/function/extraction/meta/scale.json @@ -103,7 +103,7 @@ "items": [], "columnType": [], "type": "String", - "placeHolder": "_min_max" + "placeHolder": "Enter suffix for the new column" }, { "id": "group_by", diff --git a/function/python/brightics/function/extraction/meta/string_split.json b/function/python/brightics/function/extraction/meta/string_split.json index dc30ed234..307ff05e8 100644 --- a/function/python/brightics/function/extraction/meta/string_split.json +++ b/function/python/brightics/function/extraction/meta/string_split.json @@ -44,25 +44,6 @@ ], "multiple": false }, - { - "id": "hold_cols", - "label": "Hold Columns", - "description": "The name of the columns to be kept in output table.", - "visibleOption": [], - "control": "ColumnSelector", - "validation": [], - "mandatory": false, - "items": [], - "columnType": [ - "Integer", - "Long", - "Float", - "Double", - "String" - ], - "multiple": true, - "rowCount": 5 - }, { "id": "delimiter", "label": "Delimiter", diff --git a/function/python/brightics/function/extraction/meta/variance_filter.json b/function/python/brightics/function/extraction/meta/variance_filter.json new file mode 100644 index 000000000..3cbef3bb6 --- /dev/null +++ b/function/python/brightics/function/extraction/meta/variance_filter.json @@ -0,0 +1,863 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "extraction", + "func": "brightics.function.extraction$variance_filter57430", + "name": "brightics.function.extraction$variance_filter", + "context": "python", + "label": "Variance Filter", + "description": "Feature selector that removes all features having variance lower than threshold.", + "tags": [ + "variance", + "extraction" + ], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "out_table": "" + }, + "meta": { + "table": { + "type": "table", + "desc": "Input data.", + "optional": false + }, + "out_table": { + "type": "table", + "desc": "Output table." + } + }, + "params": [ + { + "id": "feature_cols", + "label": "Feature Columns", + "description": " Columns to select as features", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "globalVariable": false, + "columnType": [ + "Integer", + "Long", + "Float", + "Double" + ], + "type": "Integer", + "multiple": true, + "rowCount": 5 + }, + { + "id": "threshold", + "label": "Variance Threshold", + "description": "Features with a training-set variance lower than this threshold will be removed. The default is to keep all features with non-zero variance, i.e. remove the features that have the same value in all samples.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Double", + "min": 0, + "max": 1, + "placeHolder": "0.0 (0.0 <= value <= 1.0)" + }, + { + "id": "group_by", + "label": "Group By", + "description": "Columns to group by.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "multiple": true + } + ] + }, + "md": { + "en": "# Variance Filter\n\n\n## Description\nFeature selector that removes all features having variance lower than threshold.\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Input data.\n#### OUTPUT\n1. **out_table**: (Table) Output table.\n#### PARAMETER\n1. **Feature Columns***: Columns to select as features\n\n2. **Variance Threshold**: Features with a training-set variance lower than this threshold will be removed. The default is to keep all features with non-zero variance, i.e. remove the features that have the same value in all samples.\n\n3. **Group By**: Columns to group by.\n\n\n### Python\n#### USAGE\n\n```\nvariance_filter(table = inputs[0], feature_cols = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], \n threshold = 0.0, group_by = None)\n```\n\n#### INPUT\n1. **table***: (Table) Input data.\n#### OUTPUT\n1. **out_table**: (Table) Output table.\n#### PARAMETER\n1. **feature_cols***: Columns to select as features\n\t* Type: *list[str]*\n2. **threshold**: Features with a training-set variance lower than this threshold will be removed. 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The output of this function is a table that contains the features filtered considering their variance values.\n\n\n++Parameters++\n1. **Feature Columns***: ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\n2. **Variance Threshold**: 0.5\n3. **Group By**: None\n\n\n\n### Python\n\n```\nfrom brightics.function.extraction import variance_filter\nres = variance_filter(table = inputs[0] ,feature_cols = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], threshold = 0.5, group_by = None)\noutput = res['out_table']\n```\nIn this script, sample_iris data is used as input of the Correlation Filter function. 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+ "description": "In this tutorial workflow, sample_iris data is used as input of the Correlation Filter function. The output of this function is a table that contains the features filtered considering their variance values.\n", + "parameter": "1. **Feature Columns***: ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\n2. **Variance Threshold**: 0.5\n3. **Group By**: None\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.extraction import variance_filter\r\nres = variance_filter(table = inputs[0] ,feature_cols = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], threshold = 0.5, group_by = None)\r\noutput = res['out_table']", + "description": "In this script, sample_iris data is used as input of the Correlation Filter function. The output of this function is a table that contains the features filtered considering their variance values.\n", + "context": "python" + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/extraction/moving_average.py b/function/python/brightics/function/extraction/moving_average.py index d462c9002..ca3c87e01 100644 --- a/function/python/brightics/function/extraction/moving_average.py +++ b/function/python/brightics/function/extraction/moving_average.py @@ -37,7 +37,7 @@ def ewma(table, group_by=None, **params): return _ewma(table, **params) -def _ewma(table, input_cols, ratio_type, custom_ratio=0.5, period_number=1): +def _ewma(table, input_cols, ratio_type='custom', custom_ratio=0.5, period_number=1): out_table = table.copy() def ewma_col(column): diff --git a/function/python/brightics/function/extraction/pca.py b/function/python/brightics/function/extraction/pca.py index 4677f3b69..101028b41 100644 --- a/function/python/brightics/function/extraction/pca.py +++ b/function/python/brightics/function/extraction/pca.py @@ -50,7 +50,7 @@ def _pca(table, input_cols, new_column_name='projected_', n_components=None, cop if n_components is None: n_components = num_feature_cols - pca = PCA(None, copy, whiten, svd_solver, tol, iterated_power, random_state=seed) + pca = PCA(n_components, copy, whiten, svd_solver, tol, iterated_power, random_state=seed) pca_model = pca.fit(table[input_cols]) column_names = [] diff --git a/function/python/brightics/function/extraction/polynomial_expansion.py b/function/python/brightics/function/extraction/polynomial_expansion.py index 62340ba82..04fdcd61d 100644 --- a/function/python/brightics/function/extraction/polynomial_expansion.py +++ b/function/python/brightics/function/extraction/polynomial_expansion.py @@ -26,12 +26,14 @@ def polynomial_expansion(table, **params): return _polynomial_expansion(table, **params) def _polynomial_expansion(table, input_cols, hold_cols=False): - out_table = pd.DataFrame() - out_table[input_cols] = table[input_cols] if hold_cols: + out_table = pd.DataFrame() + out_table[input_cols] = table[input_cols] hold_cols = list(set(hold_cols) - set(input_cols)) out_table[hold_cols] = table[hold_cols] + else: + out_table = table.copy() for i in range(len(input_cols)): for j in range(i, len(input_cols)): out_table[input_cols[i] + '_' + input_cols[j]] = np.array(table[input_cols[i]]) * np.array(table[input_cols[j]]) - return {'out_table' : out_table} + return {'out_table' : out_table} \ No newline at end of file diff --git a/function/python/brightics/function/extraction/scale.py b/function/python/brightics/function/extraction/scale.py index e3811f60f..dd52fdca4 100644 --- a/function/python/brightics/function/extraction/scale.py +++ b/function/python/brightics/function/extraction/scale.py @@ -19,6 +19,7 @@ from brightics.common.groupby import _function_by_group from brightics.function.utils import _model_dict from brightics.common.utils import check_required_parameters +from brightics.common.repr import BrtcReprBuilder, strip_margin, pandasDF2MD, dict2MD, plt2MD, keyValues2MD def scale(table, group_by=None, **params): @@ -61,7 +62,25 @@ def _scale(table, input_cols, scaler, suffix=None): out_model['used_scaler'] = scaler out_model['scaler'] = scale out_model['suffix'] = suffix - + rb = BrtcReprBuilder() + params = { + "Input columns": input_cols, + "Normalization method": scaler, + "Suffix": suffix + } + summary_table = pd.DataFrame() + summary_table['Input columns'] = input_cols + summary_table['Normalization method'] = [scaler] * len(input_cols) + summary_table['New column names'] = scaled_cols + rb.addMD(strip_margin(""" + | ## Label Encoder Model + | ### Parameters + | {params} + | + | ### Summary table + | {summary_table} + """.format(params=dict2MD(params), summary_table=pandasDF2MD(summary_table)))) + out_model['_repr_brtc_'] = rb.get() return {'out_table' : out_table, 'model' : out_model} @@ -73,7 +92,10 @@ def scale_model(table, model, **params): return _scale_model(table, model, **params) -def _scale_model(table, model): +def _scale_model(table, model): + if (table.shape[0]==0): + out_table = pd.DataFrame(columns=table.columns) + return {'out_table' : out_table} scaled_cols = [] for col in model['input_cols']: scaled_cols.append(col + model['suffix']) @@ -81,5 +103,4 @@ def _scale_model(table, model): out_table = table.copy() scaled_table = model['scaler'].transform(out_table[model['input_cols']]) out_table[scaled_cols] = pd.DataFrame(data=scaled_table) - return {'out_table' : out_table} diff --git a/function/python/brightics/function/extraction/string_split.py b/function/python/brightics/function/extraction/string_split.py index e81cb9992..81feb2528 100644 --- a/function/python/brightics/function/extraction/string_split.py +++ b/function/python/brightics/function/extraction/string_split.py @@ -30,7 +30,7 @@ def string_split(table, **params): def _string_split(table, input_col, hold_cols=None, delimiter=',', output_col_name='split', output_col_cnt=3, output_col_type='double', start_pos=0, end_pos=0): - out_table = pd.DataFrame() + out_table = table.copy() output_arr = [x[start_pos:-end_pos].split(delimiter, output_col_cnt) \ if not pd.isnull(x) \ @@ -84,4 +84,4 @@ def _string_split(table, input_col, hold_cols=None, delimiter=',', output_col_na for hold_col in hold_cols: out_table[hold_col] = table[hold_col] - return {'out_table' : out_table} + return {'out_table' : out_table} \ No newline at end of file diff --git a/function/python/brightics/function/extraction/variance_filter.py b/function/python/brightics/function/extraction/variance_filter.py new file mode 100644 index 000000000..6a901eb91 --- /dev/null +++ b/function/python/brightics/function/extraction/variance_filter.py @@ -0,0 +1,47 @@ +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +from brightics.common.repr import BrtcReprBuilder, strip_margin, plt2MD, pandasDF2MD +from brightics.common.groupby import _function_by_group +from brightics.common.utils import check_required_parameters +from brightics.common.utils import get_default_from_parameters_if_required +from brightics.common.validation import validate, greater_than, greater_than_or_equal_to + +import matplotlib.pyplot as plt +import seaborn as sns +from scipy import stats +import pandas as pd +import numpy as np +from sklearn.feature_selection import VarianceThreshold + +def variance_filter(table, group_by=None, **params): + check_required_parameters(_variance_filter, params, ['table']) + + if group_by is not None: + return _function_by_group(_variance_filter, table, group_by=group_by, **params) + else: + return _variance_filter(table, **params) + + +def _variance_filter(table, feature_cols, threshold=0.0): + data = table[feature_cols] + selector = VarianceThreshold(threshold=threshold) + selector.fit(data) + remain_label_index = selector.get_support() + output = selector.transform(data) + out_table = pd.DataFrame(output, columns=pd.Series(feature_cols)[remain_label_index]) + + return {'out_table': out_table} \ No newline at end of file diff --git a/function/python/brightics/function/io/__init__.py b/function/python/brightics/function/io/__init__.py index d4a868e05..7ac1b98d3 100644 --- a/function/python/brightics/function/io/__init__.py +++ b/function/python/brightics/function/io/__init__.py @@ -38,5 +38,3 @@ from .create_table import create_table from .create_table import set_data - -#from .image import image_load diff --git a/function/python/brightics/function/io/image.py b/function/python/brightics/function/io/image.py deleted file mode 100644 index 9ab8d0791..000000000 --- a/function/python/brightics/function/io/image.py +++ /dev/null @@ -1,106 +0,0 @@ -import uuid - -import matplotlib.pyplot as plt -from typing import List, Any, Union - -import glob -import pandas as pd -import random -import os -import pyarrow as pa -import pyarrow.parquet as pq -import pathlib -import numpy as np -import time -import cv2 - -from brightics.common.data.utils import get_unique_column_name -from brightics.common.datatypes.image import Image -from brightics.common.validation import raise_runtime_error - - -# -# labelling -# dir : (label)/files.... -# none : no label -# file_prefix : (label)_files... -# -def image_load(path, labeling='dir', image_col='image', n_sample=None, size_limit=640, auto_resize_limit=False): - def _is_big_image(img, limit): - return max(img.height, img.width) > limit - - if labeling == 'dir': - images_file_list = glob.glob('''{}/*/*'''.format(path)) - if n_sample is not None: - images_file_list = random.sample(images_file_list, n_sample) - npy_images = [(cv2.imread(x), x) for x in images_file_list] - label = [os.path.split(os.path.dirname(os.path.abspath(x[1])))[1] for x in npy_images if x[0] is not None] - else: - images_file_list = glob.glob('''{}/*'''.format(path)) - if n_sample is not None: - images_file_list = random.sample(images_file_list, n_sample) - npy_images = [(cv2.imread(x), x) for x in images_file_list] - label = None - - loaded_images = [Image(x[0], origin=x[1]) for x in npy_images if x[0] is not None] - - # check the size of loaded images - if any([x for x in loaded_images if _is_big_image(x, size_limit)]): - if auto_resize_limit: - encoded_images = [x.resize_limit(size_limit).tobytes() for x in loaded_images] - else: - raise_runtime_error('Cannot load images with size over {}px.'.format(size_limit)) - else: - encoded_images = [x.tobytes() for x in loaded_images] - - label_col = '{}_label'.format(image_col) - out_df = pd.DataFrame({image_col: encoded_images}) - if label is not None: - out_df[label_col] = label - - return {'out_table': out_df} - - -def image_unload(table, input_col, path, type='png', label_col=None, labelling='dir'): - if not _is_image_col(table, input_col): - raise_runtime_error('{} is not an image type column.'.format(input_col)) - - pathlib.Path(path).mkdir(parents=True, exist_ok=True) - - if label_col is None: - for i, x in enumerate(table[input_col]): - img_npy = Image.from_bytes(x).data - # if type == 'png': - out_file_name = '{}/{}.{}'.format(path, i, type) - print(out_file_name) - cv2.imwrite(out_file_name, img_npy) - - -def save_to_npy(table, input_col, label_col, data_output_path, label_output_path, flatten=True): - _check_image_col(table, input_col) - - data_npy = np.array( - [Image.from_bytes(x).data.flatten() if flatten else Image.from_bytes(x).data for x in table[input_col]]) - label_npy = np.array(table[label_col]) - np.save(file=data_output_path, arr=data_npy) - np.save(file=label_output_path, arr=label_npy) - - -def _is_image_col(table, input_col, n_sample=3): - if table[input_col].dtype != object: - return False - - _n_table = len(table) - if _n_table == 0: - # ignore validation - return True - - _n_sample = min(n_sample, _n_table) - sampled_data = table[input_col].sample(_n_sample) - - return all([Image.is_image(x) for x in sampled_data]) - - -def _check_image_col(table, input_col): - if not _is_image_col(table, input_col): - raise_runtime_error("input column {} is not an image column.".format(input_col)) diff --git a/function/python/brightics/function/io/load.py b/function/python/brightics/function/io/load.py index 966c56bd5..74e61de73 100644 --- a/function/python/brightics/function/io/load.py +++ b/function/python/brightics/function/io/load.py @@ -22,9 +22,10 @@ import brightics.common.data.table_data_reader as table_reader import brightics.common.data.utils as util -import brightics.common.json as data_json +import brightics.common.datajson as data_json from brightics.common.datasource import DbEngine from brightics.common.validation import raise_runtime_error +from brightics.common.exception import BrighticsFunctionException as BFE def read_csv(path, engine='python', delimiter=',', na_filter=False, strip_col=False, quoting=3, encoding='utf-8'): @@ -34,7 +35,26 @@ def read_csv(path, engine='python', delimiter=',', na_filter=False, strip_col=Fa quoting = 3 dir_data = os.getcwd() + '/data' path = os.path.join(dir_data, path) - return {'table': table_reader.read_csv(path, engine, delimiter, na_filter, strip_col, quoting, encoding)} + res = pd.DataFrame() + if os.path.isfile(path): + res = table_reader.read_csv(path, engine, delimiter, na_filter, strip_col, quoting, encoding) + elif os.path.isdir(path): + for f in os.listdir(path): + if f.endswith('.csv'): + f_path = os.path.join(path, f) + try: + tmp = table_reader.read_csv(f_path, engine, delimiter, na_filter, strip_col, quoting, encoding) + except: + raise BFE.from_errors([{'0100': 'Can not read ' + f_path + '.'}]) + if res.shape != (0, 0) and not res.columns.equals(tmp.columns): + raise BFE.from_errors([{'0100': 'Files under ' + path + ' do not have same schema.'}]) + res = pd.concat([res, tmp]) + else: + raise BFE.from_errors([{'0100': 'Path ' + path + ' is incorrect.'}]) + for i, col in enumerate(res.columns): + res = res.rename(columns={'Unnamed: {i}'.format(i=i): 'Unnamed_{i}'.format(i=i)}) + + return {'table': res} def load_model(path): @@ -85,7 +105,7 @@ def load(partial_path=['/brightics@samsung.com/upload/sample_iris.csv']): if partial_path.endswith("/"): partial_path = partial_path[:-1] - + return {'table': table_reader.read_parquet(util.make_data_path_from_key(partial_path))} diff --git a/function/python/brightics/function/io/meta/image_load.json b/function/python/brightics/function/io/meta/image_load.json deleted file mode 100644 index cae5ad72c..000000000 --- a/function/python/brightics/function/io/meta/image_load.json +++ /dev/null @@ -1,76 +0,0 @@ -{ - "script": { - "type": "", - "content": "" - }, - "specJson": { - "category": "io", - "func": "brightics.function.io$image_load", - "name": "brightics.function.io$image_load", - "context": "python", - "label": "Image Load", - "description": "Load image files.", - "tags": [], - "version": "3.6", - "outputs": { - "out_table": "" - }, - "meta": { - "out_table": { - "type": "table" - } - }, - "params": [ - { - "id": "path", - "label": "Input Path", - "description": "Directory where the image files exists.", - "mandatory": true, - "items": [], - "visibleOption": [], - "control": "InputBox", - "columnType": [], - "validation": [], - "type": "String" - }, - { - "id": "labeling", - "label": "Labeling", - "description": "Method to add label to image values.", - "mandatory": false, - "items": [ - { - "label": "Directory", - "value": "dir", - "description": "Assume the image files are saved as (Input Path)/(Label)/(Image Files).", - "default": true - }, - { - "label": "None", - "value": "none", - "description": "Assume the image files are saved as (Input Path)/(Image Files). Label values are not added.", - "default": false - } - ], - "visibleOption": [], - "control": "RadioButton", - "columnType": [], - "validation": [] - }, - { - "id": "image_col", - "label": "Image Column Name", - "description": "Column name for image values.", - "mandatory": false, - "items": [], - "visibleOption": [], - "control": "InputBox", - "columnType": [], - "validation": [], - "placeHolder": "image", - "type": "String" - } - ] - }, - "md": "" -} \ No newline at end of file diff --git a/function/python/brightics/function/io/meta/image_unload.json.notreleased b/function/python/brightics/function/io/meta/image_unload.json.notreleased deleted file mode 100644 index b7c69c8ee..000000000 --- a/function/python/brightics/function/io/meta/image_unload.json.notreleased +++ /dev/null @@ -1,53 +0,0 @@ -{ - "script": { - "type": "", - "content": "" - }, - "specJson": { - "category": "io", - "func": "brightics.function.io$image_unload", - "name": "brightics.function.io$image_unload", - "context": "python", - "label": "Image Unload", - "description": "Unload image files.", - "tags": [], - "version": "3.6", - "inputs": { - "table": "" - }, - "meta": { - "table": { - "type": "table" - } - }, - "params": [ - { - "id": "input_col", - "label": "Input Column", - "description": "", - "mandatory": true, - "items": [], - "visibleOption": [], - "control": "ColumnSelector", - "columnType": [ - "Image" - ], - "validation": [], - "multiple": false - }, - { - "id": "path", - "label": "Output Path", - "description": "", - "mandatory": true, - "items": [], - "visibleOption": [], - "control": "InputBox", - "columnType": [], - "validation": [], - "type": "String" - } - ] - }, - "md": "" -} \ No newline at end of file diff --git a/function/python/brightics/function/io/read_excel.py b/function/python/brightics/function/io/read_excel.py index 998d2f328..03cccec57 100644 --- a/function/python/brightics/function/io/read_excel.py +++ b/function/python/brightics/function/io/read_excel.py @@ -1,9 +1,12 @@ """ Copyright 2019 Samsung SDS + Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at + http://www.apache.org/licenses/LICENSE-2.0 + Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. @@ -16,4 +19,4 @@ def read_excel(path, sheet_index=0): table = pd.read_excel(path, sheet_name=sheet_index) - return {"table": table} \ No newline at end of file + return {"table": table} diff --git a/function/python/brightics/function/io/unload.py b/function/python/brightics/function/io/unload.py index 663bc0092..bd8ee4a62 100644 --- a/function/python/brightics/function/io/unload.py +++ b/function/python/brightics/function/io/unload.py @@ -24,25 +24,38 @@ import boto3 from brightics.function.utils import _model_dict_scala, _model_dict -import brightics.common.json as data_json +import brightics.common.datajson as data_json from brightics.common.datasource import DbEngine from brightics.common.validation import raise_runtime_error from brightics.brightics_data_api import _write_dataframe import brightics.common.data.utils as data_utils from brightics.common.utils import check_required_parameters import brightics.common.data.table_data_reader as table_reader -import brightics.common.data.utils as util +from brightics.brightics_java_gateway import brtc_java_gateway as gateway + + +def could_delete_path(path): + path = path if path[-1] == '/' else path + '/' + if gateway.data_root in path and gateway.data_root != path: + return True + return False def unload(table, partial_path, mode="overwrite"): - path = data_utils.make_data_path_from_key(partial_path[0]) + path = partial_path[0] if isinstance(partial_path, list) else partial_path + if path[0].startswith('.'): + raise_runtime_error('Cannot use a string to start with ".(dot)".') + if '..' in path: + raise_runtime_error('Cannot use a string "..(double dot)".') + path = path if path[0].startswith('/') else '/' + path + path = data_utils.make_data_path_from_key(path) + if not could_delete_path(path): + raise_runtime_error('Please check a path String and a type of path. Cannot use a root of directory for the path.') if os.path.isdir(path): shutil.rmtree(path) if mode=="append": try: - old_frame = table_reader.read_parquet( - util.make_data_path_from_key(partial_path[0]) - ) + old_frame = table_reader.read_parquet(path) new_frame = pd.concat([old_frame, table], axis=0, ignore_index=True) _write_dataframe(new_frame, path) except: @@ -202,9 +215,9 @@ def write_to_db(table, **params): return _write_to_db(table, **params) -def _write_to_db(table, tableName, datasource, ifExists='fail'): +def _write_to_db(table, tableName, datasource, schema=None, ifExists='fail'): if not isinstance(table, pd.DataFrame): raise_runtime_error('table is not pandas.DataFrame') with DbEngine(**datasource) as engine: - table.to_sql(tableName, engine, if_exists=ifExists, index=False) + table.to_sql(tableName, engine, schema=schema, if_exists=ifExists, index=False) diff --git a/function/python/brightics/function/manipulation/__init__.py b/function/python/brightics/function/manipulation/__init__.py index 48585a885..4377529d5 100644 --- a/function/python/brightics/function/manipulation/__init__.py +++ b/function/python/brightics/function/manipulation/__init__.py @@ -24,7 +24,7 @@ from brightics.common.utils import check_required_parameters from brightics.common.utils import get_default_from_parameters_if_required from brightics.common.validation import validate, greater_than_or_equal_to, raise_error, require_param -# from .timeseries_distance import timeseries_distance +from .timeseries_distance import timeseries_distance diff --git a/function/python/brightics/function/manipulation/meta/timeseries_distance.json b/function/python/brightics/function/manipulation/meta/timeseries_distance.json new file mode 100644 index 000000000..8318af184 --- /dev/null +++ b/function/python/brightics/function/manipulation/meta/timeseries_distance.json @@ -0,0 +1,937 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "manipulation", + "func": "brightics.function.manipulation$timeseries_distance", + "name": "brightics.function.manipulation$timeseries_distance", + "context": "python", + "label": "Time Series Distance", + "description": "This function calculates distance between 2 arrays A and B according to the selected distance type. \n\nReferences:\nhttps://en.wikipedia.org/wiki/Distance", + "tags": [ + "datetime", + "time series", + "distance", + "time series distance" + ], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "out_table": "" + }, + "meta": { + "table": { + "type": "table", + "desc": "Data in a table.", + "optional": false + }, + "out_table": { + "type": "table", + "desc": "Output table with distance column added" + } + }, + "params": [ + { + "id": "input_col_1", + "label": "Input Timeseries 1", + "description": "Column name of the first Timeseries Array (Double) or String type. 'Sax' must be chosen for 'Distance Type' to use String type column.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "columnType": [ + "Integer[]", + "Long[]", + "Float[]", + "Double[]", + "String", + "Integer", + "Long", + "Double" + ], + "multiple": false + }, + { + "id": "input_col_2", + "label": "Input Timeseries 2", + "description": "Column name of the second Timeseries Array (Double) or String type. 'Sax' must be chosen for 'Distance Type' to use String type column.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "columnType": [ + "Integer[]", + "Long[]", + "Float[]", + "Double[]", + "String", + "Integer", + "Long", + "Double" + ], + "multiple": false + }, + { + "id": "distance_type", + "label": "Distance type", + "description": "Distance method between 2 double arrays. It must be one of 'Sax', 'Dtw', 'Euclidean', 'Correlation', 'L1Distance', 'EuclideanWithInterpolation', 'L1DistanceWithInterpolation'.", + "visibleOption": [], + "control": "DropDownList", + "validation": [], + "mandatory": true, + "placeHolder": "Please Choose: ", + "items": [ + { + "label": "Sax", + "value": "Sax", + "default": true + }, + { + "label": "Dtw", + "value": "Dtw", + "default": false + }, + { + "label": "Euclidean", + "value": "Euclidean", + "default": false + }, + { + "label": "Correlation", + "value": "Correlation", + "default": false + }, + { + "label": "L1Distance", + "value": "L1Distance", + "default": false + }, + { + "label": "EuclideanWithInterpolation", + "value": "EuclideanWithInterpolation", + "default": false + }, + { + "label": "L1DistanceWithInterpolation", + "value": "L1DistanceWithInterpolation", + "default": false + } + ], + "columnType": [] + }, + { + "id": "alphabet", + "label": "Alphabet", + "description": "Alphabet size for 'Sax' strings. If distance type is 'Sax', you must fix one of 'Alphabet Column' or 'Alphabet'. It is 'Sax' related option. (between 2 and 26)", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [ + "Integer" + ], + "type": "Integer", + "placeHolder": "26", + "defaultValue": 26 + } + ], + "summary": "This function calculates distance between 2 arrays.", + "tutorialLink": "https://www.brightics.ai/kr/docs/ai/v3.7/tutorials/13_preprocessing_timeSeriesDistance?type=insight" + }, + "md": { + "en": "# Time Series Distance\nThis function calculates distance between 2 arrays.\n\n## Description\nThis function calculates distance between 2 arrays A and B according to the selected distance type. \n\n+ References:\nhttps://en.wikipedia.org/wiki/Distance\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data in a table.\n#### OUTPUT\n1. **out_table**: (Table) Output table with distance column added\n#### PARAMETER\n1. **Input Timeseries 1***: Column name of the first Timeseries Array (Double) or String type. 'Sax' must be chosen for 'Distance Type' to use String type column.\n\n2. **Input Timeseries 2***: Column name of the second Timeseries Array (Double) or String type. 'Sax' must be chosen for 'Distance Type' to use String type column.\n\n3. **Distance type***: Distance method between 2 double arrays. It must be one of 'Sax', 'Dtw', 'Euclidean', 'Correlation', 'L1Distance', 'EuclideanWithInterpolation', 'L1DistanceWithInterpolation'.\n\n4. **Alphabet**: Alphabet size for 'Sax' strings. If distance type is 'Sax', you must fix one of 'Alphabet Column' or 'Alphabet'. It is 'Sax' related option. (between 2 and 26)\n\n\t* Default value: 26 \n\n### Python\n#### USAGE\n\n```\ntimeseries_distance(table, input_col_1, input_col_2, distance_type = \"Sax\", alphabet = 26)\n```\n\n#### INPUT\n1. **table***: (Table) Data in a table.\n#### OUTPUT\n1. **out_table**: (Table) Output table with distance column added\n#### PARAMETER\n1. **input_col_1***: Column name of the first Timeseries Array (Double) or String type. 'Sax' must be chosen for 'Distance Type' to use String type column.\n\t* Type: *str*\n2. **input_col_2***: Column name of the second Timeseries Array (Double) or String type. 'Sax' must be chosen for 'Distance Type' to use String type column.\n\t* Type: *str*\n3. **distance_type***: Distance method between 2 double arrays. It must be one of 'Sax', 'Dtw', 'Euclidean', 'Correlation', 'L1Distance', 'EuclideanWithInterpolation', 'L1DistanceWithInterpolation'.\n\t* Type: *str*\n\t* Default / Range: Sax ( Sax | Dtw | Euclidean | Correlation | L1Distance | EuclideanWithInterpolation | L1DistanceWithInterpolation )\n4. **alphabet**: Alphabet size for 'Sax' strings. If distance type is 'Sax', you must fix one of 'Alphabet Column' or 'Alphabet'. It is 'Sax' related option. (between 2 and 26)\n\t* Type: *int*\n\n\t* Default value: 26 \n\n\n## Example\n### VA\n**[Related Tutorial]**\n\n\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, sample_TimeSeriesDistance1 data is used to measure timeseries distance. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Input Timeseries 1***: \"sax1\"\n2. **Input Timeseries 2***: \"sax2\"\n3. **Distance type***: \"Sax\"\n4. **Alphabet**: 26\n\n\n\n### Python\n\n```\nfrom brightics.function.manipulation import timeseries_distance\nres = timeseries_distance(table = inputs[0], input_col_1 = \"sax1\", input_col_2 = \"sax2\", distance_type = \"Sax\", alphabet = 26)\noutput = res['out_table']\n```\nIn this python script, sample_TimeSeriesDistance1 data is used to measure timeseries distance for \"sax1\" and \"sax2\".\n\n\n", + "kr": "" + }, + "exampleModels": [ + { + "fileName": "timeseries_distance_j.json", + "label": "timeseries_distance", + "contents": { + "type": "files", + "data": [ + { + "type": "file", + "data": { + "id": "mc9pqwqq38ayb8kd", + "project_id": "p5dmzu78qb564crk", + "label": "timeseries_distance", + "contents": { + "mid": "mc9pqwqq38ayb8kd", + "type": "data", + "title": "timeseries_distance", + "functions": [ + { + "persist-mode": "auto", + "func": "brightics.function.io$load13889", + "name": "brightics.function.io$load", + "param": { + "partial_path": [ + "/brightics@samsung.com/upload/sample_TimeSeriesDistance1.csv" + ] + }, + "display": { + "label": "Load", + "diagram": { + "position": { + "x": 270, + "y": 220 + } + }, + "sheet": { + "out": { + "partial": [ + { + "panel": [ + { + "id": "default-1593510547183", + "chartOption": { + "colorSet": [ + "#FD026C", + "#4682B8", + "#A5D22D", + "#F5CC0A", + "#FE8C01", + "#6B9494", + "#B97C46", + "#84ACD0", + "#C2E173", + "#F9DD5B", + "#FE569D", + "#FEB356", + "#9CB8B8", + "#D0A884", + "#2E6072", + "#6D8C1E", + "#A48806", + "#A90148", + "#A95E01", + "#476363", + "#7B532F" + ], + "title": { + "show": true, + "text": "", + "subtext": "", + "rotate": 0, + "textStyle": { + "color": "#000000", + "fontSize": 12, + "fontFamily": "" + }, + "subtextStyle": { + "color": "#000000", + "fontSize": 12, + "fontFamily": "" + }, + "left": "50%", + 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", + "model_image": null, + "creator": "brightics@samsung.com", + "create_time": "2020-06-30T09:49:02.000Z", + "updater": "brightics@samsung.com", + "update_time": "2020-06-30T09:51:36.000Z", + "event_key": "2020_095136.000", + "type": "data", + "tag": "null", + "from_version": "0.0" + } + } + ], + "version": "3.6" + } + } + ], + "sampleImages": [ + { + "fileName": "timeseries_distance_j.png", + "label": "timeseries_distance_j.png", + "base64": 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+ "description": "In this tutorial workflow, sample_TimeSeriesDistance1 data is used to measure timeseries distance. The parameter settings used in the function are shown below.", + "parameter": "1. **Input Timeseries 1***: \"sax1\"\n2. **Input Timeseries 2***: \"sax2\"\n3. **Distance type***: \"Sax\"\n4. **Alphabet**: 26\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.manipulation import timeseries_distance\r\nres = timeseries_distance(table = inputs[0], input_col_1 = \"sax1\", input_col_2 = \"sax2\", distance_type = \"Sax\", alphabet = 26)\r\noutput = res['out_table']", + "context": "python", + "description": "In this python script, sample_TimeSeriesDistance1 data is used to measure timeseries distance for \"sax1\" and \"sax2\"." + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/manipulation/outlier_detection.py b/function/python/brightics/function/manipulation/outlier_detection.py index 4b387623d..8f334ed9d 100644 --- a/function/python/brightics/function/manipulation/outlier_detection.py +++ b/function/python/brightics/function/manipulation/outlier_detection.py @@ -81,7 +81,6 @@ def _outlier_detection_tukey_carling(table, input_cols, outlier_method='tukey', 'Input Columns': input_cols, 'Outlier Method': outlier_method, 'Multiplier': multiplier, - 'Number of Outliers in a Row': number_of_removal, 'Result Type': result_type, 'New Column Prefix': new_column_prefix } diff --git a/function/python/brightics/function/manipulation/timeseries_distance.py b/function/python/brightics/function/manipulation/timeseries_distance.py new file mode 100644 index 000000000..a13cf5326 --- /dev/null +++ b/function/python/brightics/function/manipulation/timeseries_distance.py @@ -0,0 +1,152 @@ +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +import ast +import pandas as pd +import numpy as np +import scipy +from brightics.common.utils import get_default_from_parameters_if_required +from brightics.common.utils import check_required_parameters +from brightics.common.exception import BrighticsFunctionException as BFE +from dtaidistance import dtw + + +def timeseries_distance(table, **params): + params = get_default_from_parameters_if_required(params, _timeseries_distance) + check_required_parameters(_timeseries_distance, params, ['table']) + return _timeseries_distance(table, **params) + + +def _timeseries_distance(table, input_col_1, input_col_2, distance_type, alphabet=26, hold_cols=[]): + temp_table = table.copy() + if len(hold_cols) > 0: + out_table = temp_table[hold_cols] + else: + out_table = table.copy() + if table[input_col_1].dtype != table[input_col_2].dtype: + raise BFE.from_errors([{'0100': 'Data types of two input timeseries must be the same.'}]) + if distance_type == 'Sax': + if alphabet < 3 or alphabet > 26: + raise BFE.from_errors( + [{'0100': 'Alphabet must be between 3 and 26 if distance_type is Sax.'}]) + if not isinstance(table[input_col_1].loc[0], str): + raise BFE.from_errors( + [{'0100': 'Data types of input timeseries must be String if distance_type is Sax.'}]) + sax_obj = SAX(alphabetSize=alphabet) + else: + sax_obj = None + if isinstance(table[input_col_1].loc[0], str): + raise BFE.from_errors( + [{'0100': 'Data types of input timeseries must be Array (Double) if distance_type is NOT Sax.'}]) + func = lambda x: ast.literal_eval(x) + try: + temp_table[input_col_1] = temp_table[input_col_1].apply(func) + temp_table[input_col_2] = temp_table[input_col_2].apply(func) + except: + pass + arr_1 = temp_table[input_col_1].values + arr_2 = temp_table[input_col_2].values + distance_list = compute_distance(arr_1, arr_2, distance_type, sax_obj) + out_table['distance'] = distance_list + return {'out_table':out_table} + + +def compute_distance(arr_1, arr_2, distance_type, sax_obj): + if distance_type == 'Sax': + distance_func = sax_obj.compare_strings + elif distance_type == 'Dtw': + distance_func = _dtw + elif distance_type == 'Euclidean' or distance_type == 'EuclideanWithInterpolation': + distance_func = _euclidean + elif distance_type == 'Correlation': + distance_func = _corr + elif distance_type == 'L1Distance' or distance_type == 'L1DistanceWithInterpolation': + distance_func = _l1 + distance_list = [] + for ind in range(len(arr_1)): + temp_1 = np.array(arr_1[ind]) + temp_2 = np.array(arr_2[ind]) + if 'Interpolation' in distance_type: + temp_1 = _interpolate(temp_1) + temp_2 = _interpolate(temp_2) + try: + distance_list.append(distance_func(temp_1, temp_2)) + except: + distance_list.append(np.nan) + return distance_list + + +def _dtw(in_1, in_2): + sim = dtw.distance(in_1, in_2) + return sim + + +def _euclidean(in_1, in_2): + return np.linalg.norm((in_1 - in_2), ord=2) + + +def _corr(in_1, in_2): + return np.corrcoef(in_1, in_2)[0, 1] + + +def _interpolate(arr): + return pd.Series(arr).interpolate().values + + +def _l1(in_1, in_2): + return np.linalg.norm((in_1 - in_2), ord=1) + + +class SAX(object): + + def __init__(self, alphabetSize): + self.aOffset = ord('a') + self.alphabetSize = alphabetSize + self.create_breakpoint() + self.beta = self.breakpoints[str(self.alphabetSize)] + self.build_letter_compare_dict() + + def compare_strings(self, sA, sB): + list_letters_a = [x for x in str(sA)] + list_letters_b = [x for x in str(sB)] + mindist = 0.0 + for i in range(0, len(list_letters_a)): + mindist += self.compareDict[list_letters_a[i] + list_letters_b[i]] ** 2 + mindist = np.sqrt(mindist) + return mindist + + def build_letter_compare_dict(self): + number_rep = range(0, self.alphabetSize) + letters = [chr(x + self.aOffset) for x in number_rep] + self.compareDict = {} + for i in range(0, len(letters)): + for j in range(0, len(letters)): + if np.abs(number_rep[i] - number_rep[j]) <= 1: + self.compareDict[letters[i] + letters[j]] = 0 + else: + high_num = np.max([number_rep[i], number_rep[j]]) - 1 + low_num = np.min([number_rep[i], number_rep[j]]) + self.compareDict[letters[i] + letters[j]] = self.beta[high_num] - self.beta[low_num] + + def create_breakpoint(self): + self.breakpoints = {} + for i in range(3, 27): + self.breakpoints[str(i)] = self.lookupEquiprobableRegions(i) + + def lookupEquiprobableRegions(self, word_length): + regions = np.arange(0, word_length, 1) / word_length + temp = scipy.stats.norm.ppf(regions)[1:].tolist() + return temp diff --git a/function/python/brightics/function/recommendation/association_rule.py b/function/python/brightics/function/recommendation/association_rule.py index c6dc4a07c..6b1c86ecd 100644 --- a/function/python/brightics/function/recommendation/association_rule.py +++ b/function/python/brightics/function/recommendation/association_rule.py @@ -404,7 +404,7 @@ def _association_rule(table, input_mode=None, array_input=None, mul_items=None, tmp.remove(None) transactions[i] = np.array(list(tmp)) elif input_mode == 'transaction': - transactions = [list(set(transaction)) for transaction in np.array(table[array_input])] + transactions = [list(set(transaction) - {None}) for transaction in np.array(table[array_input])] else: if items is None: raise Exception('Select Item Column') diff --git a/function/python/brightics/function/recommendation/collaborative_filtering.py b/function/python/brightics/function/recommendation/collaborative_filtering.py index e5f62f652..5cb063fe0 100644 --- a/function/python/brightics/function/recommendation/collaborative_filtering.py +++ b/function/python/brightics/function/recommendation/collaborative_filtering.py @@ -265,7 +265,8 @@ def _collaborative_filtering_train(table, user_col , item_col, rating_col, N=10, model = _model_dict('collaborative filtering') model['weighted'] = weighted model['k'] = k - model['similar_coeff'] = similar_coeff + model['method'] = method + model['centered_ratings'] = centered_ratings model['item_encoder'] = item_encoder model['user_encoder'] = user_encoder model['item_users'] = item_users @@ -319,7 +320,8 @@ def _collaborative_filtering_predict(table, model, prediction_col='prediction', user_avg = model['user_avg'] weighted = model['weighted'] k = model['k'] - similar_coeff = model['similar_coeff'] + method = model['method'] + centered_ratings = model['centered_ratings'] item_encoder = model['item_encoder'] user_encoder = model['user_encoder'] item_users = model['item_users'] @@ -327,6 +329,17 @@ def _collaborative_filtering_predict(table, model, prediction_col='prediction', user_col = model['user_col'] item_col = model['item_col'] based = model['based'] + + if method == 'cosine': + similar_coeff = cosine_similarity(centered_ratings) + elif method == 'pearson': + result = [] + for i in centered_ratings.toarray(): + result.append(i - np.average(i)) + similar_coeff = cosine_similarity(result) + elif method == 'jaccard': + similar_coeff = 1 - pairwise_distances(centered_ratings.toarray(), metric="hamming") + tmp_user = np.array(table[user_col]) tmp_item = np.array(table[item_col]) valid_indices = [i for i in range(len(tmp_user)) if tmp_user[i] in user_encoder.classes_ and tmp_item[i] in item_encoder.classes_] diff --git a/function/python/brightics/function/recommendation/meta/association_rule.json b/function/python/brightics/function/recommendation/meta/association_rule.json index 15fdc8bd5..62118e4f2 100644 --- a/function/python/brightics/function/recommendation/meta/association_rule.json +++ b/function/python/brightics/function/recommendation/meta/association_rule.json @@ -242,7 +242,7 @@ "summary": "This function is a recommender model using Association Rule.", "tutorialLink": "https://www.brightics.ai/kr/docs/ai/v3.7/tutorials/30_Purchase_based_product_recom" }, - "md": "# Association Rule\nThis function is a recommender model using Association Rule.\n\n## Description\nAssociation rule is a rule-based method for discovering interesting relations between variables in large databases. This function computes the association rule using the FP-growth algorithm. FP stands for frequent pattern. It requires an input table of database consisting of transactions, where a transaction is a set of items. Association rule typically does not consider the order of items either within a transaction or across transactions. \n\nAn example rule for the supermarket domain could be {butter, bread}=> milk, meaning that if butter and bread are bought, then the customer is likely to buy milk. Several measures are presented including `support`, `confidence`, `lift`, and `conviction`. `Support` is an indication of how frequently the itemset appears in the dataset. `Confidence` is an indication of how often the rule has been found to be true. `Lift` is the ratio of the observed support to that ex pected if X and Y were independent. `Conviction` means the ratio of the expected frequency that X occurs without Y (that is to say, the frequency that the rule makes an incorrect prediction)\n\nReference\n- \n- \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data in a Table. It must contain at least one column for transaction data.\n#### OUTPUT\n1. **out_table**: (Table) Discovered Association Rules in a table.\n#### PARAMETER\n1. **Input Type**: Type of Input.\n - Available Options : `User - single item`, `User - multiple items`, `Array`\n\n2. **Input Column**: Column to select as input.\n\n3. **Item Columns**: For Input Type \"User - multiple items\"\n\n4. **User Column***: For Input Type \"User-single item\"\n\n5. **Item Column***: For Input Type \"User-single item\"\n\n6. **Min Support**: The minimal support level. \n\n7. **Min Confidence**: The minimal confidence. \n\n8. **Min Lift**: The minimal lift.\n\n9. **Max Lift**: The maximal lift.\n\n10. **Min Conviction**: The minimal conviction.\n\n11. **Max Conviction**: The maximal conviction.\n\n12. **Group By**: Columns to group by.\n\n\n### Python\n#### USAGE\n\n```\nassociation_rule(table, input_mode=None, array_input=None, mul_items=None, items=None, user_name=None, min_support=0.01, min_confidence=0.8, min_lift=-math.inf, max_lift=math.inf, min_conviction=-math.inf, max_conviction=math.inf,\ngroup_by=None)\n```\n\n#### INPUT\n1. **table***: (Table) Data in a Table. It must contain at least one column for transaction data.\n#### OUTPUT\n1. **out_table**: (Table) Discovered Association Rules in a table.\n#### PARAMETER\n1. **input_mode**: Type of Input.\n\t* Type: *str*\n\t* Default / Range: item_user ( item_user | user_multiple | transaction )\n2. **array_input**: Column to select as input.\n\t* Type: *str*\n\t* Default value: None\n3. **mul_items**: Item columns for `input_mode=user_multiple`.\n\t* Type: *list[str]*\n\t* Default value: None\n4. **user_name***: User column for `input_mode=item_user`.\n\t* Type: *str*\n\t* Default value: None\n5. **items***: Item columns for `input_mode=item_user`.\n\t* Type: _list_[_str_] \n\t* Default value: None\n6. **min_support**: The minimal support level. \n\t* Type: *float*\n\t* Default / Range: 0.01 (0 <= value <= 1)\n7. **min_confidence**: The minimal confidence. \n\t* Type: *float*\n\t* Default / Range: 0.8 (0 <= value <= 1)\n8. **min_lift**: The minimal lift.\n\t* Type: *float*\n * Default / Range: -∞ (value <= 0)\n9. **max_lift**: The maximal lift.\n\t* Type: *float*\n * Default / Range: ∞ (0 <= value)\n10. **min_conviction**: The minimal conviction.\n\t* Type: *float*\n * Default / Range: -∞ (value <= 0)\n11. **max_conviction**: The maximal conviction.\n\t* Type: *float*\n * Default / Range: ∞ (0 <= value)\n12. **group_by**: Columns to group by.\n\t* Type: *list[str]*\n\n## Example\n### VA\n**[Related Tutorial]**\n\n\n**[Sample Model]**\n\n\n\n\nIn this tutorial workflow, sample_AssociationRule data is used to conduct Association Rule function. The generated result is can be visualized by Association Rule Visualization function. The parameter settings used in the function are shown below.\n\n\n++Parameters++\n1. **Input Type**: Array\n2. **Input Column**: item_array\n3. **Min Support**: 0.01\n4. **Min Confidence**: 0.8\n5. **Min Lift**: 0\n6. **Max Lift**: 100\n7. **Min Conviction**: 0\n8. **Max Conviction**: 100\n9. **Group By**: None\n\n\n### Python\n\n```\nfrom brightics.function.recommendation import association_rule\nimport math\ninput_table = inputs[0]\nresult = association_rule(table=input_table,\n input_mode='transaction',\n array_input='item_array',\n mul_items=None,\n items=None,\n user_name=None,\n min_support=0.01,\n min_confidence=0.8,\n min_lift=-math.inf,\n max_lift=math.inf,\n min_conviction=-math.inf,\n max_conviction=math.inf,\n group_by=None)\noutput = result['model']\n```\nIn this python script, sample_AssociationRule data is used to conduct Association Rule function. The generated result is can be visualized by Association Rule Visualization function. \n\n", + "md": "# Association Rule\nThis function is a recommender model using Association Rule.\n\n## Description\nAssociation rule is a rule-based method for discovering interesting relations between variables in large databases. This function computes the association rule using the FP-growth algorithm. FP stands for frequent pattern. It requires an input table of database consisting of transactions, where a transaction is a set of items. Association rule typically does not consider the order of items either within a transaction or across transactions. \n\nAn example rule for the supermarket domain could be {butter, bread}=> milk, meaning that if butter and bread are bought, then the customer is likely to buy milk. Several measures are presented including `support`, `confidence`, `lift`, and `conviction`. `Support` is an indication of how frequently the itemset appears in the dataset. `Confidence` is an indication of how often the rule has been found to be true. `Lift` is the ratio of the observed support to that ex pected if X and Y were independent. `Conviction` means the ratio of the expected frequency that X occurs without Y (that is to say, the frequency that the rule makes an incorrect prediction)\n\nReference\n- \n- \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data in a Table. It must contain at least one column for transaction data.\n#### OUTPUT\n1. **out_table**: (Table) Discovered Association Rules in a table.\n#### PARAMETER\n1. **Input Type**: Type of Input.\n - Available Options : `User - single item`, `User - multiple items`, `Array`\n\n2. **Input Column**: Column to select as input.\n\n3. **Item Columns**: For Input Type \"User - multiple items\"\n\n4. **User Column***: For Input Type \"User-single item\"\n\n5. **Item Column***: For Input Type \"User-single item\"\n\n6. **Min Support**: The minimal support level. \n\n7. **Min Confidence**: The minimal confidence. \n\n8. **Min Lift**: The minimal lift.\n\n9. **Max Lift**: The maximal lift.\n\n10. **Min Conviction**: The minimal conviction.\n\n11. **Max Conviction**: The maximal conviction.\n\n12. **Group By**: Columns to group by.\n\n\n### Python\n#### USAGE\n\n```\nassociation_rule(table, input_mode=None, array_input=None, mul_items=None, items=None, user_name=None, min_support=0.01, min_confidence=0.8, min_lift=-math.inf, max_lift=math.inf, min_conviction=-math.inf, max_conviction=math.inf,\ngroup_by=None)\n```\n\n#### INPUT\n1. **table***: (Table) Data in a Table. It must contain at least one column for transaction data.\n#### OUTPUT\n1. **out_table**: (Table) Discovered Association Rules in a table.\n#### PARAMETER\n1. **input_mode**: Type of Input.\n\t* Type: *str*\n\t* Default / Range: item_user ( item_user | user_multiple | transaction )\n2. **array_input**: Column to select as input.\n\t* Type: *str*\n\t* Default value: None\n3. **mul_items**: Item columns for `input_mode=user_multiple`.\n\t* Type: *list[str]*\n\t* Default value: None\n4. **user_name***: User column for `input_mode=item_user`.\n\t* Type: *str*\n\t* Default value: None\n5. **items***: Item columns for `input_mode=item_user`.\n\t* Type: _list_[_str_] \n\t* Default value: None\n6. **min_support**: The minimal support level. \n\t* Type: *float*\n\t* Default / Range: 0.01 (0 <= value <= 1)\n7. **min_confidence**: The minimal confidence. \n\t* Type: *float*\n\t* Default / Range: 0.8 (0 <= value <= 1)\n8. **min_lift**: The minimal lift.\n\t* Type: *float*\n * Default / Range: -∞ (value <= 0)\n9. **max_lift**: The maximal lift.\n\t* Type: *float*\n * Default / Range: ∞ (0 <= value)\n10. **min_conviction**: The minimal conviction.\n\t* Type: *float*\n * Default / Range: -∞ (value <= 0)\n11. **max_conviction**: The maximal conviction.\n\t* Type: *float*\n * Default / Range: ∞ (0 <= value)\n12. **group_by**: Columns to group by.\n\t* Type: *list[str]*\n\n## Example\n### VA\n**[Related Tutorial]**\n\n\n**[Sample Model]**\n\n\n\n\nIn this tutorial workflow, sample_AssociationRule data is used to conduct Association Rule function. The generated result is can be visualized by Association Rule Visualization function. The parameter settings used in the function are shown below.\n\n\n++Parameters++\n1. **Input Type**: Array\n2. **Input Column**: item_array\n3. **Min Support**: 0.01\n4. **Min Confidence**: 0.8\n5. **Min Lift**: 0\n6. **Max Lift**: 100\n7. **Min Conviction**: 0\n8. **Max Conviction**: None\n9. **Group By**: None\n\n\n### Python\n\n```\nfrom brightics.function.recommendation import association_rule\nimport math\ninput_table = inputs[0]\nresult = association_rule(table=input_table,\n input_mode='transaction',\n array_input='item_array',\n mul_items=None,\n items=None,\n user_name=None,\n min_support=0.01,\n min_confidence=0.8,\n min_lift=-math.inf,\n max_lift=math.inf,\n min_conviction=-math.inf,\n max_conviction=math.inf,\n group_by=None)\noutput = result['model']\n```\nIn this python script, sample_AssociationRule data is used to conduct Association Rule function. The generated result is can be visualized by Association Rule Visualization function. \n\n", "exampleModels": [ { "fileName": "brightics.function.recommendation$association_rule.json", @@ -275,8 +275,7 @@ "min_lift": 0, "min_conviction": 0, "min_support": 0.01, - "max_lift": 100, - "max_conviction": 100 + "max_lift": 100 }, "display": { "label": "Association Rule", diff --git a/function/python/brightics/function/recommendation/meta/association_rule_visualization.json b/function/python/brightics/function/recommendation/meta/association_rule_visualization.json index f1326f005..8ac95fa4d 100644 --- a/function/python/brightics/function/recommendation/meta/association_rule_visualization.json +++ b/function/python/brightics/function/recommendation/meta/association_rule_visualization.json @@ -168,8 +168,7 @@ "min_lift": 0, "min_conviction": 0, "min_support": 0.01, - "max_lift": 100, - "max_conviction": 100 + "max_lift": 100 }, "display": { "label": "Association Rule", diff --git a/function/python/brightics/function/regression/__init__.py b/function/python/brightics/function/regression/__init__.py index 613e42efa..5ca6ecfbf 100644 --- a/function/python/brightics/function/regression/__init__.py +++ b/function/python/brightics/function/regression/__init__.py @@ -34,3 +34,5 @@ from .regression_predict import regression_predict from .isotonic_regression import isotonic_regression_train from .isotonic_regression import isotonic_regression_predict +from .pls_regression import pls_regression_train +from .pls_regression import pls_regression_predict \ No newline at end of file diff --git a/function/python/brightics/function/regression/ada_boost_regression.py b/function/python/brightics/function/regression/ada_boost_regression.py index 50ef6df6d..b1e3869ee 100644 --- a/function/python/brightics/function/regression/ada_boost_regression.py +++ b/function/python/brightics/function/regression/ada_boost_regression.py @@ -121,6 +121,10 @@ def ada_boost_regression_predict(table, model, **params): def _ada_boost_regression_predict(table, model, pred_col_name='prediction'): + if (table.shape[0] == 0): + new_cols = table.columns.tolist() + [pred_col_name] + out_table = pd.DataFrame(columns=new_cols) + return {'out_table': out_table} out_table = table.copy() regressor = model['regressor'] _, test_data = check_col_type(table, model['params']['feature_cols']) diff --git a/function/python/brightics/function/regression/knn_regression.py b/function/python/brightics/function/regression/knn_regression.py index 8e7eaecbe..55951e474 100644 --- a/function/python/brightics/function/regression/knn_regression.py +++ b/function/python/brightics/function/regression/knn_regression.py @@ -36,7 +36,10 @@ def knn_regression(train_table, test_table, **params): def _knn_regression(train_table, test_table, feature_cols, label_col, k=5, algorithm='auto', leaf_size=30, p=2, pred_col_name='prediction'): - + if (test_table.shape[0] == 0): + new_cols = test_table.columns.tolist() + [pred_col_name] + out_table = pd.DataFrame(columns=new_cols) + return {'out_table': out_table} _, X_train = check_col_type(train_table, feature_cols) y_train = train_table[label_col] _, X_test = check_col_type(test_table, feature_cols) diff --git a/function/python/brightics/function/regression/meta/ada_boost_regression_train.json b/function/python/brightics/function/regression/meta/ada_boost_regression_train.json index ea72e9b78..499346970 100644 --- a/function/python/brightics/function/regression/meta/ada_boost_regression_train.json +++ b/function/python/brightics/function/regression/meta/ada_boost_regression_train.json @@ -74,7 +74,7 @@ }, { "id": "max_depth", - "label": "Maximum Depth", + "label": "Max Depth", "description": "The maximum depth of the base estimator. In this Brightics Studio, the base estimator for Adaboost Regression model is Decision Tree Regression model. Note that it is one of the main parameters to tune to obtain good results by controlling the complexity of the base estimmators.", "visibleOption": [], "control": "InputBox", diff --git a/function/python/brightics/function/regression/meta/decision_tree_regression_predict.json b/function/python/brightics/function/regression/meta/decision_tree_regression_predict.json index e1dd2752b..91bb64e25 100644 --- a/function/python/brightics/function/regression/meta/decision_tree_regression_predict.json +++ b/function/python/brightics/function/regression/meta/decision_tree_regression_predict.json @@ -1566,4 +1566,4 @@ "base64": 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" } ] -} +} \ No newline at end of file diff --git a/function/python/brightics/function/regression/meta/decision_tree_regression_train.json b/function/python/brightics/function/regression/meta/decision_tree_regression_train.json index 3eb311ed2..fdfe1b663 100644 --- a/function/python/brightics/function/regression/meta/decision_tree_regression_train.json +++ b/function/python/brightics/function/regression/meta/decision_tree_regression_train.json @@ -1232,7 +1232,7 @@ }, { "persist-mode": "auto", - "func": "brightics.function.regression$decision_tree_regression_predict", + "func": "brightics.function.regression$decision_tree_regression_predictedict", "name": "brightics.function.regression$decision_tree_regression_predict", "param": {}, "display": { @@ -1758,4 +1758,4 @@ "base64": 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" } ] -} +} \ No newline at end of file diff --git a/function/python/brightics/function/regression/meta/glm_train.json b/function/python/brightics/function/regression/meta/glm_train.json index f8bee3b55..726e8cdbc 100644 --- a/function/python/brightics/function/regression/meta/glm_train.json +++ b/function/python/brightics/function/regression/meta/glm_train.json @@ -98,7 +98,7 @@ "default": false }, { - "label": "Gammma", + "label": "Gamma", "value": "gamma", "default": false }, diff --git a/function/python/brightics/function/regression/meta/isotonic_regression_predict.json b/function/python/brightics/function/regression/meta/isotonic_regression_predict.json index 073e443ed..8b6885550 100644 --- a/function/python/brightics/function/regression/meta/isotonic_regression_predict.json +++ b/function/python/brightics/function/regression/meta/isotonic_regression_predict.json @@ -1563,4 +1563,4 @@ "base64": 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" } ] -} +} \ No newline at end of file diff --git a/function/python/brightics/function/regression/meta/mlp_regression_predict.json b/function/python/brightics/function/regression/meta/mlp_regression_predict.json index 7ef6fd370..93f78e40d 100644 --- a/function/python/brightics/function/regression/meta/mlp_regression_predict.json +++ b/function/python/brightics/function/regression/meta/mlp_regression_predict.json @@ -53,10 +53,13 @@ ], "summary": "This function predicts data using a MLP Regression model." }, - "md": "# MLP Regression Predict\nThis function predicts data using a MLP Regression model.\n\n## Description\nMLP stands for Multi-layer Perceptron which is a neural network model with supervised learning. Given a set of features X(x1, x2, ..., xn) and a target y, it can learn a non-linear function approximator for either classification or regression. MLP consists of at least three layers of nodes: an input layer, a hidden layer and an output layer. Except for the input nodes, each node is a neuron that uses a nonlinear activation function. It is different from logistic regression, in that between the input and the output layer, there can be one or more non-linear layers, called hidden layers.\n \nThe advantages of MLP are capability to learn non-linear models and capability to learn models in real-time(online learnin) using `partial_fit`. On the other hand, the disadvantages of MLP includes 1) MLP with hidden layers have a non-convex loss function where there exists more than one local minimum. Therefore differnet random weight initializations can lead to different validation accuracy, 2) MLP requires tuning a number of hyperparameters such as the number of hidden neurons, lyers, and iterations, 3) MLP is sensitive to feature scaling. \n\nReference\n- \n- \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data in a Table. It must contain `Feature Columns` used in model training.\n2. **model***: (Model) Trained MLP Regression model. \n\n#### OUTPUT\n1. **out_table**: (Table) A table that contains input `table`'s columns and prediction column as `Prediction Column Name`.\n \n#### PARAMETER\n1. **Prediction Column Name**: (Column) A name of prediction column.\n\n\n### Python\n#### USAGE\n\n```\nmlp_regression_predict(table, model, prediction_col='prediction')\n```\n\n#### INPUT\n1. **table***: (Table)\nData in a Table. It must contain `feature_cols` used in model training.\n2. **model***: (Model) Trained MLP Regression model. \n#### OUTPUT\n1. **out_table**: (Table) A table that contains input `table`'s columns and prediction column as `prediction_col`.\n\n## PARAMETER\n1. **prediction_col**: A name of prediction column.\n\t* Type: _str_\n\t* Default value: prediction\n\n## Example\n### VA\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, sample_iris data is used to train and predict a MLP Regression. The generated model is afterward applied to a test dataset to predict the value of _petal\\_width_ by using MLP Regression Predict function. The parameter settings used in the function are shown below. \n\n++Parameters++\n1. **Prediction Column Name**: prediction\n\n\n### Python\n\n```\nfrom brightics.function.regression import mlp_regression_predict\ninput_table = inputs[1]\ninput_model = models[0]\nresult = mlp_regression_predict(table=input_table, model=input_model, prediction_col='prediction')\noutput = result['out_table']\n```\nIn this python script, trained MLP Regression model is applied to a test dataset to predict the value of _petal\\_width_ by using MLP Regression Predict function.\n", + "md": { + "en": "# MLP Regression Predict\nThis function predicts data using a MLP Regression model.\n\n## Description\nMLP stands for Multi-layer Perceptron which is a neural network model with supervised learning. Given a set of features X(x1, x2, ..., xn) and a target y, it can learn a non-linear function approximator for either classification or regression. MLP consists of at least three layers of nodes: an input layer, a hidden layer and an output layer. Except for the input nodes, each node is a neuron that uses a nonlinear activation function. It is different from logistic regression, in that between the input and the output layer, there can be one or more non-linear layers, called hidden layers.\n \nThe advantages of MLP are capability to learn non-linear models and capability to learn models in real-time(online learnin) using `partial_fit`. On the other hand, the disadvantages of MLP includes 1) MLP with hidden layers have a non-convex loss function where there exists more than one local minimum. Therefore differnet random weight initializations can lead to different validation accuracy, 2) MLP requires tuning a number of hyperparameters such as the number of hidden neurons, lyers, and iterations, 3) MLP is sensitive to feature scaling. \n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) \n2. **model***: (Model) \n#### OUTPUT\n1. **out_table**: (Table) \n#### PARAMETER\n1. **Prediction Column Name**: A name of prediction column.\n\n\n### Python\n#### USAGE\n\n```\nmlp_regression_predict(table = , model = , prediction_col = )\n```\n\n#### INPUT\n1. **table***: (Table) \n2. **model***: (Model) \n#### OUTPUT\n1. **out_table**: (Table) \n#### PARAMETER\n1. **prediction_col**: A name of prediction column.\n\t* Type: *str*\n\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\n\n\n++Parameters++\n1. **Prediction Column Name**: \n\n\n\n### Python\n\n```\nfrom brightics.function.regression import mlp_regression_predict\nres = mlp_regression_predict(table = , model = , prediction_col = )\nres['out_table']\n```\n\n", + "kr": "" + }, "exampleModels": [ { - "fileName": "brightics.function.regression$mlp_regression.json", + "fileName": "mlp_regression.json", "label": "MLP REGESSION", "contents": { "type": "files", @@ -534,7 +537,7 @@ }, { "persist-mode": "auto", - "func": "brightics.function.regression$mlp_regression_train", + "func": "brightics.function.regression$mlp_regression_traintrain", "name": "brightics.function.regression$mlp_regression_train", "param": { "activation": "tanh", @@ -805,7 +808,7 @@ }, { "persist-mode": "auto", - "func": "brightics.function.regression$mlp_regression_predict", + "func": "brightics.function.regression$mlp_regression_predictedict", "name": "brightics.function.regression$mlp_regression_predict", "param": {}, "display": { @@ -1550,9 +1553,12 @@ ], "sampleImages": [ { - "fileName": "brightics.function.regression$mlp_regression_predict.png", - "label": "brightics.function.regression$mlp_regression_predict.png", - 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" + "fileName": "mlp_regression.PNG", + "label": "mlp_regression.PNG", + "base64": 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+ "parameter": "1. **Prediction Column Name**: \n", + "index": 0 } - ] + ], + "scriptExamples": [] } diff --git a/function/python/brightics/function/regression/meta/mlp_regression_train.json b/function/python/brightics/function/regression/meta/mlp_regression_train.json index 11d823720..a9b0e5d1b 100644 --- a/function/python/brightics/function/regression/meta/mlp_regression_train.json +++ b/function/python/brightics/function/regression/meta/mlp_regression_train.json @@ -292,10 +292,13 @@ ], "summary": "This function trains a MLP Regression model which can learn non-linear models." }, - "md": "# MLP Regression Train\nThis function trains a MLP Regression model which can learn non-linear models.\n\n## Description\nMLP stands for Multi-layer Perceptron which is a neural network model with supervised learning. Given a set of features X(x1, x2, ..., xn) and a target y, it can learn a non-linear function approximator for either classification or regression. MLP consists of at least three layers of nodes: an input layer, a hidden layer and an output layer. Except for the input nodes, each node is a neuron that uses a nonlinear activation function. It is different from logistic regression, in that between the input and the output layer, there can be one or more non-linear layers, called hidden layers.\n \nThe advantages of MLP are capability to learn non-linear models and capability to learn models in real-time(online learning) using `partial_fit`. On the other hand, the disadvantages of MLP includes 1) MLP with hidden layers have a non-convex loss function where there exists more than one local minimum. Therefore differnet random weight initializations can lead to different validation accuracy, 2) MLP requires tuning a number of hyperparameters such as the number of hidden neurons, lyers, and iterations, 3) MLP is sensitive to feature scaling. \n\nReference\n- \n- \n\n\n## Properties\n### VA\n#### INPUT\n1. **table** : (Table)\nTraining data in a Table. It must contain at least one column for labels. \n\n#### OUTPUT\n1. **model** : (Model) Trained MLP Regression model.\n\n\n#### PARAMETER\n1. **Feature Columns***: Feature Column names. Only values in numeric type are possible.\n\n2. **Label column***: The column designated for the labels of data. It should be numeric type as it is for a regression.\n\n3. **Hidden Layer Sizes**: The _i_ th element represents the number of neurons in the _i_ th hidden layer.\n\n4. **Activation Function**: Activation function for the hidden layer.\n * Available Options: `identity`, `logistic`, `tanh`, `relu`\n\n5. **Solver**: The solver for weight optimization.\n * Available Options: `lbfgs`, `sgd`, `adam`\n\n6. **Alpha**: L2 penalty (regularization term) parameter.\n\n7. **Batch Size Auto**: Batch Size Mode. When set to `True`, batch_size=min(200, n_samples).\n\t* Available Options: `True`, `False`\n\n8. **Batch Size**: Size of minibatches for stochastic optimizers. If the `solver` is `lbfgs`, the classifier will not use minibatch. \n\n9. **Learning Rate**: Step size shrinkage used in update to prevents overfitting. After each boosting step, we can directly get the weights of new features, and the `learning rate` shrinks the feature weights to make the boosting process more conservative.\n * Available Options: `constant`, `invscaling`, `adaptive`\n\n10. **Learning Rate Initial**: The initial `learning rate` used. It controls the step-size in updating the weights. Only used when `solver='sgd'` or `‘adam’`.\n\n11. **Max Iteration**: Maximum number of iterations. The `solver` iterates until convergence (determined by `Tolerance`) or this number of iterations. For stochastic solvers(‘sgd’, ‘adam’), note that this determines the number of epochs (how many times each data point will be used), not the number of gradient steps.\n\n12. **Seed**: Random seed. Use the same seed in order to reproduce the same results.\n\n13. **Tolerance**: Tolerance for the optimization. When the loss or score is not improving by at least tolerance for n_iter_no_change consecutive iterations, unless `learning_rate` is set to `'adaptive'`, convergence is considered to be reached and training stops.\n\n14. **Group By**: Columns to group by.\n\n\n### Python\n#### USAGE\n\n```\nmlp_regression_train(table, feature_cols, label_col, hidden_layer_sizes=(100, ), activation='relu', solver='adam', alpha=0.0001, batch_size_auto=True, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, max_iter=200, random_state=None, tol=0.0001, group_by=None)\n```\n\n#### INPUT\n1. **table** : (Table) Training data in a Table. It must contain at least one column for labels. \n\n\n#### OUTPUT\n1. **model** : (Model) Trained MLP Regression model.\n\n#### PARAMETER\n1. **feature_cols***: Feature Column names. Only values in numeric type are possible.\n\t* Type: *list[str]*\n2. **label_col***: The column designated for the labels of data. It should be numeric type as it is for a regression.\n\t* Type: *str*\n3. **hidden_layer_sizes**: The _i_ th element represents the number of neurons in the _i_ th hidden layer.\n\t* Type: *_tuple_(_int_)*\n4. **activation**: Activation function for the hidden layer.\n\t* Type: *str*\n\t* Default / Range: relu ( identity | logistic | tanhrelu | relu )\n5. **solver**: The solver for weight optimization\n\t* Type: *str*\n\t* Default / Range: adam ( lbfgs | sgdadam | adam )\n6. **alpha**: L2 penalty (regularization term) parameter\n\t* Type: *float*\n\t* Default / Range: 0.0001 (value>=0)\n7. **batch_size_auto**: Batch Size Mode. When set to `True`, batch_size=min(200, n_samples)\n\t* Type: *_bool_*\n\t* Default value: True \n8. **batch_size**: Size of minibatches for stochastic optimizers. If the `solver` is `lbfgs`, the classifier will not use minibatch. \n\t* Type: *str* or *int*\n * Default / Range: auto (value>0)\n9. **learning_rate**: Step size shrinkage used in update to prevents overfitting. After each boosting step, we can directly get the weights of new features, and the `learning rate` shrinks the feature weights to make the boosting process more conservative.\n\t* Type: *str*\n\t* Default / Range: constant ( constant | invscaling | adaptive )\n10. **learning_rate_init**: The initial `learning rate` used. It controls the step-size in updating the weights. Only used when `solver='sgd'` or `‘adam’`.\n\t* Type: *float*\n * Default / Range: 0.001 (value>=0)\n11. **max_iter**: Maximum number of iterations. The `solver` iterates until convergence (determined by `tol`) or this number of iterations. For stochastic solvers(‘sgd’, ‘adam’), note that this determines the number of epochs (how many times each data point will be used), not the number of gradient steps.\n\t* Type: *int*\n\t* Default / Range: 200 (value>=1)\n12. **random_state**: Random seed. Use the same seed in order to reproduce the same results.\n\t* Type: *int*\n13. **tol**: Tolerance for the optimization. When the loss or score is not improving by at least tolerance for n_iter_no_change consecutive iterations, unless `learning_rate` is set to `'adaptive'`, convergence is considered to be reached and training stops.\n\t* Type: *float*\n * Default / Range: 0.0001 (value>0)\n14. **group_by**: Columns to group by.\n\t* Type: *list[str]*\n\n## Example\n### VA\n**[Sample Model]**\n\n\n\n\nIn this tutorial workflow, sample_iris data is used to train a MLP Regression model.\nThe generated model is afterward applied to a test dataset to predict the value of _petal\\_width_ by MLP Regression Predict function. The parameter settings used in the function are shown below. \n\n++Parameters++\n1. **Feature Columns***: sepal_length, sepal_width \n2. **Label column***: petal_width\n3. **Hidden Layer Sizes**: (100, )\n4. **Activation Function**: tanh\n5. **Solver**: sgd\n6. **Alpha**: 0.0001\n7. **Batch Size Auto**: False\n8. **Batch Size**: 100\n9. **Learning Rate**: adaptive\n10. **Learning Rate Initial**: 0.002 \n11. **Max Iteration**: 200\n12. **Seed**: 0\n13. **Tolerance**: 0.0001\n14. **Group By**: None\n\n\n### Python\n\n```\nfrom brightics.function.regression import mlp_regression_train\ninput_table = inputs[0]\nresult = mlp_regression_train(table=input_table,\n feature_cols=['sepal_length', 'sepal_width'],\n label_col='petal_width',\n hidden_layer_sizes=(100, ),\n activation='tanh',\n solver='sgd',\n alpha=0.0001,\n batch_size_auto=False,\n batch_size=100, \n learning_rate='adaptive',\n learning_rate_init=0.002,\n max_iter=200,\n random_state=0,\n tol=0.0001, \n group_by=None)\noutput = result['model']\n```\nIn this python script, sample_iris data is used to train a MLP Regression model. The generated model is afterward applied to a test dataset to predict the value of _petal\\_width_ by using MLP Regression Predict function. ", + "md": { + "en": "# MLP Regression Train\nThis function trains a MLP Regression model which can learn non-linear models.\n\n## Description\nMLP stands for Multi-layer Perceptron which is a neural network model with supervised learning. Given a set of features X(x1, x2, ..., xn) and a target y, it can learn a non-linear function approximator for either classification or regression. MLP consists of at least three layers of nodes: an input layer, a hidden layer and an output layer. Except for the input nodes, each node is a neuron that uses a nonlinear activation function. It is different from logistic regression, in that between the input and the output layer, there can be one or more non-linear layers, called hidden layers.\n \nThe advantages of MLP are capability to learn non-linear models and capability to learn models in real-time(online learning) using `partial_fit`. On the other hand, the disadvantages of MLP includes 1) MLP with hidden layers have a non-convex loss function where there exists more than one local minimum. Therefore differnet random weight initializations can lead to different validation accuracy, 2) MLP requires tuning a number of hyperparameters such as the number of hidden neurons, lyers, and iterations, 3) MLP is sensitive to feature scaling. \n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) \n#### OUTPUT\n1. **model**: (Model) \n#### PARAMETER\n1. **Feature Columns***: Feature Column names. Only values in numeric type are possible.\n\n2. **Label column***: The column designated for the labels of data. It should be numeric type as it is for a regression.\n\n3. **Hidden Layer Sizes**: The _i_ th element represents the number of neurons in the _i_ th hidden layer.\n\n4. **Activation Function**: Activation function for the hidden layer.\n\n5. **Solver**: The solver for weight optimization\n\n6. **Alpha**: L2 penalty (regularization term) parameter\n\n7. **Batch Size Auto**: Batch Size Mode. When set to `True`, batch_size=min(200, n_samples)\n\n\t* Default value: true \n8. **Batch Size**: Size of minibatches for stochastic optimizers. If the `solver` is `lbfgs`, the classifier will not use minibatch. \n\n9. **Learning Rate**: Step size shrinkage used in update to prevents overfitting. After each boosting step, we can directly get the weights of new features, and the `learning rate` shrinks the feature weights to make the boosting process more conservative.\n\n10. **Learning Rate Initial**: The initial `learning rate` used. It controls the step-size in updating the weights. Only used when `solver='sgd'` or `‘adam’`.\n\n11. **Max Iteration**: Maximum number of iterations. The `solver` iterates until convergence (determined by `Tolerance`) or this number of iterations. For stochastic solvers(‘sgd’, ‘adam’), note that this determines the number of epochs (how many times each data point will be used), not the number of gradient steps.\n\n12. **Seed**: Random seed. Use the same seed in order to reproduce the same results.\n\n13. **Tolerance**: Tolerance for the optimization. When the loss or score is not improving by at least tolerance for n_iter_no_change consecutive iterations, unless `learning_rate` is set to `'adaptive'`, convergence is considered to be reached and training stops.\n\n14. **Group By**: Columns to group by.\n\n\n### Python\n#### USAGE\n\n```\nmlp_regression_train(table = , feature_cols = , label_col = , hidden_layer_sizes = , activation = , solver = , alpha = , batch_size_auto = true, batch_size = , learning_rate = , learning_rate_init = , max_iter = , random_state = , tol = , group_by = )\n```\n\n#### INPUT\n1. **table***: (Table) \n#### OUTPUT\n1. **model**: (Model) \n#### PARAMETER\n1. **feature_cols***: Feature Column names. Only values in numeric type are possible.\n\t* Type: *list[str]*\n2. **label_col***: The column designated for the labels of data. It should be numeric type as it is for a regression.\n\t* Type: *str*\n3. **hidden_layer_sizes**: The _i_ th element represents the number of neurons in the _i_ th hidden layer.\n\t* Type: *str*\n4. **activation**: Activation function for the hidden layer.\n\t* Type: *str*\n\t* Default / Range: ( identity | logistic | tanhrelu | relu )\n5. **solver**: The solver for weight optimization\n\t* Type: *str*\n\t* Default / Range: ( lbfgs | sgdadam | adam )\n6. **alpha**: L2 penalty (regularization term) parameter\n\t* Type: *double*\n\t* Default / Range: 0.0001 (value>=0)\n7. **batch_size_auto**: Batch Size Mode. When set to `True`, batch_size=min(200, n_samples)\n\t* Type: *bool*\n\t* Default value: true \n8. **batch_size**: Size of minibatches for stochastic optimizers. If the `solver` is `lbfgs`, the classifier will not use minibatch. \n\t* Type: *int*\n\n9. **learning_rate**: Step size shrinkage used in update to prevents overfitting. After each boosting step, we can directly get the weights of new features, and the `learning rate` shrinks the feature weights to make the boosting process more conservative.\n\t* Type: *str*\n\t* Default / Range: constant ( constant | invscaling | adaptive )\n10. **learning_rate_init**: The initial `learning rate` used. It controls the step-size in updating the weights. Only used when `solver='sgd'` or `‘adam’`.\n\t* Type: *double*\n\n11. **max_iter**: Maximum number of iterations. The `solver` iterates until convergence (determined by `Tolerance`) or this number of iterations. For stochastic solvers(‘sgd’, ‘adam’), note that this determines the number of epochs (how many times each data point will be used), not the number of gradient steps.\n\t* Type: *int*\n\t* Default / Range: 200 (value>=1)\n12. **random_state**: Random seed. Use the same seed in order to reproduce the same results.\n\t* Type: *int*\n\n13. **tol**: Tolerance for the optimization. When the loss or score is not improving by at least tolerance for n_iter_no_change consecutive iterations, unless `learning_rate` is set to `'adaptive'`, convergence is considered to be reached and training stops.\n\t* Type: *double*\n\n14. **group_by**: Columns to group by.\n\t* Type: *list[str]*\n\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, sample_iris data is used to train a MLP Regression model. The generated model is afterward applied to a test dataset to predict the value of petal_width by MLP Regression Predict function. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Feature Columns***: sepal_length, sepal_width\n2. **Label column***: petal_width\n3. **Hidden Layer Sizes**: (100,)\n4. **Activation Function**: tanh\n5. **Solver**: sgd\n6. **Alpha**: 0.0001\n7. **Batch Size Auto**: False\n8. **Batch Size**: adaptive\n9. **Learning Rate**: 0.002\n10. **Learning Rate Initial**: 0.002\n11. **Max Iteration**: 200\n12. **Seed**: 0\n13. **Tolerance**: 0.001\n14. **Group By**: \n\n\n\n### Python\n\n```\nfrom brightics.function.regression import mlp_regression_train\nres = mlp_regression_train(table = , feature_cols = , label_col = , hidden_layer_sizes = , activation = , solver = , alpha = , batch_size_auto = true, batch_size = , learning_rate = , learning_rate_init = , max_iter = , random_state = , tol = , group_by = )\nres['model']\n```\n\n", + "kr": "" + }, "exampleModels": [ { - "fileName": "brightics.function.regression$mlp_regression.json", + "fileName": "mlp_regression.json", "label": "MLP REGESSION", "contents": { "type": "files", @@ -773,7 +776,7 @@ }, { "persist-mode": "auto", - "func": "brightics.function.regression$mlp_regression_train", + "func": "brightics.function.regression$mlp_regression_traintrain", "name": "brightics.function.regression$mlp_regression_train", "param": { "activation": "tanh", @@ -1044,7 +1047,7 @@ }, { "persist-mode": "auto", - "func": "brightics.function.regression$mlp_regression_predict", + "func": "brightics.function.regression$mlp_regression_predictedict", "name": "brightics.function.regression$mlp_regression_predict", "param": {}, "display": { @@ -1789,9 +1792,13 @@ ], "sampleImages": [ { - "fileName": "brightics.function.regression$mlp_regression_train.png", - "label": "brightics.function.regression$mlp_regression_train.png", - "base64": 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" + "fileName": "mlp_regression.PNG", + "label": "mlp_regression.PNG", + "base64": 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The generated model is afterward applied to a test dataset to predict the value of petal_width by MLP Regression Predict function. The parameter settings used in the function are shown below." } - ] + ], + "scriptExamples": [] } diff --git a/function/python/brightics/function/regression/meta/pls_regression_predict.json b/function/python/brightics/function/regression/meta/pls_regression_predict.json new file mode 100644 index 000000000..c6d354300 --- /dev/null +++ b/function/python/brightics/function/regression/meta/pls_regression_predict.json @@ -0,0 +1,1583 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "regression", + "func": "brightics.function.regression$pls_regression_predict", + "name": "brightics.function.regression$pls_regression_predict", + "context": "python", + "label": "PLS Regression Predict", + "description": "Predict data using a trained PLS model", + "tags": [ + "PLS", + "regression", + "PLS regression", + "PLS regression predict", + "predict" + ], + "version": "3.6", + "inputs": { + "table": "", + "model": "" + }, + "outputs": { + "out_table": "" + }, + "meta": { + "table": { + "type": "table" + }, + "model": { + "type": "model" + }, + "out_table": { + "type": "table" + } + }, + "params": [ + { + "id": "prediction_col", + "label": "Prediction Column Name", + "description": "Column name for prediction. Postfix is added for multiple predictions", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "type": "String", + "placeHolder": "prediction" + } + ] + }, + "md": { + "en": "# PLS Regression Predict\n\n\n## Description\nPredict data using a trained PLS model\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) \n2. **model***: (Model) \n#### OUTPUT\n1. **out_table**: (Table) \n#### PARAMETER\n1. **Prediction Column Name**: Column name for prediction. Postfix is added for multiple predictions\n\n\n### Python\n#### USAGE\n\n```\npls_regression_predict(table = , model = , prediction_col = )\n```\n\n#### INPUT\n1. **table***: (Table) \n2. **model***: (Model) \n#### OUTPUT\n1. **out_table**: (Table) \n#### PARAMETER\n1. **prediction_col**: Column name for prediction. Postfix is added for multiple predictions\n\t* Type: *str*\n\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, sample_iris data is used to train and predict a PLS regression. The generated model is afterward applied to a test data set to predict the value of petal_width , by using PLS Regression Predict function. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Prediction Column Name**: prediction\n\n\n\n### Python\n\n```\nfrom brightics.function.regression import pls_regression_predict\ninput_table = inputs[1]\ninput_model = models[0]\nres = pls_regression_predict(table = input_table,\n\t\t\t\t\t\t\t model = input_model,\n\t\t\t\t\t\t\t prediction_col = 'prediction')\nres['out_table']\n```\nIn this python script, trained PLS Regression model is applied to a test data set to predict the value of petal_width, by using pls_regression_predict function.\n\n\n", + "kr": "" + }, + "exampleModels": [ + { + "fileName": "pls_regression.json", + "label": "test model", + "contents": { + "type": "files", + "data": [ + { + "type": "file", + "data": { + "id": "m9g8m4js6fbgu8tn", + "project_id": "p5dmzu78qb564crk", + "label": "test model", + "contents": { + "mid": "m9g8m4js6fbgu8tn", + "type": "data", + "title": "test model", + "functions": [ + { + "persist-mode": "auto", + 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", + "description": "In this tutorial workflow, sample_iris data is used to train and predict a PLS regression. The generated model is afterward applied to a test data set to predict the value of petal_width , by using PLS Regression Predict function. The parameter settings used in the function are shown below.", + "parameter": "1. **Prediction Column Name**: prediction\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.regression import pls_regression_predict\r\ninput_table = inputs[1]\r\ninput_model = models[0]\r\nres = pls_regression_predict(table = input_table,\r\n\t\t\t\t\t\t\t model = input_model,\r\n\t\t\t\t\t\t\t prediction_col = 'prediction')\r\nres['out_table']", + "description": "In this python script, trained PLS Regression model is applied to a test data set to predict the value of petal_width, by using pls_regression_predict function.", + "context": "python" + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/regression/meta/pls_regression_train.json b/function/python/brightics/function/regression/meta/pls_regression_train.json new file mode 100644 index 000000000..2b75fd345 --- /dev/null +++ b/function/python/brightics/function/regression/meta/pls_regression_train.json @@ -0,0 +1,1672 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "regression", + "func": "brightics.function.regression$pls_regression_train", + "name": "brightics.function.regression$pls_regression_train", + "context": "python", + "label": "PLS Regression Train", + "description": "This function fits a PLS regression model using the given data.", + "tags": [ + "PLS", + "regression", + "PLS regression", + "PLS regression train", + "train" + ], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "model": "" + }, + "meta": { + "table": { + "type": "table" + }, + "model": { + "type": "model" + } + }, + "params": [ + { + "id": "feature_cols", + "label": "Feature Columns", + "description": "", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "columnType": [ + "Integer", + "Long", + "Float", + "Double", + "Double[]" + ], + "multiple": true, + "rowCount": 5 + }, + { + "id": "label_cols", + "label": "Label Columns", + "description": "", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "columnType": [ + "Integer", + "Long", + "Float", + "Double", + "Double[]" + ], + "multiple": true + }, + { + "id": "n_components", + "label": "Number of components", + "description": "Number of components to keep", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "targetTable": [], + "type": "Integer", + "min": 1, + "placeHolder": "2 (value>=1)" + }, + { + "id": "scale", + "label": "Scale", + "description": "Whether to scale data or not", + "visibleOption": [], + "control": "BooleanRadio", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "targetTable": [] + }, + { + "id": "max_iter", + "label": "Max Iteration", + "description": 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OUTPUT\n1. **model**: (Model) \n#### PARAMETER\n1. **Feature Columns***: \n\n2. **Label Columns***: \n\n3. **Number of components**: Number of components to keep\n\n4. **Scale**: Whether to scale data or not\n\n5. **Max Iteration**: Maximum number of iterations\n\n6. **Tolerance**: Tolerance for the optimization\n\n7. **Group By**: \n\n\n### Python\n#### USAGE\n\n```\npls_regression_train(table = , feature_cols = , label_cols = , n_components = , scale = , max_iter = , tol = , group_by = )\n```\n\n#### INPUT\n1. **table***: (Table) \n#### OUTPUT\n1. **model**: (Model) \n#### PARAMETER\n1. **feature_cols***: \n\t* Type: *list[str]*\n2. **label_cols***: \n\t* Type: *list[str]*\n3. **n_components**: Number of components to keep\n\t* Type: *int*\n\t* Default / Range: 2 (value>=1)\n4. **scale**: Whether to scale data or not\n\t* Type: *bool*\n5. **max_iter**: Maximum number of iterations\n\t* Type: *int*\n\t* Default / Range: 500 (value>=1)\n6. **tol**: Tolerance for the optimization\n\t* 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The generated model is afterward applied to a test data set to predict the value of petal_width , by using PLS Regression Predict function. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Feature Columns***: sepal_length, sepal_width, peta_length\n2. **Label Columns***: petal_width\n3. **Number of components**: 2\n4. **Scale**: False\n5. **Max Iteration**: 500\n6. **Tolerance**: 0.000001\n7. **Group By**: None\n\n\n\n### Python\n\n```\nfrom brightics.function.regression import pls_regression_train\nres = pls_regression_train(table = input_table,\n\t\t\t\t\t\t feature_cols = ['sepal_length', 'sepal_width'],\n\t\t\t\t\t\t label_cols = ['petal_width'],\n\t\t\t\t\t\t group_by = None)\nres['model']\n```\nIn this python script, sample_iris data is used to train a PLS regression. 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", + "description": "In this tutorial workflow, sample_iris data is used to train a PLS regression. The generated model is afterward applied to a test data set to predict the value of petal_width , by using PLS Regression Predict function. The parameter settings used in the function are shown below.", + "parameter": "1. **Feature Columns***: sepal_length, sepal_width, peta_length\n2. **Label Columns***: petal_width\n3. **Number of components**: 2\n4. **Scale**: False\n5. **Max Iteration**: 500\n6. **Tolerance**: 0.000001\n7. **Group By**: None\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.regression import pls_regression_train\r\nres = pls_regression_train(table = input_table,\r\n\t\t\t\t\t\t feature_cols = ['sepal_length', 'sepal_width'],\r\n\t\t\t\t\t\t label_cols = ['petal_width'],\r\n\t\t\t\t\t\t group_by = None)\r\nres['model']", + "description": "In this python script, sample_iris data is used to train a PLS regression. The generated model is afterward applied to a test data set to predict the value of petal_width , by using PLS Regression Predict function.", + "context": "python" + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/regression/meta/random_forest_regression_train.json b/function/python/brightics/function/regression/meta/random_forest_regression_train.json index a21f92eb1..3abe084a2 100644 --- a/function/python/brightics/function/regression/meta/random_forest_regression_train.json +++ b/function/python/brightics/function/regression/meta/random_forest_regression_train.json @@ -101,7 +101,7 @@ }, { "id": "max_depth", - "label": "Maximum Depth", + "label": "Max Depth", "description": " The maximum depth of the tree. It restricts the depth of each individual tree to prevent overfitting. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than `Minimum Number of Samples to Split`.", "visibleOption": [], "control": "InputBox", @@ -115,7 +115,7 @@ }, { "id": "min_samples_split", - "label": "Minimum Number of Samples to Split", + "label": "Min Sample Split", "description": "The minimum number of samples required to split an internal node. A very small number will usually mean the tree will overfit, whereas a large number will prevent the tree from learning the data.", "visibleOption": [], "control": "InputBox", @@ -129,7 +129,7 @@ }, { "id": "min_samples_leaf", - "label": "Minimum Number of Samples in a Leaf", + "label": "Min Sample Leaf", "description": "The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least `Minimum Number of Samples in a Leaf` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression. While `Minimum Number of Samples to Split` can create arbitrarily small leaves, `Minimum Number of Samples in a Leaf` guarantees that each leaf has a minimum size, avoiding low-variance, over-fit leaf nodes in regression problems.", "visibleOption": [], "control": "InputBox", diff --git a/function/python/brightics/function/regression/meta/regression_predict.json b/function/python/brightics/function/regression/meta/regression_predict.json index 9103c45a4..7d6dcf5ce 100644 --- a/function/python/brightics/function/regression/meta/regression_predict.json +++ b/function/python/brightics/function/regression/meta/regression_predict.json @@ -50,10 +50,10 @@ ], "summary": "This function predict data using a regression model." }, - "md": "# Regression Predict\nThis function predict data using a regression model.\n\n## Description\nRegression analysis is primarily used for two conceptually distinct purposes. First, regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Second, in some situations regression analysis can be used to infer causal relationships between the independent and dependent variables. Importantly, regressions by themselves only reveal relationships between a dependent variable and a collection of independent variables in a fixed dataset. To use regressions for prediction or to infer causal relationships, respectively, a researcher must carefully justify why existing relationships have predictive power for a new context or why a relationship between two variables has a causal interpretation. The latter is especially important when researchers hope to estimate causal relationships using observational data.\n\nReference: \n+ \n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data in a Table. It must contain Feature Columns that was used when train the model.\n2. **model***: (Model) Trained regression model.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contain input table's columns and prediction column as `Prediction Colmn Name`.\n#### PARAMETER\n1. **Prediction Column Name**: A name of prediction column.\n\n\n### Python\n#### USAGE\n\n```\nregression_predict(table, model, prediction_col='prediction')\n```\n\n#### INPUT\n1. **table***: (Table) Data in a Table. It must contain Feature Columns that was used when train the model.\n2. **model***: (Model) Trained regression model.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contain input table's columns and prediction column as `Prediction Colmn Name`.\n#### PARAMETER\n1. **prediction_col**: A name of prediction column.\n\t* Type: *str*\n\t* Default / Range: 'prediction'\n\n## Example\n### VA\n\n\n\n**[Sample Model]**\n\n\n\n\n\n
In this tutorial workflow, sample_iris data is used to train and predict a regression model.\nThe generated model is afterward applied to a test data set to predict the value of _pretal\\_width_ , by using Regression Predict function. \nThe parameter settings used in the function are shown below. \n\n##### ++Parameters++\n1. **Prediction Column Name** : 'prediction'\n\n### Python\n```\nfrom brightics.function.regression import regression_predict\ninput_table = inputs[1]\ninput_model = models[0]\nresult = regression_predict(table=input_table,\n model=input_model,\n prediction_col='prediction')\noutput = result['out_table']\n```\n\n
In this python script, a trained regression model is applied to a test data set to predict the value of _petal/_width_, by using Regression Predict function.\n\n", + "md": "# Regression Predict\nThis function predict data using a regression model.\n\n## Description\nRegression analysis is primarily used for two conceptually distinct purposes. First, regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Second, in some situations regression analysis can be used to infer causal relationships between the independent and dependent variables. Importantly, regressions by themselves only reveal relationships between a dependent variable and a collection of independent variables in a fixed dataset. To use regressions for prediction or to infer causal relationships, respectively, a researcher must carefully justify why existing relationships have predictive power for a new context or why a relationship between two variables has a causal interpretation. The latter is especially important when researchers hope to estimate causal relationships using observational data.\n\nReference: \n+ \n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data in a Table. It must contain Feature Columns that was used when train the model.\n2. **model***: (Model) Trained regression model.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contain input table's columns and prediction column as `Prediction Colmn Name`.\n#### PARAMETER\n1. **Prediction Column Name**: A name of prediction column.\n\n\n### Python\n#### USAGE\n\n```\nregression_predict(table, model, prediction_col='prediction')\n```\n\n#### INPUT\n1. **table***: (Table) Data in a Table. It must contain Feature Columns that was used when train the model.\n2. **model***: (Model) Trained regression model.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contain input table's columns and prediction column as `Prediction Colmn Name`.\n#### PARAMETER\n1. **prediction_col**: A name of prediction column.\n\t* Type: *str*\n\t* Default / Range: 'prediction'\n\n## Example\n### VA\n\n\n\n**[Sample Model]**\n\n\n\n\n\n
In this tutorial workflow, sample_iris data is used to train and predict a regression model.\nThe generated model is afterward applied to a test data set to predict the value of _pretal\\_width_ , by using Regression Predict function. \nThe parameter settings used in the function are shown below. \n\n##### ++Parameters++\n1. **Prediction Column Name** : 'prediction'\n\n### Python\n```\nfrom brightics.function.regression import regression_predict\ninput_table = inputs[1]\ninput_model = models[0]\nresult = regression_predict(table=input_table,\n model=input_model,\n prediction_col='prediction')\noutput = result['out_table']\n```\n\n
In this python script, a trained regression model is applied to a test data set to predict the value of _petal/_width_, by using Regression Predict function.\n\n", "exampleModels": [ { - "fileName": "brightics.function.regression$regression_predict.json", + "fileName": "regression_predict.json", "label": "test model", "contents": { "type": "files", @@ -1037,7 +1037,7 @@ }, { "persist-mode": "auto", - "func": "brightics.function.regression$regression_predict", + "func": "brightics.function.regression$regression_predict23233", "name": "brightics.function.regression$regression_predict", "param": {}, "display": { @@ -1558,9 +1558,12 @@ ], "sampleImages": [ { - "fileName": "brightics.function.regression$regression_predict.png", - "label": "brightics.function.regression$regression_predict.png", - "base64": 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" + "fileName": "regression_predict.PNG", + "label": "regression_predict.PNG", + "base64": 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+ "parameter": "1. **Prediction Column Name**: Prediction\n", + "index": 0 } - ] -} \ No newline at end of file + ], + "scriptExamples": [] +} diff --git a/function/python/brightics/function/regression/meta/xgb_regression_train.json b/function/python/brightics/function/regression/meta/xgb_regression_train.json index b9acddd14..eb5dd94b2 100644 --- a/function/python/brightics/function/regression/meta/xgb_regression_train.json +++ b/function/python/brightics/function/regression/meta/xgb_regression_train.json @@ -74,7 +74,7 @@ { "id": "objectibe", "label": "Objective", - "description": "The learning task and the corresponding learning objective.", + "description": "Specify the learning task and the corresponding learning objective or a custom objective function to be used. The objective options are below:\n\treg:linear - Regression task with squared loss\n\tcount:poisson - Poisson regression for count data, output mean of poisson distribution\n\tsurvival:cox - Cox regression for right censored survival time data (negative values are considered right censored)\n\trank:pairwise - Use LambdaMART to perform pairwise ranking where the pairwise loss is minimized\n\treg:gamma - gamma regression with log-link. Output is a mean of gamma distribution. It might be useful, e.g., for modeling insurance claims severity, or for any outcome that might be gamma-distributed\n\treg:tweedie - Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any outcome that might be Tweedie-distributed", "visibleOption": [], "control": "DropDownList", "validation": [], @@ -119,7 +119,7 @@ { "id": "max_depth", "label": "Max Depth", - "description": "Maximum depth of the tree. For example, depth 0 means 1 leaf node and depth 1 means 1 internal node + 2 leaf nodes. Normally 6 is suggested for the depth. Increasing this value will make the model more complex and more likely to overfit. Beware that XGBoost aggressively consumes memory when training a deep tree.", + "description": "Maximum depth of the tree. For example, depth 0 means 1 leaf node and depth 1 means 1 internal node + 2 leaf nodes. Normally 6 is suggested for the depth. Increasing this value will make the model more complex and more likely to overfit. 0 is only accepted in loss guided growing policy when tree_method is set as hist and it indicates no limit on depth. Beware that XGBoost aggressively consumes memory when training a deep tree.", "visibleOption": [], "control": "InputBox", "validation": [], @@ -250,7 +250,7 @@ ], "summary": "This function trains a XGBoost model for regression which is highly efficient, flexible and portable." }, - "md": "# XGB Regression Train\nThis function trains a XGBoost model for regression which is highly efficient, flexible and portable.\n\n## Description\nXGBoost stands for 'Extreme Gradient Boosting', where the term 'Gradient Boosting' originates from the paper Greedy Function Approximation: A Gradient Boosting Machine, by Friedman. \n\nXGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples. To sum up, XGBoost is an optimized distributed gradient boosting.\n\nReference\n- \n- \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Training data in a Table. It must contain at least one column for labels. \n#### OUTPUT\n1. **model**: (Model) Trained XGBoost Regression model. \n#### PARAMETER\n1. **Feature Columns***: Feature column names. Only values in numeric type are possible.\n\n2. **Label Column***: The column designated for the labels of data. It should be numeric type as it is for regression.\n\n3. **Objective**: The learning task and the corresponding learning objective.\n * Available Options : `Linear regression with squared loss`, `Poisson regression for count data`, `Cox regression for right censored survival time`, `Pairwise ranking`, `Gamma regression with log-link`, `Tweedie regression with log-link`\n\n4. **Max Depth**: Maximum depth of the tree. For example, depth 0 means 1 leaf node and depth 1 means 1 internal node + 2 leaf nodes. Normally 6 is suggested for the depth. Increasing this value will make the model more complex and more likely to overfit. Beware that XGBoost aggressively consumes memory when training a deep tree.\n\n5. **Learning Rate**: Step size shrinkage used in update to prevents overfitting. After each boosting step, we can directly get the weights of new features, and the `learning rate` shrinks the feature weights to make the boosting process more conservative. 0.3 is suggested.\n\n6. **Number of Trees**: Number of generated trees. If 1, then no bootstrapping is used. If > 1, then bootstrapping will be\n performed.\n\n7. **Importance Type**: `Importance Type` is a feature importance type property.\n * Available Options : `Gain`, `Weight`, `Cover`, `Total Gain`, `Total Cover`\n\n8. **Fraction of Samples**: The `fraction of samples` is to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. `Fraction of samples` interacts with the parameter `Number of Trees`. Choosing `Fraction of samples` < 1.0 leads to a reduction of variance and an increase in bias.\n\n9. **Seed**: Random seed. Use the same seed in order to reproduce the same results.\n\n10. **Group By**: Columns to group by\n\n\n### Python\n#### USAGE\n\n```\nxgb_regression_train(table, feature_cols, label_col, max_depth=4, learning_rate=0.01, n_estimators=100, random_state=0, silent=True, objective='reg:linear', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, seed=None, missing=None, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None, sample_weight_eval_set=None, importance_type='gain')\n```\n\n#### INPUT\n1. **table***: (Table) Training data in a Table. It must contain at least one column for labels. \n#### OUTPUT\n1. **model**: (Model) Trained XGBoost Regression model.\n#### PARAMETER\n1. **feature_cols***: Feature column names. Only values in numeric type are possible.\n\t* Type: *list[str]*\n2. **label_col***: The column designated for the labels of data. It should be numeric type as it is for regression.\n\t* Type: *str*\n3. **objective**: The learning task and the corresponding learning objective.\n\t* Type: *str*\n\t* Default / Range: reg:linear ( reg:linear | count:poisson | survival:cox | rank:pairwise | reg:gamma | reg:tweedie )\n4. **max_depth**: Maximum depth of the tree. For example, depth 0 means 1 leaf node and depth 1 means 1 internal node + 2 leaf nodes. Normally 6 is suggested for the depth. Increasing this value will make the model more complex and more likely to overfit. Beware that XGBoost aggressively consumes memory when training a deep tree.\n\t* Type: *int*\n\t* Default / Range: 3 (1 <= value)\n5. **learning_rate**: Step size shrinkage used in update to prevents overfitting. After each boosting step, we can directly get the weights of new features, and the `learning rate` shrinks the feature weights to make the boosting process more conservative. 0.3 is suggested.\n\t* Type: *float*\n\t* Default / Range: 0.1 (0 <= value)\n6. **n_estimators**: Number of generated trees. If 1, then no bootstrapping is used. If > 1, then bootstrapping will be\n performed.\n\t* Type: *int*\n\t* Default / Range: 100 (1 <= value)\n7. **importance_type**: `importance type` is a feature importance type property.\n\t* Type: *str*\n\t* Default / Range: gain ( gain | weight | cover | total_gain | total_cover )\n8. **subsample**: Subsample to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. `subsample` interacts with the parameter `n_estimators`. Choosing `subsample` < 1.0 leads to a reduction of variance and an increase in bias.\n\t* Type: *float*\n\t* Default / Range: 1.0 (0 < value <= 1.0)\n9. **random_state**: Random seed. Use the same seed in order to reproduce the same results.\n\t* Type: *int*\n10. **group_by**: Columns to group by.\n\t* Type: *list[str]*\n11. **silent** : Whether print messages during construction\n - Type : _bool_ \n - Default : True\n12. **booster** : Specify which booster to use\n - Type : _str_ \n - Default / Range : gbtree ( gbtree | gblinear | dart )\n13. **n_jobs** : Number of parallel threads used to run xgboost.\n - Type : _int_ \n - Default / Range: 1 (1 <= value)\n14. **nthread** : Number of threads to use for loading data from numpy array. If -1, uses maximum threads available on the system.\n - Type : _int_ \n - Default / Range: None (-1 <= value)\n15. **gamma** : Minimum loss reduction required to make a further partition on a leaf node of the tree.\n - Type : _float_ \n - Default / Range: 0 (0 <= value)\n16. **min_child_weight** : Minimum sum of instance weight(hessian) needed in a child.\n - Type : _int_ \n - Default / Range: 1 (0 <= value)\n17. **max_delta_step** : Maximum delta step we allow each tree’s weight estimation to be.\n - Type : _int_ \n - Default / Range: 0 (0 <= value)\n18. **colsample_bytree** : `subsample` ratio of columns when constructing each tree.\n - Type : _float_ \n - Default / Range: 1 (0 <= value)\n19. **colsample_bylevel** : `subsample` ratio of columns for each level.\n - Type : _float_ \n - Default / Range: 1 (0 <= value)\n20. **reg_alpha** : L1 regularization term on weights.\n - Type : _float_ \n - Default value: 0\n21. **reg_lambda** : L2 regularization term on weights.\n - Type : _float_ \n - Default value: 1\n22. **scale_pos_weight** : Balancing of positive and negative weights.\n - Type : _float_ \n - Default / Range: 1 (0 <= value)\n23. **base_score** : The initial prediction score of all instances, global bias.\n - Type : _float_ \n - Default / Range: 0.5 (0 <= value)\n24. **seed** : Random seed. Use the same seed in order to reproduce the same results.\n - Type : _int_ \n - Default / Range: 0 (0 <= value)\n25. **missing** : Value in the data which needs to be present as a missing value.\n - Type : _float_ \n - Default / Range: None (0 <= value)\n26. **sample_weight** : instance weights\n - Type : _list_[_float_] \n - Default / Range: None (0 <= value)\n27. **eval_set** : A list of (X, y) tuple pairs to use as validation sets, for which metrics will be computed. Validation metrics will help us track the performance of the model.\n - Type : _list_[_tuple_] \n - Default value: None\n28. **eval_metric** : If a str, should be a built-in evaluation metric to use. If a list of str, should be the list of multiple built-in evaluation metrics to use. If callable, a custom evaluation metric. The call signature is func(y_predicted, y_true) where y_true will be a DMatrix object such that you may need to call the get_label method. It must return a str, value pair where the str is a name for the evaluation and value is the value of the evaluation function. The callable custom objective is always minimized.\n default metric will be assigned according to objective. 1) poisson-nloglik: for Poisson regression (count:poisson), 2) gamma-nloglik: for gamma regression (reg:gamma), 3) cox-nloglik: for Cox proportional hazards regression (survival:cox), 4) gamma-deviance: for gamma regression (reg:gamma)\n - Type : _str_ or _list[_str_]\n - Default / Range : None ( rmse | rmsle | mae )\n29. **early_stopping_rounds** : Activates early stopping. Cross-Validation metric (average of validation metric computed over CV folds) needs to improve at least once in every `early_stopping_rounds` round(s) to continue training. The last entry in the evaluation history will represent the best iteration. If there’s more than one metric in the eval_metric parameter given in params, the last metric will be used for early stopping.\n - Type : _int_ \n - Default / Range: None (0 <= value)\n30. **verbose** : If `verbose` and an `eval_set` is used, writes the evaluation metric measured on the validation set to stderr.\n - Type : _bool_ \n - Default value: True\n31. **xgb_model** : file name of stored XGBoost model or `booster` instance XGBoost model to be loaded before training (allows training continuation).\n - Type : _str_ \n - Default value: None\n32. **sample_weight_eval_set** : A list of the form [L\\_1, L\\_2, …, L\\_n], where each L\\_i is a list of instance weights on the i-th validation set.\n - Type : _list_[_str_] \n - Default value: None\n\n## Example\n### VA\n**[Sample Model]**\n\n\n\n\nIn this tutorial workflow, sample_iris data is used to train a XGBoost for regression. The generated model is afterward applied to a test dataset to predict the value of _petal\\_width_ by using XGBoost Regression Predict function. The parameter settings used in the function are shown below. \n\n++Parameters++\n1. **Feature Columns***: sepal\\_length, sepal\\_width\n2. **Label Column***: petal\\_width\n3. **Objective**: Linear regression with squared loss\n4. **Max Depth**: 3\n5. **Learning Rate**: 0.1 \n6. **Number of Trees**: 100\n7. **Importance Type**: Gain\n8. **Fraction of Samples**: 1.0\n9. **Seed**: 0\n10. **Group By**: None \n\n\n### Python\n\n```\nfrom brightics.function.regression import xgb_regression_train\ninput_table = inputs[0]\nresult = xgb_regression_train(table=input_table,\n feature_cols=['sepal_length', 'sepal_width'],\n label_col=['petal_width'], max_depth=4, learning_rate=0.01, n_estimators=100,\n random_state=0, silent=True, objective='reg:linear', booster='gbtree', n_jobs=1,\n nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1,\n colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n base_score=0.5, seed=None, missing=None, sample_weight=None, eval_set=None,\n eval_metric=None, early_stopping_rounds=None, verbose=True,\n xgb_model=None, sample_weight_eval_set=None, importance_type='gain')\noutput = result['model']\n```\nIn this python script, sample_iris data is used to train a XGBoost for regression. The generated model is afterward applied to a test dataset to predict the value of _petal\\_width_ by using XGBoost Regression Predict function.\n", + "md": "# XGB Regression Train\nThis function trains a XGBoost model for regression which is highly efficient, flexible and portable.\n\n## Description\nXGBoost stands for 'Extreme Gradient Boosting', where the term 'Gradient Boosting' originates from the paper Greedy Function Approximation: A Gradient Boosting Machine, by Friedman. \n\nXGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples. To sum up, XGBoost is an optimized distributed gradient boosting.\n\nReference\n- \n- \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Training data in a Table. It must contain at least one column for labels. \n#### OUTPUT\n1. **model**: (Model) Trained XGBoost Regression model. \n#### PARAMETER\n1. **Feature Columns***: Feature column names. Only values in numeric type are possible.\n\n2. **Label Column***: The column designated for the labels of data. It should be numeric type as it is for regression.\n\n3. **Objective**: Specify the learning task and the corresponding learning objective or a custom objective function to be used. The objective options are below:\n\treg:linear - Regression task with squared loss\n\tcount:poisson - Poisson regression for count data, output mean of poisson distribution\n\tsurvival:cox - Cox regression for right censored survival time data (negative values are considered right censored)\n\trank:pairwise - Use LambdaMART to perform pairwise ranking where the pairwise loss is minimized\n\treg:gamma - gamma regression with log-link. Output is a mean of gamma distribution. It might be useful, e.g., for modeling insurance claims severity, or for any outcome that might be gamma-distributed\n\treg:tweedie - Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any outcome that might be Tweedie-distributed\n * Available Options : `Linear regression with squared loss`, `Poisson regression for count data`, `Cox regression for right censored survival time`, `Pairwise ranking`, `Gamma regression with log-link`, `Tweedie regression with log-link`\n\n4. **Max Depth**: Maximum depth of the tree. For example, depth 0 means 1 leaf node and depth 1 means 1 internal node + 2 leaf nodes. Normally 6 is suggested for the depth. Increasing this value will make the model more complex and more likely to overfit. 0 is only accepted in loss guided growing policy when tree_method is set as hist and it indicates no limit on depth. Beware that XGBoost aggressively consumes memory when training a deep tree.\n\n5. **Learning Rate**: Step size shrinkage used in update to prevents overfitting. After each boosting step, we can directly get the weights of new features, and the `learning rate` shrinks the feature weights to make the boosting process more conservative. 0.3 is suggested.\n\n6. **Number of Trees**: Number of generated trees. If 1, then no bootstrapping is used. If > 1, then bootstrapping will be\n performed.\n\n7. **Importance Type**: `Importance Type` is a feature importance type property.\n * Available Options : `Gain`, `Weight`, `Cover`, `Total Gain`, `Total Cover`\n\n8. **Fraction of Samples**: The `fraction of samples` is to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. `Fraction of samples` interacts with the parameter `Number of Trees`. Choosing `Fraction of samples` < 1.0 leads to a reduction of variance and an increase in bias.\n\n9. **Seed**: Random seed. Use the same seed in order to reproduce the same results.\n\n10. **Group By**: Columns to group by\n\n\n### Python\n#### USAGE\n\n```\nxgb_regression_train(table, feature_cols, label_col, max_depth=4, learning_rate=0.01, n_estimators=100, random_state=0, silent=True, objective='reg:linear', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, seed=None, missing=None, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None, sample_weight_eval_set=None, importance_type='gain')\n```\n\n#### INPUT\n1. **table***: (Table) Training data in a Table. It must contain at least one column for labels. \n#### OUTPUT\n1. **model**: (Model) Trained XGBoost Regression model.\n#### PARAMETER\n1. **feature_cols***: Feature column names. Only values in numeric type are possible.\n\t* Type: *list[str]*\n2. **label_col***: The column designated for the labels of data. It should be numeric type as it is for regression.\n\t* Type: *str*\n3. **objective**: Specify the learning task and the corresponding learning objective or a custom objective function to be used. The objective options are below:\n\treg:linear - Regression task with squared loss\n\tcount:poisson - Poisson regression for count data, output mean of poisson distribution\n\tsurvival:cox - Cox regression for right censored survival time data (negative values are considered right censored)\n\trank:pairwise - Use LambdaMART to perform pairwise ranking where the pairwise loss is minimized\n\treg:gamma - gamma regression with log-link. Output is a mean of gamma distribution. It might be useful, e.g., for modeling insurance claims severity, or for any outcome that might be gamma-distributed\n\treg:tweedie - Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any outcome that might be Tweedie-distributed\n\t* Type: *str*\n\t* Default / Range: reg:linear ( reg:linear | count:poisson | survival:cox | rank:pairwise | reg:gamma | reg:tweedie )\n4. **max_depth**: Maximum depth of the tree. For example, depth 0 means 1 leaf node and depth 1 means 1 internal node + 2 leaf nodes. Normally 6 is suggested for the depth. Increasing this value will make the model more complex and more likely to overfit. 0 is only accepted in loss guided growing policy when tree_method is set as hist and it indicates no limit on depth. Beware that XGBoost aggressively consumes memory when training a deep tree.\n\t* Type: *int*\n\t* Default / Range: 3 (1 <= value)\n5. **learning_rate**: Step size shrinkage used in update to prevents overfitting. After each boosting step, we can directly get the weights of new features, and the `learning rate` shrinks the feature weights to make the boosting process more conservative. 0.3 is suggested.\n\t* Type: *float*\n\t* Default / Range: 0.1 (0 <= value)\n6. **n_estimators**: Number of generated trees. If 1, then no bootstrapping is used. If > 1, then bootstrapping will be\n performed.\n\t* Type: *int*\n\t* Default / Range: 100 (1 <= value)\n7. **importance_type**: `importance type` is a feature importance type property.\n\t* Type: *str*\n\t* Default / Range: gain ( gain | weight | cover | total_gain | total_cover )\n8. **subsample**: Subsample to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. `subsample` interacts with the parameter `n_estimators`. Choosing `subsample` < 1.0 leads to a reduction of variance and an increase in bias.\n\t* Type: *float*\n\t* Default / Range: 1.0 (0 < value <= 1.0)\n9. **random_state**: Random seed. Use the same seed in order to reproduce the same results.\n\t* Type: *int*\n10. **group_by**: Columns to group by.\n\t* Type: *list[str]*\n11. **silent** : Whether print messages during construction\n - Type : _bool_ \n - Default : True\n12. **booster** : Specify which booster to use\n - Type : _str_ \n - Default / Range : gbtree ( gbtree | gblinear | dart )\n13. **n_jobs** : Number of parallel threads used to run xgboost.\n - Type : _int_ \n - Default / Range: 1 (1 <= value)\n14. **nthread** : Number of threads to use for loading data from numpy array. If -1, uses maximum threads available on the system.\n - Type : _int_ \n - Default / Range: None (-1 <= value)\n15. **gamma** : Minimum loss reduction required to make a further partition on a leaf node of the tree.\n - Type : _float_ \n - Default / Range: 0 (0 <= value)\n16. **min_child_weight** : Minimum sum of instance weight(hessian) needed in a child.\n - Type : _int_ \n - Default / Range: 1 (0 <= value)\n17. **max_delta_step** : Maximum delta step we allow each tree’s weight estimation to be.\n - Type : _int_ \n - Default / Range: 0 (0 <= value)\n18. **colsample_bytree** : `subsample` ratio of columns when constructing each tree.\n - Type : _float_ \n - Default / Range: 1 (0 <= value)\n19. **colsample_bylevel** : `subsample` ratio of columns for each level.\n - Type : _float_ \n - Default / Range: 1 (0 <= value)\n20. **reg_alpha** : L1 regularization term on weights.\n - Type : _float_ \n - Default value: 0\n21. **reg_lambda** : L2 regularization term on weights.\n - Type : _float_ \n - Default value: 1\n22. **scale_pos_weight** : Balancing of positive and negative weights.\n - Type : _float_ \n - Default / Range: 1 (0 <= value)\n23. **base_score** : The initial prediction score of all instances, global bias.\n - Type : _float_ \n - Default / Range: 0.5 (0 <= value)\n24. **seed** : Random seed. Use the same seed in order to reproduce the same results.\n - Type : _int_ \n - Default / Range: 0 (0 <= value)\n25. **missing** : Value in the data which needs to be present as a missing value.\n - Type : _float_ \n - Default / Range: None (0 <= value)\n26. **sample_weight** : instance weights\n - Type : _list_[_float_] \n - Default / Range: None (0 <= value)\n27. **eval_set** : A list of (X, y) tuple pairs to use as validation sets, for which metrics will be computed. Validation metrics will help us track the performance of the model.\n - Type : _list_[_tuple_] \n - Default value: None\n28. **eval_metric** : If a str, should be a built-in evaluation metric to use. If a list of str, should be the list of multiple built-in evaluation metrics to use. If callable, a custom evaluation metric. The call signature is func(y_predicted, y_true) where y_true will be a DMatrix object such that you may need to call the get_label method. It must return a str, value pair where the str is a name for the evaluation and value is the value of the evaluation function. The callable custom objective is always minimized.\n default metric will be assigned according to objective. 1) poisson-nloglik: for Poisson regression (count:poisson), 2) gamma-nloglik: for gamma regression (reg:gamma), 3) cox-nloglik: for Cox proportional hazards regression (survival:cox), 4) gamma-deviance: for gamma regression (reg:gamma)\n - Type : _str_ or _list[_str_]\n - Default / Range : None ( rmse | rmsle | mae )\n29. **early_stopping_rounds** : Activates early stopping. Cross-Validation metric (average of validation metric computed over CV folds) needs to improve at least once in every `early_stopping_rounds` round(s) to continue training. The last entry in the evaluation history will represent the best iteration. If there’s more than one metric in the eval_metric parameter given in params, the last metric will be used for early stopping.\n - Type : _int_ \n - Default / Range: None (0 <= value)\n30. **verbose** : If `verbose` and an `eval_set` is used, writes the evaluation metric measured on the validation set to stderr.\n - Type : _bool_ \n - Default value: True\n31. **xgb_model** : file name of stored XGBoost model or `booster` instance XGBoost model to be loaded before training (allows training continuation).\n - Type : _str_ \n - Default value: None\n32. **sample_weight_eval_set** : A list of the form [L\\_1, L\\_2, …, L\\_n], where each L\\_i is a list of instance weights on the i-th validation set.\n - Type : _list_[_str_] \n - Default value: None\n\n## Example\n### VA\n**[Sample Model]**\n\n\n\n\nIn this tutorial workflow, sample_iris data is used to train a XGBoost for regression. The generated model is afterward applied to a test dataset to predict the value of _petal\\_width_ by using XGBoost Regression Predict function. The parameter settings used in the function are shown below. \n\n++Parameters++\n1. **Feature Columns***: sepal\\_length, sepal\\_width\n2. **Label Column***: petal\\_width\n3. **Objective**: Linear regression with squared loss\n4. **Max Depth**: 3\n5. **Learning Rate**: 0.1 \n6. **Number of Trees**: 100\n7. **Importance Type**: Gain\n8. **Fraction of Samples**: 1.0\n9. **Seed**: 0\n10. **Group By**: None \n\n\n### Python\n\n```\nfrom brightics.function.regression import xgb_regression_train\ninput_table = inputs[0]\nresult = xgb_regression_train(table=input_table,\n feature_cols=['sepal_length', 'sepal_width'],\n label_col=['petal_width'], max_depth=4, learning_rate=0.01, n_estimators=100,\n random_state=0, silent=True, objective='reg:linear', booster='gbtree', n_jobs=1,\n nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1,\n colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n base_score=0.5, seed=None, missing=None, sample_weight=None, eval_set=None,\n eval_metric=None, early_stopping_rounds=None, verbose=True,\n xgb_model=None, sample_weight_eval_set=None, importance_type='gain')\noutput = result['model']\n```\nIn this python script, sample_iris data is used to train a XGBoost for regression. The generated model is afterward applied to a test dataset to predict the value of _petal\\_width_ by using XGBoost Regression Predict function.\n", "exampleModels": [ { "fileName": "brightics.function.regression$xgb_regression.json", diff --git a/function/python/brightics/function/regression/pls_regression.py b/function/python/brightics/function/regression/pls_regression.py new file mode 100644 index 000000000..319366c33 --- /dev/null +++ b/function/python/brightics/function/regression/pls_regression.py @@ -0,0 +1,106 @@ +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +from brightics.common.utils import get_default_from_parameters_if_required +from brightics.common.utils import check_required_parameters +from brightics.common.groupby import _function_by_group +from sklearn.metrics import mean_absolute_error +from sklearn.metrics import mean_squared_error +from sklearn.metrics import r2_score +import pandas as pd +from brightics.common.repr import strip_margin +from brightics.common.repr import BrtcReprBuilder +from brightics.common.repr import pandasDF2MD +from brightics.function.utils import _model_dict +from sklearn.cross_decomposition import PLSRegression as PLS +from brightics.common.validation import validate +from brightics.common.classify_input_type import check_col_type + + +def pls_regression_train(table, group_by=None, **params): + params = get_default_from_parameters_if_required( + params, _pls_regression_train) + check_required_parameters(_pls_regression_train, params, ['table']) + if group_by is not None: + grouped_model = _function_by_group( + _pls_regression_train, table, group_by=group_by, **params) + return grouped_model + else: + return _pls_regression_train(table, **params) + + +def _pls_regression_train(table, feature_cols, label_cols, n_components=2, scale=True, max_iter=500, tol=1e-6): + pls_model = PLS(n_components=n_components, scale=scale, max_iter=max_iter, tol=tol) + _, features = check_col_type(table, feature_cols) + _, labels = check_col_type(table, label_cols) + pls_model.fit(features, labels) + predict = pls_model.predict(features) + _mean_absolute_error = mean_absolute_error(labels, predict) + _mean_squared_error = mean_squared_error(labels, predict) + _r2_score = r2_score(labels, predict) + result_table = pd.DataFrame.from_items([ + ['Metric', ['Mean Absolute Error', 'Mean Squared Error', 'R2 Score']], + ['Score', [_mean_absolute_error, _mean_squared_error, _r2_score]] + ]) + label_name = { + 'n_components': 'Number of components', + 'scale': "Scale", + 'max_iter': 'Max iteration', + 'tol': 'Tolerance' + } + get_param = pls_model.get_params() + param_table = pd.DataFrame.from_items([ + ['Parameter', list(label_name.values())], + ['Value', [get_param[x] for x in list(label_name.keys())]] + ]) + rb = BrtcReprBuilder() + rb.addMD(strip_margin(""" + | ### PLS Regression Result + | {result} + | ### Parameters + | {list_parameters} + """.format(result=pandasDF2MD(result_table), list_parameters=pandasDF2MD(param_table) + ))) + model = _model_dict('pls_regression_model') + model['feature_cols'] = feature_cols + model['label'] = label_cols + model['mean_absolute_error'] = _mean_absolute_error + model['mean_squared_error'] = _mean_squared_error + model['r2_score'] = _r2_score + model['max_iter'] = max_iter + model['tol'] = tol + model['pls_model'] = pls_model + model['_repr_brtc_'] = rb.get() + return {'model': model} + +def pls_regression_predict(table, model, **params): + check_required_parameters(_pls_regression_predict, + params, ['table', 'model']) + if '_grouped_data' in model: + return _function_by_group(_pls_regression_predict, table, model, **params) + else: + return _pls_regression_predict(table, model, **params) + + +def _pls_regression_predict(table, model, prediction_col='prediction'): + result = table.copy() + feature_cols = model['feature_cols'] + _, features = check_col_type(result, feature_cols) + pls_model = model['pls_model'] + prediction = pls_model.predict(features) + for i in range(prediction.shape[-1]): + result[prediction_col+"_{}".format(i)] = prediction[:, i] + return {'out_table': result} diff --git a/function/python/brightics/function/regression/random_forest_regression.py b/function/python/brightics/function/regression/random_forest_regression.py index 85fd52cd2..8508ed7b8 100644 --- a/function/python/brightics/function/regression/random_forest_regression.py +++ b/function/python/brightics/function/regression/random_forest_regression.py @@ -105,7 +105,7 @@ def _random_forest_regression_train(table, feature_cols, label_col, 'min_impurity_decrease': min_impurity_decrease, 'random_state': random_state} - model = dict() + model = _model_dict('random_forest_regression_model') model['regressor'] = regressor model['params'] = params diff --git a/function/python/brightics/function/regression/regression_predict.py b/function/python/brightics/function/regression/regression_predict.py index 4815b167f..b5dd42eb3 100644 --- a/function/python/brightics/function/regression/regression_predict.py +++ b/function/python/brightics/function/regression/regression_predict.py @@ -18,6 +18,12 @@ from .linear_regression import linear_regression_predict from .decision_tree_regression import decision_tree_regression_predict from .random_forest_regression import random_forest_regression_predict +from .ada_boost_regression import ada_boost_regression_predict +from .glm import glm_predict +from .isotonic_regression import isotonic_regression_predict +from .mlp_regression import mlp_regression_predict +from .xgb_regression import xgb_regression_predict +from .pls_regression import pls_regression_predict import numpy as np @@ -40,5 +46,16 @@ def regression_predict(table, model, prediction_col='prediction'): return random_forest_regression_predict(table = table, model = model, prediction_col = prediction_col) if 'forest_regression' in tmp_model['_type'] or 'gbt_regression' in tmp_model['_type']: return random_forest_regression_predict(table = table, model = model, prediction_col = prediction_col) + if tmp_model['_type'] == 'ada_boost_regression_model': + return ada_boost_regression_predict(table=table, model=model, pred_col_name=prediction_col) + if tmp_model['_type'] == 'glm_model': + return glm_predict(table=table, model=model, prediction_col=prediction_col) + if tmp_model['_type'] == 'mlp_regression_model': + return mlp_regression_predict(table=table, model=model, prediction_col=prediction_col) + if tmp_model['_type'] == 'xgb_regression_model': + return xgb_regression_predict(table=table, model=model, prediction_col=prediction_col) + if tmp_model['_type'] == 'isotonic_regression_model': + return isotonic_regression_predict(table=table, model=model, prediction_col=prediction_col) + if tmp_model['_type'] == 'pls_regression_model': + return pls_regression_predict(table=table, model=model, prediction_col=prediction_col) raise_runtime_error('''It is not supported yet.''') - diff --git a/function/python/brightics/function/regression/test/isotonic_regression_test.py b/function/python/brightics/function/regression/test/isotonic_regression_test.py new file mode 100644 index 000000000..efdc6c438 --- /dev/null +++ b/function/python/brightics/function/regression/test/isotonic_regression_test.py @@ -0,0 +1,46 @@ +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +from brightics.function.regression.isotonic_regression import isotonic_regression_train +from brightics.function.regression.isotonic_regression import isotonic_regression_predict +from brightics.common.datasets import load_iris +import unittest +import pandas as pd +import numpy as np +import HtmlTestRunner +import os + + +class IsotonicRegression(unittest.TestCase): + + def setUp(self): + print("*** Isotonic Regression Train/Predict UnitTest Start ***") + self.testdata = load_iris() + + def tearDown(self): + print("*** Isotonic Regression Train/Predict UnitTest End ***") + + def test(self): + isotonic_train = isotonic_regression_train(self.testdata, feature_col='sepal_length', label_col='petal_width') + predict = isotonic_regression_predict(self.testdata, isotonic_train['model'])['out_table']['prediction'] + desired_label = [0.44, 0.44, 0.2166667, 0.2166667, 0.44] + np.testing.assert_array_almost_equal(desired_label, [round(x, 7) for x in predict.values[:5]], 7, 'incorrect prediction') + + +if __name__ == '__main__': + filepath = os.path.dirname(os.path.abspath(__file__)) + reportFoler = filepath + "/../../../../../../../reports" + unittest.main(testRunner=HtmlTestRunner.HTMLTestRunner(combine_reports=True, output=reportFoler)) diff --git a/function/python/brightics/function/regression/xgb_regression.py b/function/python/brightics/function/regression/xgb_regression.py index 3c59d7ba5..0f9435920 100644 --- a/function/python/brightics/function/regression/xgb_regression.py +++ b/function/python/brightics/function/regression/xgb_regression.py @@ -16,6 +16,7 @@ from random import randint import pandas as pd +import numpy as np import matplotlib.pyplot as plt from xgboost import XGBRegressor from xgboost import plot_importance, plot_tree @@ -44,37 +45,37 @@ def xgb_regression_train(table, group_by=None, **params): def _xgb_regression_train(table, feature_cols, label_col, max_depth=3, learning_rate=0.1, n_estimators=100, - silent=True, objectibe='reg:linear', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1, - max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1, - scale_pos_weight=1, base_score=0.5, random_state=None, seed=None, missing=None, - sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, - xgb_model=None, sample_weight_eval_set=None, importance_type='gain'): + silent=True, objectibe='reg:linear', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1, + max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1, + scale_pos_weight=1, base_score=0.5, random_state=None, seed=None, missing=None, + sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, + xgb_model=None, sample_weight_eval_set=None, importance_type='gain'): if random_state is None: random_state = randint(-2**31, 2**31-1) regressor = XGBRegressor(max_depth=max_depth, - learning_rate=learning_rate, - n_estimators=n_estimators, - silent=silent, - objective=objectibe, - booster=booster, - n_jobs=n_jobs, - nthread=nthread, - gamma=gamma, - min_child_weight=min_child_weight, - max_delta_step=max_delta_step, - subsample=subsample, - colsample_bytree=colsample_bytree, - colsample_bylevel=colsample_bylevel, - reg_alpha=reg_alpha, - reg_lambda=reg_lambda, - scale_pos_weight=scale_pos_weight, - base_score=base_score, - random_state=random_state, - seed=seed, - missing=missing, - importance_type=importance_type) + learning_rate=learning_rate, + n_estimators=n_estimators, + silent=silent, + objective=objectibe, + booster=booster, + n_jobs=n_jobs, + nthread=nthread, + gamma=gamma, + min_child_weight=min_child_weight, + max_delta_step=max_delta_step, + subsample=subsample, + colsample_bytree=colsample_bytree, + colsample_bylevel=colsample_bylevel, + reg_alpha=reg_alpha, + reg_lambda=reg_lambda, + scale_pos_weight=scale_pos_weight, + base_score=base_score, + random_state=random_state, + seed=seed, + missing=missing, + importance_type=importance_type) feature_names, features = check_col_type(table, feature_cols) label = table[label_col] regressor.fit(features, label, @@ -85,10 +86,11 @@ def _xgb_regression_train(table, feature_cols, label_col, max_depth=3, learning_ get_param = regressor.get_params() feature_importance = regressor.feature_importances_ # plt.rcdefaults() - plot_importance(regressor) - plt.tight_layout() - fig_plot_importance = plt2MD(plt) - plt.clf() + # plot_importance(regressor) + # plt.tight_layout() + # fig_plot_importance = plt2MD(plt) + fig_plot_importance = _plot_feature_importances(feature_cols, regressor) + # plt.clf() # plt.rcParams['figure.dpi'] = figure_dpi # plot_tree(regressor) # fig_plot_tree_UT = plt2MD(plt) @@ -166,3 +168,22 @@ def _xgb_regression_predict(table, model, prediction_col='prediction', out_table[prediction_col] = prediction return {'out_table': out_table} + + +def _plot_feature_importances(feature_cols, regressor): + + feature_importance = regressor.feature_importances_ + indices = np.argsort(feature_importance) + sorted_feature_cols = np.array(feature_cols)[indices] + + plt.barh(range(len(indices)), feature_importance[indices], color='b', align='center') + for i, v in enumerate(feature_importance[indices]): + plt.text(v, i, " {:.2f}".format(v), color='b', va='center', fontweight='bold') + + plt.yticks(range(len(indices)), sorted_feature_cols) + plt.xlabel("Feature importance") + plt.ylabel("Feature") + plt.tight_layout() + fig_feature_importances = plt2MD(plt) + plt.close() + return fig_feature_importances \ No newline at end of file diff --git a/function/python/brightics/function/statistics/__init__.py b/function/python/brightics/function/statistics/__init__.py index 58449a09e..e46b030b4 100644 --- a/function/python/brightics/function/statistics/__init__.py +++ b/function/python/brightics/function/statistics/__init__.py @@ -1,39 +1,41 @@ -""" - Copyright 2019 Samsung SDS - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -""" - -from .profile_table import profile_table -from .correlation import correlation -from .pairplot import pairplot -from .anova import bartletts_test -from .anova import oneway_anova -from .anova import twoway_anova -from .anova import tukeys_range_test -from .ttest import one_sample_ttest -from .ttest import two_sample_ttest_for_stacked_data -from .ttest import paired_ttest -from .ftest import ftest_for_stacked_data -from .chi_square_test import chi_square_test_of_independence -from .summary import statistic_summary, statistic_derivation, string_summary -from .cross_table import cross_table -from .levene import levenes_test -from .duncan_test import duncan_test -from .ljung_box_test import ljung_box_test -from .mann_whitney_test import mann_whitney_test -from .wilcoxon_test import wilcoxon_test -from .friedman_test import friedman_test -from .kruskal_wallis_test import kruskal_wallis_test -from .normality_test import normality_test -from .kernel_density_estimation import kernel_density_estimation +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +from .profile_table import profile_table +from .correlation import correlation +from .pairplot import pairplot +from .anova import bartletts_test +from .anova import oneway_anova +from .anova import twoway_anova +from .anova import tukeys_range_test +from .ttest import one_sample_ttest +from .ttest import two_sample_ttest_for_stacked_data +from .ttest import paired_ttest +from .ftest import ftest_for_stacked_data +from .chi_square_test import chi_square_test_of_independence +from .summary import statistic_summary, statistic_derivation, string_summary +from .cross_table import cross_table +from .cross_table import cross_table2 +from .levene import levenes_test +from .duncan_test import duncan_test +from .ljung_box_test import ljung_box_test +from .mann_whitney_test import mann_whitney_test +from .wilcoxon_test import wilcoxon_test +from .wilcoxon_test2 import wilcoxon_test2 +from .friedman_test import friedman_test +from .kruskal_wallis_test import kruskal_wallis_test +from .normality_test import normality_test +from .kernel_density_estimation import kernel_density_estimation diff --git a/function/python/brightics/function/statistics/anova.py b/function/python/brightics/function/statistics/anova.py index 3826dede5..2733c0553 100644 --- a/function/python/brightics/function/statistics/anova.py +++ b/function/python/brightics/function/statistics/anova.py @@ -169,7 +169,8 @@ def _twoway_anova(table, response_cols, factor_cols): """)) if n == 1: - groups = table[factor_cols].unique() + _factor_col = factor_cols[0] + groups = table[_factor_col].unique() groups.sort() sum_len = np.sum([len(str(group)) for group in groups]) @@ -181,7 +182,7 @@ def _twoway_anova(table, response_cols, factor_cols): result['_grouped_data'][response_col] = dict() if n == 1: - ax = sns.boxplot(x=factor_cols, y=response_col, data=table, order=groups) + ax = sns.boxplot(x=_factor_col, y=response_col, data=table, order=groups) if sum_len > 512: ax.set_xticklabels(ax.get_xticklabels(), rotation=90) elif sum_len > 64: diff --git a/function/python/brightics/function/statistics/correlation.py b/function/python/brightics/function/statistics/correlation.py index 29402a9b3..f0be6654e 100644 --- a/function/python/brightics/function/statistics/correlation.py +++ b/function/python/brightics/function/statistics/correlation.py @@ -42,19 +42,21 @@ def _correlation(table, vars, method='pearson', display_plt=True, height=2.5, co size = len(vars) result_arr = [] - + cov_xy = table[vars].cov() + for i in range(size): for j in range(i): + cov_temp = cov_xy[vars[i]][vars[j]] if method == 'pearson': r, p = stats.pearsonr(table[vars[i]], table[vars[j]]) elif method == 'spearman': r, p = stats.spearmanr(table[vars[i]], table[vars[j]]) else: r, p = stats.kendalltau(table[vars[i]], table[vars[j]]) + + result_arr.append([vars[i], vars[j], r, p, cov_temp]) - result_arr.append([vars[i], vars[j], r, p]) - - df_result = pd.DataFrame(result_arr, columns=['x', 'y', 'corr', 'p_value']) + df_result = pd.DataFrame(result_arr, columns=['x', 'y', 'corr', 'p_value', 'cov']) rb = BrtcReprBuilder() if display_plt: diff --git a/function/python/brightics/function/statistics/cross_table.py b/function/python/brightics/function/statistics/cross_table.py index ff81c9fbb..c3647b17a 100644 --- a/function/python/brightics/function/statistics/cross_table.py +++ b/function/python/brightics/function/statistics/cross_table.py @@ -104,3 +104,107 @@ def _cross_table(table, input_cols_1, input_cols_2, result='N', margins=False): model['_repr_brtc_'] = rb.get() return {'model': model} + + +def cross_table2(table, group_by=None, **params): + check_required_parameters(_cross_table2, params, ['table']) + if group_by is not None: + return _function_by_group(_cross_table2, table, group_by=group_by, **params) + else: + return _cross_table2(table, **params) + + +def _cross_table2(table, input_cols_1, input_cols_2, result=None, + add_info=None, row_total_str='row_total', col_total_str='col_total', contents_col='contents'): + df1 = [table[col] for col in input_cols_1] + df2 = [table[col] for col in input_cols_2] + if result is None: + result = ['N'] + if add_info is None: + add_info = [] + if not result: + raise_runtime_error("Please check 'result'.") + tables = [] + + if 'N' in result: + tmp_table1 = pd.crosstab(df1, df2, margins=False) + + if 'row_tot' in add_info: + tmp_sum = pd.DataFrame(tmp_table1.sum(axis=1), columns=[row_total_str]) + tmp_table1[row_total_str] = tmp_sum + if 'column_tot' in add_info: + tmp_sum = pd.DataFrame(tmp_table1.sum(axis=0), columns=[col_total_str]).T + tmp_table1 = pd.concat([tmp_table1, tmp_sum]) + + tmp_table1[contents_col] = ['N'] * tmp_table1.shape[0] + tables.append(tmp_table1) + + if 'N / Row Total' in result: + tmp_table2 = pd.crosstab(df1, df2, normalize='index') + tmp_table2[contents_col] = ['N / Row Total'] * tmp_table2.shape[0] + tables.append(tmp_table2) + + if 'N / Column Total' in result: + tmp_table3 = pd.crosstab(df1, df2, normalize='columns') + tmp_table3[contents_col] = ['N / Column Total'] * tmp_table3.shape[0] + tables.append(tmp_table3) + + if 'N / Total' in result: + tmp_table4 = pd.crosstab(df1, df2, normalize='all') + + if 'row_tot' in add_info: + tmp_sum = pd.DataFrame(tmp_table4.sum(axis=1), columns=[row_total_str]) + tmp_table4[row_total_str] = tmp_sum + if 'column_tot' in add_info: + tmp_sum = pd.DataFrame(tmp_table4.sum(axis=0), columns=[col_total_str]).T + tmp_table4 = pd.concat([tmp_table4, tmp_sum]) + + tmp_table4[contents_col] = ['N / Total'] * tmp_table4.shape[0] + tables.append(tmp_table4) + result_table = pd.concat(tables) + + if len(input_cols_1) == 1: + result_table['new_index'] = result_table.index + else: + result_table['new_index'] = ['_'.join([str(x).replace('.', '_') for x in idx]) for idx in result_table.index] + result_table = result_table.sort_values(by=['new_index', contents_col]) + result_table = result_table.drop(['new_index'], axis=1) + + # each row and column name + row_names = list(result_table.index)[:] + if len(input_cols_1) == 1: + joined_row_name = [str(i) for i in row_names] + else: + if 'column_tot' in add_info: + if ('N' not in result) and ('N / Total' not in result): + joined_row_name = ['_'.join(str(s) for s in row_names[i]) for i in range(len(row_names))] + elif ('N' in result) and ('N / Total' in result): + joined_row_name = ['_'.join(str(s) for s in row_names[i]) for i in range(len(row_names) - 2)] + [row_names[-2]] + [row_names[-1]] + else: + joined_row_name = ['_'.join(str(s) for s in row_names[i]) for i in range(len(row_names) - 1)] + [row_names[-1]] + else: + joined_row_name = ['_'.join(str(s) for s in row_names[i]) for i in range(len(row_names))] + + # joined_row_name = joined_row_name.astype(str) + + column_names = list(result_table.columns)[:] + if len(input_cols_2) == 1: + joined_column_name = [str(i).replace('.','_') for i in column_names] + else: + if 'row_tot' in add_info: + if ('N' not in result) and ('N / Total' not in result): + joined_column_name = ['_' + '_'.join(str(s) for s in column_names[i]).replace('.','_') for i in range(len(column_names))] + else: + joined_column_name = ['_' + '_'.join(str(s) for s in column_names[i]).replace('.','_') for i in range(len(column_names) - 1)] + [column_names[-1][0]] + else: + joined_column_name = ['_' + '_'.join(str(s) for s in column_names[i]).replace('.','_') for i in range(len(column_names))] + + input_cols = input_cols_1 + input_cols_2 + + # cross table + result_table.insert(loc=0, column=' ', value=joined_row_name) + result_table.columns = np.append('_'.join(input_cols), joined_column_name) + # result_table = result_table.rename(columns={result_table.columns[1]: contents_col}) + result_table.reset_index(drop=True, inplace=True) + + return {'out_table': result_table} \ No newline at end of file diff --git a/function/python/brightics/function/statistics/friedman_test.py b/function/python/brightics/function/statistics/friedman_test.py index 805c284b6..21458863a 100644 --- a/function/python/brightics/function/statistics/friedman_test.py +++ b/function/python/brightics/function/statistics/friedman_test.py @@ -53,21 +53,23 @@ def _friedman_test(table, response_cols, factor_col): samples[name] = groups[name] else: samples[name] = groups[name].sample(n=min_length) + group_name = [] + stats = [] + pvals = [] for response_col in response_cols: - stats, pval = friedmanchisquare(*[x[response_col] for x in samples.values()]) - rb.addMD(strip_margin(""" - | ## {response_col} by {factor_col} - | - | ### Statistics value: {stats} - | - | ### P value: {pval} - """.format(response_col=response_col, factor_col=factor_col, stats=stats, pval=pval))) + stat, pval = friedmanchisquare(*[x[response_col] for x in samples.values()]) + group_name.append(response_col + ' by ' + factor_col) + stats.append(stat) + pvals.append(pval) name = response_col + '_' + factor_col result[name] = dict() - result[name]['Statistics'] = stats + result[name]['Statistics'] = stat result[name]['P value'] = pval + rb.addMD(strip_margin(""" + | {table} + """.format(table=pandasDF2MD(pd.DataFrame({'': group_name, 'Test Statistics': stats, 'P Value': pvals}))))) result['_repr_brtc_'] = rb.get() return {'result': result} diff --git a/function/python/brightics/function/statistics/kruskal_wallis_test.py b/function/python/brightics/function/statistics/kruskal_wallis_test.py index 4b5d7b6bb..2155c6898 100644 --- a/function/python/brightics/function/statistics/kruskal_wallis_test.py +++ b/function/python/brightics/function/statistics/kruskal_wallis_test.py @@ -47,21 +47,27 @@ def _kruskal_wallis_test(table, response_cols, factor_col, nan_policy='propagate for name, group in table.groupby(factor_col): groups[name] = group + group_name = [] + df = [len(groups) - 1] * len(response_cols) + stats = [] + pvals = [] for response_col in response_cols: - stats, pval = kruskal(*[x[response_col] for x in groups.values()]) - rb.addMD(strip_margin(""" - | ## {response_col} by {factor_col} - | - | ### Statistics value: {stats} - | - | ### P value: {pval} - """.format(response_col=response_col, factor_col=factor_col, stats=stats, pval=pval))) + stat, pval = kruskal(*[x[response_col] for x in groups.values()]) + group_name.append(response_col + ' by ' + factor_col) + stats.append(stat) + pvals.append(pval) name = response_col + '_' + factor_col result[name] = dict() - result[name]['Statistics'] = stats + result[name]['Statistics'] = stat result[name]['P value'] = pval + rb.addMD(strip_margin(""" + | {table} + """.format(table=pandasDF2MD(pd.DataFrame({'': group_name, + 'Degree of Freedom': df, + 'Test Statistics': stats, + 'P value': pvals}))))) result['_repr_brtc_'] = rb.get() return {'result': result} diff --git a/function/python/brightics/function/statistics/mann_whitney_test.py b/function/python/brightics/function/statistics/mann_whitney_test.py index 4cab3e188..7a3f6991d 100644 --- a/function/python/brightics/function/statistics/mann_whitney_test.py +++ b/function/python/brightics/function/statistics/mann_whitney_test.py @@ -48,21 +48,23 @@ def _mann_whitney_test(table, response_col, factor_col, use_continuity=True): uniq_factor = table[factor_col].unique() for name in uniq_factor: groups[name] = np.array(table[response_col])[np.where(table[factor_col] == name)] + group_name = [] + stats = [] + pvals = [] for name1, name2 in itertools.combinations(uniq_factor, 2): - stats, pval = mannwhitneyu(groups[name1], groups[name2], use_continuity=use_continuity) - rb.addMD(strip_margin(""" - | ## {name1} vs {name2} - | - | ### Statistics U value: {stats} - | - | ### P value: {pval} - """.format(name1=name1, name2=name2, stats=stats, pval=pval))) + name = str(name1) + ' vs ' + str(name2) + stat, pval = mannwhitneyu(groups[name1], groups[name2], use_continuity=use_continuity) + group_name.append(name) + stats.append(stat) + pvals.append(pval) - name = str(name1) + '_' + str(name2) result[name] = dict() - result[name]['Statistics'] = stats + result[name]['Statistics'] = stat result[name]['P value'] = pval + rb.addMD(strip_margin(""" + | {table} + """.format(table=pandasDF2MD(pd.DataFrame({'': group_name, 'Test Statistics': stats, 'P Value': pvals}))))) result['_repr_brtc_'] = rb.get() return {'result': result} diff --git a/function/python/brightics/function/statistics/meta/cross_table.json b/function/python/brightics/function/statistics/meta/cross_table.json index e64737060..a9c420897 100644 --- a/function/python/brightics/function/statistics/meta/cross_table.json +++ b/function/python/brightics/function/statistics/meta/cross_table.json @@ -4,6 +4,7 @@ "content": "" }, "specJson": { + "deprecated": true, "category": "statistics", "func": "brightics.function.statistics$cross_table00483", "name": "brightics.function.statistics$cross_table", diff --git a/function/python/brightics/function/statistics/meta/cross_table2.json b/function/python/brightics/function/statistics/meta/cross_table2.json new file mode 100644 index 000000000..d7bf336f7 --- /dev/null +++ b/function/python/brightics/function/statistics/meta/cross_table2.json @@ -0,0 +1,984 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "statistics", + "func": "brightics.function.statistics$cross_table2", + "name": "brightics.function.statistics$cross_table2", + "context": "python", + "label": "Cross Table", + "description": "In statistics, a crosstable is a type of table in a matrix format that displays the (multivariate) frequency distribution of the variables. They are heavily used in survey research, business intelligence, engineering and scientific research. They provide a basic picture of the interrelation between two variables and can help find interactions between them. This function provides cross table of the given columns.", + "tags": [], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "out_table": "" + }, + "meta": { + "table": { + "type": "table", + "desc": "Data in a Table.", + "optional": false + }, + "out_table": { + "type": "table", + "desc": "Cross Table result of the given `Input Columns`." + } + }, + "params": [ + { + "id": "input_cols_1", + "label": "Input Columns 1", + "description": "The first input column name.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "columnType": [ + "Integer", + "Long", + "Float", + "Double", + "String" + ], + "multiple": true + }, + { + "id": "input_cols_2", + "label": "Input Columns 2", + "description": "The second input column name.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "columnType": [ + "Integer", + "Long", + "Float", + "Double", + "String" + ], + "multiple": true + }, + { + "id": "result", + "label": "Contents", + "description": "Contents to be presented in the cross table.", + "visibleOption": [], + "control": "CheckBox", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "N", + "value": "N", + "default": true + }, + { + "label": "N / Row Total", + "value": "N / Row Total", + "default": false + }, + { + "label": "N / Column Total", + "value": "N / Column Total", + "default": false + }, + { + "label": "N / Total", + "value": "N / Total", + "default": false + } + ], + "columnType": [] + }, + { + "id": "add_info", + "label": "Additional Information", + "description": "Add row/column margins (subtotals).", + "visibleOption": [], + "control": "CheckBox", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "Row Total", + "value": "row_tot", + "default": true + }, + { + "label": "Column Total", + "value": "column_tot", + "default": true + } + ], + "columnType": [] + }, + { + "id": "row_total_str", + "label": "Row Total Column Name", + "description": "Name of the column containing row totals.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "placeHolder": "row_total", + "type": "String" + }, + { + "id": "col_total_str", + "label": "Column Total Row Name", + "description": "Name of the row containing column totals.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "String", + "placeHolder": "col_total" + }, + { + "id": "group_by", + "label": "Group By", + "description": "Columns to group by.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "multiple": true + }, + { + "id": "contents_col", + "label": "Contents Column Name", + "description": "Name of the column which contains content type of each row.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "globalVariable": false, + "placeHolder": "contents", + "type": "String", + "defaultValue": "" + } + ], + "summary": "This function is to create Cross Table." + }, + "md": { + "en": "# Cross Table\nThis function is to create Cross Table.\n\n## Description\nIn statistics, a crosstable is a type of table in a matrix format that displays the (multivariate) frequency distribution of the variables. They are heavily used in survey research, business intelligence, engineering and scientific research. They provide a basic picture of the interrelation between two variables and can help find interactions between them. This function provides cross table of the given columns.\n\nReference\n- \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data in a Table.\n#### OUTPUT\n1. **out_table**: (Table) Cross Table result of the given `Input Columns`.\n#### PARAMETER\n1. **Input Columns 1***: The first input column name.\n\n2. **Input Columns 2***: The second input column name.\n\n3. **Contents**: Contents to be presented in the cross table.\n * Available Options: `N`, `N / Row Total`, `N / Column Total`, `N / Total`\n\n4. **Additional Information**: Add row/column margins (subtotals).\n * Available Options: `Row Total`, `Column Total`\n\n5. **Row Total Column Name**: Name of the column containing row totals.\n\n6. **Column Total Row Name**: Name of the row containing column totals.\n\n7. **Group By**: Columns to group by.\n\n8. **Contents Column Name**: Name of the column which contains content type of each row.\n\t* Default value: contents\n\n### Python\n#### USAGE\n\n```\ncross_table2(table, input_cols_1, input_cols_2, result=None, add_info=None, row_total_str='row_total', col_total_str='col_total', contents_col='contents')\n```\n\n#### INPUT\n1. **table***: (Table) Data in a Table.\n#### OUTPUT\n1. **out_table**: (Table) Cross Table result of the given `Input Columns`.\n#### PARAMETER\n1. **input_cols_1***: The first input column name.\n\t* Type: *list[str]*\n2. **input_cols_2***: The second input column name.\n\t* Type: *list[str]*\n3. **result**: Contents to be presented in the cross table.\n\t* Type: *str*\n\t* Default / Range: N ( N | N / Row Total | N / Column Total | N / Total )\n4. **add_info**: Add row/column margins (subtotals).\n\t* Type: *str*\n\t* Default / Range: row_tot ( row_tot | column_tot )\n5. **row_total_str**: Name of the column containing row totals.\n\t* Type: *str*\n6. **col_total_str**: Name of the row containing column totals.\n\t* Type: *str*\n7. **group_by**: Columns to group by.\n\t* Type: *list[str]*\n8. **contents_col**: Name of the column which contains content type of each row.\n\t* Type: *str*\n\t* Default value: \n\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, sample_adult data is used to create Cross Table. This table includes workclass and race to see the interrelation between them. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Input Columns 1***: workclass\n2. **Input Columns 2***: race\n3. **Contents**: N, N / Row Total, N / Column Total, N / Total\n4. **Additional Information**: Row Total, Column Total\n5. **Row Total Column Name**: a\n6. **Column Total Row Name**: b\n7. **Group By**: None\n8. **Contents Column Name**: contents\n\n\n\n### Python\n\n```\nfrom brightics.function.statistics import cross_table2\ninput_table = inputs[0]\nresult = cross_table2(table=input_table,\n input_cols_1=['workclass'],\n input_cols_2=['race'],\n\t\t\t\t\t result=['N', 'N / Row Total', 'N / Column Total', 'N / Total'],\n\t\t\t\t\t add_info=['row_tot', 'column_tot'],\n\t\t\t\t\t row_total_str='row_total',\n\t\t\t\t\t col_total_str='col_total',\n\t\t\t\t\t group_by=None,\n\t\t\t\t\t contents_col='contents')\nout_table = result['out_table']\n```\nIn this Python script, sample_adult data is used to create Cross Table. This table includes workclass and race to see the interrelation between them. 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", + "model_image": null, + "creator": "brightics@samsung.com", + "create_time": "2020-07-02T03:11:21.000Z", + "updater": "brightics@samsung.com", + "update_time": "2020-07-02T07:16:11.000Z", + "event_key": "2020_071611.000", + "type": "data", + "tag": "null", + "from_version": "0.0" + } + } + ], + "version": "3.6" + } + } + ], + "sampleImages": [ + { + "fileName": "brightics.function.statistics$cross_table.png", + "label": "brightics.function.statistics$cross_table.png", + "base64": 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+ "description": "In this tutorial workflow, sample_adult data is used to create Cross Table. This table includes workclass and race to see the interrelation between them. The parameter settings used in the function are shown below.", + "parameter": "1. **Input Columns 1***: workclass\n2. **Input Columns 2***: race\n3. **Contents**: N, N / Row Total, N / Column Total, N / Total\n4. **Additional Information**: Row Total, Column Total\n5. **Row Total Column Name**: a\n6. **Column Total Row Name**: b\n7. **Group By**: None\n8. **Contents Column Name**: contents\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.statistics import cross_table2\r\ninput_table = inputs[0]\r\nresult = cross_table2(table=input_table,\r\n input_cols_1=['workclass'],\r\n input_cols_2=['race'],\r\n\t\t\t\t\t result=['N', 'N / Row Total', 'N / Column Total', 'N / Total'],\r\n\t\t\t\t\t add_info=['row_tot', 'column_tot'],\r\n\t\t\t\t\t row_total_str='row_total',\r\n\t\t\t\t\t col_total_str='col_total',\r\n\t\t\t\t\t group_by=None,\r\n\t\t\t\t\t contents_col='contents')\r\nout_table = result['out_table']", + "description": "In this Python script, sample_adult data is used to create Cross Table. This table includes workclass and race to see the interrelation between them. The parameter settings used in the function are shown below.", + "context": "python" + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/statistics/meta/kruskal_wallis_test.json b/function/python/brightics/function/statistics/meta/kruskal_wallis_test.json index 1725c99a5..fea055e0e 100644 --- a/function/python/brightics/function/statistics/meta/kruskal_wallis_test.json +++ b/function/python/brightics/function/statistics/meta/kruskal_wallis_test.json @@ -8,7 +8,7 @@ "func": "brightics.function.statistics$kruskal_wallis_test", "name": "brightics.function.statistics$kruskal_wallis_test", "context": "python", - "label": "Kruskal-Wallis Test", + "label": "Kruskal Wallis Test", "description": "The Kruskal-Wallis test is a nonparametric test, and is used when the assumptions of one-way ANOVA are not met. Both the Kruskal-Wallis test and one-way ANOVA assess for significant differences on a continuous dependent variable by a categorical independent variable (with two or more groups). In the ANOVA, we assume that the dependent variable is normally distributed and there is approximately equal variance on the scores across groups. However, when using the Kruskal-Wallis Test, we do not have to make any of these assumptions. Therefore, the Kruskal-Wallis test can be used for both continuous and ordinal-level dependent variables. However, like most non-parametric tests, the Kruskal-Wallis Test is not as powerful as the ANOVA.\n\nReference\n+ ", "tags": [ "statistics" diff --git a/function/python/brightics/function/statistics/meta/mann_whitney_test.json b/function/python/brightics/function/statistics/meta/mann_whitney_test.json index 345bae219..7952b6e3c 100644 --- a/function/python/brightics/function/statistics/meta/mann_whitney_test.json +++ b/function/python/brightics/function/statistics/meta/mann_whitney_test.json @@ -8,7 +8,7 @@ "func": "brightics.function.statistics$mann_whitney_test", "name": "brightics.function.statistics$mann_whitney_test", "context": "python", - "label": "Mann-Whitney Test", + "label": "Mann Whitney Test", "description": "In statistics, the Mann–Whitney U test (also called the Mann–Whitney–Wilcoxon (MWW), Wilcoxon rank-sum test, or Wilcoxon–Mann–Whitney test) is a nonparametric test of the null hypothesis that it is equally likely that a randomly selected value from one sample will be less than or greater than a randomly selected value from a second sample. This test can be used to investigate whether two independent samples were selected from populations having the same distribution when the dependent variable is either ordinal or continuous, but not normally distributed. \n\nReference\n+ \n+ ", "tags": [ "statistics" diff --git a/function/python/brightics/function/statistics/meta/normality_test.json b/function/python/brightics/function/statistics/meta/normality_test.json index 79607f18e..f67f9adc5 100644 --- a/function/python/brightics/function/statistics/meta/normality_test.json +++ b/function/python/brightics/function/statistics/meta/normality_test.json @@ -664,25 +664,8 @@ } ], "preferences": {}, - "variables": { - "path": { - "type": "array[string]", - "value": [ - "" - ] - }, - "out_alias": { - "type": "string", - "value": "" - } - }, - "variableRef": [ - { - "param": { - "out-alias": "out_alias" - } - } - ], + "variables": {}, + "variableRef": [], "innerModels": {}, "inData": [], "outData": [], diff --git a/function/python/brightics/function/statistics/meta/one_sample_ttest.json b/function/python/brightics/function/statistics/meta/one_sample_ttest.json index c7b2f0ab7..302c1852c 100644 --- a/function/python/brightics/function/statistics/meta/one_sample_ttest.json +++ b/function/python/brightics/function/statistics/meta/one_sample_ttest.json @@ -381,7 +381,7 @@ }, { "persist-mode": "auto", - "func": "brightics.function.statistics$one_sample_ttest", + "func": "brightics.function.statistics$one_sample_ttest84543", "name": "brightics.function.statistics$one_sample_ttest", "param": { "alternatives": [ diff --git a/function/python/brightics/function/statistics/meta/statistic_derivation.json b/function/python/brightics/function/statistics/meta/statistic_derivation.json index 79c5bb607..179dd0a5e 100644 --- a/function/python/brightics/function/statistics/meta/statistic_derivation.json +++ b/function/python/brightics/function/statistics/meta/statistic_derivation.json @@ -81,17 +81,17 @@ "default": true }, { - "label": "Variance", + "label": "Variance of Population", "value": "variance", "default": false }, { - "label": "Standard Deviation", + "label": "Standard Deviation of Population", "value": "stddev", "default": false }, { - "label": "Skewness", + "label": "Skewness of Sample", "value": "skewness", "default": false }, @@ -101,12 +101,12 @@ "default": false }, { - "label": "Number of row", + "label": "Number of Row", "value": "nrow", "default": false }, { - "label": "Number of value", + "label": "Number of Value", "value": "num_of_value", "default": false }, @@ -164,7 +164,7 @@ "items": [], "columnType": [], "type": "Double", - "placeHolder": "50 (0 <= value <= 100)", + "placeHolder": "(0 <= value <= 100)", "min": 0, "max": 100 }, @@ -179,7 +179,7 @@ "items": [], "columnType": [], "type": "Double", - "placeHolder": "0.1 (0.0 <= value < 0.5)", + "placeHolder": "(0.0 <= value < 0.5)", "min": 0, "max": 0.5 }, diff --git a/function/python/brightics/function/statistics/meta/statistic_summary.json b/function/python/brightics/function/statistics/meta/statistic_summary.json index 704cc8027..5b0824c0e 100644 --- a/function/python/brightics/function/statistics/meta/statistic_summary.json +++ b/function/python/brightics/function/statistics/meta/statistic_summary.json @@ -81,17 +81,17 @@ "default": true }, { - "label": "Variance", + "label": "Variance of Population", "value": "variance", "default": false }, { - "label": "Standard Deviation", + "label": "Standard Deviation of Population", "value": "stddev", "default": false }, { - "label": "Skewness", + "label": "Skewness of Sample", "value": "skewness", "default": false }, @@ -101,12 +101,12 @@ "default": false }, { - "label": "Number of row", + "label": "Number of Row", "value": "nrow", "default": false }, { - "label": "Number of value", + "label": "Number of Value", "value": "num_of_value", "default": false }, @@ -164,7 +164,7 @@ "items": [], "columnType": [], "type": "Double", - "placeHolder": "50 (0 <= value <= 100)", + "placeHolder": "(0 <= value <= 100)", "min": 0, "max": 100 }, @@ -179,7 +179,7 @@ "items": [], "columnType": [], "type": "Double", - "placeHolder": "0.1 (0.0 <= value < 0.5)", + "placeHolder": "(0.0 <= value < 0.5)", "min": 0, "max": 0.5 }, diff --git a/function/python/brightics/function/statistics/meta/wilcoxon_test.json b/function/python/brightics/function/statistics/meta/wilcoxon_test.json index 22cd5c723..acdf576cf 100644 --- a/function/python/brightics/function/statistics/meta/wilcoxon_test.json +++ b/function/python/brightics/function/statistics/meta/wilcoxon_test.json @@ -4,6 +4,7 @@ "content": "" }, "specJson": { + "deprecated": true, "category": "statistics", "func": "brightics.function.statistics$wilcoxon_test", "name": "brightics.function.statistics$wilcoxon_test", diff --git a/function/python/brightics/function/statistics/meta/wilcoxon_test2.json b/function/python/brightics/function/statistics/meta/wilcoxon_test2.json new file mode 100644 index 000000000..eefad7252 --- /dev/null +++ b/function/python/brightics/function/statistics/meta/wilcoxon_test2.json @@ -0,0 +1,711 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "statistics", + "func": "brightics.function.statistics$wilcoxon_test2", + "name": "brightics.function.statistics$wilcoxon_test2", + "context": "python", + "label": "Wilcoxon Test", + "description": "The Wilcoxon test(Wilcoxon signed-rank test) is a non-parametric statistical hypothesis test used to compare two related samples, matched samples, or repeated measurements on a single sample to assess whether their population mean ranks differ. It can be used as an alternative to the paired Student's t-test when the distribution of the differences between the two samples cannot be assumed to be normally distributed. This test is a nonparametric test that can be used to determine whether two dependent samples were selected from populations having the same distribution.\n\nReference\n+ ", + "tags": [ + "statistics" + ], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "result": "" + }, + "meta": { + "table": { + "type": "table", + "desc": "Data in a Table.", + "optional": false + }, + "result": { + "type": "model", + "desc": "It shows the result of Wilcoxon Test." + } + }, + "params": [ + { + "id": "first_col", + "label": "First Column", + "description": "Column to select as the first column.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "columnType": [ + "Integer", + "Long", + "Float", + "Double" + ], + "multiple": false + }, + { + "id": "second_col", + "label": "Second Column", + "description": "Column to select as the second column.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "columnType": [ + "Integer", + "Long", + "Float", + "Double" + ], + "multiple": false + }, + { + "id": "zero_method", + "label": "Zero Method", + "description": "Choose a method of handling ties at zero. `pratt` includes zero-differences in the ranking process, but drops the ranks of the zeros. `wilcox` discards all zero-differences, the default. `zsplit` includes zero-differences in the ranking process and split the zero rank between positive and negative ones.", + "visibleOption": [], + "control": "RadioButton", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "pratt", + "value": "pratt", + "default": false + }, + { + "label": "wilcox", + "value": "wilcox", + "default": true + }, + { + "label": "zsplit", + "value": "zsplit", + "default": false + } + ], + "columnType": [], + "targetTable": [] + }, + { + "id": "correction", + "label": "Correction", + "description": "If `True`, apply continuity correction by adjusting the Wilcoxon rank statistic by 0.5 towards the mean value when computing the z-statistic.", + "visibleOption": [], + "control": "BooleanRadio", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "targetTable": [], + "defaultValue": false + }, + { + "id": "group_by", + "label": "Group By", + "description": "Columns to group by.\n", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "multiple": true + } + ], + "summary": "This function is for Wilcoxon Test in statistics." + }, + "md": "# Wilcoxon Test\r\nThis function is for Wilcoxon Test in statistics.\r\n\r\n## Description\r\nThe Wilcoxon test(Wilcoxon signed-rank test) is a non-parametric statistical hypothesis test used to compare two related samples, matched samples, or repeated measurements on a single sample to assess whether their population mean ranks differ. It can be used as an alternative to the paired Student's t-test when the distribution of the differences between the two samples cannot be assumed to be normally distributed. This test is a nonparametric test that can be used to determine whether two dependent samples were selected from populations having the same distribution.\r\n\r\nReference\r\n+ \r\n\r\n\r\n\r\n## Properties\r\n### VA\r\n#### INPUT\r\n1. **table***: (Table) Data in a Table.\r\n#### OUTPUT\r\n1. **result**: (Model) It shows the result of Wilcoxon Test.\r\n#### PARAMETER\r\n1. **Response Column***: Column to select as response.\r\n\r\n2. **Factor Column***: Column to select as factor.\r\n\r\n3. **Zero Method**: Choose a method of handling ties at zero. `pratt` includes zero-differences in the ranking process, but drops the ranks of the zeros. `wilcox` discards all zero-differences, the default. `zsplit` includes zero-differences in the ranking process and split the zero rank between positive and negative ones.\r\n * Available Options : `pratt`, `wilcox`, `zsplit` \r\n4. **Correction**: If `True`, apply continuity correction by adjusting the Wilcoxon rank statistic by 0.5 towards the mean value when computing the z-statistic.\r\n\t* Available Options : `True`, `False`\r\n5. **Group By**: Columns to group by.\r\n\r\n\r\n\r\n### Python\r\n#### USAGE\r\n\r\n```\r\nwilcoxon_test2(table, response_col, factor_col, zero_method='wilcox', correction=False, group_by=None)\r\n```\r\n\r\n#### INPUT\r\n1. **table***: (Table) Data in a Table.\r\n#### OUTPUT\r\n1. **result**: (Model) It shows the result of Wilcoxon Test.\r\n#### PARAMETER\r\n1. **response_col***: Column to select as response.\r\n\t* Type: *str*\r\n2. **factor_col***: Column to select as factor.\r\n\t* Type: *str*\r\n3. **zero_method**: Choose a method of handling ties at zero. `pratt` includes zero-differences in the ranking process, but drops the ranks of the zeros. `wilcox` discards all zero-differences, the default. `zsplit` includes zero-differences in the ranking process and split the zero rank between positive and negative ones.\r\n\t* Type: *str*\r\n\t* Default / Range: wilcox ( prattwilcox | wilcox | zsplit )\r\n4. **correction**: If `True`, apply continuity correction by adjusting the Wilcoxon rank statistic by 0.5 towards the mean value when computing the z-statistic.\r\n - Type: _bool_\r\n\t* Default / Range: False\r\n5. **group_by**: Columns to group by.\r\n - Type : _list_[_str_]\r\n\r\n\r\n## Example\r\n### VA\r\n\r\n\r\n\r\n**[Sample Model]**\r\n\r\n\r\n\r\n\r\n\r\n
In this tutorial workflow, sample_iris data is used for Wilcoxon Test. _sepal\\_length_ is used as `Response Column` and _species_ is used as `Factor Column`. The parameter settings used in the function are shown below. \r\n\r\n++Parametersres++\r\n1. **Response Column*** : sepal_length\r\n2. **Factor Column*** : species\r\n3. **Zero Method** : wilcox\r\n4. **Crrection** : False\r\n5. **Group By** : None\r\n\r\n### Python\r\n```\r\nfrom brightics.function.statistics import wilcoxon_test2\r\ninput_table = inputs[0]\r\nresult = wilcoxon_test2(table=input_table, \r\n response_col='sepal_length',\r\n factor_col='species', \r\n zero_method='wilcox',\r\n correction=False, \r\n group_by=None)\r\noutput = result['result']\r\n```\r\n\r\n
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", + "description": "In this tutorial workflow, sample_iris data is used for Wilcoxon Test. sepal_length is used as `First Column` and sepal_width is used as `Second Column`. The parameter settings used in the function are shown below.", + "parameter": "1. **First Column***: sepal_length\n2. **Second Column***: sepal_width\n3. **Zero Method**: wilcox\n4. **Correction**: false\n5. **Group By**: None\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.statistics import wilcoxon_test2\r\ninput_table = inputs[0]\r\nresult = wilcoxon_test2(table=input_table, \r\n first_col='sepal_length',\r\n second_col='sepal_width', \r\n zero_method='wilcox',\r\n correction=False, \r\n group_by=None)\r\noutput = result['result']", + "description": "In this tutorial workflow, sample_iris data is used for Wilcoxon Test. sepal_length is used as `First Column` and sepal_width is used as `Second Column`.", + "context": "python" + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/statistics/wilcoxon_test2.py b/function/python/brightics/function/statistics/wilcoxon_test2.py new file mode 100644 index 000000000..89b5b2290 --- /dev/null +++ b/function/python/brightics/function/statistics/wilcoxon_test2.py @@ -0,0 +1,64 @@ +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +from brightics.common.repr import BrtcReprBuilder, strip_margin, plt2MD, \ + pandasDF2MD, keyValues2MD +from brightics.common.groupby import _function_by_group +from brightics.common.utils import check_required_parameters +from brightics.common.utils import get_default_from_parameters_if_required +from brightics.common.validation import validate +from brightics.common.validation import from_under +from brightics.common.exception import BrighticsFunctionException as BFE + +import pandas as pd +import numpy as np +from scipy.stats import wilcoxon +import itertools + + +def wilcoxon_test2(table, group_by=None, **params): + check_required_parameters(_wilcoxon_test2, params, ['table']) + + params = get_default_from_parameters_if_required(params, _wilcoxon_test2) + + if group_by is not None: + return _function_by_group(_wilcoxon_test2, table, group_by=group_by, **params) + else: + return _wilcoxon_test2(table, **params) + + +def _wilcoxon_test2(table, first_col, second_col, zero_method='wilcox', correction=False): + result = dict() + rb = BrtcReprBuilder() + rb.addMD("""## Wilcoxon Test Result""") + + alter_hypothesis = [] + stats = [] + pvals = [] + + stat, pval = wilcoxon(x=table[first_col], y=table[second_col], zero_method=zero_method, correction=correction) + alter_hypothesis.append('Median of the differences != 0') + stats.append(stat) + pvals.append(pval) + + result_table = pd.DataFrame({'Alternative hypothesis': alter_hypothesis, 'Sum of differences ranks': stats, 'P-value': pvals}) + + rb.addMD(strip_margin(""" + | {table} + """.format(table=pandasDF2MD(result_table)))) + result['_repr_brtc_'] = rb.get() + + return {'result': result} diff --git a/function/python/brightics/function/textanalytics/__init__.py b/function/python/brightics/function/textanalytics/__init__.py index 5198cba64..fdc9487c3 100644 --- a/function/python/brightics/function/textanalytics/__init__.py +++ b/function/python/brightics/function/textanalytics/__init__.py @@ -19,10 +19,18 @@ from .lda import lda from .lda import lda2 from .lda import lda3 +from .lda import lda4 +from .topic_name_extraction import topic_name_extraction +from .gsdmm import gsdmm +from .dtm import dtm +from .dtm import dim from .tfidf import tfidf from .tfidf import tfidf2 +from .tfidf import tfidf3 from .tokenizer import tokenizer_eng from .tokenizer import tokenizer_kor +from .tokenizer2 import tokenizer_eng2 +from .tokenizer2 import tokenizer_kor2 from .bow import bow from .doc_doc_mtx import doc_doc_mtx from .doc_term_mtx import doc_term_mtx @@ -35,12 +43,21 @@ from .word2vec import word2vec_similarity from .word2vec import word2vec_similarity2 from .word2vec import word2vec_model +from .fasttext import fasttext +from .fasttext import fasttext_similarity +from .fasttext import fasttext_model from .doc2vec import doc2vec from .doc2vec import doc2vec_model from .doc_summarize import doc_summarizer_kor from .doc_summarize import doc_summarizer_eng from .extract_senti_words import extract_senti_words from .split_sentences import split_sentences +from .split_sentences import split_sentences2 from .search import search from .search import search_user_dict from .search import search2 +from .regex import regex +from .ner import ner_eng +from .ner import ner_kor +from .ner import ner_crf_train +from .ner import ner_crf_predict \ No newline at end of file diff --git a/function/python/brightics/function/textanalytics/data/english_ner_model.pickle b/function/python/brightics/function/textanalytics/data/english_ner_model.pickle new file mode 100644 index 000000000..5524821e6 Binary files /dev/null and b/function/python/brightics/function/textanalytics/data/english_ner_model.pickle differ diff --git a/function/python/brightics/function/textanalytics/data/korean_ner_model.pickle b/function/python/brightics/function/textanalytics/data/korean_ner_model.pickle new file mode 100644 index 000000000..4cd9d2e52 Binary files /dev/null and b/function/python/brightics/function/textanalytics/data/korean_ner_model.pickle differ diff --git a/function/python/brightics/function/textanalytics/data/regex_format_dict.py b/function/python/brightics/function/textanalytics/data/regex_format_dict.py new file mode 100644 index 000000000..cb2c9d285 --- /dev/null +++ b/function/python/brightics/function/textanalytics/data/regex_format_dict.py @@ -0,0 +1,41 @@ +IPV4_SEG = r'(?:25[0-5]|2[0-4]\d|1\d{2}|[1-9]?\d)' +DATE_SEP = r'(?:-|/| |\.|\. )' +DATE_SEP_US = r'(?:-|/| |\.|\. |,|, )' +YEAR = r'(?:\d{4}|\d{2})' +MONTH = r'(?:1[0-2]|0?[1-9]|Jan|January|Feb|February|Mar|March|Apr|April|May|Jun|June|Jul|July|Aug|August|Sep|Septermber|Oct|October|Nov|November|Dec|December)' +MONTH_NUM = r'(?:1[0-2]|0?[1-9])' +MONTH_NUM_2DIGIT = r'(?:1[0-2]|0[1-9])' +DAY = r'(?:3[01]|[12]\d|0?[1-9])' +DAY_2DIGIT = r'(?:3[01]|[12]\d|0[1-9])' +YEAR_SEP_KOR = r'[년年] ?' +MONTH_SEP_KOR = r'[월月] ?' +DAY_SEP_KOR = r'[일日]' +DATE_PATTERNS = [YEAR + DATE_SEP + MONTH + DATE_SEP + DAY, + YEAR + YEAR_SEP_KOR + MONTH_NUM + MONTH_SEP_KOR + DAY + DAY_SEP_KOR, + YEAR + YEAR_SEP_KOR + MONTH_NUM + MONTH_SEP_KOR, + MONTH_NUM + MONTH_SEP_KOR + DAY + DAY_SEP_KOR, + MONTH + DATE_SEP_US + DAY + DATE_SEP_US + YEAR, + DAY + DATE_SEP_US + MONTH + DATE_SEP_US + YEAR, + YEAR + MONTH_NUM_2DIGIT + DAY_2DIGIT, + MONTH_NUM_2DIGIT + DAY_2DIGIT + YEAR, + DAY_2DIGIT + MONTH_NUM_2DIGIT + YEAR] + +pattern_dict = {'alphabet': r'[a-zA-Z]+', + 'hangul': r'[ㄱ-ㅣ가-힣]+', + 'numeric': r'\d+', + 'emoji': 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+ 'quotation_mark': r'[\'\"]', + 'special_character': r'[^\w\s]', + 'blank': r'\s', + 'e_mail': r'[\w-]+(?:\.[\w-]+)*@[a-zA-Z0-9-]+(?:\.[a-zA-Z0-9-]+)*', + 'url': r'https?://[a-zA-Z0-9.=?/&%$-_]+|www\.[a-zA-Z0-9.=?/&%$-_]+', + 'html': r'<[^>]*>', + 'rt': r'(?:rt|RT) @[\w]*: |@[a-zA-Z0-9.=?/-_]+', + 'cell_phone_number': r'010-\d{4}-\d{4}', + 'phone_number': r'\d{2,3}-\d{3,4}-\d{4}', + 'zip_code': r'\d{5}', + 'hashtag': r'#[a-zA-Z0-9_ㄱ-ㅣ가-힣]+', + 'ipv4': IPV4_SEG + r'(?:\.' + IPV4_SEG + '){3}', + 'chinese': r'[⺀-⺙⺛-⻳⼀-⿕々〇〡-〩〸-〺〻㐀-䶵一-鿃豈-鶴侮-頻並-龎]+', + 'japanese': r'[、-ヾ㌀-㍰㍻-㍿㋀-㋋㏠-㏾㋐-㋿｡-ﾝ]+', + 'date': r'|'.join(DATE_PATTERNS)} diff --git a/function/python/brightics/function/textanalytics/data/word2vec_questions_words.txt b/function/python/brightics/function/textanalytics/data/word2vec_questions_words.txt new file mode 100644 index 000000000..bd2963b2e --- /dev/null +++ b/function/python/brightics/function/textanalytics/data/word2vec_questions_words.txt @@ -0,0 +1,19558 @@ +: capital-common-countries +Athens Greece Baghdad Iraq +Athens Greece Bangkok Thailand +Athens Greece Beijing China +Athens Greece Berlin Germany +Athens Greece Bern Switzerland +Athens Greece Cairo Egypt +Athens Greece Canberra Australia +Athens Greece Hanoi Vietnam +Athens Greece Havana Cuba +Athens Greece Helsinki Finland +Athens Greece Islamabad Pakistan +Athens Greece Kabul Afghanistan +Athens Greece London England +Athens Greece Madrid Spain +Athens Greece Moscow Russia +Athens Greece Oslo Norway +Athens Greece Ottawa Canada +Athens Greece Paris France +Athens Greece Rome Italy +Athens Greece Stockholm Sweden +Athens Greece Tehran Iran +Athens Greece Tokyo Japan +Baghdad Iraq Bangkok Thailand +Baghdad Iraq Beijing China +Baghdad Iraq Berlin Germany +Baghdad Iraq Bern Switzerland +Baghdad Iraq Cairo Egypt +Baghdad Iraq Canberra Australia +Baghdad Iraq Hanoi Vietnam +Baghdad Iraq Havana Cuba +Baghdad Iraq Helsinki Finland +Baghdad Iraq Islamabad Pakistan +Baghdad Iraq Kabul 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Thailand Gaborone Botswana +Bangkok Thailand Georgetown Guyana +Bangkok Thailand Hanoi Vietnam +Bangkok Thailand Harare Zimbabwe +Bangkok Thailand Havana Cuba +Bangkok Thailand Helsinki Finland +Bangkok Thailand Islamabad Pakistan +Bangkok Thailand Jakarta Indonesia +Bangkok Thailand Kabul Afghanistan +Bangkok Thailand Kampala Uganda +Bangkok Thailand Kathmandu Nepal +Bangkok Thailand Khartoum Sudan +Bangkok Thailand Kiev Ukraine +Banjul Gambia Beijing China +Banjul Gambia Beirut Lebanon +Banjul Gambia Belgrade Serbia +Banjul Gambia Belmopan Belize +Banjul Gambia Berlin Germany +Banjul Gambia Bern Switzerland +Banjul Gambia Bishkek Kyrgyzstan +Banjul Gambia Bratislava Slovakia +Banjul Gambia Brussels Belgium +Banjul Gambia Bucharest Romania +Banjul Gambia Budapest Hungary +Banjul Gambia Bujumbura Burundi +Banjul Gambia Cairo Egypt +Banjul Gambia Canberra Australia +Banjul Gambia Caracas Venezuela +Banjul Gambia Chisinau Moldova +Banjul Gambia Conakry Guinea +Banjul Gambia Copenhagen 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Burundi +Beijing China Cairo Egypt +Beijing China Canberra Australia +Beijing China Caracas Venezuela +Beijing China Chisinau Moldova +Beijing China Conakry Guinea +Beijing China Copenhagen Denmark +Beijing China Dakar Senegal +Beijing China Damascus Syria +Beijing China Dhaka Bangladesh +Beijing China Doha Qatar +Beijing China Dublin Ireland +Beijing China Dushanbe Tajikistan +Beijing China Funafuti Tuvalu +Beijing China Gaborone Botswana +Beijing China Georgetown Guyana +Beijing China Hanoi Vietnam +Beijing China Harare Zimbabwe +Beijing China Havana Cuba +Beijing China Helsinki Finland +Beijing China Islamabad Pakistan +Beijing China Jakarta Indonesia +Beijing China Kabul Afghanistan +Beijing China Kampala Uganda +Beijing China Kathmandu Nepal +Beijing China Khartoum Sudan +Beijing China Kiev Ukraine +Beijing China Kigali Rwanda +Beijing China Kingston Jamaica +Beirut Lebanon Belgrade Serbia +Beirut Lebanon Belmopan Belize +Beirut Lebanon Berlin Germany +Beirut Lebanon Bern Switzerland +Beirut Lebanon Bishkek Kyrgyzstan +Beirut Lebanon Bratislava Slovakia +Beirut Lebanon Brussels Belgium +Beirut Lebanon Bucharest Romania +Beirut Lebanon Budapest Hungary +Beirut Lebanon Bujumbura Burundi +Beirut Lebanon Cairo Egypt +Beirut Lebanon Canberra Australia +Beirut Lebanon Caracas Venezuela +Beirut Lebanon Chisinau Moldova +Beirut Lebanon Conakry Guinea +Beirut Lebanon Copenhagen Denmark +Beirut Lebanon Dakar Senegal +Beirut Lebanon Damascus Syria +Beirut Lebanon Dhaka Bangladesh +Beirut Lebanon Doha Qatar +Beirut Lebanon Dublin Ireland +Beirut Lebanon Dushanbe Tajikistan +Beirut Lebanon Funafuti Tuvalu +Beirut Lebanon Gaborone Botswana +Beirut Lebanon Georgetown Guyana +Beirut Lebanon Hanoi Vietnam +Beirut Lebanon Harare Zimbabwe +Beirut Lebanon Havana Cuba +Beirut Lebanon Helsinki Finland +Beirut Lebanon Islamabad Pakistan +Beirut Lebanon Jakarta Indonesia +Beirut Lebanon Kabul Afghanistan +Beirut Lebanon Kampala Uganda +Beirut Lebanon Kathmandu Nepal +Beirut Lebanon Khartoum Sudan +Beirut Lebanon Kiev Ukraine +Beirut Lebanon Kigali Rwanda +Beirut Lebanon Kingston Jamaica +Beirut Lebanon Libreville Gabon +Belgrade Serbia Belmopan Belize +Belgrade Serbia Berlin Germany +Belgrade Serbia Bern Switzerland +Belgrade Serbia Bishkek Kyrgyzstan +Belgrade Serbia Bratislava Slovakia +Belgrade Serbia Brussels Belgium +Belgrade Serbia Bucharest Romania +Belgrade Serbia Budapest Hungary +Belgrade Serbia Bujumbura Burundi +Belgrade Serbia Cairo Egypt +Belgrade Serbia Canberra Australia +Belgrade Serbia Caracas Venezuela +Belgrade Serbia Chisinau Moldova +Belgrade Serbia Conakry Guinea +Belgrade Serbia Copenhagen Denmark +Belgrade Serbia Dakar Senegal +Belgrade Serbia Damascus Syria +Belgrade Serbia Dhaka Bangladesh +Belgrade Serbia Doha Qatar +Belgrade Serbia Dublin Ireland +Belgrade Serbia Dushanbe Tajikistan +Belgrade Serbia Funafuti Tuvalu +Belgrade Serbia Gaborone Botswana +Belgrade Serbia Georgetown Guyana +Belgrade Serbia Hanoi Vietnam +Belgrade 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Germany Cairo Egypt +Berlin Germany Canberra Australia +Berlin Germany Caracas Venezuela +Berlin Germany Chisinau Moldova +Berlin Germany Conakry Guinea +Berlin Germany Copenhagen Denmark +Berlin Germany Dakar Senegal +Berlin Germany Damascus Syria +Berlin Germany Dhaka Bangladesh +Berlin Germany Doha Qatar +Berlin Germany Dublin Ireland +Berlin Germany Dushanbe Tajikistan +Berlin Germany Funafuti Tuvalu +Berlin Germany Gaborone Botswana +Berlin Germany Georgetown Guyana +Berlin Germany Hanoi Vietnam +Berlin Germany Harare Zimbabwe +Berlin Germany Havana Cuba +Berlin Germany Helsinki Finland +Berlin Germany Islamabad Pakistan +Berlin Germany Jakarta Indonesia +Berlin Germany Kabul Afghanistan +Berlin Germany Kampala Uganda +Berlin Germany Kathmandu Nepal +Berlin Germany Khartoum Sudan +Berlin Germany Kiev Ukraine +Berlin Germany Kigali Rwanda +Berlin Germany Kingston Jamaica +Berlin Germany Libreville Gabon +Berlin Germany Lilongwe Malawi +Berlin Germany Lima Peru +Berlin Germany Lisbon Portugal +Bern Switzerland Bishkek Kyrgyzstan +Bern Switzerland Bratislava Slovakia +Bern Switzerland Brussels Belgium +Bern Switzerland Bucharest Romania +Bern Switzerland Budapest Hungary +Bern Switzerland Bujumbura Burundi +Bern Switzerland Cairo Egypt +Bern Switzerland Canberra Australia +Bern Switzerland Caracas Venezuela +Bern Switzerland Chisinau Moldova +Bern Switzerland Conakry Guinea +Bern Switzerland Copenhagen Denmark +Bern Switzerland Dakar Senegal +Bern Switzerland Damascus Syria +Bern Switzerland Dhaka Bangladesh +Bern Switzerland Doha Qatar +Bern Switzerland Dublin Ireland +Bern Switzerland Dushanbe Tajikistan +Bern Switzerland Funafuti Tuvalu +Bern Switzerland Gaborone Botswana +Bern Switzerland Georgetown Guyana +Bern Switzerland Hanoi Vietnam +Bern Switzerland Harare Zimbabwe +Bern Switzerland Havana Cuba +Bern Switzerland Helsinki Finland +Bern Switzerland Islamabad Pakistan +Bern Switzerland Jakarta Indonesia +Bern Switzerland Kabul Afghanistan +Bern Switzerland Kampala Uganda +Bern Switzerland Kathmandu Nepal +Bern Switzerland Khartoum Sudan +Bern Switzerland Kiev Ukraine +Bern Switzerland Kigali Rwanda +Bern Switzerland Kingston Jamaica +Bern Switzerland Libreville Gabon +Bern Switzerland Lilongwe Malawi +Bern Switzerland Lima Peru +Bern Switzerland Lisbon Portugal +Bern Switzerland Ljubljana Slovenia +Bishkek Kyrgyzstan Bratislava Slovakia +Bishkek Kyrgyzstan Brussels Belgium +Bishkek Kyrgyzstan Bucharest Romania +Bishkek Kyrgyzstan Budapest Hungary +Bishkek Kyrgyzstan Bujumbura Burundi +Bishkek Kyrgyzstan Cairo Egypt +Bishkek Kyrgyzstan Canberra Australia +Bishkek Kyrgyzstan Caracas Venezuela +Bishkek Kyrgyzstan Chisinau Moldova +Bishkek Kyrgyzstan Conakry Guinea +Bishkek Kyrgyzstan Copenhagen Denmark +Bishkek Kyrgyzstan Dakar Senegal +Bishkek Kyrgyzstan Damascus Syria +Bishkek Kyrgyzstan Dhaka Bangladesh +Bishkek Kyrgyzstan Doha Qatar +Bishkek Kyrgyzstan Dublin Ireland +Bishkek Kyrgyzstan Dushanbe Tajikistan +Bishkek Kyrgyzstan Funafuti Tuvalu +Bishkek Kyrgyzstan Gaborone Botswana +Bishkek Kyrgyzstan Georgetown Guyana +Bishkek Kyrgyzstan Hanoi Vietnam +Bishkek Kyrgyzstan Harare Zimbabwe +Bishkek Kyrgyzstan Havana Cuba +Bishkek Kyrgyzstan Helsinki Finland +Bishkek Kyrgyzstan Islamabad Pakistan +Bishkek Kyrgyzstan Jakarta Indonesia +Bishkek Kyrgyzstan Kabul Afghanistan +Bishkek Kyrgyzstan Kampala Uganda +Bishkek Kyrgyzstan Kathmandu Nepal +Bishkek Kyrgyzstan Khartoum Sudan +Bishkek Kyrgyzstan Kiev Ukraine +Bishkek Kyrgyzstan Kigali Rwanda +Bishkek Kyrgyzstan Kingston Jamaica +Bishkek Kyrgyzstan Libreville Gabon +Bishkek Kyrgyzstan Lilongwe Malawi +Bishkek Kyrgyzstan Lima Peru +Bishkek Kyrgyzstan Lisbon Portugal +Bishkek Kyrgyzstan Ljubljana Slovenia +Bishkek Kyrgyzstan London England +Bratislava Slovakia Brussels Belgium +Bratislava Slovakia Bucharest Romania +Bratislava Slovakia Budapest Hungary +Bratislava Slovakia Bujumbura Burundi +Bratislava Slovakia Cairo Egypt +Bratislava Slovakia Canberra Australia +Bratislava Slovakia Caracas Venezuela +Bratislava Slovakia Chisinau Moldova +Bratislava Slovakia Conakry Guinea +Bratislava Slovakia Copenhagen Denmark +Bratislava Slovakia Dakar Senegal +Bratislava Slovakia Damascus Syria +Bratislava Slovakia Dhaka Bangladesh +Bratislava Slovakia Doha Qatar +Bratislava Slovakia Dublin Ireland +Bratislava Slovakia Dushanbe Tajikistan +Bratislava Slovakia Funafuti Tuvalu +Bratislava Slovakia Gaborone Botswana +Bratislava Slovakia Georgetown Guyana +Bratislava Slovakia Hanoi Vietnam +Bratislava Slovakia Harare Zimbabwe +Bratislava Slovakia Havana Cuba +Bratislava Slovakia Helsinki Finland +Bratislava Slovakia Islamabad Pakistan +Bratislava Slovakia Jakarta Indonesia +Bratislava Slovakia Kabul Afghanistan +Bratislava Slovakia Kampala Uganda +Bratislava Slovakia Kathmandu Nepal +Bratislava Slovakia Khartoum Sudan +Bratislava Slovakia Kiev Ukraine +Bratislava Slovakia Kigali Rwanda +Bratislava Slovakia Kingston Jamaica +Bratislava Slovakia 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+Canberra Australia Doha Qatar +Canberra Australia Dublin Ireland +Canberra Australia Dushanbe Tajikistan +Canberra Australia Funafuti Tuvalu +Canberra Australia Gaborone Botswana +Canberra Australia Georgetown Guyana +Canberra Australia Hanoi Vietnam +Canberra Australia Harare Zimbabwe +Canberra Australia Havana Cuba +Canberra Australia Helsinki Finland +Canberra Australia Islamabad Pakistan +Canberra Australia Jakarta Indonesia +Canberra Australia Kabul Afghanistan +Canberra Australia Kampala Uganda +Canberra Australia Kathmandu Nepal +Canberra Australia Khartoum Sudan +Canberra Australia Kiev Ukraine +Canberra Australia Kigali Rwanda +Canberra Australia Kingston Jamaica +Canberra Australia Libreville Gabon +Canberra Australia Lilongwe Malawi +Canberra Australia Lima Peru +Canberra Australia Lisbon Portugal +Canberra Australia Ljubljana Slovenia +Canberra Australia London England +Canberra Australia Luanda Angola +Canberra Australia Lusaka Zambia +Canberra Australia Madrid Spain 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+apartment hotel 3.33 +cup cone 3.17 +horse ox 3.02 +throat nose 2.8 +bone teeth 4.17 +bone elbow 3.78 +bacon bean 1.22 +cup jar 5.13 +proof fact 7.3 +appointment engagement 6.75 +birthday year 1.67 +word clue 2.53 +author creator 8.02 +atom carbon 3.1 +archbishop bishop 7.05 +letter paragraph 4.0 +page paragraph 3.03 +steeple chapel 7.08 +muscle bone 3.65 +muscle tongue 5.0 +boy soldier 2.15 +belly abdomen 8.13 +guy girl 3.33 +bed chair 3.5 +clothes jacket 5.15 +gun knife 3.65 +tin metal 5.63 +bottle container 7.93 +hen turkey 6.13 +meat bread 1.67 +arm bone 3.83 +neck spine 5.32 +apple lemon 4.05 +agony grief 7.63 +assignment task 8.7 +night dawn 2.95 +dinner soup 3.72 +calf bull 4.93 +snow storm 4.8 +nail hand 3.42 +dog horse 2.38 +arm neck 1.58 +ball glove 1.75 +flu fever 6.08 +fee salary 3.72 +nerve brain 3.88 +beast animal 7.83 +dinner chicken 2.85 +girl maid 2.93 +child boy 5.75 +alcohol wine 7.42 +nose mouth 3.73 +street car 2.38 +bell door 2.2 +box hat 1.3 +belief impression 5.95 +bias opinion 5.6 +attention awareness 8.73 +anger mood 4.1 +elegance style 5.72 +beauty age 1.58 +book theme 2.58 +friend mother 2.53 +vitamin iron 5.55 +car factory 2.75 +pact condition 2.45 +chapter choice 0.48 +arithmetic rhythm 2.35 +winner presence 1.08 +belief flower 0.4 +winner goal 3.23 +trick size 0.48 +choice vein 0.98 +hymn conquest 0.68 +endurance band 0.4 +jail choice 1.08 +condition boy 0.48 +flower endurance 0.4 +hole agreement 0.3 +doctor temper 0.48 +fraternity door 0.68 +task woman 0.68 +fraternity baseball 0.88 +cent size 0.4 +presence door 0.48 +mouse management 0.48 +task highway 0.48 +liquor century 0.4 +task straw 0.68 +island task 0.3 +night chapter 0.48 +pollution president 0.68 +gun trick 0.48 +bath trick 0.58 +diet apple 1.18 +cent wife 0.58 +chapter tail 0.3 +course stomach 0.58 +hymn straw 0.4 +dentist colonel 0.4 +wife straw 0.4 +hole wife 0.68 +pupil president 0.78 +bath wife 0.48 +people cent 0.48 +formula log 1.77 +woman fur 0.58 +apple sunshine 0.58 +gun dawn 1.18 +meal waist 0.98 +camera president 0.48 +liquor band 0.68 +stomach vein 2.35 +gun fur 0.3 +couch baseball 0.88 +worker camera 0.68 +deck mouse 0.48 +rice boy 0.4 +people gun 0.68 +cliff tail 0.3 +ankle window 0.3 +princess island 0.3 +container mouse 0.3 +wagon container 2.65 +people balloon 0.48 +dollar people 0.4 +bath balloon 0.4 +stomach bedroom 0.4 +bicycle bedroom 0.4 +log bath 0.4 +bowl tail 0.48 +go come 2.42 +take steal 6.18 +listen hear 8.17 +think rationalize 8.25 +occur happen 9.32 +vanish disappear 9.8 +multiply divide 1.75 +plead beg 9.08 +begin originate 8.2 +protect defend 9.13 +kill destroy 5.9 +create make 8.72 +accept reject 0.83 +ignore avoid 6.87 +carry bring 5.8 +leave enter 0.95 +choose elect 7.62 +lose fail 7.33 +encourage discourage 1.58 +achieve accomplish 8.57 +make construct 8.33 +listen obey 4.93 +inform notify 9.25 +receive give 1.47 +borrow beg 2.62 +take obtain 7.1 +advise recommend 8.1 +imitate portray 6.75 +win succeed 7.9 +think decide 5.13 +greet meet 6.17 +agree argue 0.77 +enjoy entertain 5.92 +destroy make 1.6 +save protect 6.58 +give lend 7.22 +understand know 7.47 +take receive 5.08 +accept acknowledge 6.88 +decide choose 8.87 +accept believe 6.75 +keep possess 8.27 +roam wander 8.83 +succeed fail 0.83 +spend save 0.55 +leave go 7.63 +come attend 8.1 +know believe 5.5 +gather meet 7.3 +make earn 7.62 +forget ignore 3.07 +multiply add 2.7 +shrink grow 0.23 +arrive leave 1.33 +succeed try 3.98 +accept deny 1.75 +arrive come 7.05 +agree differ 1.05 +send receive 1.08 +win dominate 5.68 +add divide 2.3 +kill choke 4.92 +acquire get 8.82 +participate join 7.7 +leave remain 2.53 +go enter 4.0 +take carry 5.23 +forget learn 1.18 +appoint elect 8.17 +engage marry 5.43 +ask pray 3.72 +go send 3.75 +take deliver 4.37 +speak hear 3.02 +analyze evaluate 8.03 +argue rationalize 4.2 +lose keep 1.05 +compare analyze 8.1 +disorganize organize 1.45 +go allow 3.62 +take possess 7.2 +learn listen 3.88 +destroy construct 0.92 +create build 8.48 +steal buy 1.13 +kill hang 4.45 +forget know 0.92 +create imagine 5.13 +do happen 4.23 +win accomplish 7.85 +give deny 1.43 +deserve earn 5.8 +get put 1.98 +locate find 8.73 +appear attend 6.28 +know comprehend 7.63 +pretend imagine 8.47 +satisfy please 7.67 +cherish keep 4.85 +argue differ 5.15 +overcome dominate 6.25 +behave obey 7.3 +cooperate participate 6.43 +achieve try 4.42 +fail discourage 3.33 +begin quit 1.28 +say participate 3.82 +come bring 2.42 +declare announce 9.08 +read comprehend 4.7 +take leave 2.47 +proclaim announce 8.18 +acquire obtain 8.57 +conclude decide 7.75 +please plead 2.98 +argue prove 4.83 +ask plead 6.47 +find disappear 0.77 +inspect examine 8.75 +verify justify 4.08 +assume predict 4.85 +learn evaluate 4.17 +argue justify 5.0 +make become 4.77 +discover originate 4.83 +achieve succeed 7.5 +give put 3.65 +understand listen 4.68 +expand grow 8.27 +borrow sell 1.73 +keep protect 5.4 +explain prove 4.1 +assume pretend 3.72 +agree please 4.13 +forgive forget 3.92 +clarify explain 8.33 +understand forgive 4.87 +remind forget 0.87 +get remain 1.6 +realize discover 7.47 +require inquire 1.82 +ignore ask 1.07 +think inquire 4.77 +reject avoid 4.78 +argue persuade 6.23 +pursue persuade 3.17 +accept forgive 3.73 +do quit 1.17 +investigate examine 8.1 +discuss explain 6.67 +owe lend 2.32 +explore discover 8.48 +complain argue 4.8 +withdraw reject 6.38 +keep borrow 2.25 +beg ask 6.0 +arrange organize 8.27 +reduce shrink 8.02 +speak acknowledge 4.67 +give borrow 2.22 +kill defend 2.63 +disappear shrink 5.8 +deliver carry 3.88 +breathe choke 1.37 +acknowledge notify 5.3 +become seem 2.63 +pretend seem 4.68 +accomplish become 4.0 +contemplate think 8.82 +determine predict 5.8 +please entertain 5.0 +remain retain 5.75 +pretend portray 7.03 +forget retain 0.63 +want choose 4.78 +lose get 0.77 +try think 2.62 +become appear 4.77 +leave ignore 4.42 +accept recommend 2.75 +leave wander 3.57 +keep give 1.05 +give allow 5.15 +bring send 2.97 +absorb learn 5.48 +acquire find 6.38 +leave appear 0.97 +create destroy 0.63 +begin go 7.42 +get buy 5.08 +collect save 6.67 +replace restore 5.73 +join add 8.1 +join marry 5.35 +accept deliver 1.58 +attach join 7.75 +put hang 3.0 +go sell 0.97 +communicate pray 3.55 +give steal 0.5 +add build 4.92 +bring restore 2.62 +comprehend satisfy 2.55 +portray decide 1.18 +organize become 1.77 +give know 0.88 +say verify 4.9 +cooperate join 5.18 +arrange require 0.98 +borrow want 1.77 +investigate pursue 7.15 +ignore explore 0.4 +bring complain 0.98 +enter owe 0.68 +portray notify 0.78 +remind sell 0.4 +absorb possess 5.0 +join acquire 2.85 +send attend 1.67 +gather attend 4.8 +absorb withdraw 2.97 +attend arrive 6.08 diff --git a/function/python/brightics/function/textanalytics/data/word2vec_wordsim353.tsv b/function/python/brightics/function/textanalytics/data/word2vec_wordsim353.tsv new file mode 100644 index 000000000..c63addcfc --- /dev/null +++ b/function/python/brightics/function/textanalytics/data/word2vec_wordsim353.tsv @@ -0,0 +1,355 @@ +# The WordSimilarity-353 Test Collection (http://www.cs.technion.ac.il/~gabr/resources/data/wordsim353/) +# Word 1 Word 2 Human (mean) +love sex 6.77 +tiger cat 7.35 +tiger tiger 10.00 +book paper 7.46 +computer keyboard 7.62 +computer internet 7.58 +plane car 5.77 +train car 6.31 +telephone communication 7.50 +television radio 6.77 +media radio 7.42 +drug abuse 6.85 +bread butter 6.19 +cucumber potato 5.92 +doctor nurse 7.00 +professor doctor 6.62 +student professor 6.81 +smart student 4.62 +smart stupid 5.81 +company stock 7.08 +stock market 8.08 +stock phone 1.62 +stock CD 1.31 +stock jaguar 0.92 +stock egg 1.81 +fertility egg 6.69 +stock live 3.73 +stock life 0.92 +book library 7.46 +bank money 8.12 +wood forest 7.73 +money cash 9.15 +professor cucumber 0.31 +king cabbage 0.23 +king queen 8.58 +king rook 5.92 +bishop rabbi 6.69 +Jerusalem Israel 8.46 +Jerusalem Palestinian 7.65 +holy sex 1.62 +fuck sex 9.44 +Maradona football 8.62 +football soccer 9.03 +football basketball 6.81 +football tennis 6.63 +tennis racket 7.56 +Arafat peace 6.73 +Arafat terror 7.65 +Arafat Jackson 2.50 +law lawyer 8.38 +movie star 7.38 +movie popcorn 6.19 +movie critic 6.73 +movie theater 7.92 +physics proton 8.12 +physics chemistry 7.35 +space chemistry 4.88 +alcohol chemistry 5.54 +vodka gin 8.46 +vodka brandy 8.13 +drink car 3.04 +drink ear 1.31 +drink mouth 5.96 +drink eat 6.87 +baby mother 7.85 +drink mother 2.65 +car automobile 8.94 +gem jewel 8.96 +journey voyage 9.29 +boy lad 8.83 +coast shore 9.10 +asylum madhouse 8.87 +magician wizard 9.02 +midday noon 9.29 +furnace stove 8.79 +food fruit 7.52 +bird cock 7.10 +bird crane 7.38 +tool implement 6.46 +brother monk 6.27 +crane implement 2.69 +lad brother 4.46 +journey car 5.85 +monk oracle 5.00 +cemetery woodland 2.08 +food rooster 4.42 +coast hill 4.38 +forest graveyard 1.85 +shore woodland 3.08 +monk slave 0.92 +coast forest 3.15 +lad wizard 0.92 +chord smile 0.54 +glass magician 2.08 +noon string 0.54 +rooster voyage 0.62 +money dollar 8.42 +money cash 9.08 +money currency 9.04 +money wealth 8.27 +money property 7.57 +money possession 7.29 +money bank 8.50 +money deposit 7.73 +money withdrawal 6.88 +money laundering 5.65 +money operation 3.31 +tiger jaguar 8.00 +tiger feline 8.00 +tiger carnivore 7.08 +tiger mammal 6.85 +tiger animal 7.00 +tiger organism 4.77 +tiger fauna 5.62 +tiger zoo 5.87 +psychology psychiatry 8.08 +psychology anxiety 7.00 +psychology fear 6.85 +psychology depression 7.42 +psychology clinic 6.58 +psychology doctor 6.42 +psychology Freud 8.21 +psychology mind 7.69 +psychology health 7.23 +psychology science 6.71 +psychology discipline 5.58 +psychology cognition 7.48 +planet star 8.45 +planet constellation 8.06 +planet moon 8.08 +planet sun 8.02 +planet galaxy 8.11 +planet space 7.92 +planet astronomer 7.94 +precedent example 5.85 +precedent information 3.85 +precedent cognition 2.81 +precedent law 6.65 +precedent collection 2.50 +precedent group 1.77 +precedent antecedent 6.04 +cup coffee 6.58 +cup tableware 6.85 +cup article 2.40 +cup artifact 2.92 +cup object 3.69 +cup entity 2.15 +cup drink 7.25 +cup food 5.00 +cup substance 1.92 +cup liquid 5.90 +jaguar cat 7.42 +jaguar car 7.27 +energy secretary 1.81 +secretary senate 5.06 +energy laboratory 5.09 +computer laboratory 6.78 +weapon secret 6.06 +FBI fingerprint 6.94 +FBI investigation 8.31 +investigation effort 4.59 +Mars water 2.94 +Mars scientist 5.63 +news report 8.16 +canyon landscape 7.53 +image surface 4.56 +discovery space 6.34 +water seepage 6.56 +sign recess 2.38 +Wednesday news 2.22 +mile kilometer 8.66 +computer news 4.47 +territory surface 5.34 +atmosphere landscape 3.69 +president medal 3.00 +war troops 8.13 +record number 6.31 +skin eye 6.22 +Japanese American 6.50 +theater history 3.91 +volunteer motto 2.56 +prejudice recognition 3.00 +decoration valor 5.63 +century year 7.59 +century nation 3.16 +delay racism 1.19 +delay news 3.31 +minister party 6.63 +peace plan 4.75 +minority peace 3.69 +attempt peace 4.25 +government crisis 6.56 +deployment departure 4.25 +deployment withdrawal 5.88 +energy crisis 5.94 +announcement news 7.56 +announcement effort 2.75 +stroke hospital 7.03 +disability death 5.47 +victim emergency 6.47 +treatment recovery 7.91 +journal association 4.97 +doctor personnel 5.00 +doctor liability 5.19 +liability insurance 7.03 +school center 3.44 +reason hypertension 2.31 +reason criterion 5.91 +hundred percent 7.38 +Harvard Yale 8.13 +hospital infrastructure 4.63 +death row 5.25 +death inmate 5.03 +lawyer evidence 6.69 +life death 7.88 +life term 4.50 +word similarity 4.75 +board recommendation 4.47 +governor interview 3.25 +OPEC country 5.63 +peace atmosphere 3.69 +peace insurance 2.94 +territory kilometer 5.28 +travel activity 5.00 +competition price 6.44 +consumer confidence 4.13 +consumer energy 4.75 +problem airport 2.38 +car flight 4.94 +credit card 8.06 +credit information 5.31 +hotel reservation 8.03 +grocery money 5.94 +registration arrangement 6.00 +arrangement accommodation 5.41 +month hotel 1.81 +type kind 8.97 +arrival hotel 6.00 +bed closet 6.72 +closet clothes 8.00 +situation conclusion 4.81 +situation isolation 3.88 +impartiality interest 5.16 +direction combination 2.25 +street place 6.44 +street avenue 8.88 +street block 6.88 +street children 4.94 +listing proximity 2.56 +listing category 6.38 +cell phone 7.81 +production hike 1.75 +benchmark index 4.25 +media trading 3.88 +media gain 2.88 +dividend payment 7.63 +dividend calculation 6.48 +calculation computation 8.44 +currency market 7.50 +OPEC oil 8.59 +oil stock 6.34 +announcement production 3.38 +announcement warning 6.00 +profit warning 3.88 +profit loss 7.63 +dollar yen 7.78 +dollar buck 9.22 +dollar profit 7.38 +dollar loss 6.09 +computer software 8.50 +network hardware 8.31 +phone equipment 7.13 +equipment maker 5.91 +luxury car 6.47 +five month 3.38 +report gain 3.63 +investor earning 7.13 +liquid water 7.89 +baseball season 5.97 +game victory 7.03 +game team 7.69 +marathon sprint 7.47 +game series 6.19 +game defeat 6.97 +seven series 3.56 +seafood sea 7.47 +seafood food 8.34 +seafood lobster 8.70 +lobster food 7.81 +lobster wine 5.70 +food preparation 6.22 +video archive 6.34 +start year 4.06 +start match 4.47 +game round 5.97 +boxing round 7.61 +championship tournament 8.36 +fighting defeating 7.41 +line insurance 2.69 +day summer 3.94 +summer drought 7.16 +summer nature 5.63 +day dawn 7.53 +nature environment 8.31 +environment ecology 8.81 +nature man 6.25 +man woman 8.30 +man governor 5.25 +murder manslaughter 8.53 +soap opera 7.94 +opera performance 6.88 +life lesson 5.94 +focus life 4.06 +production crew 6.25 +television film 7.72 +lover quarrel 6.19 +viewer serial 2.97 +possibility girl 1.94 +population development 3.75 +morality importance 3.31 +morality marriage 3.69 +Mexico Brazil 7.44 +gender equality 6.41 +change attitude 5.44 +family planning 6.25 +opera industry 2.63 +sugar approach 0.88 +practice institution 3.19 +ministry culture 4.69 +problem challenge 6.75 +size prominence 5.31 +country citizen 7.31 +planet people 5.75 +development issue 3.97 +experience music 3.47 +music project 3.63 +glass metal 5.56 +aluminum metal 7.83 +chance credibility 3.88 +exhibit memorabilia 5.31 +concert virtuoso 6.81 +rock jazz 7.59 +museum theater 7.19 +observation architecture 4.38 +space world 6.53 +preservation world 6.19 +admission ticket 7.69 +shower thunderstorm 6.31 +shower flood 6.03 +weather forecast 8.34 +disaster area 6.25 +governor office 6.34 +architecture century 3.78 diff --git a/function/python/brightics/function/textanalytics/doc2vec.py b/function/python/brightics/function/textanalytics/doc2vec.py index f0b7e9ee1..590bdfb42 100644 --- a/function/python/brightics/function/textanalytics/doc2vec.py +++ b/function/python/brightics/function/textanalytics/doc2vec.py @@ -17,15 +17,18 @@ from random import randint import pandas as pd from gensim.models.doc2vec import Doc2Vec, TaggedDocument +import matplotlib.pyplot as plt +from sklearn.manifold import TSNE from brightics.common.utils import check_required_parameters from brightics.common.utils import get_default_from_parameters_if_required from brightics.common.validation import validate from brightics.common.validation import greater_than_or_equal_to -from brightics.common.repr import BrtcReprBuilder +from brightics.common.repr import BrtcReprBuilder from brightics.common.repr import strip_margin from brightics.function.utils import _model_dict from brightics.common.repr import dict2MD +from brightics.common.repr import plt2MD def doc2vec(table, **params): @@ -37,7 +40,8 @@ def doc2vec(table, **params): greater_than_or_equal_to(params, 1, 'min_count'), greater_than_or_equal_to(params, 1, 'train_epoch'), greater_than_or_equal_to(params, 1, 'workers'), - greater_than_or_equal_to(params, 1, 'negative')] + greater_than_or_equal_to(params, 1, 'negative'), + greater_than_or_equal_to(params, 1, 'topn')] validate(*param_validation_check) return _doc2vec(table, **params) @@ -45,8 +49,7 @@ def doc2vec(table, **params): def _doc2vec(table, input_col, dm=1, vector_size=100, window=10, min_count=1, max_vocab_size=None, train_epoch=100, workers=1, alpha=0.025, min_alpha=0.025, seed=None, hs=1, negative=5, ns_exponent=0.75, - hashfxn=hash): - + topn=30, hashfxn=hash): if seed is None: random_state = seed seed = randint(0, 0xffffffff) @@ -94,13 +97,44 @@ def _doc2vec(table, input_col, dm=1, vector_size=100, window=10, min_count=1, 'Negative': negative, 'Negative sampling exponent': ns_exponent} + # tsne visualization + length = len(vocab) + if length < topn: + topn = length + topn_words = sorted(vocab, key=vocab.get, reverse=True)[:topn] + + X = d2v[topn_words] + tsne = TSNE(n_components=min(2, topn), random_state=seed) + X_tsne = tsne.fit_transform(X) + df = pd.DataFrame(X_tsne, index=topn_words, columns=['x', 'y']) + + fig = plt.figure() + fig.set_size_inches(50, 40) + ax = fig.add_subplot(1, 1, 1) + + ax.scatter(df['x'], df['y'], s=1000) + ax.tick_params(axis='both', which='major', labelsize=50) + + for word, pos in df.iterrows(): + ax.annotate(word, pos, fontsize=80) + plt.show() + fig = plt2MD(plt) + plt.clf() + rb = BrtcReprBuilder() rb.addMD(strip_margin(""" | ## Doc2Vec Result | + | ### Total Number of words + | {length} + | + | ### Top {topn} Words + | {topn_words} + | {fig} + | | ### Parameters | {params} - """.format(params=dict2MD(params)))) + """.format(length=length, topn=topn, topn_words=topn_words, fig=fig, params=dict2MD(params)))) model = _model_dict('doc2vec_model') model['params'] = params diff --git a/function/python/brightics/function/textanalytics/doc_summarize.py b/function/python/brightics/function/textanalytics/doc_summarize.py index 612386b57..ce142564f 100644 --- a/function/python/brightics/function/textanalytics/doc_summarize.py +++ b/function/python/brightics/function/textanalytics/doc_summarize.py @@ -18,6 +18,7 @@ from brightics.common.utils import get_default_from_parameters_if_required from brightics.common.validation import validate from brightics.common.validation import greater_than, greater_than_or_equal_to, over_under +from brightics.common.validation import raise_runtime_error import pandas as pd import numpy as np from nltk import tokenize @@ -73,16 +74,16 @@ def _tokenize_for_summarize(text): import kss splitted_array = kss.split_sentences(text) else: - from brightics.function.textanalytics.pykss import pykss - splitted_array = pykss.split_sentences(text) + from . import split_sentences_kss as kss2 + splitted_array = kss2.kss.pykss.split_sentences(text) # Tokenizer tokenized_table = _tokenizer_kor(texts=splitted_array, pos_extraction=['Noun']) len_doc = len(tokenized_table) - + # Tokenizer afterprocess tokenized_sentence = ([' '.join(tokenized_table[i]) for i in range(len_doc)]) - + return tokenized_sentence, splitted_array, len_doc @@ -101,7 +102,7 @@ def _text_summarizer(text, ratio, num_sentence, damping_factor): # Documents correlation matrix tokenized_sentence, splitted_array, len_doc = _tokenize_for_summarize(text=text) - if (ratio != 2): + if ratio is not None: num_sentence = np.maximum(int(len_doc * ratio), 1) else: num_sentence = np.minimum(len_doc, num_sentence) @@ -117,16 +118,18 @@ def _text_summarizer(text, ratio, num_sentence, damping_factor): return summarized_text, result_table, num_sentence -def _doc_summarizer_kor(table, input_col, hold_cols=None, result_type='summarized_document', new_col_name='summarized_document', ratio=2, num_sentence=1, damping_factor=0.85): +def _doc_summarizer_kor(table, input_col, hold_cols=None, result_type='summarized_document', + new_col_name='summarized_document', ratio=None, num_sentence=1, damping_factor=0.85): doc_col = table[input_col].values len_doc_col = len(doc_col) - + if hold_cols is None: - hold_cols = [input_col] - out_table = table[hold_cols] + out_table = table.copy() + else: + out_table = table[hold_cols] - if (result_type == 'summarized_document'): + if result_type == 'summarized_document': summarizer = np.vectorize(_text_summarizer) result_table = summarizer(doc_col, ratio=ratio, num_sentence=num_sentence, damping_factor=damping_factor)[0] out_table[new_col_name] = result_table @@ -142,37 +145,49 @@ def _sum_result(i): return {'out_table': out_table} -def _doc_summarizer_eng(table, input_col, hold_cols=None, result_type='summarized_document', new_col_name='summarized_document', ratio=2, num_sentence=1): +def _doc_summarizer_eng(table, input_col, hold_cols=None, result_type='summarized_document', + new_col_name='summarized_document', ratio=None, num_sentence=1): doc_col = table[input_col].values len_doc_col = len(doc_col) - + if hold_cols is None: - hold_cols = [input_col] - out_table = table[hold_cols] + out_table = table.copy() + else: + out_table = table[hold_cols] table_list = [] for i in range(len_doc_col): - len_doc = len(summarize(doc_col[i], ratio=1, split=True)) - if (ratio != 2): - _num_sentence = np.maximum(int(len_doc * ratio), 1) - _ratio = ratio + try: + len_doc = len(summarize(doc_col[i], ratio=1, split=True)) + except ValueError as e: + if str(e) == "input must have more than one sentence": + summarized_doc = doc_col[i] + summarized_sents = [doc_col[i]] + _num_sentence = 1 + else: + raise else: - _num_sentence = np.minimum(len_doc, num_sentence) - _ratio = (_num_sentence / len_doc if len_doc != 0 else 1) - if (result_type == 'summarized_document'): - summarized_col = [summarize(doc_col[i], ratio=_ratio)] + if ratio is not None: + _num_sentence = np.maximum(int(len_doc * ratio), 1) + _ratio = ratio + else: + _num_sentence = np.minimum(len_doc, num_sentence) + _ratio = (_num_sentence / len_doc if len_doc != 0 else 1) + summarized_doc = summarize(doc_col[i], ratio=_ratio, split=False) + summarized_sents = summarize(doc_col[i], ratio=_ratio, split=True) + + if result_type == 'summarized_document': + summarized_col = [summarized_doc] else: - summarized_sentences = summarize(doc_col[i], ratio=_ratio, split=True) - summarized_col = np.insert(np.transpose([summarized_sentences[0:_num_sentence]]), 0, i + 1, axis=1) - table_list.append(summarized_col) + summarized_col = np.insert(np.transpose([summarized_sents[0:_num_sentence]]), 0, i + 1, axis=1) + table_list.append(summarized_col) result_table = np.concatenate(table_list, axis=0) - if (result_type == 'summarized_document'): + if result_type == 'summarized_document': out_table[new_col_name] = result_table else: out_table = pd.DataFrame(result_table, columns=['doc_id', 'sentence']) out_table['doc_id'] = out_table['doc_id'].astype(int) return {'out_table': out_table} - diff --git a/function/python/brightics/function/textanalytics/dtm.py b/function/python/brightics/function/textanalytics/dtm.py new file mode 100644 index 000000000..2b724011e --- /dev/null +++ b/function/python/brightics/function/textanalytics/dtm.py @@ -0,0 +1,264 @@ +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +from brightics.common.repr import BrtcReprBuilder, strip_margin, pandasDF2MD, dict2MD +from brightics.function.utils import _model_dict +from brightics.common.groupby import _function_by_group +from brightics.common.utils import check_required_parameters +from brightics.common.utils import get_default_from_parameters_if_required +from brightics.common.validation import validate, greater_than_or_equal_to, raise_runtime_error +from brightics.common.exception import BrighticsFunctionException + +import numpy as np +import pandas as pd +import pyLDAvis as plv +from gensim.models.wrappers import DtmModel +from gensim.models.coherencemodel import CoherenceModel +from gensim import corpora +import pathlib +import platform +import os + + +def dtm(table, group_by=None, **params): + check_required_parameters(_dtm, params, ['table']) + params = get_default_from_parameters_if_required(params, _dtm) + param_validation_check = [greater_than_or_equal_to(params, 2, 'num_topic'), + greater_than_or_equal_to(params, 2, 'num_topic_word'), + greater_than_or_equal_to(params, 1, 'max_iter')] + validate(*param_validation_check) + if group_by is not None: + return _function_by_group(_dtm, table, group_by=group_by, **params) + else: + return _dtm(table, **params) + + +def _dtm(table, input_col, topic_name='topic', num_topic=5, num_topic_word=10, max_iter=20, time_slice=None, + coherence='u_mass', vis_time=0, seed=None): + running_os = platform.system() + is_os_64bit = platform.machine().endswith('64') + if running_os == 'Linux': + if is_os_64bit: + dtm_filename = 'dtm-linux64' + else: + dtm_filename = 'dtm-linux32' + elif running_os == 'Windows': + if is_os_64bit: + dtm_filename = 'dtm-win64.exe' + else: + dtm_filename = 'dtm-win32.exe' + else: # Mac + dtm_filename = 'dtm-darwin64' + dtm_path = os.path.join(str(pathlib.Path(__file__).parent.absolute()), 'dtm', dtm_filename) + tokenized_doc = np.array(table[input_col]) + num_doc = len(tokenized_doc) + if time_slice is None: + time_slice = [num_doc] + elif sum(time_slice) != num_doc: + raise_runtime_error("The sum of time slice list does not match the number of documents.") + if vis_time < 0 or vis_time >= len(time_slice): + raise_runtime_error("Invalid time parameter: {}".format(vis_time)) + dictionary = corpora.Dictionary(tokenized_doc) + corpus = [dictionary.doc2bow(text) for text in tokenized_doc] + dtm_params = {"corpus": corpus, + "id2word": dictionary, + "time_slices": time_slice, + "num_topics": num_topic, + "lda_sequence_max_iter": max_iter, + "model": 'dtm'} + if seed is not None: + dtm_params["rng_seed"] = seed + dtm_model = DtmModel(dtm_path, **dtm_params) + + topic_time = [[dtm_model.show_topic(topicid=id, time=t, topn=num_topic_word) for id in range(num_topic)] + for t in range(len(time_slice))] + topic_time = [[["{}: {}".format(tup[1], tup[0]) for tup in topic] for topic in time] for time in topic_time] + timeline = ["{} ({} docs)".format(ind, t) for ind, t in enumerate(time_slice)] + columns = ["topic_{}".format(i + 1) for i in range(num_topic)] + topic_table = pd.DataFrame(topic_time, columns=columns) + topic_table['time'] = timeline + topic_table = topic_table[['time'] + columns] + + prop_arr = dtm_model.gamma_ + out_table = pd.DataFrame.copy(table, deep=True) + if topic_name in table.columns: + raise BrighticsFunctionException.from_errors( + [{'0100': "Existing table contains Topic Column Name. Please choose again."}]) + out_table[topic_name] = [item.argmax() + 1 for item in prop_arr] + out_table['topic_distribution'] = prop_arr.tolist() + + coherence_topic_arr = [dtm_model.dtm_coherence(time) for time in range(len(time_slice))] + if coherence == 'u_mass': + coh_arr = [CoherenceModel(topics=item, dictionary=dictionary, corpus=corpus, coherence='u_mass').get_coherence() + for item in coherence_topic_arr] + else: + coh_arr = [CoherenceModel(topics=item, dictionary=dictionary, corpus=corpus, texts=tokenized_doc, + coherence='c_v').get_coherence() for item in coherence_topic_arr] + + doc_topic, topic_term, doc_lengths, term_frequency, vocab = dtm_model.dtm_vis(corpus, vis_time) + prepared_data = plv.prepare(topic_term, doc_topic, doc_lengths, vocab, term_frequency, sort_topics=False) + html_result = plv.prepared_data_to_html(prepared_data) + + params = {'Input column': input_col, + 'Topic column name': topic_name, + 'Number of topics': num_topic, + 'Number of words for each topic': num_topic_word, + 'Maximum number of iterations': max_iter, + 'Time slice': time_slice, + 'Coherence measure': coherence, + 'Time to visualize': vis_time} + rb = BrtcReprBuilder() + rb.addMD(strip_margin(""" + | ## Dynamic Topic Modeling Result + | ### Summary + | + """)) + rb.addHTML(html_result) + rb.addMD(strip_margin(""" + | ### Coherence for each period + | {coh_arr} + | + | ### Parameters + | {params} + """.format(coh_arr=coh_arr, params=dict2MD(params)))) + + model = _model_dict('dtm_model') + model['params'] = params + model['dtm_model'] = dtm_model + model['coherences'] = coh_arr + model['corpus'] = corpus + model['_repr_brtc_'] = rb.get() + + return {'out_table': out_table, 'topic_table': topic_table, 'model': model} + + +def dim(table, group_by=None, **params): + check_required_parameters(_dim, params, ['table']) + params = get_default_from_parameters_if_required(params, _dim) + param_validation_check = [greater_than_or_equal_to(params, 2, 'num_topic'), + greater_than_or_equal_to(params, 2, 'num_topic_word'), + greater_than_or_equal_to(params, 1, 'max_iter')] + validate(*param_validation_check) + if group_by is not None: + return _function_by_group(_dim, table, group_by=group_by, **params) + else: + return _dim(table, **params) + + +def _dim(table, input_col, topic_name='topic', num_topic=5, num_topic_word=10, max_iter=20, time_slice=None, + coherence='u_mass', vis_time=0, seed=None): + running_os = platform.system() + is_os_64bit = platform.machine().endswith('64') + if running_os == 'Linux': + if is_os_64bit: + dtm_filename = 'dtm-linux64' + else: + dtm_filename = 'dtm-linux32' + elif running_os == 'Windows': + if is_os_64bit: + dtm_filename = 'dtm-win64.exe' + else: + dtm_filename = 'dtm-win32.exe' + else: # Mac + dtm_filename = 'dtm-darwin64' + dtm_path = os.path.join(str(pathlib.Path(__file__).parent.absolute()), 'dtm', dtm_filename) + tokenized_doc = np.array(table[input_col]) + num_doc = len(tokenized_doc) + if time_slice is None: + time_slice = [num_doc] + elif sum(time_slice) != num_doc: + raise_runtime_error("The sum of time slice list does not match the number of documents.") + if vis_time < 0 or vis_time >= len(time_slice): + raise_runtime_error("Invalid time parameter: {}".format(vis_time)) + dictionary = corpora.Dictionary(tokenized_doc) + corpus = [dictionary.doc2bow(text) for text in tokenized_doc] + dim_params = {"corpus": corpus, + "id2word": dictionary, + "time_slices": time_slice, + "num_topics": num_topic, + "lda_sequence_max_iter": max_iter, + "model": 'fixed'} + if seed is not None: + dim_params["rng_seed"] = seed + dtm_model = DtmModel(dtm_path, **dim_params) + + topic_time = [[dtm_model.show_topic(topicid=topic_id, time=t, topn=num_topic_word) for topic_id in range(num_topic)] + for t in range(len(time_slice))] + topic_time = [[["{}: {}".format(tup[1], tup[0]) for tup in topic] for topic in time] for time in topic_time] + timeline = ["{} ({} docs)".format(ind, t) for ind, t in enumerate(time_slice)] + columns = ["topic_{}".format(i + 1) for i in range(num_topic)] + topic_table = pd.DataFrame(topic_time, columns=columns) + topic_table['time'] = timeline + topic_table = topic_table[['time'] + columns] + + prop_arr = dtm_model.gamma_ + out_table = pd.DataFrame.copy(table, deep=True) + if topic_name in table.columns: + raise BrighticsFunctionException.from_errors( + [{'0100': "Existing table contains Topic Column Name. Please choose again."}]) + out_table[topic_name] = [item.argmax() + 1 for item in prop_arr] + out_table['topic_distribution'] = prop_arr.tolist() + + # original influence table: influences_time[time_slice][document_no][topic_no] + influence_arr = np.vstack(dtm_model.influences_time) + influence_table = pd.DataFrame(influence_arr, columns=columns) + time_id = np.concatenate([id * np.ones(duration) for id, duration in enumerate(time_slice)]) + influence_table['time'] = time_id + influence_table = influence_table[['time'] + columns] + + coherence_topic_arr = [dtm_model.dtm_coherence(time) for time in range(len(time_slice))] + if coherence == 'u_mass': + coh_arr = [CoherenceModel(topics=item, dictionary=dictionary, corpus=corpus, coherence='u_mass').get_coherence() + for item in coherence_topic_arr] + else: + coh_arr = [CoherenceModel(topics=item, dictionary=dictionary, corpus=corpus, texts=tokenized_doc, + coherence='c_v').get_coherence() for item in coherence_topic_arr] + + doc_topic, topic_term, doc_lengths, term_frequency, vocab = dtm_model.dtm_vis(corpus, vis_time) + prepared_data = plv.prepare(topic_term, doc_topic, doc_lengths, vocab, term_frequency, sort_topics=False) + html_result = plv.prepared_data_to_html(prepared_data) + + params = {'Input column': input_col, + 'Topic column name': topic_name, + 'Number of topics': num_topic, + 'Number of words for each topic': num_topic_word, + 'Maximum number of iterations': max_iter, + 'Time slice': time_slice, + 'Coherence measure': coherence, + 'Time to visualize': vis_time} + rb = BrtcReprBuilder() + rb.addMD(strip_margin(""" + | ## Document Influence Model Result + | ### Summary + | + """)) + rb.addHTML(html_result) + rb.addMD(strip_margin(""" + | ### Coherence for each period + | {coh_arr} + | + | ### Parameters + | {params} + """.format(coh_arr=coh_arr, params=dict2MD(params)))) + + model = _model_dict('dtm_model') + model['params'] = params + model['dtm_model'] = dtm_model + model['coherences'] = coh_arr + model['corpus'] = corpus + model['_repr_brtc_'] = rb.get() + + return {'out_table': out_table, 'topic_table': topic_table, 'influence_table': influence_table, 'model': model} diff --git a/function/python/brightics/function/textanalytics/dtm/dtm-darwin64 b/function/python/brightics/function/textanalytics/dtm/dtm-darwin64 new file mode 100755 index 000000000..eb02e3048 Binary files /dev/null and b/function/python/brightics/function/textanalytics/dtm/dtm-darwin64 differ diff --git a/function/python/brightics/function/textanalytics/dtm/dtm-linux32 b/function/python/brightics/function/textanalytics/dtm/dtm-linux32 new file mode 100755 index 000000000..22b1b1b73 Binary files /dev/null and b/function/python/brightics/function/textanalytics/dtm/dtm-linux32 differ diff --git a/function/python/brightics/function/textanalytics/dtm/dtm-linux64 b/function/python/brightics/function/textanalytics/dtm/dtm-linux64 new file mode 100755 index 000000000..c27bb92bb Binary files /dev/null and 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b/function/python/brightics/function/textanalytics/extract_senti_words.py @@ -29,8 +29,9 @@ def _extract_senti_words(table, input_col, user_dict=None, hold_cols=None): len_text = len(table) if hold_cols is None: - hold_cols = [input_col] - out_table = table[hold_cols] + out_table = table.copy() + else: + out_table = table[hold_cols] text_data = table[input_col].values basic_dict = dict(pd.read_csv('brightics/function/textanalytics/data/senti_words_basic_dict_kor.csv').values) diff --git a/function/python/brightics/function/textanalytics/fasttext.py b/function/python/brightics/function/textanalytics/fasttext.py new file mode 100644 index 000000000..cf115b08c --- /dev/null +++ b/function/python/brightics/function/textanalytics/fasttext.py @@ -0,0 +1,280 @@ +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +from gensim.models import FastText +import matplotlib.pyplot as plt +from sklearn.manifold import TSNE +import pandas as pd +import numpy as np + +from brightics.common.repr import BrtcReprBuilder +from brightics.common.repr import strip_margin +from brightics.function.utils import _model_dict +from brightics.common.repr import dict2MD +from brightics.common.repr import plt2MD +from brightics.common.repr import pandasDF2MD +from brightics.common.utils import check_required_parameters +from brightics.common.utils import get_default_from_parameters_if_required +from brightics.common.validation import validate +from brightics.common.validation import greater_than_or_equal_to + + +def fasttext(table, **params): + check_required_parameters(_fasttext, params, ['table']) + + params = get_default_from_parameters_if_required(params, _fasttext) + param_validation_check = [greater_than_or_equal_to(params, 1, 'size'), + greater_than_or_equal_to(params, 1, 'window'), + greater_than_or_equal_to(params, 1, 'min_count'), + greater_than_or_equal_to(params, 1, 'train_epoch'), + greater_than_or_equal_to(params, 1, 'workers'), + greater_than_or_equal_to(params, 1, 'negative'), + greater_than_or_equal_to(params, 1, 'topn'), + greater_than_or_equal_to(params, 1, 'min_n'), + greater_than_or_equal_to(params, 1, 'max_n'), + greater_than_or_equal_to(params, 1, 'bucket')] + validate(*param_validation_check) + return _fasttext(table, **params) + + +def _fasttext(table, input_col, sg=1, size=100, window=5, min_count=1, + max_vocab_size=None, train_epoch=100, workers=1, alpha=0.025, + min_alpha=0.025, seed=None, hs=1, negative=5, ns_exponent=0.75, + topn=30, hashfxn=hash, min_n=3, max_n=6, bucket=2000000): + if isinstance(sg, str): + sg = int(sg) + algo = {1: 'Skip-gram', 0: 'CBOW'}[sg] + + tagged_sents = table[input_col].apply(list).tolist() + ft = FastText(sentences=tagged_sents, + sg=sg, + size=size, + window=window, + alpha=alpha, + min_alpha=min_alpha, + seed=seed, + min_count=min_count, + max_vocab_size=max_vocab_size, + workers=workers, + iter=train_epoch, + hs=hs, + negative=negative, + ns_exponent=ns_exponent, + hashfxn=hashfxn, + min_n=min_n, + max_n=max_n, + bucket=bucket) + + ft.init_sims(replace=True) + vocab = ft.wv.vocab + + analogies_score, sections = ft.wv.evaluate_word_analogies( + 'brightics/function/textanalytics/data/word2vec_questions_words.txt') + pearson_1, spearman_1, oov_ratio_1 = ft.wv.evaluate_word_pairs( + 'brightics/function/textanalytics/data/word2vec_wordsim353.tsv') + pearson_2, spearman_2, oov_ratio_2 = ft.wv.evaluate_word_pairs( + 'brightics/function/textanalytics/data/word2vec_simlex999.tsv') + + params = {'Input column': input_col, + 'Training algorithm': algo, + 'Word vector dimensionality': size, + 'Window': window, + 'Minimum word count': min_count, + 'Max vocabulary size': max_vocab_size, + 'Train epoch': train_epoch, + 'Number of workers': workers, + 'Alpha': alpha, + 'Minimum alpha': min_alpha, + 'Seed': seed, + 'Hierarchical softmax': hs, + 'Negative': negative, + 'Negative sampling exponent': ns_exponent} + + # tsne visualization + length = len(vocab) + if length < topn: + topn = length + topn_words = sorted(vocab, key=vocab.get, reverse=True)[:topn] + + X = ft[topn_words] + tsne = TSNE(n_components=min(2, topn), random_state=seed) + X_tsne = tsne.fit_transform(X) + df = pd.DataFrame(X_tsne, index=topn_words, columns=['x', 'y']) + + fig = plt.figure() + fig.set_size_inches(50, 40) + ax = fig.add_subplot(1, 1, 1) + + ax.scatter(df['x'], df['y'], s=1000) + ax.tick_params(axis='both', which='major', labelsize=50) + + for word, pos in df.iterrows(): + ax.annotate(word, pos, fontsize=80) + plt.show() + fig = plt2MD(plt) + plt.clf() + + rb = BrtcReprBuilder() + rb.addMD(strip_margin(""" + | ## FastText Result + | + | ### Total Number of words + | {length} + | + | ### Top {topn} Words + | {topn_words} + | {fig} + | + | ### Word analogy score + | {analogies_score} + | + | ### Word correlation scores + | #### Pearson correlation coefficient with 2-tailed p-value (WordSim353) + | {pearson_1_1}, {pearson_1_2} + | #### Spearman rank-order correlation coefficient with 2-tailed p-value (WordSim353) + | {spearman_1_1}, {spearman_1_2} + | #### The ratio of pairs with unknown words (WordSim353) + | {oov_ratio_1} + | #### Pearson correlation coefficient with 2-tailed p-value (SimLex999) + | {pearson_2_1}, {pearson_2_2} + | #### Spearman rank-order correlation coefficient with 2-tailed p-value (SimLex999) + | {spearman_2_1}, {spearman_2_2} + | #### The ratio of pairs with unknown words (SimLex999) + | {oov_ratio_2} + | + | ### Parameters + | {params} + """.format(length=length, analogies_score=analogies_score, + pearson_1_1=pearson_1[0], pearson_1_2=pearson_1[1], spearman_1_1=spearman_1[0], + spearman_1_2=spearman_1[1], oov_ratio_1=oov_ratio_1, + pearson_2_1=pearson_2[0], pearson_2_2=pearson_2[1], spearman_2_1=spearman_2[0], + spearman_2_2=spearman_2[1], oov_ratio_2=oov_ratio_2, + topn=topn, topn_words=topn_words, params=dict2MD(params), fig=fig))) + + vocab = list(ft.wv.vocab) + + model = _model_dict('fasttext_model') + model['params'] = params + model['vocab'] = vocab + model['ft'] = ft.wv + model['_repr_brtc_'] = rb.get() + + out_table = pd.DataFrame({'words': vocab, 'word_vectors': ft.wv[vocab].tolist()}) + return {'model': model, 'out_table': out_table} + + +def _feature_vec(words, model, num_features): + feature_vector = np.zeros(num_features, dtype="float64") + word_set = set(model.index2word) + num_words = 0 + for word in words: + if word in word_set: + feature_vector = np.add(feature_vector, model[word]) + num_words = num_words + 1. + feature_vector = np.divide(feature_vector, num_words) + return feature_vector + + +def _avg_feature_vecs(docs, model, num_features): + doc_feature_vectors = np.zeros((len(docs), num_features), dtype="float64") + counter = 0. + for doc in docs: + doc_feature_vectors[int(counter)] = _feature_vec(doc, model, num_features) + counter = counter + 1. + return doc_feature_vectors + + +def fasttext_model(table, model, **params): + check_required_parameters(_fasttext_model, params, ['table', 'model']) + return _fasttext_model(table, model, **params) + + +def _fasttext_model(table, model): + doc = table[model['params']['Input column']] + ft_model = model['ft'] + num_features = model['params']['Word vector dimensionality'] + + out_table = table.copy() + out_table['feature_vectors'] = _avg_feature_vecs(doc, ft_model, num_features).tolist() + return {'out_table': out_table} + + +def fasttext_similarity(table, model, **params): + check_required_parameters(_fasttext_similarity, params, ['table', 'model']) + + params = get_default_from_parameters_if_required(params, _fasttext_similarity) + param_validation_check = [greater_than_or_equal_to(params, 1, 'topn')] + validate(*param_validation_check) + return _fasttext_similarity(table, model, **params) + + +def _fasttext_similarity(table, model, positive_col=None, negative_col=None, topn=1): + if positive_col is None and negative_col is None: + raise ValueError('The similarity cannot be computed with no input.') + + if positive_col is None: + columns = [negative_col] + elif negative_col is None: + columns = [positive_col] + else: + columns = [positive_col, negative_col] + + df_dropped = table[columns].dropna(how='all') + ft_model = model['ft'] + + if positive_col is None: + res = df_dropped.apply(lambda _: _ft_sim_neg(ft_model, negative=_[negative_col], topn=topn), axis=1) + elif negative_col is None: + res = df_dropped.apply(lambda _: _ft_sim_pos(ft_model, positive=_[positive_col], topn=topn), axis=1) + else: + res = df_dropped.apply( + lambda _: _ft_sim_both(ft_model, positive=_[positive_col], negative=_[negative_col], topn=topn), axis=1) + + out_table = pd.DataFrame(sum(res, []), columns=columns + ['synonym', 'similarity']) + return {'out_table': out_table} + + +def _split_token(text): + if text is not None: + return [token.strip() for token in text.split(",")] + return None + + +def _ft_sim_pos(model, positive, topn=1): + split_positive = _split_token(positive) + result = model.most_similar(positive=split_positive, topn=topn) + res = [[positive] + list(result[i]) for i in range(len(result))] + return res + + +def _ft_sim_neg(model, negative, topn=1): + split_negative = _split_token(negative) + result = model.most_similar(negative=split_negative, topn=topn) + res = [[negative] + list(result[i]) for i in range(len(result))] + return res + + +def _ft_sim_both(model, positive=None, negative=None, topn=1): + split_positive = _split_token(positive) + split_negative = _split_token(negative) + result = model.most_similar(positive=split_positive, negative=split_negative, topn=topn) + if positive is None: + res = [[None] + [negative] + list(result[i]) for i in range(len(result))] + elif negative is None: + res = [[positive] + [None] + list(result[i]) for i in range(len(result))] + else: + res = [[positive] + [negative] + list(result[i]) for i in range(len(result))] + return res diff --git a/function/python/brightics/function/textanalytics/gsdmm.py b/function/python/brightics/function/textanalytics/gsdmm.py new file mode 100644 index 000000000..ecd4098ed --- /dev/null +++ b/function/python/brightics/function/textanalytics/gsdmm.py @@ -0,0 +1,154 @@ +""" + Copyright 2020 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +from brightics.common.repr import BrtcReprBuilder, strip_margin, pandasDF2MD, dict2MD +from brightics.function.utils import _model_dict +from brightics.common.utils import check_required_parameters +from brightics.common.utils import get_default_from_parameters_if_required +from brightics.common.validation import validate, greater_than_or_equal_to +from brightics.common.exception import BrighticsFunctionException + +from sklearn.preprocessing import normalize +import numpy as np +import pandas as pd +from .short_text_topic_modeling_gsdmm import gsdmm_rwalk +import functools +import pyLDAvis + + +def gsdmm(table, **params): + check_required_parameters(_gsdmm, params, ['table']) + params = get_default_from_parameters_if_required(params, _gsdmm) + param_validation_check = [greater_than_or_equal_to(params, 2, 'K'), + greater_than_or_equal_to(params, 0.0, 'alpha'), + greater_than_or_equal_to(params, 0.0, 'beta'), + greater_than_or_equal_to(params, 1, 'max_iter'), + greater_than_or_equal_to(params, 1, 'num_topic_words')] + + validate(*param_validation_check) + return _gsdmm(table, **params) + + +def _count_to_ratio_raw(word_count): + if not word_count: + return {} + else: + word_count_list = word_count.items() + words = [pair[0] for pair in word_count_list] + counts = [[pair[1]] for pair in word_count_list] + counts_normalized = normalize(counts, norm='l1', axis=0) + word_ratio_raw = {word: ratio[0] for word, ratio in zip(words, counts_normalized)} + return word_ratio_raw + + +def _gen_table(word_ratio_raw, num_topic_words): + if not word_ratio_raw: + return [""] + else: + word_ratio_sorted = sorted(word_ratio_raw.items(), key=lambda item: item[1], reverse=True) + word_ratio = [["{}: {}".format(word, ratio), word, ratio] for word, ratio in word_ratio_sorted] + return np.transpose(word_ratio[:num_topic_words]).tolist() + + +def _gsdmm(table, input_col, topic_name='topic', K=10, alpha=0.1, beta=0.1, max_iter=50, num_topic_words=3): + docs = np.array(table[input_col]) + docs_set = [set(doc) for doc in docs] + docs_preprocessed = [list(doc_set) for doc_set in docs_set] + vocab_set = list(set.union(*docs_set)) + vocab_size = len(vocab_set) + + # initialize and train a GSDMM model + mgp = gsdmm_rwalk.MovieGroupProcess(K=K, alpha=alpha, beta=beta, n_iters=max_iter) + topics = mgp.fit(docs_preprocessed, vocab_size) + + # generate topic table + topic_word_count = mgp.cluster_word_distribution + topic_words_raw = [[ind, _count_to_ratio_raw(word_count)] + for ind, word_count in enumerate(topic_word_count) if word_count] + topic_words = [[item[0]] + _gen_table(item[1], num_topic_words) for item in topic_words_raw] + + # reset topic ids + nonempty_topic_indices = [item[0] for item in topic_words] + reset_topic_ind = {old_ind: (new_ind + 1) for new_ind, old_ind in enumerate(nonempty_topic_indices)} + topics = [reset_topic_ind[old_ind] for old_ind in topics] + topic_words = [[reset_topic_ind[old_item[0]]] + old_item[1:] for old_item in topic_words] + + # generate output dataframes + out_table = pd.DataFrame.copy(table, deep=True) + if topic_name in table.columns: + raise BrighticsFunctionException.from_errors( + [{'0100': "Existing table contains the topic column name. Please choose another name."}]) + out_table[topic_name] = topics + columns = ['index', 'vocabularies_weights', 'vocabularies', 'weights'] + topic_table = pd.DataFrame(topic_words, columns=columns) + topic_table['weights'] = topic_table['weights'].apply(pd.to_numeric) + + # pyLDAvis + if len(topic_words) == 1: + html_result = None + else: + topic_words_dicts = [item[1] for item in topic_words_raw] + topic_term_dists = [[topic_words_dict.get(word, 0) for word in vocab_set] for topic_words_dict in + topic_words_dicts] + num_docs = len(topics) + num_topics = len(topic_words_raw) + doc_topic_dists = np.zeros((num_docs, num_topics)) + for doc_id, topic_id in enumerate(topics): + doc_topic_dists[doc_id][topic_id - 1] = 1.0 + doc_lengths = [len(doc) for doc in docs_preprocessed] + vocab_count = functools.reduce( + lambda dict_1, dict_2: {word: dict_1.get(word, 0) + dict_2.get(word, 0) for word in + set(dict_1).union(dict_2)}, + topic_word_count) + term_frequency = [vocab_count.get(word) for word in vocab_set] + + prepared_data = pyLDAvis.prepare(topic_term_dists, doc_topic_dists, doc_lengths, vocab_set, term_frequency) + html_result = pyLDAvis.prepared_data_to_html(prepared_data) + + # generate report + params = {'Input column': input_col, + 'Topic column name': topic_name, + 'K': K, + 'Alpha': alpha, + 'Beta': beta, + 'Maximum number of iterations': max_iter, + 'Number of words for each topic': num_topic_words} + rb = BrtcReprBuilder() + rb.addMD(strip_margin(""" + | ## GSDMM Result + | ### Summary + | + """)) + if html_result is not None: + rb.addHTML(html_result) + rb.addMD(strip_margin(""" + | + """)) + rb.addMD(strip_margin(""" + | ### Final Number of Topics + | {num_topics} + | + | ### Parameters + | {params} + """.format(num_topics=len(topic_words_raw), params=dict2MD(params)))) + + # create model + model = _model_dict('lda_model') + model['params'] = params + model['gsdmm_model'] = mgp + model['_repr_brtc_'] = rb.get() + + return {'out_table': out_table, 'topic_table': topic_table, 'model': model} diff --git a/function/python/brightics/function/textanalytics/lda.py b/function/python/brightics/function/textanalytics/lda.py index 4747be17d..f933ae467 100644 --- a/function/python/brightics/function/textanalytics/lda.py +++ b/function/python/brightics/function/textanalytics/lda.py @@ -25,8 +25,130 @@ from sklearn.feature_extraction.text import CountVectorizer from sklearn.decomposition import LatentDirichletAllocation +from sklearn.preprocessing import normalize import numpy as np import pandas as pd +import pyLDAvis +import pyLDAvis.sklearn as ldavis + + +def lda4(table, group_by=None, **params): + check_required_parameters(_lda4, params, ['table']) + params = get_default_from_parameters_if_required(params, _lda4) + param_validation_check = [greater_than_or_equal_to(params, 2, 'num_voca'), + greater_than_or_equal_to(params, 2, 'num_topic'), + from_to( + params, 2, params['num_voca'], 'num_topic_word'), + greater_than_or_equal_to(params, 1, 'max_iter'), + greater_than(params, 1.0, 'learning_offset')] + + validate(*param_validation_check) + if group_by is not None: + return _function_by_group(_lda4, table, group_by=group_by, **params) + else: + return _lda4(table, **params) + + +def _lda4(table, input_col, topic_name='topic', num_voca=1000, num_topic=5, num_topic_word=10, max_iter=20, + learning_method='online', learning_offset=10., random_state=None): + # generate model + corpus = np.array(table[input_col]) + if isinstance(corpus[0], np.ndarray): + tf_vectorizer = CountVectorizer( + preprocessor=' '.join, stop_words='english', max_df=0.95, min_df=2, max_features=num_voca) + else: + tf_vectorizer = CountVectorizer( + max_df=0.95, min_df=2, max_features=num_voca, stop_words='english') + term_count = tf_vectorizer.fit_transform(corpus) + tf_feature_names = tf_vectorizer.get_feature_names() + if learning_method == 'online': + lda_model = LatentDirichletAllocation(n_components=num_topic, max_iter=max_iter, + learning_method=learning_method, + learning_offset=learning_offset, random_state=random_state).fit( + term_count) + elif learning_method == 'batch': + lda_model = LatentDirichletAllocation( + n_components=num_topic, max_iter=max_iter, learning_method=learning_method, random_state=random_state).fit( + term_count) + else: + raise_runtime_error("Please check 'learning_method'.") + log_likelihood = lda_model.score(term_count) + perplexity = lda_model.perplexity(term_count) + + # create topic table + vocab_weights_list = [] + vocab_list = [] + weights_list = [] + topic_term_prob = normalize(lda_model.components_, norm='l1') + for vector in topic_term_prob: + pairs = [] + for term_idx, value in enumerate(vector): + pairs.append((abs(value), tf_feature_names[term_idx])) + pairs.sort(key=lambda x: x[0], reverse=True) + vocab_weights = [] + vocab = [] + weights = [] + for pair in pairs[:num_topic_word]: + vocab_weights.append("{}: {}".format(pair[1], pair[0])) + vocab.append(pair[1]) + weights.append(pair[0]) + vocab_weights_list.append(vocab_weights) + vocab_list.append(vocab) + weights_list.append(weights) + topic_table = pd.DataFrame( + {'vocabularies_weights': vocab_weights_list, 'vocabularies': vocab_list, 'weights': weights_list}) + topic_table['index'] = [idx + 1 for idx in topic_table.index] + topic_table = topic_table[['index', 'vocabularies_weights', 'vocabularies', 'weights']] + + # create output table + doc_topic = lda_model.transform(term_count) + out_table = pd.DataFrame.copy(table, deep=True) + topic_dist_name = topic_name + '_distribution' + if topic_name in table.columns or topic_dist_name in table.columns: + raise BrighticsFunctionException.from_errors( + [{'0100': "Existing table contains Topic Column Name. Please choose again."}]) + out_table[topic_name] = [doc_topic[i].argmax() + 1 for i in range(len(corpus))] + out_table[topic_dist_name] = doc_topic.tolist() + + # pyLDAvis + prepared_data = ldavis.prepare(lda_model, term_count, tf_vectorizer) + html_result = pyLDAvis.prepared_data_to_html(prepared_data) + + # generate report + params = {'Input column': input_col, + 'Topic column name': topic_name, + 'Number of topics': num_topic, + 'Number of words for each topic': num_topic_word, + 'Maximum number of iterations': max_iter, + 'Learning method': learning_method, + 'Learning offset': learning_offset, + 'Seed': random_state} + rb = BrtcReprBuilder() + rb.addMD(strip_margin(""" + | ## Latent Dirichlet Allocation Result + | ### Summary + | + """)) + rb.addHTML(html_result) + rb.addMD(strip_margin(""" + | + | ### Log Likelihood + | {log_likelihood} + | + | ### Perplexity + | {perplexity} + | + | ### Parameters + | {params} + """.format(log_likelihood=log_likelihood, perplexity=perplexity, params=dict2MD(params)))) + + # create model + model = _model_dict('lda_model') + model['params'] = params + model['lda_model'] = lda_model + model['_repr_brtc_'] = rb.get() + + return {'out_table': out_table, 'topic_table': topic_table, 'model': model} def lda3(table, group_by=None, **params): diff --git a/function/python/brightics/function/textanalytics/meta/bow.json b/function/python/brightics/function/textanalytics/meta/bow.json index c62900aa2..a62e5c59d 100644 --- a/function/python/brightics/function/textanalytics/meta/bow.json +++ b/function/python/brightics/function/textanalytics/meta/bow.json @@ -632,8 +632,8 @@ }, { "persist-mode": "auto", - "func": "brightics.function.textanalytics$tokenizer_eng51532", - "name": "brightics.function.textanalytics$tokenizer_eng", + "func": "brightics.function.textanalytics$tokenizer_eng2", + "name": "brightics.function.textanalytics$tokenizer_eng2", "param": { "stemming": false, "pos_extraction": [ diff --git a/function/python/brightics/function/textanalytics/meta/dim.json b/function/python/brightics/function/textanalytics/meta/dim.json new file mode 100644 index 000000000..51adb131a --- /dev/null +++ b/function/python/brightics/function/textanalytics/meta/dim.json @@ -0,0 +1,1470 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "textanalytics", + "func": "brightics.function.textanalytics$dim", + "name": "brightics.function.textanalytics$dim", + "context": "python", + "label": "Document Influence Model", + "description": "Document Influences Model (DIM), which is based on the dynamic topic model, is a probabilistic model that captures how past articles exhibit varying influence on future articles. It is assumed that an article’s influence on the future is corroborated by how the language of its field changes subsequent to its publication. In the model, the influence of each article is encoded as a hidden variable and posterior inference reveals the influential articles of the collection.\n\nReference\n\n+ Gerrish & Blei, 2010", + "tags": [ + "dtm", + "topic modeling", + "dim" + ], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "out_table": "", + "topic_table": "", + "influence_table": "", + "model": "" + }, + "meta": { + "table": { + "type": "table", + "desc": "Data including text documents.", + "optional": false + }, + "out_table": { + "type": "table", + "desc": "Topic classification result attached to the input table." + }, + "topic_table": { + "type": "table", + "desc": "Table which consists of each topic's vocabulary and weights." + }, + "influence_table": { + "type": "table", + "desc": "Table which shows how much each document gave an influence to each topic." + }, + "model": { + "type": "model", + "desc": "Result model." + } + }, + "params": [ + { + "id": "input_col", + "label": "Input Column", + "description": "Text data column to select as an input. Data type should be of string array type.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "globalVariable": false, + "columnType": [ + "String[]" + ], + "multiple": false + }, + { + "id": "topic_name", + "label": "Topic Column Name", + "description": "Column name for topic modeling result.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "String", + "placeHolder": "topic" + }, + { + "id": "num_topic", + "label": "Number of Topics", + "description": "The number of topics that will be contained in the entire documents. It should be greater than or equal to two.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Integer", + "min": 2, + "placeHolder": "5 (value >= 2)" + }, + { + "id": "num_topic_word", + "label": "Number of Terminologies", + "description": "Maximum number of terminologies you want to show for particular topics. It should be greater than or equal to two. Terminology number should be less than or equal to vocabulary size also.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "placeHolder": "10 (value >= 2)", + "type": "Integer", + "min": 2 + }, + { + "id": "max_iter", + "label": "Iterations", + "description": "Maximum number of iterations you want to run your model. It should be at least one.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Integer", + "min": 1, + "placeHolder": "20 (value >= 1)" + }, + { + "id": "time_slice", + "label": "Time Slice", + "description": "The numbers of documents for each period. The sum of the numbers should be equal to the total number of documents. Default value is a singleton with the number of documents alone.", + "visibleOption": [], + "control": "ArrayInput", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "type": "Integer", + "placeHolder": "(value >= 1)", + "min": 1 + }, + { + "id": "coherence", + "label": "Coherence Measure", + "description": "Coherence measure of the resulting topic modeling.", + "visibleOption": [], + "control": "RadioButton", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "UMass", + "value": "u_mass", + "default": true + }, + { + "label": "C_V", + "value": "c_v", + "default": false + } + ], + "columnType": [] + }, + { + "id": "vis_time", + "label": "Time to visualize", + "description": "Time period to visualize the result. pyLDAvis visualization for the chosen time period will be generated in the model tab. It should be greater than or equal to zero, and less than the number of time periods.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Integer", + "min": 0, + "placeHolder": "0 (0 <= value <= # time slices)" + }, + { + "id": "seed", + "label": "Seed", + "description": "The seed used by the random number generator.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Integer", + "placeHolder": "" + } + ], + "summary": "This function builds a topic model for text documents with regard to time. Also provides an influence table, which consists of information how much each document from each time period gave influence to each topic." + }, + "md": { + "en": "# Document Influence Model\nThis function builds a topic model for text documents with regard to time. Also provides an influence table, which consists of information how much each document from each time period gave influence to each topic.\n\n## Description\nDocument Influences Model (DIM), which is based on the dynamic topic model, is a probabilistic model that captures how past articles exhibit varying influence on future articles. It is assumed that an article’s influence on the future is corroborated by how the language of its field changes subsequent to its publication. In the model, the influence of each article is encoded as a hidden variable and posterior inference reveals the influential articles of the collection.\n\nReference\n\n+ Gerrish & Blei, 2010\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data including text documents.\n#### OUTPUT\n1. **out_table**: (Table) Topic classification result attached to the input table.\n2. **topic_table**: (Table) Table which consists of each topic's vocabulary and weights.\n3. **influence_table**: (Table) Table which shows how much each document gave an influence to each topic.\n4. **model**: (Model) Result model.\n#### PARAMETER\n1. **Input Column***: Text data column to select as an input. Data type should be of string array type.\n\n2. **Topic Column Name**: Column name for topic modeling result.\n\n3. **Number of Topics**: The number of topics that will be contained in the entire documents. It should be greater than or equal to two.\n\n4. **Number of Terminologies**: Maximum number of terminologies you want to show for particular topics. It should be greater than or equal to two. Terminology number should be less than or equal to vocabulary size also.\n\n5. **Iterations**: Maximum number of iterations you want to run your model. It should be at least one.\n\n6. **Time Slice**: The numbers of documents for each period. The sum of the numbers should be equal to the total number of documents. Default value is a singleton with the number of documents alone.\n\n7. **Coherence Measure**: Coherence measure of the resulting topic modeling.\n + Available Options: `UMass`, `C_V`\n\n8. **Time to visualize**: Time period to visualize the result. pyLDAvis visualization for the chosen time period will be generated in the model tab. It should be greater than or equal to zero, and less than the number of time periods.\n\n9. **Seed**: The seed used by the random number generator.\n\n\n### Python\n#### USAGE\n\n```\ndim(table= ,input_col= ,topic_name= ,num_topic= ,num_topic_word= ,max_iter= ,time_slice= ,coherence= ,vis_time= ,seed=)\n```\n\n#### INPUT\n1. **table***: (Table) Data including text documents.\n#### OUTPUT\n1. **out_table**: (Table) Topic classification result attached to the input table.\n2. **topic_table**: (Table) Table which consists of each topic's vocabulary and weights.\n3. **influence_table**: (Table) Table which shows how much each document gave an influence to each topic.\n4. **model**: (Model) Result model.\n#### PARAMETER\n1. **input_col***: Text data column to select as an input. Data type should be of string array type.\n\t* Type: *str*\n2. **topic_name**: Column name for topic modeling result.\n\t* Type: *str*\n3. **num_topic**: The number of topics that will be contained in the entire documents. It should be greater than or equal to two.\n\t* Type: *int*\n\t* Default / Range: 5 (value >= 2)\n4. **num_topic_word**: Maximum number of terminologies you want to show for particular topics. It should be greater than or equal to two. Terminology number should be less than or equal to vocabulary size also.\n\t* Type: *int*\n\t* Default / Range: 10 (value >= 2)\n5. **max_iter**: Maximum number of iterations you want to run your model. It should be at least one.\n\t* Type: *int*\n\t* Default / Range: 20 (value >= 1)\n6. **time_slice**: The numbers of documents for each period. The sum of the numbers should be equal to the total number of documents. Default value is a singleton with the number of documents alone.\n\t* Type: *str*\n7. **coherence**: Coherence measure of the resulting topic modeling.\n\t* Type: *str*\n\t* Default / Range: u_mass ( u_mass | c_v )\n8. **vis_time**: Time period to visualize the result. pyLDAvis visualization for the chosen time period will be generated in the model tab. It should be greater than or equal to zero, and less than the number of time periods.\n\t* Type: *int*\n\t* Default / Range: 0 (0 <= value <= # time slices)\n9. **seed**: The seed used by the random number generator.\n\t* Type: *int*\n\n\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, sample_LatentDirichletAllocation data is used to make topic model and get the result. The result includes assigned topic and contributions of each word to assigned topic. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Input Column***: tokenized_text\n2. **Topic Column Name**: topic\n3. **Number of Topics**: 3\n4. **Number of Terminologies**: 10\n5. **Iterations**: 20\n6. **Time Slice**: 2, 3, 3\n7. **Coherence Measure**: UMass\n8. **Time to visualize**: 0\n9. **Seed**: None\n\n\n\n### Python\n\n```\nfrom brightics.function.textanalytics import dim\ninput_table = inputs[0]\nresult = dim(table=input_table, input_col='tokenized_text',\n topic_name='topic', num_topic=3, num_topic_word=10, max_iter=20, time_slice=[2, 3, 3],\n coherence='u_mass', vis_time=0, seed=None)\noutput = result['out_table']\n```\nIn this tutorial workflow, sample_LatentDirichletAllocation data is used to make topic model and get the result. The result includes assigned topic and contributions of each word to assigned topic. 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+ "description": "In this tutorial workflow, sample_LatentDirichletAllocation data is used to make topic model and get the result. The result includes assigned topic and contributions of each word to assigned topic. The parameter settings used in the function are shown below.", + "parameter": "1. **Input Column***: tokenized_text\n2. **Topic Column Name**: topic\n3. **Number of Topics**: 3\n4. **Number of Terminologies**: 10\n5. **Iterations**: 20\n6. **Time Slice**: 2, 3, 3\n7. **Coherence Measure**: UMass\n8. **Time to visualize**: 0\n9. **Seed**: None\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.textanalytics import dim\r\ninput_table = inputs[0]\r\nresult = dim(table=input_table, input_col='tokenized_text',\r\n topic_name='topic', num_topic=3, num_topic_word=10, max_iter=20, time_slice=[2, 3, 3],\r\n coherence='u_mass', vis_time=0, seed=None)\r\noutput = result['out_table']", + "description": "In this tutorial workflow, sample_LatentDirichletAllocation data is used to make topic model and get the result. The result includes assigned topic and contributions of each word to assigned topic. The parameter settings used in the function are shown below.", + "context": "python" + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/textanalytics/meta/doc2vec.json b/function/python/brightics/function/textanalytics/meta/doc2vec.json index 095fb7fb0..7ce21fa79 100644 --- a/function/python/brightics/function/textanalytics/meta/doc2vec.json +++ b/function/python/brightics/function/textanalytics/meta/doc2vec.json @@ -9,7 +9,7 @@ "name": "brightics.function.textanalytics$doc2vec", "context": "python", "label": "Doc2Vec", - "description": "Doc2vec is an unsupervised algorithm to generate vectors for documents. The algorithm is an adaptation of word2vec which can generate vectors for words. The vectors generated by doc2vec can be used for tasks like finding similarity between sentences/paragraphs/documents. Unlike sequence models like RNN, where word sequence is captured in generated sentence vectors, doc2vec sentence vectors are word order independent.\n\nReference: \n+ \n+ \n+ ", + "description": "Doc2vec is an unsupervised algorithm to generate vectors for documents. The algorithm is an adaptation of word2vec which can generate vectors for words. The vectors generated by doc2vec can be used for tasks like finding similarity between sentences/paragraphs/documents. Unlike sequence models like RNN, where word sequence is captured in generated sentence vectors, doc2vec sentence vectors are word order independent.\n\nReference: \n+ \n+ \n+ \n+ \"Distributed representations of sentences and documents\", Quoc Le and Tomas Mikolov, 2014", "tags": [], "version": "3.6", "inputs": { @@ -227,11 +227,25 @@ "columnType": [], "placeHolder": "0.75", "type": "Integer" + }, + { + "id": "topn", + "label": "Top N frequent words", + "description": "Number of top N frequent words to be illustrated on 2D graph.", + "mandatory": false, + "items": [], + "visibleOption": [], + "control": "InputBox", + "columnType": [], + "validation": [], + "placeHolder": "30 (value >= 2)", + "type": "Integer", + "min": 2 } ], "summary": "This function trains Doc2Vec model from tokenized text data." }, - "md": "# Doc2Vec\nThis function trains Doc2Vec model from tokenized text data.\n\n## Description\nDoc2vec is an unsupervised algorithm to generate vectors for documents. The algorithm is an adaptation of word2vec which can generate vectors for words. The vectors generated by doc2vec can be used for tasks like finding similarity between sentences/paragraphs/documents. Unlike sequence models like RNN, where word sequence is captured in generated sentence vectors, doc2vec sentence vectors are word order independent.\n\nReference: \n+ \n+ \n+ \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Text data in a Table.\n#### OUTPUT\n1. **doc_table**: (Table) Output document Table. It contains input Table's columns and documnet_vectors column.\n2. **word_table**: (Table) Output word Table. It contains input words columns and word_vectors column.\n3. **model**: (Model) Doc2Vec model.\n#### PARAMETER\n1. **Input column***: Columns to select as input. Only List of String type columns are possible.\n\n2. **Training Algorithm**: Training algorithm. In the distributed bag of words(`PV-DBOW`) model, the distributed representations of context (or surrounding words) are combined to predict the word in the middle. While in the distributed memory(`PV-DM`) model, the distributed representation of the input word is used to predict the context. `PV-DM` generally performs better than `PV-DBOW`.\n - Available Options : `PV-DM`, `PV-DBOW`\n\n3. **Dimension of Vectors**: Dimensionality of the feature vectors.\n\n4. **Window Size**: Maximum distance between the current and predicted word within a sentence.\n\n5. **Minimum count**: Ignores all words with total frequency lower than this.\n\n6. **Alpha**: The initial learning rate.\n\n7. **Minimum alpha**: Learning rate will linearly drop to `Minimum alpha` as training progresses.\n\n8. **Seed**: Random seed. Use the same seed in order to reproduce the same results.\n\n9. **Train epoch**: Number of iterations (epochs) over the corpus.\n\n10. **Number of workers**: Use these many worker threads to train the model (=faster training with multicore machines).\n\n11. **Hierarchical softmax**: If it is True, hierarchical softmax will be used for model training. If it is False, and Negative is non-zero, negative sampling will be used.\n - Available Options: `True`, `False`\n\n12. **Negative**: If `Negative` > 0, negative sampling will be used, the int for negative specifies how many noise words should be drawn (usually between 5-20).\n\n13. **Negative sampling exponent**: The exponent used to shape the negative sampling distribution. A value of 1.0 samples exactly in proportion to the frequencies, 0.0 samples all words equally, while a negative value samples low-frequency words more than high-frequency words. The popular default value of 0.75 was chosen by the original Word2Vec paper.\n\n\n### Python\n#### USAGE\n\n```\ndoc2vec(table, input_col, dm=1, vector_size=100, window=10, min_count=1, max_vocab_size=None, train_epoch=100, workers=1, alpha=0.025, min_alpha=0.025, seed=None, hs=1, negative=5, ns_exponent=0.75, hashfxn=hash)\n```\n\n#### INPUT\n1. **table***: (Table) Text data in a Table.\n#### OUTPUT\n1. **doc_table**: (Table) Output document Table. It contains input Table's columns and documnet_vectors column.\n2. **word_table**: (Table) Output word Table. It contains input words columns and word_vectors column.\n3. **model**: (Model) Doc2Vec model.\n#### PARAMETER\n1. **input_col***: Columns to select as input. Only List of String type columns are possible.\n\t* Type: *str*\n2. **dm**: Training algorithm(1:PV-DM, 0:PV-DBOW). In the distributed bag of words(PV-DBOW) model, the distributed representations of context (or surrounding words) are combined to predict the word in the middle. While in the distributed memory(`PV-DM`) model, the distributed representation of the input word is used to predict the context. PV-DM generally performs better than PV-DBOW.\n\t* Type: *int*\n\t* Default / Range: 1 ( 1 | 0 )\n3. **vector_size**: Dimensionality of the feature vectors.\n\t* Type: *int*\n\t* Default / Range: 100 (value >= 1)\n4. **window**: Maximum distance between the current and predicted word within a sentence.\n\t* Type: *int*\n\t* Default / Range: 5 (value >= 1)\n5. **min_count**: Ignores all words with total frequency lower than this.\n\t* Type: *int*\n\t* Default / Range: 1 (value >= 1)\n6. **alpha**: The initial learning rate.\n\t* Type: *float*\n\t* Default / Range: 0.025 (value > 0)\n7. **min_alpha**: Learning rate will linearly drop to `min_alpha` as training progresses.\n\t* Type: *float*\n\t* Default / Range: 0.025 (value > 0)\n8. **seed**: Random seed. Use the same seed in order to reproduce the same results.\n\t* Type: *int*\n9. **train_epoch**: Number of iterations (epochs) over the corpus.\n\t* Type: *int*\n\t* Default / Range: 100 (value >= 1)\n10. **workers**: Use these many worker threads to train the model (=faster training with multicore machines).\n\t* Type: *int*\n\t* Default / Range: 1 (value >= 1)\n11. **hs**: If it is True, hierarchical softmax will be used for model training. If it is False, and Negative is non-zero, negative sampling will be used.\n\t* Type: *bool*\n\t* Default value: True \n12. **negative**: If `negative` > 0, negative sampling will be used, the int for negative specifies how many noise words should be drawn (usually between 5-20).\n\t* Type: *int*\n\t* Default / Range: 5 (5 <= value <= 20)\n13. **ns_exponent**: The exponent used to shape the negative sampling distribution. A value of 1.0 samples exactly in proportion to the frequencies, 0.0 samples all words equally, while a negative value samples low-frequency words more than high-frequency words. The popular default value of 0.75 was chosen by the original Word2Vec paper.\n\t* Type: *int*\n\t* Default / Range: 0.75 (0 <= value <= 1)\n\n\n## Example\n### VA\n\n\n**[Sample Model]**\n\n\n\n\n\n
In this tutorial workflow, simple text data Table created by Create Table function are used as input of Doc2Vec function. The outputs of this function are a Doc2Vec model, a document Table, and a word Table.\nThe parameter settings used in the function are shown below. \n\n##### ++Parameters++\n1. **Input Column*** : tokenized_document\n2. **Training Algorithm** : PV-DM\n3. **Dimension of Vectors** : 100\n4. **Window Size** : 5\n5. **Minimum Count** : 1\n6. **Alpha**: 0.025\n7. **Minimum Alpha** : 0.025\n8. **Seed** : None\n9. **Train epoch** : 100\n10. **Number of Workers** : 1\n11. **Hierarchical softmax** : False\n12. **Negative** : 5\n13. **Negative sampling exponent** : 0.75\n\n### Python\n```\nfrom brightics.function.textanalytics import doc2vec\ninput_table = inputs[0]\nresult = doc2vec(table=input_table, input_col='tokenized_document',\n dm=1, vector_size=100, window=10, min_count=1,\n max_vocab_size=None, train_epoch=100, workers=1,\n alpha=0.025, min_alpha=0.025, seed=None, hs=0,\n negative=5, ns_exponent=0.75, hashfxn=hash)\ndoc_table = result['doc_table']\nword_table = result['word_table']\noutput_model = result['model']\n```\n\n
In this python script, sample_data_tokenizer_eng data is used as input of Doc2Vec function. The outputs of this function are a Doc2Vec model, a document Table, and a word Table.\n", + "md": "# Doc2Vec\nThis function trains Doc2Vec model from tokenized text data.\n\n## Description\nDoc2vec is an unsupervised algorithm to generate vectors for documents. The algorithm is an adaptation of word2vec which can generate vectors for words. The vectors generated by doc2vec can be used for tasks like finding similarity between sentences/paragraphs/documents. Unlike sequence models like RNN, where word sequence is captured in generated sentence vectors, doc2vec sentence vectors are word order independent.\n\nReference: \n+ \n+ \n+ \n+ \"Distributed representations of sentences and documents\", Quoc Le and Tomas Mikolov, 2014\n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Text data in a Table.\n#### OUTPUT\n1. **doc_table**: (Table) Output document Table. It contains input Table's columns and documnet_vectors column.\n2. **word_table**: (Table) Output word Table. It contains input words columns and word_vectors column.\n3. **model**: (Model) Doc2Vec model.\n#### PARAMETER\n1. **Input column***: Columns to select as input. Only List of String type columns are possible.\n\n2. **Training Algorithm**: Training algorithm. In the distributed bag of words(`PV-DBOW`) model, the distributed representations of context (or surrounding words) are combined to predict the word in the middle. While in the distributed memory(`PV-DM`) model, the distributed representation of the input word is used to predict the context. `PV-DM` generally performs better than `PV-DBOW`.\n - Available Options : `PV-DM`, `PV-DBOW`\n\n3. **Dimension of Vectors**: Dimensionality of the feature vectors.\n\n4. **Window Size**: Maximum distance between the current and predicted word within a sentence.\n\n5. **Minimum count**: Ignores all words with total frequency lower than this.\n\n6. **Alpha**: The initial learning rate.\n\n7. **Minimum alpha**: Learning rate will linearly drop to `Minimum alpha` as training progresses.\n\n8. **Seed**: Random seed. Use the same seed in order to reproduce the same results.\n\n9. **Train epoch**: Number of iterations (epochs) over the corpus.\n\n10. **Number of workers**: Use these many worker threads to train the model (=faster training with multicore machines).\n\n11. **Hierarchical softmax**: If it is True, hierarchical softmax will be used for model training. If it is False, and Negative is non-zero, negative sampling will be used.\n - Available Options: `True`, `False`\n\n12. **Negative**: If `Negative` > 0, negative sampling will be used, the int for negative specifies how many noise words should be drawn (usually between 5-20).\n\n13. **Negative sampling exponent**: The exponent used to shape the negative sampling distribution. A value of 1.0 samples exactly in proportion to the frequencies, 0.0 samples all words equally, while a negative value samples low-frequency words more than high-frequency words. The popular default value of 0.75 was chosen by the original Word2Vec paper.\n\n14. **Top N frequent words**: Number of top N frequent words to be illustrated on 2D graph.\n\n\n### Python\n#### USAGE\n\n```\ndoc2vec(table, input_col, dm=1, vector_size=100, window=10, min_count=1, max_vocab_size=None, train_epoch=100, workers=1, alpha=0.025, min_alpha=0.025, seed=None, hs=1, negative=5, ns_exponent=0.75, hashfxn=hash)\n```\n\n#### INPUT\n1. **table***: (Table) Text data in a Table.\n#### OUTPUT\n1. **doc_table**: (Table) Output document Table. It contains input Table's columns and documnet_vectors column.\n2. **word_table**: (Table) Output word Table. It contains input words columns and word_vectors column.\n3. **model**: (Model) Doc2Vec model.\n#### PARAMETER\n1. **input_col***: Columns to select as input. Only List of String type columns are possible.\n\t* Type: *str*\n2. **dm**: Training algorithm(1:PV-DM, 0:PV-DBOW). In the distributed bag of words(PV-DBOW) model, the distributed representations of context (or surrounding words) are combined to predict the word in the middle. While in the distributed memory(`PV-DM`) model, the distributed representation of the input word is used to predict the context. PV-DM generally performs better than PV-DBOW.\n\t* Type: *int*\n\t* Default / Range: 1 ( 1 | 0 )\n3. **vector_size**: Dimensionality of the feature vectors.\n\t* Type: *int*\n\t* Default / Range: 100 (value >= 1)\n4. **window**: Maximum distance between the current and predicted word within a sentence.\n\t* Type: *int*\n\t* Default / Range: 5 (value >= 1)\n5. **min_count**: Ignores all words with total frequency lower than this.\n\t* Type: *int*\n\t* Default / Range: 1 (value >= 1)\n6. **alpha**: The initial learning rate.\n\t* Type: *float*\n\t* Default / Range: 0.025 (value > 0)\n7. **min_alpha**: Learning rate will linearly drop to `min_alpha` as training progresses.\n\t* Type: *float*\n\t* Default / Range: 0.025 (value > 0)\n8. **seed**: Random seed. Use the same seed in order to reproduce the same results.\n\t* Type: *int*\n9. **train_epoch**: Number of iterations (epochs) over the corpus.\n\t* Type: *int*\n\t* Default / Range: 100 (value >= 1)\n10. **workers**: Use these many worker threads to train the model (=faster training with multicore machines).\n\t* Type: *int*\n\t* Default / Range: 1 (value >= 1)\n11. **hs**: If it is True, hierarchical softmax will be used for model training. If it is False, and Negative is non-zero, negative sampling will be used.\n\t* Type: *bool*\n\t* Default value: True \n12. **negative**: If `negative` > 0, negative sampling will be used, the int for negative specifies how many noise words should be drawn (usually between 5-20).\n\t* Type: *int*\n\t* Default / Range: 5 (5 <= value <= 20)\n13. **ns_exponent**: The exponent used to shape the negative sampling distribution. A value of 1.0 samples exactly in proportion to the frequencies, 0.0 samples all words equally, while a negative value samples low-frequency words more than high-frequency words. The popular default value of 0.75 was chosen by the original Word2Vec paper.\n14. **topn**: Number of top N frequent words to be illustrated on 2D graph.\n\t* Type: *int*\n\t* Default / Range: 30 (value >= 2)\n\t* Type: *int*\n\t* Default / Range: 0.75 (0 <= value <= 1)\n\n\n## Example\n### VA\n\n\n**[Sample Model]**\n\n\n\n\n\n
In this tutorial workflow, simple text data Table created by Create Table function are used as input of Doc2Vec function. The outputs of this function are a Doc2Vec model, a document Table, and a word Table.\nThe parameter settings used in the function are shown below. \n\n##### ++Parameters++\n1. **Input Column*** : tokenized_document\n2. **Training Algorithm** : PV-DM\n3. **Dimension of Vectors** : 100\n4. **Window Size** : 5\n5. **Minimum Count** : 1\n6. **Alpha**: 0.025\n7. **Minimum Alpha** : 0.025\n8. **Seed** : None\n9. **Train epoch** : 100\n10. **Number of Workers** : 1\n11. **Hierarchical softmax** : False\n12. **Negative** : 5\n13. **Negative sampling exponent** : 0.75\n\n### Python\n```\nfrom brightics.function.textanalytics import doc2vec\ninput_table = inputs[0]\nresult = doc2vec(table=input_table, input_col='tokenized_document',\n dm=1, vector_size=100, window=10, min_count=1,\n max_vocab_size=None, train_epoch=100, workers=1,\n alpha=0.025, min_alpha=0.025, seed=None, hs=0,\n negative=5, ns_exponent=0.75, hashfxn=hash)\ndoc_table = result['doc_table']\nword_table = result['word_table']\noutput_model = result['model']\n```\n\n
In this python script, sample_data_tokenizer_eng data is used as input of Doc2Vec function. The outputs of this function are a Doc2Vec model, a document Table, and a word Table.\n", "exampleModels": [ { "fileName": "brightics.function.textanalytics$doc2vec.json", @@ -960,8 +974,8 @@ }, { "persist-mode": "auto", - "func": "brightics.function.textanalytics$tokenizer_eng51532", - "name": "brightics.function.textanalytics$tokenizer_eng", + "func": "brightics.function.textanalytics$tokenizer_eng2", + "name": "brightics.function.textanalytics$tokenizer_eng2", "param": { "stemming": false, "pos_extraction": [ diff --git a/function/python/brightics/function/textanalytics/meta/doc2vec_model.json b/function/python/brightics/function/textanalytics/meta/doc2vec_model.json index a376aa083..96d5bdb99 100644 --- a/function/python/brightics/function/textanalytics/meta/doc2vec_model.json +++ b/function/python/brightics/function/textanalytics/meta/doc2vec_model.json @@ -9,7 +9,7 @@ "name": "brightics.function.textanalytics$doc2vec_model", "context": "python", "label": "Doc2Vec Model", - "description": "Doc2vec is an unsupervised algorithm to generate vectors for documents. The algorithm is an adaptation of word2vec which can generate vectors for words. The vectors generated by doc2vec can be used for tasks like finding similarity between sentences/paragraphs/documents. Unlike sequence models like RNN, where word sequence is captured in generated sentence vectors, doc2vec sentence vectors are word order independent.\n\nThis function calculates feature vectors to represent documents from a input document Table with a input Doc2Vec model.\n\nReference: \n+ \n+ \n+ ", + "description": "Doc2vec is an unsupervised algorithm to generate vectors for documents. The algorithm is an adaptation of word2vec which can generate vectors for words. The vectors generated by doc2vec can be used for tasks like finding similarity between sentences/paragraphs/documents. Unlike sequence models like RNN, where word sequence is captured in generated sentence vectors, doc2vec sentence vectors are word order independent.\n\nThis function calculates feature vectors to represent documents from a input document Table with a input Doc2Vec model.\n\nReference: \n+ \n+ \n+ \n+ \"Distributed representations of sentences and documents\", Quoc Le and Tomas Mikolov, 2014", "tags": [], "version": "3.6", "inputs": { @@ -33,7 +33,7 @@ "params": [], "summary": "This funcion generates vectors to represent documents with Doc2Vec." }, - "md": "# Doc2Vec Model\nThis funcion generates vectors to represent documents with Doc2Vec.\n\n## Description\nDoc2vec is an unsupervised algorithm to generate vectors for documents. The algorithm is an adaptation of word2vec which can generate vectors for words. The vectors generated by doc2vec can be used for tasks like finding similarity between sentences/paragraphs/documents. Unlike sequence models like RNN, where word sequence is captured in generated sentence vectors, doc2vec sentence vectors are word order independent.\n\nThis function calculates feature vectors to represent documents from a input document Table with a input Doc2Vec model.\n\nReference: \n+ \n+ \n+ \n\n\n\n## Properties\n### VA\n#### INPUT\n1. **model***: (Model) Trained Doc2Vec Model.\n2. **table***: (Table) Text data in a Table.\n#### OUTPUT\n1. **out_table**: (Table) Output document Table. It contains input Table's columns and documnet_vectors column.\n#### PARAMETER\n\n### Python\n#### USAGE\n\n```\ndoc2vec_model(model, table)\n```\n\n#### INPUT\n1. **model***: (Model) Trained Doc2Vec Model.\n2. **table***: (Table) Text data in a Table.\n#### OUTPUT\n1. **out_table**: (Table) Output document Table. It contains input Table's columns and documnet_vectors column.\n#### PARAMETER\n\n## Example\n### VA\n\n\n**[Sample Model]**\n\n\n\n\n\n
In this tutorial workflow, sample_data_tokenizer_eng data is used as input of Doc2Vec function after tokenizing. And the Doc2Vec output model and output Table are used as input of Doc2Vec Model function. The output of this function is a Table that contains document_vector of each document.\nThe parameter settings used in the function are shown below. \n\n\n### Python\n```\nfrom brightics.function.textanalytics import doc2vec_model\ninput_model = models[0]\ninput_table = inputs[0]\nresult = doc2vec_model(model=input_model, table=input_table)\noutput = result['out_table']\n```\n\n
In this python script, sample_data_tokenizer_eng data is used as input of Doc2Vec function after tokenizing. And the Doc2Vec output model and output Table are used as input of Doc2Vec Model function. The output of this function is a Table that contains document_vector of each document.\n", + "md": "# Doc2Vec Model\nThis funcion generates vectors to represent documents with Doc2Vec.\n\n## Description\nDoc2vec is an unsupervised algorithm to generate vectors for documents. The algorithm is an adaptation of word2vec which can generate vectors for words. The vectors generated by doc2vec can be used for tasks like finding similarity between sentences/paragraphs/documents. Unlike sequence models like RNN, where word sequence is captured in generated sentence vectors, doc2vec sentence vectors are word order independent.\n\nThis function calculates feature vectors to represent documents from a input document Table with a input Doc2Vec model.\n\nReference: \n+ \n+ \n+ \n+ \"Distributed representations of sentences and documents\", Quoc Le and Tomas Mikolov, 2014\n\n\n\n## Properties\n### VA\n#### INPUT\n1. **model***: (Model) Trained Doc2Vec Model.\n2. **table***: (Table) Text data in a Table.\n#### OUTPUT\n1. **out_table**: (Table) Output document Table. It contains input Table's columns and documnet_vectors column.\n#### PARAMETER\n\n### Python\n#### USAGE\n\n```\ndoc2vec_model(model, table)\n```\n\n#### INPUT\n1. **model***: (Model) Trained Doc2Vec Model.\n2. **table***: (Table) Text data in a Table.\n#### OUTPUT\n1. **out_table**: (Table) Output document Table. It contains input Table's columns and documnet_vectors column.\n#### PARAMETER\n\n## Example\n### VA\n\n\n**[Sample Model]**\n\n\n\n\n\n
In this tutorial workflow, sample_data_tokenizer_eng data is used as input of Doc2Vec function after tokenizing. And the Doc2Vec output model and output Table are used as input of Doc2Vec Model function. The output of this function is a Table that contains document_vector of each document.\nThe parameter settings used in the function are shown below. \n\n\n### Python\n```\nfrom brightics.function.textanalytics import doc2vec_model\ninput_model = models[0]\ninput_table = inputs[0]\nresult = doc2vec_model(model=input_model, table=input_table)\noutput = result['out_table']\n```\n\n
In this python script, sample_data_tokenizer_eng data is used as input of Doc2Vec function after tokenizing. And the Doc2Vec output model and output Table are used as input of Doc2Vec Model function. The output of this function is a Table that contains document_vector of each document.\n", "exampleModels": [ { "fileName": "brightics.function.textanalytics$doc2vec_model.json", @@ -296,8 +296,8 @@ }, { "persist-mode": "auto", - "func": "brightics.function.textanalytics$tokenizer_eng51532", - "name": "brightics.function.textanalytics$tokenizer_eng", + "func": "brightics.function.textanalytics$tokenizer_eng2", + "name": "brightics.function.textanalytics$tokenizer_eng2", "param": { "stemming": false, "pos_extraction": [ diff --git a/function/python/brightics/function/textanalytics/meta/doc_doc_mtx.json b/function/python/brightics/function/textanalytics/meta/doc_doc_mtx.json index 6a69a3637..154c285f0 100644 --- a/function/python/brightics/function/textanalytics/meta/doc_doc_mtx.json +++ b/function/python/brightics/function/textanalytics/meta/doc_doc_mtx.json @@ -1255,8 +1255,8 @@ }, { "persist-mode": "auto", - "func": "brightics.function.textanalytics$tokenizer_eng51532", - "name": "brightics.function.textanalytics$tokenizer_eng", + "func": "brightics.function.textanalytics$tokenizer_eng2", + "name": "brightics.function.textanalytics$tokenizer_eng2", "param": { "stemming": false, "pos_extraction": [ diff --git a/function/python/brightics/function/textanalytics/meta/doc_summarizer_eng.json b/function/python/brightics/function/textanalytics/meta/doc_summarizer_eng.json index e09f7c207..27aa5a101 100644 --- a/function/python/brightics/function/textanalytics/meta/doc_summarizer_eng.json +++ b/function/python/brightics/function/textanalytics/meta/doc_summarizer_eng.json @@ -65,27 +65,10 @@ "columnType": [], "targetTable": [] }, - { - "id": "hold_cols", - "label": "Hold Columns", - "description": "Column to hold in output Table.", - "visibleOption": [ - { - "id": "result_type", - "value": "summarized_document" - } - ], - "control": "ColumnSelector", - "validation": [], - "mandatory": false, - "items": [], - "columnType": [], - "multiple": true - }, { "id": "ratio", "label": "Compression Ratio", - "description": "Number between 0 and 1 that determines the proportion of the number of sentences of the original text to be chosen for the summary.", + "description": "Number between 0 and 1 that determines the proportion of the number of sentences of the original text to be chosen for the summary. If None, the ratio is set to the proportion of `Number of Sentences` parameter to the total number of sentences.", "visibleOption": [], "control": "InputBox", "validation": [], @@ -135,7 +118,7 @@ ], "summary": "This function summarizes English documents." }, - "md": "# Documents Summarizer (English)\nThis function summarizes English documents.\n\n## Description\nThis function summarizes the given text, by extracting one or more important sentences from the text.\n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) English documents in a Table.\n#### OUTPUT\n1. **out_table**: (Table) Summarized English documents in a Table.\n#### PARAMETER\n1. **Input Column***: Column to select as input. The column must contain a English document in each row.\n\n2. **Result Type**: Output type of summarization. If `Summarized Document`, output table shows summarized document in `New Column Name`.\n - Available Options : `Summarized Document`, `Sentence`\n\n3. **Hold Columns**: Column to hold in output Table.\n\n4. **Compression Ratio**: Number between 0 and 1 that determines the proportion of the number of sentences of the original text to be chosen for the summary.\n\n5. **Number of Sentences**: Determines how many sentences will the output contains. If `Compression Ratio` is provided, then this parameter will be ignored.\n\n6. **New Column Name**: New column name for summarized English documents.\n\n\n### Python\n#### USAGE\n\n```\ndoc_summarizer_eng(table, input_col, result_type='sentence', hold_cols=None, ratio, num_sentence=1, new_col_name='summarized_document')\n```\n\n#### INPUT\n1. **table***: (Table) English documents in a Table.\n#### OUTPUT\n1. **out_table**: (Table) Summarized English documents in a Table.\n#### PARAMETER\n1. **input_col***: Column to select as input. The column must contain a English document in each row.\n\t* Type: *str*\n2. **result_type**: Output type of summarization. If `summarized_document`, output table shows summarized document in `new_col_name`.\n - Available Options : `summarized_document`, `sentence`\n\t* Type: *str*\n\t* Default / Range: sentence ( summarized_document | sentence )\n3. **hold_cols**: Column to hold in output Table.\n\t* Type: *list[str]*\n4. **ratio**: Number between 0 and 1 that determines the proportion of the number of sentences of the original text to be chosen for the summary.\n\t* Type: *float*\n\t* Default / Range: ( 0 < value <= 1)\n5. **num_sentence**: Determines how many sentences will the output contains. If `Compression Ratio` is provided, then this parameter will be ignored.\n\t* Type: *int*\n\t* Default / Range: 1 ( value >= 1 )\n6. **new_col_name**: New column name for summarized English documents.\n\t* Type: *str*\n\t* Default / Range: summarized_document\n\n## Example\n### VA\n\n\n**[Sample Model]**\n\n\n\n\n
In this tutorial workflow, sample text data is used for summarization. With Documents Summarizer(English) function, summarized document is returned in output table. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Input Column***: text \n2. **Result Type**: Sentence \n3. **Hold Columns**: None \n4. **Compression Ratio**: 0.2 \n5. **Number of Sentences**: 1 \n6. **New Column Name**: summarized_document \n\n\n### Python\n\n```\nfrom brightics.function.textanalytics import doc_summarizer_eng\ninput_table = inputs[0]\nresult = doc_summarizer_eng(table=input_table, input_col='text', result_type='sentence', hold_cols=None, ratio=0.2)\noutput = result['out_table']\n```\n\n
In this python script, sample text data table which contains the Matrix synopsis is used for summarization. With _doc\\_summarizer\\_eng_ function, summarized document is returned in output table.\n\n", + "md": "# Documents Summarizer (English)\nThis function summarizes English documents.\n\n## Description\nThis function summarizes the given text, by extracting one or more important sentences from the text.\n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) English documents in a Table.\n#### OUTPUT\n1. **out_table**: (Table) Summarized English documents in a Table.\n#### PARAMETER\n1. **Input Column***: Column to select as input. The column must contain a English document in each row.\n\n2. **Result Type**: Output type of summarization. If `Summarized Document`, output table shows summarized document in `New Column Name`.\n - Available Options : `Summarized Document`, `Sentence`\n\n3. **Compression Ratio**: Number between 0 and 1 that determines the proportion of the number of sentences of the original text to be chosen for the summary.\n\n4. **Number of Sentences**: Determines how many sentences will the output contains. If `Compression Ratio` is provided, then this parameter will be ignored.\n\n5. **New Column Name**: New column name for summarized English documents.\n\n\n### Python\n#### USAGE\n\n```\ndoc_summarizer_eng(table, input_col, result_type='sentence', ratio, num_sentence=1, new_col_name='summarized_document')\n```\n\n#### INPUT\n1. **table***: (Table) English documents in a Table.\n#### OUTPUT\n1. **out_table**: (Table) Summarized English documents in a Table.\n#### PARAMETER\n1. **input_col***: Column to select as input. The column must contain a English document in each row.\n\t* Type: *str*\n2. **result_type**: Output type of summarization. If `summarized_document`, output table shows summarized document in `new_col_name`.\n - Available Options : `summarized_document`, `sentence`\n\t* Type: *str*\n\t* Default / Range: sentence ( summarized_document | sentence )\n3. **ratio**: Number between 0 and 1 that determines the proportion of the number of sentences of the original text to be chosen for the summary.\n\t* Type: *float*\n\t* Default / Range: ( 0 < value <= 1)\n4. **num_sentence**: Determines how many sentences will the output contains. If `Compression Ratio` is provided, then this parameter will be ignored.\n\t* Type: *int*\n\t* Default / Range: 1 ( value >= 1 )\n5. **new_col_name**: New column name for summarized English documents.\n\t* Type: *str*\n\t* Default / Range: summarized_document\n\n## Example\n### VA\n\n\n**[Sample Model]**\n\n\n\n\n
In this tutorial workflow, sample text data is used for summarization. With Documents Summarizer(English) function, summarized document is returned in output table. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Input Column***: text \n2. **Result Type**: Sentence \n3. **Compression Ratio**: 0.2 \n4. **Number of Sentences**: 1 \n5. **New Column Name**: summarized_document \n\n\n### Python\n\n```\nfrom brightics.function.textanalytics import doc_summarizer_eng\ninput_table = inputs[0]\nresult = doc_summarizer_eng(table=input_table, input_col='text', result_type='sentence', ratio=0.2)\noutput = result['out_table']\n```\n\n
In this python script, sample text data table which contains the Matrix synopsis is used for summarization. With _doc\\_summarizer\\_eng_ function, summarized document is returned in output table.\n\n", "exampleModels": [ { "fileName": "brightics.function.textanalytics$doc_summarizer_eng.json", @@ -160,7 +143,7 @@ "name": "brightics.function.io$load", "param": { "partial_path": [ - "/brightics@samsung.com/upload/text_eng.csv" + "/brightics@samsung.com/upload/sample_text_eng.csv" ] }, "display": { @@ -402,7 +385,7 @@ "name": "brightics.function.textanalytics$doc_summarizer_eng", "param": { "result_type": "sentence", - "input_col": "text_hold", + "input_col": "text", "num_sentence": 1, "ratio": 0.2 }, @@ -864,12 +847,7 @@ } ], "preferences": {}, - "variables": { - "path": { - "type": "string", - "value": "" - } - }, + "variables": {}, "variableRef": [], "innerModels": {}, "inData": [], diff --git a/function/python/brightics/function/textanalytics/meta/doc_summarizer_kor.json b/function/python/brightics/function/textanalytics/meta/doc_summarizer_kor.json index a3a0f0125..00809efd0 100644 --- a/function/python/brightics/function/textanalytics/meta/doc_summarizer_kor.json +++ b/function/python/brightics/function/textanalytics/meta/doc_summarizer_kor.json @@ -4,13 +4,12 @@ "content": "" }, "specJson": { - "isFunction": false, "category": "textanalytics", "func": "brightics.function.textanalytics$doc_summarizer_kor77426", "name": "brightics.function.textanalytics$doc_summarizer_kor", "context": "python", "label": "Documents Summarizer (Korean)", - "description": "This function summarizes Korean documents.", + "description": "This function summarizes the given text, by extracting one or more important sentences from the text.", "tags": [], "version": "3.6", "inputs": { @@ -21,32 +20,38 @@ }, "meta": { "table": { - "type": "table" + "type": "table", + "desc": "Korean documents in a Table.", + "optional": false }, "out_table": { - "type": "table" + "type": "table", + "desc": "Summarized Korean documents in a Table." } }, "params": [ { "id": "input_col", "label": "Input Column", - "description": "", - "mandatory": true, - "items": [], + "description": "Column to select as input. The column must contain a Korean document in each row.", "visibleOption": [], "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], "columnType": [ "String" ], - "validation": [], "type": "String", "multiple": false }, { "id": "result_type", "label": "Result Type", - "description": "Output type of summarization.", + "description": "Output type of summarization. If `Summarized Document`, output table shows summarized document in `New Column Name`.\n - Available Options : `Summarized Document`, `Scored Sentence`", + "visibleOption": [], + "control": "RadioButton", + "validation": [], "mandatory": false, "items": [ { @@ -60,41 +65,21 @@ "default": true } ], - "visibleOption": [], - "control": "RadioButton", "columnType": [], - "validation": [], "targetTable": [] }, - { - "id": "hold_cols", - "label": "Hold Columns", - "description": "Column to select as hold.", - "mandatory": false, - "items": [], - "visibleOption": [ - { - "id": "result_type", - "value": "summarized_document" - } - ], - "control": "ColumnSelector", - "columnType": [], - "validation": [], - "multiple": true - }, { "id": "ratio", "label": "Compression Ratio", - "description": "Number between 0 and 1 that determines the proportion of the number of sentences of the original text to be chosen for the summary.", - "mandatory": false, - "items": [], + "description": "Number between 0 and 1 that determines the proportion of the number of sentences of the original text to be chosen for the summary. If None, the ratio is set to the proportion of `Number of Sentences` parameter to the total number of sentences.", "visibleOption": [], "control": "InputBox", - "columnType": [], "validation": [], + "mandatory": false, + "items": [], + "columnType": [], "targetTable": [], - "placeHolder": "", + "placeHolder": "(0 <= value <= 1)", "type": "Double", "min": 0, "max": 1 @@ -102,28 +87,28 @@ { "id": "num_sentence", "label": "Number of Sentences", - "description": "Determines how many sentences will the output contain. If ratio is provided, then this parameter will be ignored.", - "mandatory": false, - "items": [], + "description": "Determines how many sentences will the output contains. If `Compression Ratio` is provided, then this parameter will be ignored.", "visibleOption": [], "control": "InputBox", - "columnType": [], "validation": [], + "mandatory": false, + "items": [], + "columnType": [], "targetTable": [], "type": "Integer", "min": 1, - "placeHolder": "1" + "placeHolder": "1 (value >= 1)" }, { "id": "damping_factor", "label": "Damping Factor", "description": "Damping Factor used in the PageRank algorithm. The default value is 0.85.", - "mandatory": false, - "items": [], "visibleOption": [], "control": "InputBox", - "columnType": [], "validation": [], + "mandatory": false, + "items": [], + "columnType": [], "targetTable": [], "placeHolder": "0.85", "type": "Double", @@ -133,9 +118,7 @@ { "id": "new_col_name", "label": "New Column Name", - "description": "New column name", - "mandatory": false, - "items": [], + "description": "New column name for summarized Korean documents.", "visibleOption": [ { "id": "result_type", @@ -143,13 +126,911 @@ } ], "control": "InputBox", - "columnType": [], "validation": [], + "mandatory": false, + "items": [], + "columnType": [], "targetTable": [], "type": "String", "placeHolder": "summarized_document" } - ] + ], + "summary": "This function summarizes Korean documents." + }, + "md": { + "en": "# Documents Summarizer (Korean)\nThis function summarizes Korean documents.\n\n## Description\nThis function summarizes the given text, by extracting one or more important sentences from the text.\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Korean documents in a Table.\n#### OUTPUT\n1. **out_table**: (Table) Summarized Korean documents in a Table.\n#### PARAMETER\n1. **Input Column***: Column to select as input. The column must contain a Korean document in each row.\n\n2. **Result Type**: Output type of summarization. If `Summarized Document`, output table shows summarized document in `New Column Name`.\n - Available Options : `Summarized Document`, `Scored Sentence`\n\n3. **Compression Ratio**: Number between 0 and 1 that determines the proportion of the number of sentences of the original text to be chosen for the summary. If None, the ratio is set to the proportion of `Number of Sentences` parameter to the total number of sentences.\n\n4. **Number of Sentences**: Determines how many sentences will the output contains. If `Compression Ratio` is provided, then this parameter will be ignored.\n\n5. **Damping Factor**: Damping Factor used in the PageRank algorithm. The default value is 0.85.\n\n6. **New Column Name**: New column name for summarized Korean documents.\n\n\n### Python\n#### USAGE\n\n```\ndoc_summarizer_kor(table = , input_col = , result_type = , ratio = , num_sentence = , damping_factor = , new_col_name = )\n```\n\n#### INPUT\n1. **table***: (Table) Korean documents in a Table.\n#### OUTPUT\n1. **out_table**: (Table) Summarized Korean documents in a Table.\n#### PARAMETER\n1. **input_col***: Column to select as input. The column must contain a Korean document in each row.\n\t* Type: *str*\n2. **result_type**: Output type of summarization. If `Summarized Document`, output table shows summarized document in `New Column Name`.\n - Available Options : `Summarized Document`, `Scored Sentence`\n\t* Type: *str*\n\t* Default / Range: ( summarized_documentscored_sentence | scored_sentence )\n3. **ratio**: Number between 0 and 1 that determines the proportion of the number of sentences of the original text to be chosen for the summary. If None, the ratio is set to the proportion of `Number of Sentences` parameter to the total number of sentences.\n\t* Type: *double*\n\t* Default / Range: (0 <= value <= 1)\n4. **num_sentence**: Determines how many sentences will the output contains. If `Compression Ratio` is provided, then this parameter will be ignored.\n\t* Type: *int*\n\t* Default / Range: 1 (value >= 1)\n5. **damping_factor**: Damping Factor used in the PageRank algorithm. The default value is 0.85.\n\t* Type: *double*\n\t* Default / Range: 0.85\n6. **new_col_name**: New column name for summarized Korean documents.\n\t* Type: *str*\n\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, sample_text_kor data is used as input of Documents Summarizer (Korean) function. The output of this function is a Table with summarized document data added to the input Table. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Input Column***: doc1\n2. **Result Type**: Scored Sentence\n3. **Compression Ratio**: None\n4. **Number of Sentences**: 3\n5. **Damping Factor**: 0.85\n6. **New Column Name**: summarized_document\n\n\n\n### Python\n\n```\nfrom brightics.function.textanalytics import doc_summarizer_kor\ninput_table = inputs[0]\nresult = doc_summarizer_kor(table=input_table, input_col='doc1', result_type='scored_sentence', new_col_name='summarized_document', num_sentence=3, damping_factor=0.85)\noutput = result['out_table']\n```\nIn this tutorial workflow, sample_nsmc data is used as input of Documents Summarizer (Korean) function. 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SiamucljMQBgSN6bs+NG0Utlm8e/7E3wMrKR8eOGirTy1wZvDUx3bUbs3SBO3T25osfnH7z8EV12uDG2MzZ6+OVau3vn3hnx4FzU8X5w+eHSu51b1eHp5/Yc/L1g+c/H5nemMARksY8NfCGUcFiOTJ2RvWfzttRU3YTg/tXWko1Wbfyw18dVwWrRY5eaFztKGmvwJkX5Go7hb6f+eOJXJwRSF5jD9XvR9uzROCIngv5G0ZRi+VvPnrIG2Bl5einJw2J6WWuXr+dS4fAHRkYEoHbeWhAv+4jEDjv/26uDU8/9fanrx04PziSlx1UkTnh4lDinVuE2JWHTr1kVLBY3h06qvpfqqTxyhCOwMVk+4fn/vcPf53EVx/d/Xv3vKIU7b/c9+r/+sHbxt0MbRW4mEP9CR21mKcMggnpB+qDKzwQOKLnxuyIUdRi+frRH3oDrKwcPn7MkJhe5sr1oZQcgTt/c0IE7um9n1Vr9eCRV7ECd+FW7qHXD+85duV2rmsnfwlpXx4/v9OoYLEcGulX/S8scQSuNbp+DdxSpVperCRxaWjyv393p1I0sbfJYrlaCz0goJ0CF3OZhZPYyzXcBON08QQuVvha0UfSg5Et/d9+/JBR16L85MKb3gArK5cHb124ej2TnDh1xvCzJrz17l7pf2h0MiXXwM3MLbz4wZm7n+ubLS9V/ceaRAVO7O7ohdt/86O9xy/dKZYtfqshIUnZe+uIUcFiuVXytrRzqXwrQ7oEbnSiIH+lpVSTpTDHLt752tPv/f69zhG4+1748MzgmGFvkhQInHkHgw8CR9aTV6+9Z9S1KKcmr6ies/0ouBbvQt277z3pUw0ympq7UJertX39g9/f8fHrB89LoVU9RAXuyp2pV/af/dbz+2+Od+3xdYS0NbmFGaOCRXn49Mte3ysr06l8zUzaBM75OzvXnXfOrJojA0MPvHxQydn/e+nA4fPecxOMdF3ggpOhvqKZp1AROLKmlCrlbxx73KhuOs9efMvrdWWlWMryu+1XFThd3Xyc55Prl511MrrAyX9vjM288fHFb7+wf++JqzfHC+JntVr9V0cunfp8TD6V/w6O5F/98NzWXUd3Hhpo/sqZtSSyS5nRZ2TEllaSzvz61mGjjhlc8R+NJOtIeI1OC2k8hTpTLKtWS1vUmxj+8NvbpRr2XwsZlJ5uXwMXlTyugSMbzfXZ4W8e/7FR4BRPDuzyelpZER8YdZ9bm1WaCFycuikcgVtebrycp5MxBK4qijaa3/La4Yd/8ckbhy+euzF+O1ccnS4Jt8YL8t8dB85LoXt455F9/c4bUSsbn23jmUcNvHKUpSBwdmXnYJ9RzRR/dfDBExMDXk8p3ilNn8Dl0vsuVCVwf7n1Lalr0ef3Btm4wMWj9lmD20UT70I17znVRutXzEZJ9bsEI0HgSELKywtS7+468lhQ5h749GcHRn7rfewm8++zjxW4ZHVzGHYFrls31xsCJ6nW6sXy4vb9Z//pJ+999dHd33/145/8+tMn95z8zssH//zf3vr6zz54/eDAqx+ek3/vP31janaD78VJupzDJaPH4YhF6Z+8/G+ntwU17a8/2vLzS3uG5xo3XKf5yeQpPAJXGOve9SLNc300/9nVkYGbE82vC2mvwCX21tidDY6lRWjsFzaZFgJHmmesPHVzdjS/aO5oZfvkqcIQuObqphged/6WunRpb1TgZN+zWq2NTZfODI719V/feWhg+/5z2z889+bhi/tPXZf6NpYvfT6SF42778UDu49elv+qAdeT6L6iJPoYI0K6mlJl/lZp7M7cRK0e2rhXlqvjk0V9dU4VaTyFKnTrdMOmpN0C5yY4zOYQvflUdzjnU79ihvrUjrpJ92DqCBxZa+r1erZfgRoQCFwr6qZQApfv0pUhmsCZRXVpuZovLVwdnjp/c0K87frYzMzcgrorS2zvxtjMc+/2//CXx3/1yeWJmbno3VotJbbyxFywYV7m4STwvPC5BWdUWu2KnHkIXyLs9emYol4VvemGa6DbyU/DMhUh12ycKj0f9ObMeaO703Pi0Ud9WuGyr319iT97W/rOBqOiPncsC4uVsUnnxsrUkjqBU/cxpPltWqtm3QJHiHURdZubXxyfSu9O6uYi0ta6ugUMT8x061Fw9/y87w++tf0vHnnr+KXh/mujSYznvZtSg4ix3ZooPPtO/5bXDr9z8pp7Pdw6HK7hVaYhhbIGgYvhsWfCshV3hUmELY+ZO8yNOUwYKpAn3QU93Jlcq8DFjEc3xZjZCBseaU+WKsuyx2WsxSkkfUfg3L3Vbt3HUKvXN86LCBzJdGq1+uzcQmG2nM4XPKcS5zK44NFrncyju45+6cFdqiI1wXiQb5ChXGFb35mvbt394ekbU8X1XA8Xd2IhaiFrEzhPgPSDZJ6xBePxNajhQF4XfX7M8RgjSbzOWBMv/WifKXChaNM15833Qv8LxthnzAjJ5mZhsVIszecL5Ql7dkdTegq1K8+9vHJn6h+fenfjfHnLm6omInAkkynPL8lKqg6WQyt4Jxbc14x2OJ9dHXn0jaP/9Tuvf+HukLEZJAncYmX52vDUzkMDdz/Xt//U9dl1fYW440wO2tnANQlc0E9E17RpmSdJffeK6JokOuZwzDmJTMVPosBprukffotoosSbW9PwGjNP2hYbTyOkU+CcvdWlinfJbcfSf23UKGobBIEjmYz/TMtUXx2SMpy2knbzWrCDKcwtnh4c2/XRhe37z77Sl8jAzcQjPPPO9XD5nQcHzt+YCO6EWE8ax5Ma+AK0FoFrGE/MUOa1vP5EG6dHIzYWP3UvwUehQZJELaF749ihNlGtY4TEmSftif+qU8c9LCKlR+CEYmmDt6+vOSJw/+fHv9lEEDiSvdTq7jMtxy2rdN3HbTFpPa8drUqtVp8pLSxWNu3esuAIli80KRM47YBZiHUJXOPLhgSxmcB5c4vAdSqW3kGfXoHr4tsDCSFJUedPhyMrLDRHtVhXzqJ2MUmiE7GxVAmcplZqWpFB1iBw/gwI/uFGlejXiQSB61QsvQ0rtQLnnHHo1qMvCSFJyU3PZvtdC+1D2k1az2vH3oh25b5+8Ek7KGUKXJz6dFjgWhikVYELBtSG9RMz82YQuI5ETMNdQ+27JiSlAqfeaj/lv26ZEJKGqIeSc/htfah2kzb0WrMnoh3NiiNQk0Dp/C7agF0SOL+H4NO1Cpw2YNxhtobdmhPyv06cwCU64mrzRpIipiErprIOu0jvKVT1PJHO38pACEmKf/sCrJ+u3MrQzQQ+FKWhUK301imBayKd/qRbEjj98JuBNx7N8DQas4rAtT/e7Quub1hHigXOpeeKHSFpDYffNk5PHoSTRE0lTi90hxMdMdWkYwIn0R3OmVV/tN5sb5LAOWkch3MIjxCBa3+s3ilNu8AJvVfsCEljeGzvZiEt6bUpIaR7UTul9mKBwE3me+uyX0JSmPKCc/MpbBZWvy2QkGxE7MJYMe3CAoETSuVFr70JIR1PvV53brPPFUa4/3TjSBvmCtKe0qpe+xJCOh7xCnPdtA0LBE5dNVJZ3rRnSBJC1pTCrPuUS+xts3BbUlrVa19CSGcjRiHroO1X9NpxBE7gqhFCuhJOnrYPTqQS0pVk44peawRO6PzLtQjp8ch+6liu4D4hyb6HJKUbp1WlbTm3QEiHY+mLs6LYJHDC3DwXwxHSodTr9cm8s59q4yMu049qVWlhLoYjpGMRi9BXQ6uxRuBGXeQfvF+LkM5EPSFJrXfQDlTb8rRLQjoT9dasQCdsx7IjcCO5mdFcYXEJhyOkvckXHXvjsb3tRrWwtLbX7oSQ9kTMQfwhSzdj2SZwLuLOOBwh7ctMseysa3a+XsY+3HaWNvdanxCy2XHszVjv7MdKgVPMcy6VkDYkzwtPu4S0vLcMCCGbF7EFY13LBrYKnLr+d44H/BKyeanWav6jyfPBugadwmlzaX9ZCt7yIIRsOOIJsmZl8k4si4/AKTjvQMimZGGxMjbp1Lhhzpx2CdXyshS4VYuQTYl3NUhGsVrgZIfV2WedmC7ywntCNhL1YKTRXEZuzrIX5/449yJrHntJyEYiViBu4K5WnipkD+uPwAVIveNxSoSsNYtLldy0Om1ayGqZsw1ZCs6hUFku3K1FyFojJpCZR/U2JzsCJ4xPFsvzvJqGkJayvFzNN84voG5pw1sisoxkSXnLjBDSNOIAYgLaepRlMiVwquRNTM+icYQ0yXK15r2fHixBlpcsNW/5EUIike2+bP3d9aVXdkczKHDqCpLxqWKpvFil5BGiZWGxot6vADYiy477GwjRI1t52dbLFl9WELX1R+Cyg5S8ufkl7swnvZylynKhNK9qnMKvdGAH+vKS5ShLU5apt3QJ6b3INl227D2+O5ppgcs5FwI7r85w/5ubnp2ZLZcXpO5VuduBZDuyVyq/dNkxnZopBasAZAxZsrJ8ZSnLsuZsA8l2ZKst227Zgst23L/vyt++92qJy/4ROEE9GcF4q+PYZCGXn5Xyly+WZ4pl+QtgKeoHLDuj8nuW0qYbm/ez59Fu2cNdpnpZk+UuS19+A/JLCH4VAJaifsBOTcvPqkdUBqifPc886gmBaxDejLH4IUuox8CGja1XrgXpbdylrC19HsUMWcLcUvPz9ukxgUtEKiCA1Rg/aQDjFwJgHcZPGkIgcAAAAACWgcABAAAAWAYCBwAAAGAZCBz0Fuf2/PSuR/aei3RXOJ/eu/1ApHuEz994ZOtdzx9X/z3w/Na77o3y0zcu6YP4fLJd66cxrVZGEp55dx683prOszbF7+353Pw0ivSf3EQAkDKOP3nv1ic/MToGOJ+2uuIHBefS3u/5RSOEX/TC6LUoMi294sUMHp75VouVPsWEShvC6T+5iWwFgYNeQlWHJDvxatbqAud6XlIt88cT96kaMKgjrrQlVJ/oSJwuQc9Rg4yf7fAUnVqZONsazghb6A0Auo2nMkl2ovYMWxA4tzgky1BysXIHDIqqUbjcktuk/oRKTcQgE2bb/cr+FN0SlzjbDUL1MyMgcNArqELmEC9wwS7dagKnKlSkEvmEikvMR6GhnIoWV6SiI3G6BH1GalbSeCIVUy+RTchisQPIGkEtShI4/4DWqgLnl8eEtT7kYWEiJcWtTqqKmnXMLFzOsEG9NYuVNp4w5szEeGEsIVnMBAgc9ATB7qPzjzi7corFI3sPhEuGO5RRQZRIHY+omE+k0sWNJCCh9ETLZbhKRr9F/Pdy63u47Oqq55ZXt747hGfDGWG2ih1AplD2JitpzGqucFb2Jz/RV3lvKNPnlEiFi4xGZH8ydiQ+mqU1Fzj306DIRL9F0vcyy2O4irqf+oS/jjPC2C9oKwgc9BbxouNXrsR9Ph9/8HDpaRCpdM2Jr4MxIwnPdszUnR6icx5TAYNiZ0wlXOWFxGoOAGkiXnSCKhFZtQ2CwZNWeVOYViFUi0LDusUnqDnGbEenHl8ehVDtcou2P57wrEbqeUzltBoEDnqLOIFrFLhVBK5RYhIKwdoqnTuS6ORiRmJMLmbqCXMeqd1uTVxbWTe6A0CqiFtVtYLQfE3XiklUoYIezJqZjFu+wpNzvc0l1N2YXMzUm8258jaXRt1bpYDH13+LQeCgt4iuwHqXpuu/Xk1iFEoI7XquhttzjCHFjcQoZK0LnFfm/Kn4ldQdVs1Aa98XANJKVOBCXVbVIL8CxAqcO6pW64Dbs15g3SITjNMtXP7kzJLVusCpqQQFUFdG9VGL39d+EDjoLZyCogtcuGo0Wb3DA8YKnKtHEauLRclT1N7iR2LW0JipuyOMn3PlcL6rhYd1a5/2qT4gAgdgA2590IqJUR+SV2RjwDiBc6tHxOpiUfKkV1ezcAmNmTFLVnTqMYM7RGtduG4rTfQwBkfgACwm7GGeSEWJVA3Xq2LQKo4rQ3FOZuDVl/g+40dilmDjW8R2ScAZVcykVfENlbbkug8A6cHwMG9djmKKi7ZrF6Yhf26xaqWwqF1Bo09jxhwacmm6VLTnmMEdYmpdnHoKqrzrRQyBA7CY5qLT8upt7OM6tDassrfEPdqEkZiTi/SWJFuR7gmVTnDHqX2UUD0BIF2ssqq2vCcWUxxaG1bZW7geOrgzFh5cG6E5OXNaSRXV1bJQ9+Ta64xTn7GWd3TtAIGD3qJ9AtdKaXBLT6K9CUkjiXR3C5PfxR1t/GyHvlGonhqlzf1G+iSSVQ8AUkT7BK6VvTi3nxh7cwlXPLX7GipH+sjdYuV3CRWrMMYUXX1Ufbpj0L6C+5E2iZi6bTUIHPQWSYakMAQuWYyihSAiQD6Nkai6E6UxVOJI3GENnVLVUKHPpLnTqcqiIlyL3T4DwtN1ZjtDlQ4gs0RMKExY4JqIUVTgTAHy0Ubi1rcYgqH0+rN6kXGnqNBn0jSzcO3S5zA0OWPmY6qo3SBwAFbQ2X3HzFU6AEgdUWVsJ9nbKUXgACyhg1LF4TcA6ACdKzVZ3ClF4ACswTk7EHuCdXOR3eIOTAUAwD0Zmnz+d7NwzmC0fyqdBoEDAAAAsAwEDgAAAMAyEDgAAAAAy0DgAAAAACwDgQMAAACwDAQOAAAAwDIQOAAAAADLQOAAAAAALAOBAwAAALAMBA4AAADAMhA4AAAAAMtA4AAAAAAsA4EDAAAAsAwEDgAAAMAyEDgAAAAAy0DgAAAAACwDgQMAAACwDAQOAAAAwDIQOAAAAADLQOAAAAAALAOBAwAAALAMBA4AAADAMhA4AAAAAMtA4AAAAAAsA4EDAAAAsAwEDgAAAMAyEDgAAAAAy0DgAAAAACwDgQMAAACwDAQOAAAAwDIQOAAAAADLQOAAAAAALAOBAwAAALAMBA4AAADAMhA4AAAAAMtA4AAAAAAsA4EDAAAAsAwEDgAAAMAyEDgAAAAAy0DgAAAAACwDgQMAAACwDAQOAAAAwDIQOAAAAADLQOAAAAAALAOBAwAAALAMBA4AAADAMhA4AAAAAMtA4AAAAACsYmLmPwDSiaiQZGsXuQAAAABJRU5ErkJggg==", + "description": "In this tutorial workflow, sample_text_kor data is used as input of Documents Summarizer (Korean) function. The output of this function is a Table with summarized document data added to the input Table. The parameter settings used in the function are shown below.", + "parameter": "1. **Input Column***: doc1\n2. **Result Type**: Scored Sentence\n3. **Compression Ratio**: None\n4. **Number of Sentences**: 3\n5. **Damping Factor**: 0.85\n6. **New Column Name**: summarized_document\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.textanalytics import doc_summarizer_kor\ninput_table = inputs[0]\nresult = doc_summarizer_kor(table=input_table, input_col='doc1', result_type='scored_sentence', new_col_name='summarized_document', num_sentence=3, damping_factor=0.85)\noutput = result['out_table']", + "context": "python", + "description": "In this tutorial workflow, sample_nsmc data is used as input of Documents Summarizer (Korean) function. The output of this function is a Table with summarized document data added to the input Table." + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/textanalytics/meta/doc_term_mtx.json b/function/python/brightics/function/textanalytics/meta/doc_term_mtx.json index 50c44c1da..d9e73be0a 100644 --- a/function/python/brightics/function/textanalytics/meta/doc_term_mtx.json +++ b/function/python/brightics/function/textanalytics/meta/doc_term_mtx.json @@ -339,8 +339,8 @@ }, { "persist-mode": "auto", - "func": "brightics.function.textanalytics$tokenizer_eng51532", - "name": "brightics.function.textanalytics$tokenizer_eng", + "func": "brightics.function.textanalytics$tokenizer_eng2", + "name": "brightics.function.textanalytics$tokenizer_eng2", "param": { "stemming": false, "pos_extraction": [ diff --git a/function/python/brightics/function/textanalytics/meta/dtm.json b/function/python/brightics/function/textanalytics/meta/dtm.json new file mode 100644 index 000000000..3c96befde --- /dev/null +++ b/function/python/brightics/function/textanalytics/meta/dtm.json @@ -0,0 +1,1460 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "textanalytics", + "func": "brightics.function.textanalytics$dtm", + "name": "brightics.function.textanalytics$dtm", + "context": "python", + "label": "Dynamic Topic Modeling", + "description": "Dynamic topic models are generative models that can be used to analyze the evolution of (unobserved) topics of a collection of documents over time. This family of models was proposed by David Blei and John Lafferty and is an extension to Latent Dirichlet Allocation (LDA) that can handle sequential documents. In LDA, both the order the words appear in a document and the order the documents appear in the corpus are oblivious to the model. Whereas words are still assumed to be exchangeable, in a dynamic topic model the order of the documents plays a fundamental role. More precisely, the documents are grouped by time slice (e.g.: years) and it is assumed that the documents of each group come from a set of topics that evolved from the set of the previous slice.\n\nReference\n\n+ https://en.wikipedia.org/wiki/Dynamic_topic_model", + "tags": [ + "dtm", + "topic modeling" + ], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "out_table": "", + "topic_table": "", + "model": "" + }, + "meta": { + "table": { + "type": "table", + "desc": "Data including text documents.", + "optional": false + }, + "out_table": { + "type": "table", + "desc": "Topic classification result attached to the input table." + }, + "topic_table": { + "type": "table", + "desc": "Table which consists of each topic's vocabulary and weights." + }, + "model": { + "type": "model", + "desc": "Result model." + } + }, + "params": [ + { + "id": "input_col", + "label": "Input Column", + "description": "Text data column to select as an input. Data type should be of string array type.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "globalVariable": false, + "columnType": [ + "String[]" + ], + "multiple": false + }, + { + "id": "topic_name", + "label": "Topic Column Name", + "description": "Column name for topic modeling result.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "String", + "placeHolder": "topic" + }, + { + "id": "num_topic", + "label": "Number of Topics", + "description": "The number of topics that will be contained in the entire documents. It should be greater than or equal to two.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Integer", + "min": 2, + "placeHolder": "5 (value >= 2)" + }, + { + "id": "num_topic_word", + "label": "Number of Terminologies", + "description": "Maximum number of terminologies you want to show for particular topics. It should be greater than or equal to two. Terminology number should be less than or equal to vocabulary size also.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "placeHolder": "10 (value >= 2)", + "type": "Integer", + "min": 2 + }, + { + "id": "max_iter", + "label": "Iterations", + "description": "Maximum number of iterations you want to run your model. It should be at least one.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Integer", + "min": 1, + "placeHolder": "20 (value >= 1)" + }, + { + "id": "time_slice", + "label": "Time Slice", + "description": "The numbers of documents for each period. The sum of the numbers should be equal to the total number of documents. Default value is a singleton with the number of documents alone.", + "visibleOption": [], + "control": "ArrayInput", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "type": "Integer", + "placeHolder": "(value >= 1)", + "min": 1 + }, + { + "id": "coherence", + "label": "Coherence Measure", + "description": "Coherence measure of the resulting topic modeling.", + "visibleOption": [], + "control": "RadioButton", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "UMass", + "value": "u_mass", + "default": true + }, + { + "label": "C_V", + "value": "c_v", + "default": false + } + ], + "columnType": [] + }, + { + "id": "vis_time", + "label": "Time to visualize", + "description": "Time period to visualize the result. pyLDAvis visualization for the chosen time period will be generated in the model tab. It should be greater than or equal to zero, and less than the number of time periods.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Integer", + "min": 0, + "placeHolder": "0 (0 <= value <= # time slices)" + }, + { + "id": "seed", + "label": "Seed", + "description": "The seed used by the random number generator.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Integer", + "placeHolder": "" + } + ], + "summary": "This function builds a topic model for text documents with regard to time." + }, + "md": { + "en": "# Dynamic Topic Modeling\nThis function builds a topic model for text documents with regard to time.\n\n## Description\nDynamic topic models are generative models that can be used to analyze the evolution of (unobserved) topics of a collection of documents over time. This family of models was proposed by David Blei and John Lafferty and is an extension to Latent Dirichlet Allocation (LDA) that can handle sequential documents. In LDA, both the order the words appear in a document and the order the documents appear in the corpus are oblivious to the model. Whereas words are still assumed to be exchangeable, in a dynamic topic model the order of the documents plays a fundamental role. More precisely, the documents are grouped by time slice (e.g.: years) and it is assumed that the documents of each group come from a set of topics that evolved from the set of the previous slice.\n\nReference\n\n+ https://en.wikipedia.org/wiki/Dynamic_topic_model\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data including text documents.\n#### OUTPUT\n1. **out_table**: (Table) Topic classification result attached to the input table.\n2. **topic_table**: (Table) Table which consists of each topic's vocabulary and weights.\n3. **model**: (Model) Result model.\n#### PARAMETER\n1. **Input Column***: Text data column to select as an input. Data type should be of string array type.\n\n2. **Topic Column Name**: Column name for topic modeling result.\n\n3. **Number of Topics**: The number of topics that will be contained in the entire documents. It should be greater than or equal to two.\n\n4. **Number of Terminologies**: Maximum number of terminologies you want to show for particular topics. It should be greater than or equal to two. Terminology number should be less than or equal to vocabulary size also.\n\n5. **Iterations**: Maximum number of iterations you want to run your model. It should be at least one.\n\n6. **Time Slice**: The numbers of documents for each period. The sum of the numbers should be equal to the total number of documents. Default value is a singleton with the number of documents alone.\n\n7. **Coherence Measure**: Coherence measure of the resulting topic modeling.\n + Available Options: `UMass`, `C_V`\n\n8. **Time to visualize**: Time period to visualize the result. pyLDAvis visualization for the chosen time period will be generated in the model tab. It should be greater than or equal to zero, and less than the number of time periods.\n\n9. **Seed**: The seed used by the random number generator.\n\n\n### Python\n#### USAGE\n\n```\ndtm(table= ,input_col= ,topic_name= ,num_topic= ,num_topic_word= ,max_iter= ,time_slice= ,coherence= ,vis_time= ,seed=)\n```\n\n#### INPUT\n1. **table***: (Table) Data including text documents.\n#### OUTPUT\n1. **out_table**: (Table) Topic classification result attached to the input table.\n2. **topic_table**: (Table) Table which consists of each topic's vocabulary and weights.\n3. **model**: (Model) Result model.\n#### PARAMETER\n1. **input_col***: Text data column to select as an input. Data type should be of string array type.\n\t* Type: *str*\n2. **topic_name**: Column name for topic modeling result.\n\t* Type: *str*\n3. **num_topic**: The number of topics that will be contained in the entire documents. It should be greater than or equal to two.\n\t* Type: *int*\n\t* Default / Range: 5 (value >= 2)\n4. **num_topic_word**: Maximum number of terminologies you want to show for particular topics. It should be greater than or equal to two. Terminology number should be less than or equal to vocabulary size also.\n\t* Type: *int*\n\t* Default / Range: 10 (value >= 2)\n5. **max_iter**: Maximum number of iterations you want to run your model. It should be at least one.\n\t* Type: *int*\n\t* Default / Range: 20 (value >= 1)\n6. **time_slice**: The numbers of documents for each period. The sum of the numbers should be equal to the total number of documents. Default value is a singleton with the number of documents alone.\n\t* Type: *list[int]*\n7. **coherence**: Coherence measure of the resulting topic modeling.\n\t* Type: *str*\n\t* Default / Range: u_mass ( u_mass | c_v )\n8. **vis_time**: Time period to visualize the result. pyLDAvis visualization for the chosen time period will be generated in the model tab. It should be greater than or equal to zero, and less than the number of time periods.\n\t* Type: *int*\n\t* Default / Range: 0 (0 <= value <= # time slices)\n9. **seed**: The seed used by the random number generator.\n\t* Type: *int*\n\n\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, sample_LatentDirichletAllocation data is used to make topic model and get the result. The result includes assigned topic and contributions of each word to assigned topic. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Input Column***: tokenized_text\n2. **Topic Column Name**: topic\n3. **Number of Topics**: 3\n4. **Number of Terminologies**: 10\n5. **Iterations**: 20\n6. **Time Slice**: 2, 3, 3\n7. **Coherence Measure**: UMass\n8. **Time to visualize**: 0\n9. **Seed**: None\n\n\n\n### Python\n\n```\nfrom brightics.function.textanalytics import dtm\ninput_table = inputs[0]\nresult = dtm(table=input_table, input_col='tokenized_text',\n topic_name='topic', num_topic=3, num_topic_word=10, max_iter=20, time_slice=[2, 3, 3],\n coherence='u_mass', vis_time=0, seed=None)\noutput = result['out_table']\n```\nIn this tutorial workflow, sample_LatentDirichletAllocation data is used to make topic model and get the result. The result includes assigned topic and contributions of each word to assigned topic. 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", + "description": "In this tutorial workflow, sample_LatentDirichletAllocation data is used to make topic model and get the result. The result includes assigned topic and contributions of each word to assigned topic. The parameter settings used in the function are shown below.", + "parameter": "1. **Input Column***: tokenized_text\n2. **Topic Column Name**: topic\n3. **Number of Topics**: 3\n4. **Number of Terminologies**: 10\n5. **Iterations**: 20\n6. **Time Slice**: 2, 3, 3\n7. **Coherence Measure**: UMass\n8. **Time to visualize**: 0\n9. **Seed**: None\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.textanalytics import dtm\r\ninput_table = inputs[0]\r\nresult = dtm(table=input_table, input_col='tokenized_text',\r\n topic_name='topic', num_topic=3, num_topic_word=10, max_iter=20, time_slice=[2, 3, 3],\r\n coherence='u_mass', vis_time=0, seed=None)\r\noutput = result['out_table']", + "description": "In this tutorial workflow, sample_LatentDirichletAllocation data is used to make topic model and get the result. The result includes assigned topic and contributions of each word to assigned topic. The parameter settings used in the function are shown below.", + "context": "python" + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/textanalytics/meta/extract_senti_words.json b/function/python/brightics/function/textanalytics/meta/extract_senti_words.json index 6e484da9f..6b77dcde6 100644 --- a/function/python/brightics/function/textanalytics/meta/extract_senti_words.json +++ b/function/python/brightics/function/textanalytics/meta/extract_senti_words.json @@ -9,7 +9,7 @@ "name": "brightics.function.textanalytics$extract_senti_words", "context": "python", "label": "Extract Sentimental Words", - "description": "This function extracts sentimental words from given documents. A data of the first column in user-dictionary should be words and the second column should be sentimental scores. A range of scores of the default dictionary is [-2, 2].", + "description": "This function extracts sentimental words from given documents. A data of the first column in user-dictionary should be words and the second column should be sentimental scores. A range of scores of the default dictionary is [-2, 2]. This function uses the sentiment lexicon dataset from Hu and Liu, KDD-2004 for English and the KNU Korean sentiment lexicon dataset for Korean.", "tags": [], "version": "3.6", "inputs": { @@ -46,23 +46,11 @@ "String[]" ], "multiple": false - }, - { - "id": "hold_cols", - "label": "Hold Columns", - "description": "Columns to select as hold.", - "visibleOption": [], - "control": "ColumnSelector", - "validation": [], - "mandatory": false, - "items": [], - "columnType": [], - "multiple": true } ], "summary": "This function is to extract Sentimental Words." }, - "md": "# Extract Sentimental Words\nThis function is to extract Sentimental Words.\n\n## Description\nThis function extracts sentimental words from given documents. A data of the first column in user-dictionary should be words and the second column should be sentimental scores. A range of scores of the default dictionary is [-2, 2].\n\nReference\n- \n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data including sentimental words in a Table.\n2. **user_dict**: (Table) User dictionary data which includes sentiment score for a word.\n#### OUTPUT\n1. **out_table**: (Table) Extracted sentiment words in a table.\n\n#### PARAMETER\n1. **Input Column***: Words column to select as input. It should be a list of string.\n\n2. **Hold Columns**: Columns to select as hold.\n\n\n### Python\n#### USAGE\n\n```\nextract_senti_words(table, input_col, user_dict=None, hold_cols=None)\n```\n\n#### INPUT\n1. **table***: (Table) Data including sentimental words in a Table.\n2. **user_dict**: (Table) User dictionary data which includes sentiment score for a word.\n#### OUTPUT\n1. **out_table**: (Table) Extracted sentiment words in a table.\n#### PARAMETER\n1. **input_col***: Words column to select as input.\n\t* Type: *list[str]*\n2. **hold_cols**: Columns to select as hold.\n\t* Type: *list[str]*\n\n## Example\n### VA\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, words data and user dictionary data are used to extract sentimental words. The result includes sentimental words extracted from the input words data. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Input Column***: tokenized_Sentence\n2. **Hold Columns**: None\n\n\n### Python\n\n```\nfrom brightics.function.textanalytics import extract_senti_words\ninput_table0 = inputs[0]\ninput_table1 = inputs[1]\nresult = extract_senti_words(table=input_table0,\n input_col='tokenized_Sentence',\n user_dict=input_table1,\n hold_cols=None)\noutput = result['out_table']\n```\nIn this python script, words data and user dictionary data are used to extract sentimental words. The result includes sentimental words extracted from the input words data.", + "md": "# Extract Sentimental Words\nThis function is to extract Sentimental Words.\n\n## Description\nThis function extracts sentimental words from given documents. A data of the first column in user-dictionary should be words and the second column should be sentimental scores. A range of scores of the default dictionary is [-2, 2]. This function uses the sentiment lexicon dataset from Hu and Liu, KDD-2004 for English and the KNU Korean sentiment lexicon dataset for Korean.\n\nReference\n+ \n+ \n+ \n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data including sentimental words in a Table.\n2. **user_dict**: (Table) User dictionary data which includes sentiment score for a word.\n#### OUTPUT\n1. **out_table**: (Table) Extracted sentiment words in a table.\n\n#### PARAMETER\n1. **Input Column***: Words column to select as input. It should be a list of string.\n\n\n### Python\n#### USAGE\n\n```\nextract_senti_words(table, input_col, user_dict=None)\n```\n\n#### INPUT\n1. **table***: (Table) Data including sentimental words in a Table.\n2. **user_dict**: (Table) User dictionary data which includes sentiment score for a word.\n#### OUTPUT\n1. **out_table**: (Table) Extracted sentiment words in a table.\n#### PARAMETER\n1. **input_col***: Words column to select as input.\n\t* Type: *list[str]*\n\n## Example\n### VA\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, words data and user dictionary data are used to extract sentimental words. The result includes sentimental words extracted from the input words data. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Input Column***: tokenized_Sentence\n\n\n### Python\n\n```\nfrom brightics.function.textanalytics import extract_senti_words\ninput_table0 = inputs[0]\ninput_table1 = inputs[1]\nresult = extract_senti_words(table=input_table0,\n input_col='tokenized_Sentence',\n user_dict=input_table1)\noutput = result['out_table']\n```\nIn this python script, words data and user dictionary data are used to extract sentimental words. The result includes sentimental words extracted from the input words data.", "exampleModels": [ { "fileName": "brightics.function.textanalytics$extract_senti_words.json", @@ -87,7 +75,7 @@ "name": "brightics.function.io$load", "param": { "partial_path": [ - "/brightics@samsung.com/upload/script1.csv" + "/brightics@samsung.com/upload/sample_extractSentimentalWords.csv" ] }, "display": { @@ -329,7 +317,7 @@ "name": "brightics.function.io$load", "param": { "partial_path": [ - "/brightics@samsung.com/upload/user_dict.txt" + "/brightics@samsung.com/upload/sample_esw_user_dict.csv" ] }, "display": { @@ -1029,8 +1017,8 @@ }, { "persist-mode": "auto", - "func": "brightics.function.textanalytics$tokenizer_kor", - "name": "brightics.function.textanalytics$tokenizer_kor", + "func": "brightics.function.textanalytics$tokenizer_kor2", + "name": "brightics.function.textanalytics$tokenizer_kor2", "param": { "normalization": true, "stemming": true, diff --git a/function/python/brightics/function/textanalytics/meta/gsdmm.json b/function/python/brightics/function/textanalytics/meta/gsdmm.json new file mode 100644 index 000000000..081ae3c6c --- /dev/null +++ b/function/python/brightics/function/textanalytics/meta/gsdmm.json @@ -0,0 +1,1644 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "textanalytics", + "func": "brightics.function.textanalytics$gsdmm", + "name": "brightics.function.textanalytics$gsdmm", + "context": "python", + "label": "GSDMM", + "description": "GSDMM stands for collapsed Gibbs Sampling algorithm for the Dirichlet Multinomial Mixture model. This function classifies given short text documents into clusters in which documents share the same topic. It is recommended to perform pre-process for the input, such as converting letters into lower case, removing stop-words, stemming for words, removing words with too high or too low frequencies, etc. This pre-process should be performed carefully to get better results.\n\nInput column should be in an array type containing tokenized terms. K is an upper bound of the number of topics, so if true number of topics is larger than K, it may not work properly as expected. Increasing number of iterations for running LDA model would give you stable results.\n\n+ Reference: Yin & Wang, 2014", + "tags": [ + "gsdmm", + "topic modeling", + "short text topic modeling" + ], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "out_table": "", + "topic_table": "", + "model": "" + }, + "meta": { + "table": { + "type": "table", + "desc": "Data including text documents.", + "optional": false + }, + "out_table": { + "type": "table", + "desc": "Topic classification result attached to the input table." + }, + "topic_table": { + "type": "table", + "desc": "Table which consists of each topic's vocabulary and weights." + }, + "model": { + "type": "model", + "desc": "Result model." + } + }, + "params": [ + { + "id": "input_col", + "label": "Input Column", + "description": " Text data column to select as an input. Data type should be of string array type.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "globalVariable": false, + "columnType": [ + "String[]" + ], + "multiple": false + }, + { + "id": "topic_name", + "label": "Topic Column Name", + "description": "Column name for topic modeling result.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "String", + "placeHolder": "topic" + }, + { + "id": "K", + "label": "K", + "description": "Upper bound of the number of topics that will be contained in the entire documents. It should be greater than or equal to two.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Integer", + "min": 2, + "placeHolder": "10 (value >= 2)" + }, + { + "id": "alpha", + "label": "Alpha", + "description": "Alpha, one of the parameters for GSDMM. It relates to the prior probability of a document choosing a cluster (completeness). If alpha gets larger, so does the probability of a document choosing an empty cluster. It should be greater than or equal to zero.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Double", + "min": 0, + "placeHolder": "0.1 (value >= 0)" + }, + { + "id": "beta", + "label": "Beta", + "description": "Beta, one of the parameters for GSDMM. It relates to homogeneity. If beta gets larger, so does the probability of a cluster containing mixed topics. It should be greater than or equal to zero.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Double", + "min": 0, + "placeHolder": "0.1 (value >= 0)" + }, + { + "id": "num_topic_words", + "label": "Number of Terminologies", + "description": "Maximum number of words to be shown for each topic. It should be greater than or equal to two.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "placeHolder": "3 (value >= 2)", + "type": "Integer", + "min": 2 + }, + { + "id": "max_iter", + "label": "Iterations", + "description": "Maximum number of iterations you want to run your model. It should be at least one.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Integer", + "min": 1, + "placeHolder": "50 (value >= 1)" + } + ], + "summary": "This function classifies given short text documents into clusters in which documents share the same topic." + }, + "md": { + "en": "# GSDMM\nThis function classifies given short text documents into clusters in which documents share the same topic.\n\n## Description\nGSDMM stands for collapsed Gibbs Sampling algorithm for the Dirichlet Multinomial Mixture model. This function classifies given short text documents into clusters in which documents share the same topic. It is recommended to perform pre-process for the input, such as converting letters into lower case, removing stop-words, stemming for words, removing words with too high or too low frequencies, etc. This pre-process should be performed carefully to get better results.\n\nInput column should be in an array type containing tokenized terms. K is an upper bound of the number of topics, so if true number of topics is larger than K, it may not work properly as expected. Increasing number of iterations for running LDA model would give you stable results.\n\n+ Reference: Yin & Wang, 2014\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data including text documents.\n#### OUTPUT\n1. **out_table**: (Table) Topic classification result attached to the input table.\n2. **topic_table**: (Table) Table which consists of each topic's vocabulary and weights.\n3. **model**: (Model) Result model.\n#### PARAMETER\n1. **Input Column***: Text data column to select as an input. Data type should be of string array type.\n\n2. **Topic Column Name**: Column name for topic modeling result.\n\n3. **K**: Upper bound of the number of topics that will be contained in the entire documents. It should be greater than or equal to two.\n\n4. **Alpha**: Alpha, one of the parameters for GSDMM. It relates to the prior probability of a document choosing a cluster (completeness). If alpha gets larger, so does the probability of a document choosing an empty cluster. It should be greater than or equal to zero.\n\n5. **Beta**: Beta, one of the parameters for GSDMM. It relates to homogeneity. If beta gets larger, so does the probability of a cluster containing mixed topics. It should be greater than or equal to zero.\n\n6. **Number of Terminologies**: Maximum number of words to be shown for each topic. It should be greater than or equal to two.\n\n7. **Iterations**: Maximum number of iterations you want to run your model. It should be at least one.\n\n\n### Python\n#### USAGE\n\n```\ngsdmm(table= ,input_col= ,topic_name= ,K= ,alpha= ,beta= ,num_topic_words= ,max_iter=)\n```\n\n#### INPUT\n1. **table***: (Table) Data including text documents.\n#### OUTPUT\n1. **out_table**: (Table) Topic classification result attached to the input table.\n2. **topic_table**: (Table) Table which consists of each topic's vocabulary and weights.\n3. **model**: (Model) Result model.\n#### PARAMETER\n1. **input_col***: Text data column to select as an input. Data type should be of string array type.\n\t* Type: *str*\n2. **topic_name**: Column name for topic modeling result.\n\t* Type: *str*\n3. **K**: Upper bound of the number of topics that will be contained in the entire documents. It should be greater than or equal to two.\n\t* Type: *int*\n\t* Default / Range: 10 (value >= 2)\n4. **alpha**: Alpha, one of the parameters for GSDMM. It relates to the prior probability of a document choosing a cluster (completeness). If alpha gets larger, so does the probability of a document choosing an empty cluster. It should be greater than or equal to zero.\n\t* Type: *double*\n\t* Default / Range: 0.1 (value >= 0)\n5. **beta**: Beta, one of the parameters for GSDMM. It relates to homogeneity. If beta gets larger, so does the probability of a cluster containing mixed topics. It should be greater than or equal to zero.\n\t* Type: *double*\n\t* Default / Range: 0.1 (value >= 0)\n6. **num_topic_words**: Maximum number of words to be shown for each topic. It should be greater than or equal to two.\n\t* Type: *int*\n\t* Default / Range: 3 (value >= 2)\n7. **max_iter**: Maximum number of iterations you want to run your model. It should be at least one.\n\t* Type: *int*\n\t* Default / Range: 50 (value >= 1)\n\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, sample_LatentDirichletAllocation data is used to make topic model and get the result. The result includes assigned topic and contributions of each word to assigned topic. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Input Column***: tokenized_text\n2. **Topic Column Name**: topic\n3. **K**: 10\n4. **Alpha**: 0.1\n5. **Beta**: 0.1\n6. **Number of Terminologies**: 3\n7. **Iterations**: 50\n\n\n\n### Python\n\n```\nfrom brightics.function.textanalytics import gsdmm\ninput_table = inputs[0]\nresult = gsdmm(table=input_table, input_col='tokenized_text',\n topic_name='topic', K=10, alpha=0.1, beta=0.1,\n max_iter=50, num_topic_words=3)\noutput = result['out_table']\n```\nIn this tutorial workflow, sample_LatentDirichletAllocation data is used to make topic model and get the result. The result includes assigned topic and contributions of each word to assigned topic. 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BWVLVOoPIhl3/viyg/XH/rxhsORZF7uEdO33tgZKW2dCpo6qH2juPOoV4ehd0nlXu9TkpNJZ25pUsegPXhNq/dwVomVnlKj8sHdQ/8y8WtR04R/Pv7ChpufzpaS1t3mKWPCdwRy+Lcbj6rgLpjvrt0niukfPblJ1NlbkZR1qy/0xiR8eViIPnnqdVlqFTvvH7UWMvfYUYZzyPh7Y2nBDtl6gYYepTc+duWh6Ipf2H5i8k40W6y4d4FptlqFcu3q/fhrO0+/+OHJA+fvyg8B6Y0HHutVcn7AMPbGzjLDf3k7Z/OtvUo1k545vy5dbX9GH0uF/IARpZo5OnXI8vr2IzkRl4jSG88kcuYm4mOfnLxpdL/e/fpEEXsYz+48eUP0xpsOXroXNfYD70dv7EyZAqgjtFMZ9DSi3Ue9u056l1S6We/3CuqNlRmm9wehN17huZy68+1jzykFTTocab+JCqWw7RET/t44P667Im87cvXvXvxEFmUxd7xyP2bd4Q29MQlZcrXio8dfVOqs22cP2zOMcO9Z3ak3ljp1yLPx0RyYJ+L0xmLGWKzU3t574ZktR0/dmClX62duRj748prbvrO3q7WGuPeVjyae2fKlaJ6bzeWdi2sF9cYji117203jqHvjxaenNagfAYw4W2/vV+qY20/OvGEtNz9fCvtHfkodU3TqkEd1zlR3byzq1dmp2b9/eddHx65PJ3KzqbxS04Q7s2lx1/tfXHn8jf1HLt2v1ZvL7o27NcaSaxbXbTi7xvICo97VtSpLegaUPRItgb2x5/MpZdM3vfFKzv387DcO/VQpZW4TscvWouI3H669/MLcG8udD4ul0RyY10t2nLjx33/2/lceNerys+8fn00G7P5Nb0xCljev71AqrF+8bG0aLZar7kEdMt17Y8nXIWdiIzpJtYjTGzfMQ/Ke/+CkKFDi/61W66nNR//T99/5n09t+53H1onF/uRHW/7yFx+kC2Ux3dx25Oq3f/3ZrUhKtND9O97YntItcvoV8t7YnpsGzJKHNU8dUm9s/1x97+2XkKnsQ6WC+W2/c9Baen5+NqQnsJGUChbI3SFHzPMLVqrqWSeGE3dvHM+WPj1966+f/ejag4QoaseuPPjqo2//j5+9/5//37t/+L2Nf/H09t/97nrRHouC9yCW/fuXdm07ei2VKy+3N3Z95Od5M7v7TGfwOgu7h3N7Dc6Mq8Ood30ve2bY7o1XrTYmjb4+/LVVduuu9sarXzOeoetbyLvkeswFhlRzyCCy+sIGpYgpvn3sOeeMdLlwHS0S/u3GYmomf3NjmEqtfvzqw999bJ2oqr//xIYn3myf/dXJMHpj5aM+/15qygJBUxbX54viXnpjEpx8raSU10Af3D0kl280m6ItDKvDx48rU8ZORId8+Njx2/dmZqKpUZ20RsTpjav1xqHJe6/tPL39y2vyLtEb/2jDoWsP4mKa+OGX1945cFH2xuJv59HLD57efPSzU1PxbHEYvXHXgubru9pTQ1ep9G7J8a7BXVrb08RO81r5WNeU1MtVIT3LeGusa21OpQ3sCe3n43545xfKE/di7dckqIa7Xhz3wvb61VdYeSnMuP5emLzPzbUG9y/C+5o4T2OBn2sY6eXzvkeOrraWnp+PJ7NKKQgTpXZ18dGnuyavXH8QSYipWn1E51Bw98bXHyY2Hbz01KYjovUVd4ne+A+e2CBuX/3ese+u3Xc/mhHtseiNxV2isv3re8fW75u8ci+2vN64/Q4PmqoZg8sz0u0q4V3YWYkzRjqPeqfOWOOxPdjXrDfvcsapHLmrN63xPsN20bPuag9MOXKt9RgLjH5skqXlbi6iVLBAR2bPy+Wrtbq781rpQt0bzxn/jurDSCdbDl3+/lsHOvmHVz/96qPGdeeFP3pyk7jl7pznYMJB98buuZ1Lpxlbm2u17umLgt6YeHI+cUOprYGePve29YD5+Y92f6LMqHT2YDaRzBSsl2boaffGtcbuiam1u89+empK3iW6YmE6kfuzn2zdc+bWTCI3cX26Zh5jfHYq8srHp0QljCTzg+6NFyxo3s4tcGLaniy2ub5Lh29haK9kkb1x4DoD1tamNIoyzo/jLrydZ8meBP3UkueBvsXktiPjP9ZiC/XGHf5euL6L2jm3eX5q9RuNLt858ZJSwQLdz8/K5R/MzCjjWmdHJybEVK37SUkHF3dvfPFOdP2+C698PCEqlbgrkS19cfF+rlR98cOTYm4mqpmoaaKyibvEjW9+du6tPecnrs8sqzd2RscCw9OOqxR0ndp1GfXKXe0RvWavXLmnKK3ae0Epku1itXrPbnMM2t27XNWmc0656/GHIuOXfdMTSvkK9Ma1j+XyzVar3XytfOHepzol/h15b/yTjYdl5e2F6JMv3PZsDRhsb+yvy+pnivY8puMCnkIpl3HNbOiNiSdfRM4ptTXQEydftR4wP//J3s+UuZTOHoreOD0WvfHeM7fX7Dqz88QN6z4zTm9sfW3m7NTsr3ac3nTwkphxnroxIyaagtwys4wETf4WLmiehsqpVO4tM/aNdu2y1+As4+pjra6sfYv9XXwNoTvtyah1r72w/S18Le6yJsSubxfAKdG+Z9W+pd18+l4x16PU+m8/SnkpfK+M8+o5r7DzXZx1+pcRcW7s+poMI397+OdKBQt0KXVbLh+Zm1PGtc6OnT4tpmqNMeiNL9+Lbfz84nPbj4s6Zt1tRvbG1hdmRG/81t4LsjdOF8pvfHpOLHPg/B3r7t6jjv2F0x4dDs94lwksBTJKeWkP4TUX5V1y5FqL2TfK/xsPaJc7sXJZHuVYlv93bpT/N+4gKy/b7hxQyleg5ybftR4wP+80XyEQ/n2qR94br9l19hu//LiTv3h6uzzeWPjD7238m+d33JhOWI80M9DeWJ3EmAm8sR11ZuPUTffyTrWlNyaenI5dVWproFVn37QeMD//8We7lLmUzh7OJsdhu3G90RRTyVd3nNqwf7JhXshEJrA3Pnb14eqtX+6auBnr5zMPmPz1UtCcL9est2d4dpNmJGiqaj3E/kZBfZrvyfg6QCf2ExCs8mjf4qqW9sOt6u2srftcM/ibtue+Qfy9cftp+P6IBBV23zxYecHVZ+Us716JN85L1P7jFfij2avqva8YUP6xw6lcFbdz03L56UhEGdc6OzpxSkzVGs3R98b35tLbjl59/M39tyMp9wmq/b1xplB+4YOT6/dPXrwTOE/qOYHvYefd3uauae1m1StoDhZQMZyHywHoDGrj4XLomU9G3t6+UVB7YzEYradqrMq6vX3jQvWKjHF2PzimlK9Ar1/9QC7fYrvxyjLy442rtUaxUutElGN5vLHwyCu7xS3yinlOBtkbd6ieHScc3opszVECV6IUX0KsxMsZpbYG2jS1Ry7fbLUu37gdVvsOH1KmiV18sm/P2UtXpudSiTHYbix+L4ls8Zfbjv9805FiuSa+lAv4e+Nms7nzxI1/en3P1QdxUd+sW/sQf+XpqaA58zyHp0K2Ozc/q5oFlVbftw7s5dzTyvZEttNM12AV4Q5rUxO8mP3cgjklOuDVU3/S7uu3H7hAbxz0fJQ/UuoaRAK/9dj0xq9efl+pYH7fOPTTRstq/6ZnY0opCBOlcAXbavy7+YP3Dh8/ceteREzVxuF440K5emjy3h//aMuZmxHnkuwiSm8sil0sU/iHV3dv/eJyLFO0bl1aAt/Yzo1trrFgx1VM/IsFDGc73Xpja53iUdbgMpbp1hvbD3c2LxuD0Xn+Ad+drIxcS99TKligfdMTcvlaveHuvFa68PfGpfL4nqf6wIW733p5lzze+EcbDl266yt+49AbB5RpkzLR8ayE3ph0zIsXtyjl1e9e3no3hfsSx72cp1rYe/jQrfsz9qPG4jzV4v+1RvO9L648tfnoh19ey9mXA/D3xpN3oq99clq00Kl8Wfngb3nxV56eCpq/N/aUqW69sTXvDCqtvm+90JTX1c51642thweuzZ/gxTq8LGoCFlN/0t7Wv1Bv7HkdXNqz/157Y/vGkffGvZxGYd2NXdbS4omH63onCqV8qcyueMtH205fuGg9xDw1TLU2+vNUN5rNq/fjj63dt/bTs1fux60lfL3xbKqw48QNsdjhi/dFS2DdusQEDc+AAeIaCwFp1xB7LHQZ9fZdQb2x/a037ZZj3xxuXXtj695Ve/eY63GvZMGaQ8Y6T556XSliim8c+mm+Zn02lC+G6hLH4e6NjeONR3VB+QVz4Pyd7/xmnyzKP1x/6OQ1a28rJaPujZ26aS+mzlECV0JvTDrmQWHu6wdXKUXWbfOtvdai8/OpTME7qENlwd7Y2xUbpudSc4llHqm79Lh7YxExj1y/f/KH6w/uPXt7JpGrN5rZYmXd3gs3phOtVqtUrV17EF+z68yz7x//+PiNZU8ilfgrTy8Fzd0bt88Z026u/Lf4stTe2NUDL7ZaBqwtKMGLdXhZ1AQsNqje2IrvQ4FOaxAJXIl9Y5df1tCy5uoHSh1z+/ax57I1a3ePSjVUJ3T1U4pYm78rlmLGv+NwfWPxZTJX2nPm9lObjqzbd+HyvVi13mjNzx+5dF+edFD8fzqR2zVx8ycbD/9m19mb00lzHctJexQETtW8Y6FzrVAHSOdR73z8pyxpfQvnS4NSMwN74/aXBvOJOU9mgZpDxjqTySmljin2PDxpLTo/H0/lPIN6hQtzbywvmpfJlaxf3Zjlu2uNxvgPvrfx71785Mr9mHWrLyM+3tg/U1RLsFOs3Stxyiu9MQnI+cSNbx5+Sqmz0lvXd1oLhe6qAH5demN/V2yJGWXNeoGGHqU3Fs3w+VuzqzYe/vmmI6L7vXQ39jCenU3mRZ98ZzZ96sbMm5+e+8G6g7/cdvzQ5D3RNvf1kMKAyd/CBU390jfXDOzBvFlab2x/X8Hz9EQCn7YnPTwrI8GLBbxQQQlYzPeTBhV2Z6ptP1D9cRZ+8upqA16QwJX08EHGMPPypa1KNZP+6djzt7Ltz74T6TB/3icopczQqSt2KSxyhtOvKL1xs9UqV+tvfHpO1LTf7D57dmr2diQ1k8hHkrmHsez1h4mPjl1fvfXY42/sf++LK1MzKfeu10uMM4KUt7en7bTGQruMeIaz62Mmaw0dRr0zjjoOateqfM2w8qU17tzP3/p2Hb47WWnpcvJU9wXbS6Hbvy/8+1SP8Lw13SN742++sLNUrbvP+qBk+b1xMFlAfdOa9i3KAlbB9Zdg1zeyb3HNAumNSXDi5bRog//GdX7XVWfeOD7X7jZE4qm8MpxDJrA33rFnd3BX7DKq650ovbFIrdGcTRZ+vfPU3z6/4xvPfvzTdw7/asepF7af+O7a/X/5zAf/9Poe0TO/+vGp//3iJyevT4v22HpYHxI0/VqwoPn7LnsBu79qlzhr7ujrypSZohnfk1Ee5TwNz6Ps+J724ttLmcApqTP37cBaOOCx/p/Uqe32y+VaeW9PXp1YizgLdFqDSNArEPSLGHEOzJz+/sRrTk371pFfbJraU6y3d14rlEK152EgT0HroSu2N2Ms78DdpUbpjWUK5dqeM7efeGO/qHiPr9333LYToog9teno1//1I1HKxKzs4IV7f736w9/uOX8/mrEes4w4w6ojZ1Q6A6EDeyz0OOrdSyqfTLVvUQaaOoRdT0ktC65iQlZo7uUjv76y7a8O/Ngpa6svbJhMWtduFGk2W3OhO0gk/L3xXHxk+x92z83p5KkbM1fvx53T2ARmsL1xx8WcnrZzhW1XPc8HjV70xmSBzBRjovg6Oxw6SWWLylgOH6U3Fl3x5Ru3lGUCjWr/Q39vLKpXrd58GMueuRn59NTUuwcvrdt3YcP+ye1Hrx68cPfag0QsU7x0N/bWngv/+Npne8/eTvRtR57g6ddCBS2g7+rYy3m0l1dmimZ8T8bby3WZ+8qVBC/g9IFBnWFg7PW4C2/nGi5Zzzng9ez2kzr6dX3jLr8RkaBXIOiHHYukKrm7uchs0XPJCZFwnz3BYVazDb10xY7paHpUpxgM7I3FdD+ZK4np2aEL997/4oooaOv3Tb574NLuU1Oi0D2M50RZ++TkzZ+/e+S1nafvRTPL/rCyyyRKHfVdpnbtz5sWGPWB9cG+0fdRnVIHnC/tb+f7PDGomJAVnXqz8bAQvZefdX/SJ5NIh3AbRvh7Y6GvJ4AZdgbdG5vxlFFXeZVx32tMVuw1uyYunrmduN2plWM3ayHjn0azmQz1YcYOpzfuvSuW563JFUZzGgV/b+ykWmvEM8XrDxMXbs9dvhe7O5fJFivyg79CuSZueWH7iee2HRf9cyLbl/a4y/SrW0Hrre/yTlW930KZKZrxPZlF9sZGXNuWPbeL9NwbBxVnz6sRwHrOAa9n0E8q4lqh8XzUB6qvcNCT9/158vylWNzvyPvbGdsUwnWumi5kTeuxK3bMjmgzhqs3Vt9H4i9RvlS9O5e+dDc6eWdOFDdR4irmOcPErDKaLmw/evXZ94/9ZvfZ25F0qQ+XC/UN1Y7j3T+olbmWfwFL5wmevQbfWFPqgPOlsyrfgFVrAgllavVGWHfu06I3HvllnJaTJffGhKy4iAmH6Poi5iG1OhC98SK6YpdEOm+9ZMPNnz+9/XceW/etl3eduTl75makk5Rv43Cl1piaST77/vHVW499fv5urlgd1eVMQx57XuvtZgcaex7ccR4/oFjf1zfXH7uUK7VQblrpZOsiu2LHSC7jtOXQ5X//+Hoxxdp08FKXsnY7krIe4MpcKv/hsWuPrd337sFLN6eTospZdxAS6jQazWy+pIzfMNGiN87mR7CNpdVqNfvhpQ/pjUmYU280RT+cyZVCf3Sx3/3pmHJLj2ZjfTjIbQn559f3fO0Hm2RF6sJ9DSd3bkVSL314UrTWp27MZIvje3W9lRx7a+pAOtX25nSnI/VvRBpSrM3s7i3kY5RiqZrJl5KZwlw8zJdr6i/xolkv3xCza+Lmnz+17SuPqkVM4b6GkzuJXGnf2dt/8fT2dXsv9OXYY0LGM7V6I1sop3PFWDJUp6QOpENvnBK/SOt3O8ScnYqIKeDyyZ0YBXpjEsrYnz4aV1xDb4zXaiSXAz08ee8H6w7+x3/Z6EwZA3XqjUvV+sW70bf3nv8/r+yeuD5dLK/gPXrGN1bTOJDjWXz7QjuGffiMtRvnsDdW95Rms+UdsOjJSM6cencuLdraP/7h5t99bJ1Sx9w69caiYYhlivKqTr/97HwiO5ozihEy6KQ1OAWMQ4vtxkJj6Kd1PXD+jlJbl4nemIQysWRumsZ4UcxDjjP5EVydLpkrnbg2veXQZTGb7CJw/0OZQrl25X7szU/P3YqkquyCOJBYW3cHtVu1cziiYwQNqtyhenw3GptDVZdjQ/okE4llWl1PTTqIFCu1qZnk9qNXN35+Ualjbgcu3LUe4Euj2ZLt8aHJe/lRbPomZAiZjWtU0DTojc15ZL447N2qRW/8zRd29hG9MQlfavWGMUhj3jGLHoztGfgXjPilJ7Kl2oguQ0XIoKPJqQQHoVReqb2l6IoL7AhDQppyVYtz7Dt02W48kt2qCSHdk82XxfCM0BsvSaUPZ0YlhPQzjWbTGJ7mh/JYrJHsVk0I6Z6U+XnftG/AhpUuvbEwksPzCCFdYuylM8cO1Ytnvmgpjm0jZMySKxif92EJ5Mx72dcKJoT0M/LzPn0aY0GL3lj+RlNZPo8kZIxSLMuj8rB0zCMJGavMJTgx9VKZG9uzoziTAiGkU+TnfVqdF0aX7cYR89/aKK6eRwgJjLxok1YfRvaRfN2YRxIyPimUKs7YxFLE0rPxEZyRixDSKWJIyh5KH7r0xtPm55FpdkEkZDxSYqPxssnjtBtNNh0TMhaJstF4eeTHCrnCsE+eSggJTL4oP+/T69g3jY43ljjqmJBxiHn5+JRuH0YOQibHpmNCRh85ieRS7csXiWX4yI+QkafVaonBqOE8TbvemLMgEjLy2JNI9AcXCiZktGm2Wlpd/3PQ+MiPkJFHDEMxGDU8SESv3lh++FEscaFgQkaWeqMZiTGJ7Kd4imvUETLKpLNFMRLZEaYv5B+ISpXLBRMyslSqdTEMddubWtJuu/HMnHGmhwYndyVkREmaF8rj+p/9Ik+mkC/ykR8ho0mpUjMGIzWtr2LJHKfkImRUkWdL1ZN2vbHcN4A9qwkZSdibenA4mQIhw0+j2TSu22SeGA99Y37QkOE8/ISMInJvam3PnqDfdmMbJ0IkZMipVM2tK0wi+858SaOJLNc+IWTIsXaEwWAUy1XrhSaEDCXFknkZEY13hNG0N5Z7IZaouYQMK42GsXWFI40HKsUeMYQMMZm83LpCWRugSpU9YggZUqq1hjHuYvTGGqPmEjKEtFotnY9dGSbO70rIcMIRIoNnfOgwF8/WOUcMIYOP3IbhHYM60ro3Ni7bFeMgPUIGHnY7HBLzg14OGCFk0CmUjMbY3AeNjcYDJE/9HU1kueIxIQNNs9mKJXNiuGl40SaF3tuNzYP05uJZrg5KyOBin5ha05M6jESWc9gQMrAUy/J4PGra8Ij2mK3HhAwojWZTNsYQdN+nWorEMuxcTUjf02y2Emm5KzWTyCEyP/XL5IrWr4EQ0r84u1KzdWVoIubG+WgiV6uzJYOQPkcMKzG43CNOc/TG1nm5hGKJU3MR0reY1dY4cEXuFIehMs95lsoUOHE1IX1M1jz51jRbjEckEsuUKzXrl0EIWXYq1fosJ0n1ojc2ifZYbmlhR0RC+pFiqcopqcdBNJnjlAqELD+NZjORNg4PobKNirGh3pyq5YsV67dCCFlG8oWye4hBoje2ROytWzGmkoQsI61WK50rugcXRo6pJCHLSansfNjHFuORsrfYJ9OFBocfE7LUiOFjnySVD/tU9MYK6y3CiV4JWULEDNK6AAD7HI6LjNzSkkgX+NSPkMVGzCBTWevDPg4wHisRPvUjZEkplCrs/9IFvbHKeLuYU8loIlcscwQyIT2lVmtYp92yRxDGUCZXajY5BJmQhSPGSa5QljuU0RWPG+PsXOYnsPFUvlzlCGRCekq5UrPPkGodoQA/euNgzmEt8VSuxIkfCOmcaq3ubFcxUG3HlLUlPxLLZPMldkckpFNEV5wvVqxdYChoK4GY7nO1EUK6pFKr2ztRG3v2Oechhh+9cXcp+WlxNJkTfym59Dwh7pTK1XapjWU4d+v4EwXN+YuYzhbZy5oQd+r1RjZfss/amuEc+yuD/flFPJUvlqqcnJ8QJ2I4FEqVeMp1iSamaguhN+6NXXlFJ1AsV2mSic4pV2qZXGk2zsEqYRBNZEUzQJNMdI5oifNFMX20dzXk5DQrzbSxX0z7t5bKFktl0STTJRNN02y1SpVaOluUxxVbH/PRFfeG3rhXyufHsWQuky+VyjWuRE9Cn3qjWa7Wc4VyIl1wnb+BjSornvhtOr/EuXg2lSkUSpVKtd7k4z8S6oiJY7XWKBQrYu4oL8NuMHZ+ae9YgRXHqGbeX188lcsaU7VqnakaCXvEVE30w+IN795KTEFbAnrjJYmprfJcIhtP5RPpfCpbBFY0MVkUPVIyUxDvZ/HGjngPt5s2DzSgKw692XhG9AxGTcsUlHcIsDIZNS2Wyvn3eTEPnmKLSngYZ+oy/nKpv2hR08RUTfx1E+8H4y8dsGJZU7V0Qbyl2x/w2eQBoVgaeuNlUd58NAwIEet008YkQ70L4WTtFGB9zJyyNsK09xQAwoJtKfqw9yM1zrZg/oepGsLFOjUSu0z3C73xIIh3J7CiKW9paM38u6u8Q4AVhVkjvMxdorxvEmDlUPbpQx/RGwMAAAAAdEdvDAAAAADQHb0xAAAAAEB39MbA0k1++KtHnt4x6b3x87XPPPKY5QcfTrnv8ri64wf2Yr6VHH/Jueux9Z977gpmfNO1xwNutFbyq61XPXfNRKe2Pu16eu4n41tPm3sxyffjK4yXqMsKAYwVY4z7yoVn4HerSO7q99JRz11GKbDv6rUwSkFFxlyb75kcXe++sWsBdDEe5SzZS70V9bnrCgGMk4UmSKOdqhmTsc4r6TxV6/odu5TiQEzV3OiNgaWSMypPrZQ1zi5Y5gLBNdcscPZd3kfJamsXKbPALTAPsyad3rpmPNB+buYCnpUYtzjP3HwydvX0fHeV8RMtdlLorewAxpec6nnHuKeOKcXKwyxWwdXPU4Lk9G45RcaaICpPw3jyziywewF0yOLpeVQvJc54ksEvAoCxIse4t+AMaKq2QE3o+Ew6r8R4iDNV60cp7sxYYQ+LaYHeGFgKs/SY3L2xb1ZnLOZeoNPtru7UrJ7uemfU384FS9ZHk7vgmtXQ9WGhp/6a3855nuZdXX8Kh6dM985Y4QJ/MwCMmFk0TO7h7y0dgqtYefhud1U5tYj5qlxbD0XGKXqeNRjfznme3Qtgm//27vXWYTyQeSQw3pxa4R3mvknOUqdqrpWYdy12qqauRKk//qma+6dYSinuiqmajd4YWDSj0JgFS6k4xpcB0y+L+agOdadTjTN4aqV3JbLaii/Voumferpv8T5t/1yw4+yw2w9o/ghW6Vd/FuPpdf6bAWDUzGbSGKTKrNEzOfMxR32noe2tMx7ucqTMDrsVGZOx/NM7PldKnLeEdi+AXRnVz3kCZr21KT8L80hgrHWcIHUtMp4KoHLVGaVweYuhspKuz8RbWNy3eO7tXoq78q2nXdaYqgWiNwaWzlvX7LJizjKD6k4nZhn11kebU1KV2xULF1xXHfe2vt45panT3wbzgU+L9Vg/XXsZz1+FgA9le56YAhgpZfBaHaAsRKbgSqUyh3yHAmhWyA4zsM5FRrKfnlJSlC+7FsCuXKXMWEl7nWqNVQspgDGlDF7jS2PkmoVIWtJUzTNTMsvFghUm4JkoJc5VdrwVpi+lmKlab+iNgaXzzsDMQukuWN2mgJJT5gJKqlkiDT1UbbXCep+YySmCSjMc0BsH1GuDuaTrdtffCV+FVS24AIBx4B2qcl7lqkKyZHWdPJl1z9Cphjh1w69LkbG/lBW1ezPcrQC6b1S5f7oOZbBtwQUAjANlqPZzqubM0zrWNA/lmQTUkHZl887N+lOKFyyDPdXJ8KM3BpbOOwOTBddTqsy62bV4SV1KszpZDKRW2G5TQ6X2eeuvKaBeBzOftvlY+49Np0cFfBcA48dbH+SEzFOazLHcdR4pdZu6mYWxtxlYu8h4ypq3N15MAXTf6GU+q3aZsiejHR8V8F0AjB2lPvRpqub+v10uFprkKM8kYK7VrmwDKcVM1XpCbwwsnXduZBadxU/IpC6l2ayJ3Vey8NTQWYm6toBSaP4gnUqnm7c0mz+CTXk4BRdYEYImZN6RGzCfC9Zt6rb4IhPwxJyCqa6tSwF0blHI8qXWKPO722VNfbj/uwAYP0rJ6stUzVyndyU9FATlmQTUUnv9asnqYymWtc6iPJypmoneGFg6bykMqJWj6o29E0fvLepTMv5OeOev/ls66DD3lUXcU3MpuMCKoNQH48ux6I098zkXc/2LKYABzIcvUKDMJ+ytw976D2A8KfXB/HK5U7WAMti1yEjqo/w1pH3L4EsxU7VO6I2BpVPqmlloepk8+Stau1gH18pFFlxfoXctoNa+BR/rvt1bNDst6fpxrFs6LglgnKhD1dfEdpo8+ce4UzH8D+kwV2s/xHNLQOlQpqFq2VQf5atybeZdvVQn9bl1WSeA8aEO1X5M1Zz/OHd1Wolb4DNxz+5cWybUstmPUuzcYvH9FB3qrW7ojYGl85VCs3g5t3SsR+pdZvdrf+mdNcoP9gImkR4dKrhdc71/CVzFVzKfjH2Lr/62edYpn6e1pPKT+ia+vj8AAMaSb24kS5A9un1zqTbvXbI+2F+aJc6pAGaRCS4InYuMl1JSfBWmSwH0MJ9Y4F3qT+r9EQRfIQUwjsyx7Ckj/ZiqeSZOSp3sZIFn4q+TvnnU8koxU7Xe0BsDS2cUMrU2mVXJmPm5aqhJnVrJgmVRipGcO0qeeZtvfib5C65B1nH/Sozypzxt95Nxr8dXOl3r9M4LzZobfJf59Ly3ABhLxkD29Yru0e0uHb3XB3tiZ3EVGfN2f5ULXonDN4czaqYyMe1UAM3bzcd6irCL9TN6irlaeI3XxF+KAYybwAlSp6maOfvyzYKCi4DnLneR8a3EsuAz8aw/YKq2/FLMVK0H9MaAfswyqswjB4hJJIABM+aCyjxygJhEAhgwpmojQm8M6Cjg88hBYRIJYAgCNh0PCpNIAIPHVG0k6I0BPQ2pDhqVPWDPIgDotyG1rKIJ9+12DgD9x1RtBOiNAQAAAAC6ozcGAAAAAOiO3hgAAAAAoDt6YwAAAACA7uiNAQAAAAC6ozcGAAAAAOiO3hgAAAAAoDt6YwAAAACA7uiNAQAAAAC6ozcGAAAAAOiO3hgAAAAAoDt6YwAAAACA7uiNAQAAAAC6ozcGAAAAAOiO3hgAAAAAoDt6YwAAAACA7uiNAQAAAAC6ozcGAAAAAOiO3hgAAAAAoDt6YwAAAACA7uiNAQAAAAC6ozcGAAAAAOiO3hgAAAAAoDt6YwAAAACA7uiNAQAAAAC6ozcGAAAAAOiO3hgAAAAAoDt6YwAAAACA7uiNAQAAAAC6ozcGAAAAAOiO3hgAAAAAoDt6YwAAAACA7uiNAQAAAAC6ozcGAAAAAOiO3hgAAAAAoDt6YwAAAACA7uiNAQAAAAC6ozcGAAAAAOiO3hgAAAAAoDt6YwAAAACA7uiNAQAAAAC6ozcGAAAAAOiO3hgAAAAAoDt6YwAAAACA7uiNAQAAAAC6ozcGAAAAAOiO3hgAAAAAoDt6YwAAAACA7uiNAQAAAAC6ozcGAAAAAOiO3hgAAAAAoDt6YwAAAACA7uiNAQAAAAB6i6b/P1aTw4IUOVpqAAAAAElFTkSuQmCC", + "description": "In this tutorial workflow, sample_LatentDirichletAllocation data is used to make topic model and get the result. The result includes assigned topic and contributions of each word to assigned topic. The parameter settings used in the function are shown below.", + "parameter": "1. **Input Column***: tokenized_text\n2. **Topic Column Name**: topic\n3. **K**: 10\n4. **Alpha**: 0.1\n5. **Beta**: 0.1\n6. **Number of Terminologies**: 3\n7. **Iterations**: 50\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.textanalytics import gsdmm\r\ninput_table = inputs[0]\r\nresult = gsdmm(table=input_table, input_col='tokenized_text',\r\n topic_name='topic', K=10, alpha=0.1, beta=0.1,\r\n max_iter=50, num_topic_words=3)\r\noutput = result['out_table']", + "description": "In this tutorial workflow, sample_LatentDirichletAllocation data is used to make topic model and get the result. The result includes assigned topic and contributions of each word to assigned topic. The parameter settings used in the function are shown below.", + "context": "python" + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/textanalytics/meta/lda2.json b/function/python/brightics/function/textanalytics/meta/lda2.json new file mode 100644 index 000000000..a0eca1dd9 --- /dev/null +++ b/function/python/brightics/function/textanalytics/meta/lda2.json @@ -0,0 +1,178 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "deprecated": true, + "category": "textanalytics", + "func": "brightics.function.textanalytics$lda204291", + "name": "brightics.function.textanalytics$lda2", + "context": "python", + "label": "Latent Dirichlet Allocation", + "description": "This function builds a topic model for text documents. The generated model is then used for classifying the documents based on topics automatically chosen by the model. To choose topics, this function adapts Gibbs sampling.\n\nWhen we have a dataset (text documents) to analyze, we need to do pre-process for the dataset. In general, we split texts into terms (words), build a vocabulary that only consider the top max_features ordered by term frequency across the corpus, and then remove (1) terms that have a document frequency strictly higher than 0.95 (2) terms that have a document frequency strictly lower than counts 2 (3) a built-in stop word list for English is used. \n\nThis pre-process should be performed carefully to get better results. This function performs this pre-process with default setting we mentioned above.\n\nAlso, choosing the number of topics based on the dataset is very important. It could be possible for you to choose either randomly till you can get your desired results or thoroughly based on knowledge of the given text. Increasing number of iterations for running LDA model would give you stable results.\n\nReference:\n+ ", + "tags": [ + "lda", + "topic modeling" + ], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "out_table": "", + "model": "" + }, + "meta": { + "table": { + "type": "table" + }, + "out_table": { + "type": "table" + }, + "model": { + "type": "model" + } + }, + "params": [ + { + "id": "input_col", + "label": "Input Column", + "description": "", + "mandatory": true, + "items": [], + "visibleOption": [], + "control": "ColumnSelector", + "columnType": [ + "String", + "String[]" + ], + "validation": [], + "multiple": false + }, + { + "id": "num_voca", + "label": "Number of Vocabularies", + "description": "The number of vocabularies that will be utilized to count their frequencies in the entire documents. It should be greater than or equal to two.", + "mandatory": false, + "items": [], + "visibleOption": [], + "control": "InputBox", + "columnType": [], + "validation": [], + "placeHolder": "1000 (value >= 2)", + "type": "Integer", + "min": 2 + }, + { + "id": "num_topic", + "label": "Number of Topics", + "description": "The number of topics that will be contained in the entire documents. It should be greater than or equal to two.", + "mandatory": false, + "items": [], + "visibleOption": [], + "control": "InputBox", + "columnType": [], + "validation": [], + "type": "Integer", + "min": 2, + "placeHolder": "3 (value >= 2)" + }, + { + "id": "num_topic_word", + "label": "Number of Terminologies", + "description": "Maximum number of terminologies you want to show for particular topics. It should be greater than or equal to two. Terminology number should be less than or equal to vocabulary size also.", + "mandatory": false, + "items": [], + "visibleOption": [], + "control": "InputBox", + "columnType": [], + "validation": [], + "placeHolder": "3 (value >= 2)", + "type": "Integer", + "min": 2 + }, + { + "id": "max_iter", + "label": "Iterations", + "description": "Maximum number of iterations you want to run your model. It should be at least one.", + "mandatory": false, + "items": [], + "visibleOption": [], + "control": "InputBox", + "columnType": [], + "validation": [], + "type": "Integer", + "min": 1, + "placeHolder": "20 (value >= 1)" + }, + { + "id": "learning_method", + "label": "Learning Method", + "description": "Method used to update variational parameters for topic word distribution. Choose 'Online' or 'Batch'.\n- 'Online': Online variational Bayes method. In each EM update, use mini-batch of training data to update the variational parameters for topic word distribution variable incrementally. The learning rate is controlled the 'Learning Offset' parameters.\n- 'Batch': Batch variational Bayes method. Use all training data in each EM update. Old variational parameters for topic word distribution will be overwritten in each iteration.", + "mandatory": false, + "items": [ + { + "label": "Online", + "value": "online", + "default": true + }, + { + "label": "Batch", + "value": "batch", + "default": false + } + ], + "visibleOption": [], + "control": "RadioButton", + "columnType": [], + "validation": [] + }, + { + "id": "learning_offset", + "label": "Learning Offset", + "description": "A (positive) parameter that downweights early iterations in online learning. It should be greater than 1.0.", + "mandatory": false, + "items": [], + "visibleOption": [ + { + "id": "learning_method", + "value": "online" + } + ], + "control": "InputBox", + "columnType": [], + "validation": [], + "placeHolder": "10.0 (value > 1.0)", + "type": "Double" + }, + { + "id": "random_state", + "label": "Seed", + "description": "The seed used by the random number generator.", + "mandatory": false, + "items": [], + "visibleOption": [], + "control": "InputBox", + "columnType": [], + "validation": [], + "type": "Integer", + "placeHolder": "" + }, + { + "id": "group_by", + "label": "Group By", + "description": "", + "mandatory": false, + "items": [], + "visibleOption": [], + "control": "ColumnSelector", + "columnType": [], + "validation": [], + "multiple": true, + "rowCount": 5 + } + ] + }, + "md": "" +} diff --git a/function/python/brightics/function/textanalytics/meta/lda3.json b/function/python/brightics/function/textanalytics/meta/lda3.json index 98c5774be..5270e01fb 100644 --- a/function/python/brightics/function/textanalytics/meta/lda3.json +++ b/function/python/brightics/function/textanalytics/meta/lda3.json @@ -4,12 +4,13 @@ "content": "" }, "specJson": { + "deprecated": true, "category": "textanalytics", "func": "brightics.function.textanalytics$lda3", "name": "brightics.function.textanalytics$lda3", "context": "python", "label": "Latent Dirichlet Allocation", - "description": "The generated topic model is then used for classifying the documents based on topics automatically chosen by the model. To choose topics, this function adapts Gibbs sampling.\n\nWhen we have a text document dataset to analyze, we need to do pre-process for the dataset. In general, we split texts into terms (words), build a vocabulary that only consider the top max_features ordered by term frequency across the corpus, and then remove (1) terms that have a document frequency strictly higher than 0.95 (2) terms that have a document frequency strictly lower than counts 2 (3) a built-in stopwords list for English is used. \n\nThis pre-processing should be performed carefully to get better results. This function performs this pre-processing with default setting we mentioned above.\n\nAlso, choosing the number of topics based on the dataset is very important. It could be possible for you to choose either randomly till you can get your desired results or thoroughly based on knowledge of the given text. Increasing number of iterations for running LDA model would give you stable results.", + "description": "The generated topic model is then used for classifying the documents based on topics automatically chosen by the model. To choose topics, this function adapts Gibbs sampling.\n\nWhen we have a text document dataset to analyze, we need to do pre-process for the dataset. In general, we split texts into terms (words), build a vocabulary that only consider the top max_features ordered by term frequency across the corpus, and then remove (1) terms that have a document frequency strictly higher than 0.95 (2) terms that have a document frequency strictly lower than counts 2 (3) a built-in stopwords list for English is used. \n\nThis pre-processing should be performed carefully to get better results. This function performs this pre-processing with default setting we mentioned above.\n\nAlso, choosing the number of topics based on the dataset is very important. It could be possible for you to choose either randomly till you can get your desired results or thoroughly based on knowledge of the given text. Increasing number of iterations for running LDA model would give you stable results.\n\nReference: \n+ \n+ \"Online Learning for Latent Dirichlet Allocation\", Matthew D. Hoffman, David M. Blei, Francis Bach, 2010", "tags": [ "lda", "topic modeling" @@ -184,7 +185,7 @@ "summary": "This function builds a topic model for text documents with LDA.", "tutorialLink": "https://www.brightics.ai/kr/docs/ai/v3.7/tutorials/35_market_saledata_lda" }, - "md": "# Latent Dirichlet Allocation\nThis function builds a topic model for text documents with LDA.\n\n## Description\nThe generated topic model is then used for classifying the documents based on topics automatically chosen by the model. To choose topics, this function adapts Gibbs sampling.\n\nWhen we have a text document dataset to analyze, we need to do pre-process for the dataset. In general, we split texts into terms (words), build a vocabulary that only consider the top max_features ordered by term frequency across the corpus, and then remove (1) terms that have a document frequency strictly higher than 0.95 (2) terms that have a document frequency strictly lower than counts 2 (3) a built-in stopwords list for English is used. \n\nThis pre-processing should be performed carefully to get better results. This function performs this pre-processing with default setting we mentioned above.\n\nAlso, choosing the number of topics based on the dataset is very important. It could be possible for you to choose either randomly till you can get your desired results or thoroughly based on knowledge of the given text. Increasing number of iterations for running LDA model would give you stable results.\n\nReference\n- \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data including text documents.\n#### OUTPUT\n1. **out_table**: (Table) Table including topic modeling result.\n#### PARAMETER\n1. **Input Column***: Text data column to select as an input. Data type should be a string or list of string.\n\n2. **Topic Column Name**: Column name for topic modeling result.\n\n3. **Number of Vocabularies**: The number of vocabularies that will be utilized to count their frequencies in the entire documents. It should be greater than or equal to two.\n\n4. **Number of Topics**: The number of topics that will be contained in the entire documents. It should be greater than or equal to two.\n\n5. **Number of Terminologies**: Maximum number of terminologies you want to show for particular topics. It should be greater than or equal to two. Terminology number should be less than or equal to vocabulary size also.\n\n6. **Iterations**: Maximum number of iterations you want to run your model. It should be at least one.\n\n7. **Learning Method**: Method used to update parameters for topic word distribution. In each EM(Expectation Maximization) update, `Online` uses mini-batch of training data to update the parameters for topic word distribution variable incrementally. `Batch` uses all training data in each EM update. Old parameters for topic word distribution will be overwritten in each iteration.\n - Available Options : `Online`, `Batch` \n\n8. **Seed**: Random seed. Use the same seed in order to reproduce the same results.\n\n9. **Group By**: Columns to group by.\n\n\n### Python\n#### USAGE\n\n```\nlda3(table, input_col, topic_name='topic', num_voca=1000, num_topic=3, num_topic_word=3, max_iter=20, learning_method='online', learning_offset=10., random_state=None, group_by=None)\n```\n\n#### INPUT\n1. **table***: (Table) Data including text documents.\n#### OUTPUT\n1. **out_table**: (Table) Table including topic modeling result.\n#### PARAMETER\n1. **input_col***: Text data column to select as an input.\n\t* Type: *str* or *list[str]*\n2. **topic_name**: Column name for topic modeling result.\n\t* Type: *str*\n\t* Default: topic\n3. **num_voca**: The number of vocabularies that will be utilized to count their frequencies in the entire documents. It should be greater than or equal to two.\n\t* Type: *int*\n\t* Default / Range: 1000 (value >= 2)\n4. **num_topic**: The number of topics that will be contained in the entire documents. It should be greater than or equal to two.\n\t* Type: *int*\n\t* Default / Range: 3 (value >= 2)\n5. **num_topic_word**: Maximum number of terminologies you want to show for particular topics. It should be greater than or equal to two. Terminology number should be less than or equal to vocabulary size also.\n\t* Type: *int*\n\t* Default / Range: 3 (value >= 2)\n6. **max_iter**: Maximum number of iterations you want to run your model. It should be at least one.\n\t* Type: *int*\n\t* Default / Range: 20 (value >= 1)\n7. **learning_method**: Method used to update parameters for topic word distribution. In each EM(Expectation Maximization) update, `Online` uses mini-batch of training data to update the parameters for topic word distribution variable incrementally. `Batch` uses all training data in each EM update. Old parameters for topic word distribution will be overwritten in each iteration.\n\t* Type: *str*\n\t* Default / Range: online ( online | batch )\n8. **learning_offset**: A (positive) parameter that downweights early iterations in online learning. It should be greater than 1.0.\n\t* Type: *float*\n * Default / Range: 10.0 (value > 1.0)\n9. **random_state**: Random seed. Use the same seed in order to reproduce the same results.\n\t* Type: *int*\n10. **group_by**: Columns to group by.\n\t* Type: *list[str]*\n\n## Example\n### VA\n**[Related Tutorial]**\n\n\n**[Sample Model]**\n\n\n\n\nIn this tutorial workflow, sample_LatentDirichletAllocation data is used to make topic model and get the result. The result includes assigned topic and contributiveness of each word to assigned topic. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Input Column***: tokenized_text\n2. **Topic Column Name**: topic\n3. **Number of Vocabularies**: 1000\n4. **Number of Topics**: 3\n5. **Number of Terminologies**: 3 \n6. **Iterations**: 20\n7. **Learning Method**: Batch\n8. **Seed**: 0\n9. **Group By**: None\n\n\n### Python\n\n```\nfrom brightics.function.textanalytics import lda3\ninput_table = inputs[0]\nresult = lda3(table=input_table,\n input_col='tokenized_text',\n topic_name='topic',\n num_voca=1000,\n num_topic=3,\n num_topic_word=3,\n max_iter=20,\n learning_method='online',\n learning_offset=10.,\n random_state=None,\n group_by=None)\noutput = result['out_table']\n```\nIn this python script, sample_LatentDirichletAllocation data is used to make topic model and get the result. The result includes assigned topic and contributiveness of each word to assigned topic.\n", + "md": "# Latent Dirichlet Allocation\nThis function builds a topic model for text documents with LDA.\n\n## Description\nThe generated topic model is then used for classifying the documents based on topics automatically chosen by the model. To choose topics, this function adapts Gibbs sampling.\n\nWhen we have a text document dataset to analyze, we need to do pre-process for the dataset. In general, we split texts into terms (words), build a vocabulary that only consider the top max_features ordered by term frequency across the corpus, and then remove (1) terms that have a document frequency strictly higher than 0.95 (2) terms that have a document frequency strictly lower than counts 2 (3) a built-in stopwords list for English is used. \n\nThis pre-processing should be performed carefully to get better results. This function performs this pre-processing with default setting we mentioned above.\n\nAlso, choosing the number of topics based on the dataset is very important. It could be possible for you to choose either randomly till you can get your desired results or thoroughly based on knowledge of the given text. Increasing number of iterations for running LDA model would give you stable results.\n\nReference\n+ \n+ \"Online Learning for Latent Dirichlet Allocation\", Matthew D. Hoffman, David M. Blei, Francis Bach, 2010 \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data including text documents.\n#### OUTPUT\n1. **out_table**: (Table) Table including topic modeling result.\n#### PARAMETER\n1. **Input Column***: Text data column to select as an input. Data type should be a string or list of string.\n\n2. **Topic Column Name**: Column name for topic modeling result.\n\n3. **Number of Vocabularies**: The number of vocabularies that will be utilized to count their frequencies in the entire documents. It should be greater than or equal to two.\n\n4. **Number of Topics**: The number of topics that will be contained in the entire documents. It should be greater than or equal to two.\n\n5. **Number of Terminologies**: Maximum number of terminologies you want to show for particular topics. It should be greater than or equal to two. Terminology number should be less than or equal to vocabulary size also.\n\n6. **Iterations**: Maximum number of iterations you want to run your model. It should be at least one.\n\n7. **Learning Method**: Method used to update parameters for topic word distribution. In each EM(Expectation Maximization) update, `Online` uses mini-batch of training data to update the parameters for topic word distribution variable incrementally. `Batch` uses all training data in each EM update. Old parameters for topic word distribution will be overwritten in each iteration.\n - Available Options : `Online`, `Batch` \n\n8. **Seed**: Random seed. Use the same seed in order to reproduce the same results.\n\n9. **Group By**: Columns to group by.\n\n\n### Python\n#### USAGE\n\n```\nlda3(table, input_col, topic_name='topic', num_voca=1000, num_topic=3, num_topic_word=3, max_iter=20, learning_method='online', learning_offset=10., random_state=None, group_by=None)\n```\n\n#### INPUT\n1. **table***: (Table) Data including text documents.\n#### OUTPUT\n1. **out_table**: (Table) Table including topic modeling result.\n#### PARAMETER\n1. **input_col***: Text data column to select as an input.\n\t* Type: *str* or *list[str]*\n2. **topic_name**: Column name for topic modeling result.\n\t* Type: *str*\n\t* Default: topic\n3. **num_voca**: The number of vocabularies that will be utilized to count their frequencies in the entire documents. It should be greater than or equal to two.\n\t* Type: *int*\n\t* Default / Range: 1000 (value >= 2)\n4. **num_topic**: The number of topics that will be contained in the entire documents. It should be greater than or equal to two.\n\t* Type: *int*\n\t* Default / Range: 3 (value >= 2)\n5. **num_topic_word**: Maximum number of terminologies you want to show for particular topics. It should be greater than or equal to two. Terminology number should be less than or equal to vocabulary size also.\n\t* Type: *int*\n\t* Default / Range: 3 (value >= 2)\n6. **max_iter**: Maximum number of iterations you want to run your model. It should be at least one.\n\t* Type: *int*\n\t* Default / Range: 20 (value >= 1)\n7. **learning_method**: Method used to update parameters for topic word distribution. In each EM(Expectation Maximization) update, `Online` uses mini-batch of training data to update the parameters for topic word distribution variable incrementally. `Batch` uses all training data in each EM update. Old parameters for topic word distribution will be overwritten in each iteration.\n\t* Type: *str*\n\t* Default / Range: online ( online | batch )\n8. **learning_offset**: A (positive) parameter that downweights early iterations in online learning. It should be greater than 1.0.\n\t* Type: *float*\n * Default / Range: 10.0 (value > 1.0)\n9. **random_state**: Random seed. Use the same seed in order to reproduce the same results.\n\t* Type: *int*\n10. **group_by**: Columns to group by.\n\t* Type: *list[str]*\n\n## Example\n### VA\n**[Related Tutorial]**\n\n\n**[Sample Model]**\n\n\n\n\nIn this tutorial workflow, sample_LatentDirichletAllocation data is used to make topic model and get the result. The result includes assigned topic and contributiveness of each word to assigned topic. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Input Column***: tokenized_text\n2. **Topic Column Name**: topic\n3. **Number of Vocabularies**: 1000\n4. **Number of Topics**: 3\n5. **Number of Terminologies**: 3 \n6. **Iterations**: 20\n7. **Learning Method**: Batch\n8. **Seed**: 0\n9. **Group By**: None\n\n\n### Python\n\n```\nfrom brightics.function.textanalytics import lda3\ninput_table = inputs[0]\nresult = lda3(table=input_table,\n input_col='tokenized_text',\n topic_name='topic',\n num_voca=1000,\n num_topic=3,\n num_topic_word=3,\n max_iter=20,\n learning_method='online',\n learning_offset=10.,\n random_state=None,\n group_by=None)\noutput = result['out_table']\n```\nIn this python script, sample_LatentDirichletAllocation data is used to make topic model and get the result. The result includes assigned topic and contributiveness of each word to assigned topic.\n", "exampleModels": [ { "fileName": "brightics.function.textanalytics$lda3.json", diff --git a/function/python/brightics/function/textanalytics/meta/lda4.json b/function/python/brightics/function/textanalytics/meta/lda4.json new file mode 100644 index 000000000..e6b4a67f1 --- /dev/null +++ b/function/python/brightics/function/textanalytics/meta/lda4.json @@ -0,0 +1,1465 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "textanalytics", + "func": "brightics.function.textanalytics$lda4", + "name": "brightics.function.textanalytics$lda4", + "context": "python", + "label": "Latent Dirichlet Allocation", + "description": "The generated topic model is then used for classifying the documents based on topics automatically chosen by the model. To choose topics, this function adapts Gibbs sampling.\n\nWhen we have a text document dataset to analyze, we need to do pre-process for the dataset. In general, we split texts into terms (words), build a vocabulary that only consider the top max_features ordered by term frequency across the corpus, and then remove (1) terms that have a document frequency strictly higher than 0.95 (2) terms that have a document frequency strictly lower than counts 2 (3) a built-in stopwords list for English is used. \n\nThis pre-processing should be performed carefully to get better results. This function performs this pre-processing with default setting we mentioned above.\n\nAlso, choosing the number of topics based on the dataset is very important. It could be possible for you to choose either randomly till you can get your desired results or thoroughly based on knowledge of the given text. Increasing number of iterations for running LDA model would give you stable results.\n\nReference: \n+ \n+ \"Online Learning for Latent Dirichlet Allocation\", Matthew D. Hoffman, David M. Blei, Francis Bach, 2010", + "tags": [ + "lda", + "topic modeling" + ], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "out_table": "", + "topic_table": "", + "model": "" + }, + "meta": { + "table": { + "type": "table", + "desc": "Data including text documents.", + "optional": false + }, + "out_table": { + "type": "table", + "desc": "Table including topic modeling result." + }, + "topic_table": { + "type": "table", + "desc": "Table which consists of each topic's vocabulary and weights." + }, + "model": { + "type": "model", + "desc": "Result model." + } + }, + "params": [ + { + "id": "input_col", + "label": "Input Column", + "description": "Text data column to select as an input.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "columnType": [ + "String", + "String[]" + ], + "multiple": false + }, + { + "id": "topic_name", + "label": "Topic Column Name", + "description": "Column name for topic modeling result.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "type": "String", + "placeHolder": "topic" + }, + { + "id": "num_voca", + "label": "Number of Vocabularies", + "description": "The number of vocabularies that will be utilized to count their frequencies in the entire documents. It should be greater than or equal to two.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "placeHolder": "1000 (value >= 2)", + "type": "Integer", + "min": 2 + }, + { + "id": "num_topic", + "label": "Number of Topics", + "description": "The number of topics that will be contained in the entire documents. It should be greater than or equal to two.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "type": "Integer", + "min": 2, + "placeHolder": "5 (value >= 2)" + }, + { + "id": "num_topic_word", + "label": "Number of Terminologies", + "description": "Maximum number of terminologies you want to show for particular topics. It should be greater than or equal to two. Terminology number should be less than or equal to vocabulary size also.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "placeHolder": "10 (value >= 2)", + "type": "Integer", + "min": 2 + }, + { + "id": "max_iter", + "label": "Iterations", + "description": "Maximum number of iterations you want to run your model. It should be at least one.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "type": "Integer", + "min": 1, + "placeHolder": "20 (value >= 1)" + }, + { + "id": "learning_method", + "label": "Learning Method", + "description": "Method used to update parameters for topic word distribution. In each EM(Expectation Maximization) update, `Online` uses mini-batch of training data to update the parameters for topic word distribution variable incrementally. `Batch` uses all training data in each EM update. Old parameters for topic word distribution will be overwritten in each iteration.", + "visibleOption": [], + "control": "RadioButton", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "Online", + "value": "online", + "default": true + }, + { + "label": "Batch", + "value": "batch", + "default": false + } + ], + "columnType": [] + }, + { + "id": "learning_offset", + "label": "Learning Offset", + "description": "A (positive) parameter that downweights early iterations in online learning. It should be greater than 1.0.", + "visibleOption": [ + { + "id": "learning_method", + "value": "online" + } + ], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "placeHolder": "10.0 (value > 1.0)", + "type": "Double" + }, + { + "id": "random_state", + "label": "Seed", + "description": "Random seed. Use the same seed in order to reproduce the same results.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "type": "Integer", + "placeHolder": "" + }, + { + "id": "group_by", + "label": "Group By", + "description": "Columns to group by.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "multiple": true, + "rowCount": 5 + } + ], + "summary": "This function builds a topic model for text documents with LDA.", + "tutorialLink": "https://www.brightics.ai/kr/docs/ai/v3.7/tutorials/35_market_saledata_lda" + }, + "md": { + "en": "# Latent Dirichlet Allocation\nThis function builds a topic model for text documents with LDA.\n\n## Description\nThe generated topic model is then used for classifying the documents based on topics automatically chosen by the model. To choose topics, this function adapts Gibbs sampling.\n\nWhen we have a text document dataset to analyze, we need to do pre-process for the dataset. In general, we split texts into terms (words), build a vocabulary that only consider the top max_features ordered by term frequency across the corpus, and then remove (1) terms that have a document frequency strictly higher than 0.95 (2) terms that have a document frequency strictly lower than counts 2 (3) a built-in stopwords list for English is used. \n\nThis pre-processing should be performed carefully to get better results. This function performs this pre-processing with default setting we mentioned above.\n\nAlso, choosing the number of topics based on the dataset is very important. It could be possible for you to choose either randomly till you can get your desired results or thoroughly based on knowledge of the given text. Increasing number of iterations for running LDA model would give you stable results.\n\nReference: \n+ \n+ \"Online Learning for Latent Dirichlet Allocation\", Matthew D. Hoffman, David M. Blei, Francis Bach, 2010\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data including text documents.\n#### OUTPUT\n1. **out_table**: (Table) Table including topic modeling result.\n2. **topic_table**: (Table) Table which consists of each topic's vocabulary and weights.\n3. **model**: (Model) Result model.\n#### PARAMETER\n1. **Input Column***: Text data column to select as an input.\n\n2. **Topic Column Name**: Column name for topic modeling result.\n\n3. **Number of Vocabularies**: The number of vocabularies that will be utilized to count their frequencies in the entire documents. It should be greater than or equal to two.\n\n4. **Number of Topics**: The number of topics that will be contained in the entire documents. It should be greater than or equal to two.\n\n5. **Number of Terminologies**: Maximum number of terminologies you want to show for particular topics. It should be greater than or equal to two. Terminology number should be less than or equal to vocabulary size also.\n\n6. **Iterations**: Maximum number of iterations you want to run your model. It should be at least one.\n\n7. **Learning Method**: Method used to update parameters for topic word distribution. In each EM(Expectation Maximization) update, `Online` uses mini-batch of training data to update the parameters for topic word distribution variable incrementally. `Batch` uses all training data in each EM update. Old parameters for topic word distribution will be overwritten in each iteration.\n\n8. **Learning Offset**: A (positive) parameter that downweights early iterations in online learning. It should be greater than 1.0.\n\n9. **Seed**: Random seed. Use the same seed in order to reproduce the same results.\n\n10. **Group By**: Columns to group by.\n\n\n### Python\n#### USAGE\n\n```\nlda4(table = , input_col = , topic_name = , num_voca = , num_topic = , num_topic_word = , max_iter = , learning_method = , learning_offset = , random_state = , group_by = )\n```\n\n#### INPUT\n1. **table***: (Table) Data including text documents.\n#### OUTPUT\n1. **out_table**: (Table) Table including topic modeling result.\n2. **topic_table**: (Table) Table which consists of each topic's vocabulary and weights.\n3. **model**: (Model) Result model.\n#### PARAMETER\n1. **input_col***: Text data column to select as an input.\n\t* Type: *str*\n2. **topic_name**: Column name for topic modeling result.\n\t* Type: *str*\n3. **num_voca**: The number of vocabularies that will be utilized to count their frequencies in the entire documents. It should be greater than or equal to two.\n\t* Type: *int*\n\t* Default / Range: 1000 (value >= 2)\n4. **num_topic**: The number of topics that will be contained in the entire documents. It should be greater than or equal to two.\n\t* Type: *int*\n\t* Default / Range: 5 (value >= 2)\n5. **num_topic_word**: Maximum number of terminologies you want to show for particular topics. It should be greater than or equal to two. Terminology number should be less than or equal to vocabulary size also.\n\t* Type: *int*\n\t* Default / Range: 10 (value >= 2)\n6. **max_iter**: Maximum number of iterations you want to run your model. It should be at least one.\n\t* Type: *int*\n\t* Default / Range: 20 (value >= 1)\n7. **learning_method**: Method used to update parameters for topic word distribution. In each EM(Expectation Maximization) update, `Online` uses mini-batch of training data to update the parameters for topic word distribution variable incrementally. `Batch` uses all training data in each EM update. Old parameters for topic word distribution will be overwritten in each iteration.\n\t* Type: *str*\n\t* Default / Range: online ( online | batch )\n8. **learning_offset**: A (positive) parameter that downweights early iterations in online learning. It should be greater than 1.0.\n\t* Type: *double*\n\n9. **random_state**: Random seed. Use the same seed in order to reproduce the same results.\n\t* Type: *int*\n\n10. **group_by**: Columns to group by.\n\t* Type: *list[str]*\n\n\n## Example\n### VA\n**[Related Tutorial]**\n\n\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, sample_LatentDirichletAllocation data is used to make topic model and get the result. The result includes assigned topic and contributiveness of each word to assigned topic. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Input Column***: tokenized_text\n2. **Topic Column Name**: topic\n3. **Number of Vocabularies**: 1000\n4. **Number of Topics**: 5\n5. **Number of Terminologies**: 10\n6. **Iterations**: 20\n7. **Learning Method**: Online\n8. **Learning Offset**: 10\n9. **Seed**: None\n10. **Group By**: None\n\n\n\n### Python\n\n```\nfrom brightics.function.textanalytics import lda4\ninput_table = inputs[0]\nresult = lda4(table=input_table,\n input_col='tokenized_text',\n topic_name='topic',\n num_voca=1000,\n num_topic=5,\n num_topic_word=10,\n max_iter=20,\n learning_method='online',\n learning_offset=10,\n random_state=None,\n group_by=None)\noutput = result['out_table']\n```\nIn this tutorial workflow, sample_LatentDirichletAllocation data is used to make topic model and get the result. 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+ "description": "In this tutorial workflow, sample_LatentDirichletAllocation data is used to make topic model and get the result. The result includes assigned topic and contributiveness of each word to assigned topic. The parameter settings used in the function are shown below.", + "parameter": "1. **Input Column***: tokenized_text\n2. **Topic Column Name**: topic\n3. **Number of Vocabularies**: 1000\n4. **Number of Topics**: 5\n5. **Number of Terminologies**: 10\n6. **Iterations**: 20\n7. **Learning Method**: Online\n8. **Learning Offset**: 10\n9. **Seed**: None\n10. **Group By**: None\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.textanalytics import lda4\r\ninput_table = inputs[0]\r\nresult = lda4(table=input_table,\r\n input_col='tokenized_text',\r\n topic_name='topic',\r\n num_voca=1000,\r\n num_topic=5,\r\n num_topic_word=10,\r\n max_iter=20,\r\n learning_method='online',\r\n learning_offset=10,\r\n random_state=None,\r\n group_by=None)\r\noutput = result['out_table']", + "context": "python", + "description": "In this tutorial workflow, sample_LatentDirichletAllocation data is used to make topic model and get the result. The result includes assigned topic and contributiveness of each word to assigned topic." + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/textanalytics/meta/n_gram.json b/function/python/brightics/function/textanalytics/meta/n_gram.json index 0ced3f0f1..661540c9f 100644 --- a/function/python/brightics/function/textanalytics/meta/n_gram.json +++ b/function/python/brightics/function/textanalytics/meta/n_gram.json @@ -536,8 +536,8 @@ }, { "persist-mode": "auto", - "func": "brightics.function.textanalytics$tokenizer_eng51532", - "name": "brightics.function.textanalytics$tokenizer_eng", + "func": "brightics.function.textanalytics$tokenizer_eng2", + "name": "brightics.function.textanalytics$tokenizer_eng2", "param": { "stemming": true, "pos_extraction": [ diff --git a/function/python/brightics/function/textanalytics/meta/ner_crf_predict.json b/function/python/brightics/function/textanalytics/meta/ner_crf_predict.json new file mode 100644 index 000000000..26cb47283 --- /dev/null +++ b/function/python/brightics/function/textanalytics/meta/ner_crf_predict.json @@ -0,0 +1,1403 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "textanalytics", + "func": "brightics.function.textanalytics$ner_crf_predict", + "name": "brightics.function.textanalytics$ner_crf_predict", + "context": "python", + "label": "NER CRF Predict", + "description": "Named-entity recognition (NER) (also known as entity identification, entity chunking and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.\n\nThis function extracts named entities from given text using a CRF model obtained from the Named Entity Recognition CRF Train function.\n\nReference\n\n+ https://en.wikipedia.org/wiki/Named-entity_recognition", + "tags": [ + "ner", + "named entity recognition", + "crf" + ], + "version": "3.6", + "inputs": { + "table": "", + "model": "" + }, + "outputs": { + "out_table": "" + }, + "meta": { + "table": { + "type": "table", + "desc": "Data including text documents.", + "optional": false + }, + "model": { + "type": "model", + "desc": "Output model from NER CRF Train function.", + "optional": false + }, + "out_table": { + "type": "table", + "desc": "Table including found named entities with their tags." + } + }, + "params": [ + { + "id": "input_cols", + "label": "Input Columns", + "description": "String column names to apply the named entity recognition. Available input type is string only.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "columnType": [ + "String" + ], + "multiple": true, + "rowCount": 5 + }, + { + "id": "language", + "label": "Language", + "description": "Language of the text data.", + "visibleOption": [], + "control": "RadioButton", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "English", + "value": "eng", + "default": true + }, + { + "label": "Korean", + "value": "kor", + "default": false + } + ], + "columnType": [] + }, + { + "id": "extraction_set", + "label": "Extraction", + "description": "Extraction of items to return.", + "visibleOption": [], + "control": "ArrayInput", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "type": "String", + "placeHolder": "" + }, + { + "id": "new_col_prefix", + "label": "Column Name Prefix", + "description": "Prefix for new column names.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "String", + "placeHolder": "named_entity" + } + ], + "summary": "This function extracts named entities from given text using a CRF model obtained from the Named Entity Recognition CRF Train function." + }, + "md": { + "en": "# NER CRF Predict\nThis function extracts named entities from given text using a CRF model obtained from the Named Entity Recognition CRF Train function.\n\n## Description\nNamed-entity recognition (NER) (also known as entity identification, entity chunking and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.\n\nThis function extracts named entities from given text using a CRF model obtained from the Named Entity Recognition CRF Train function.\n\nReference\n\n+ https://en.wikipedia.org/wiki/Named-entity_recognition\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data including text documents.\n2. **model***: (Model) Output model from NER CRF Train function.\n#### OUTPUT\n1. **out_table**: (Table) Table including found named entities with their tags.\n#### PARAMETER\n1. **Input Columns***: String column names to apply the named entity recognition. Available input type is string only.\n\n2. **Language**: Language of the text data.\n\n3. **Extraction**: Extraction of items to return. If None, every possible named entity tag will be chosen.\n\n4. **Column Name Prefix**: Prefix for new column names.\n\n\n### Python\n#### USAGE\n\n```\nner_crf_predict(table = , model = , input_cols = , language = , extraction_set = , new_col_prefix = )\n```\n\n#### INPUT\n1. **table***: (Table) Data including text documents.\n2. **model***: (Model) Output model from NER CRF Train function.\n#### OUTPUT\n1. **out_table**: (Table) Table including found named entities with their tags.\n#### PARAMETER\n1. **input_cols***: String column names to apply the named entity recognition. Available input type is string only.\n\t* Type: *list[str]*\n2. **language**: Language of the text data.\n\t* Type: *str*\n\t* Default / Range: eng ( eng | kor )\n3. **extraction_set**: Extraction of items to return. If None, every possible named entity tag will be chosen.\n\t* Type: *str*\n4. **new_col_prefix**: Prefix for new column names.\n\t* Type: *str*\n\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, text_eng data is used to predict named entities. The output model from NER CRF Train function is applied to predict named entity category of each word. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Input Columns***: text\n2. **Language**: English\n3. **Extraction**: None\n4. **Column Name Prefix**:named_entity \n\n\n\n### Python\n\n```\nfrom brightics.function.textanalytics import ner_crf_predict\ninput_table = inputs[0]\ninput_model = models[0]\nresult = ner_crf_predict(table=input_table, model=input_model, input_cols=['text'],\n language='eng', new_col_prefix='named_entity', extraction_set=None)\noutput = result['out_table']\n```\nIn this tutorial workflow, text_eng data is used to predict named entities. 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", + "description": "In this tutorial workflow, text_eng data is used to predict named entities. The output model from NER CRF Train function is applied to predict named entity category of each word. The parameter settings used in the function are shown below.", + "parameter": "1. **Input Columns***: text\n2. **Language**: English\n3. **Extraction**: None\n4. **Column Name Prefix**:named_entity \n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.textanalytics import ner_crf_predict\r\ninput_table = inputs[0]\r\ninput_model = models[0]\r\nresult = ner_crf_predict(table=input_table, model=input_model, input_cols=['text'],\r\n language='eng', new_col_prefix='named_entity', extraction_set=None)\r\noutput = result['out_table']", + "context": "python", + "description": "In this tutorial workflow, text_eng data is used to predict named entities. The output model from NER CRF Train function is applied to predict named entity category of each word." + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/textanalytics/meta/ner_crf_train.json b/function/python/brightics/function/textanalytics/meta/ner_crf_train.json new file mode 100644 index 000000000..7defcfa41 --- /dev/null +++ b/function/python/brightics/function/textanalytics/meta/ner_crf_train.json @@ -0,0 +1,1486 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "textanalytics", + "func": "brightics.function.textanalytics$ner_crf_train", + "name": "brightics.function.textanalytics$ner_crf_train", + "context": "python", + "label": "NER CRF Train", + "description": "Named-entity recognition (NER) (also known as entity identification, entity chunking and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.\n\nThis function trains a CRF model to perform the named entity recognition. An input corpus must contain four columns: sentence column which identifies each sentence, token column, pos column, and label column. It is assumed that the IOB system is used and each label starts with letters 'I', 'O', or 'B'.\n\nReference\n\n+ https://en.wikipedia.org/wiki/Named-entity_recognition", + "tags": [ + "ner", + "named entity recognition", + "crf" + ], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "model": "" + }, + "meta": { + "table": { + "type": "table", + "desc": "Train data including sentence info, token info, part of speech, and named entity label.", + "optional": false + }, + "model": { + "type": "model", + "desc": "Trained model which will be used in the NER CRF Predict function." + } + }, + "params": [ + { + "id": "sentence_col", + "label": "Sentence Column", + "description": "Sentence column which identifies each sentence. It may contain sentence ID, sentence itself, etc. This value for tokens in the same sentence should be identical.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "columnType": [ + "Integer", + "Float", + "Double", + "Long", + "String" + ], + "multiple": false + }, + { + "id": "token_col", + "label": "Token Column", + "description": "Token column which contains a tokenized item.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "columnType": [ + "String" + ], + "multiple": false + }, + { + "id": "pos_col", + "label": "POS Column", + "description": "POS column which contains a part of speech information.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "columnType": [ + "String" + ], + "multiple": false + }, + { + "id": "label_col", + "label": "Label Column", + "description": "Label column which contains a named entity label.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "columnType": [ + "String" + ], + "multiple": false + }, + { + "id": "algorithm", + "label": "Algorithm", + "description": "Training algorithm for CRF.", + "visibleOption": [], + "control": "RadioButton", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "L-BFGS Gradient Descent", + "value": "lbfgs", + "default": true + }, + { + "label": "L2 Stochastic Gradient Descent", + "value": "l2sgd", + "default": false + }, + { + "label": "Averaged Perceptron", + "value": "ap", + "default": false + }, + { + "label": "Passive Aggressive", + "value": "pa", + "default": false + }, + { + "label": "Adaptive Regularization Of Weight Vector", + "value": "arow", + "default": false + } + ], + "columnType": [] + }, + { + "id": "c1", + "label": "c1", + "description": "The coefficient for L1 regularization. Affects when algorithm is set to L-BFGS.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "targetTable": [], + "placeHolder": "0.1 (value >= 0)", + "type": "Double", + "min": 0 + }, + { + "id": "c2", + "label": "c2", + "description": "The coefficient for L2 regularization. Affects when algorithm is set to L2 SGD or L-BFGS.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "targetTable": [], + "placeHolder": "0.1 (value >= 0)", + "type": "Double", + "min": 0 + }, + { + "id": "max_iterations", + "label": "Maximum number of iterations", + "description": "The maximum number of iterations for optimization algorithm. Suggested default values for each training algorithm: unlimited for L-BFGS, 1000 for L2 SGD, and 100 for AP, PA, and AROW.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "targetTable": [], + "type": "Integer", + "min": 1, + "placeHolder": "100 (value >= 1)" + }, + { + "id": "all_possible_transitions", + "label": "Use all possible transitions", + "description": "Specify whether CRF generates transition features that do not even occur in the training data.", + "visibleOption": [], + "control": "BooleanRadio", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "defaultValue": true + } + ], + "summary": "This function trains a CRF model to perform the named entity recognition." + }, + "md": { + "en": "# NER CRF Train\nThis function trains a CRF model to perform the named entity recognition.\n\n## Description\nNamed-entity recognition (NER) (also known as entity identification, entity chunking and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.\n\nThis function trains a CRF model to perform the named entity recognition. An input corpus must contain four columns:\n1. sentence column which identifies each sentence,\n2. token column which contains each word,\n3. pos column which contains part of speech of each word, and\n4. label column which contains named entity label of each word. It is assumed that the IOB system is used and each label starts with letters 'I', 'O', or 'B'.\n\nReference\n\n+ https://en.wikipedia.org/wiki/Named-entity_recognition\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Train data including sentence info, token info, part of speech, and named entity label.\n#### OUTPUT\n1. **model**: (Model) Trained model which will be used in the NER CRF Predict function.\n#### PARAMETER\n1. **Sentence Column***: Sentence column which identifies each sentence. It may contain sentence ID, sentence itself, etc. This value for tokens in the same sentence should be identical.\n\n2. **Token Column***: Token column which contains a tokenized item.\n\n3. **POS Column***: POS column which contains a part of speech information.\n\n4. **Label Column***: Label column which contains a named entity label. It is assumed that the IOB system is used and each label starts with letters 'I', 'O', or 'B'.\n\n5. **Algorithm**: Training algorithm for CRF.\n + Available Items: `L-BFGS Gradient Descent`, `L2 Stochastic Gradient Descent`, `Averaged Perceptron`, `Passive Aggressive`, `Adaptive Regularization Of Weight Vector`\n\n6. **c1**: The coefficient for L1 regularization. Affects when algorithm is set to `L-BFGS Gradient Descent`.\n\n7. **c2**: The coefficient for L2 regularization. Affects when algorithm is set to `L2 Stochastic Gradient Descent` or `L-BFGS Gradient Descent`.\n\n8. **Maximum number of iterations**: The maximum number of iterations for optimization algorithm. Suggested default values for each training algorithm: unlimited for `L-BFGS Gradient Descent`, 1000 for `L2 Stochastic Gradient Descent`, and 100 for `Averaged Perceptron`, `Passive Aggressive`, `Adaptive Regularization Of Weight Vector`.\n\n9. **Use all possible transitions**: Specify whether CRF generates transition features that do not even occur in the training data.\n\n\t* Default value: true \n\n### Python\n#### USAGE\n\n```\nner_crf_train(table = , sentence_col = , token_col = , pos_col = , label_col = , algorithm = , c1 = , c2 = , max_iterations = , all_possible_transitions = true)\n```\n\n#### INPUT\n1. **table***: (Table) Train data including sentence info, token info, part of speech, and named entity label.\n#### OUTPUT\n1. **model**: (Model) Trained model which will be used in the NER CRF Predict function.\n#### PARAMETER\n1. **sentence_col***: Sentence column which identifies each sentence. It may contain sentence ID, sentence itself, etc. This value for tokens in the same sentence should be identical.\n\t* Type: *str*\n2. **token_col***: Token column which contains a tokenized item.\n\t* Type: *str*\n3. **pos_col***: POS column which contains a part of speech information.\n\t* Type: *str*\n4. **label_col***: Label column which contains a named entity label.\n\t* Type: *str*\n5. **algorithm**: Training algorithm for CRF.\n\t* Type: *str*\n\t* Default / Range: lbfgs ( lbfgs | l2sgd | ap | pa | arow )\n6. **c1**: The coefficient for L1 regularization. Affects when algorithm is set to lbfgs.\n\t* Type: *double*\n\t* Default / Range: 0.1 (value >= 0)\n7. **c2**: The coefficient for L2 regularization. Affects when algorithm is set to l2sgd or lbfgs.\n\t* Type: *double*\n\t* Default / Range: 0.1 (value >= 0)\n8. **max_iterations**: The maximum number of iterations for optimization algorithm. Suggested default values for each training algorithm: unlimited for lbfgs, 1000 for l2sgd, and 100 for ap, pa, and arow.\n\t* Type: *int*\n\t* Default / Range: 100 (value >= 1)\n9. **all_possible_transitions**: Specify whether CRF generates transition features that do not even occur in the training data.\n\t* Type: *bool*\n\t* Default value: true \n\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, ner_crf_train data is used to train a NER CRF model and to predict named entities. The generated model is afterward applied to another text data to predict named entity category of each word in the NER CRF Predict function. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Sentence Column***: sent\n2. **Token Column***: word\n3. **POS Column***: pos\n4. **Label Column***: tag\n5. **Algorithm**: L-BFGS Gradient Descent\n6. **c1**: 0.1\n7. **c2**: 0.1\n8. **Maximum number of iterations**: 100\n9. **Use all possible transitions**: true\n\n\n\n### Python\n\n```\nfrom brightics.function.textanalytics import ner_crf_train\ninput_table = inputs[0]\nresult = ner_crf_train(table=input_table,\n sentence_col='sent', token_col='word', pos_col='pos', label_col='tag',\n algorithm='lbfgs', c1=0.1, c2=0.1,\n max_iterations=100, all_possible_transitions=True)\nmodel = result['model']\n```\nIn this tutorial workflow, ner_crf_train data is used to train a NER CRF model and to predict named entities. 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", + "description": "In this tutorial workflow, ner_crf_train data is used to train a NER CRF model and to predict named entities. The generated model is afterward applied to another text data to predict named entity category of each word in the NER CRF Predict function. The parameter settings used in the function are shown below.", + "parameter": "1. **Sentence Column***: sent\n2. **Token Column***: word\n3. **POS Column***: pos\n4. **Label Column***: tag\n5. **Algorithm**: L-BFGS Gradient Descent\n6. **c1**: 0.1\n7. **c2**: 0.1\n8. **Maximum number of iterations**: 100\n9. **Use all possible transitions**: true\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.textanalytics import ner_crf_train\r\ninput_table = inputs[0]\r\nresult = ner_crf_train(table=input_table,\r\n sentence_col='sent', token_col='word', pos_col='pos', label_col='tag',\r\n algorithm='lbfgs', c1=0.1, c2=0.1,\r\n max_iterations=100, all_possible_transitions=True)\r\nmodel = result['model']", + "description": "In this tutorial workflow, ner_crf_train data is used to train a NER CRF model and to predict named entities. The generated model is afterward applied to another text data to predict named entity category of each word in the NER CRF Predict function.", + "context": "python" + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/textanalytics/meta/ner_eng.json b/function/python/brightics/function/textanalytics/meta/ner_eng.json new file mode 100644 index 000000000..c00dbd594 --- /dev/null +++ b/function/python/brightics/function/textanalytics/meta/ner_eng.json @@ -0,0 +1,1116 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "textanalytics", + "func": "brightics.function.textanalytics$ner_eng", + "name": "brightics.function.textanalytics$ner_eng", + "context": "python", + "label": "NER (English)", + "description": "Named-entity recognition (NER) (also known as entity identification, entity chunking and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.\n\nThere are three methods available; NLTK uses the NLTK chunker method, Spacy uses Spacy NER method, and CRF uses pre-trained CRF model which has been obtained by training with GMB corpus.\n\nReference\n\n+ https://en.wikipedia.org/wiki/Named-entity_recognition", + "tags": [ + "ner", + "named entity recognition" + ], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "out_table": "" + }, + "meta": { + "table": { + "type": "table", + "desc": "Data including text documents.", + "optional": false + }, + "out_table": { + "type": "table", + "desc": "Table including found named entities with their tags." + } + }, + "params": [ + { + "id": "input_cols", + "label": "Input Columns", + "description": "String column names to apply the named entity recognition. Available input type is string only.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "columnType": [ + "String" + ], + "multiple": true, + "rowCount": 5 + }, + { + "id": "method", + "label": "Method", + "description": "Method for Named Entity Recognition.", + "visibleOption": [], + "control": "RadioButton", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "NLTK", + "value": "nltk", + "default": true + }, + { + "label": "Spacy", + "value": "spacy", + "default": false + }, + { + "label": "CRF", + "value": "crf", + "default": false + } + ], + "columnType": [] + }, + { + "id": "ne_extraction_nltk", + "label": "Extraction", + "description": "Extraction of items to return for NLTK mode.", + "visibleOption": [ + { + "id": "method", + "value": "nltk" + } + ], + "control": "CheckBox", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "FACILITY", + "value": "FACILITY", + "default": true + }, + { + "label": "GPE (Geo-Political Entities)", + "value": "GPE", + "default": true + }, + { + "label": "GSP (Geographical-Social-Political Entities)", + "value": "GSP", + "default": true + }, + { + "label": "LOCATION", + "value": "LOCATION", + "default": true + }, + { + "label": "ORGANIZATION", + "value": "ORGANIZATION", + "default": true + }, + { + "label": "PERSON", + "value": "PERSON", + "default": true + } + ], + "columnType": [] + }, + { + "id": "ne_extraction_spacy", + "label": "Extraction", + "description": "Extraction of items to return for Spacy mode.", + "visibleOption": [ + { + "id": "method", + "value": "spacy" + } + ], + "control": "CheckBox", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "CARDINAL", + "value": "CARDINAL", + "default": true + }, + { + "label": "DATE", + "value": "DATE", + "default": true + }, + { + "label": "EVENT", + "value": "EVENT", + "default": true + }, + { + "label": "FAC (Facilities)", + "value": "FAC", + "default": true + }, + { + "label": "GPE (Geo-Political Entities)", + "value": "GPE", + "default": true + }, + { + "label": "LANGUAGE", + "value": "LANGUAGE", + "default": true + }, + { + "label": "LAW", + "value": "LAW", + "default": true + }, + { + "label": "LOC (Location)", + "value": "LOC", + "default": true + }, + { + "label": "MONEY", + "value": "MONEY", + "default": true + }, + { + "label": "NORP (Nationalities Or Religious/Political Groups)", + "value": "NORP", + "default": true + }, + { + "label": "ORDINAL", + "value": "ORDINAL", + "default": true + }, + { + "label": "ORG (Organizations)", + "value": "ORG", + "default": true + }, + { + "label": "PERCENT", + "value": "PERCENT", + "default": true + }, + { + "label": "PERSON", + "value": "PERSON", + "default": true + }, + { + "label": "PRODUCT", + "value": "PRODUCT", + "default": true + }, + { + "label": "QUANTITY", + "value": "QUANTITY", + "default": true + }, + { + "label": "TIME", + "value": "TIME", + "default": true + }, + { + "label": "WORK_OF_ART", + "value": "WORK_OF_ART", + "default": true + } + ], + "columnType": [] + }, + { + "id": "ne_extraction_crf", + "label": "Extraction", + "description": "Extraction of items to return for CRF mode.", + "visibleOption": [ + { + "id": "method", + "value": "crf" + } + ], + "control": "CheckBox", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "ART (Artifact)", + "value": "ART", + "default": true + }, + { + "label": "EVE (Event)", + "value": "EVE", + "default": true + }, + { + "label": "GEO (Geographical Entities)", + "value": "GEO", + "default": true + }, + { + "label": "GPE (Geo-Political Entities)", + "value": "GPE", + "default": true + }, + { + "label": "NAT (Natural Phenomenon)", + "value": "NAT", + "default": true + }, + { + "label": "ORG (Organizations)", + "value": "ORG", + "default": true + }, + { + "label": "PER (Person)", + "value": "PER", + "default": true + }, + { + "label": "TIM (Time)", + "value": "TIM", + "default": true + } + ], + "columnType": [] + }, + { + "id": "new_col_prefix", + "label": "Column Name Prefix", + "description": "Prefix for new column names.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "String", + "placeHolder": "named_entity" + } + ], + "summary": "This function extracts named entities from given text." + }, + "md": { + "en": "# NER (English)\nThis function extracts named entities from given text.\n\n## Description\nNamed-entity recognition (NER) (also known as entity identification, entity chunking and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.\n\nThere are three methods available;\n1. `NLTK` uses the NLTK chunker method,\n2. `Spacy` uses Spacy NER method, and\n3. `CRF` uses pre-trained CRF model which has been obtained by training with GMB corpus.\n\nReference\n\n+ https://en.wikipedia.org/wiki/Named-entity_recognition\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data including text documents.\n#### OUTPUT\n1. **out_table**: (Table) Table including found named entities with their tags.\n#### PARAMETER\n1. **Input Columns***: String column names to apply the named entity recognition. Available input type is string only.\n\n2. **Method**: Method for Named Entity Recognition.\n\n3. **Extraction**: Extraction of items to return for NLTK mode. This option appears only when Method is set to `NLTK`. Initially all available options will be chosen as default.\n + Available Options: `FACILITY`, `GPE (Geo-Political Entities)`, `GSP (Geographical-Social-Political Entities)`, `LOCATION`, `ORGANIZATION`, `PERSON`\n4. **Extraction**: Extraction of items to return for Spacy mode. This option appears only when Method is set to `Spacy`. Initially all available options will be chosen as default.\n + Abailable Options: `CARDINAL`, `DATE`, `EVENT`, `FAC (Facilities)`, `GPE (Geo-Political Entities)`, `LANGUAGE`, `LAW`, `LOC (Location)`, `MONEY`, `NORP (Nationalities Or Religious/Political Groups)`, `ORDINAL`, `ORG (Organizations)`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART`\n5. **Extraction**: Extraction of items to return for CRF mode. This option appears only when Method is set to `CRF`. Initially all available options will be chosen as default.\n + Abailable Options: `ART (Artifact)`, `EVE (Event)`, `GEO (Geographical Entities)`, `GPE (Geo-Political Entities)`, `NAT (Natural Phenomenon)`, `ORG (Organizations)`, `PER (Person)`, `TIM (Time)`\n6. **Column Name Prefix**: Prefix for new column names.\n\n\n### Python\n#### USAGE\n\n```\nner_eng(table = , input_cols = , method = , ne_extraction_nltk = , ne_extraction_spacy = , ne_extraction_crf = , new_col_prefix = )\n```\n\n#### INPUT\n1. **table***: (Table) Data including text documents.\n#### OUTPUT\n1. **out_table**: (Table) Table including found named entities with their tags.\n#### PARAMETER\n1. **input_cols***: String column names to apply the named entity recognition. Available input type is string only.\n\t* Type: *list[str]*\n2. **method**: Method for Named Entity Recognition.\n\t* Type: *str*\n\t* Default / Range: nltk ( nltk | spacy | crf )\n3. **ne_extraction_nltk**: Extraction of items to return for NLTK mode. This option appears only when method='nltk'. Initially all available options will be chosen as default.\n\t* Type: *str*\n\t* Default / Range: FACILITY ( FACILITYGPE | GPEGSP | GSPLOCATION | LOCATIONORGANIZATION | ORGANIZATIONPERSON | PERSON )\n4. **ne_extraction_spacy**: Extraction of items to return for Spacy mode. This option appears only when method='spacy'. Initially all available options will be chosen as default.\n\t* Type: *str*\n\t* Default / Range: CARDINAL ( CARDINALDATE | DATEEVENT | EVENTFAC | FACGPE | GPELANGUAGE | LANGUAGELAW | LAWLOC | LOCMONEY | MONEYNORP | NORPORDINAL | ORDINALORG | ORGPERCENT | PERCENTPERSON | PERSONPRODUCT | PRODUCTQUANTITY | QUANTITYTIME | TIMEWORK_OF_ART | WORK_OF_ART )\n5. **ne_extraction_crf**: Extraction of items to return for CRF mode. This option appears only when method='crf'. Initially all available options will be chosen as default.\n\t* Type: *str*\n\t* Default / Range: ART ( ARTEVE | EVEGEO | GEOGPE | GPENAT | NATORG | ORGPER | PERTIM | TIM )\n6. **new_col_prefix**: Prefix for new column names.\n\t* Type: *str*\n\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, sample_data_tokenizer_eng data is used as input of NER (English) function. The output of this function is a Table with all numbers within the text data. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Input Columns***: document\n2. **Method**: NLTK\n3. **Extraction**: Facility, GPE (Geo-Political Entities), GSP (Geographical-Social-Political Entities), LOCATION, ORGANIZATION, PERSON\n4. **Column Name Prefix**: named_entity\n\n\n\n### Python\n\n```\nfrom brightics.function.textanalytics import ner_eng\ninput_table = inputs[0]\nresult = ner_eng(table=input_table, input_cols=['document'], method='nltk',\n ne_extraction_nltk=None, ne_extraction_spacy=None, ne_extraction_crf=None,\n new_col_prefix='named_entity')\noutput = result['out_table']\n```\nIn this tutorial workflow, sample_data_tokenizer_eng data is used as input of NER (English) function. 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", + "model_image": null, + "creator": "brightics@samsung.com", + "create_time": "2020-06-18T01:32:03.000Z", + "updater": "brightics@samsung.com", + "update_time": "2020-06-18T01:32:36.000Z", + "event_key": "2020_013236.000", + "type": "data", + "tag": "null", + "from_version": "0.0" + } + } + ], + "version": "3.6" + } + } + ], + "sampleImages": [ + { + "fileName": "ner_eng.png", + "label": "ner_eng.png", + "base64": 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", + "description": "In this tutorial workflow, sample_data_tokenizer_eng data is used as input of NER (English) function. The output of this function is a Table with all numbers within the text data. The parameter settings used in the function are shown below.", + "parameter": "1. **Input Columns***: document\n2. **Method**: NLTK\n3. **Extraction**: Facility, GPE (Geo-Political Entities), GSP (Geographical-Social-Political Entities), LOCATION, ORGANIZATION, PERSON\n4. **Column Name Prefix**: named_entity\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.textanalytics import ner_eng\r\ninput_table = inputs[0]\r\nresult = ner_eng(table=input_table, input_cols=['document'], method='nltk',\r\n ne_extraction_nltk=None, ne_extraction_spacy=None, ne_extraction_crf=None,\r\n new_col_prefix='named_entity')\r\noutput = result['out_table']", + "description": "In this tutorial workflow, sample_data_tokenizer_eng data is used as input of NER (English) function. The output of this function is a Table with all numbers within the text data.", + "context": "python" + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/textanalytics/meta/ner_kor.json b/function/python/brightics/function/textanalytics/meta/ner_kor.json new file mode 100644 index 000000000..e99aa7ee7 --- /dev/null +++ b/function/python/brightics/function/textanalytics/meta/ner_kor.json @@ -0,0 +1,104 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "isFunction": false, + "category": "textanalytics", + "func": "brightics.function.textanalytics$ner_kor", + "name": "brightics.function.textanalytics$ner_kor", + "context": "python", + "label": "NER (Korean)", + "description": "Extracts named entities from given text. It uses pre-trained CRF model which has been obtained by training with Korean NER Corpus.", + "tags": [ + "ner", + "named entity recognition" + ], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "out_table": "" + }, + "meta": { + "table": { + "type": "table" + }, + "out_table": { + "type": "table" + } + }, + "params": [ + { + "id": "input_cols", + "label": "Input Columns", + "description": "String column names to apply the named entity recognition. Available input type is string only.", + "mandatory": true, + "items": [], + "visibleOption": [], + "control": "ColumnSelector", + "columnType": [ + "String" + ], + "validation": [], + "multiple": true, + "rowCount": 5 + }, + { + "id": "ne_extraction_crf", + "label": "Extraction", + "description": "Extraction of items to return.", + "mandatory": false, + "items": [ + { + "label": "DT (Date)", + "value": "DT", + "default": true + }, + { + "label": "LC (Location)", + "value": "LC", + "default": true + }, + { + "label": "OG (Organization)", + "value": "OG", + "default": true + }, + { + "label": "PS (Person)", + "value": "PS", + "default": true + }, + { + "label": "TI (Time)", + "value": "TI", + "default": true + } + ], + "visibleOption": [], + "control": "CheckBox", + "columnType": [], + "validation": [] + }, + { + "id": "new_col_prefix", + "label": "Column Name Prefix", + "description": "Prefix for new column names", + "mandatory": false, + "items": [], + "visibleOption": [], + "globalVariable": false, + "control": "InputBox", + "columnType": [], + "validation": [], + "targetTable": [], + "type": "String", + "placeHolder": "named_entity" + } + ] + }, + "md": "" +} \ No newline at end of file diff --git a/function/python/brightics/function/textanalytics/meta/regex.json b/function/python/brightics/function/textanalytics/meta/regex.json new file mode 100644 index 000000000..6c59210f4 --- /dev/null +++ b/function/python/brightics/function/textanalytics/meta/regex.json @@ -0,0 +1,1051 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "textanalytics", + "func": "brightics.function.textanalytics$regex", + "name": "brightics.function.textanalytics$regex", + "context": "python", + "label": "Regular Expression", + "description": "A regular expression (shortened as regex or regexp; also referred to as rational expression) is a sequence of characters that define a search pattern. Usually such patterns are used by string searching algorithms for \"find\" or \"find and replace\" operations on strings, or for input validation. It is a technique developed in theoretical computer science and formal language theory.\n\nYou can choose a regular expression pattern from the pre-defined patterns, or input your own custom pattern. You can also make use of user-dictionary, which contains pattern names in the first column and regular expression patterns in the second column. The user dictionary patterns will be loaded by inputting the corresponding pattern name. The user dictionary may have duplicate pattern names; corresponding patterns will be merged into a single pattern.\n\nReference\n+ https://en.wikipedia.org/wiki/Regular_expression", + "tags": [ + "regex", + "regular expression" + ], + "version": "3.6", + "inputs": { + "table": "", + "user_dict": "" + }, + "outputs": { + "out_table": "" + }, + "meta": { + "table": { + "type": "table", + "desc": "Data including text documents.", + "optional": false + }, + "user_dict": { + "type": "table", + "desc": "User dictionary containing pre-defined regular expression patterns. The first column contains pattern names which a user had set for convenience, and the second column contains regular expression patterns.", + "optional": true + }, + "out_table": { + "type": "table", + "desc": "Table including result of a desired text manipulation." + } + }, + "params": [ + { + "id": "input_cols", + "label": "Input Columns", + "description": "String column names to apply the regex function. It should be of string type.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "columnType": [ + "String" + ], + "multiple": true, + "rowCount": 5 + }, + { + "id": "transformation_mode", + "label": "Transformation Mode", + "description": "What to do on the selected column. Extract: Find substrings matching the given pattern. Replace: The found matching substrings will be replaced to another given pattern. Remove: The found matching substrings will be removed. Split: Split the string by the given pattern. Default: Extract", + "visibleOption": [], + "control": "RadioButton", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "Extract", + "value": "extract", + "default": true + }, + { + "label": "Replace", + "value": "replace", + "default": false + }, + { + "label": "Remove", + "value": "remove", + "default": false + }, + { + "label": "Split", + "value": "split", + "default": false + } + ], + "columnType": [] + }, + { + "id": "find_mode", + "label": "Find Mode", + "description": "How many substrings to be transformed. All: All the matching substrings. First: Only the first matching substring. Default: All", + "visibleOption": [], + "control": "RadioButton", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "All", + "value": "all", + "default": true + }, + { + "label": "First", + "value": "first", + "default": false + } + ], + "columnType": [] + }, + { + "id": "pattern", + "label": "Pattern", + "description": "Input pattern.", + "visibleOption": [], + "control": "DropDownList", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "Alphabet (Latin)", + "value": "alphabet", + "default": true + }, + { + "label": "Numeric", + "value": "numeric", + "default": false + }, + { + "label": "Blank", + "value": "blank", + "default": false + }, + { + "label": "Korean character (Hangul)", + "value": "hangul", + "default": false + }, + { + "label": "Chinese character", + "value": "chinese", + "default": false + }, + { + "label": "Japanese character", + "value": "japanese", + "default": false + }, + { + "label": "Emoji", + "value": "emoji", + "default": false + }, + { + "label": "Quotation mark", + "value": "quotation_mark", + "default": false + }, + { + "label": "Special character", + "value": "special_character", + "default": false + }, + { + "label": "E-mail address", + "value": "e_mail", + "default": false + }, + { + "label": "URL", + "value": "url", + "default": false + }, + { + "label": "HTML", + "value": "html", + "default": false + }, + { + "label": "IP address (IPv4)", + "value": "ipv4", + "default": false + }, + { + "label": "RT", + "value": "rt", + "default": false + }, + { + "label": "Hashtag", + "value": "hashtag", + "default": false + }, + { + "label": "Date", + "value": "date", + "default": false + }, + { + "label": "Cell phone number", + "value": "cell_phone_number", + "default": false + }, + { + "label": "Phone number", + "value": "phone_number", + "default": false + }, + { + "label": "Zip code", + "value": "zip_code", + "default": false + }, + { + "label": "From user dictionary", + "value": "user_dictionary", + "default": false + }, + { + "label": "Custom", + "value": "custom", + "default": false + } + ], + "columnType": [] + }, + { + "id": "user_dict_pattern", + "label": "Pattern From User Dictionary", + "description": "Regex pattern from the given user dictionary. This option is available only when Pattern option is set to \"From user dictionary\".", + "visibleOption": [ + { + "id": "pattern", + "value": "user_dictionary" + } + ], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "String", + "placeHolder": "" + }, + { + "id": "custom_pattern", + "label": "Custom Pattern", + "description": "Custom regex pattern. This option is available only when Pattern option is set to \"Custom\".", + "visibleOption": [ + { + "id": "pattern", + "value": "custom" + } + ], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "String", + "placeHolder": "" + }, + { + "id": "replacement_string", + "label": "Replacement String", + "description": "String with which the source pattern will be replaced. This option is available only when Transformation Mode is set to \"Replace\".", + "visibleOption": [ + { + "id": "transformation_mode", + "value": "replace" + } + ], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "String", + "placeHolder": "" + } + ], + "summary": "This function extracts, replaces, removes, or splits strings using regex patterns." + }, + "md": { + "en": "# Regular Expression\nThis function extracts, replaces, removes, or splits strings using regex patterns.\n\n## Description\nA regular expression (shortened as regex or regexp; also referred to as rational expression) is a sequence of characters that define a search pattern. Usually such patterns are used by string searching algorithms for \"find\" or \"find and replace\" operations on strings, or for input validation. It is a technique developed in theoretical computer science and formal language theory.\n\nYou can choose a regular expression pattern from the pre-defined patterns, or input your own custom pattern. You can also make use of user-dictionary, which contains pattern names in the first column and regular expression patterns in the second column. The user dictionary patterns will be loaded by inputting the corresponding pattern name. The user dictionary may have duplicate pattern names; corresponding patterns will be merged into a single pattern.\n\nReference\n+ https://en.wikipedia.org/wiki/Regular_expression\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data including text documents.\n2. **user_dict***: (Table) User dictionary containing pre-defined regular expression patterns. The first column contains pattern names which a user had set for convenience, and the second column contains regular expression patterns.\n#### OUTPUT\n1. **out_table**: (Table) Table including result of a desired text manipulation.\n#### PARAMETER\n1. **Input Columns***: String column names to apply the regex function. It should be of string type.\n\n2. **Transformation Mode**: What to do on the selected column.\n + Available Options: `Extract`, `Replace`, `Remove`, `Split` (Default: `Extract`)\n + `Extract`: Find substrings matching the given pattern.\n + `Replace`: The found matching substrings will be replaced to another given pattern.\n + `Remove`: The found matching substrings will be removed.\n + `Split`: Split the string by the given pattern. \n\n3. **Find Mode**: How many substrings to be transformed.\n + Available Options: `All`, `First` (Default: `All`)\n + `All`: All the matching substrings.\n + `First`: Only the first matching substring.\n\n4. **Pattern***: Input pattern.\n + Available Options: `Alphabet (Latin)`,`Numeric`, `Blank`, `Korean character (Hangul)`, `Chinese character`, `Japanese character`, `Emoji`, `Quotation mark`, `Special character`, `E-mail address`, `URL`, `HTML`, `IP address (IPv4)`, `RT`, `Hashtag`, `Date`, `Cell phone number`, `Phone number`, `Zip code`, `From user dictionary`, `Custom`\n\n5. **Pattern From User Dictionary**: Regex pattern from the given user dictionary. This option is available only when Pattern option is set to \"From user dictionary\".\n\n6. **Custom Pattern**: Custom regex pattern. This option is available only when Pattern option is set to \"Custom\".\n\n7. **Replacement String**: String with which the source pattern will be replaced. This option is available only when Transformation Mode is set to \"Replace\".\n\n\n### Python\n#### USAGE\n\n```\nregex(table, user_dict, input_cols, transformation_mode=, find_mode=, pattern=, user_dict_pattern=, custom_pattern=, replacement_string=)\n```\n\n#### INPUT\n1. **table***: (Table) Data including text documents.\n2. **user_dict***: (Table) User dictionary containing pre-defined regular expression patterns. The first column contains pattern names which a user had set for convenience, and the second column contains regular expression patterns.\n#### OUTPUT\n1. **out_table**: (Table) Table including result of a desired text manipulation.\n#### PARAMETER\n1. **input_cols***: String column names to apply the regex function. It should be of string type.\n\t* Type: *list[str]*\n2. **transformation_mode**: What to do on the selected column. Extract: Find substrings matching the given pattern. Replace: The found matching substrings will be replaced to another given pattern. Remove: The found matching substrings will be removed. Split: Split the string by the given pattern. Default: Extract\n\t* Type: *str*\n\t* Default / Range: extract ( extract | replace | remove | split )\n3. **find_mode**: How many substrings to be transformed. All: All the matching substrings. First: Only the first matching substring. Default: All\n\t* Type: *str*\n\t* Default / Range: all ( all | first )\n4. **pattern**: Input pattern.\n\t* Type: *str*\n\t* Default / Range: alphabet ( alphabet | numeric | blank | hangul | chinese | japanese | emoji | quotation_mark | special_character | e_mail | url | html | ipv4 | rt | hashtag | date | cell_phone_number | phone_number | zip_code | user_dictionary | custom )\n5. **user_dict_pattern**: Regex pattern from the given user dictionary. This option is available only when Pattern option is set to \"From user dictionary\".\n\t* Type: *str*\n6. **custom_pattern**: Custom regex pattern. This option is available only when Pattern option is set to \"Custom\".\n\t* Type: *str*\n7. **replacement_string**: String with which the source pattern will be replaced. This option is available only when Transformation Mode is set to \"Replace\".\n\t* Type: *str*\n\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, text_eng data is used as input of Regular Expression function. The output of this function is a Table with all numbers within the text data. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Input Columns***: text\n2. **Transformation Mode**: Extract\n3. **Find Mode**: All\n4. **Pattern**: Numeric\n5. **Pattern From User Dictionary**: \n6. **Custom Pattern**: \n7. **Replacement String**: \n\n\n\n### Python\n\n```\nfrom brightics.function.textanalytics import regex\ninput_table = inputs[0]\nresult = regex(table=input_table, input_cols=['text'],\n transformation_mode='extract', find_mode='all', pattern='numeric',\n user_dict_pattern='', custom_pattern='', replacement_string='', user_dict=None)\noutput = result['out_table']\n```\nIn this tutorial workflow, text_eng data is used as input of Regular Expression function. The output of this function is a Table with all numbers within the text data.\n\n\n", + "kr": "" + }, + "exampleModels": [ + { + "fileName": "Regular Expression.json", + "label": "Regular Expression", + "contents": { + "type": "files", + "data": [ + { + "type": "file", + "data": { + "id": "mbpcnp347c2rwx28", + "project_id": "p7puh97cexbjrebu", + "label": "Regular Expression", + "contents": { + "mid": "mbpcnp347c2rwx28", + "type": "data", + "title": "Regular Expression", + "functions": [ + { + "persist-mode": "auto", + "func": "brightics.function.io$load13889", + "name": "brightics.function.io$load", + "param": { + "partial_path": [ + "/brightics@samsung.com/upload/sample_text_eng.csv" + ] + }, + "display": { + "label": "Load", + "diagram": { + "position": { + "x": 270, + "y": 120 + } + }, + "sheet": { + "out": { + "partial": [ + { + "panel": [ + { + "id": "default-1592375862952", + "chartOption": { + "colorSet": [ + "#FD026C", + "#4682B8", + "#A5D22D", + "#F5CC0A", + "#FE8C01", + 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", + "model_image": null, + "creator": "brightics@samsung.com", + "create_time": "2020-06-17T06:35:02.000Z", + "updater": "brightics@samsung.com", + "update_time": "2020-06-17T06:50:42.000Z", + "event_key": "2020_065042.000", + "type": "data", + "tag": "null", + "from_version": "0.0" + } + } + ], + "version": "3.6" + } + } + ], + "sampleImages": [ + { + "fileName": "regex.png", + "label": "regex.png", + "base64": 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", + "description": "In this tutorial workflow, text_eng data is used as input of Regular Expression function. The output of this function is a Table with all numbers within the text data. The parameter settings used in the function are shown below.", + "parameter": "1. **Input Columns***: text\n2. **Transformation Mode**: Extract\n3. **Find Mode**: All\n4. **Pattern**: Numeric\n5. **Pattern From User Dictionary**: \n6. **Custom Pattern**: \n7. **Replacement String**: \n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.textanalytics import regex\r\ninput_table = inputs[0]\r\nresult = regex(table=input_table, input_cols=['text'],\r\n transformation_mode='extract', find_mode='all', pattern='numeric',\r\n user_dict_pattern='', custom_pattern='', replacement_string='', user_dict=None)\r\noutput = result['out_table']", + "description": "In this tutorial workflow, text_eng data is used as input of Regular Expression function. The output of this function is a Table with all numbers within the text data. The parameter settings used in the function are shown below.", + "context": "python" + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/textanalytics/meta/search2.json b/function/python/brightics/function/textanalytics/meta/search2.json index 1ebfc776c..8f34a69e5 100644 --- a/function/python/brightics/function/textanalytics/meta/search2.json +++ b/function/python/brightics/function/textanalytics/meta/search2.json @@ -53,19 +53,6 @@ ], "multiple": true }, - { - "id": "hold_cols", - "label": "Hold Columns", - "description": "Result table includes hold columns in input table as well as the result of the operation. If None, all columns are included.", - "visibleOption": [], - "control": "ColumnSelector", - "validation": [], - "mandatory": false, - "items": [], - "globalVariable": false, - "columnType": [], - "multiple": true - }, { "id": "bool_search", "label": "Boolean Search", @@ -132,7 +119,7 @@ ], "summary": "This funcion search text in a document." }, - "md": "# Text Search\nThis funcion search text in a document.\n\n## Description\nText Search is to search text in a document. Keywords and the elements of Keyword Dictionary are the search text. If a search text is contained in the first column of Synonym Dictionary, then Text Search uses the synonyms as the original keyword.\n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Text data in a Table. It must contain at least one _str_ type column as input.\n2. **keyword_dict**: (Table) Keyword dictionary. The first column of the Table must be search keywords.\n3. **synonym_dict**: (Table) Synonym dictionary. The first column of the Table must be keywords and the second column must be synonyms of keywords.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contain search result. The setting value of `How to Handle Missing Data` parameter determines whether or not rows that do not include search keywords are deleted.\n#### PARAMETER\n1. **Input Columns***: Columns to select as input. Only String type columns are possible.\n\n2. **Hold Columns**: Result table includes hold columns in input table as well as the result of the operation. If None, all columns are included.\n\n3. **Boolean Search**: A type of search allowing users to combine keywords with operators such as And and Or.\n - Available Options: `Or`, `And`\n\n4. **Keywords**: Additional search keywords.\n\n5. **How to Handle Missing Data**: How to handle missing data.\n - Available Options: `Not to remove`, `To remove rows with all values missing`, `To remove rows with any values missing`\n\n\n### Python\n#### USAGE\n\n```\nsearch2(table, input_cols, hold_cols=None, bool_search=\"or\", keyword_dict=None, keywords=None, synonym_dict=None, remove_na=\"no\")\n```\n\n#### INPUT\n1. **table***: (Table) Text data in a Table. It must contain at least one _str_ type column as input.\n2. **keyword_dict**: (Table) Keyword dictionary. The first column of the Table must be search keywords.\n3. **synonym_dict**: (Table) Synonym dictionary. The first column of the Table must be keywords and the second column must be synonyms of keywords.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contain search result. The setting value of `remove_na` parameter determines whether or not rows that do not include search keywords are deleted.\n#### PARAMETER\n1. **input_cols***: Columns to select as input. Only String type columns are possible.\n\t* Type: *list[str]*\n2. **hold_cols**: Result table includes hold columns in input table as well as the result of the operation. If None, all columns are included.\n\t* Type: *list[str]*\n3. **bool_search**: A type of search allowing users to combine keywords with operators such as And and Or.\n\t* Type: *str*\n\t* Default / Range: or ( or | and )\n4. **keywords**: Additional search keywords.\n\t* Type: *list[str]*\n5. **remove_na**: How to handle missing data.\n\t* Type: *str*\n\t* Default / Range: no ( no | all | any )\n\n## Example\n### VA\n\n\n**[Sample Model]**\n\n\n\n\n\n
In this tutorial workflow, sample_data_tokenizer_eng and User_dict data are used as input of Text Search function. This function search keywords from input text data then, return search result Table data.\nThe parameter settings used in the function are shown below. \n\n##### ++Parameters++\n1. **Input Columns*** : document\n2. **Hold Columns** : None\n3. **Boolean Search** : Or\n4. **Keywords** : None\n5. **How to Handle Missing Data** : Not to remove\n\n\n### Python\n```\nfrom brightics.function.textanalytics import search2\ninput_table = inputs[0]\nkeyword_dict = inputs[1]\nsynonym_dict = inputs[2]\nresult = search2(table=input_table,\n keyword_dict=keyword_dict,\n synonym_dict=synonym_dict,\n input_cols=['document'],\n hold_cols=None,\n bool_search='or',\n keywords=None,\n remove_na='no')\noutput = result['out_table']\n```\n\n
In this python script, simple text data, sample_data_tokenizer_eng and User_dict data are used as input of search2 function. This function search keywords from input text data then, return search result Table data.\n", + "md": "# Text Search\nThis funcion search text in a document.\n\n## Description\nText Search is to search text in a document. Keywords and the elements of Keyword Dictionary are the search text. If a search text is contained in the first column of Synonym Dictionary, then Text Search uses the synonyms as the original keyword.\n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Text data in a Table. It must contain at least one _str_ type column as input.\n2. **keyword_dict**: (Table) Keyword dictionary. The first column of the Table must be search keywords.\n3. **synonym_dict**: (Table) Synonym dictionary. The first column of the Table must be keywords and the second column must be synonyms of keywords.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contain search result. The setting value of `How to Handle Missing Data` parameter determines whether or not rows that do not include search keywords are deleted.\n#### PARAMETER\n1. **Input Columns***: Columns to select as input. Only String type columns are possible.\n\n2. **Boolean Search**: A type of search allowing users to combine keywords with operators such as And and Or.\n - Available Options: `Or`, `And`\n\n3. **Keywords**: Additional search keywords.\n\n4. **How to Handle Missing Data**: How to handle missing data.\n - Available Options: `Not to remove`, `To remove rows with all values missing`, `To remove rows with any values missing`\n\n\n### Python\n#### USAGE\n\n```\nsearch2(table, input_cols, bool_search=\"or\", keyword_dict=None, keywords=None, synonym_dict=None, remove_na=\"no\")\n```\n\n#### INPUT\n1. **table***: (Table) Text data in a Table. It must contain at least one _str_ type column as input.\n2. **keyword_dict**: (Table) Keyword dictionary. The first column of the Table must be search keywords.\n3. **synonym_dict**: (Table) Synonym dictionary. The first column of the Table must be keywords and the second column must be synonyms of keywords.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contain search result. The setting value of `remove_na` parameter determines whether or not rows that do not include search keywords are deleted.\n#### PARAMETER\n1. **input_cols***: Columns to select as input. Only String type columns are possible.\n\t* Type: *list[str]*\n2. **bool_search**: A type of search allowing users to combine keywords with operators such as And and Or.\n\t* Type: *str*\n\t* Default / Range: or ( or | and )\n3. **keywords**: Additional search keywords.\n\t* Type: *list[str]*\n4. **remove_na**: How to handle missing data.\n\t* Type: *str*\n\t* Default / Range: no ( no | all | any )\n\n## Example\n### VA\n\n\n**[Sample Model]**\n\n\n\n\n\n
In this tutorial workflow, sample_data_tokenizer_eng and sample_data_ts_user_dict data are used as input of Text Search function. This function search keywords from input text data then, return search result Table data.\nThe parameter settings used in the function are shown below. \n\n##### ++Parameters++\n1. **Input Columns*** : document\n2. **Boolean Search** : Or\n3. **Keywords** : None\n4. **How to Handle Missing Data** : Not to remove\n\n\n### Python\n```\nfrom brightics.function.textanalytics import search2\ninput_table = inputs[0]\nkeyword_dict = inputs[1]\nsynonym_dict = inputs[2]\nresult = search2(table=input_table,\n keyword_dict=keyword_dict,\n synonym_dict=synonym_dict,\n input_cols=['document'],\n bool_search='or',\n keywords=None,\n remove_na='no')\noutput = result['out_table']\n```\n\n
In this python script, simple text data, sample_data_tokenizer_eng and sample_data_ts_user_dict data are used as input of search2 function. This function search keywords from input text data then, return search result Table data.\n", "exampleModels": [ { "fileName": "brightics.function.textanalytics$search2.json", @@ -381,7 +368,7 @@ "name": "brightics.function.io$load", "param": { "partial_path": [ - "/brightics@samsung.com/upload/User_dict.csv" + "/brightics@samsung.com/upload/sample_ts_user_dict.csv" ] }, "display": { @@ -1094,7 +1081,7 @@ "name": "brightics.function.io$load", "param": { "partial_path": [ - "/brightics@samsung.com/upload/User_dict.csv" + "/brightics@samsung.com/upload/sample_ts_user_dict.csv" ] }, "display": { diff --git a/function/python/brightics/function/textanalytics/meta/split_sentences.json b/function/python/brightics/function/textanalytics/meta/split_sentences.json index accd0b170..27d555a29 100644 --- a/function/python/brightics/function/textanalytics/meta/split_sentences.json +++ b/function/python/brightics/function/textanalytics/meta/split_sentences.json @@ -4,6 +4,7 @@ "content": "" }, "specJson": { + "deprecated": true, "category": "textanalytics", "func": "brightics.function.textanalytics$split_sentences19300", "name": "brightics.function.textanalytics$split_sentences", diff --git a/function/python/brightics/function/textanalytics/meta/split_sentences2.json b/function/python/brightics/function/textanalytics/meta/split_sentences2.json new file mode 100644 index 000000000..fb5bb4a17 --- /dev/null +++ b/function/python/brightics/function/textanalytics/meta/split_sentences2.json @@ -0,0 +1,922 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "textanalytics", + "func": "brightics.function.textanalytics$split_sentences2", + "name": "brightics.function.textanalytics$split_sentences2", + "context": "python", + "label": "Split Sentences", + "description": "To split sentences is a basic preprocessing method for text analysis.", + "tags": [], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "out_table": "" + }, + "meta": { + "table": { + "type": "table", + "desc": "Text data in a Table.", + "optional": false + }, + "out_table": { + "type": "table", + "desc": "Text data table with all sentences split." + } + }, + "params": [ + { + "id": "input_col", + "label": "Input Column", + "description": "Column to select as input. Only String type column are possible.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "columnType": [], + "multiple": false + }, + { + "id": "language", + "label": "Language", + "description": "Language selection option. Korean and English are available", + "visibleOption": [], + "control": "RadioButton", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "Korean", + "value": "kor", + "default": true + }, + { + "label": "English", + "value": "eng", + "default": false + }, + { + "label": "Mixed (Korean and English)", + "value": "mixed", + "default": false + } + ], + "columnType": [], + "targetTable": [] + }, + { + "id": "doc_id_col_name", + "label": "Document ID Column Name", + "description": "Document ID column name to be appeared in the output", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "String", + "placeHolder": "doc_id" + }, + { + "id": "sentence_id_col_name", + "label": "Sentence ID Column Name", + "description": "Sentence ID column name to be appeared in the output", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "String", + "placeHolder": "sentence_id" + }, + { + "id": "sentence_col_name", + "label": "Sentence Column Name", + "description": "Sentence column name to be appeared in the output", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "String", + "placeHolder": "sentence" + }, + { + "id": "duplicate_original", + "label": "Duplicate original texts", + "description": "If True, the original text will be appearing in all the rows. Otherwise, the original text will be shown only for the first row for each text.", + "visibleOption": [], + "control": "BooleanRadio", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "defaultValue": false + } + ], + "summary": "This function splits text into sentences." + }, + "md": { + "en": "# Split Sentences\nThis function splits text into sentences.\n\n## Description\nTo split sentences is a basic preprocessing method for text analysis.\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Text data in a Table.\n#### OUTPUT\n1. **out_table**: (Table) Text data table with all sentences split.\n#### PARAMETER\n1. **Input Column***: Column to select as input. Only String type column are possible.\n\n2. **Language**: Language selection option.\n + Available Options: `Korean`, `English`, `Mixed (Korean and English)`\n\n3. **Document ID Column Name**: Document ID column name to be appeared in the output\n\n4. **Sentence ID Column Name**: Sentence ID column name to be appeared in the output\n\n5. **Sentence Column Name**: Sentence column name to be appeared in the output\n\n6. **Duplicate original texts**: If True, the original text will be appearing in all the rows. Otherwise, the original text will be shown only for the first row for each text.\n\n\t* Default value: false \n\n### Python\n#### USAGE\n\n```\nsplit_sentences2(table = , input_col = , language = , doc_id_col_name = , sentence_id_col_name = , sentence_col_name = , duplicate_original = false)\n```\n\n#### INPUT\n1. **table***: (Table) Text data in a Table.\n#### OUTPUT\n1. **out_table**: (Table) Text data table with all sentences split.\n#### PARAMETER\n1. **input_col***: Column to select as input. Only String type column are possible.\n\t* Type: *str*\n2. **language**: Language selection option. Korean and English are available\n\t* Type: *str*\n\t* Default / Range: kor ( kor | eng | mixed )\n3. **doc_id_col_name**: Document ID column name to be appeared in the output\n\t* Type: *str*\n4. **sentence_id_col_name**: Sentence ID column name to be appeared in the output\n\t* Type: *str*\n5. **sentence_col_name**: Sentence column name to be appeared in the output\n\t* Type: *str*\n6. **duplicate_original**: If True, the original text will be appearing in all the rows. Otherwise, the original text will be shown only for the first row for each text.\n\t* Type: *bool*\n\t* Default value: false \n\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, sample_data_tokenizer_eng data is used as input of Split Sentences function. The output of this function is a Table with splited sentences data. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Input Column***: document\n2. **Language**: English\n3. **Document ID Column Name**: doc_id\n4. **Sentence ID Column Name**: sentence_id\n5. **Sentence Column Name**: sentence\n6. **Duplicate original texts**: false\n\n\n\n### Python\n\n```\nfrom brightics.function.textanalytics import split_sentences2\ninput_table = inputs[0]\nresult = split_sentences2(table=input_table, input_col='document', language='eng',\n doc_id_col_name='doc_id', sentence_id_col_name='sentence_id',\n sentence_col_name='sentence', duplicate_original=False)\noutput = result['out_table']\n```\nIn this tutorial workflow, sample_data_tokenizer_eng data is used as input of Split Sentences function. 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Split Sentences sample model.

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", + "description": "In this tutorial workflow, sample_data_tokenizer_eng data is used as input of Split Sentences function. The output of this function is a Table with splited sentences data. The parameter settings used in the function are shown below.", + "parameter": "1. **Input Column***: document\n2. **Language**: English\n3. **Document ID Column Name**: doc_id\n4. **Sentence ID Column Name**: sentence_id\n5. **Sentence Column Name**: sentence\n6. **Duplicate original texts**: false\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.textanalytics import split_sentences2\r\ninput_table = inputs[0]\r\nresult = split_sentences2(table=input_table, input_col='document', language='eng',\r\n doc_id_col_name='doc_id', sentence_id_col_name='sentence_id',\r\n sentence_col_name='sentence', duplicate_original=False)\r\noutput = result['out_table']", + "description": "In this tutorial workflow, sample_data_tokenizer_eng data is used as input of Split Sentences function. The output of this function is a Table with splited sentences data.", + "context": "python" + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/textanalytics/meta/stopwords_remover_user_dict.json b/function/python/brightics/function/textanalytics/meta/stopwords_remover_user_dict.json index 3b84bcf99..744e045ef 100644 --- a/function/python/brightics/function/textanalytics/meta/stopwords_remover_user_dict.json +++ b/function/python/brightics/function/textanalytics/meta/stopwords_remover_user_dict.json @@ -42,19 +42,9 @@ "validation": [], "mandatory": true, "items": [], - "columnType": [], - "multiple": true - }, - { - "id": "hold_cols", - "label": "Hold Columns", - "description": "Result table includes hold columns in input table as well as the result of the operation. If None, all columns are included.", - "visibleOption": [], - "control": "ColumnSelector", - "validation": [], - "mandatory": false, - "items": [], - "columnType": [], + "columnType": [ + "String[]" + ], "multiple": true }, { @@ -98,7 +88,7 @@ ], "summary": "This function removes stopwords from tokenized text data." }, - "md": "# Stopwords Remover\nThis function removes stopwords from tokenized text data.\n\n## Description\nIn order to screen only meaningful word tokens from the text data, it is necessary to remove the meaningless word tokens. The word meaningless here refers to words that appear frequently in sentences but are not very helpful in analyzing the sentences. For example, words such as I, my, me, over, and suffix often appear in sentences, but contribute little to actual semantic analysis. These words are called stopwords.\n\nReference: \n+ \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Text data in a Table.\n2. **user_dict**: (Table) User defined stopword dictionary. The first column of the Table must be stopwords.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contain input table's columns and stopwords-removed text data.\n#### PARAMETER\n1. **Input Columns***: Columns to select as input. Only List of String type columns are possible.\n\n2. **Hold Columns**: Result table includes hold columns in input table as well as the result of the operation. If None, all columns are included.\n\n3. **Default Dictionary**: Whether to use default dictionary.\n - Available Options : `True`, `False`\n\n4. **Stop Words**: User list of stopwords.\n\n5. **Prefix**: Prefix for new column name.\n\n\n### Python\n#### USAGE\n\n```\nstopwords_remover_user_dict(table, input_cols, hold_cols=None, default_dict=False, stop_words=None, prefix='stopwords', user_dict=None)\n```\n\n#### INPUT\n1. **table***: (Table) Text data in a Table.\n2. **user_dict**: (Table) User defined stopword dictionary. The first column of the Table must be stopwords.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contain input table's columns and stopwords-removed text data.\n#### PARAMETER\n1. **input_cols***: Columns to select as input. Only List of String type columns are possible.\n\t* Type: *list[str]*\n2. **hold_cols**: Result table includes hold columns in input table as well as the result of the operation. If None, all columns are included.\n\t* Type: *list[str]*\n3. **default_dict**: Whether to use default dictionary.\n\t* Type: *bool*\n\t* Default value: False \n4. **stop_words**: User list of stopwords.\n\t* Type: *list[str]*\n5. **prefix**: Prefix for new column name.\n\t* Type: *str*\n\t* Default / Range: 'stopwords'\n\n## Example\n### VA\n\n\n**[Sample Model]**\n\n\n\n\n\n
In this tutorial workflow, tokenized sample_data_tokenizer_eng data is used as input of Synonym Converter function. The ouput of this function is a Table that has input Table's columns and stopwords-removed text data column.\nThe parameter settings used in the function are shown below. \n\n##### ++Parameters++\n1. **Input Columns*** : tokenized_text\n2. **Hold Columns** : None\n3. **Default Dictionary** : True\n4. **Stop Words** : like, monday\n5. **Prefix** : stopwords\n\n\n### Python\n```\nfrom brightics.function.textanalytics import stopwords_remover_user_dict\ninput_table = inputs[0]\nuser_dict = inputs[1]\nresult = stopwords_remover_user_dict(table=input_table,\n user_dict=user_dict,\n input_cols=['tokenized_text'],\n hold_cols=None,\n default_dict=True,\n stop_words=['like', 'monday'],\n prefix='stopwords')\noutput = result['out_table']\n```\n\n
In this python script, tokenized sample_data_tokenizer_eng data is used as input of synonym_converter_user_dict function. The ouput of this function is a Table that has input Table's columns and stopwords-removed text data column.\n\n", + "md": "# Stopwords Remover\nThis function removes stopwords from tokenized text data.\n\n## Description\nIn order to screen only meaningful word tokens from the text data, it is necessary to remove the meaningless word tokens. The word meaningless here refers to words that appear frequently in sentences but are not very helpful in analyzing the sentences. For example, words such as I, my, me, over, and suffix often appear in sentences, but contribute little to actual semantic analysis. These words are called stopwords.\n\nReference: \n+ \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Text data in a Table.\n2. **user_dict**: (Table) User defined stopword dictionary. The first column of the Table must be stopwords.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contain input table's columns and stopwords-removed text data.\n#### PARAMETER\n1. **Input Columns***: Columns to select as input. Only List of String type columns are possible.\n\n2. **Default Dictionary**: Whether to use default dictionary.\n - Available Options : `True`, `False`\n\n3. **Stop Words**: User list of stopwords.\n\n4. **Prefix**: Prefix for new column name.\n\n\n### Python\n#### USAGE\n\n```\nstopwords_remover_user_dict(table, input_cols, default_dict=False, stop_words=None, prefix='stopwords', user_dict=None)\n```\n\n#### INPUT\n1. **table***: (Table) Text data in a Table.\n2. **user_dict**: (Table) User defined stopword dictionary. The first column of the Table must be stopwords.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contain input table's columns and stopwords-removed text data.\n#### PARAMETER\n1. **input_cols***: Columns to select as input. Only List of String type columns are possible.\n\t* Type: *list[str]*\n2. **default_dict**: Whether to use default dictionary.\n\t* Type: *bool*\n\t* Default value: False \n3. **stop_words**: User list of stopwords.\n\t* Type: *list[str]*\n4. **prefix**: Prefix for new column name.\n\t* Type: *str*\n\t* Default / Range: 'stopwords'\n\n## Example\n### VA\n\n\n**[Sample Model]**\n\n\n\n\n\n
In this tutorial workflow, tokenized sample_data_tokenizer_eng data is used as input of Synonym Converter function. The ouput of this function is a Table that has input Table's columns and stopwords-removed text data column.\nThe parameter settings used in the function are shown below. \n\n##### ++Parameters++\n1. **Input Columns*** : tokenized_text\n2. **Default Dictionary** : True\n3. **Stop Words** : like, monday\n4. **Prefix** : stopwords\n\n\n### Python\n```\nfrom brightics.function.textanalytics import stopwords_remover_user_dict\ninput_table = inputs[0]\nuser_dict = inputs[1]\nresult = stopwords_remover_user_dict(table=input_table,\n user_dict=user_dict,\n input_cols=['tokenized_text'],\n default_dict=True,\n stop_words=['like', 'monday'],\n prefix='stopwords')\noutput = result['out_table']\n```\n\n
In this python script, tokenized sample_data_tokenizer_eng data is used as input of synonym_converter_user_dict function. The ouput of this function is a Table that has input Table's columns and stopwords-removed text data column.\n\n", "exampleModels": [ { "fileName": "brightics.function.textanalytics$stopwords_remover_user_dict.json", @@ -826,8 +816,8 @@ }, { "persist-mode": "auto", - "func": "brightics.function.textanalytics$tokenizer_eng51532", - "name": "brightics.function.textanalytics$tokenizer_eng", + "func": "brightics.function.textanalytics$tokenizer_eng2", + "name": "brightics.function.textanalytics$tokenizer_eng2", "param": { "stemming": false, "pos_extraction": [ diff --git a/function/python/brightics/function/textanalytics/meta/synonym_converter_user_dict.json b/function/python/brightics/function/textanalytics/meta/synonym_converter_user_dict.json index efecec963..e22f9536c 100644 --- a/function/python/brightics/function/textanalytics/meta/synonym_converter_user_dict.json +++ b/function/python/brightics/function/textanalytics/meta/synonym_converter_user_dict.json @@ -42,19 +42,9 @@ "validation": [], "mandatory": true, "items": [], - "columnType": [], - "multiple": true - }, - { - "id": "hold_cols", - "label": "Hold Columns", - "description": "Result table includes hold columns in input table as well as the result of the operation. If None, all columns are included.", - "visibleOption": [], - "control": "ColumnSelector", - "validation": [], - "mandatory": false, - "items": [], - "columnType": [], + "columnType": [ + "String[]" + ], "multiple": true }, { @@ -88,7 +78,7 @@ ], "summary": "This function converts similar words into a representative word." }, - "md": "# Synonym Converter\nThis function converts similar words into a representative word.\n\n## Description\nIn text analysis, text data can be processed more effectively by changing synonyms into representative words. This reduces the size of word corpus, allowing data to ve processed using less memory. Also, This also increases the number of sentence(or document) samples for a word.\n\nThe first column of `user_dict` must be representative words and the second column must be synonyms.\n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Text data in a Table.\n2. **user_dict**: (Table) User defined synonym dictionary. The first column of the Table must be representative words and the second column must be synonyms.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contain input table's columns and synonym removed columns.\n#### PARAMETER\n1. **Input Columns***: Columns to select as input. Only List of words type columns are possible.\n\n2. **Hold Columns**: Result table includes hold columns in input table as well as the result of the operation. If None, all columns are included.\n\n3. **Synonym List**: User list of synonyms. To change 'apple', 'banana' and 'carrot' to 'fruits', enter : fruits, apple, banana, carrot\n\n4. **Prefix**: Prefix for new column name.\n\n\n### Python\n#### USAGE\n\n```\nsynonym_converter(table, input_cols, hold_cols=None, default_dict=False, synonym_list=None, prefix='synonym', user_dict=None)\n```\n\n#### INPUT\n1. **table***: (Table) Text data in a Table.\n2. **user_dict**: (Table) User defined synonym dictionary. The first column of the Table must be representative words and the second column must be synonyms.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contain input table's columns and synonym removed columns.\n#### PARAMETER\n1. **input_cols***: Columns to select as input. Only List of words type columns are possible.\n\t* Type: *list[str]*\n2. **hold_cols**: Result table includes hold columns in input table as well as the result of the operation. If None, all columns are included.\n\t* Type: *list[str]*\n3. **synonym_list**: User list of synonyms. To change 'apple', 'banana' and 'carrot' to 'fruits', enter : fruits, apple, banana, carrot\n\t* Type: *list[str]*\n4. **prefix**: Prefix for new column name.\n\t* Type: *str*\n\t* Default / Range: 'synonym'\n\n## Example\n### VA\n\n\n**[Sample Model]**\n\n\n\n\n\n
In this tutorial workflow, sample_data_tokenizer_eng data and synonym_dict Table that is created by Create Table function are used as input of Synonym Converter function.\nThe parameter settings used in the function are shown below. \n\n##### ++Parameters++\n1. **Input Columns*** : tokenized_text\n2. **Hold Columns** : None\n3. **Synonym List** : None\n4. **Prefix** : synonym\n\n\n### Python\n```\nfrom brightics.function.textanalytics import synonym_converter_user_dict\ninput_table = inputs[0]\nuser_dict = inputs[1]\nresult = synonym_converter_user_dict(table=input_table,\n user_dict=user_dict,\n input_cols=['tokenized_text'],\n hold_cols=None,\n synonym_list=None,\n prefix='synonym')\noutput = result['out_table']\n```\n\n
In this python script, sample_data_tokenizer_eng data and synonym_dict Table that is created by Create Table function are used as input of synonym_converter_user_dict.\n\n", + "md": "# Synonym Converter\nThis function converts similar words into a representative word.\n\n## Description\nIn text analysis, text data can be processed more effectively by changing synonyms into representative words. This reduces the size of word corpus, allowing data to ve processed using less memory. Also, This also increases the number of sentence(or document) samples for a word.\n\nThe first column of `user_dict` must be representative words and the second column must be synonyms.\n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Text data in a Table.\n2. **user_dict**: (Table) User defined synonym dictionary. The first column of the Table must be representative words and the second column must be synonyms.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contain input table's columns and synonym removed columns.\n#### PARAMETER\n1. **Input Columns***: Columns to select as input. Only List of words type columns are possible.\n\n2. **Synonym List**: User list of synonyms. To change 'apple', 'banana' and 'carrot' to 'fruits', enter : fruits, apple, banana, carrot\n\n3. **Prefix**: Prefix for new column name.\n\n\n### Python\n#### USAGE\n\n```\nsynonym_converter(table, input_cols, default_dict=False, synonym_list=None, prefix='synonym', user_dict=None)\n```\n\n#### INPUT\n1. **table***: (Table) Text data in a Table.\n2. **user_dict**: (Table) User defined synonym dictionary. The first column of the Table must be representative words and the second column must be synonyms.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contain input table's columns and synonym removed columns.\n#### PARAMETER\n1. **input_cols***: Columns to select as input. Only List of words type columns are possible.\n\t* Type: *list[str]*\n2. **synonym_list**: User list of synonyms. To change 'apple', 'banana' and 'carrot' to 'fruits', enter : fruits, apple, banana, carrot\n\t* Type: *list[str]*\n3. **prefix**: Prefix for new column name.\n\t* Type: *str*\n\t* Default / Range: 'synonym'\n\n## Example\n### VA\n\n\n**[Sample Model]**\n\n\n\n\n\n
In this tutorial workflow, sample_data_tokenizer_eng data and synonym_dict Table that is created by Create Table function are used as input of Synonym Converter function.\nThe parameter settings used in the function are shown below. \n\n##### ++Parameters++\n1. **Input Columns*** : tokenized_text\n2. **Synonym List** : None\n3. **Prefix** : synonym\n\n\n### Python\n```\nfrom brightics.function.textanalytics import synonym_converter_user_dict\ninput_table = inputs[0]\nuser_dict = inputs[1]\nresult = synonym_converter_user_dict(table=input_table,\n user_dict=user_dict,\n input_cols=['tokenized_text'],\n synonym_list=None,\n prefix='synonym')\noutput = result['out_table']\n```\n\n
In this python script, sample_data_tokenizer_eng data and synonym_dict Table that is created by Create Table function are used as input of synonym_converter_user_dict.\n\n", "exampleModels": [ { "fileName": "brightics.function.textanalytics$synonym_converter_user_dict.json", @@ -815,8 +805,8 @@ }, { "persist-mode": "auto", - "func": "brightics.function.textanalytics$tokenizer_eng51532", - "name": "brightics.function.textanalytics$tokenizer_eng", + "func": "brightics.function.textanalytics$tokenizer_eng2", + "name": "brightics.function.textanalytics$tokenizer_eng2", "param": { "stemming": false, "pos_extraction": [ diff --git a/function/python/brightics/function/textanalytics/meta/term_term_mtx.json b/function/python/brightics/function/textanalytics/meta/term_term_mtx.json index 349c79ae7..779eb8481 100644 --- a/function/python/brightics/function/textanalytics/meta/term_term_mtx.json +++ b/function/python/brightics/function/textanalytics/meta/term_term_mtx.json @@ -334,8 +334,8 @@ }, { "persist-mode": "auto", - "func": "brightics.function.textanalytics$tokenizer_eng51532", - "name": "brightics.function.textanalytics$tokenizer_eng", + "func": "brightics.function.textanalytics$tokenizer_eng2", + "name": "brightics.function.textanalytics$tokenizer_eng2", "param": { "stemming": false, "pos_extraction": [ @@ -1074,482 +1074,6 @@ }, "fid": "f397a9hvz863mra5" }, - { - "persist-mode": "auto", - "func": "pythonScript", - "name": "PythonScript", - "inputs": { - "inputs": [], - "models": [], - "images": [] - }, - "outputs": { - "output": "tu38s7jvqsyw2zfx" - }, - "param": { - "script": "#\n# NOTE : Enter \"Out Table Alias\"\n# ex)\n# inputDataframe = inputs[0]\n# result = inputDataframe\n#\nfrom brightics.function.textanalytics import term_term_mtx\ninput_table = inputs[1]\ninput_model = models[0]\nresult = term_term_mtx(table=input_table, model=input_model, input_col='tokenized_description2', result_type='dense')\noutput = result['out_table']" - }, - "display": { - "label": "Python Script", - "diagram": { - 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}, - "grid": { - "left": "10%", - "right": "10%", - "top": "60px", - "bottom": "60px", - "width": "auto", - "height": "auto" - }, - "xAxis": [ - { - "selected": [], - "title": { - "show": true, - "text": "", - "subtext": "", - "rotate": 0, - "textStyle": { - "color": "#000000", - "fontSize": 12, - "fontFamily": "" - }, - "subtextStyle": { - "color": "#000000", - "fontSize": 12, - "fontFamily": "" - }, - "left": "50%", - "bottom": "5px" - }, - "axisTick": { - "show": true - }, - "axisLine": { - "onZero": false - }, - "scale": true, - "axisLabel": { - "show": true, - "rotate": 0, - "textStyle": { - "color": "#000000", - "fontSize": 12, - "fontFamily": "" - } - }, - "zlevel": 1 - } - ], - "yAxis": [ - { - "selected": [], - "title": { - "show": true, - "text": "", - "subtext": "", - "rotate": -90, - "textStyle": { - "color": "#000000", - "fontSize": 12, - "fontFamily": "" - }, - "subtextStyle": { - "color": "#000000", - "fontSize": 12, - "fontFamily": "" - }, - "left": "8px", - "top": "50%" - }, - "axisTick": { - "show": true - }, - "axisLine": { - "onZero": false - }, - "scale": true, - "axisLabel": { - "show": true, - "rotate": 0, - "textStyle": { - "color": "#000000", - "fontSize": 12, - "fontFamily": "" - } - }, - "zlevel": 1 - } - ], - "colorBy": [ - { - "selected": [] - } - ], - "legend": { - "show": true, - "orientation": "horizontal", - "left": "50%", - "right": "auto", - "top": "10px", - "bottom": "auto", - "textStyle": { - "color": "#000000", - "fontSize": 12, - "fontFamily": "", - "fontStyle": "normal", - "fontWeight": "normal", - "fontDecoration": "none" - } - }, - "noDataMessage": "", - "plotOptions": { - "table": { - "formatter": [] - }, - "map": { - "geoData": {} - } - }, - "source": { - "dataType": "lazy", - "lazyData": [] - }, - "guide": { - "message": "Set data options using Chart Setting Button." - } - }, - "dataSource": { - "selectable": true - }, - "style": {}, - "group": "6cu2w9v6juamba4qjjazg3xi" - } - ], - "layout": { - "type": "panel", - "id": "default-1583990471381" - } - } - ], - "full": [ - { - "panel": [], - "layout": {} - } - ] - }, - "out": { - "partial": [ - { - "panel": [ - { - "id": "default-1583990471656", - "chartOption": { - "colorSet": [ - "#FD026C", - "#4682B8", - "#A5D22D", - "#F5CC0A", - "#FE8C01", - "#6B9494", - "#B97C46", - "#84ACD0", - "#C2E173", - "#F9DD5B", - "#FE569D", - "#FEB356", - "#9CB8B8", - "#D0A884", - "#2E6072", - "#6D8C1E", - "#A48806", - "#A90148", - "#A95E01", - "#476363", - "#7B532F" - ], - "title": { - "show": true, - "text": "", - "subtext": "", - "rotate": 0, - "textStyle": { - "color": "#000000", - "fontSize": 12, - "fontFamily": "" - }, - "subtextStyle": { - "color": "#000000", - "fontSize": 12, - "fontFamily": "" - }, - "left": "50%", - "top": "0px" - }, - "toolbar": { - "show": false, - "right": "8px", - "top": "8px", - "orientation": "horizontal", - "menu": {} - }, - "chart": { - "border": "1px #000000 none", - "background": "rgba(255, 255, 255,1)", - "animationDuration": 1000, - "type": "table" - }, - "tooltip": { - 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"id": "default-1583990471656" - } - } - ], - "full": [ - { - "panel": [], - "layout": {} - } - ] - } - } - }, - "version": "3.6", - "context": "python", - "meta": { - "inputs": { - "type": "table", - "range": { - "min": 1, - "max": 10 - } - }, - "models": { - "type": "model", - "range": { - "min": 1, - "max": 10 - } - }, - "images": { - "type": "image", - "range": { - "min": 1, - "max": 10 - } - }, - "output": { - "type": "table" - } - }, - "fid": "fe7u9bs7auxznj3g" - }, { "persist-mode": "auto", "func": "brightics.function.textanalytics$term_term_mtx98834", diff --git a/function/python/brightics/function/textanalytics/meta/tfidf3.json b/function/python/brightics/function/textanalytics/meta/tfidf3.json new file mode 100644 index 000000000..4051be253 --- /dev/null +++ b/function/python/brightics/function/textanalytics/meta/tfidf3.json @@ -0,0 +1,182 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "textanalytics", + "func": "brightics.function.textanalytics$tfidf376642", + "name": "brightics.function.textanalytics$tfidf3", + "context": "python", + "label": "TF-IDF", + "description": "This is a function to calculate TF-IDF, abbreviated term for term frequency-inverse document frequency. \n\nReference:\n+ ", + "tags": [], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "out_table": "", + "model": "" + }, + "meta": { + "table": { + "type": "table", + "optional": false + }, + "out_table": { + "type": "table" + }, + "model": { + "type": "model" + } + }, + "params": [ + { + "id": "input_col", + "label": "Input Column", + "description": "", + "mandatory": true, + "items": [], + "visibleOption": [], + "control": "ColumnSelector", + "columnType": [ + "String", + "String[]" + ], + "validation": [], + "multiple": false + }, + { + "id": "max_df", + "label": "Maximum Document Frequency", + "description": "When building the vocabulary, ignore terms that have a document frequency strictly higher than the given threshold (corpus-specific stop words).", + "mandatory": false, + "items": [], + "visibleOption": [], + "control": "InputBox", + "columnType": [], + "validation": [], + "targetTable": [], + "placeHolder": "the number of documents", + "type": "Integer" + }, + { + "id": "min_df", + "label": "Minimum Document Frequency", + "description": "When building the vocabulary, ignore terms that have a document frequency strictly lower than the given threshold. This value is also called cut-off in the literature.", + "mandatory": false, + "items": [], + "visibleOption": [], + "control": "InputBox", + "columnType": [], + "validation": [], + "targetTable": [], + "placeHolder": "1 (value >= 0)", + "type": "Integer", + "min": 0 + }, + { + "id": "max_features", + "label": "Maximum Number of Vocabularies", + "description": "The number of vocabularies that will be utilized to count their frequencies in the entire documents. It should be greater than or equal to two.", + "mandatory": false, + "items": [], + "visibleOption": [], + "control": "InputBox", + "columnType": [], + "validation": [], + "targetTable": [], + "placeHolder": "100 (value >= 2)", + "type": "Integer", + "min": 2 + }, + { + "id": "use_idf", + "label": "Using IDF", + "description": "Weighting scheme for IDF. Currently it is providing \"Unary\" and \"Inverse Document Frequency\" only.", + "mandatory": false, + "items": [], + "visibleOption": [], + "control": "BooleanRadio", + "columnType": [], + "validation": [], + "targetTable": [], + "defaultValue": true + }, + { + "id": "norm", + "label": "Norm", + "description": "Norm used to normalize term vectors.", + "mandatory": false, + "items": [ + { + "label": "L1", + "value": "l1", + "default": false + }, + { + "label": "L2", + "value": "l2", + "default": true + } + ], + "visibleOption": [], + "control": "RadioButton", + "columnType": [], + "validation": [], + "targetTable": [] + }, + { + "id": "smooth_idf", + "label": "Smooth IDF", + "description": "Smooth idf weights by adding one to document frequencies, as if an extra document was seen containing every term in the collection exactly once. Prevents zero divisions.", + "mandatory": false, + "items": [], + "visibleOption": [], + "control": "BooleanRadio", + "columnType": [], + "validation": [], + "targetTable": [], + "defaultValue": true + }, + { + "id": "sublinear_tf", + "label": "Sublinear TF", + "description": "Apply sublinear tf scaling, i.e. replace \"tf\" with \"1 + log(tf)\".", + "mandatory": false, + "items": [], + "visibleOption": [], + "control": "BooleanRadio", + "columnType": [], + "validation": [], + "targetTable": [], + "defaultValue": false + }, + { + "id": "res_type", + "label": "Result Type", + "description": "Result type", + "mandatory": false, + "items": [ + { + "label": "Sparse", + "value": "sparse", + "default": true + }, + { + "label": "Dense", + "value": "dense", + "default": false + } + ], + "visibleOption": [], + "control": "RadioButton", + "columnType": [], + "validation": [], + "targetTable": [] + } + ] + }, + "md": "" +} \ No newline at end of file diff --git a/function/python/brightics/function/textanalytics/meta/tokenizer_eng.json b/function/python/brightics/function/textanalytics/meta/tokenizer_eng.json index b9219aa34..2e3ab0b13 100644 --- a/function/python/brightics/function/textanalytics/meta/tokenizer_eng.json +++ b/function/python/brightics/function/textanalytics/meta/tokenizer_eng.json @@ -4,6 +4,7 @@ "content": "" }, "specJson": { + "deprecated": true, "category": "textanalytics", "func": "brightics.function.textanalytics$tokenizer_eng51532", "name": "brightics.function.textanalytics$tokenizer_eng", diff --git a/function/python/brightics/function/textanalytics/meta/tokenizer_eng2.json b/function/python/brightics/function/textanalytics/meta/tokenizer_eng2.json new file mode 100644 index 000000000..49e6bb3f3 --- /dev/null +++ b/function/python/brightics/function/textanalytics/meta/tokenizer_eng2.json @@ -0,0 +1,1147 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "textanalytics", + "func": "brightics.function.textanalytics$tokenizer_eng2", + "name": "brightics.function.textanalytics$tokenizer_eng2", + "context": "python", + "label": "Tokenizer (English)", + "description": "Tokenization is the process of demarcating and possibly classifying sections of a string of input characters. The resulting tokens are then passed on to some other form of processing. The process can be considered a sub-task of parsing input.\n\nIn corpus linguistics, part-of-speech tagging (POS tagging or PoS tagging or POST), also called grammatical tagging or word-category disambiguation, is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context—i.e., its relationship with adjacent and related words in a phrase, sentence, or paragraph. A simplified form of this is commonly taught to school-age children, in the identification of words as nouns, verbs, adjectives, adverbs, etc.\n\nThis function uses NLTK Tokenizer. The first column of the user-dictionary consists of compound words that the user wants to be kept, and the second column, which is optional, consists of tokens (separated by commas) to be combined.\n\nReference: \n+ \n+ ", + "tags": [], + "version": "3.6", + "inputs": { + "table": "", + "user_dict": "" + }, + "outputs": { + "out_table": "" + }, + "meta": { + "table": { + "type": "table", + "optional": false + }, + "user_dict": { + "type": "table", + "optional": true + }, + "out_table": { + "type": "table" + } + }, + "params": [ + { + "id": "input_cols", + "label": "Input Columns", + "description": "Columns to select as input. Only String type columns are possible.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "columnType": [ + "String" + ], + "multiple": true + }, + { + "id": "new_col_prefix", + "label": "New Column Prefix", + "description": "Name of the new column to be added.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "type": "String", + "placeHolder": "tokenized" + }, + { + "id": "stemming", + "label": "Stemming", + "description": "Whether to use stemming; the process of reducing inflected words to their word stem, base or root form.\n - Available Options : `True`, `False`", + "visibleOption": [], + "control": "BooleanRadio", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "defaultValue": false + }, + { + "id": "pos_extraction", + "label": "Extraction", + "description": "Extraction of items to return.\n - Available Options : `Coordinating Conjunction`, `Cardinal Digit`, `Determiner`, `Existential There`, `Foreign Word`, `Preposition/Subordinating Conjunction`, `Adjective`, `Adjective, Comparative`, `Adjective, Superlative`, `List Marker`, `Modal`, `Noun, Singular`, `Noun Plural`, `Proper Noun, Singular`, `Proper Noun, Plural`, `Predeterminer`, `Possessive Ending`, `Personal Pronoun`, `Possessive Pronoun`, `Adverb`, `Adverb, Comparative`, `Adverb, Superlative`, `Particle`, `To`, `Interjection`, `Verb, Base`, `Verb, Past Tense`, `Verb, Gerund/Present Participle`, `Verb, Past Participle`, `Verb, Sing. Present, Non-3d`, `Verb, 3rd Person Sing. Present`, `Wh-determiner`, `Wh-pronoun`, `Possessive Wh-pronoun`, `Wh-abverb`", + "visibleOption": [], + "control": "CheckBox", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "Coordinating Conjunction", + "value": "CC", + "default": true + }, + { + "label": "Cardinal Digit", + "value": "CD", + "default": false + }, + { + "label": "Determiner", + "value": "DT", + "default": false + }, + { + "label": "Existential There", + "value": "EX", + "default": false + }, + { + "label": "Foreign Word", + "value": "FW", + "default": false + }, + { + "label": "Preposition/Subordinating Conjunction", + "value": "IN", + "default": false + }, + { + "label": "Adjective", + "value": "JJ", + "default": true + }, + { + "label": "Adjective, Comparative", + "value": "JJR", + "default": true + }, + { + "label": "Adjective, Superlative", + "value": "JJS", + "default": true + }, + { + "label": "List Marker", + "value": "LS", + "default": false + }, + { + "label": "Modal", + "value": "MD", + "default": false + }, + { + "label": "Noun, Singular", + "value": "NN", + "default": true + }, + { + "label": "Noun Plural", + "value": "NNS", + "default": true + }, + { + "label": "Proper Noun, Singular", + "value": "NNP", + "default": true + }, + { + "label": "Proper Noun, Plural", + "value": "NNPS", + "default": true + }, + { + "label": "Predeterminer", + "value": "PDT", + "default": false + }, + { + "label": "Possessive Ending", + "value": "POS", + "default": false + }, + { + "label": "Personal Pronoun", + "value": "PRP", + "default": false + }, + { + "label": "Possessive Pronoun", + "value": "PRP$", + "default": false + }, + { + "label": "Adverb", + "value": "RB", + "default": true + }, + { + "label": "Adverb, Comparative", + "value": "RBR", + "default": true + }, + { + "label": "Adverb, Superlative", + "value": "RBS", + "default": true + }, + { + "label": "Particle", + "value": "RP", + "default": false + }, + { + "label": "To", + "value": "TO", + "default": false + }, + { + "label": "Interjection", + "value": "UH", + "default": false + }, + { + "label": "Verb, Base", + "value": "VB", + "default": true + }, + { + "label": "Verb, Past Tense", + "value": "VBD", + "default": true + }, + { + "label": "Verb, Gerund/Present Participle", + "value": "VBG", + "default": false + }, + { + "label": "Verb, Past Participle", + "value": "VBN", + "default": true + }, + { + "label": "Verb, Sing. Present, Non-3d", + "value": "VBP", + "default": true + }, + { + "label": "Verb, 3rd Person Sing. Present", + "value": "VBZ", + "default": true + }, + { + "label": "Wh-determiner", + "value": "WDT", + "default": false + }, + { + "label": "Wh-pronoun", + "value": "WP", + "default": false + }, + { + "label": "Possessive Wh-pronoun", + "value": "WP$", + "default": false + }, + { + "label": "Wh-abverb", + "value": "WRB", + "default": false + } + ], + "columnType": [] + }, + { + "id": "is_tagged", + "label": "Part of Speech Tagging", + "description": "Whether to show parts of speech with tokenized words.\n - Available Options : `True`, `False`", + "visibleOption": [], + "control": "BooleanRadio", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "defaultValue": false + }, + { + "id": "lower_case", + "label": "Lower Case", + "description": "Whether to convert all characters to lower cases.\n - Available Options : `True`, `False`", + "mandatory": false, + "items": [], + "visibleOption": [], + "control": "BooleanRadio", + "columnType": [], + "validation": [], + "defaultValue": true + } + ], + "summary": "This function tokenizes english string data into parts of speech(POS)." + }, + "md": "# Tokenizer (English)\nThis function tokenizes english string data into parts of speech(POS).\n\n## Description\nTokenization is the process of demarcating and possibly classifying sections of a string of input characters. The resulting tokens are then passed on to some other form of processing. The process can be considered a sub-task of parsing input.\n\nIn corpus linguistics, part-of-speech tagging (POS tagging or PoS tagging or POST), also called grammatical tagging or word-category disambiguation, is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context—i.e., its relationship with adjacent and related words in a phrase, sentence, or paragraph. A simplified form of this is commonly taught to school-age children, in the identification of words as nouns, verbs, adjectives, adverbs, etc. \n\nThis function uses NLTK Tokenizer. The first column of the user-dictionary consists of compound words that the user wants to be kept, and the second column, which is optional, consists of tokens (separated by commas) to be combined.\n\nReference: \n+ \n+ \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Text data in a Table.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contain input table's columns and tokenized text column.\n#### PARAMETER\n1. **Input Columns***: Columns to select as input. Only String type columns are possible.\n\n2. **New Column Prefix**: Name of the new column to be added.\n\n3. **Stemming**: Whether to use stemming; the process of reducing inflected words to their word stem, base or root form.\n - Available Options : `True`, `False`\n\n4. **Extraction**: Extraction of items to return.\n - Available Options : `Coordinating Conjunction`, `Cardinal Digit`, `Determiner`, `Existential There`, `Foreign Word`, `Preposition/Subordinating Conjunction`, `Adjective`, `Adjective, Comparative`, `Adjective, Superlative`, `List Marker`, `Modal`, `Noun, Singular`, `Noun Plural`, `Proper Noun, Singular`, `Proper Noun, Plural`, `Predeterminer`, `Possessive Ending`, `Personal Pronoun`, `Possessive Pronoun`, `Adverb`, `Adverb, Comparative`, `Adverb, Superlative`, `Particle`, `To`, `Interjection`, `Verb, Base`, `Verb, Past Tense`, `Verb, Gerund/Present Participle`, `Verb, Past Participle`, `Verb, Sing. Present, Non-3d`, `Verb, 3rd Person Sing. Present`, `Wh-determiner`, `Wh-pronoun`, `Possessive Wh-pronoun`, `Wh-abverb`\n\n5. **Part of Speech Tagging**: Whether to show parts of speech with tokenized words.\n - Available Options : `True`, `False`\n\n6. **Lower Case**: Whether to convert all characters to lower cases.\n - Available Options : `True`, `False`\n\n\n### Python\n#### USAGE\n\n```\ntokenizer_eng2(table, input_cols, new_col_prefix='tokenized', stemming=True, pos_extraction=None, is_tagged=False, lower_case=False)\n```\n\n#### INPUT\n1. **table***: (Table) Text data in a Table.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contain input table's columns and tokenized text column.\n#### PARAMETER\n1. **input_cols***: Columns to select as input. Only String type columns are possible.\n\t* Type: *list[str]*\n2. **new_col_prefix**: Name of the new column to be added.\n\t* Type: *str*\n\t* Default / Range: 'tokenized'\n3. **stemming**: Whether to use stemming; the process of reducing inflected words to their word stem, base or root form.\n\t* Type: *bool*\n\t* Default / Range: False \n4. **pos_extraction**: Extraction of items to return.\n\t* Type: *list[str]*\n\t* Default / Range: CC ( CC | CD | DT | EX | FW | INJJ | JJJJR | JJRJJS | JJS | LS | MDNN | NNNNS | NNSNNP | NNPNNPS | NNPS | PDT | POS | PRP | PRP$RB | RBRBR | RBRRBS | RBS | RP | TO | UHVB | VBVBD | VBD | VBGVBN | VBNVBP | VBPVBZ | VBZ | WDT | WP | WP$ | WRB )\n5. **is_tagged**: Whether to show parts of speech with tokenized words.\n\t* Type: *bool*\n\t* Default value: False\n6. **lower_case**: Whether to convert all characters to lower cases.\n\t* Type: *bool*\n\t* Default value: False \n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\n\n\n
In this tutorial workflow, sample_data_tokenizer_eng data is used as input of Tokenizer(English) function. The output of this function is a Table with tokenized text data added to the input Table.\nThe parameter settings used in the function are shown below. \n\n##### ++Parameters++\n1. **Input Columns*** : document\n2. **New Column Prefix** : tokenized\n3. **Stemming** : False\n4. **Extraction** : Select All\n5. **Part of Speech Tagging** : True\n6. **Lower Case** : False\n\n### Python\n```\nfrom brightics.function.textanalytics import tokenizer_eng2\ninput_table = inputs[0]\nresult = tokenizer_eng2(table=input_table, input_cols=['document'],\n new_col_prefix='tokenized',\n stemming=False, pos_extraction=None, is_tagged=True, lower_case=False)\noutput = result['out_table']\n```\n\n
In this python script, sample_data_tokenizer_eng data is used as input of tokenizer_eng function. The output of this function is a Table with tokenized text data added to the input Table.\n\n", + "exampleModels": [ + { + "fileName": "brightics.function.textanalytics$tokenizer_eng.json", + "label": "Tokenizer(English)", + "contents": { + "type": "files", + "data": [ + { + "type": "file", + "data": { + "id": "m3h4sb5ugnxrmwej", + "project_id": "phvesm6fsjmzbzfa", + "label": "Tokenizer(English)", + "contents": { + "mid": "m3h4sb5ugnxrmwej", + "type": "data", + "title": "Tokenizer(English)", + "functions": [ + { + "persist-mode": "auto", + "func": "brightics.function.io$load13889", + "name": "brightics.function.io$load", + "param": { + "partial_path": [ + "/brightics@samsung.com/upload/sample_data_tokenizer_eng.csv" + ] + }, + "display": { + "label": "Load", + "diagram": { + "position": { + "x": 270, + "y": 120 + } + }, + "sheet": { + "out": { + "partial": [ + { + "panel": [ + { + "id": "default-1584947362154", + "chartOption": { + "colorSet": [ + "#FD026C", + "#4682B8", + "#A5D22D", + 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true, + "text": "", + "subtext": "", + "rotate": 0, + "textStyle": { + "color": "#000000", + "fontSize": 12, + "fontFamily": "" + }, + "subtextStyle": { + "color": "#000000", + "fontSize": 12, + "fontFamily": "" + }, + "left": "50%", + "bottom": "5px" + }, + "axisTick": { + "show": true + }, + "axisLine": { + "onZero": false + }, + "scale": true, + "axisLabel": { + "show": true, + "rotate": 0, + "textStyle": { + "color": "#000000", + "fontSize": 12, + "fontFamily": "" + } + }, + "zlevel": 1 + } + ], + "yAxis": [ + { + "selected": [], + "title": { + "show": true, + "text": "", + "subtext": "", + "rotate": -90, + "textStyle": { + "color": "#000000", + "fontSize": 12, + "fontFamily": "" + }, + "subtextStyle": { + "color": "#000000", + "fontSize": 12, + "fontFamily": "" + }, + "left": "8px", + "top": "50%" + }, + "axisTick": { + "show": true + }, + "axisLine": { + "onZero": false + }, + "scale": true, + "axisLabel": { + "show": true, + "rotate": 0, + "textStyle": { + "color": "#000000", + "fontSize": 12, + "fontFamily": "" + } + }, + "zlevel": 1 + } + ], + "colorBy": [ + { + "selected": [] + } + ], + "legend": { + "show": true, + "orientation": "horizontal", + "left": "50%", + "right": "auto", + "top": "10px", + "bottom": "auto", + "textStyle": { + "color": "#000000", + "fontSize": 12, + "fontFamily": "", + "fontStyle": "normal", + "fontWeight": "normal", + "fontDecoration": "none" + } + }, + "noDataMessage": "", + "plotOptions": { + "table": { + "formatter": [] + }, + "map": { + "geoData": {} + } + }, + "source": { + "dataType": "lazy", + "lazyData": [] + }, + "guide": { + "message": "Set data options using Chart Setting Button." + } + }, + "dataSource": { + "selectable": true + }, + "style": {}, + "group": "6xsyyuh4w6t34xm7jejk64dn" + } + ], + "layout": { + "type": "panel", + "id": "default-1584947362154" + } + } + ], + "full": [ + { + "panel": [], + "layout": {} + } + ] + } + } + }, + "meta": { + "table": { + "type": "table" + } + }, + "context": "python", + 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"#B97C46", + "#84ACD0", + "#C2E173", + "#F9DD5B", + "#FE569D", + "#FEB356", + "#9CB8B8", + "#D0A884", + "#2E6072", + "#6D8C1E", + "#A48806", + "#A90148", + "#A95E01", + "#476363", + "#7B532F" + ], + "title": { + "show": true, + "text": "", + "subtext": "", + "rotate": 0, + "textStyle": { + "color": "#000000", + "fontSize": 12, + "fontFamily": "" + }, + "subtextStyle": { + "color": "#000000", + "fontSize": 12, + "fontFamily": "" + }, + "left": "50%", + "top": "0px" + }, + "toolbar": { + "show": false, + "right": "8px", + "top": "8px", + "orientation": "horizontal", + "menu": {} + }, + "chart": { + "border": "1px #000000 none", + "background": "rgba(255, 255, 255,1)", + "animationDuration": 1000, + "type": "table" + }, + "tooltip": { + "triggerOn": "mousemove" + }, + "grid": { + "left": "10%", + "right": "10%", + "top": "60px", + "bottom": "60px", + "width": "auto", + "height": "auto" + }, + "xAxis": [ + { + "selected": [], + "title": { + "show": true, + "text": "", + "subtext": "", + 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Tokenizer(English) sample model.

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" + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/textanalytics/meta/tokenizer_kor.json b/function/python/brightics/function/textanalytics/meta/tokenizer_kor.json index 1887b06cd..2aac2dde9 100644 --- a/function/python/brightics/function/textanalytics/meta/tokenizer_kor.json +++ b/function/python/brightics/function/textanalytics/meta/tokenizer_kor.json @@ -4,6 +4,7 @@ "content": "" }, "specJson": { + "deprecated": true, "category": "textanalytics", "func": "brightics.function.textanalytics$tokenizer_kor51577", "name": "brightics.function.textanalytics$tokenizer_kor", diff --git a/function/python/brightics/function/textanalytics/meta/tokenizer_kor2.json b/function/python/brightics/function/textanalytics/meta/tokenizer_kor2.json new file mode 100644 index 000000000..932b0319f --- /dev/null +++ b/function/python/brightics/function/textanalytics/meta/tokenizer_kor2.json @@ -0,0 +1,1056 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "textanalytics", + "func": "brightics.function.textanalytics$tokenizer_kor2", + "name": "brightics.function.textanalytics$tokenizer_kor2", + "context": "python", + "label": "Tokenizer (Korean)", + "description": "In corpus linguistics, part-of-speech tagging (POS tagging or PoS tagging or POST), also called grammatical tagging or word-category disambiguation, is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context—i.e., its relationship with adjacent and related words in a phrase, sentence, or paragraph. A simplified form of this is commonly taught to school-age children, in the identification of words as nouns, verbs, adjectives, adverbs, etc.\n\nThis function uses Twitter Korean Tokenizer. The first column of the user-dictionary consists of compound words that the user wants to be kept, and the second column, which is optional, consists of tokens (separated by commas) to be combined.\n\nReference: \n+ \n+ ", + "tags": [], + "version": "3.6", + "inputs": { + "table": "", + "user_dict": "" + }, + "outputs": { + "out_table": "" + }, + "meta": { + "table": { + "type": "table", + "optional": false + }, + "user_dict": { + "type": "table", + "optional": true + }, + "out_table": { + "type": "table" + } + }, + "params": [ + { + "id": "input_cols", + "label": "Input Columns", + "description": "Columns to select as input. Only String type columns are possible.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "columnType": [ + "String" + ], + "multiple": true + }, + { + "id": "new_col_prefix", + "label": "New Column Prefix", + "description": "Name of the new column to be added.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "type": "String", + "placeHolder": "tokenized" + }, + { + "id": "normalization", + "label": "Normalization", + "description": "Whether to use normalization; the process of canonicalizing tokens so that matches occur despite superficial differences in the character sequences of the tokens.\n - Available Options : `True'|'False`", + "visibleOption": [], + "control": "BooleanRadio", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "defaultValue": true + }, + { + "id": "stemming", + "label": "Stemming", + "description": "Whether to use stemming; the process of reducing inflected words to their word stem, base or root form.\n - Available Options : `True'|'False`", + "visibleOption": [], + "control": "BooleanRadio", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "defaultValue": true + }, + { + "id": "pos_extraction", + "label": "Extraction", + "description": "Extraction of items to return.\n - Available Options : `Noun`, `Verb`, `Adjective`, `Adverb`, `Determiner`, `Exclamation`, `Josa`, `Eomi`, `PreEomi`, `Conjunction`, `Modifier`, `VerbPrefix`, `Suffix`, `Unknown`, `Korean`, `Foreign`, `Number`, `KoreanParticle`, `Alpha`, `Punctuation`, `Hashtag`, `ScreenName`, `Email`, `URL`, `CashTag`, `Space`, `Others`", + "visibleOption": [], + "control": "CheckBox", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "Noun", + "value": "Noun", + "default": true + }, + { + "label": "Verb", + "value": "Verb", + "default": false + }, + { + "label": "Adjective", + "value": "Adjective", + "default": false + }, + { + "label": "Adverb", + "value": "Adverb", + "default": false + }, + { + "label": "Determiner", + "value": "Determiner", + "default": false + }, + { + "label": "Exclamation", + "value": "Exclamation", + "default": false + }, + { + "label": "Josa", + "value": "Josa", + "default": false + }, + { + "label": "Eomi", + "value": "Eomi", + "default": false + }, + { + "label": "PreEomi", + "value": "PreEomi", + "default": false + }, + { + "label": "Conjunction", + "value": "Conjunction", + "default": false + }, + { + "label": "Modifier", + "value": "Modifier", + "default": false + }, + { + "label": "VerbPrefix", + "value": "VerbPrefix", + "default": false + }, + { + "label": "Suffix", + "value": "Suffix", + "default": false + }, + { + "label": "Unknown", + "value": "Unknown", + "default": false + }, + { + "label": "Korean", + "value": "Korean", + "default": false + }, + { + "label": "Foreign", + "value": "Foreign", + "default": false + }, + { + "label": "Number", + "value": "Number", + "default": false + }, + { + "label": "KoreanParticle", + "value": "KoreanParticle", + "default": false + }, + { + "label": "Alpha", + "value": "Alpha", + "default": false + }, + { + "label": "Punctuation", + "value": "Punctuation", + "default": false + }, + { + "label": "Hashtag", + "value": "Hashtag", + "default": false + }, + { + "label": "ScreenName", + "value": "ScreenName", + "default": false + }, + { + "label": "Email", + "value": "Email", + "default": false + }, + { + "label": "URL", + "value": "URL", + "default": false + }, + { + "label": "CashTag", + "value": "CashTag", + "default": false + }, + { + "label": "Space", + "value": "Space", + "default": false + }, + { + "label": "Others", + "value": "Others", + "default": false + } + ], + "columnType": [] + }, + { + "id": "is_tagged", + "label": "Part of Speech Tagging", + "description": "Whether to show parts of speech with tokenized words.\n - Available Options : `True', 'False`", + "visibleOption": [], + "control": "BooleanRadio", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "defaultValue": false + } + ], + "summary": "This function tokenizes korean string data into parts of speech(POS)." + }, + "md": "# Tokenizer (Korean)\nThis function tokenizes korean string data into parts of speech(POS).\n\n## Description\nIn corpus linguistics, part-of-speech tagging (POS tagging or PoS tagging or POST), also called grammatical tagging or word-category disambiguation, is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context—i.e., its relationship with adjacent and related words in a phrase, sentence, or paragraph. A simplified form of this is commonly taught to school-age children, in the identification of words as nouns, verbs, adjectives, adverbs, etc. \n\nThis function uses Twitter Korean Tokenizer. The first column of the user-dictionary consists of compound words that the user wants to be kept, and the second column, which is optional, consists of tokens (separated by commas) to be combined.\n\nReference: \n+ \n+ \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Text data in a Table.\n#### OUTPUT\n1. **table**: (Table) Output Table. It contain input table's columns and tokenized text column.\n#### PARAMETER\n1. **Input Columns***: Columns to select as input. Only String type columns are possible.\n\n2. **New Column Prefix**: Name of the new column to be added.\n\n3. **Normalization**: Whether to use normalization; the process of canonicalizing tokens so that matches occur despite superficial differences in the character sequences of the tokens.\n * Available Options: `True`, `False`\n4. **Stemming**: Whether to use stemming; the process of reducing inflected words to their word stem, base or root form.\n * Available Options: `True`, `False`\n5. **Extraction**: Extraction of items to return.\n - Available Options: `Noun`, `Verb`, `Adjective`, `Adverb`, `Determiner`, `Exclamation`, `Josa`, `Eomi`, `PreEomi`, `Conjunction`, `Modifier`, `VerbPrefix`, `Suffix`, `Unknown`, `Korean`, `Foreign`, `Number`, `KoreanParticle`, `Alpha`, `Punctuation`, `Hashtag`, `ScreenName`, `Email`, `URL`, `CashTag`, `Space`, `Others`\n\n6. **Part of Speech Tagging**: Whether to show parts of speech with tokenized words.\n * Available Options: `True`, `False`\n\n### Python\n#### USAGE\n\n```\ntokenizer_kor2(table, input_cols, new_col_prefix='tokenized', normalization=True, stemming=True, pos_extraction=None, is_tagged=False)\n```\n\n#### INPUT\n1. **table***: (Table) Text data in a Table.\n#### OUTPUT\n1. **table**: (Table) Output Table. It contain input table's columns and tokenized text column.\n#### PARAMETER\n1. **input_cols***: Columns to select as input. Only String type columns are possible.\n\t* Type: *list[str]*\n2. **new_col_prefix**: Name of the new column to be added.\n\t* Type: *str*\n\t* Default value: 'tokenized'\n3. **normalization**: Whether to use normalization; the process of canonicalizing tokens so that matches occur despite superficial differences in the character sequences of the tokens.\n\t* Type: *bool*\n\t* Default value: True \n4. **stemming**: Whether to use stemming; the process of reducing inflected words to their word stem, base or root form.\n\t* Type: *bool*\n\t* Default value: True \n5. **pos_extraction**: Extraction of items to return.\n\t* Type: *list[str]*\n\t* Default / Range: Noun ( Noun | Verb | Adjective | Adverb | Determiner | Exclamation | Josa | Eomi | PreEomi | Conjunction | Modifier | VerbPrefix | Suffix | Unknown | Korean | Foreign | Number | KoreanParticle | Alpha | Punctuation | Hashtag | ScreenName | Email | URL | CashTag | Space | Others )\n6. **is_tagged**: Whether to show parts of speech with tokenized words.\n\t* Type: *bool*\n\t* Default value: False \n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\n\n\n
In this tutorial workflow, sample_nsmc data is used as input of Tokenizer(Korean) function. The output of this function is a Table with tokenized text data added to the input Table.\nThe parameter settings used in the function are shown below. \n\n##### ++Parameters++\n1. **Input Columns*** : doc1, doc2\n2. **New Column Prefix** : tokenized\n3. **Normalization** : True\n4. **Stemming** : True\n5. **Extraction** : Select All\n6. **Part of Speech Tagging** : False\n\n### Python\n```\nfrom brightics.function.textanalytics import tokenizer_kor2\ninput_table = inputs[0]\nresult = tokenizer_kor2(table=input_table, input_cols=['doc1', 'doc2'],\n new_col_prefix='tokenized', normalization=True,\n stemming=True, pos_extraction=None, is_tagged=False)\noutput = result['out_table']\n```\n\n
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Tokenizer(Korean) sample model.

", + "model_image": null, + "creator": "brightics@samsung.com", + "create_time": "2020-03-23T07:09:12.000Z", + "updater": "brightics@samsung.com", + "update_time": "2020-03-23T07:35:34.000Z", + "event_key": "2020_073534.000", + "type": "data", + "tag": "null", + "from_version": "0.0" + } + } + ], + "version": "3.6" + } + } + ], + "sampleImages": [ + { + "fileName": "brightics.function.textanalytics$tokenizer_kor.PNG", + "label": "brightics.function.textanalytics$tokenizer_kor.PNG", + "base64": 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It takes an output topic table from topic modeling functions, such as Latent Dirichlet Allocation or GSDMM, as the input table. Additionally, either (1) an output model from the Word2Vec function as an input model or (2) a similarity matrix for words as an input table is needed. For the former case, note that both Word2vec and the topic modeling function should have the same input function, and for the latter case, the first column (the column names, resp.) should contain the words as indices of rows. (columns, resp.) The output table has five additional columns, each of which is derived from distinct weighting options. The first column uses the weights as given in the topic table, the second uses harmonic weights [1, 1/2, 1/3, 1/4, ...], the third uses geometric weights [1, 1/2, 1/4, 1/8, ...], the fourth uses log weights [log(N), log(N-1), log(N-2), ...] where N is the number of topic words, and the fifth uses constant weights [1, 1, 1, ...].", + "tags": [ + "lda", + "topic modeling" + ], + "version": "3.6", + "inputs": { + "table": "", + "sim_table": "", + "model": "" + }, + "outputs": { + "out_table": "" + }, + "meta": { + "table": { + "type": "table", + "desc": "Data including text documents.", + "optional": false + }, + "sim_table": { + "type": "table", + "desc": "Word similarity matrix table. The first column should contain the words as indices of rows, and the column names should be the corresponding words.", + "optional": true + }, + "model": { + "type": "model", + "desc": "Word2vec output model.", + "optional": true + }, + "out_table": { + "type": "table", + "desc": "Plausible names for each topic." + } + }, + "params": [ + { + "id": "topn", + "label": "Top N topic names", + "description": "The number of best topic names to be shown for each method.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Integer", + "min": 1, + "placeHolder": "10 (value >= 1)" + } + ], + "summary": "This function suggests some topic names based on the topic vocabularies." + }, + "md": { + "en": "# Topic Name Extraction\nThis function suggests some topic names based on the topic vocabularies.\n\n## Description\nThis function suggests some topic names based on the topic vocabularies. It takes an output topic table from topic modeling functions, such as Latent Dirichlet Allocation or GSDMM, as the input table. Additionally, either\n 1) an output model from the Word2Vec function as an input model or\n 2) a similarity matrix for words as an input table is needed.\n\nFor the former case, note that both Word2vec and the topic modeling function should have the same input function, and for the latter case, the first column (the column names, resp.) should contain the words as indices of rows. (columns, resp.)\n\nThe output table has five additional columns, each of which is derived from distinct weighting options.\n 1) The first column \"topic_name_original\" uses the weights as given in the topic table,\n 2) the second column \"topic_name_harmonic\" uses harmonic weights [1, 1/2, 1/3, 1/4, ...],\n 3) the third column \"topic_name_geometric\" uses geometric weights [1, 1/2, 1/4, 1/8, ...],\n 4) the fourth column \"topic_name_log\" uses log weights [log(N), log(N-1), log(N-2), ...] where N is the number of topic words, and\n 5) the fifth column \"topic_name_constant\" uses constant weights [1, 1, 1, ...].\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data including text documents.\n2. **sim_table***: (Table) Word similarity matrix table. The first column should contain the words as indices of rows, and the column names should be the corresponding words.\n3. **model***: (Model) Word2vec output model.\n#### OUTPUT\n1. **out_table**: (Table) Plausible names for each topic.\n#### PARAMETER\n1. **Top N topic names**: The number of best topic names to be shown for each method.\n\n\n### Python\n#### USAGE\n\n```\ntopic_name_extraction(table = , sim_table = , model = , topn = )\n```\n\n#### INPUT\n1. **table***: (Table) Data including text documents.\n2. **sim_table***: (Table) Word similarity matrix table. The first column should contain the words as indices of rows, and the column names should be the corresponding words.\n3. **model***: (Model) Word2vec output model.\n#### OUTPUT\n1. **out_table**: (Table) Plausible names for each topic.\n#### PARAMETER\n1. **topn**: The number of best topic names to be shown for each method.\n\t* Type: *int*\n\t* Default / Range: 10 (value >= 1)\n\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, sample_LatentDirichletAllocation data is used as an input of Topic Name Extraction function. The result includes each topic's plausible names. The parameter settings used in the function are shown below.\n\n++Inputs++\n1. **table**: topic_table (from Latent Dirichlet Allocation)\n2. **sim_table**: None\n3. **model**: model (from Word2Vec)\n\n++Parameters++\n1. **Top N topic names**: 10\n\n\n\n### Python\n\n```\nfrom brightics.function.textanalytics import topic_name_extraction\ninput_table = inputs[1]\ninput_model = models[1]\nresult = topic_name_extraction(table=input_table, sim_table=None, model=input_model, topn=10)\noutput = result['out_table']\n```\nIn this tutorial workflow, sample_LatentDirichletAllocation data is used as an input of Topic Name Extraction function. 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1HTbg3fky7cQPtaNfOdQA5deSh9hoMoX6up/NjzlcGxOVHWhicXF+MZ5wtgxY0+/0ulMIryqF+0mdSbWHnFsdSUZ1uWSyy7NdZ7xq3MRWZtG+9FbJ27V28b6Y7J+3euvbVIK0+Xdbv6RaV/KtvUHgHH8lW6n8a3b5kFGHfP+EZcvi4R6bRW7fqdoQPHjow+fiJdXh+tmhq3g8hZr0SkC41/1qqciHShXE9cJlbKYV5ngGXdg/l39a46hm6OyN+FOXQzv1OX+mavusYG1nuo79b765ImJOCtWpheXtqW3zP4FYZupNOyvl5eWVPePSTyRseffLJvp7CZ0ZvyLiHxxFbesXv7MTu5vNHJVarVfGl99FlMtGi7L9z/+OzdD8/c/fjs8K4L949eG7s5Lu5sxtrPMXQjnRnjxVnR132mR9S0/UcPb3b01pZWzTp00y968aJUrsyvpvtHnx+88vDT3pGPzihl7ZNzw/svPTg/NDk+u1osla2L1H9997TYQ0uGbuYqTmUuR+XlX223LowFnsuqUl7ENnT36m0j3TF5/15TLWnx7LKWluJ2H+oP3eyPpJ3xMHrdPaKkc1q163eGtFZtM6M3WjUtLgeRxzzLfsA6apoa+zZSPXGUr41jv4lZIsQXrTt0M+6GsbHtwlqZcvne7VXIth9JnZJLmpdgtmqhGbrNxwI6q1pN5WZiyYV4WvoNgxSGbiTyKZRdTnCIJ7PSsRw95tBN08jobVbt5FKZ9nwCvdTJiTYulSs+mF5++8j1//mrM198/9yvTtwSa9T3Tt5+beelf3jr+Ks7L5298zSWyJpnkDB0I52QYqVUrsqfWJcvlKzHclRpQ7cdak1rfPTWrlbNOXQTLdnscqr7+vi/f9D7z+/0vLFP+RS/Hb0jb3fdEFXu5ffOiPo2MbeaLZTMM+OaPnQzV4YSc225qaGbsQJswdDNvEq/Y/L+Azp02+judWg6tlUzh26aRkZvtGpGwjR0q+3/lXdf0++25U4ad0nSpKGb/RsnTUlYWrVwDN3ml9Ziqyn9UQxYfnz4+p98a9/nft6TyNR7l80tD93qtEEtSHu/OglF+uZuf+fOh3/T9+pfXvjmP11+453RA/fjtk9/0z4tK8KkoZumkdGb+JGgP0atjdTJ5Qqlm+Nz4p8/OXL96ujzlWRONHaZfEn8uZrKTczFf3n81lc+PP/B6bvmiSHb6uTM9siT6+8k7XHv5PzOFpo//6J3tHIT6c73x8FR/J2d6AZp4MeHvs+g/Xx5nln8aOz4v/T/SNQ04auDvzz27HKpop9rIJLNF6VjOXq0oZumwdFbG1s1aegmVqfxVO7dE7f+/YPzey+OPltKrGXyaa2sZQvz8fSZ20/EAvXl984OP11cL+vNepOHbpZD2HWpKeI9dKtzjDR/6GauOY0vIe/fa6olFY2Na4jbfZBvZd+mkYFaI9tEP7Rq0tBN08jorUNbNVtcDqIAD92ckzVH7RK0jeV76CxTLt+7vQptXNlIMxK6Vi00Z7q16+TeDSP6NlHO/vArO0WlG30W0y91hKEbiV7WiunXhz7Wip1k98QZfSP1fUOkwzliXIdumvqjt/U2vWRe6uTuPll49/it1/deufFoNp7OW98QSaxdi+tl0cx9cnb4hwcHjt94LNo7cTlDt6bH+NJG7TW6Og/hHLoZ2wSpVT03OyhVM80XBt5+nHiubySqX9RPDLEO3TSNjN7a1apJQ7dcYX3/pdG3jlzfe3F0ZjlZKotKVqtr5UplaS1z5f70Nz+9+MHpoXuTerlp7tDNWbK8F4q1bRoodM0euhn7txyn8v4bHLq5baZ8g7V77nYf5MojbeOx8rem04dutGoa16Gbpv7orUNbNVu8Dl7lQlt1ki40/mn7KW9eqBUQuZ64NBvqUe99/Dprae0Oa/T64FJh5O/C+dVdvnf7fjq9wrQlYWzVQjN0S6Zz+uMXsBy/Mf65n/doRe37+/uHn7o3RgzdSPTy6u3fSMXOau+Ts/p2UV+g1hm6abxGb9aeqZWxdnLZQqnr2tg3Pum79uB5MlsYfRY7dPWh1bGBsbVM/v7U0m9ODX3lw/PTS0mxZO2UoVv7oq8ka22f0Qh68P1x2H7xb2gPxn+GgPyIuTQ/JNUxq3+6/MZCdkXbcr0c8QWqc+imqT96a1erZh26ldbLsyupr350Qaw/J+ZWM/lSz+CEVNZEq5bIFE7dUs53E/8slpRFtS9DN3diKSitM61rQnOh6FLWzAPfqIqObXwfunmp3Vbev9ea07F89X4QtG/H7X7KO5G3MfdpbOB4QDp9SUyrpqkzdNN4jd46tFWzxfUgclSn2iXGZubhWStZ5q6MW3nXK+Mm5m49KphjD0osX9r8WnX2LF+yiaFb7Z8blUTiT0LaqoVh6KZ+HlYmW+/Fm+3N+aHJP//eof/4hU9EXXtt5+WZWLLiqNAM3UjEcmTqolTmnMYS09rGheK67aCOlg2Hbhrb6C3WzveptHZy07GkaNFe3XVJrFRF3Xr/1NAf/PvOv3yz6/e//On/9c29/+XbB/7wKzvHZ1eK6+W+4am//sFRsbhN54pN7uQaiGPlGa0YbV+tg3Q2gs3N9ot/Y3sw/gNYe+U2Jbue/4fL35OKmOSn9/fpW794Ee13H/caumlcRm9tbdWsQ7dktnhzfO5ffnlK9GblSnVuJfVnr+7/01f3/8m39v3uF3d89o3Doqa9feSG6NPEOvbVnZd+0X1T3PFqtdrcoZtldSczD2pz6ajSi5tt6WiqFcxGhm7STlwPyfrfgq3SbnnoVru3dsYGbkVD3olzG/d9WkqKfYPOWgbTqpk2HLppbKM3WjU9Hse4vWSZaseve/lSmUeoy8jM/HJ2loPaFpc9KLEc+I4Bn5N+W2fVcvneHVXI/Q5HtEFtc8LbqoXmTLdsvg0fQt9gRI27P7X0n7++R9S1z3xph6iSopPTrzPSiqGbVEpcapNcFFzKgaU+ims38dVJh+VzV38g1Tin9x8d1bd+8eL5XOzZ7FIkDT8Yk5q2Ok73XRh78mx2cXVxJak/NC2PtZO7/nDmN6fufHz2rvarAtHJfe7nPSvJnLj2g9NDZ28/1To5sSK9N7X0rZ2Xjg2MzS6nWjJ0k+qVfQPH0K3WXVlKn1nBVB7NotjeWjwt60O5k/NsH617ti2t7TXW8s2au7J+OTPGtZbC67J8dY3xOCgtpnWp6ajhlodX2eeGK1uXO2B/eAXbI2zdQ517Ylzl/A/Q6pyduSFVMFfmb1ATqaxUCqLk6KmTUu1y0kZv+44eunFnaHpuuY2tmnXoNreSOnz14Q8O9A89WRBXaUO3rv5He/ru/9fXD4se7Eu/6X37yA1xlah5n5wb/tWJ24Njs6IANnnoJmItDsoRatzKMi+zFplaKbNVFWuJE7Ee8nqkwqXGeg9dl4Le34Lj2JT371XGPaqWvZBa74y1aBjZsDSpse9TKjL2nyaWB6oDQqtm6r95y1q+6tt/7PDIgzFaNSNex7gS6893r4NdOcwtB6mtBLnVKxGpIjkOaks89lC73LU8KpRvx96EOKuWy/fuWoXsd7iz6kwrs9lWLZ3NWwdKbRSaoVuu3UO3rmtjX/nwvBfRq/3uF3dope2Pv7pLtHSPni/rt1TT7KGbVJsM1uJob9pMHj2ZpP5XJ52W5+lFqbq5+reBt/QbvHhx8Vq/1NN0srEnz5ZW2vbhMNZOrm94SnRsBy4/UBu5F1OLa8NPF0vlyj++fWLn+RHR0l25P51W3xxkfHb17a4buy/cm5hbDc6vT7XWrVa7LAXN1gjqvBovO3MnUidnX9dZGHfedZ+1u2Rd+OlcV78uq2iv5asjbt+1xm1vpjff1e+558pWvgMeX8ilK3Vy7VPb/lPmZ/f3SxXM1ZWFu9r28cSadFx3sp7ec21s1axDt2dLiV3n7/28+6b2Nruiyg08nFmMZ7r6H332jSPxdF5cLoqYdkPR2onl67k7T8uVbQ3dCAlgaNW2iVZtu7G3aoRsP5tt1YLzMaYhGLrNqn+267NjzPzo0IBWuRrUP1p7Gz+R5g7dzJWeuR4zF4fGes/Yj7nuMhddzuWisY1lhdn25RAJVEbjk1J1c/V3F7+j3+DFi/7BG1I308nGnz5v4ycyWzs50aiJTm7PxftaJ6fF7OT0f6t5PLv6s6ODYjX7ZC7+6PnyT7sGhQfTnp8e01hch27mhd71ytrJmZXKOpAyLvSaGVkHZHo7WLvEURUtszwjlrmVfq1xtz3nVpahm8sOzbg+JpYv56K2pWUWpl9oXmJ2vY5LLDv3vPPSA+h4PD1/6Ajys2b79q1PZVvznTsfShXM1cnpfm37ZDolHdedrOdCbxtbNevQ7Xksse/ifbHsHDE+IUGLOXTT/63m6MD4+6funFWHboevPhQ1TexBv46QkIdWbZto1babwPx8J5HJZlu1Yikor5rnTLdG82nvyN/9uNvLX77Zpb2nm/D7X/70b394TPpEhaYO3YwVlPVEBvcLLZHXdeYXslZGc23G0I1YM5mak6qbq3/p/5F+gxcvLg9ck7qZTjb2dHopGJ3cvakl0cm9c2xQ/Xw/vZtz7eRGn8W+vety17VHz2M+vtrCbcDk2qVJF5r/3LFHnpQpkedfSvSbGCXR+bsKZ9n0HrqZtdE5UbJUS+NuSFM5z7KsxTHPUrLpoVvtbsjfhbkrl8fc/KIbDN1qA0rP78XtZ4rbt1ZvstnS/OTeHqmCueqbu61tv5ZISMd1J+s5H5Qz3WKJ7NnbT7/56cWBhzPaS7G0OIduouLtPH/vvZO3rz9SXl6qX0pIVEKrtk20atuN1LkRsu1stlXLFznTbZPaPnQTpS1XXPcy9nxFe0834W9+eHQ1latUbA1cM4duriu0epXO3KdKX3e5Duka+OqkE1N9Uf37S9+VCpzTO6MH9Bu8eCF6lwePJyPpxp27UqNWx5GT3WL76bnlpWC8UchaJv/B6bv/9qszqWyxXK5oGzg7OdHm9Y8+/+8/OiZWp9pH0fsUl6Gbay2SC505JDLZRjbmXMmFXs3cZj0NDt0sJbR2J+111U4vzm4TRpe4lvR639EGj570Xbh/U/IXdRR/aQPze6mRfta4/fhw+9YCM3TrfnZFqmCunqWVdwoTWVhaujE01LirN29evD5w/uqVM319J8+d7T7dc/hE9479u6QSYTrZe+7m3RGp2rSMqFTS/fGy88Cec5cvjU08a2OrZh26ibI2Prvydz/u7h4YL5TW9S0cQzexcC1XKt/bc+Xn3TdnV1LM3Ej0EvlWbXBo5OT5czsO7JaKkmbXwX1HTh7vPnO65/y5MxcvHjtT78NhJDsP7jl1oXfy+QKt2rbC0I34nc22aplswTpQaqNQDN0S4s9MLrifXnrtwfOX3un53VeUM92+/MH54aeLopPTrzMSgKGbyxpJxdCNbCWfPj4lFTinoZUxbePSeoA+s9l3DX56afeZHrGleauFYHwk1nq5cvbO0299emn3hXuL8Yy2gbOTG5tZ+eTc8BffP/dsMSFuol/qQ/wburkOd9zoJXHLQzfzQnsfWW/opn93fgzdpDrvSKuGbpZL7MxnodGhm3lhu4duy/k1qYI5fW/oI33rFy9WExnzcN6yWaWAPO2/eetE75ldh/ZKRUPovXxZLIOlW7VA/U8v1ew6uOfKjRvTszH1Ju1s1axDN7HsXEnm3th39Z2jgwMPZ/QtHEO3ZLZwYXjqtZ2XD1x+UCiVtQsJiVii2qoNPxg7deG8VJGEfUcPnu47Pzg07CybDX566e5D+8SW5q1o1QgJVNrSqvkiNGe6JdM5/cELWC7fm/7Kh+e1bu+rH124an8rNzNtH7oZKzFBW4zJyz+GbmRTSZdy//PaT6QyZ/Wrh0f0TZU3P8pJR3SUbDh0k8ZtJv3RaXmsnZz459RiQiw7v/Sb3mMD45OLa6LtrqhvbzT0ZKFarYp/Tcyt7ugdeWNf/96Lo4mMv4tqH4Zu791z1kCPqmjNFodult9e2HfeQLWM2NBNj+WHi0b/7kI3dBPZ8Deo44lpbUtxjEiH8/Y9n18ZeTB+qk9eSe49enDo/gNp42arP3TbdXCvZdxW065WzTp0E/8srpf7R5+/3XXjnWOD4qp0vliuVEefxfb23c8V16uia09mRfP26s5Lv+i+eetx/UNqszH+P8v/z404DkCPiteaiKJUK0e0fNFLJFu1gdvy+GzX4X0Xrl59ODElbWm14dBNGreZ9Een5Wl3q2YpZW42e/KaKC+tKyz39ljvXltrLPE/7W3VtiwEQ7f5mPJnPJnVHr6gRTRtoib+3pd2/MNbx0Vvp1/qSLvf08251JGXf+YXcqtTdGDEJZOpuZcH3pYqneZn9/frG714US5XtKM4quoM3bzGbfPqn+36PaTUyYkn6Ml8/Du7r3x3z5V9l0aHny4+W0zMraRml1NTi2sjk4uf9o6IQve9PVdO3XwSS2RL6809081xlq4a6UL7Px11soHx1paGbmZJdOneXO+2LcEYutU6aZfH3Ny/4yHd8A6Y353+pd1+eLntxH0I2LbsfXJWqmaav+r71o2lUX2jJq9OH05Mnr96ZddB24lvVwdvSps1ldfQzWvc1t5WTRq6iSVocb189NqYKGtvHblxdfT52MzKTCw5v5qeWU6Jv5+9/eStI9dffu/sh2fuioqXUyuhPzEPJYWjSogEZ+im35NaHWik4SShS8RatUsD160Vaf+xI1cHB59Mz0ubOdUZunmN2zq7VTNbBXferY4zenvQmsKiVVTr3WtbjSVNSxBatc0KzZluy/G0/vgFLNrQ7bNvHF5OZuu8J8j2h27utKbNsV6qXeKxyrLs1ui3zEbQvMTSO9KBEdfkyoV9T87989UfmPXuazffNd+9UktwzuxtEtehm9e4Tae2tgUfV3qbidTJiZQr1VS2KDq2l3528i/eOPKtTy/+7KjyiVdf++jCn3/v0L/88vSevtFPe+/91+8dPnvn6UrKx5NZXOdQZr0yOyS5gpnVyeir5GlOrcTpNdDxhdxmPXJnJm2zQUl03G35TjY4dKv/mHgwvvEGhm7mNo5HT3A8gMZ3an+E5R8xIuZO9LvttoC370SL/Ci1P3eWH71x9xOzpv33i9/+9cOuueyyfvWLF+LItR3OzTHxbPbKjUFrVem9ckXapnmcQzevcZtVu1o1aeimRdS3wbFZ0aT9P98+8C+/OPXm/v5fdN/84YGBf3z7xN/96Nh391y98Wj25V+d/f7+/vHZFb/e0s08uDz/V3sejy1eEDZYjkgUEo1W7fn8ymnLicA79u8eHBp+vrAqbebFdejmNW7TdXSrtkHX0fiPbLdmoGkJXkdBmpSAtGqNC83QbWG5bS+qr5+n8/Fb43Ojz2L1fxPS3KGb52a1XkpqBC1qfV6dr8XQjdTPYm71WXphrSh/zFO0X1iqkYZuG4zbLNLZ2ufotTLOTk55T/FyZX41Pfx08eztJ3v67u84N7yjd+TA5Qe9dyZFfVuMZybmVsXlX/5N75H+Rwu+La09Fn613wHY1AqRs6/ynHDZ1Lbf/NDNu4oad8wcRdmY31qjq1yjFFsX4Ru0v5saurns7c139W0aHLp5Pxrmc+TYg4jL0M3tmw1Esuv55+nFuWys+sI2kCmtl0VDIh3LzXPt5i1reTlx7oy0QZNYh26NjNs07WrVXIdulapYoBaezMX7R2eODYztPD/yybnhXRfuHb/++Pqj2anFNbF8vTjy7K0jN767+8rYzEq2UNJvufUY/8N37HH+V9fD0I20L+Ft1R5PznT1nKgVpUP7hh+OSdvUJw3dNhi3WXRqq+by83prcWsGmhaGbh2WgLRqjQjJ0C2mPGrtOr/XlzR76KbGto5ylhvrGkm51uj8bFtaFo3i8pYWShKtiOZgLZm1HcgRZQ7dGh+3acTjoz9YrY2lk5PfPry4Xo6n8uOzK/cml0QD93Q+vpbOa58MI9q+yYW1X/fc+eGBa2Jlu7SWcX5izOZTb+FnG+tIbZ9LX+WyK3v9tK9pmzF0U2Krw/YS3fAq1/juPHfrJM/C6g/dRCw7VC6X22tH8Zc3UCIPGW2PsNuPD5edGHc4HCv/XKG0oDYkrXR7ZNRcKAqXr1+XNmgGbejW+LhN0b5WzXXopqVSrYraNbOcfDi9PDK59GA6trCaFs+jdq0od71Dkz86OPDjQwN3Jha2+3l/liPXs31qdOhmlguN6wGywTZyyTIrgHkfapTbet1n+36k+2k9qC1VJSRHNAlLq3bqfK9ZA/d2HXzybFbaYEPm0K3xcZumU1s1tx/6ztTaAPOQN+uAuESqUQqlvNRauGFzA73syNVJKjhq7L2H2QfKFc+4ais11lqorXep/qNBgpG2tGobCsnQbTEu/mzXR9H7ki0P3QgJXUQPl8kWFpeTtqM4ukbHn2x23KZp10fRf+G9s5/54o7/9vrhaw9mbo3P3xqfc+X8Halo3Z4trr138va3d10+fuOxaOZ8fX83YkbvWW2/EWlujC7ZNphrQYyut9Vfd9Mpltbjyba9/Orx5Iy54BRGx59KG/iup/fcJsZtmva1anefLPzv/77zt17++K3D1+vUNNcXJcTTucv3nn3+l6ff7xm6M7GwnfPdbEs7x3BNj+NylwWhvOzU2WZh7tuYO3FZ6yq0FaPLbb2GbtY5Wo2lNLlvoGB1GuyEqFW7ceeuWf0OHz8qXdug63eGNjtu03Rqq2Yc2hsdyOaoyz7h0srIBkM3C3Xm5XK5wlr6zP3bqOXUeZXn0G3DGuuxgSLw7Uonp72tWn2heXmpsJZqw68aqlWleG3fBwzdSKRTqVSTmXwilVtZS0tHLupoy1khb+7v/9NX92kVqQ7r59BbI4rYx2eH/+KNI71DkyvJgH6udNij94hNWbXWmmBz5Wwutls45lNj9LXWljo4yRdKyXQunsyKRZd05Lbe9GzMXHbuOrhncmZR2iAg2tKqPXq+/Dc/6Pq9L+2Qipjks28ciaddXimWyRfvPl3413dP//DgwNjMqn7ppiOtUc0DzfsECjWOBaF5Q/MSc7Bl7krexjyE9a9urF2NI8u5T5e75xy6mYtY7/1Yhm76d2ReYm5DgpIwtmoTUzN7jxzQSt+ew/ula1ujI1s1y6HtwlrWLAXKnJpZJlMu0/zacM1aJYzyYnY+zr3Jl9TupL5zYwNrM7OVGmsZuum7ql1iL+mk3QlUq1ZHeIZusbXFdvyqYWxm5X/89MT2/dmr+7XiyNCNRDLZXFE+ZtEA8bjpj2ALc2t8/vv7+v+Pr+3+rZdtrZvEq5MrlNYn5lb3Xrz/8ntnz95+st0XZBHX6O1dU1attfW5rNWtpH5PmjJb9CGi69DeSDsghu4/MOdup/vOS9cGQptatXgq3319/G9/eLT+3M1r6FapVDP50uV70z8+NPD9/f2xxJZekOVY7LksNUU2HLq5LRrlC122URaNtpvY473s9B66Oe6qEvlCx2LV6xsnAUgYW7UzfRfMujcxNSNd2xod2arV5llu7N2CUZFcr3UpCOb21trijFxtvKqW5cu51c+t1FjzS1v6E0cVJYFI0Fo1LyE600152UJpXX5Ze7NjvlGIXxi6kUhG+9yrWfmwhSftsRKPm/4ItjCJTOHuk4UDlx982juyw9vos5h+A0dyxfWpxbW9fffvTS6Zb/FL/I3W3tVZRW8rZkNpqt/7NiV6Ax3MlbnoN9RDVek9gqP38mVz/XnlxqB0bQC0p1UrqW8ufurmhPLO4o5SZjp6baxOvYqnc1fuTXcPjKfzxUqdT6P3iMt6rKGhlXxDj3WdsQBWV4Cbm2rZ1sPmbjceunl8FemGtjumZXN3j7QwoWvVbg3fMyvejaFh6doW6OBWbTNDt1pZUEgHvktBcB172WLboVEtXaqNnAaGbo3UWNfq7XFD0s4Es1VzFaaXlwrJdKtfynT78fw/vn3CRwzdSPRSqVSVI3RRPmCxAfURE4+e/jiGKuJur6XzhRITNxLNBPPj/CafLxw4dlhbgh483jWrvo1a0LS+VfMruUIpmS3o/9hUnFNsG8sirVVDN2M/EnO3DN06LmFs1Y6dPqWVu1Pne6WrWqdDW7UGJlzW1Ib78kxqE0M3r0LK0I14JJitmquQDd3a8rIFQkj98NrS7WjLyxYIIfWjvGDBcbQGweDQsLYKFW6PjErXBkEHtmrmqtJLbQXYkqGb9f5o2zh2y9Ct4xK6Vu3B+FOz1j2amJSubbHOa9U2NXQzNna7iUtBcB+6WXbiPvlq4C4xdOuwBLZVcwrX0E358Nf8Nj5YihDSjMRWU/NheDl9AInHTTx6+uNICAlGRKehHqGB+8h5YWZh9UB3l7YQPdF7Rro2ADqwVbO/EsqVuUrcaOjmvhyVLnTZRr0P+j5dBmpbGLq5LjsbWQYzdAtmQteq9V29qhW6Y6d7pKtarCNbtQYmXEaM8lJjLV+NDt2cF8rVxqVqqbeyTMHc9ryVGsvQLQwJcqvmFL6h2+paG15XTwjxSqG4Lg5M3s1ta7THTTyG+qNJCAlARKehHqEB7eT6B29qa1Hh4eM2nwDi0Hmtmrk8c1mdmqtEY53mWMs5FnLm6R7OS8ylpmO3xopR3abOteY+Gxi61bZxXuK4bwzdgp3QtWrTc8v7jh7UqtzA7TvStS3Wka2aWXY8mIe8WV7EJWZ9q5WITQ/dHCM2y+TLext9V2573kqNZegWhgS8VZOE7OWl2uvqi6VWv0cvIcQr2vvyYjva8h69hBDXiB5DOTAD/M5Hk88Xdh/ery1HL/Rfla5tvw5r1cwlpW0BaUS+duOhm2UbO9sMq+425hd1MndifF2NstR0G5a5L7wt3ylDt3AkdK2a+REKOw7snng2J13bFh3WqjU4dJMHWLXi45zKqZSy4Dp0q/MVLeXFXrgM5gb2wqjtfys11lGoRRi6BSrBb9UkYRu6ab9BZYFKSDBSLCm/Ow3FRzUHl/roiUdSf0wJIW2NsToN9O9OL/T3ayvSPUcOTM/FpGvbraNaNefJX/bUVnfqORSOtZzXQs62trSsOS2xnAmiqJ2zJmKbuylfy1jQ1taQ1psrX91rWGbblf2rMHQLRcLYqp04d0Yrce38CAWrjmvVGhq6mWXKMj6rFRazAlhriLKl+9BNxPpFrUXJXh7tUzx5J9Zr1XK3lRrL0C3wCUWrZhW6oZsuX+Sd3Qhpf1bUM3t5bel2aI+eeCT1x5QQ0r6I7sJ6eAbW2NNpbUUqjDwcl64NCFo1QoKQMLZqe7v015beGrkvXdUWtGqEBCdhadWswjd0m11SPiB/OZ7WH3VCSJuSyyufhDXvOEixWdpjKB5P/ZElhLQporsQB6PWaQTckZ7j2qL00sB16aq2o1UjJCAJY6v2ZHpeK27C46kZ6dp2oVUjJCAJUatmCuWZbtqH76SzBf2BJ4S0PNVqdUn7nObwvJw+uNTHUDye4lHVH19CSMsj+gpxJIblA/56L1/WFqXdZ05JVwUBrRohbU9IW7Xhh+Nacdt1aK90VTvRqhESgISrVTOF9eWlmvX1iv7wE0Jam7VUVjoesX3iUdUfX0JIayM6Cul4DLjBu8PaunTH/t0zCyvStcFBq0ZIuxLSVu367TtacTtyslu6Kgho1QhpV0LXqpnCOnTTppu8coGQtiSXV19LHwvNu1eGg/p4isdWf5QJIS2M9mqFEP3udHxqRluXCvfGHkvXBgGtGiFtTHhbtbOXLmqV7fSFC9JV7UerRkj7ErpWzRTuM92ERDqnPwmEkJZkvVyZV3uOcL2WPvi0x1M8tuIR1h9rQkhLInoJ68EYFuZ7jV++Hri3dbOiVSOkxQl1q3b4xDGtsl25MShd1Xa0aoS0KyFt1TShH7oJGd4xhJBWpVqtxuIp9dAL3+9Ow0B5VMUjzDuGENKyiC7CfhiGxqkLvdrS9NjpHumqoKFVI6RlCXurppU1Yej+A+mqYKBVI6TVCW+rpgn50M14W9B8gbN8CWlFVhPKB8/PhfC03tBQH1vxOOuPOCGkmRH9g37ohfAzYfoHb2lL04PHu6SrAoRWjZDWJtSt2sziqjl0e/B4Uro2KGjVCGlhQt2qaUJ/ptus8ZdCkWaOkOYmnlTbOLSEeLT1x50Q0pyIzkE73MxeIlxuDd/Tlqa7Du+TrgoUWjVCWpawt2rTszFz6PZoYkq6Nmho1QhpdsLeqmmi8PJS9Sxf5QX2eZo5QpqWtaT6GVih/Q1DyKiPs3jM9UefEOJ3RM+gHm6ifwjri+XvPXpsrk5nFlalawOGVo2QpicCrdrUzKJZ1sanZqRrg4VWjZAmJwKtmiYaQ7eaHC9eIKQJiWsvVUDLiUdefw4IIf5FdAvSsRZG40+nzdXpk+k56drAolUjpBmJRqs28WzOLGvi79K1wUSrRkgzEo1WTRO1oZuQzub1J4oQsu2sl8vL+tvxoj3E4y+eBf35IIRsO6JPkI6ykHo2t2SuTh8G9s2P3NCqEeJjotSqjU/NmGVtamZRujawaNUI8TeRadU00Ru6KWceriWzfKAMIdtPvlBaWE7aDzG0gXgWeA9yQrYf0RvoL78K+esUTDv279ZWp3dHH0lXBRitGiG+JWKt2qOJKXPoNj0bk64NMlo1QnxJ9Fo1IYJnummWVlOF4rr+1BFCNp9kWv0NAx9UGgTqsyCeEf25IYRsPqIrEL2B7cgKv/1HD2ur08GhYemq4KNVI2SbiV6r9uDxpDl0m1kM+FtV2tGqEbLtRLJVEyI6dDPeQDSRzvFrVEI2m3yhZNQ75X2vEQzKcyGeF36PSshmIzoB0Q/oh1K0Pg2mq+eEtjq9cmNQuiroaNUI2Uai2qqNjk+YQzfpqjCgVSNki4lwqyZE9ky3uVhiXv2Fw+JKMpMr6E8mIaRuSutl8414560HFALAfEbWklnxTOnPGSGkbkQPIDoB5QgSXUEsOi9V0Bw73RPWoZtAq0bI5hPtVi3kQ7faMyKeI1o1QhpMtFs1IbpDN7vYaiqbL+rPKiHEEdEZrKW0188zbgu02ugtxeiNkHoRP/fFT3/z2ImkcA/d7GjVCKmfTmjVwj5009CqEdJgOqFVEzpj6BZbm1X/srSSTGXy5UpFf5IJIeorFFbNj5lXTzpACBjPlHjueBUDIdaIn/LiZ734iS8OEOWnf6TLWnSGbrRqhHinc1q1aAzddLRqhHiko1o1oVPOdFM//KL2lgcra5lMrlAu09KRzk2huJ5I5RYtn3g1G7nXz0eb9fkSz6N4NnlLctLJET/TxU928fPdPC7Un/sRfJGCVZTOdKNVI0RKB7ZqkRq60aoRYk9ntmpC5wzdLBZrT+3SSnItmc3mi6VSmY+uJ9GOKHP5QimVya8mMtZXJTBrCzvrMyieWfH8imdZPNcsVkm0I35qi5/d4ie4+Dmu/bJUlVB/ytcOimiL1tDNglaNdGRo1SI2dDPRqpHODK2apiOHbqLYeZzBuLicjK2mVtbS8UQmnswC4ZbIiB/qy/H00mpqXn5PSvWfUXyjys6lP5u251Q87+LZF/8HxP8EyhqiIJERP6PFT2rrqR9WXj/foyqyQzdaNXQIWjW7qA7ddLRq6AS0ag4dOnRzUn7/EOOUH0SO8msE/TcJ2tvlzHbYLxY6k/Ysa8+4Kt5pv1BC5PFTWxPhoZsTTzqiiVbNIuJDNwtaNUQeP7VNDN08iVIIhJr0XxqQ/ocAoSP9l0ZHDd2cpP8eQOhI/6XROUM3L9L/ECB0pP/SEBi6AQAAhFKHD90ARAxDNwDRw9ANAAAglBi6AYgShm4AooehGwAAQCgxdAMQJQzdAEQPQzd0son9r7/x0iuaHefla2vOv29upvvpVXmbka5fvPR694j9QpurOyx7ML+c9T5YWe+Pss03uiaMf2oGfuq8Gw+7v2Huof6d0Ymd/GL/Q+lCAKFlqTOOomFhrRUal4rhVmTsrLVR2lIpicZVLvdEuZ9y1fWqohtXV51SKuvf4ehh6IYO4V4HGqx4hg2LSZ1+b6NW0N6qWVu+9wcsmzls4Vuov8OQY+iGjuHaZW1pcdpITVAaP3nRp9QTYycu7ZO9VbN8xbqLR2v1q/stGDpiNcrQDR1LqXRm76UWHa8DXi1/jfRM3p2cVtTsrZvHl1N3ZW283HpEvSLb6qO6ijZuqG3QQKWz11MA4WWvM2qJ8ypcyoFfv8VxKzJ2ah0zqoe9cNkqqq00adwaTa8q6nW5K7eeMtoYuqEjuNYBax1zqTMOGxeTOv3eBq2grVVTv9CmS3Ej34JCuScNbBZWDN3QGVy7LFudsXVZNtptzatsS1oP6jb2Bsm2Gt2oVVM2Nr6ErcezszeidRe8VkoljPhqlKEbOpSjXtRpp9zWhxZqQVF51jvnzpV9uvVMak20bulcQ6plUfuK1ntlrYbmZnXutkG5bxHu3oCO4age1uWcnW196ORRZGwcFcZSguR7ohbJWjul/NNebL2qqNfldTh3Hm0M3RB5HnVArjNqX+e5bGusmCj79Ch6da6SWjW15bN8Fe8F6ua+hZpIL1AZuiH6PLosR61QSoTLGs25xPPu9xTKtdqXs2yj7sS6c6n+2LopdQ+Wr+i1am58wSuJ/mqUoRs6lFJK7I2XtdaozZlRd2y9lEzdUrnWsUO5l7Jzv9bZmcm71cq0uKGz4ErsG6j30yB1nJHu3oBO4VITLL2Oeq3Z0CgFwas6eRcZZ4GyshQrub6p9cdaUW27Va91qaJelytqHaS+Te0qZf+edzJ6GLoh2jzrgKOSWC9Rb2VfPboXE3uxqlM96hYW+24bHrrV/Ra0v5uFzrZZpBeoDN0Qcdqh7dZlOQqU5RJ1Y8+j3jIUkwuOepVyQ+Uv0uX2uuSoP+Z9s66RvS7xYKuxaik22L9T9f5EeTXK0A0dylnXvLoi7fJviBZKLxNunZPbDutxL53OSZxlzSxxVGo7dVfG/VFrnFnItvxbCAAB5lITnCWldvk3XheVzShrLttsWGRs1Dop9WdGqVT3Y36JOo2aVxWVL7c0l4Jjh84SF2UM3dAh3OqAy4qxfjNTv1Wr0+/VucqlibLVKFs/ZlPnW5C+F8eWdQpp2DF0Q6eQuyyX7sW+gvOkFShbMXGSykjdEipVGGflbOgrCpZ92r+XjluNMnRDh3L2K2otcCkf0uXqDV0Wos565E0tNI4a6lK/5HJs4XmVtnPrfd5wCbrhBgCCz9GvqFXC5dCWL/dYE9apP1bq8lIhfSHtq6isO6lTKr2uki53Vm/JZqpx6DF0Q4eQj2vnirGBNVv94qBc69Hv1bnKo1SqdVXleZfqfAsuV9ltuEFoMXRDp5BLh8tybMOGRyE3dR6kouEsXMoGer2SSqVL5WyoBFkXvJ2+GmXoho5lW2dq/VNjHYxaFBxNW/1Ozkpt3Zz9mdtu61Q09ybPwlI6N/zuGr/zAALLtg40l3yNdDBquZDryYZFxsbRWtXaRGuxrddUeRUi+XKtv/ReyjbUpEYFQzd0CLkOuDRIxsSqdolsk92Oe78nX+W4J8pXqV0i1UOLet/CRgV8c/U5TBi6oVPIR7FLg9RAP6PVigZ6nvqVytZZyffEpXK6lC+Zuv/aN9jhq1GGbuhkRk+jlZgGyodGLSJydWuwUkgFqEYtdlKzqJYnj7vUQL9lu59GMXWtd9Euc0DnMHoa7dh3aeDcudWfRoqMjbkTpZbab1jblVp1tzl0U2jrWJ1b5WygAY0Ehm7oEHIdcPZsrnXMbrPdjrK9RzExr5JbNZe7YY7SzEtUG30L6pcwSGWzVlSjhqEbOoV8FLv0bHVKkKrhiZvgts6tFRmlMJqVSm7VlM3slbPeElWl7VmuUeq3rH9F1zuzmfocLgzdAJ1SPho71F0rYAOVQl8lujZJ7sWrzhywgX7L9X4aBdq222iXOaBTKQd7Q6sy+0rPemFDN9eYO3EpXGYnV28O6FWI6hcotdDZ7jxDNyB65DrgLFAN9kWb6XbU8uJeTGpXSRXP5W541L2GvwW1ptn30MA3G1IM3dApHEexs0DVK1nqzTfR7dRZVGqUHWobyCXL2VbVbbTqLXgNHbcaZeiGDuUoFl5LQfVyWwlw33KjSqEVIM9i537zOk2VfJXzXjnvuUEpu/VvCyB0zMGWcYlXgyVXAI8t69QfwXkTc7fO/Vt2VadUel21UXVVWzdLBdto+0hh6IYO4Tiu5YrXyLS9bnFwdk1md1TnKkepVP9pK8XO4uxxufe34LgDrkU7Ehi6oVM4uixHBfAqHfptvauZG6lo1P3qcqmUC46lAMrUqxqpTnKvWGefUcDQDR3LVsiU4uLVq6lFwb6lSymp28l53srgVWg2qLbWWikVL/Urav+UezXHN+v9VQCEh61dc1n4mbSWyLalS/1xFhkbe2HRdqL/075/tcKYW9ZZGHtVUelydQ+WctphfZuEoRs6hLM+qKXAOPbrVbwaryKjq9Pv1WsF5SbKfq1UD208vwWprDm+uzqFNOwYuqFTuHRZtn5JrSSuh7lts0YpVcW2FLXVQ3vNcVQYWx2T2zALe/WzsjeNLt9dxFejDN3QydSapbFXLrkQaOtJnWv5s1cuhbpzbeFnu7lFbXvLxnZKXbPfN51Lpa5zP7Va6XqVVmfdvykA4aIt4TS2+qAWB8eyUOfe5TiKjLPNqrMT61X24qaUO7l2qRxV1PNy67cpOL5T914wkhi6oUO41wd1oahx1DeXxsaxE0f35dlH1bvK2arZapT1Kkcp9voWrJfLV6lNnVRyI4OhGzqFo8tSWZdsljpjKR1SC2SyXOvWBSklRb5cLZUa6SqXVs1r41q9tRVJC70GdvRqlKEbEGzuFdlHUW7dAASQY93rJ2XnHXOam8DQDWi/prdqdpFenTJ0A4Kgqa2aQ/RXowzdgKBz/gbVT1H/xQKA4PE82W27lKVvB53mJjB0A4Kgua2aTcRXpwzdgGBoWqvm1AGrUYZuQPA1r8ES9bSzFqgAAqEpDZZSKlt3skkwMHQDgqFFszBluhfpk3kZugFB0aJZWEesRhm6AQAAhBJDNwBRwtANQPQwdAMAAAglhm4AooShG4DoYegGAAAQSgzdAEQJQzcA0cPQDQAAIJQYugGIEoZuAKKHoRsAAEAoMXQDECUM3QBED0M3AACAUGLoBiBKGLoBiB6GbgAAAKHE0A1AlDB0AxA9DN0AAABCiaEbgChh6AYgehi6AQAAhBJDNwBRwtANQPQwdAMAAAglhm4AooShG4DoYegGAAAQSgzdAEQJQzcA0cPQDQAAIJQYugGIEoZuAKKHoRsAAEAoMXQDECUM3QBED0M3AACAUGLoBiBKGLoBiB6GbgAAAKHE0A1AlDB0AxA9DN0AAABCiaEbgChh6AYgehi6AQAAhBJDNwBRwtANQPQwdAMAAAglhm4AooShG4DoYegGAAAQSgzdAEQJQzcA0cPQDQAAIJQYugGIEoZuAKKHoRsAAEAoMXQDECUM3QBED0M3AACAUGLoBiBKGLoBiB6GbgAAAKHE0A1AlDB0AxA9DN0AAABCiaEbgChh6AYgehi6AQAAhBJDNwBRwtANQPQwdAMAAAglhm4AooShG4DoYegGAAAQSgzdAEQJQzcA0cPQDQAAIJQYugGIEoZuAKKHoRsAAEAoMXQDECUM3QBED0M3AACAUGLoBiBKGLoBiB6GbgAAAKHE0A1AlDB0AxA9DN0AAABCiaEbgChh6AYgehi6AQAAhBJDNwBRwtANQPQwdAMAAAglhm4AooShG4DoYegGAAAQSgzdAEQJQzcA0cPQDQAAIJQYugGIEoZuAKKHoRsAAEAoMXQDECUM3QBED0M3AACAUGLoBiBKGLoBiB6GbgAAAKHE0A1AlDB0AxA9DN0AAABCiaEbgChh6AYgehi6AQAAhBJDNwBRwtANQPQwdAMAAAglhm4AooShG4DoYegGAAAQSgzdAEQJQzcA0cPQDQAAIJQYugGIEoZuAKKHoRsAAEAoMXQDECUM3QBED0M3AACAUGLoBiBKGLoBiB6GbgAAAKHE0A1AlDB0AxA9DN0AAABCiaEbgChh6AYgehi6AQAAhBJDNwBRwtANQPQwdAMAAAglhm4AooShG4DoYegGAAAQSgzdAEQJQzcA0cPQDQAAIJQYugGIEoZuAKKHoRsAAEAoMXQDECUM3QBED0M3AACAUGLoBiBKGLoBiB6GbgAAAKHE0A1AlDB0AxA9DN0AAABCiaEbgChh6AYgehi6AQAAhBJDNwBRwtANQPQwdAMAAAglhm4AooShG4DoYegGAAAQSgzdAEQJQzcA0cPQDQAAIJQYugGIEoZuAKKHoRsAAEAoMXQDECUM3QBED0M3AACAUGLoBiBKGLoBiB6GbgAAAKHE0A1AlDB0AxA9DN0AAABCiaEbgChh6AYgehi6AQAAhBJDNwBRwtANQPQwdAMAAAilDhq6LYo/4/KFMSDkltbmxZ/Kf2/FrPW/d0di6AYgehi6AQAAhFLHnum2sJxYXE7GVlNAqC2tpMR/5vlYQvofrnJMmTsAQzcA0cPQDQAAIJSiO3SLW0/5ia2mEqlcNl8sFNfL5coLQiKXarVaWi/nC6VUJr+ayCwuJ7X//Mp5cMpfXKdyEcTQDUD0MHQDAAAIpegN3cxZ28JyIpHK5QulSqWqjyUI6aSU1svpbGE5nq4dHcarUCOMoRuA6GHoBgAAEEqRPNNtNZHJF0r64IGQjs96uZzK5JdWtHPfEurLTiP7ylOGbgCih6EbAABAKEVi6BbX3k5eSKZz6+tlfdJACLEnVyhpJ77N246gSGHoBiB6GLoBAACEUviHbvo7VSVSuXKFN2sjZOPk8kXjrDchaqe8MXQDED0M3QAAAEIpzEO3+NyiMi9YWUuXOLuNkE0mnS3YD6iIYOgGIHoYugEAAITSiXOntdXp5esD0lWhkMkV9BECIWSTWS9XVtYy+tEUi8jHm96590CrabsP7ZeuAoCQYugGAAAQSj3nz2kL1L7+fumqgFuOc4IbIT4knclrx5T5yb+hNnDrtlbTDh3vkq4CgJBi6AYAABBKp/vOawvU3suXpauCKLY2u6j8ZS2Z1QcGhJBtJ18ozUflTLe+a9e0mtZ95pR0FQCEFEM3AACAUDp76aK2QBV/ka4KrFQmr48KCCE+Zb1csXy6QoiZv0g403dBugoAQoqhGwAAQCj1XrmsLVBPh2SBmsnyJm6ENCWVSjUWT0lHXOiYHw7Td+2adBUAhBRDNwAAgFDq6+/XFqgne89KVwWJ/sI3Jm6ENDXVaujnbge6u7Sa1n/zlnQVAIQUQzcAAIBQunR9QFugdp85LV0VNGkmboQ0P5VKNdSvM911aK9W024N35OuAoCQYugGAAAQSlduDGoL1EPHj0pXBUoyzfu4EdKirK+XF5YT8+LQC9unKzyZntMKmjDyaFy6FgBCiqEbAABAKN198EhboO44sFu6KjhWExl9GEAIaUkKxXXpMAyFWyP3zaHb9GxMuhYAQoqhGwAAQCg9eTZrrlEnns1J1wbB4nKyUqnqkwBCSKuSyuSlgzH4ei/rnwzT1XNcugoAwouhGwAAQFiZb4E08iCIr8bKF0r6DIAQ0tqsJjLS8RhwR3qOa9Xs4sCAdBUAhBdDNwAAgLA6dvqktkwduH1HuqqdYsqfiVROX/0TQlqe9fXyfHje1u3Z7JJWyoThB2PStQAQXgzdAAAAwqr3iv6CrN4rV6Sr2mh+KbG4kqxWeWEpIe1MOluQjs3AGnk4bg7dJp8vStcCQHgxdAMAAAir63eGtGXqsdM90lXtlc0X9XU/IaR9WVpNKYekevJpkJmfxXz01EnpKgAINYZuAAAAYXXv0WNtpSo8nV6Qrm2L+djacjytr/gJIW1NLl+UjtAAmppd3Nd1UKtjff3XpGsBINQYugEAAITY4RPHtMXqwK3b0lXtkuPzEwgJTJbj6bmlQL+5W//NW1oREx48npSuBYBQY+gGAAAQYubLsrpOHpeuaoPYWmw1pa/1CSEBSK5QEsfm7KLjaA2G5/MrB7u7tCJ28txZ6VoACDuGbgAAACE29nRaW68KD8afSte2lPq+UZlcQV/rE0KCkaWV5Lx0tAaG+caUwtD9B9K1ABB2DN0AAADC7UTvGW3J2netzW+HNB9L6Kt8QkhgksrklcMzkC8yPXyyWytfR052S1cBQAQwdAMAAAi3wbvD2qp158E9o+062U198dpaMquv8luVdK44ubC2p+/+O0cHf3z4+tb86sTtvuGpWKLVd56Q1qRcrtiO1sAwXx0vDNwOyrtSAoCPGLoBAACE27PZJfOz/4607Z3d4uLPQnFdX+W3KktrmR3nhv/i+0c+88Ud/+HfPtqaP/7qrq9+dOHC8JS+U0Iil5W1tP2Abb/R8Qlz4rav69DkTCA+fxkA/MXQDQAAIPSs74t07vIl6doWmI+tLSy34bWl96dif/rq/t955ZM/+PKn/+nru7dG3PyPvrrrG5/06TslJHLJZAvqoaoMxwPi2Okes2oNDo9I1wJANDB0AwAAiILjZ0+bK9iHE5PStU2lfTBivOWvLRUZerLwO6988lsvf/SrE7efzsc3bWHtyVz8H98+/ttf+Phf3z2t75SQyGW9XFlZSy+vpRdigXhnN+sLS0/3nZeuBYDIYOgGAAAQBVOzi+YiVpiejUkbNOjKjRuj40+kCzeiLOMzuaK+vm9hbj+e114ieujKw2q1ql+qplKpxtP5x7OrYpvBsbl7U0uxRLZYKutXGxE3+vwvT4k9fO7np/SLCIl0iqX1tWTWfgi31K2R+2al2nPkgHQtAEQJQzcAAICIGLhzx1zKCo8mpqQNGtF9RnnN17HTPZsdvZXW5XlWC2IduukXvXhRWq8srmUGx+aODYzvvnD/47N3Pzpz99PzI139Y5fvTT9dWCuul63zuX999zRDN9JpKZbKy/GUdBS3wLWbt6xlqsWn5QJAizF0AwAAiI4T52ovMhWG7j+QNtiQNnTbod58U6M3fSnf2jiHbpVqdSGe6bk58cr75/7hreOv7br83snbH5we+tHBgX9+p+fzvzz96547z5YS+VLtMx8YupGOzWoiIx3ITXXx2jWzOglXbgxKGwBAxDB0AwAAiJTBoWHrsrb/5q3ZhVVpmzq0oZum8dFbbDWlL+JbG2noVqlW4+nc+6fufP3jvp0X7j2eXY0lsvF0fi2TX03lni0lem5OfHvX5S9/cH5kcnG9XNF2stWh22LPm2+89IqwZ0i/RM29PeqFivfu6ZepGX5Pu/zNM/P6Jb5l/uy7zq84tEO/G9JV5sYv7RjWL7LEvPa1s4v6RSTMubY48sPhXf90+Y2/6vvWv177yfuPjj5OPtevUxNryfluT5/Pn73UZ9YW4dETznEDEH0M3QAAAKJmdHxi18G95uL20PGj/YO3Jp8vSJu5sg7dNI2M3tryKQoi0tAtWygdvvrwrSPXd54fmVpMKC8jtbzRW7lSXVzL9A1PfeOTvk/ODo8+i2mXb/lMN3OqZR111UZa0tzKHMa5jbq2GcfQzRjw2en3Z+nMa/ol9nGhEnOS+G7Pkn4RCWmy6/kfj+z+ywvfdDo0eUHfSH2LN+lw9tfYk2d9/f27D+0zS0r3mVMNliMACDuGbgAAABH05NmsWNmaq1xhz+EDF69dG3s6LW0pcQ7dNPVHb8l0Tl/BtzbWodt6uTK3kvraxxc+OnN3bGYlWyidH5o8ceOx1eiz2Fomf/z6+Gu7Lh/pf1RU34duy0M3t5PCzKGVynJSm2Mu5meknTvumDRK856smfO4JpyOR1qc14c+lmZtVta5W7w5n6tw79HjM30XrGVEOH/lsrQZAEQYQzcAAIBomllcuXJjcG/XQWnR29Vz4sLVq7fvjU7NLko3EbyGbhqv0Vs6W9CX762NdeiWyhVvPZ7/3C9O9Q5NlivVuZXUn726///8X3v+89f3/M4rn/yXbx/4gy9/+vaRG5VqNVsofevTi7/ovhVLZKvV6tbf080cUZknr9VOItPUplrGaXFNOYPMPnRze92rcZ6dNoZzGxcqaepkkLQy3c+uSFM2pyfJWW3jQtG3k90mns3dGrnfe+XKoeNHraVDONJzfPDusLQ9AEQbQzcAAIAom5pZdB29aQ6fONZ3tX/g9u1bw/eGH449eDzZ1XNc2sbJPnqLi6+SzRW11XuLYx26za+mu/offX9//52JBXGVNnQ7cPnBjt6RP//eocezq1/49bm3j9wQV1Wq1Y/O3P3Vids3x+fE37fxQQqOt2kzZlvv7TD+ok+vjEGY7Qwy+2lx0os9LRM983WstRmZ5Z3jxIUbD8vsQzePM9pcpnXmnqXLLbG/lJVT5IKRz1/7sTRic/pwrFvfWjzRsYS1btQ3s7A6+Xxh7On06PjE0OjDweGRa7dunbt88UD3EWuhMDFuA9CxGLoBAABEX/3R23b09J4T+88VSvravbWxDt2ml5K7L9z7efdN7c3asoXSpZFn2iTus28ciafzd58sPHq+rN3wSP+jX/fc0c6J28bQTX6dpjEd2zNkjqK0k+A2PidOV5uauWygfxX7IMzGa+jmePs5+Z4rcdxJ81YW9jP1LLM/C9fZHGldZjJL0nzN1RcG3tZv8OLFxWtXpUPbF4zbAHQ4hm4AAACdYno2NvxgrK//2pGT3dLaeMt6LvSKPecDMHR7Hkvuuzj606OD96ZsL+A0h276v9UcHRh7/9Sdc3eeVipVcas3D1zbfcHrJLF6sZ9iZkyylKGV8XftzC/pRDOXmZd5vpgxsbIM3WyjtNrlxm0tky/3oVttg9rIzLzn5tlz8ulyxq2MfTpO63Pc59qQzpwtknZkND4pzddc/d3F7+g3ePGif/CGdGhv2Y79u7rPnLo6ePP+2AafegwAkcfQDQAAoBNNPl+4NXL/3OWLXadOHOzu2nvk4I79u6XFcyN6zitnugVh6La0ljl968m3dl688Wi29pGlbkO3arW668K9907eHng4U7F8vOlWYp2mGeMwbYxlzLCUOZf17/KtzEgXmsM1+ws2ncMyEceJbJbUJm72WZi8f3MOqE3Q7ENDLfqu9O9CHtKJqPt0H/yRFuZpalaar7n63NUf6jd48eLy9WvSod2gnQd27+06eOj40WOnT17o7x9+MDazsGotNQDQyRi6AQAAQPd8YeXoqRPSotqLWGyfu9T3cOKpuGEQ3tMtX1x/NLPy//2k+/j18UJxXd/CMXSrVquVavWNfVd/fuzmzHJymzO32uhqx7DXZO29e9aXnSox/lk770yN/VQyy561q7W43tZl/qXFOnGTX/VpP9tO/nLmWWwutK/i8V2Q9qdcrfztxdekEZvT2/f26Td48WLsyeTo+ESDHk1MPp6amZxZmF1kvgYA9TB0AwAAQE39Ty/V7Dq458qNG9OzMfNWmVz7P720XKkuJ7Pf3XPl58duDo7pH8soIg3dUrni5fvTr+68tP/SaL5Um81tNbUTxN6zT9Zqc6sde6QXZrZo6GbuwbG9FutJc4491Bu6aSfZMXQLcj4aOy6N2JxuxR5qG5fWy+axDADwEUM3AAAA1NQfujnHbZpUxvaOaS2Ldegm/lkolS+NPHvr8PVfdN8cfrqYyZcq1erI5NInZ4ezhVL1xYvVVO7ag5nv7LnyztHBwbE5bSfbjDF7MtRmZOY8Tme+ILQVQ7eNJm5KzG3e3POe7bWlIvY74xaGbkHOWjH1uas/kKZsVu+MHtA3ffEimc5JRzQAwBcM3QAAAFDjNXTzGrdp1pJZffne2khDt2q1Wlwvi7+/uvPSW0du3BibnVpcW4inY2uZxbWM+Hvf8NTbXTc+/8vTn5wdvj8VK5TK2n62E3PgpbG+1Zp0VW0iJr19mxbpQo+hm/X0NP0iy+DP+BLWeV+doZg8FnSbGEovSq3FZdKnjups3xRpX8YS015ztx8O7ypV9NM8K5XqfCwhHdEAAF8wdAMAAECNc+hWf9wmzC7Fl+NpbQHf4khDNy3pXHHg4eyrOy/+t9cPf+G9sz8+NPDeydtvHb7+uZ/3/OPbJ76352r/6PNXfn3uR4cGJuZWt/mWbkrqnFNmu8o6vTJfvGlub15ibOYxdLPs09jS8sZt2vzLOuyzTMRcYh8L2u587Sr9DjjHcPJ9rt3E+/w40sqsFdOfjJ/4+0vfMcdtXxh4+9Tza/rVauLJrHREAwD8wtANAAAANdah24bjNtPCckJfwbc2rkO3cqW6ls4/er7SNzx1+OrDHeeGPz47/GnvSNe1sSv3px/Prq5l8r1Dkz8+dP37+/vFP7Pb/ehVc/bkHDZ5X2Wbx9XUZmReQzd5Umaj3txx/pqFfBqa9W7Id959P65n59nxgtNgpfqi+iy9MLb2bCG7ol9kJJXJS8cyAMBHDN0AAABQow3dGh+3mdbXfXip5mbjOnTTUqlWc4X16aXE/amlOxML9yaX5lbS2bw+X1tJ5k7fevLm/v6fdt0YfrqY3tanr1qGU44BWe0d3xxXidSuFbymcm43tE67XpM+BsFjnKdxvPazduddXxZa51Q4I5bBosA5buFJIsVbuQFAczF0AwAAQE1P77nNjtvmYsqfOWOe1crcfbL4+1/+9Ldf/vido4P3p2L3p5ZcPZ5dXS9X9NsYWU3lLo5MvfSzng9P3x2ZXMoXt/9JpoSEI9lccWklaTuKAQBNwNANAAAA26MO3dZSbfgshYfTy3/1Ztdnvrjjt7/w8e++8omXv/7B0Xha/nzVarWayhVvP57//C9P/+jgwOO5Vf0KQiKXSrWazuaT6fxqIsPHJgBAyzB0AwAAgA+WVlL6+r6FWUlmj1x99Nc/OPp7X9qhvc7U1WffOOIcuomUK5V0rnj+7uSPDg786NDASjJXdpwQR0gEks0X1eM0bj1mAQDNxtANAAAA/ii1/G3diuvluZVU9/Xxj88Ov39qyMuByw9y3q8eXUlm+4anjlx9lMoWKlUfPs6UkKBlNZERR+i845gFADQVQzcAAAD4I5VxOZssFMnmS4lMQf8HIdFKpVIVh+es44AFADQbQzcAA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+ "description": "In this tutorial workflow, sample_LatentDirichletAllocation data is used to make topic model and get the result. The result includes each topic's plausible names. The parameter settings used in the function are shown below.", + "parameter": "1. **Top N topic names**: 10\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.textanalytics import topic_name_extraction\r\ninput_table = inputs[1]\r\ninput_model = models[1]\r\nresult = topic_name_extraction(table=input_table, sim_table=None, model=input_model, topn=10)\r\noutput = result['out_table']", + "description": "In this tutorial workflow, sample_LatentDirichletAllocation data is used to make topic model and get the result. The result includes each topic's plausible names.", + "context": "python" + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/textanalytics/meta/word2vec.json b/function/python/brightics/function/textanalytics/meta/word2vec.json index 5c25a8feb..4db5bc963 100644 --- a/function/python/brightics/function/textanalytics/meta/word2vec.json +++ b/function/python/brightics/function/textanalytics/meta/word2vec.json @@ -9,7 +9,7 @@ "name": "brightics.function.textanalytics$word2vec", "context": "python", "label": "Word2Vec", - "description": "Word2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a vector space, typically of several hundred dimensions, with each unique word in the corpus being assigned a corresponding vector in the space. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space.\n\nReference: \n+ ", + "description": "Word2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a vector space, typically of several hundred dimensions, with each unique word in the corpus being assigned a corresponding vector in the space. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space.\n\nReference: \n+ \n+ \"Distributed representations of words and phrases and their compositionality\", Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean, 2013", "tags": [], "version": "3.6", "inputs": { @@ -241,7 +241,7 @@ ], "summary": "This function trains Word2Vec model from tokenized text data." }, - "md": "# Word2Vec\nThis function trains Word2Vec model from tokenized text data.\n\n## Description\nWord2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a vector space, typically of several hundred dimensions, with each unique word in the corpus being assigned a corresponding vector in the space. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space.\n\nReference: \n+ \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Text data in a Table.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contains words column and word_vectors column.\n2. **model**: (Model) Word2Vec model.\n#### PARAMETER\n1. **Input Column***: Columns to select as input. Only List of String type columns are possible.\n\n2. **Training Algorithm**: Training algorithm. In the `CBOW` model, the distributed representations of context (or surrounding words) are combined to predict the word in the middle. While in the `Skip-gram` model, the distributed representation of the input word is used to predict the context. `Skip-gram` works well with a small amount of the training data, represents well even rare words or phrases. `CBOW` is several times faster to train than the `Skip-gram`, slightly better accuracy for the frequent words.\n - Available Options : `Skip-gram`, `CBOW`\n\n3. **Dimension of Vectors**: Dimensionality of the word vectors.\n\n4. **Window Size**: Maximum distance between the current and predicted word within a sentence.\n\n5. **Minimum Count**: Ignores all words with total frequency lower than this.\n\n6. **Alpha**: The initial learning rate.\n\n7. **Minimum alpha**: Learning rate will linearly drop to min_alpha as training progresses.\n\n8. **Seed**: Random seed. Use the same seed in order to reproduce the same results.\n\n9. **Train epoch**: Number of iterations (epochs) over the corpus.\n\n10. **Number of Workers**: Use these many worker threads to train the model (=faster training with multicore machines).\n\n11. **Hierarchical softmax**: If it is True, hierarchical softmax will be used for model training. If it is False, and Negative is non-zero, negative sampling will be used.\n - Available Options: `True`, `False`\n \n12. **Negative**: If `Negative` > 0, negative sampling will be used, the int for negative specifies how many noise words should be drawn (usually between 5-20).\n\n13. **Negative sampling exponent**: The exponent used to shape the negative sampling distribution. A value of 1.0 samples exactly in proportion to the frequencies, 0.0 samples all words equally, while a negative value samples low-frequency words more than high-frequency words. The popular default value of 0.75 was chosen by the original Word2Vec paper.\n\n14. **Top N frequent words**: Number of top-N frequent words to return.\n\n\n### Python\n#### USAGE\n\n```\nword2vec(table, input_col, sg=1, size=100, window=5, min_count=1, max_vocab_size=None, train_epoch=100, workers=1, alpha=0.025, min_alpha=0.025, seed=None, hs=1, negative=5, ns_exponent=0.75, topn=30, hashfxn=hash)\n```\n\n#### INPUT\n1. **table***: (Table) Text data in a Table.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contains words column and word_vectors column.\n2. **model**: (Model) Word2Vec model.\n#### PARAMETER\n1. **input_col***: Columns to select as input. Only List of String type columns are possible.\n\t* Type: *str*\n2. **sg**: Training algorithm(1:Skip-gram, 0:CBOW). In the CBOW model, the distributed representations of context (or surrounding words) are combined to predict the word in the middle. While in the Skip-gram model, the distributed representation of the input word is used to predict the context. Skip-gram works well with a small amount of the training data, represents well even rare words or phrases. CBOW is several times faster to train than the Skip-gram, slightly better accuracy for the frequent words.\n\t* Type: *int*\n\t* Default / Range: 1 ( 1 | 0 )\n3. **size**: Dimensionality of the word vectors.\n\t* Type: *int*\n\t* Default / Range: 100 (value >= 1)\n4. **window**: Maximum distance between the current and predicted word within a sentence.\n\t* Type: *int*\n\t* Default / Range: 5 (value >= 1)\n5. **min_count**: Ignores all words with total frequency lower than this.\n\t* Type: *int*\n\t* Default / Range: 1 (value >= 1)\n6. **alpha**: The initial learning rate.\n\t* Type: *float*\n\t* Default / Range: 0.025 (value > 0)\n7. **min_alpha**: Learning rate will linearly drop to min_alpha as training progresses.\n\t* Type: *float*\n\t* Default / Range: 0.025 (value > 0)\n8. **seed**: Random seed. Use the same seed in order to reproduce the same results.\n\t* Type: *int*\n9. **train_epoch**: Number of iterations (epochs) over the corpus.\n\t* Type: *int*\n\t* Default / Range: 100 (value >= 1)\n10. **workers**: Use these many worker threads to train the model (=faster training with multicore machines).\n\t* Type: *int*\n\t* Default / Range: 1 (value >= 1)\n11. **hs**: If it is True, hierarchical softmax will be used for model training. If it is False, and Negative is non-zero, negative sampling will be used.\n\t* Type: *bool*\n\t* Default value: True \n12. **negative**: If `Negative` > 0, negative sampling will be used, the int for negative specifies how many noise words should be drawn (usually between 5-20).\n\t* Type: *int*\n\t* Default / Range: 5 (value >= 1)\n13. **ns_exponent**: The exponent used to shape the negative sampling distribution. A value of 1.0 samples exactly in proportion to the frequencies, 0.0 samples all words equally, while a negative value samples low-frequency words more than high-frequency words. The popular default value of 0.75 was chosen by the original Word2Vec paper.\n\t* Type: *float*\n\t* Default / Range: 0.75 (0 <= value <= 1)\n14. **topn**: Number of top-N frequent words to return.\n\t* Type: *int*\n\t* Default / Range: 30 (value >= 2)\n\n## Example\n### VA\n\n\n**[Sample Model]**\n\n\n\n\n\n
In this tutorial workflow, sample_data_tokenizer_eng data is used as input of Word2Vec function after tokenizing. The outputs of this function are a Word2Vec model and a Table with words and word_vectors columns.\nThe parameter settings used in the function are shown below. \n\n##### ++Parameters++\n1. **Input Column*** : tokenized_document\n2. **Training Algorithm** : Skip-gram\n3. **Dimension of Vectors** : 100\n4. **Window Size** : 5\n5. **Minimum Count** : 1\n6. **Alpha**: 0.025\n7. **Minimum Alpha** : 0.025\n8. **Seed** : None\n9. **Train epoch** : 100\n10. **Number of Workers** : 1\n11. **Hierarchical softmax** : False\n12. **Negative** : 5\n13. **Negative sampling exponent** : 0.75\n14. **Top N frequent words** : 30\n\n### Python\n```\nfrom brightics.function.textanalytics import word2vec\ninput_table = inputs[0]\nresult = word2vec(table=input_table, input_col='tokenized_document', sg=1,\n size=100, window=5, min_count=1, max_vocab_size=None,\n train_epoch=100, workers=1, alpha=0.025, min_alpha=0.025,\n seed=None, hs=0, negative=5, ns_exponent=0.75, topn=30,\n hashfxn=hash)\noutput_table = result['out_table']\noutput_model = result['model']\n```\n\n
In this python script, sample_data_tokenizer_eng data is used as input of Word2Vec function after tokenizing. The output of this function is a Word2Vec model and Table with words and word_vectors columns.\n\n", + "md": "# Word2Vec\nThis function trains Word2Vec model from tokenized text data.\n\n## Description\nWord2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a vector space, typically of several hundred dimensions, with each unique word in the corpus being assigned a corresponding vector in the space. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space.\n\nReference: \n+ \n+ \"Distributed representations of words and phrases and their compositionality\", Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean, 2013\n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Text data in a Table.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contains words column and word_vectors column.\n2. **model**: (Model) Word2Vec model.\n#### PARAMETER\n1. **Input Column***: Columns to select as input. Only List of String type columns are possible.\n\n2. **Training Algorithm**: Training algorithm. In the `CBOW` model, the distributed representations of context (or surrounding words) are combined to predict the word in the middle. While in the `Skip-gram` model, the distributed representation of the input word is used to predict the context. `Skip-gram` works well with a small amount of the training data, represents well even rare words or phrases. `CBOW` is several times faster to train than the `Skip-gram`, slightly better accuracy for the frequent words.\n - Available Options : `Skip-gram`, `CBOW`\n\n3. **Dimension of Vectors**: Dimensionality of the word vectors.\n\n4. **Window Size**: Maximum distance between the current and predicted word within a sentence.\n\n5. **Minimum Count**: Ignores all words with total frequency lower than this.\n\n6. **Alpha**: The initial learning rate.\n\n7. **Minimum alpha**: Learning rate will linearly drop to min_alpha as training progresses.\n\n8. **Seed**: Random seed. Use the same seed in order to reproduce the same results.\n\n9. **Train epoch**: Number of iterations (epochs) over the corpus.\n\n10. **Number of Workers**: Use these many worker threads to train the model (=faster training with multicore machines).\n\n11. **Hierarchical softmax**: If it is True, hierarchical softmax will be used for model training. If it is False, and Negative is non-zero, negative sampling will be used.\n - Available Options: `True`, `False`\n \n12. **Negative**: If `Negative` > 0, negative sampling will be used, the int for negative specifies how many noise words should be drawn (usually between 5-20).\n\n13. **Negative sampling exponent**: The exponent used to shape the negative sampling distribution. A value of 1.0 samples exactly in proportion to the frequencies, 0.0 samples all words equally, while a negative value samples low-frequency words more than high-frequency words. The popular default value of 0.75 was chosen by the original Word2Vec paper.\n\n14. **Top N frequent words**: Number of top-N frequent words to return.\n\n\n### Python\n#### USAGE\n\n```\nword2vec(table, input_col, sg=1, size=100, window=5, min_count=1, max_vocab_size=None, train_epoch=100, workers=1, alpha=0.025, min_alpha=0.025, seed=None, hs=1, negative=5, ns_exponent=0.75, topn=30, hashfxn=hash)\n```\n\n#### INPUT\n1. **table***: (Table) Text data in a Table.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contains words column and word_vectors column.\n2. **model**: (Model) Word2Vec model.\n#### PARAMETER\n1. **input_col***: Columns to select as input. Only List of String type columns are possible.\n\t* Type: *str*\n2. **sg**: Training algorithm(1:Skip-gram, 0:CBOW). In the CBOW model, the distributed representations of context (or surrounding words) are combined to predict the word in the middle. While in the Skip-gram model, the distributed representation of the input word is used to predict the context. Skip-gram works well with a small amount of the training data, represents well even rare words or phrases. CBOW is several times faster to train than the Skip-gram, slightly better accuracy for the frequent words.\n\t* Type: *int*\n\t* Default / Range: 1 ( 1 | 0 )\n3. **size**: Dimensionality of the word vectors.\n\t* Type: *int*\n\t* Default / Range: 100 (value >= 1)\n4. **window**: Maximum distance between the current and predicted word within a sentence.\n\t* Type: *int*\n\t* Default / Range: 5 (value >= 1)\n5. **min_count**: Ignores all words with total frequency lower than this.\n\t* Type: *int*\n\t* Default / Range: 1 (value >= 1)\n6. **alpha**: The initial learning rate.\n\t* Type: *float*\n\t* Default / Range: 0.025 (value > 0)\n7. **min_alpha**: Learning rate will linearly drop to min_alpha as training progresses.\n\t* Type: *float*\n\t* Default / Range: 0.025 (value > 0)\n8. **seed**: Random seed. Use the same seed in order to reproduce the same results.\n\t* Type: *int*\n9. **train_epoch**: Number of iterations (epochs) over the corpus.\n\t* Type: *int*\n\t* Default / Range: 100 (value >= 1)\n10. **workers**: Use these many worker threads to train the model (=faster training with multicore machines).\n\t* Type: *int*\n\t* Default / Range: 1 (value >= 1)\n11. **hs**: If it is True, hierarchical softmax will be used for model training. If it is False, and Negative is non-zero, negative sampling will be used.\n\t* Type: *bool*\n\t* Default value: True \n12. **negative**: If `Negative` > 0, negative sampling will be used, the int for negative specifies how many noise words should be drawn (usually between 5-20).\n\t* Type: *int*\n\t* Default / Range: 5 (value >= 1)\n13. **ns_exponent**: The exponent used to shape the negative sampling distribution. A value of 1.0 samples exactly in proportion to the frequencies, 0.0 samples all words equally, while a negative value samples low-frequency words more than high-frequency words. The popular default value of 0.75 was chosen by the original Word2Vec paper.\n\t* Type: *float*\n\t* Default / Range: 0.75 (0 <= value <= 1)\n14. **topn**: Number of top-N frequent words to return.\n\t* Type: *int*\n\t* Default / Range: 30 (value >= 2)\n\n## Example\n### VA\n\n\n**[Sample Model]**\n\n\n\n\n\n
In this tutorial workflow, sample_data_tokenizer_eng data is used as input of Word2Vec function after tokenizing. The outputs of this function are a Word2Vec model and a Table with words and word_vectors columns.\nThe parameter settings used in the function are shown below. \n\n##### ++Parameters++\n1. **Input Column*** : tokenized_document\n2. **Training Algorithm** : Skip-gram\n3. **Dimension of Vectors** : 100\n4. **Window Size** : 5\n5. **Minimum Count** : 1\n6. **Alpha**: 0.025\n7. **Minimum Alpha** : 0.025\n8. **Seed** : None\n9. **Train epoch** : 100\n10. **Number of Workers** : 1\n11. **Hierarchical softmax** : False\n12. **Negative** : 5\n13. **Negative sampling exponent** : 0.75\n14. **Top N frequent words** : 30\n\n### Python\n```\nfrom brightics.function.textanalytics import word2vec\ninput_table = inputs[0]\nresult = word2vec(table=input_table, input_col='tokenized_document', sg=1,\n size=100, window=5, min_count=1, max_vocab_size=None,\n train_epoch=100, workers=1, alpha=0.025, min_alpha=0.025,\n seed=None, hs=0, negative=5, ns_exponent=0.75, topn=30,\n hashfxn=hash)\noutput_table = result['out_table']\noutput_model = result['model']\n```\n\n
In this python script, sample_data_tokenizer_eng data is used as input of Word2Vec function after tokenizing. The output of this function is a Word2Vec model and Table with words and word_vectors columns.\n\n", "exampleModels": [ { "fileName": "brightics.function.textanalytics$word2vec.json", @@ -486,8 +486,8 @@ }, { "persist-mode": "auto", - "func": "brightics.function.textanalytics$tokenizer_eng51532", - "name": "brightics.function.textanalytics$tokenizer_eng", + "func": "brightics.function.textanalytics$tokenizer_eng2", + "name": "brightics.function.textanalytics$tokenizer_eng2", "param": { "stemming": false, "pos_extraction": [ diff --git a/function/python/brightics/function/textanalytics/meta/word2vec_model.json b/function/python/brightics/function/textanalytics/meta/word2vec_model.json index 7b24323c2..250980829 100644 --- a/function/python/brightics/function/textanalytics/meta/word2vec_model.json +++ b/function/python/brightics/function/textanalytics/meta/word2vec_model.json @@ -9,7 +9,7 @@ "name": "brightics.function.textanalytics$word2vec_model", "context": "python", "label": "Word2Vec Model", - "description": "Word2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a vector space, typically of several hundred dimensions, with each unique word in the corpus being assigned a corresponding vector in the space. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space.\n\nThis function calculates feature vectors of documents (average of word vectors) from input document Table with a Word2Vec model.\n\nReference: \n+ ", + "description": "Word2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a vector space, typically of several hundred dimensions, with each unique word in the corpus being assigned a corresponding vector in the space. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space.\n\nThis function calculates feature vectors of documents (average of word vectors) from input document Table with a Word2Vec model.\n\nReference: \n+ \n+ \"Distributed representations of words and phrases and their compositionality\", Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean, 2013", "tags": [], "version": "3.6", "inputs": { @@ -33,7 +33,7 @@ "params": [], "summary": "This function calculates average vector to represent documents with Word2Vec." }, - "md": "# Word2Vec Model\nThis function calculates average vector to represent documents with Word2Vec.\n\n## Description\nWord2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a vector space, typically of several hundred dimensions, with each unique word in the corpus being assigned a corresponding vector in the space. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space.\n\nThis function calculates feature vectors of documents (average of word vectors) from input document Table with a Word2Vec model.\n\nReference: \n+ \n\n\n## Properties\n### VA\n#### INPUT\n1. **model***: (Model) Trained Word2Vec model.\n2. **table***: (Table) Text data in a Table. It must contain a document column whose name is same as input model's input column.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contains input Table's columns and feature_vector column.\n#### PARAMETER\n\n### Python\n#### USAGE\n\n```\nword2vec_model(table, model)\n```\n\n#### INPUT\n1. **model***: (Model) Trained Word2Vec model.\n2. **table***: (Table) Text data in a Table. It must contain a document column whose name is same as input model's input column.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contains input Table's columns and feature_vector column.\n#### PARAMETER\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\n\n\n
In this tutorial workflow, sample_data_tokenizer_eng data is used as input of Word2Vec function after tokenizing. And the Word2Vec output model and tokenized sample_data_tokenizer_eng Table are used as input of Word2Vec Model function. The ouput of this function is a Table that contains feature vector of each document.\nThe parameter settings used in the function are shown below. \n\n##### ++Parameters++\n\n\n### Python\n```\nfrom brightics.function.textanalytics import word2vec_model\ninput_table = inputs[0]\ninput_model = models[0]\nresult = word2vec_model(table=input_table, model=input_model)\noutput = result['out_table']\n```\n\n
In this python script, sample_data_tokenizer_eng data is used as input of Word2Vec function after tokenizing. And the Word2Vec output model and tokenized sample_data_tokenizer_eng Table are used as input of word2vec_model function. The ouput of this function is a Table that contains feature vector of each document.\n", + "md": "# Word2Vec Model\nThis function calculates average vector to represent documents with Word2Vec.\n\n## Description\nWord2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a vector space, typically of several hundred dimensions, with each unique word in the corpus being assigned a corresponding vector in the space. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space.\n\nThis function calculates feature vectors of documents (average of word vectors) from input document Table with a Word2Vec model.\n\nReference: \n+ \n+ \"Distributed representations of words and phrases and their compositionality\", Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean, 2013\n\n\n## Properties\n### VA\n#### INPUT\n1. **model***: (Model) Trained Word2Vec model.\n2. **table***: (Table) Text data in a Table. It must contain a document column whose name is same as input model's input column.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contains input Table's columns and feature_vector column.\n#### PARAMETER\n\n### Python\n#### USAGE\n\n```\nword2vec_model(table, model)\n```\n\n#### INPUT\n1. **model***: (Model) Trained Word2Vec model.\n2. **table***: (Table) Text data in a Table. It must contain a document column whose name is same as input model's input column.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contains input Table's columns and feature_vector column.\n#### PARAMETER\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\n\n\n
In this tutorial workflow, sample_data_tokenizer_eng data is used as input of Word2Vec function after tokenizing. And the Word2Vec output model and tokenized sample_data_tokenizer_eng Table are used as input of Word2Vec Model function. The ouput of this function is a Table that contains feature vector of each document.\nThe parameter settings used in the function are shown below. \n\n##### ++Parameters++\n\n\n### Python\n```\nfrom brightics.function.textanalytics import word2vec_model\ninput_table = inputs[0]\ninput_model = models[0]\nresult = word2vec_model(table=input_table, model=input_model)\noutput = result['out_table']\n```\n\n
In this python script, sample_data_tokenizer_eng data is used as input of Word2Vec function after tokenizing. And the Word2Vec output model and tokenized sample_data_tokenizer_eng Table are used as input of word2vec_model function. The ouput of this function is a Table that contains feature vector of each document.\n", "exampleModels": [ { "fileName": "brightics.function.textanalytics$word2vec_model.json", @@ -278,8 +278,8 @@ }, { "persist-mode": "auto", - "func": "brightics.function.textanalytics$tokenizer_eng51532", - "name": "brightics.function.textanalytics$tokenizer_eng", + "func": "brightics.function.textanalytics$tokenizer_eng2", + "name": "brightics.function.textanalytics$tokenizer_eng2", "param": { "stemming": false, "pos_extraction": [ diff --git a/function/python/brightics/function/textanalytics/meta/word2vec_similarity2.json b/function/python/brightics/function/textanalytics/meta/word2vec_similarity2.json index 1196fc75e..667d5e894 100644 --- a/function/python/brightics/function/textanalytics/meta/word2vec_similarity2.json +++ b/function/python/brightics/function/textanalytics/meta/word2vec_similarity2.json @@ -9,7 +9,7 @@ "name": "brightics.function.textanalytics$word2vec_similarity2", "context": "python", "label": "Word2Vec Similarity", - "description": "Word2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a vector space, typically of several hundred dimensions, with each unique word in the corpus being assigned a corresponding vector in the space. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space.\n\nWord2Vec Similarity finds the top-N most similar words. Positive words contribute positively towards the similarity, negative words negatively.\nThis method computes cosine similarity between a simple mean of the projection weight vectors of the given words and the vectors for each word in the model. The method corresponds to the word-analogy and distance scripts in the original word2vec implementation.\n\nReference: \n+ \n+ ", + "description": "Word2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a vector space, typically of several hundred dimensions, with each unique word in the corpus being assigned a corresponding vector in the space. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space.\n\nWord2Vec Similarity finds the top-N most similar words. Positive words contribute positively towards the similarity, negative words negatively.\nThis method computes cosine similarity between a simple mean of the projection weight vectors of the given words and the vectors for each word in the model. The method corresponds to the word-analogy and distance scripts in the original word2vec implementation.\n\nReference: \n+ \n+ \n+ \"Distributed representations of words and phrases and their compositionality\", Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean, 2013", "tags": [], "version": "3.6", "inputs": { @@ -75,7 +75,7 @@ ], "summary": "This function caluate Word2Vec Similarity." }, - "md": "# Word2Vec Similarity\nThis function caluate Word2Vec Similarity.\n\n## Description\nWord2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a vector space, typically of several hundred dimensions, with each unique word in the corpus being assigned a corresponding vector in the space. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space.\n\nWord2Vec Similarity finds the top-N most similar words. Positive words contribute positively towards the similarity, negative words negatively.\nThis method computes cosine similarity between a simple mean of the projection weight vectors of the given words and the vectors for each word in the model. The method corresponds to the word-analogy and distance scripts in the original word2vec implementation.\n\nReference: \n+ \n+ \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Words data in a Table. the words should be in the train data.\n2. **model***: (Model) Trained Word2Vec model.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contains Word2Vec similarity.\n#### PARAMETER\n1. **Positive Column**: Column of words that contribute positively.\n\n2. **Negative Column**: Column of words that contribute negatively.\n\n3. **Top N similar words**: Number of top-N similar words to return.\n\n\n### Python\n#### USAGE\n\n```\nword2vec_similarity2(table, model, positive_col=None, negative_col=None, topn=1)\n```\n\n#### INPUT\n1. **table***: (Table) Words data in a Table. the words should be in the train data.\n2. **model***: (Model) Trained Word2Vec model.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contains Word2Vec similarity.\n#### PARAMETER\n1. **positive_col**: Column of words that contribute positively.\n\t* Type: *str*\n2. **negative_col**: Column of words that contribute negatively.\n\t* Type: *str*\n3. **topn**: Number of top-N similar words to return.\n\t* Type: *int*\n\t* Default / Range: (value >= 1)\n\n\n## Example\n### VA\n\n\n**[Sample Model]**\n\n\n\n\n\n
In this tutorial workflow, sample_data_tokenizer_eng data is used as input of Word2Vec function after tokenizing. And the Word2Vec output model and output Table are uesd as input of Word2Vec Similarity function. The output of this function is a Table that contain synonyms and similarity of words.\nThe parameter settings used in the function are shown below. \n\n##### ++Parameters++\n1. **Positive Column**: words\n2. **Negative Column**: None\n3. **Top N similar words**: 1\n\n### Python\n```\nfrom brightics.function.textanalytics import word2vec_similarity2\ninput_table = inputs[0]\ninput_model = models[0]\nresult = word2vec_similarity2(table=input_table, model=input_model,\n positive_col='words', negative_col=None,\n topn=1)\noutput = result['out_table']\n```\n\n
In this python script, sample_data_tokenizer_eng data is used as input of Word2Vec function after tokenizing. And the Word2Vec output model and output Table are uesd as input of Word2Vec Similarity function. The output of this function is a Table that contain synonyms and similarity of words.\n\n\n", + "md": "# Word2Vec Similarity\nThis function caluate Word2Vec Similarity.\n\n## Description\nWord2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a vector space, typically of several hundred dimensions, with each unique word in the corpus being assigned a corresponding vector in the space. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space.\n\nWord2Vec Similarity finds the top-N most similar words. Positive words contribute positively towards the similarity, negative words negatively.\nThis method computes cosine similarity between a simple mean of the projection weight vectors of the given words and the vectors for each word in the model. The method corresponds to the word-analogy and distance scripts in the original word2vec implementation.\n\nReference: \n+ \n+ \n+ \"Distributed representations of words and phrases and their compositionality\", Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean, 2013\n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Words data in a Table. the words should be in the train data.\n2. **model***: (Model) Trained Word2Vec model.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contains Word2Vec similarity.\n#### PARAMETER\n1. **Positive Column**: Column of words that contribute positively.\n\n2. **Negative Column**: Column of words that contribute negatively.\n\n3. **Top N similar words**: Number of top-N similar words to return.\n\n\n### Python\n#### USAGE\n\n```\nword2vec_similarity2(table, model, positive_col=None, negative_col=None, topn=1)\n```\n\n#### INPUT\n1. **table***: (Table) Words data in a Table. the words should be in the train data.\n2. **model***: (Model) Trained Word2Vec model.\n#### OUTPUT\n1. **out_table**: (Table) Output Table. It contains Word2Vec similarity.\n#### PARAMETER\n1. **positive_col**: Column of words that contribute positively.\n\t* Type: *str*\n2. **negative_col**: Column of words that contribute negatively.\n\t* Type: *str*\n3. **topn**: Number of top-N similar words to return.\n\t* Type: *int*\n\t* Default / Range: (value >= 1)\n\n\n## Example\n### VA\n\n\n**[Sample Model]**\n\n\n\n\n\n
In this tutorial workflow, sample_data_tokenizer_eng data is used as input of Word2Vec function after tokenizing. And the Word2Vec output model and output Table are uesd as input of Word2Vec Similarity function. The output of this function is a Table that contain synonyms and similarity of words.\nThe parameter settings used in the function are shown below. \n\n##### ++Parameters++\n1. **Positive Column**: words\n2. **Negative Column**: None\n3. **Top N similar words**: 1\n\n### Python\n```\nfrom brightics.function.textanalytics import word2vec_similarity2\ninput_table = inputs[0]\ninput_model = models[0]\nresult = word2vec_similarity2(table=input_table, model=input_model,\n positive_col='words', negative_col=None,\n topn=1)\noutput = result['out_table']\n```\n\n
In this python script, sample_data_tokenizer_eng data is used as input of Word2Vec function after tokenizing. And the Word2Vec output model and output Table are uesd as input of Word2Vec Similarity function. The output of this function is a Table that contain synonyms and similarity of words.\n\n\n", "exampleModels": [ { "fileName": "brightics.function.textanalytics$word2vec_similarity2.json", @@ -320,8 +320,8 @@ }, { "persist-mode": "auto", - "func": "brightics.function.textanalytics$tokenizer_eng51532", - "name": "brightics.function.textanalytics$tokenizer_eng", + "func": "brightics.function.textanalytics$tokenizer_eng2", + "name": "brightics.function.textanalytics$tokenizer_eng2", "param": { "stemming": false, "pos_extraction": [ diff --git a/function/python/brightics/function/textanalytics/ner.py b/function/python/brightics/function/textanalytics/ner.py new file mode 100644 index 000000000..63d81b0f0 --- /dev/null +++ b/function/python/brightics/function/textanalytics/ner.py @@ -0,0 +1,482 @@ +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +import nltk +from bs4 import BeautifulSoup +from nltk.tokenize import word_tokenize +from nltk import ne_chunk +from nltk.tree import Tree +import numpy as np +import pandas as pd +import en_core_web_sm +import re +import sklearn_crfsuite +import pickle + +from brightics.function.textanalytics.split_sentences import split_sentences +from brightics.function.textanalytics.tokenizer2 import tokenizer_kor2, tokenizer_eng2 +from brightics.function.utils import _model_dict +from brightics.common.repr import BrtcReprBuilder +from brightics.common.repr import strip_margin +from brightics.common.repr import dict2MD +from brightics.common.utils import check_required_parameters +from brightics.common.utils import get_default_from_parameters_if_required +from brightics.common.validation import raise_runtime_error +from brightics.common.validation import validate, greater_than_or_equal_to + +REPLACE_NO_SPACE = re.compile(r"[.;:!\'?,\"()\[\]]") +REGEX_EXTRACT_POSTAG_KOR = re.compile(r'^(.+)/([\S]+)$') +REGEX_EXTRACT_POSTAG_ENG = re.compile(r'^(.+)\(([\S]+)\)$') +REGEX_CHINESE = re.compile(r'[⺀-⺙⺛-⻳⼀-⿕々〇〡-〩〸-〺〻㐀-䶵一-鿃豈-鶴侮-頻並-龎]+') +METHOD_NLTK = 'nltk' +METHOD_SPACY = 'spacy' +METHOD_STANFORD = 'stanford' +METHOD_CRF = 'crf' +DEFAULT_LABELS_NLTK = ['FACILITY', 'GPE', 'GSP', 'LOCATION', 'ORGANIZATION', 'PERSON'] +DEFAULT_LABELS_SPACY = ['CARDINAL', 'DATE', 'EVENT', 'FAC', 'GPE', 'LANGUAGE', 'LAW', 'LOC', 'MONEY', 'NORP', 'ORDINAL', + 'ORG', 'PERCENT', 'PERSON', 'PRODUCT', 'QUANTITY', 'TIME', 'WORK_OF_ART'] +DEFAULT_LABELS_CRF_ENG = ['ART', 'EVE', 'GEO', 'GPE', 'NAT', 'ORG', 'PER', 'TIM'] +DICT_NE_LABEL_KOR = {'PS': 'PERSON', + 'DT': 'DATE', + 'OG': 'ORGANIZATION', + 'LC': 'LOCATION', + 'TI': 'TIME'} +DICT_NE_LABEL_ENG = {'geo': 'GEOGRAPHICAL_ENTITY', + 'org': 'ORGANIZATION', + 'per': 'PERSON', + 'gpe': 'GEO_POLITICAL_ENTITY', + 'tim': 'TIME', + 'art': 'ARTIFACT', + 'eve': 'EVENT', + 'nat': 'NATURAL_PHENOMENON'} + + +def ner_eng(table, **params): + check_required_parameters(_ner_eng, params, ['table']) + return _ner_eng(table, **params) + + +def _ner_eng(table, input_cols, method='nltk', ne_extraction_nltk=None, ne_extraction_spacy=None, + ne_extraction_crf=None, new_col_prefix='named_entity'): + out_table = table.copy() + if ne_extraction_nltk is None: + ne_extraction_nltk = DEFAULT_LABELS_NLTK + if ne_extraction_spacy is None: + ne_extraction_spacy = DEFAULT_LABELS_SPACY + if ne_extraction_crf is None: + ne_extraction_crf = DEFAULT_LABELS_CRF_ENG + + if method == METHOD_CRF: + # Load a CRF model pre-trained with GMB corpus and sklearn_crfsuite. + # https://www.kaggle.com/abhinavwalia95/entity-annotated-corpus/version/3#ner_dataset.csv + with open('brightics/function/textanalytics/data/english_ner_model.pickle', 'rb') as f: + crf_model = pickle.load(f) + model = {'crf_model': crf_model} + res = _ner_crf_predict(table, model, input_cols, language='eng', new_col_prefix=new_col_prefix, + extraction_set=ne_extraction_crf) + out_table = res['out_table'] + + else: + def process_text(text): + if method == METHOD_NLTK: + return process_text_nltk(text, ne_extraction_nltk) + elif method == METHOD_SPACY: + return process_text_spacy(text, ne_extraction_spacy) + elif method == METHOD_STANFORD: + return process_text_stanford(text) + else: + raise_runtime_error("Invalid method name.") + + for col in input_cols: + docs = table[col] + docs_result = docs.apply(process_text) + out_table['{prefix}_{col}'.format(prefix=new_col_prefix, col=col)] = docs_result + + return {'out_table': out_table} + + +def process_text_nltk(text, extraction_set): + text_filtered_html = BeautifulSoup(text).get_text() + text_preprocessed = REPLACE_NO_SPACE.sub("", text_filtered_html) + text_tokenized = word_tokenize(text_preprocessed) + text_tagged = nltk.pos_tag(text_tokenized) + chunk_tree = ne_chunk(text_tagged) + named_entities = [" ".join([token for token, pos in chunk.leaves()]) + "({label})".format(label=chunk.label()) + for chunk in chunk_tree if type(chunk) == Tree and chunk.label() in extraction_set] + return named_entities + + +def process_text_spacy(text, extraction_set): + nlp = en_core_web_sm.load() + doc = nlp(text) + named_entities = ["{token}({label})".format(token=word.text, label=word.label_) + for word in doc.ents if word.label_ in extraction_set] + return named_entities + + +# TODO: Available after implementation of Stanford library +def process_text_stanford(text): + from nltk.tag import StanfordNERTagger + tagger = StanfordNERTagger('english.all.3class.distsim.crf.ser.gz') + tagged = tagger.tag(text.split()) + named_entities = ["{token}({label})".format(token=word, label=label) + for word, label in tagged if label != 'O'] + return named_entities + + +def ner_kor(table, **params): + check_required_parameters(_ner_kor, params, ['table']) + return _ner_kor(table, **params) + + +# TODO: Incomplete right now since the corpus is compatible with MeCab Tokenizer +def _ner_kor(table, input_cols, ne_extraction_crf=None, new_col_prefix='named_entity'): + # Load a CRF model pre-trained with KoreanNERCorpus and sklearn_crfsuite. + # https://github.com/machinereading/KoreanNERCorpus + with open('brightics/function/textanalytics/data/korean_ner_model.pickle', 'rb') as f: + crf_model = pickle.load(f) + model = {'crf_model': crf_model} + res = _ner_crf_predict(table, model, input_cols, language='kor', new_col_prefix=new_col_prefix, + extraction_set=ne_extraction_crf) + out_table = res['out_table'] + return {'out_table': out_table} + + +def _transform_token_kor(pos_tagged_token): # Rule-based transformation from Tw into MeCab pos system + tag = REGEX_EXTRACT_POSTAG_KOR.search(pos_tagged_token) + token, pos = tag.group(1), tag.group(2) + if pos == 'Adjective': + pos = 'VA' + elif pos == 'Adverb': + pos = 'MAG' + elif pos == 'Alpha': + pos = 'SL' + elif pos == 'Conjunction': + pos = 'MAJ' + elif pos == 'Determiner': + pos = 'MM' + elif pos == 'Eomi': + if token in ['가', '다', '데요', '까', '라', '니다', '니다.', '거든', '고', '냐', '네', '느냐', '는다', '는데', '다', + '다네', '더라고', '며', '습니다', '아', '아라', '아서다', '어', '어요', '요', '을까', '자', '지만']: + pos = 'EF' + elif token in ['기', '음']: + pos = 'ETN' + elif token in ['다는', '냐는', '느냐는', '는', '는다는', '다는', '단', '더라는', '던', '라는', '란', '려는', '려던', '은', + '을', '자는', '잘']: + pos = 'ETM' + else: + pos = 'EC' + elif pos == 'Exclamation': + pos = 'IC' + elif pos == 'Foreign': + if REGEX_CHINESE.findall(token): + pos = 'SH' + else: + pos = 'SL' + elif pos == 'Josa': + if token in ['이', '가', '께서']: + pos = 'JKS' + elif token == '의': + pos = 'JKG' + elif token in ['을', '를']: + pos = 'JKO' + elif token in ['같이', '께', '랑', '로', '로부터', '로서', '로써', '루', '만큼', '보다', '서', '서부터', '에', '에게', + '에다', '에서', '에서부터', '으로', '으로부터', '으로서', '으로써', '처럼', '하고', '한테', '한테서']: + pos = 'JKB' + elif token in ['여', '야']: + pos = 'JKV' + elif token in ['고', '라고', '이라고']: + pos = 'JKQ' + elif token in ['까지', '는', '다', '대로', '도', '두', '란', '마다', '마저', '만', '밖에', '부터', '뿐', '사', + '야', '요', '은', '이나', '이나마', '이란', '이야', '조차']: + pos = 'JX' + elif token in ['과', '나', '와', '이나', '이랑']: + pos = 'JC' + else: + pos = 'JKB' + elif pos == 'Noun': + if token in ['거기', '그', '그것', '그곳', '그녀', '그대', '나', '내', '너', '네', '누구', '당신', '머', '무엇', '뭐', + '아무개', '어디', '여기', '여러분', '우리', '이', '이거', '이것', '이곳', '저', '저희', '제']: + pos = 'NP' + elif token in ['첫째', '둘째', '셋째', '넷째', '하나', '둘', '셋', '넷', '다섯', '여섯', '일곱', '여덟', '아홉', '열', + '일', '이', '삼', '사', '오', '육', '칠', '팔', '구', '십', '백', '천', '만', '십만', '백만', '천만', + '억', '십억', '백억', '천억', '조', '십조', '백조', '천조', '경', '수십', '수백', '수천', '수만', '수십만', + '수백만', '수천만', '수억', '수십억', '수백억', '수천억']: + pos = 'NR' + elif token in ['가량', '가지', '간', '개', '개교', '개국', '개년', '개사', '개선', '개소', '개월', '거', '거리', '건', + '것', '격', '겸', '고', '구', '군데', '권', '그루', '기', '기가', '나름', '내', '년', '년대', '년도', + '년차', '놈', '달러', '대', '대로', '데', '도', '동', '듯', '등', '때문', '량', '리', '마리', '마일', + '만', '만큼', '말', '매', '명', '바', '바퀴', '박', '발', '발짝', '번', '부', '분', '뿐', '살', '석', + '설', '세', '센트', '셈', '수', '시', '식', '심', '씨', '양', '어치', '엔', '외', '원', '월', '위', + '이', '인방', '인승', '인치', '일', '자', '장', '적', '전', '점', '정', '조', '주', '주년', '주일', '줄', + '중', '즈음', '지', '쪽', '쯤', '차', '차전', '채', '척', '천', '초', '측', '킬로미터', '터', '톤', '톨', + '통', '투', '판', '편', '평', '푼', '한', '할', '행', '호', '화', '회', '미터', '밀리미터', '센티미터', + '센치미터', '리터', '밀리리터', '데시리터', '킬로리터', '킬로', '킬로그램', '그램', '톤', '밀리그램', + '나노미터', '나노그램', '파운드', '유로', '위안']: + pos = 'NNB' + else: + pos = 'NNG' + elif pos == 'Number': + pos = 'SN' + elif pos == 'PreEomi': + pos = 'EP' + elif pos == 'Punctuation': + if token in ['!', ',', '.', '?', '．']: + pos = 'SF' + elif token == '…': + pos = 'SE' + elif token in ['(', '[']: + pos = 'SSO' + elif token in [')', ']']: + pos = 'SSC' + else: + pos = 'SC' + elif pos == 'Suffix': + pass + elif pos == 'Verb': + if token == '이': + pos = 'VCP' + elif token == '아니': + pos = 'VCN' + elif token in ['가', '계시', '나', '나가', '내', '놓', '달', '두', '드리', '들', '말', '못하', '버리', '보', '싶', '않', + '오', '있', '주', '지', '프', '하']: + pos = 'VX' + else: + pos = 'VV' + + return token, pos, '' + + +def _transform_tokens_kor(pos_tagged_tokens): + return [_transform_token_kor(token) for token in pos_tagged_tokens] + + +def _transform_token_eng(pos_tagged_token): + tag = REGEX_EXTRACT_POSTAG_ENG.search(pos_tagged_token) + return tag.group(1), tag.group(2), '' + + +def _transform_tokens_eng(pos_tagged_tokens): + return [_transform_token_eng(token) for token in pos_tagged_tokens] + + +""" + The following functions word2features, sent2features, sent2labels, and sent2tokens are excerpted from + sklearn-crfsuite tutorial page. + © Copyright 2015, Mikhail Korobov. + https://github.com/TeamHG-Memex/sklearn-crfsuite/blob/master/docs/CoNLL2002.ipynb +""" + + +def word2features(sent, i): + word = sent[i][0] + postag = sent[i][1] + + features = { + 'bias': 1.0, + 'word.lower()': word.lower(), + 'word[-3:]': word[-3:], + 'word[-2:]': word[-2:], + 'word.isupper()': word.isupper(), + 'word.istitle()': word.istitle(), + 'word.isdigit()': word.isdigit(), + 'postag': postag, + 'postag[:2]': postag[:2], + } + if i > 0: + word1 = sent[i-1][0] + postag1 = sent[i-1][1] + features.update({ + '-1:word.lower()': word1.lower(), + '-1:word.istitle()': word1.istitle(), + '-1:word.isupper()': word1.isupper(), + '-1:postag': postag1, + '-1:postag[:2]': postag1[:2], + }) + else: + features['BOS'] = True + + if i < len(sent)-1: + word1 = sent[i+1][0] + postag1 = sent[i+1][1] + features.update({ + '+1:word.lower()': word1.lower(), + '+1:word.istitle()': word1.istitle(), + '+1:word.isupper()': word1.isupper(), + '+1:postag': postag1, + '+1:postag[:2]': postag1[:2], + }) + else: + features['EOS'] = True + + return features + + +def sent2features(sent): + return [word2features(sent, i) for i in range(len(sent))] + + +def sent2labels(sent): + return [label for token, postag, label in sent] + + +def sent2tokens(sent): + return [token for token, postag, label in sent] + + +def gen_ne_list(token_label_list, extraction_set=None): + res = [] + current_ne = '' + current_tag = '' + for token_label in token_label_list: + token = token_label[0] + label = token_label[1] + if label[0] == 'I': + current_ne = ' '.join([current_ne, token]) + else: + if (extraction_set is not None and current_tag in extraction_set) \ + or (extraction_set is None and current_tag != ''): + res.append("{ne}({tag})".format(ne=current_ne, tag=current_tag)) + if label[0] == 'B': + current_tag = label[2:].upper() + current_ne = token + else: + current_tag = '' + if (extraction_set is not None and current_tag in extraction_set) \ + or (extraction_set is None and current_tag != ''): + res.append("{ne}({tag})".format(ne=current_ne, tag=current_tag)) + return res + + +def ner_crf_train(table, **params): + check_required_parameters(_ner_crf_train, params, ['table']) + params = get_default_from_parameters_if_required(params, _ner_crf_train) + param_validation_check = [greater_than_or_equal_to(params, 0, 'c1'), + greater_than_or_equal_to(params, 0, 'c2'), + greater_than_or_equal_to(params, 1, 'max_iterations')] + validate(*param_validation_check) + + return _ner_crf_train(table, **params) + + +def _ner_crf_train(table, sentence_col, token_col, pos_col, label_col, algorithm='lbfgs', c1=0.1, c2=0.1, + max_iterations=100, all_possible_transitions=True): + df = table.apply(lambda row: [row[sentence_col], [(row[token_col], row[pos_col], row[label_col])]], + axis=1, result_type='expand') + df.columns = ['sent_id', 'sent'] + input_df = df.groupby('sent_id')['sent'].sum() + train_set = input_df.values + features_train = [sent2features(s) for s in train_set] + label_train = [sent2labels(s) for s in train_set] + crf_params = {"algorithm": algorithm, + "max_iterations": max_iterations, + "all_possible_transitions": all_possible_transitions} + if algorithm == 'lbfgs': + crf_params['c1'] = c1 + crf_params['c2'] = c2 + elif algorithm == 'l2sgd': + crf_params['c2'] = c2 + crf = sklearn_crfsuite.CRF(**crf_params) + crf_model = crf.fit(features_train, label_train) + + appearing_labels = list(set([label[2:].upper() for label in table[label_col].values if label[0] == 'B'])) + num_tokens = len(df.index) + num_sentences = len(input_df.index) + + params = {'Sentence column': sentence_col, + 'Token column': token_col, + 'POS column': pos_col, + 'Label column': label_col, + 'Algorithm': algorithm, + 'c1': c1, + 'c2': c2, + 'Maximum iterations': max_iterations, + 'All possible transitions': all_possible_transitions} + + rb = BrtcReprBuilder() + rb.addMD(strip_margin(""" + | ## Named Entity Recognition CRF Train Result + | ### Number of tokens + | {num_tokens} + | + | ### Number of sentences + | {num_sentences} + | + | ### Appearing labels + | {appearing_labels} + | + | ### Parameters + | {params} + """.format(num_tokens=num_tokens, num_sentences=num_sentences, appearing_labels=appearing_labels, + params=dict2MD(params)))) + + model = _model_dict('crf_ner_model') + model['sentence'] = sentence_col + model['token'] = token_col + model['pos'] = pos_col + model['label'] = label_col + model['algorithm'] = algorithm + model['c1'] = c1 + model['c2'] = c2 + model['max_iterations'] = max_iterations + model['all_possible_transitions'] = all_possible_transitions + model['crf_model'] = crf_model + model['_repr_brtc_'] = rb.get() + + return {'model': model} + + +def ner_crf_predict(table, **params): + check_required_parameters(_ner_crf_predict, params, ['table']) + return _ner_crf_predict(table, **params) + + +def _ner_crf_predict(table, model, input_cols, language='eng', new_col_prefix='named_entity', extraction_set=None): + out_table = table.copy() + crf_model = model['crf_model'] + + for col in input_cols: + # Split sentences and tokenize using Brightics functions + df_split_sentences = split_sentences(table, input_col=col, language=language)['out_table'].reset_index() + if language == 'eng': + df_tokenized = tokenizer_eng2(df_split_sentences, input_cols=['sentence'], is_tagged=True, + stemming=False, lower_case=False)['out_table'] + else: # language == 'kor' + df_tokenized = tokenizer_kor2(df_split_sentences, input_cols=['sentence'], is_tagged=True, + stemming=False)['out_table'] + col_doc_id = df_tokenized['doc_id'] + col_tokenized = df_tokenized['tokenized_sentence'] + + # Transform to CRF input format + if language == 'eng': + transformer = _transform_tokens_eng + else: # language == 'kor': + transformer = _transform_tokens_kor + col_transformed = col_tokenized.apply(transformer) + features_raw = col_transformed.tolist() + features = [sent2features(sentence) for sentence in features_raw] + + # Run the model + labels = crf_model.predict(features) + token_labels = [[[token, label] for (token, pos, _), label in zip(i, j)] + for i, j in zip(features_raw, labels)] + ne_list = [gen_ne_list(token_label_list, extraction_set) for token_label_list in token_labels] + df = pd.DataFrame({'doc_id': col_doc_id, 'ne_list': ne_list}) + df_group_by_doc_id = df.groupby('doc_id')['ne_list'].sum() + col_ne_list = df_group_by_doc_id.reset_index(drop=True) + out_table['{prefix}_{col}'.format(prefix=new_col_prefix, col=col)] = col_ne_list + + return {'out_table': out_table} \ No newline at end of file diff --git a/function/python/brightics/function/textanalytics/regex.py b/function/python/brightics/function/textanalytics/regex.py new file mode 100644 index 000000000..863f65690 --- /dev/null +++ b/function/python/brightics/function/textanalytics/regex.py @@ -0,0 +1,84 @@ +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +from brightics.common.utils import check_required_parameters +from brightics.common.exception import BrighticsFunctionException +from .data import regex_format_dict +import re + + +def regex(table, **params): + check_required_parameters(_regex, params, ['table']) + return _regex(table, **params) + + +def _regex(table, input_cols, transformation_mode='extract', find_mode='all', pattern='', + user_dict_pattern='', custom_pattern='', replacement_string='', user_dict=None): + out_table = table.copy() + pattern_dict = regex_format_dict.pattern_dict + user_pattern_dict = {} + if user_dict is not None: + user_patterns = user_dict.values + for user_pattern in user_patterns: + user_pattern_name = user_pattern[0] + user_pattern_content = user_pattern[1] + user_pattern_dict[user_pattern_name] = user_pattern_dict.get(user_pattern_name, []) + [user_pattern_content] + user_pattern_dict = {key: r'|'.join(value) for key, value in user_pattern_dict.items()} + + if pattern == '': + raise BrighticsFunctionException.from_errors([{'0100': "Please choose a pattern."}]) + if pattern == 'custom': + raw_pattern = custom_pattern + elif pattern == 'user_dictionary': + raw_pattern = user_pattern_dict.get(user_dict_pattern) + if raw_pattern is None: + raise BrighticsFunctionException.from_errors( + [{'0100': user_dict_pattern + " is not a valid pattern name in the user dictionary."}]) + else: + raw_pattern = pattern_dict.get(pattern) + regex_pattern = re.compile(raw_pattern) + + def transformation(text): + if transformation_mode == 'extract': + if find_mode == 'first': + result = regex_pattern.search(text) + if result is None: + return "" + else: + return result.group() + else: # find_mode == 'all' + return regex_pattern.findall(text) + elif transformation_mode == 'replace': + if find_mode == 'first': + return regex_pattern.sub(replacement_string, text, 1) + else: # find_mode == 'all' + return regex_pattern.sub(replacement_string, text) + elif transformation_mode == 'remove': + if find_mode == 'first': + return regex_pattern.sub("", text, 1) + else: # find_mode == 'all' + return regex_pattern.sub("", text) + else: # transformation_mode == 'split' + if find_mode == 'first': + return regex_pattern.split(text, 1) + else: # find_mode == 'all' + return regex_pattern.split(text) + + for col in input_cols: + result_col = table[col].apply(transformation) + out_table['regex_' + col] = result_col + + return {'out_table': out_table} diff --git a/function/python/brightics/function/textanalytics/search.py b/function/python/brightics/function/textanalytics/search.py index 96d773270..88179daab 100644 --- a/function/python/brightics/function/textanalytics/search.py +++ b/function/python/brightics/function/textanalytics/search.py @@ -139,7 +139,8 @@ def _search2(table, input_cols, hold_cols=None, bool_search="or", keyword_dict=N raise ValueError('At least one of Search Words and User Dictionary must be included.') input_table = table[input_cols] - + columns = table.columns + if hold_cols is None: hold_table = table.drop(input_cols, axis=1) length_ht = len(table.columns) - len(input_cols) @@ -166,5 +167,8 @@ def _search2(table, input_cols, hold_cols=None, bool_search="or", keyword_dict=N out_table = pd.concat([input_table[cond], hold_table], axis=1).dropna(thresh=length_ht + 1).reset_index(drop=True) else: out_table = pd.concat([input_table[cond], hold_table], axis=1) - + + if hold_cols is None: + out_table = out_table[columns] + return {'out_table': out_table} diff --git a/function/python/brightics/function/textanalytics/short_text_topic_modeling_gsdmm/__init__.py b/function/python/brightics/function/textanalytics/short_text_topic_modeling_gsdmm/__init__.py new file mode 100644 index 000000000..abe31a458 --- /dev/null +++ b/function/python/brightics/function/textanalytics/short_text_topic_modeling_gsdmm/__init__.py @@ -0,0 +1,17 @@ +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +from . import gsdmm_rwalk diff --git a/function/python/brightics/function/textanalytics/short_text_topic_modeling_gsdmm/gsdmm_rwalk.py b/function/python/brightics/function/textanalytics/short_text_topic_modeling_gsdmm/gsdmm_rwalk.py new file mode 100644 index 000000000..94e69e245 --- /dev/null +++ b/function/python/brightics/function/textanalytics/short_text_topic_modeling_gsdmm/gsdmm_rwalk.py @@ -0,0 +1,226 @@ +""" +MIT License + +Copyright (c) 2017 Ryan Walker + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +""" + + +from numpy.random import multinomial +from numpy import log, exp +from numpy import argmax +import json + + +class MovieGroupProcess: + def __init__(self, K=8, alpha=0.1, beta=0.1, n_iters=30): + ''' + A MovieGroupProcess is a conceptual model introduced by Yin and Wang 2014 to + describe their Gibbs sampling algorithm for a Dirichlet Mixture Model for the + clustering short text documents. + Reference: http://dbgroup.cs.tsinghua.edu.cn/wangjy/papers/KDD14-GSDMM.pdf + Imagine a professor is leading a film class. At the start of the class, the students + are randomly assigned to K tables. Before class begins, the students make lists of + their favorite films. The teacher reads the role n_iters times. When + a student is called, the student must select a new table satisfying either: + 1) The new table has more students than the current table. + OR + 2) The new table has students with similar lists of favorite movies. + :param K: int + Upper bound on the number of possible clusters. Typically many fewer + :param alpha: float between 0 and 1 + Alpha controls the probability that a student will join a table that is currently empty + When alpha is 0, no one will join an empty table. + :param beta: float between 0 and 1 + Beta controls the student's affinity for other students with similar interests. A low beta means + that students desire to sit with students of similar interests. A high beta means they are less + concerned with affinity and are more influenced by the popularity of a table + :param n_iters: + ''' + self.K = K + self.alpha = alpha + self.beta = beta + self.n_iters = n_iters + + # slots for computed variables + self.number_docs = None + self.vocab_size = None + self.cluster_doc_count = [0 for _ in range(K)] + self.cluster_word_count = [0 for _ in range(K)] + self.cluster_word_distribution = [{} for i in range(K)] + + @staticmethod + def from_data(K, alpha, beta, D, vocab_size, cluster_doc_count, cluster_word_count, cluster_word_distribution): + ''' + Reconstitute a MovieGroupProcess from previously fit data + :param K: + :param alpha: + :param beta: + :param D: + :param vocab_size: + :param cluster_doc_count: + :param cluster_word_count: + :param cluster_word_distribution: + :return: + ''' + mgp = MovieGroupProcess(K, alpha, beta, n_iters=30) + mgp.number_docs = D + mgp.vocab_size = vocab_size + mgp.cluster_doc_count = cluster_doc_count + mgp.cluster_word_count = cluster_word_count + mgp.cluster_word_distribution = cluster_word_distribution + return mgp + + @staticmethod + def _sample(p): + ''' + Sample with probability vector p from a multinomial distribution + :param p: list + List of probabilities representing probability vector for the multinomial distribution + :return: int + index of randomly selected output + ''' + return [i for i, entry in enumerate(multinomial(1, p)) if entry != 0][0] + + def fit(self, docs, vocab_size): + ''' + Cluster the input documents + :param docs: list of list + list of lists containing the unique token set of each document + :param V: total vocabulary size for each document + :return: list of length len(doc) + cluster label for each document + ''' + alpha, beta, K, n_iters, V = self.alpha, self.beta, self.K, self.n_iters, vocab_size + + D = len(docs) + self.number_docs = D + self.vocab_size = vocab_size + + # unpack to easy var names + m_z, n_z, n_z_w = self.cluster_doc_count, self.cluster_word_count, self.cluster_word_distribution + cluster_count = K + d_z = [None for i in range(len(docs))] + + # initialize the clusters + for i, doc in enumerate(docs): + + # choose a random initial cluster for the doc + z = self._sample([1.0 / K for _ in range(K)]) + d_z[i] = z + m_z[z] += 1 + n_z[z] += len(doc) + + for word in doc: + if word not in n_z_w[z]: + n_z_w[z][word] = 0 + n_z_w[z][word] += 1 + + for _iter in range(n_iters): + total_transfers = 0 + + for i, doc in enumerate(docs): + + # remove the doc from it's current cluster + z_old = d_z[i] + + m_z[z_old] -= 1 + n_z[z_old] -= len(doc) + + for word in doc: + n_z_w[z_old][word] -= 1 + + # compact dictionary to save space + if n_z_w[z_old][word] == 0: + del n_z_w[z_old][word] + + # draw sample from distribution to find new cluster + p = self.score(doc) + z_new = self._sample(p) + + # transfer doc to the new cluster + if z_new != z_old: + total_transfers += 1 + + d_z[i] = z_new + m_z[z_new] += 1 + n_z[z_new] += len(doc) + + for word in doc: + if word not in n_z_w[z_new]: + n_z_w[z_new][word] = 0 + n_z_w[z_new][word] += 1 + + cluster_count_new = sum([1 for v in m_z if v > 0]) + print("In stage %d: transferred %d clusters with %d clusters populated" % ( + _iter, total_transfers, cluster_count_new)) + if total_transfers == 0 and cluster_count_new == cluster_count and _iter > 25: + print("Converged. Breaking out.") + break + cluster_count = cluster_count_new + self.cluster_word_distribution = n_z_w + return d_z + + def score(self, doc): + ''' + Score a document + Implements formula (3) of Yin and Wang 2014. + http://dbgroup.cs.tsinghua.edu.cn/wangjy/papers/KDD14-GSDMM.pdf + :param doc: list[str]: The doc token stream + :return: list[float]: A length K probability vector where each component represents + the probability of the document appearing in a particular cluster + ''' + alpha, beta, K, V, D = self.alpha, self.beta, self.K, self.vocab_size, self.number_docs + m_z, n_z, n_z_w = self.cluster_doc_count, self.cluster_word_count, self.cluster_word_distribution + + p = [0 for _ in range(K)] + + # We break the formula into the following pieces + # p = N1*N2/(D1*D2) = exp(lN1 - lD1 + lN2 - lD2) + # lN1 = log(m_z[z] + alpha) + # lN2 = log(D - 1 + K*alpha) + # lN2 = log(product(n_z_w[w] + beta)) = sum(log(n_z_w[w] + beta)) + # lD2 = log(product(n_z[d] + V*beta + i -1)) = sum(log(n_z[d] + V*beta + i -1)) + + lD1 = log(D - 1 + K * alpha) + doc_size = len(doc) + for label in range(K): + lN1 = log(m_z[label] + alpha) + lN2 = 0 + lD2 = 0 + for word in doc: + lN2 += log(n_z_w[label].get(word, 0) + beta) + for j in range(1, doc_size + 1): + lD2 += log(n_z[label] + V * beta + j - 1) + p[label] = exp(lN1 - lD1 + lN2 - lD2) + + # normalize the probability vector + pnorm = sum(p) + pnorm = pnorm if pnorm > 0 else 1 + return [pp / pnorm for pp in p] + + def choose_best_label(self, doc): + ''' + Choose the highest probability label for the input document + :param doc: list[str]: The doc token stream + :return: + ''' + p = self.score(doc) + return argmax(p), max(p) diff --git a/function/python/brightics/function/textanalytics/split_sentences.py b/function/python/brightics/function/textanalytics/split_sentences.py index df5d5505b..e143514fc 100644 --- a/function/python/brightics/function/textanalytics/split_sentences.py +++ b/function/python/brightics/function/textanalytics/split_sentences.py @@ -18,6 +18,8 @@ from nltk import tokenize import pandas as pd import numpy as np +import platform +from brightics.common.exception import BrighticsFunctionException def split_sentences(table, **params): @@ -26,21 +28,19 @@ def split_sentences(table, **params): def _split_sentences(table, input_col, language='kor'): - - doc_col = table[input_col].values + doc_col = table[input_col].values + os = platform.system() - if (language == 'kor'): - import platform - os = platform.system() + if language == 'kor': if os == 'Linux': import kss sent_tokenizer = kss.split_sentences - else: - from brightics.function.textanalytics.pykss import pykss - sent_tokenizer = pykss.split_sentences - elif (language == 'eng'): + else: # os == 'Windows' + from . import split_sentences_kss as kss2 + sent_tokenizer = kss2.kss.pykss.split_sentences + else: # language == 'eng' sent_tokenizer = tokenize.sent_tokenize - + def _splitter(doc_id, text): sentence_col = sent_tokenizer(text) len_sentence_col = len(sentence_col) @@ -50,5 +50,74 @@ def _splitter(doc_id, text): data_table = np.concatenate([_splitter(i, doc_col[i]) for i in range(len(doc_col))], axis=1) out_table = pd.DataFrame(np.transpose(data_table), columns=['doc_id', 'sentence_id', 'sentence']) - + + return {'out_table': out_table} + + +def split_sentences2(table, **params): + check_required_parameters(_split_sentences2, params, ['table']) + return _split_sentences2(table, **params) + + +def _split_sentences2(table, input_col, language='kor', doc_id_col_name='doc_id', sentence_id_col_name='sentence_id', + sentence_col_name='sentence', duplicate_original=False): + if doc_id_col_name in table.columns: + raise BrighticsFunctionException.from_errors( + [{'0100': "Document ID column name {} already exists in the input table. Please choose another one." + .format(doc_id_col_name)}]) + if sentence_id_col_name in table.columns: + raise BrighticsFunctionException.from_errors( + [{'0100': "Sentence ID column name {} already exists in the input table. Please choose another one." + .format(sentence_id_col_name)}]) + if sentence_col_name in table.columns: + raise BrighticsFunctionException.from_errors( + [{'0100': "Sentence column name {} already exists in the input table. Please choose another one." + .format(sentence_col_name)}]) + + doc_col = table[input_col].values + os = platform.system() + if os == 'Linux': + import kss + sent_tokenizer_kor = kss.split_sentences + else: # os == 'Windows' + from . import split_sentences_kss as kss2 + sent_tokenizer_kor = kss2.kss.pykss.split_sentences + sent_tokenizer_eng = tokenize.sent_tokenize + + if language == 'kor': + sent_tokenizer = sent_tokenizer_kor + elif language == 'eng': + sent_tokenizer = sent_tokenizer_eng + else: # language == 'mixed' + def sent_tokenizer(text): + kor_sents = sent_tokenizer_kor(text) + sents = [sent_tokenizer_eng(sent) for sent in kor_sents] + return [y for x in sents for y in x] # flattened + + num_doc = len(doc_col) + doc_id_col = list(range(1, num_doc + 1)) + sent_list_col = [sent_tokenizer(text) for text in doc_col] + table[doc_id_col_name] = doc_id_col + column_list = table.columns.tolist() + table[sentence_col_name] = sent_list_col + num_sent_col = [len(sent_list) for sent_list in sent_list_col] + + # to be shortened when pandas explode is available + values = np.array(sent_list_col) + values_flattened = np.concatenate(values).ravel() + col = table[sentence_col_name] + col_exploded = pd.Series(values_flattened, index=col.index.repeat(num_sent_col), name=col.name) + out_table = table.drop([sentence_col_name], axis=1).join(col_exploded).reindex(columns=table.columns, copy=False) + + sent_id_col = sum([list(range(1, num_sent + 1)) for num_sent in num_sent_col], []) + out_table[sentence_id_col_name] = sent_id_col + + if not duplicate_original: + column_list_original = column_list.copy() + column_list_original.remove(doc_id_col_name) + out_table[column_list_original] = out_table[column_list_original].where(out_table[sentence_id_col_name] == 1, None) + + column_list_new = column_list + [sentence_id_col_name, sentence_col_name] + out_table = out_table[column_list_new] + return {'out_table': out_table} diff --git a/function/python/brightics/function/textanalytics/split_sentences_kss/__init__.py b/function/python/brightics/function/textanalytics/split_sentences_kss/__init__.py new file mode 100644 index 000000000..801980af4 --- /dev/null +++ b/function/python/brightics/function/textanalytics/split_sentences_kss/__init__.py @@ -0,0 +1,17 @@ +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +from . import kss diff --git a/function/python/brightics/function/textanalytics/split_sentences_kss/kss.py b/function/python/brightics/function/textanalytics/split_sentences_kss/kss.py new file mode 100644 index 000000000..ac3d99395 --- /dev/null +++ b/function/python/brightics/function/textanalytics/split_sentences_kss/kss.py @@ -0,0 +1,281 @@ +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +""" + A KSS(Korean Sentence Splitter) module. + + This is pure Python version of the project kss 1.2.5 + https://pypi.org/project/kss/. + + We choose to rewrite kss in pure Python because we want the module + works in all Python implementations without C++ build. +""" + +# -*- coding: utf-8 -*- + +DEFAULT = 'DEFAULT' +DA = 'DA' +YO = 'YO' +SB = 'SB' +COMMON = 'COMMON' + +NONE = 0 +PREV = 1 << 0 +CONT = 1 << 1 +NEXT = 1 << 2 +NEXT1 = 1 << 3 +NEXT2 = 1 << 4 + +mapDA = {'갔': PREV, + '간': PREV, + '겠': PREV, + '겼': PREV, + '같': PREV, + '놨': PREV, + '녔': PREV, + '니': PREV, + '낸': PREV, + '냈': PREV, + '뒀': PREV, + '때': PREV, + '랐': PREV, + '럽': PREV, + '렵': PREV, + '렸': PREV, + '린': PREV, + '뤘': PREV, + '밌': PREV, + '봤': PREV, + '섰': PREV, + '샜': PREV, + '않': PREV, + '았': PREV, + '없': PREV, + '었': PREV, + '였': PREV, + '온': PREV, + '웠': PREV, + '이': PREV, + '인': PREV, + '있': PREV, + '졌': PREV, + '쳤': PREV, + '챘': PREV, + '팠': PREV, + '펐': PREV, + '했': PREV, + '혔': PREV, + '가': NEXT, + '고': NEXT | NEXT2, + '는': NEXT | NEXT2, + '라': NEXT, + '를': NEXT, + '만': NEXT, + '며': NEXT | NEXT2, + '면': NEXT | NEXT1 | NEXT2, + '서': PREV | NEXT2, + '싶': PREV | NEXT, + '죠': NEXT, + '죵': NEXT, + '쥬': NEXT, + '하': PREV | NEXT1, + '해': NEXT1, + '도': NEXT2} + +mapYO = {'가': PREV, + '구': PREV, + '군': PREV, + '걸': PREV, + '까': PREV, + '께': PREV, + '껴': PREV, + '네': PREV, + '나': PREV, + '데': PREV, + '든': PREV, + '서': PREV, + '세': PREV, + '아': PREV, + '어': PREV, + '워': PREV, + '에': PREV, + '예': PREV, + '을': PREV, + '져': PREV, + '줘': PREV, + '지': PREV, + '춰': PREV, + '해': PREV, + '고': PREV | NEXT2, + '는': NEXT, + '라': NEXT1, + '를': NEXT, + '며': NEXT2, + '면': PREV | NEXT2, + '하': NEXT1} + +mapSB = {'가': PREV, + '까': PREV, + '거': PREV, + '걸': PREV, + '껄': PREV, + '나': PREV, + '니': PREV, + '다': PREV, + '도': PREV, + '든': PREV, + '랴': PREV, + '래': PREV, + '마': PREV, + '봐': PREV, + '서': PREV, + '아': PREV, + '어': PREV, + '오': PREV, + '요': PREV, + '을': PREV, + '자': PREV, + '지': PREV, + '죠': PREV, + '고': PREV | NEXT2, + '는': NEXT, + '라': PREV | NEXT, + '며': NEXT2, + '면': NEXT2, + '하': NEXT1} + +mapCOMMON = {'ㅅ': CONT, + 'ㅎ': CONT, + 'ㅋ': CONT, + 'ㅠ': CONT, + 'ㅜ': CONT, + '^': CONT, + ';': CONT, + '.': CONT, + '?': CONT, + '!': CONT, + ')': CONT, + '~': CONT, + '…': CONT, + ',': CONT} + +chmap = {DA: mapDA, + YO: mapYO, + SB: mapSB, + COMMON: mapCOMMON} + + +class pykss: + @staticmethod + def split_sentences(text): + result = [] + prev_chr = '' + cur_sentence = '' + stk = [] + cur_stat = 'DEFAULT' + + def _push_pop_symbol(symbol): + if stk: + if stk[-1] == symbol: + del stk[-1] + else: + stk.append(symbol) + else: + stk.append(symbol) + + for chr_string in text: + if cur_stat == 'DEFAULT': + if chr_string == '´': + chr_string = '\'' + + if chr_string in {'"', '\''}: + _push_pop_symbol(chr_string) + elif chr_string in {'‘', '“'}: + stk.append(chr_string) + elif chr_string in {'’', '”'}: + if stk: + del stk[-1] + elif chr_string == '다': + if not stk and (chmap[DA].get(prev_chr, NONE) & PREV): + cur_stat = 'DA' + elif chr_string == '요': + if not stk and (chmap[YO].get(prev_chr, NONE) & PREV): + cur_stat = 'YO' + elif chr_string in {'!', '?', '.'}: + if not stk and (chmap[SB].get(prev_chr, NONE) & PREV): + cur_stat = 'SB' + else: + cur_stat_chr_string = chmap[cur_stat].get(chr_string, NONE) + cur_stat_prev_chr = chmap[cur_stat].get(prev_chr, NONE) + + if chr_string == ' ' or ( + chmap[COMMON].get(chr_string, NONE) & CONT): + if cur_stat_prev_chr & NEXT1: + result.append(cur_sentence.strip()) + cur_sentence = prev_chr + cur_stat = DEFAULT + elif cur_stat_chr_string & NEXT: + if cur_stat_prev_chr & NEXT1: + cur_sentence += prev_chr + cur_stat = DEFAULT + elif cur_stat_chr_string & NEXT1: + if cur_stat_prev_chr & NEXT1: + result.append(cur_sentence.strip()) + cur_sentence = prev_chr + cur_stat = DEFAULT + elif cur_stat_chr_string & NEXT2: + if cur_stat_prev_chr & NEXT1: + cur_sentence += prev_chr + else: + result.append(cur_sentence.strip()) + cur_sentence = '' + cur_stat = DEFAULT + elif cur_stat_chr_string == NONE or (cur_stat_chr_string & PREV): + result.append(cur_sentence.strip()) + cur_sentence = '' + + if cur_stat_prev_chr & NEXT1: + cur_sentence += prev_chr + cur_stat = DEFAULT + + if chr_string in {'\'', '\"'}: + _push_pop_symbol(chr_string) + elif chr_string in {'‘', '“'}: + stk.append(chr_string) + elif chr_string in {'’', '”'}: + if stk: + del stk[-1] + else: + pass + + if cur_stat == 'DEFAULT' or not ( + chmap[cur_stat].get(chr_string, NONE) & NEXT1): + cur_sentence += chr_string + prev_chr = chr_string + + if cur_sentence: + result.append(cur_sentence.strip()) + cur_sentence = '' + + flag = chmap.get(cur_stat) + flag = NONE if flag is None else flag.get(prev_chr, NONE) + + if flag & NEXT1: + cur_sentence += prev_chr + result.append(cur_sentence.strip()) + cur_sentence = '' + + return result diff --git a/function/python/brightics/function/textanalytics/synonym_converter.py b/function/python/brightics/function/textanalytics/synonym_converter.py index 66041a028..55bcc2d3e 100644 --- a/function/python/brightics/function/textanalytics/synonym_converter.py +++ b/function/python/brightics/function/textanalytics/synonym_converter.py @@ -20,9 +20,9 @@ def synonym_converter(table, **params): - check_required_parameters(_synonym_converter, params, ['table']) + check_required_parameters(_synonym_converter, params, ['table']) return _synonym_converter(table, **params) - + def synonym_converter_user_dict(table, **params): check_required_parameters(_synonym_converter, params, ['table']) @@ -30,7 +30,7 @@ def synonym_converter_user_dict(table, **params): def _synonym_converter(table, input_cols, hold_cols=None, default_dict=False, synonym_list=None, prefix='synonym', user_dict=None): - + if hold_cols is not None: hold_table = pd.DataFrame() hold_table[hold_cols] = table[hold_cols] @@ -39,24 +39,33 @@ def _synonym_converter(table, input_cols, hold_cols=None, default_dict=False, sy # initial dictionary synonym_dict = dict() - if (synonym_list is not None): + if synonym_list is not None: for word_synonym in synonym_list: - synonym_dict.update({synonym.strip() : word_synonym.split(',')[0] for synonym in word_synonym.split(',')[1:]}) - + synonym_dict.update( + {synonym.strip(): word_synonym.split(',')[0] for synonym in word_synonym.split(',')[1:]}) + # default dictionary update - if (default_dict == True): + if default_dict: synonym_dict.update(dict()) # To do dict() => default dictionary # user_dict update - if (user_dict is not None): + if user_dict is not None: user_dictionary = user_dict.values for word_synonym in user_dictionary: - synonym_dict.update({synonym.strip() : word_synonym[0] for synonym in word_synonym[1].split(',')}) + synonym_dict.update({synonym.strip(): word_synonym[0] for synonym in word_synonym[1].split(',')}) - values = [[[synonym_dict.get(word, word) for word in word_list] for word_list in table[column]] for column in input_cols] + values = [[[synonym_dict.get(word, word) for word in word_list] for word_list in table[column]] for column in + input_cols] + # np.transpose works wrong when the number of rows or columns is equal to 1 + if len(values) == 1: + values_tr = [[word_list] for word_list in values[0]] + elif len(values[0]) == 1: + values_tr = [[col[0] for col in values]] + else: + values_tr = np.transpose(values) value_columns = ['{}_{}'.format(prefix, column) for column in input_cols] - value_table = pd.DataFrame(np.transpose(values), columns=value_columns) - + value_table = pd.DataFrame(values_tr, columns=value_columns) + out_table = pd.concat([hold_table, value_table.reset_index(drop=True)], axis=1) - - return {'out_table' : out_table} + + return {'out_table': out_table} diff --git a/function/python/brightics/function/textanalytics/tfidf.py b/function/python/brightics/function/textanalytics/tfidf.py index cc5b00e2f..881c1c98a 100644 --- a/function/python/brightics/function/textanalytics/tfidf.py +++ b/function/python/brightics/function/textanalytics/tfidf.py @@ -27,6 +27,107 @@ from operator import itemgetter from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfTransformer +from sklearn.feature_extraction.text import TfidfVectorizer + + +def tfidf3(table, group_by=None, **params): + check_required_parameters(_tfidf3, params, ['table']) + params = get_default_from_parameters_if_required(params, _tfidf3) + param_validation_check = [greater_than_or_equal_to(params, 0, 'min_df'), + greater_than_or_equal_to(params, 2, 'max_features'), + greater_than(params, 0, 'max_df')] + validate(*param_validation_check) + if group_by is not None: + return _function_by_group(_tfidf3, table, group_by=group_by, **params) + else: + return _tfidf3(table, **params) + + +def _tfidf3(table, input_col, res_type='sparse', + analyzer='word', binary=False, + decode_error='strict', dtype=np.int64, + encoding='utf-8', input='content', + lowercase=True, max_df=1.0, + max_features=100, min_df=1, + ngram_range=(1, 1), norm='l2', + preprocessor=None, smooth_idf=True, + stop_words=None, strip_accents=None, + sublinear_tf=False, token_pattern='(?u)\\b\\w\\w+\\b', + tokenizer=None, use_idf=True, vocabulary=None): + + corpus = np.array(table[input_col]) + + if isinstance(corpus[0], np.ndarray): + preprocessor = ' '.join + + vectorizer = TfidfVectorizer( + analyzer=analyzer, binary=binary, decode_error=decode_error, + dtype=dtype, encoding=encoding, input=input, lowercase=lowercase, + max_df=max_df, max_features=max_features, min_df=min_df, + ngram_range=ngram_range, norm=norm, preprocessor=preprocessor, + smooth_idf=smooth_idf, stop_words=stop_words, strip_accents=strip_accents, + sublinear_tf=sublinear_tf, token_pattern=token_pattern, + tokenizer=tokenizer, use_idf=use_idf, vocabulary=vocabulary) + + tfidf = vectorizer.fit(corpus) + tfidf_csr = tfidf.transform(corpus) + + vocab_idx = tfidf.vocabulary_ + vocab = sorted(vocab_idx, key=lambda k: vocab_idx[k]) + + if res_type == 'sparse': + indptr = tfidf_csr.indptr + indices = tfidf_csr.indices + data = tfidf_csr.data + + idx2word = {idx: word for idx, word in enumerate(vocab)} + vocab_col = [idx2word[k] for k in indices] + + if use_idf is True: + idf = tfidf.idf_ + idx2val = {idx: val for idx, val in enumerate(idf)} + idf_col = [idx2val[k] for k in indices] + else: + idf_col = [1] * len(indices) + + num_doc = len(corpus) + repeat_num = [indptr[i + 1] - indptr[i] for i in range(num_doc)] + docid_col = np.repeat(range(num_doc), repeat_num) + doc_col = np.repeat(corpus, repeat_num) + + res_dict = {'document_id': docid_col, input_col: doc_col, 'term': vocab_col, 'idf': idf_col, 'tfidf': data} + out_table = pd.DataFrame(res_dict, columns=res_dict.keys()) + + elif res_type == 'dense': + out_table = pd.DataFrame(tfidf_csr.toarray(), columns=vocab) + + params = { + 'Input Column': input_col, + 'Max DF': max_df, + 'Min DF': min_df, + 'Maximum Number of Vocabularies': max_features, + 'Using IDF': use_idf, + 'Norm': norm, + 'Smooth IDF': smooth_idf, + 'Sublinear TF': sublinear_tf, + 'Result Type': res_type + } + + rb = BrtcReprBuilder() + rb.addMD(strip_margin(""" + | ## TF-IDF Result + | + | ### Parameters + | + | {params} + """.format(params=dict2MD(params)))) + + model = _model_dict('tfidf') + model['vectorizer'] = vectorizer + model['params'] = params + model['_repr_brtc_'] = rb.get() + + return {'out_table': out_table, 'model': model} def tfidf(table, group_by=None, **params): # This will be deprecated. diff --git a/function/python/brightics/function/textanalytics/tokenizer.py b/function/python/brightics/function/textanalytics/tokenizer.py index 54077f84e..9ae73ac6e 100644 --- a/function/python/brightics/function/textanalytics/tokenizer.py +++ b/function/python/brightics/function/textanalytics/tokenizer.py @@ -125,4 +125,4 @@ def _tokenizer_eng(table, input_cols, hold_cols=None, new_col_prefix='tokenized' pos_doc_list.append(pos_list) out_table['{prefix}_{col}'.format(prefix=new_col_prefix, col=input_cols[i])] = pos_doc_list - return {'out_table': out_table} + return {'out_table': out_table} \ No newline at end of file diff --git a/function/python/brightics/function/textanalytics/tokenizer2.py b/function/python/brightics/function/textanalytics/tokenizer2.py new file mode 100644 index 000000000..1bad747f5 --- /dev/null +++ b/function/python/brightics/function/textanalytics/tokenizer2.py @@ -0,0 +1,197 @@ +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +import numpy as np +import pandas as pd +from nltk.stem import PorterStemmer +from nltk.tokenize import sent_tokenize, word_tokenize +import nltk +from bs4 import BeautifulSoup +import re +from random import randint + +from brightics.common.utils import check_required_parameters + +LEAF_KEY = True +REPLACE_NO_SPACE = re.compile("[.;:!\'?,\"()\[\]]") +REPLACE_WITH_SPACE = re.compile("()|(\-)|(\/)") +POS_ENG_DEFAULT = ["CC", "CD", "DT", "EX", "FW", "IN", "JJ", "JJR", "JJS", "LS", "MD", + "NN", "NNS", "NNP", "NNPS", "PDT", "POS", "PRP", "PRP$", "RB", "RBR", "RBS", + "RP", "TO", "UH", "VB", "VBD", "VBG", "VBN", "VBP", "VBZ", "WDT", "WP", "WP$", "WRB"] + +""" +Handling compound words: replace every compound words in the given text to numbers which is not contained in the +text, apply the tokenizer, and replace the numbers back to the corresponding compound words. +""" + + +def _encode(text_table, user_dict, lower_case): + encode_ind = randint(1000, 10000) # a random integer + encode_list = [] + decode_dict = {} + if user_dict is not None: + user_dict_arr = user_dict.values.tolist() + texts_arr = text_table.values.tolist() + whole_text = ' '.join([' '.join(row) for row in texts_arr]) + numbers_in_text = [int(item) for item in re.compile(r'\d+').findall(whole_text)] + for row in user_dict_arr: + compound_word = row[0] + if len(row) > 1 and row[1] is not None and row[1] != np.nan and row[1] != "": + pos = row[1] + else: + pos = None + while encode_ind in numbers_in_text: + encode_ind += 1 + encode_word = ' {} '.format(encode_ind) + encode_list.append((compound_word, encode_word)) + decode_dict[str(encode_ind)] = (compound_word, pos) + encode_ind += 1 + table_encoded = text_table.applymap(lambda text: _substitution(text, encode_list, lower_case)) + else: + table_encoded = text_table + return table_encoded, decode_dict + + +def _substitution(text, encode_list, lower_case): + if lower_case: + text = text.lower() + for compound_word, encode_word in encode_list: + text = re.compile(compound_word).sub(encode_word, text) + return text + + +def _extract_kor(list_tokens_tagged, is_tagged, decode_dict, *pos_extraction): + list_tokens_tagged_decoded = [] + for token_tagged in list_tokens_tagged: + if token_tagged[0] in decode_dict: + compound_word, pos = decode_dict[token_tagged[0]] + if pos is None: + pos = 'Noun' + list_tokens_tagged_decoded.append([compound_word, pos]) + else: + list_tokens_tagged_decoded.append(token_tagged) + + if pos_extraction: + list_tokens_tagged_filtered = [token_tagged for token_tagged in list_tokens_tagged_decoded + if token_tagged[1] in set(pos_extraction)] + else: + list_tokens_tagged_filtered = list_tokens_tagged_decoded + + if is_tagged is False: + res = [token_tagged[0] for token_tagged in list_tokens_tagged_filtered] + else: + res = ['{text}/{pos}'.format(text=token_tagged[0], pos=token_tagged[1]) + for token_tagged in list_tokens_tagged_filtered] + return res + + +def tokenizer_kor2(table, **params): + check_required_parameters(_tokenizer_kor2, params, ['table']) + return _tokenizer_kor2(table, **params) + + +def _tokenizer_kor2(table, input_cols, hold_cols=None, new_col_prefix='tokenized', + normalization=True, stemming=True, pos_extraction=None, is_tagged=False, user_dict=None): + from twkorean import TwitterKoreanProcessor as Tw # import here since twkorean cannot be loaded in Enterprise ver. + + if pos_extraction is None: + pos_extraction = [] + + table_encoded, decode_dict = _encode(table[input_cols], user_dict, False) + + tokenizer = Tw(normalization=normalization, stemming=stemming) + tokenize_vec = np.vectorize(tokenizer.tokenize, otypes=[object])(table_encoded) + + columns = ['{prefix}_{col}'.format(prefix=new_col_prefix, col=col) for col in input_cols] + tokenized_table = pd.DataFrame(np.vectorize(_extract_kor, otypes=[object])( + tokenize_vec, is_tagged, decode_dict, *pos_extraction), columns=columns) + + if hold_cols is None: + out_table = pd.concat([table, tokenized_table], axis=1) + else: + out_table = pd.concat([table[hold_cols], tokenized_table], axis=1) + + return {'out_table': out_table} + + +def doc_list_stemming_eng(word_tok_list): + ps = PorterStemmer() + return [ps.stem(word_tok) for word_tok in word_tok_list] + + +def preprocess_reviews_eng(text, lower_case): + if lower_case: + text = REPLACE_NO_SPACE.sub("", text.lower()) + else: + text = REPLACE_NO_SPACE.sub("", text) + return text + + +def _extract_pos_eng(tagged, pos_extraction, is_tagged, decode_dict): + token, pos = tagged + if token in decode_dict: + token, pos = decode_dict[token] + if pos is None: + pos = 'NN' + + if pos in pos_extraction: + if is_tagged: + return '{token}({pos})'.format(token=token, pos=pos) + else: + return '{token}'.format(token=token) + else: + return None + + +def _transform_tagged_list_eng(tagged_list, pos_extraction, is_tagged, decode_dict): + tagged_list_transformed = [_extract_pos_eng(tagged, pos_extraction, is_tagged, decode_dict) + for tagged in tagged_list] + return [item for item in tagged_list_transformed if item is not None] + + +def tokenizer_eng2(table, **params): + check_required_parameters(_tokenizer_eng2, params, ['table']) + return _tokenizer_eng2(table, **params) + + +def _tokenizer_eng2(table, input_cols, hold_cols=None, new_col_prefix='tokenized', + lower_case=True, stemming=True, pos_extraction=None, is_tagged=False, user_dict=None): + if hold_cols is None: + out_table = table.copy() + else: + out_table = table[hold_cols] + + if pos_extraction is None: + pos_extraction = POS_ENG_DEFAULT + + table_encoded, decode_dict = _encode(table[input_cols], user_dict, lower_case) + + def _process(text): + text_filtered_html = BeautifulSoup(text).get_text() + text_preprocessed = preprocess_reviews_eng(text_filtered_html, lower_case) + text_tokenized = word_tokenize(text_preprocessed) + if stemming: + text_tokenized = doc_list_stemming_eng(text_tokenized) + text_tagged = nltk.pos_tag(text_tokenized) + text_result = _transform_tagged_list_eng(text_tagged, pos_extraction, is_tagged, decode_dict) + return text_result + + for col in input_cols: + docs = table_encoded[col] + docs_result = docs.apply(_process) + out_table['{prefix}_{col}'.format(prefix=new_col_prefix, col=col)] = docs_result + + return {'out_table': out_table} \ No newline at end of file diff --git a/function/python/brightics/function/textanalytics/topic_name_extraction.py b/function/python/brightics/function/textanalytics/topic_name_extraction.py new file mode 100644 index 000000000..1dd1f7ac6 --- /dev/null +++ b/function/python/brightics/function/textanalytics/topic_name_extraction.py @@ -0,0 +1,81 @@ +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +from brightics.common.utils import check_required_parameters +from brightics.common.utils import get_default_from_parameters_if_required +from brightics.common.validation import raise_runtime_error +from brightics.common.validation import validate, greater_than_or_equal_to + +import numpy as np +import pandas as pd + + +def topic_name_extraction(table, **params): + check_required_parameters(_topic_name_extraction, params, ['table']) + params = get_default_from_parameters_if_required(params, _topic_name_extraction) + param_validation_check = [greater_than_or_equal_to(params, 1, 'topn')] + validate(*param_validation_check) + return _topic_name_extraction(table, **params) + + +def _topic_name_extraction(table, sim_table=None, model=None, topn=10): + out_table = table.copy() + if model is None: + if sim_table is None: + raise_runtime_error("Either a model or a similarity table is needed as an input.") + else: + if 'w2v' in model: + input_model = model['w2v'] + elif 'ft' in model: + input_model = model['ft'] + + topics_vocab_weight_arr = np.array(table[['vocabularies', 'weights']]) + num_topic_vocab = len(topics_vocab_weight_arr[0][0]) + harmonic_weights = np.array([1.0 / (i + 1) for i in range(num_topic_vocab)]) + geometric_weights = np.array([1.0 / 2 ** i for i in range(num_topic_vocab)]) + log_weights = np.array([np.log(num_topic_vocab - i) for i in range(num_topic_vocab)]) + constant_weights = np.ones(num_topic_vocab) + + def topic_name_suggestion(vocab, weights): + if model is not None: # similarity: Word2vec model + vectors = np.transpose(np.array([input_model.wv.get_vector(word) for word in vocab])) + weighted_averages = np.dot(np.array( + [weights, harmonic_weights, geometric_weights, log_weights, constant_weights]), np.transpose(vectors)) + topic_name_weight_tuples = [input_model.wv.similar_by_vector(avg, topn=topn) for avg in weighted_averages] + res = [['{}: {}'.format(tup[0], tup[1]) for tup in item] for item in topic_name_weight_tuples] + return res + else: # similarity: matrix + # Assume that sim_table has words as indices for both rows and columns. columns: column name, rows: 1st col + vocab_set = sim_table.columns.values[1:] + row_indices = [pd.Index(sim_table.iloc[:, 0]).get_loc(word) for word in vocab] + sim_table_vocab = np.array(sim_table.iloc[row_indices, 1:]) + weighted_sims = np.dot(np.array( + [weights, harmonic_weights, geometric_weights, log_weights, constant_weights]), sim_table_vocab) + res = [_extract(item, vocab_set, topn) for item in weighted_sims] + return res + + topics_name_arr = [topic_name_suggestion(row[0], row[1]) for row in topics_vocab_weight_arr] + columns = ['topic_name_original', 'topic_name_harmonic', 'topic_name_geometric', 'topic_name_log', + 'topic_name_constant'] + out_table[columns] = pd.DataFrame(topics_name_arr) + return {'out_table': out_table} + + +def _extract(weighted_sim_list, vocab_set, topn): + list_zipped = zip(vocab_set, weighted_sim_list) + list_sorted = sorted(list_zipped, key=lambda tup: tup[1]) + list_cut = list_sorted[:topn] + return ['{}: {}'.format(tup[0], tup[1]) for tup in list_cut] diff --git a/function/python/brightics/function/textanalytics/word2vec.py b/function/python/brightics/function/textanalytics/word2vec.py index d3857fc9b..c9cfdfd1f 100644 --- a/function/python/brightics/function/textanalytics/word2vec.py +++ b/function/python/brightics/function/textanalytics/word2vec.py @@ -74,8 +74,15 @@ def _word2vec(table, input_col, sg=1, size=100, window=5, min_count=1, hashfxn=hashfxn) w2v.init_sims(replace=True) - vocab = w2v.wv.vocab - + vocab = w2v.wv.vocab + + analogies_score, sections = w2v.wv.evaluate_word_analogies( + 'brightics/function/textanalytics/data/word2vec_questions_words.txt') + pearson_1, spearman_1, oov_ratio_1 = w2v.wv.evaluate_word_pairs( + 'brightics/function/textanalytics/data/word2vec_wordsim353.tsv') + pearson_2, spearman_2, oov_ratio_2 = w2v.wv.evaluate_word_pairs( + 'brightics/function/textanalytics/data/word2vec_simlex999.tsv') + params = {'Input column': input_col, 'Training algorithm': algo, 'Word vector dimensionality': size, @@ -126,9 +133,31 @@ def _word2vec(table, input_col, sg=1, size=100, window=5, min_count=1, | {topn_words} | {fig} | + | ### Word analogy score + | {analogies_score} + | + | ### Word correlation scores + | #### Pearson correlation coefficient with 2-tailed p-value (WordSim353) + | {pearson_1_1}, {pearson_1_2} + | #### Spearman rank-order correlation coefficient with 2-tailed p-value (WordSim353) + | {spearman_1_1}, {spearman_1_2} + | #### The ratio of pairs with unknown words (WordSim353) + | {oov_ratio_1} + | #### Pearson correlation coefficient with 2-tailed p-value (SimLex999) + | {pearson_2_1}, {pearson_2_2} + | #### Spearman rank-order correlation coefficient with 2-tailed p-value (SimLex999) + | {spearman_2_1}, {spearman_2_2} + | #### The ratio of pairs with unknown words (SimLex999) + | {oov_ratio_2} + | | ### Parameters | {params} - """.format(length=length, topn=topn, topn_words=topn_words, params=dict2MD(params), fig=fig))) + """.format(length=length, analogies_score=analogies_score, + pearson_1_1=pearson_1[0], pearson_1_2=pearson_1[1], spearman_1_1=spearman_1[0], + spearman_1_2=spearman_1[1], oov_ratio_1=oov_ratio_1, + pearson_2_1=pearson_2[0], pearson_2_2=pearson_2[1], spearman_2_1=spearman_2[0], + spearman_2_2=spearman_2[1], oov_ratio_2=oov_ratio_2, + topn=topn, topn_words=topn_words, params=dict2MD(params), fig=fig))) vocab = list(w2v.wv.vocab) @@ -249,18 +278,20 @@ def _word2vec_similarity2(table, model, positive_col=None, negative_col=None, to if positive_col is None: columns = [negative_col] elif negative_col is None: - columns = [positive_col] + columns = [positive_col] else: - columns = [positive_col, negative_col] - + columns = [positive_col, negative_col] + df_dropped = table[columns].dropna(how='all') - + if positive_col is None: res = df_dropped.apply(lambda _: _w2v_sim_neg(model, negative=_[negative_col], topn=topn), axis=1) elif negative_col is None: res = df_dropped.apply(lambda _: _w2v_sim_pos(model, positive=_[positive_col], topn=topn), axis=1) else: - res = df_dropped.apply(lambda _: _w2v_sim_both(model, positive=_[positive_col], negative=_[negative_col], topn=topn), axis=1) - + res = df_dropped.apply( + lambda _: _w2v_sim_both(model, positive=_[positive_col], negative=_[negative_col], topn=topn), axis=1) + out_table = pd.DataFrame(sum(res, []), columns=columns + ['synonym', 'similarity']) return {'out_table': out_table} + diff --git a/function/python/brightics/function/timeseries/__init__.py b/function/python/brightics/function/timeseries/__init__.py index 5e97a4619..776cbe612 100644 --- a/function/python/brightics/function/timeseries/__init__.py +++ b/function/python/brightics/function/timeseries/__init__.py @@ -23,4 +23,5 @@ from .arima import arima_predict from .arima import auto_arima_train from .arima import auto_arima_predict -from .autocorrelation import autocorrelation \ No newline at end of file +from .autocorrelation import autocorrelation +from .linear_sampling import linear_sampling diff --git a/function/python/brightics/function/timeseries/linear_sampling.py b/function/python/brightics/function/timeseries/linear_sampling.py new file mode 100644 index 000000000..5edaa8c04 --- /dev/null +++ b/function/python/brightics/function/timeseries/linear_sampling.py @@ -0,0 +1,40 @@ +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +from brightics.common.utils import get_default_from_parameters_if_required +from brightics.common.validation import validate, greater_than, from_to +from brightics.common.utils import check_required_parameters +from brightics.common.groupby import _function_by_group + + +def linear_sampling(table, group_by=None, **params): + params = get_default_from_parameters_if_required(params, _linear_sampling) + param_validation_check = [greater_than(params, 0.0, 'train_ratio'), + greater_than(params, 0.0, 'test_ratio')] + + validate(*param_validation_check) + check_required_parameters(_linear_sampling, params, ['table']) + if group_by is not None: + return _function_by_group(_linear_sampling, table, group_by=group_by, **params) + else: + return _linear_sampling(table, **params) + + +def _linear_sampling(table, train_ratio=0.7, test_ratio=0.3): + index = int(table.shape[0] * train_ratio / (train_ratio + test_ratio)) + train_table = table[:index] + test_table = table[index:] + return {'train_table': train_table, 'test_table': test_table} diff --git a/function/python/brightics/function/timeseries/meta/autocorrelation.json b/function/python/brightics/function/timeseries/meta/autocorrelation.json index 185abde4c..036439238 100644 --- a/function/python/brightics/function/timeseries/meta/autocorrelation.json +++ b/function/python/brightics/function/timeseries/meta/autocorrelation.json @@ -618,14 +618,7 @@ } ], "preferences": {}, - "variables": { - "path": { - "type": "array[string]", - "value": [ - "" - ] - } - }, + "variables": {}, "variableRef": [], "innerModels": {}, "inData": [], diff --git a/function/python/brightics/function/timeseries/meta/linear_sampling.json b/function/python/brightics/function/timeseries/meta/linear_sampling.json new file mode 100644 index 000000000..568277f35 --- /dev/null +++ b/function/python/brightics/function/timeseries/meta/linear_sampling.json @@ -0,0 +1,881 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "transform", + "func": "brightics.function.timeseries$linear_sampling", + "name": "brightics.function.timeseries$linear_sampling", + "context": "python", + "label": "Linear Sampling", + "description": "This function divides timeseries data into train and test data according to timeseries order.\n\nReference:\nhttps://en.wikipedia.org/wiki/Sampling_(statistics)", + "tags": [ + "transform", + "linear sampling", + "sampling", + "split data" + ], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "train_table": "", + "test_table": "" + }, + "meta": { + "table": { + "type": "table", + "desc": "Data in a table.", + "optional": false + }, + "train_table": { + "type": "table", + "desc": "Training dataset in a table." + }, + "test_table": { + "type": 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+ "description": "In this tutorial workflow, Linear Sampling function is used to split the sample_timeseries data into training and test data. The parameter settings used in the function are shown below.", + "parameter": "1. **Train Ratio**: 0.7\n2. **Test Ratio**: 0.3\n3. **Group By**: None\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.timeseries import linear_sampling\nres = linear_sampling(table = inputs[0] , train_ratio = 0.7, test_ratio = 0.3, group_by = None)\noutput0 = res['train_table']\noutput1 = res['test_table']", + "description": "In this python script, Linear Sampling function is used to split the sample_timeseries data into training and test data.", + "context": "python" + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/timeseries/test/spcrule_test.py b/function/python/brightics/function/timeseries/test/spcrule_test.py new file mode 100644 index 000000000..4148b5c97 --- /dev/null +++ b/function/python/brightics/function/timeseries/test/spcrule_test.py @@ -0,0 +1,76 @@ +""" + Copyright 2019 Samsung SDS + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + http://www.apache.org/licenses/LICENSE-2.0 + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +from brightics.common.datasets import load_iris +from brightics.function.timeseries import spcrule,spcrule_summ +import unittest +import pandas as pd +import numpy as np +import HtmlTestRunner +import os + + +class SpcruleTest(unittest.TestCase): + def setUp(self): + print("*** Spc ruleset AD with/without summary UnitTest Start ***") + testdata = load_iris() + setosa = testdata[testdata.species=='setosa'] + versicolor = testdata[testdata.species=='versicolor'] + versicolor_test = versicolor[versicolor.sepal_length>6.5] + testset = setosa.append(versicolor_test) + testset = testset.reset_index(drop=True) + testset['time'] = testset.index.astype(int) + self.testdata = testset + + def tearDown(self): + print("*** Spc ruleset AD with/without summary UnitTest End ***") + + def test_first(self): + spc = spcrule(self.testdata,time_col='time',value_col='sepal_length',ruleset_id='0') + DF1 = spc['out_table'].values + DF2 = spc['out_table2'].values + np.testing.assert_string_equal(DF1[0][4], 'lack of samples') + np.testing.assert_equal(DF1[0][5], None) + np.testing.assert_string_equal(DF1[len(DF1)-1][4], '1') + np.testing.assert_equal(DF1[len(DF1)-1][5], True) + np.testing.assert_equal(DF2[0][3][-1],DF1[-1][1]) + np.testing.assert_equal(DF2[0][3][-2],DF1[-2][1]) + np.testing.assert_equal(DF2[0][3][-3],DF1[-3][1]) + np.testing.assert_equal(DF2[0][3][-4],DF1[-4][1]) + np.testing.assert_equal(DF2[0][3][-4],DF1[-4][1]) + np.testing.assert_equal(DF2[0][0],DF1[-1][2]) + np.testing.assert_equal(DF2[0][1],DF1[-1][3]) + self.summary = td['out_table2'] + + def test_second(self): + spc = spcrule(self.testdata,time_col='time',value_col='sepal_length',ruleset_id='0') + self.summary = td['out_table2'] + spc2 = spcrule_summ(self.testdata,self.summary,time_col='time',value_col='sepal_length',ruleset_id='0') + DF1 = spc2['out_table'].values + DF2 = spc2['out_table2'].values + np.testing.assert_string_equal(DF1[0][4], 'old_data') + np.testing.assert_string_equal(DF1[len(DF1)-1][4], '1') + np.testing.assert_equal(DF1[len(DF1)-1][5], True) + np.testing.assert_equal(DF2[0][3][-1],DF1[-1][1]) + np.testing.assert_equal(DF2[0][3][-2],DF1[-2][1]) + np.testing.assert_equal(DF2[0][3][-3],DF1[-3][1]) + np.testing.assert_equal(DF2[0][3][-4],DF1[-4][1]) + np.testing.assert_equal(DF2[0][3][-4],DF1[-4][1]) + np.testing.assert_equal(DF2[0][0],DF1[-1][2]) + np.testing.assert_equal(DF2[0][1],DF1[-1][3]) + + +if __name__ == '__main__': + filepath = os.path.dirname(os.path.abspath(__file__)) + reportFoler = filepath + "/../../../../../../../reports" + unittest.main(testRunner=HtmlTestRunner.HTMLTestRunner(combine_reports=True, output=reportFoler)) \ No newline at end of file diff --git a/function/python/brightics/function/transform/SMOTE.py b/function/python/brightics/function/transform/SMOTE.py new file mode 100644 index 000000000..78e020de5 --- /dev/null +++ b/function/python/brightics/function/transform/SMOTE.py @@ -0,0 +1,71 @@ +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +from brightics.common.groupby import _function_by_group +from brightics.common.utils import check_required_parameters +from brightics.common.utils import get_default_from_parameters_if_required +from brightics.common.validation import validate +from brightics.common.validation import greater_than_or_equal_to +from brightics.common.validation import raise_error +from brightics.common.classify_input_type import check_col_type + +import sklearn.utils as sklearn_utils +from sklearn import svm +from sklearn import preprocessing +from imblearn.over_sampling import SMOTE as SMOTE_LIB +import pandas as pd +import numpy as np + + +def SMOTE(table, group_by=None, **params): + check_required_parameters(_SMOTE, params, ['table']) + params = get_default_from_parameters_if_required(params, _SMOTE) + + if group_by is not None: + return _function_by_group(_SMOTE, table, group_by=group_by, **params) + else: + return _SMOTE(table, **params) + + +def _SMOTE(table, label_col, sampling_strategy='not majority', seed=None, k_neighbors=5, m_neighbors=10, + out_step=0.5, kind='regular', svm_estimator='svc', n_jobs=1): + + features = table.drop([label_col], axis=1) + y = table[label_col] + + if(sklearn_utils.multiclass.type_of_target(y) == 'continuous'): + raise_error('0718', 'label_col') + + lab_encoder = preprocessing.LabelEncoder() + y_encoder = lab_encoder.fit_transform(y) + + if (kind == 'svm'): + svc_model = svm.SVC() + else: + svc_model = None + + sm = SMOTE_LIB(sampling_strategy=sampling_strategy, random_state=seed, k_neighbors=k_neighbors, m_neighbors=m_neighbors, + out_step=out_step, kind=kind, svm_estimator=svc_model, n_jobs=n_jobs) + + X_res, y_res = sm.fit_resample(features, y_encoder) + y_decoder = lab_encoder.inverse_transform(y_res) + + df = pd.DataFrame(data=X_res, columns=features.columns) + df1 = pd.DataFrame(data=y_decoder, columns=[label_col]) + + out_table = df.join(df1) + + return {'out_table' : out_table} diff --git a/function/python/brightics/function/transform/__init__.py b/function/python/brightics/function/transform/__init__.py index 3cd0e1454..e07666c59 100644 --- a/function/python/brightics/function/transform/__init__.py +++ b/function/python/brightics/function/transform/__init__.py @@ -18,6 +18,7 @@ from .missing_data import delete_missing_data from .reshaping import pivot from .reshaping import pivot2 +from .reshaping import pivot3 from .reshaping import unpivot from .reshaping import distinct from .reshaping import transpose @@ -30,4 +31,12 @@ from .sql import execute as sql_execute from .sql import execute2 as sql_execute2 from .svd import svd +from .svd import svd2 +from .svd import svd_model from .transpose_time_series import transpose_time_series +from .SMOTE import SMOTE +from .under_sampling import under_sampling +from .explode import explode +from .tsne import tsne +from .savgol_filter import savgol_filter + diff --git a/function/python/brightics/function/transform/explode.py b/function/python/brightics/function/transform/explode.py new file mode 100644 index 000000000..ba931a4f4 --- /dev/null +++ b/function/python/brightics/function/transform/explode.py @@ -0,0 +1,58 @@ +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +import numpy as np +import pandas as pd +from pandas.core.dtypes.missing import isna +from brightics.common.utils import check_required_parameters +from brightics.common.validation import raise_runtime_error + + +def explode(table, **params): + check_required_parameters(_explode, params, ['table']) + return _explode(table, **params) + + +def _explode(table, input_col): + col = table[input_col] + is_arr_col_list = [isinstance(item, np.ndarray) or isinstance(item, list) for item in col] + is_non_arr_col_list = [not item for item in is_arr_col_list] + is_arr_col = all(is_arr_col_list) + is_non_arr_col = all(is_non_arr_col_list) + if not is_arr_col and not is_non_arr_col: + raise_runtime_error("{} is an invalid column to explode or un-explode.".format(input_col)) + elif is_arr_col: # explode + values = np.array(col) + values_flattened = np.concatenate(values).ravel() + counts = [len(item) for item in values] + col_exploded = pd.Series(values_flattened, index=col.index.repeat(counts), name=col.name) + out_table = table.drop([input_col], axis=1).join(col_exploded).reindex(columns=table.columns, copy=False) + else: # un-explode + group_cols = table.columns.tolist() + group_cols.remove(input_col) + + group_id = 'tmp_group_id' + while group_id in table.columns: + group_id += '_' + group_idx = table[group_cols].drop_duplicates() + group_idx[group_id] = np.arange(group_idx.shape[0]) + table = table.merge(group_idx, on=group_cols) + + out_table = table.groupby(group_id)[input_col].apply(list).reset_index(name=input_col) \ + .merge(group_idx, on=group_id).reindex(columns=table.columns).drop(group_id, axis=1) + out_table[input_col] = out_table[input_col].map(lambda lst: [item for item in lst if not isna(item)]) + + return {'out_table': out_table} diff --git a/function/python/brightics/function/transform/meta/SMOTE.json b/function/python/brightics/function/transform/meta/SMOTE.json new file mode 100644 index 000000000..726dc3d34 --- /dev/null +++ b/function/python/brightics/function/transform/meta/SMOTE.json @@ -0,0 +1,1499 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "transform", + "func": "brightics.function.transform$SMOTE47342", + "name": "brightics.function.transform$SMOTE", + "context": "python", + "label": "Over Sampling (SMOTE)", + "description": "SMOTE is one of the most popular approach to generate new dataset samples. SMOTE first selects a class instance A at random and finds its k nearest class neighbors. The synthetic instance is then created by selecting one of the k nearest neighbors B at random and connecting A and B to form a line segment in the feature space. The synthetic instances are generated as a convex combination of the two chosen instances A and B.\n\nReferences:\nhttps://en.wikipedia.org/wiki/Oversampling_and_undersampling_in_data_analysis", + "tags": [ + "sampling", + "transform" + ], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "out_table": "" + }, + "meta": { + "table": { + "type": "table", + "desc": "Data in a table.", + "optional": false + }, + "out_table": { + "type": "table", + "desc": "Oversampled data. " + } + }, + "params": [ + { + "id": "label_col", + "label": "Label Column", + "description": "The column designated for the labels of data.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "globalVariable": false, + "columnType": [ + "Integer", + "Long", + "Float", + "Double", + "String" + ], + "multiple": false + }, + { + "id": "sampling_strategy", + "label": "Sampling Strategy", + "description": "Sampling information to resample the data set.", + "visibleOption": [], + "control": "RadioButton", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "Minority", + "value": "minority", + "default": false + }, + { + "label": "Not Minority", + "value": "not minority", + "default": false + }, + { + "label": "Not Majority", + "value": "not majority", + "default": true + }, + { + "label": "All", + "value": "all", + "default": false + } + ], + "globalVariable": false, + "columnType": [], + "targetTable": [] + }, + { + "id": "seed", + "label": "Seed", + "description": "Control the randomization of the algorithm.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Integer" + }, + { + "id": "k_neighbors", + "label": "K Neighbors", + "description": "The number of instances to choose in the nearest neighbors belonging to the same class.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Integer", + "min": 1, + "defaultValue": 5 + }, + { + "id": "m_neighbors", + "label": "M Neighbors", + "description": "The number of nearest neighbors to use to determine if a minority sample is in danger. Only available when the parameter Kind is one of {'Borderline1', 'Borderline2', 'SVM'}", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Integer", + "defaultValue": 10 + }, + { + "id": "kind", + "label": "Kind", + "description": "The type of SMOTE algorithm to use one of the following options: 'regular', 'borderline1', 'borderline2', 'svm'.", + "visibleOption": [], + "control": "RadioButton", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "Regular", + "value": "regular", + "default": true + }, + { + "label": "Borderline1", + "value": "borderline1", + "default": false + }, + { + "label": "Borderline2", + "value": "borderline2", + "default": false + }, + { + "label": "SVM", + "value": "svm", + "default": false + } + ], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "String" + }, + { + "id": "out_step", + "label": "Out Step", + "description": "Step size when extrapolating", + "visibleOption": [ + { + "id": "kind", + "value": "svm" + } + ], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Double", + "defaultValue": 0.5 + }, + { + "id": "n_jobs", + "label": "N Jobs", + "description": "The number of threads to open if possible.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Integer", + "min": 1, + "defaultValue": 1 + }, + { + "id": "group_by", + "label": "Group By", + "description": "Columns to group by", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "multiple": true, + "rowCount": 5 + } + ], + "summary": "This function oversamples the input data using Synthetic Minority Over-sampling Technique.", + "tutorialLink": "https://www.brightics.ai/kr/docs/ai/v3.7/tutorials/121_class_imbalance?type=insight" + }, + "md": { + "en": "# Over Sampling (SMOTE)\nThis function oversamples the input data using Synthetic Minority Over-sampling Technique.\n\n## Description\nSMOTE is one of the most popular approach to generate new dataset samples. SMOTE first selects a class instance A at random and finds its k nearest class neighbors. The synthetic instance is then created by selecting one of the k nearest neighbors B at random and connecting A and B to form a line segment in the feature space. The synthetic instances are generated as a convex combination of the two chosen instances A and B.\n\n+ References:\nhttps://en.wikipedia.org/wiki/Oversampling_and_undersampling_in_data_analysis\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data in a table.\n#### OUTPUT\n1. **out_table**: (Table) Oversampled data. \n#### PARAMETER\n1. **Label Column***: The column designated for the labels of data.\n\n2. **Sampling Strategy**: Sampling information to resample the data set.\n\n3. **Seed**: Control the randomization of the algorithm.\n\n4. **K Neighbors**: The number of instances to choose in the nearest neighbors belonging to the same class.\n\n\t* Default value: 5 \n5. **M Neighbors**: The number of nearest neighbors to use to determine if a minority sample is in danger. 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Only available when the parameter Kind is one of {'Borderline1', 'Borderline2', 'SVM'}\n\t* Type: *int*\n\n\t* Default value: 10 \n6. **kind**: The type of SMOTE algorithm to use one of the following options: 'regular', 'borderline1', 'borderline2', 'svm'.\n\t* Type: *str*\n\t* Default / Range: regular ( regular | borderline1 | borderline2 | svm )\n7. **out_step**: Step size when extrapolating\n\t* Type: *double*\n\n\t* Default value: 0.5 \n8. **n_jobs**: The number of threads to open if possible.\n\t* Type: *int*\n\t* Default / Range: \n\t* Default value: 1 \n9. **group_by**: Columns to group by\n\t* Type: *list[str]*\n\n\n## Example\n### VA\n**[Related Tutorial]**\n\n\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, modified sample_iris data is used for oversampling the minority class. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Label Column***: \"species\" \n2. **Sampling Strategy**: \"minority\"\n3. **Seed**: None\n4. **K Neighbors**: 5\n5. **M Neighbors**: 10\n6. **Kind**: \"regular\"\n7. **Out Step**: 0.5\n8. **N Jobs**: 1\n9. **Group By**: None\n\n\n\n### Python\n\n```\nfrom brightics.function.transform import SMOTE\nres = SMOTE(table = inputs[0], label_col = \"species\", sampling_strategy = \"minority\", k_neighbors = 5, m_neighbors = 10, kind = \"regular\", out_step = 0.5, n_jobs = 1, group_by = None)\noutput = res['out_table']\n```\nIn this tutorial workflow, modified sample_iris data is used for oversampling the minority class.\n\n\n", + "kr": "" + }, + "exampleModels": [ + { + "fileName": "SMOTE.json", + "label": "SMOTE", + "contents": { + "type": "files", + "data": [ + { + "type": "file", + "data": { + "id": "mybh6kxb83p6rmgs", + "project_id": "p5dmzu78qb564crk", + "label": "SMOTE", + "contents": { + "mid": 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+ "description": "In this tutorial workflow, modified sample_iris data is used for oversampling the minority class. The parameter settings used in the function are shown below.", + "parameter": "1. **Label Column***: \"species\" \n2. **Sampling Strategy**: \"minority\"\n3. **Seed**: None\n4. **K Neighbors**: 5\n5. **M Neighbors**: 10\n6. **Kind**: \"regular\"\n7. **Out Step**: 0.5\n8. **N Jobs**: 1\n9. **Group By**: None\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.transform import SMOTE\nres = SMOTE(table = inputs[0], label_col = \"species\", sampling_strategy = \"minority\", k_neighbors = 5, m_neighbors = 10, kind = \"regular\", out_step = 0.5, n_jobs = 1, group_by = None)\noutput = res['out_table']", + "context": "python", + "description": "In this tutorial workflow, modified sample_iris data is used for oversampling the minority class." + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/transform/meta/delete_missing_data.json b/function/python/brightics/function/transform/meta/delete_missing_data.json index 6bf942ac6..c04b4203c 100644 --- a/function/python/brightics/function/transform/meta/delete_missing_data.json +++ b/function/python/brightics/function/transform/meta/delete_missing_data.json @@ -59,6 +59,43 @@ ], "multiple": true, "rowCount": 5 + }, + { + "id": "row_or_column", + "label": "Delete row or column", + "description": "Delete row or column", + "mandatory": false, + "items": [ + { + "label": "Row", + "value": "index", + "default": true + }, + { + "label": "Column", + "value": "columns", + "default": false + } + ], + "visibleOption": [], + "control": "RadioButton", + "columnType": [], + "validation": [] + }, + { + "id": "missing_ratio", + "label": "Missing values ratio", + "description": "Delete data with more than x% missing values. Default value = 1 (delete nothing)", + "mandatory": false, + "items": [], + "visibleOption": [], + "control": "InputBox", + "columnType": [], + "validation": [], + "placeHolder": "0 (0 <= value <= 1)", + "type": "Double", + "min": 0, + "max": 1 } ], "summary": "Remove rows when the chosen columns have abnormal values.", diff --git a/function/python/brightics/function/transform/meta/distinct.json b/function/python/brightics/function/transform/meta/distinct.json index 89ecaa8a0..4a3f97ad2 100644 --- a/function/python/brightics/function/transform/meta/distinct.json +++ b/function/python/brightics/function/transform/meta/distinct.json @@ -46,18 +46,6 @@ "columnType": [], "multiple": true }, - { - "id": "hold_cols", - "label": "Hold Columns", - "description": "Columns to show with Input Columns. If not selected, whole columns are shown.", - "visibleOption": [], - "control": "ColumnSelector", - "validation": [], - "mandatory": false, - "items": [], - "columnType": [], - "multiple": true - }, { "id": "group_by", "label": "Group By", diff --git a/function/python/brightics/function/transform/meta/explode.json b/function/python/brightics/function/transform/meta/explode.json new file mode 100644 index 000000000..06f68dc7b --- /dev/null +++ b/function/python/brightics/function/transform/meta/explode.json @@ -0,0 +1,1310 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "transform", + "func": "brightics.function.transform$explode", + "name": "brightics.function.transform$explode", + "context": "python", + "label": "Explode And Unexplode", + "description": "This function explodes a column whose values are lists, and vice versa. To explode a column means to transform each element of an array type to a row, replicating any other column's values. Unexplode does the \"undo\" action of Explode, which is basically identical to grouping by other columns.", + "tags": [], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "out_table": "" + }, + "meta": { + "table": { + "type": "table", + "desc": "Input data table.", + "optional": false + }, + "out_table": { + "type": "table", + "desc": "Output data table with the chosen column exploded or unexploded." + } + }, + "params": [ + { + "id": "input_col", + "label": "Input Column", + "description": "Choose a column of the input data. If all values of the column are list-like, this function explodes the column, and if all values of the column are not list-like, un-explodes the column.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "columnType": [ + "Integer", + "Float", + "Double", + "Long", + "String", + "Integer[]", + "Float[]", + "Double[]", + "Long[]", + "String[]" + ], + "multiple": false + } + ], + "summary": "This function explodes a column whose values are lists, and vice versa." + }, + "md": { + "en": "# Explode And Unexplode\nThis function explodes a column whose values are lists, and vice versa.\n\n## Description\nThis function explodes a column whose values are lists, and vice versa. To explode a column means to transform each element of an array type to a row, replicating any other column's values. Unexplode does the \"undo\" action of Explode, which is basically identical to grouping by other columns.\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Input data table.\n#### OUTPUT\n1. **out_table**: (Table) Output data table with the chosen column exploded or unexploded.\n#### PARAMETER\n1. **Input Column***: Choose a column of the input data. If all values of the column are list-like, this function explodes the column, and if all values of the column are not list-like, un-explodes the column.\n\n\n### Python\n#### USAGE\n\n```\nexplode(table = , input_col = )\n```\n\n#### INPUT\n1. **table***: (Table) Input data table.\n#### OUTPUT\n1. **out_table**: (Table) Output data table with the chosen column exploded or unexploded.\n#### PARAMETER\n1. **input_col***: Choose a column of the input data. If all values of the column are list-like, this function explodes the column, and if all values of the column are not list-like, un-explodes the column.\n\t* Type: *str*\n\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, text_eng data is used as an input of Explode And Unexplode function. The Tokenizer function returns a column consisting of list of tokens, and the Explode And Unexplode function will list all items in a single column with other column values replicated. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Input Column***: tokenized_text\n\n\n\n### Python\n\n```\nfrom brightics.function.transform import explode\ninput_table = inputs[0]\nresult = explode(table=input_table, input_col='tokenized_text')\noutput = result['out_table']\n```\nIn this tutorial workflow, text_eng data is used as an input of Explode And Unexplode function. 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2w/5utXQ2ZKfTyhdbc+dHaqmNIg1Gw16011LIM2v3o2jF0IYTWlifBue/XLNw+IDO/e330oPlTzkJcWbUp9ws1wRDhA3bXj1tjPj/72W3t+8T/2/Pz/fvzUa+ffvZZrG5VmszUT9zFvsik16GpNjY0TgtpfXjGl+VL1w8+uffeFbS9vOXL00uRstpQrVcVBEawSmeLJq9NPbxq77/UPNx04V7VueEmmtD1Fyo/uAWdhKeCi5Z3JDUdm+LU6UuxhbN4o/S1Ll/qEF/4oun9G5jUH/CEuXJ+nrr18duN3D/xKxLR/3PvwL4//fu/EMfNvurIFbWHeeKPENEEv1jSTC2b0x0JNabXWOH1t5qm3PvmzB9as23tGBLpbieyOI5f/9bn3v/7gun97cbtZrmctJCLZmZWBI9x5X2e4prTnbLNLUJLfmsf7wpQOUaVG5e3r++4//IqIaYIfjj276tKOqVJ7RIDIRkI4yzS8PaVBRbqu+vFrH4hg9+c/Xfvgyn1XJlPmUZcwpSh+ul2Y+emR3xgxTmHrzYNmIX1wiFKdY4bblBp0tabmAxq6FFP6yee3hR19dvOnp67NZIsVedBHs9UqlGvnbiaXbT363DuH9py4brS+YUoHLvMp2W8wjqbULjP8x+uvdVd2K9HM4IkTK9LVduN4IhXzuQlKNLPxs6bGPtWBrHIktIiFjnL6hIXH1x00VmsTkW0qlb/3dx986d5V33h0k1Gmd/Uekeykro30/beu46jR9kFvF+pZN/0snxxd7TKGD3Ra3+7qHsnbiavH81Hv0E445WfoddtogTqbunbXJ88oAc1g/2T7uRZKoRsDEl5Tmg/rqNdNB879y3PvGyFPJG2f3/ReWxxTimKmXK1499hzSoCT2Xl7zCw6Px/vQbx+ptTAz5pOJYOZfCVkm1LhP4uV2vLdJ59Yf/DIxYlytS7ys7c/Pi/zwfGr1VpD/PXFdw8/sf5j4VqbzaUtdBQhUxqYrNjbzjKDNqULV09XUL13wNpw9UMljsk8dOx3Zrn5+VLc29qUOKbgZ02DWpByEaZUqFpvHL04OZM2N4tutFrvjl14etPYy1uOGEd6l+0bfbC+4bYT06Jcu1K7zJvAdYrVhehKC+1UzX2kXbPsK1uxon2WdWX7fqyzOtpU+4Ie2akd5K0I5hENVFMq2XX1LAFZ6CJ1Mz/17X2PKKFM5nCiXS/CNq4tjKbUGOdWLAUz+aoXbfn04t/9cuMdd2tR76mNY1NzHiONMaUoZnr9whYltLlJls3OwGK5KlfqmNHZlBq4rGkmEdDSu0K2KW00mtem0r9++5AIUOL/W63WY+sO/tkDa77x2KYv3rNCFPvLn6//h1+9nS6UhS/ddODcXa/svDKZEt61f3NKrTTIMWSruzxSnDjJStekXHCRD2rRWvoT7u0K5vvqu6lehC5nbysRzM3ma3vN0vPzU2FdHaQvKBHME9maTuqLt1Wq6soCw9HiTGkfJdknT4xa4HKP7RY6M8vqcB07CfRKC9v+VsZZrbzLaNhRxbvF0DMDtK/W0189ooHLlDp8sgpZ6CL15MlVShBTuOuTZ8RPv1E4F7KJCeHtKRU5kfHIQqhKrT527raxhtuX71t13+vtNS1tDcOUtlvgdNr13JJSwCtXkAKi+CumFHkrXyspcc2Tt6/vM8o3mk3hx+LK/rExJVfzQ1jT/Z+MXb0xMTGTCmpFECHblFbrjX2nbizbenTzx+eNPwlT+vNV+87fSv7lL9a/8/H5NXtOG6ZU/GgdPHvr8XUHdx65nMwWh2FKOwY0V4rTzrekUOlMcZxXkENru4VeSeOUtMk7YxNIEdJRxhljpavZkdbTjFn3I5/u/6Ackou1n4lXDJcejlzYur76hJVHoUv6vdBx3pt0BfmDcD4T+za6vK9hqJeGtjsPPmmWnp9PzmWVUBAnlNjVgXd3bDv1+YVbk7MiVasHNE8+Eqa0HWe8KpERCuR6J13TUWv80kI5jvmkbXJNFAU8o4ocN9Sw2ZarL1RRh/dlyiukCDnftX3DZKGL0fXcpBLBPDkwdcIoX63VZecVOKE0pdPaf4NqfrMlwtkDb+zx43sv7fjC3cuNwPfnP10rjlyfdkwYG7QpdQYjG79UqY1vAueAcIAcOjF7UQlqnjz+2XLzhPn5d7e/r6Qyo8ytqdm5TMF8NENX25TWGtsPX35t+/EdRy4bfxJ2VDA+m/vrhzbsOnZlYjZ3+MJ4TZ9Hevzy5IvvHRGRcHIuP2hT2jWgOVOcduySApoztTKQXsXnJTTaF1mgKfW8psfV2jhyTUueeZj3g3LJ610bdMg7tb8uM+/NKqYmkWoG6fN7Ib2KlFwqON61R7YakH706fNKBPPkZn7KKH9rYkKp16PMwcOHRarWecXHwenmTGbvyeuduZ3QNnFotlrZQuXwhYlVH556Yv3HP1+579E3Dyx7/+jOo1em5vJBLT5nKzzVAcVAH4wfVsKXJ787/55RXtSOtvkKAeEcvpsS/w3clD60er+RhPWCMKgnrzravwdrSu10wU4I1EYsK4HwLSClU2pSIsCUIof+MPmZEtQ8ue/QS+YJ8/Pv796pJDGjzG1hStOhMKW7j119dduxrZ9eNP+myzal5r91Hb889fKWo2v3nhGm9MjFiefeOSS4ped5S5CX1+oe0Bypmx2p5F5H66AVu6wr2GUkA2nmf+0j1qu4nJgsydcZf7UKWy/h8pb21TrHc2//6e82NewQ7bqr9pF2mut6YtJZavy3zlIehevJ2E/PfsL2q9jXdJcRsg92fCbD0D/vf1SJYJ6cSV01yk9OTyv1epT55OhRkao1AjKlPapcrd+YyWz8w7kHV+771pPvfO3+NV+8Z8Wf/mT13/1y4/eX7frttmMiymUKwazVZEitdwgtQZuu7VHClyfPnHrTPGF+3jZfYSC8w3cDN6Wvbjv+7aff8+Obj2825pQKRID7p19vuTg+a56pa6Cm1DOKdQltakph509yeTtTwZQih44mzilBzZOHj79unjA//97ObUoSM8rcnpoLQ09pvdE8enHipS1HVn14qtFsr7rraUo/OXf7yQ0fbzt8KdHPO7eCjGTAeglo9j9fXelykkKqP9RkntLJILluxmW9bFk3IDDDo3VEipbW6Wb0tq8mvVkPeb+o5Bs9cJvS9m24fkS8Artt+617Ux64eld2efkiTtmPqP3j5fnWXO0FQen7PgtUKlzNjRvlxycnlXo9yhw8fESkao1meE1ps9m6OZNZs+f03z3y1h/9yBzaZnPHD9/40r0rf7Xh42OXJ42xIYFIrXcILUHbb32ihC9PfnPubaN8i57SHgl8Tmm11ihWan6IxM6YUyq488Xt4ogIf+aZugZpSj2yOk2+v/TOYVdmcuB5EVdLP0K6kuWMEtQ8WXt5l1G+2WqdvXg1rnywf5+Sn3Xg/Q92HT/z+fh0ajYEPaXic5nNFp/eNPbo2gPFck380yjgNqXNZnPrpxd/8Jtd524lRXwzj/ZB7sjTU0CTPKGJI0K2LZMbM5p5hVbXS3v7w/a5Uu7ojKtOzCDsczVVfTGl0tNT32nn61sndjGlXvej/Eh5pNeeLx0aU/rS2Y1KBHPz7X2PNFqmYxmfSiihIE4ogcubDdp/17391v6xT6/cmBSpWlBzSntRrljddvjyn/9srduRGtxx9/Kv3b/m6Y1j9nq8wxemFPVR59M3lAjmyQfjh43ytXpDdl6BE15TWiqHd/XdPSevf/eFbcac0p+v2nfmukcsCd6U2tmAgpJhOC6CKUW+eu70eiWuubmRN79N8d6qtJfVdwW79++7cnPCOisUq++K/681mm/94fPH1h185+PzOWuRc7cpPXVtZtn7R4V3TeXLSovb0uSOPD0FNLcpdYSpTqbUzPYWb0qlWCr5qE6m1Dzd82pueRfzeSyqPIoNxpQ6noNEO5nu1ZRaBwM3pb1MlV9xcZtZWtx4yLZP6C9K+FLR7ej6dzcdPXnaPEVf/qNaC3hQWwcdvTT5yzcP/NGPVgjzaeRjbu64+w2Rzm08cM48B6GIy28zeZtv73skXzNbYfLFcG1VGk5Tqs0pDWpH5q7ac+Laj377gRHOHly579B5c2CPoqBNqVXGLqYmB54XwZQiX90qTP/j3oeV6Caz7spus+j8fCpTcFbqWNHVlDrtqMb4dGp6domzMRcv2ZQKnbuZXPnhqQdX7t19/OrEbK7eaGaLlRW7T14cn221WqVq7fyt5Kvbjj21cey9sYv9XgjEHXl6CWiyKV37mdIAJ+Q+4tJiTalkPhcaLT0tmVvexXweiyqPYoMypaZcbtzvCkKeF7EOBm5KhV4997YSx2Tu+uSZbM0c4FCphmuZyr6jBLE2bjtqkND+G9Q+pZ/fTK7Zc3r1R6dFBDPazFqt+csTczPSgJTNB8//z//3jpGJdeC/PrThgTf2mOf0LI9vuyHfihMC9e3eyBXDq1Nzl5U4prDr9iGz6Px8MpVzVOqgCaMpNTa/yuRK5jMLmX78muZIv/KT1f/y3Puf30yYR10KeE6pO0VTg5EdU+SLWGkKgQZ56cTsxe/sf0wJcAZvXNhqFgrfIuN9p4MpddtRk4QW1swHNHQpplS40BNXph5evf/RtQeE7TxzPXE7mZ2ay4v07tpU+sjFidd3fPazFXuf3jS279QN4Vf7Om3Mw0R1D2jqP10pUQ/J1uJMqfW6AsftCXnetkM9poDexTwelJc8irneqVdgt34jfC1l95tXL+vxQDwv0kMLwjD1wpkNSjQz+MEnv76SbTc6z6bj3NAmUEKZhp8dlSgsMMPpl9bvPyvSsDvuXi5iVElfgqTZaq368NQHx6+mrYWLXtt+/M8eWGNkYh348n2r/vsT5hS73uVb/Xus9YGob/eGKQ21OqxMKW+8XArfiLbwDt8NcFGQzjJM6Xee3SrioDUhy0NLN6XeGKHElU+0jygFzNAjtW1bwaj9QtYRKf0i0CBvJctp4T//SVq18uFjvxubdrSgJFN5pTrHDE9TumXXdm87KhHU9gmKKRWqNZpTc4VXth75519v+fZT7z2yZv/LW448u/nTH7/24T888fYPfrNLmNWX3jvyr8+9f+jCuPCl5ml9kJfX6hrQ3CmgVcAyNu0Q59c9uBhTat+GZyuh67YX7usMeT2TtuXzwSzsca77ndqx3Xpc0sV7u3n7mh4NnX5XEPJ6Al4fRMDaM3H0gcPL7Jj23QO/Wnt5V7HeHq5VKIVrkNsgcAS0Huyo1X8QzGzM98Yu/O0jb33p3pXCiN5MZMSRRrP53Re2/fDVXcKXGmVefPew32xSGVHm6w+uM07pXR7fdkO91vog1Ld7w5SGXTfyk698vulbe35hh7UnT646NWduBSfUbLamwzcfIbymdDoZ2FC3zro0Pnfk4sS5m0l7jRBPDdaU+hazA4R/QtPOXSSnqkKgQV00UUyIqGePbbOVyhaVuhw/FFMq7OjZi1eUMp4ENdTNbUpF9KrVm7cT2WOXJnccufzm3jMrPjgp0rvNB8/tPXn9/K3ZRKZ45nrijV0nv79s5+7jV2f7NnTF04B1DWgeKaCviXLQLu/lhVw340zarJfwwLiIdwE74es5BbSuIwde/xhuYN6zx/Ps9E5t+rVPaYdPRMjrCXi92VAoVcldz01OFR0L6QvFe4a8jR7NVvViR23GZ9JBrd926Pz4A2/s+er9q//hic3Ci971ys5/f2XH1x5Y841HNz285g8iZGWLld57Sr/5+Gbzuj3L49tuqOdaH4D6dm+Y0mio3mzcLszcyE/JTWyGZtNh7DwIrykV9HV1jWFr0KZUlyPPcA2Fkv+qxU3ryo4YagVW4ziBBi1ejWZzLtZTSW1sU9q7HTUWBckFtCGe25TaqtYayUzxwu3Zk1enz95IXJ/OiGTOaHErlGviyLObP31m05gwrrPZvvhSDxNlqVNA683wOI2T8yUGYUo1Sb2pjuNCPaeAXsHZ8TQ8MO/Z43l6vVMh6YLa/agnqk/Y6+ZdP0+OX4qFfUbOTye0KoRsIZDBYcS0Hu2ozVRA/QeTc/mthy7d+eK2rz+49ovWVgiCrz+47q5lO1d8cDKZLW46cO5bT3afUyrC472/+9C8bs/y+LYbcn3n5foo1yBXzqbUL7lytSObfZZd2DwiDR7xfRW/iOQMYu4bE5KCoXjLnrmiM/wqL4FCo1q9EdrhbKE2pYHvCrMULdqUIhQ5NZstYbcm9WmTo4AwpQuwoxKz6bz5yIar//b4ZpG3ffeFbccuTR27NOlHytUdWqk1Lk/MPbVx7MkNn3x04nquWA3ztoQRlpUsOm3kQGWZ0mHnjubreia+oVK5UgtnZ8KA2LBAO2oTyK4wzVZrOl3YsP/sw2v2/+A3u7730o7vvbT9q/ev/sajmx5as3/H0cuZQuXQhfGHVu//wo+Wd1x9d/m/Pvf+m3sWXPEWYUrdyPVdcn02Xu07hg+0/2k37jiNpQO7jnuZUq/XVdqMnG7TgW1KpTYvm4g0PI2OGo1mNl9S6m+oCLUpzeYD6FVotVoi2C2d59/BlKI4q95oCiOayZViP4PUzc3xhHKkR6b0uU/D1w9/s+svfrbWzsP8kLeEkXVlMvX8O4eEpz1ycSJbDO9mXVGWlfYNxCL20M0yNJmpsyuVD4eKpWomX5rLFKaTcd79pb+Ih2Y+vkBlzCm965UdO4+acSxbrGz99NKf6ushKbHO5kv3rvzV+o+nUgtuLlyUKTULt4/Y9d2ylJZNVQcyCEl19qRVo6VXl0ypVanbRtG8rNuU2me57tYdK+ybkXysaUqtI5ZHtS4b/ran2KtWb2QL5XSumJgL10K7noTZlKbEEzQf6hB1/PKkiGtLxxgvJ8CUoljKam/TNnCC3tCeVSDb+u0/deNnK/Z+7f7VdirmiZ8pLVXrp6/PLN994t9e3H74wnixHOExLOGVmcbJw+H6JiktVhjIy3WQmbwOxHsvVc1my1lhoSdCsixlszX/xu4TO45ctkd8NJqt69PpFbtP/u0jb7lXPLpDd6SPrTt46MJ4deGdvYswpZJDUzyn1Wwkdy2aAUG+vtpj6bB8LnupSTmo3pt9QTkOKAftf8p3YttdvYx1Wfl+zOdDZ2nQSkdqmY9Q95QKGkNfrHLPiWtK5FoimFIUSyXmcuM40gWhTyvN5APY7GouV/r0/Pj6fWdFftaBq5Mp8wSXCuXa5zcTr+/47MpkqloLYLTeCMhM/gY1gtfOR20CcIZGLuvK48OhYqmqV9VRmYbQJzKTiUyr47qPw5G4hYvjc9POhZdK1fq1qfSbH51+4I0933xi81fvX/3Fe1Z85b5Vf/PwW997eceL7x0+fGHC3kJmQVqEKZWqtmJK212abhwBwTaZAsXvWX9ydk46X0i5N9etGnLesHKrhpzGVb4rlWE3eyFFU8koBbQQm1I9gcsXhz2CV5jS7zy7tY9gSlH8VKs3tEqqb54OCyK064p3lfjQZ7OlWkC72iA0aI3IOm2DoFQO76j+ZquVKVT2nbrx4ntH7vntB3e+sO0Hy3Y9vu7gpoPnx5O5RTexDc2UOk2m7QZdjUohNaUhbYEaEZWrEVs5POw9pYGM4EUIdVY2XxbVcxJTuigq+lbvCKHwqNFsatVTbw2HhRLajeUHJ8uUOnsyhWyHtghT6jB+HrIvZeB46RCYUudLo+CV0hvaxl0VNrSE3ZQKApmChRDqIG1AyDRjdxeO/tBS2WC2m0cI+SlX0BraYBEYKW99xMZQSP7Qc7Jl26H1YEqdHs9PtodsI5W3zbDsHhWHrLpQz9dVDtr/9Hybehkfc4uCldHQFiFHKgi1KTUeZSo7ci1wCIVZxbIx8woWz6glcAiFXNOzLLe7WPTu5WwQs+WDlIdFlGlbuB5MqWRxfe2ifUS7jt1P2/aBtv9s+2GXn3S5R9frto+4TbVdpv3qqnG13iM2NQQyGtqitfZH2HtKJ/X/1oLYBQsh5CljD5hoNb+FB+O5jVwCh1CIVShV7LoJiyGRnkqGYrmjoUrygQryQNZeTKns62T8nWG7S9a8rP/NtJ2hl1eUHKaE92BdN5Zn9rboTCgNUqJKGh4qQoTdlI7rLXBpRrshFA6V6CZdMsZc3EaTzlKEQqEZukmXhuHnc4taxjbiaptDC7tv01RvplRT23lqSI6ubfmkiyvjda1/aj7Wo9dUl18HptNSSvfZluRdxY3ZNlV+s07v6nxraMjKF42GtohNs4rAnFIDZpYiFAbp+y+nItf8FkIy1mZ6CKEAZWRvbLm8dCYTGdraApNsStFoq9VqicoYxTwtMqZ0BNd2QyhssrI36A9s+IlQsGq2WtHaxy/k0NYWmDClyJKohqIyRnE+QjRMqWH3iyU2/EQoMNUbzckE2Vs/SabY8gqhIJXOFkVNZOhHXzB+ICrVmvlw0TCFKUW6KtW6qIaRG7hrEJme0olpbRp9gyUrEQpI5s7y7OPXJ4wJ8/kibW0IBaNSRd9ZnpjWVxJzuRFb7ygcwpQiXcZSlBElMqbU6IZmEC9CgYiBu4ODCfMIDV+NZlPbBkZfdQz6hu7wM6wujlAQMgbuRneGfHR6Si1Gcnk3hIJUpar3J5C99R39kc7MZkduKwWEgpY59AMGQ7FcNR80QmgoKpb0zRGiPPQjYqbUGPBWItghNCw1Glp/ArNJB0qKMSAIDVGZvNGfQFgbIJUqY0AQGpKqtYZW7xKY0iAg2CE0BLVarUjPT4gQrFqJ0HDEZITBo7n96WS2zjogCA1eRueBsw5GkkiaUm37nQQTsRAauBjhNiT0pk3mJiA0aBVK+p7yWo2jm3SAGAsaz8xm2bkUoYGq2WzpG8hHcg8YhWj2lOoTsaaTWXb5Q2hwspbbZU/54ZFlgRCEBqZi2ZhzRUwbHsKX0l+K0IDUaDYNRxoPojp812AykWEcL0J9V7PZmk0bo3bJ3oaI3tyWyRXNjwEh1D/Zo3Zj0J8QFSb17uiZ2VytThcCQn2WqFaicsk1LupE2JQaix4JiiXWPUKob9LDnDY5gQ3lA0BfUCqVKbAcL0J9VFZf2WicPtKAmExkypWa+WEghJasSrU+FbsVKKPdU6pNxDL6FhjzhlA/VCxVWWg3DMzM5Zg2j9DS1Wg2Z9PaTAQiW1BoXdN6qpYvVsxPBSG0BOULZbmKxYaIm1K9M8foz0mQwyG0BLVarXSuKFcuCBxyOISWolLZbmWjjzRQrD7quXShwRRThBYrUX2sFShj2MoWeVNqYX42LF+J0CIkUjdzPXGGt4WFjNG3MJsu0NyG0EIlUrdU1mxlYxJpqJikuQ2hRalQqsR7xEdsTKk+MkfP4WZmc8Uys0wR6km1WsNc08iqQRBCMrlSs8k0U4S6S9STXKFsDKHCjoYNbekjvekzmcqXq8wyRagnlSs1a/lJczB8LImPKTWwpy4kU7kSs+oR8le1Vrd7EjRwpCHF7LueTGSy+RIj3xDyk7Cj+WLFHPRBQIsCIs9mDwWEOqhSq7d3jJ9O2Yu8xpK4mVKLlNE+OjOXEz9R7N2MkKxSudqOcYkMK1KGHxHQ7J+idLbIgF6EZNXrjWy+ZK1FmWHl8GhgNRwkU/liqcqS4wjZEtWhUKokU9KOLyOQqsXVlFpYIU+k4MVyFXeKRlnlSi2TK00l4zwhYXSYmc2KLBx3ikZZwovmiyJvs0a1xXHlj3gzro0EaX9qqWyxVBbuFHuKRlTNVqtUqaWzRWPuqNm+NjI9B3E3pfYnapGYy2XypVK5xlbOKPaqN5rlaj1XKM+mC9LkeLoRIo/4NO0PcTqZTWUKhVKlUq03aXdDsZbI2Kq1RqFYEUmbsZ2yhjbcoz2UACKHFs2cH18ylctqqVq1TqqG4i6RqgkjKr7wcr/oaAa0+JtSBwnVo07PZpOp/Gw6n8oWASKNyNKEOZnLFMT3WXyxJ51Tqsb1Me3Y0dgzlcyIZF2LaZmC8g0BiCZaTEukcu5RHvo8HWYfxAdtGSTtl0v9oEVME6ma+HUT3wftlw4gspipW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+ "description": "In this tutorial workflow, text_eng data is used as an input of Explode And Unexplode function. The Tokenizer function returns a column consisting of list of tokens, and the Explode And Unexplode function will list all items in a single column with other column values replicated. The parameter settings used in the function are shown below.", + "parameter": "1. **Input Column***: tokenized_text\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.transform import explode\r\ninput_table = inputs[0]\r\nresult = explode(table=input_table, input_col='tokenized_text')\r\noutput = result['out_table']", + "description": "In this tutorial workflow, text_eng data is used as an input of Explode And Unexplode function. The Tokenizer function returns a column consisting of list of tokens, and the Explode And Unexplode function will list all items in a single column with other column values replicated.", + "context": "python" + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/transform/meta/flatten_json.json b/function/python/brightics/function/transform/meta/flatten_json.json index 1f377dfcb..424d94159 100644 --- a/function/python/brightics/function/transform/meta/flatten_json.json +++ b/function/python/brightics/function/transform/meta/flatten_json.json @@ -1361,5 +1361,4 @@ "base64": 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" } ] -} - +} \ No newline at end of file diff --git a/function/python/brightics/function/transform/meta/get_table.json b/function/python/brightics/function/transform/meta/get_table.json index 2cef587b9..b7acc87fe 100644 --- a/function/python/brightics/function/transform/meta/get_table.json +++ b/function/python/brightics/function/transform/meta/get_table.json @@ -833,4 +833,4 @@ "base64": 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} ] -} +} \ No newline at end of file diff --git a/function/python/brightics/function/transform/meta/pivot2.json b/function/python/brightics/function/transform/meta/pivot2.json index 317adbbec..727ad94c3 100644 --- a/function/python/brightics/function/transform/meta/pivot2.json +++ b/function/python/brightics/function/transform/meta/pivot2.json @@ -4,6 +4,7 @@ "content": "" }, "specJson": { + "deprecated": true, "category": "transform", "func": "brightics.function.transform$pivot_twopivot", "name": "brightics.function.transform$pivot2", diff --git a/function/python/brightics/function/transform/meta/pivot3.json b/function/python/brightics/function/transform/meta/pivot3.json new file mode 100644 index 000000000..343360b57 --- /dev/null +++ b/function/python/brightics/function/transform/meta/pivot3.json @@ -0,0 +1,922 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "transform", + "func": "brightics.function.transform$pivot3", + "name": "brightics.function.transform$pivot3", + "context": "python", + "label": "Pivot", + "description": "A pivot table is a table of statistics that summarizes the data of a more extensive table (such as from a database, spreadsheet, or business intelligence program). This summary might include sums, averages, or other statistics, which the pivot table groups together in a meaningful way. Pivot tables are a technique in data processing. They arrange and rearrange (or \"pivot\") statistics in order to draw attention to useful information.", + "tags": [ + "transform", + "preprocessing" + ], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "out_table": "" + }, + "meta": { + "table": { + "type": "table", + "desc": "Data in a table", + "optional": false + }, + "out_table": { + "type": "table", + "desc": "Pivoted table" + } + }, + "params": [ + { + "id": "index", + "label": "Rows", + "description": "Grouper, all combinations of elements of these columns are shown the left part of tables.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "multiple": true, + "rowCount": 5 + }, + { + "id": "columns", + "label": "Columns", + "description": "Grouper, all combinations of elements of these columns are shown the top part of tables.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "multiple": true + }, + { + "id": "values", + "label": "Values", + "description": "Columns to aggregate.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "columnType": [], + "multiple": true, + "rowCount": 5 + }, + { + "id": "aggfunc", + "label": "Aggregate Function", + "description": "Aggregate functions\n - Available Options: MAX, MIN, MEAN, SUM, VAR, STDEV, CNT, MEDIAN, Q1, Q3, DISTINCT", + "visibleOption": [], + "control": "CheckBox", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "Max", + "value": "max", + "default": false + }, + { + "label": "Min", + "value": "min", + "default": false + }, + { + "label": "Mean", + "value": "mean", + "default": true + }, + { + "label": "Sum", + "value": "sum", + "default": true + }, + { + "label": "Variance of Sample", + "value": "var", + "default": false + }, + { + "label": "Standard Deviation of Sample", + "value": "std", + "default": false + }, + { + "label": "Count", + "value": "count", + "default": false + }, + { + "label": "Median", + "value": "median", + "default": false + }, + { + "label": "Q1", + "value": "q1", + "default": false + }, + { + "label": "Q3", + "value": "q3", + "default": false + }, + { + "label": "Distinct", + "value": "distinct", + "default": false + } + ], + "columnType": [] + } + ], + "summary": "This function returns pivot table." + }, + "md": "# Pivot\nThis function returns pivot table.\n\n## Description\nA pivot table is a table of statistics that summarizes the data of a more extensive table (such as from a database, spreadsheet, or business intelligence program). This summary might include sums, averages, or other statistics, which the pivot table groups together in a meaningful way. Pivot tables are a technique in data processing. They arrange and rearrange (or \"pivot\") statistics in order to draw attention to useful information.\n\nReference\n- \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data in a table\n#### OUTPUT\n1. **out_table**: (Table) Pivoted table\n#### PARAMETER\n1. **Rows**: Grouper, all combinations of elements of these columns are shown the left part of tables.\n\n2. **Columns**: Grouper, all combinations of elements of these columns are shown the top part of tables.\n\n3. **Values***: Columns to aggregate.\n\n4. **Aggregate Function**: Aggregate functions\n - Available Options: MAX, MIN, MEAN, SUM, VAR, STDEV, CNT, MEDIAN, Q1, Q3, DISTINCT\n\n\n### Python\n#### USAGE\n\n```\npivot3(table, values, aggfunc, index=None, columns=None)\n```\n\n#### INPUT\n1. **table***: (Table) Data in a table\n#### OUTPUT\n1. **out_table**: (Table) Pivoted table\n#### PARAMETER\n1. **index**: Grouper, all combinations of elements of these columns are shown the left part of tables.\n\t* Type: *list[str]*\n2. **columns**: Grouper, all combinations of elements of these columns are shown the top part of tables.\n\t* Type: *list[str]*\n3. **values***: Columns to aggregate.\n\t* Type: *list[str]*\n4. **aggfunc**: Aggregate functions\n - Available Options: MAX, MIN, MEAN, SUM, VAR, STDEV, CNT, MEDIAN, Q1, Q3, DISTINCT\n\t* Type: *str*\n\t* Default / Range: ( max | minmean | meansum | sum | var | std | count | median | q1 | q3 | distinct )\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\n\nIn this tutorial workflow, Pivot is used to calculate pivot table. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Rows**: Species\n2. **Values***: sepal_length\n3. **Aggregate Function**: MEAN, SUM\n\n\n### Python\n\n```\nfrom brightics.function.transform import pivot3\ninput_table = inputs[0]\nresult = pivot3(table = input_table, index = ['species'], values = ['sepal_length'], aggfunc = ['mean', 'sum'])\nout_table = result['out_table']\n```\n\nIn this python script, Pivot is used to calculate pivot table.\n", + "exampleModels": [ + { + "fileName": "Pivot.json", + "label": "Pivot", + "contents": { + "type": "files", + "data": [ + { + "type": "file", + "data": { + "id": "mrnqbd58hwn7rbx2", + "project_id": "pzuy9kd3uqtj7ruh", + "label": "Pivot", + "contents": { + "mid": "mrnqbd58hwn7rbx2", + "type": "data", + "title": "Pivot", + "functions": [ + { + "persist-mode": "auto", + "func": "brightics.function.io$load13889", + "name": "brightics.function.io$load", + "param": {}, + "display": { + "label": "Load", + 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+ } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/transform/meta/savgol_filter.json b/function/python/brightics/function/transform/meta/savgol_filter.json new file mode 100644 index 000000000..576034de6 --- /dev/null +++ b/function/python/brightics/function/transform/meta/savgol_filter.json @@ -0,0 +1,980 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "transform", + "func": "brightics.function.transform$savgol_filter", + "name": "brightics.function.transform$savgol_filter", + "context": "python", + "label": "Savitzky-Golay Filter", + "description": "A Savitzky–Golay filter is a digital filter that can be applied to a set of digital data points for the purpose of smoothing the data, that is, to increase the precision of the data without distorting the signal tendency\n\nReference:\n+ ", + "tags": [ + "savgol_filter" + ], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "out_table": "" + }, + "meta": { + "table": { + "type": "table" + }, + "out_table": { + "type": "table" + } + }, + "params": [ + { + "id": "input_cols", + "label": "Input Columns", + "description": "", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "globalVariable": false, + "columnType": [ + "Integer", + "Long", + "Float", + "Double" + ], + "multiple": true + }, + { + "id": "window_length", + "label": "Window length", + "description": "The length of the filter window (i.e. the number of coefficients). window_length must be a positive odd integer.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "placeHolder": "1 (value >= 1)", + "type": "Integer", + "min": 1 + }, + { + "id": "polyorder", + "label": "Polyorder", + "description": "The order of the polynomial used to fit the samples. polyorder must be less than window_length.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "placeHolder": "0 (value >= 0)", + "type": "Integer", + "min": 0 + }, + { + "id": "deriv", + "label": "Derivative order", + "description": "The order of the derivative to compute. This must be a nonnegative integer. The default is 0, which means to filter the data without differentiating.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Integer", + "min": 0, + "placeHolder": "0 (value >= 0)" + }, + { + "id": "delta", + "label": "Delta", + "description": "The spacing of the samples to which the filter will be applied. This is only used if deriv > 0. Default is 1.0.", + "visibleOption": [ + { + "script": "return #deriv# > 0;" + } + ], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Double", + "min": 0, + "placeHolder": "1.0 (value >= 0.0)" + }, + { + "id": "axis", + "label": "Axis", + "description": "The axis of the array x along which the filter is to be applied. Default is -1.\n\n", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Integer", + "placeHolder": "-1" + }, + { + "id": "mode", + "label": "Mode", + "description": "Must be ‘mirror’, ‘constant’, ‘nearest’, ‘wrap’ or ‘interp’. This determines the type of extension to use for the padded signal to which the filter is applied. When mode is ‘constant’, the padding value is given by cval. See the Notes for more details on ‘mirror’, ‘constant’, ‘wrap’, and ‘nearest’. When the ‘interp’ mode is selected (the default), no extension is used. Instead, a degree polyorder polynomial is fit to the last window_length values of the edges, and this polynomial is used to evaluate the last window_length // 2 output values.", + "visibleOption": [], + "control": "RadioButton", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "mirror", + "value": "mirror", + "default": false + }, + { + "label": "constant", + "value": "constant", + "default": false + }, + { + "label": "nearest", + "value": "nearest", + "default": false + }, + { + "label": "wrap", + "value": "wrap", + "default": false + }, + { + "label": "interp", + "value": "interp", + "default": true + } + ], + "globalVariable": false, + "columnType": [], + "targetTable": [] + }, + { + "id": "cval", + "label": "cval", + "description": "Value to fill past the edges of the input if mode is ‘constant’. Default is 0.0.", + "visibleOption": [ + { + "id": "mode", + "value": "constant" + } + ], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "placeHolder": "0.0", + "type": "Double" + }, + { + "id": "group_by", + "label": "Group By", + "description": "Columns to group by.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "multiple": true + } + ] + }, + "md": { + "en": "# Savitzky-Golay Filter\n\n\n## Description\nA Savitzky–Golay filter is a digital filter that can be applied to a set of digital data points for the purpose of smoothing the data, that is, to increase the precision of the data without distorting the signal tendency\n\nReference:\n+ \n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) \n#### OUTPUT\n1. **out_table**: (Table) \n#### PARAMETER\n1. **Input Columns***: Input columns.\n\n2. **Window length**: The length of the filter window (i.e. the number of coefficients). window_length must be a positive odd integer.\n\n3. **Polyorder**: The order of the polynomial used to fit the samples. polyorder must be less than window_length.\n\n4. **Derivative order**: The order of the derivative to compute. This must be a nonnegative integer. The default is 0, which means to filter the data without differentiating.\n\n5. **Delta**: The spacing of the samples to which the filter will be applied. This is only used if deriv > 0. Default is 1.0.\n\n6. **Axis**: The axis of the array x along which the filter is to be applied. Default is -1.\n\n\n\n7. **Mode**: Must be ‘mirror’, ‘constant’, ‘nearest’, ‘wrap’ or ‘interp’. This determines the type of extension to use for the padded signal to which the filter is applied. When mode is ‘constant’, the padding value is given by cval. See the Notes for more details on ‘mirror’, ‘constant’, ‘wrap’, and ‘nearest’. When the ‘interp’ mode is selected (the default), no extension is used. Instead, a degree polyorder polynomial is fit to the last window_length values of the edges, and this polynomial is used to evaluate the last window_length // 2 output values.\n\n8. **cval**: Value to fill past the edges of the input if mode is ‘constant’. Default is 0.0.\n\n9. **Group By**: Columns to group by.\n\n\n### Python\n#### USAGE\n\n```\nsavgol_filter(table = , input_cols = , window_length = , polyorder = , deriv = , delta = , axis = , mode = , cval = , group_by = )\n```\n\n#### INPUT\n1. **table***: (Table) \n#### OUTPUT\n1. **out_table**: (Table) \n#### PARAMETER\n1. **input_cols***: Input columns.\n\t* Type: *list[str]*\n2. **window_length**: The length of the filter window (i.e. the number of coefficients). window_length must be a positive odd integer.\n\t* Type: *int*\n\t* Default / Range: 1 (value >= 1)\n3. **polyorder**: The order of the polynomial used to fit the samples. polyorder must be less than window_length.\n\t* Type: *int*\n\t* Default / Range: 0 (value >= 0)\n4. **deriv**: The order of the derivative to compute. This must be a nonnegative integer. The default is 0, which means to filter the data without differentiating.\n\t* Type: *int*\n\t* Default / Range: 0 (value >= 0)\n5. **delta**: The spacing of the samples to which the filter will be applied. This is only used if deriv > 0. Default is 1.0.\n\t* Type: *double*\n\t* Default / Range: 1.0 (value >= 0.0)\n6. **axis**: The axis of the array x along which the filter is to be applied. Default is -1.\n\n\n\t* Type: *int*\n\n7. **mode**: Must be ‘mirror’, ‘constant’, ‘nearest’, ‘wrap’ or ‘interp’. This determines the type of extension to use for the padded signal to which the filter is applied. When mode is ‘constant’, the padding value is given by cval. See the Notes for more details on ‘mirror’, ‘constant’, ‘wrap’, and ‘nearest’. When the ‘interp’ mode is selected (the default), no extension is used. Instead, a degree polyorder polynomial is fit to the last window_length values of the edges, and this polynomial is used to evaluate the last window_length // 2 output values.\n\t* Type: *str*\n\t* Default / Range: ( mirror | constant | nearest | wrapinterp | interp )\n8. **cval**: Value to fill past the edges of the input if mode is ‘constant’. Default is 0.0.\n\t* Type: *double*\n\n9. **group_by**: Columns to group by.\n\t* Type: *list[str]*\n\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\nThe sample workflow is an example for the usage of Savitzky-Golay Filter, applied on a sample time series. It uses ‘interp’ mode extension, for the padded signal to which the filter is applied. \n\n++Parameters++\n1. **Input Columns***: data, data2\n2. **Window length**: 3\n3. **Polyorder**: 0\n4. **Derivative order**: 0\n5. **Axis**: -1\n6. **Mode**: interp\n\n\n\n### Python\n\n```\nfrom brightics.function.transform import savgol_filter\n\ninput_table = inputs[0]\n\nresult = savgol_filter(table = input_table,\n input_cols = ['data', 'data2'],\n window_length = 3,\n polyorder = 0,\n deriv = 0,\n axis = -1 ,\n mode = 'interp ')\n\noutput = result['out_table']\n```\nThis is the script expression for the usage of Savitzky-Golay Filter, with the same setting to the VA example model. \n\n\n", + "kr": "" + }, + "exampleModels": [ + { + "fileName": "Savitzky-Golay Filter.json", + "label": "Savitzky-Golay Filter", + "contents": { + "type": "files", + "data": [ + { + "type": "file", + "data": { + "id": "m2eejtbrrmxa6u76", + "project_id": "p5dmzu78qb564crk", + "label": "Savitzky-Golay Filter", + 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+ "description": "The sample workflow is an example for the usage of Savitzky-Golay Filter, applied on a sample time series. It uses ‘interp’ mode extension, for the padded signal to which the filter is applied. ", + "parameter": "1. **Input Columns***: data, data2\n2. **Window length**: 3\n3. **Polyorder**: 0\n4. **Derivative order**: 0\n5. **Axis**: -1\n6. **Mode**: interp\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.transform import savgol_filter\r\n\r\ninput_table = inputs[0]\r\n\r\nresult = savgol_filter(table = input_table,\r\n input_cols = ['data', 'data2'],\r\n window_length = 3,\r\n polyorder = 0,\r\n deriv = 0,\r\n axis = -1 ,\r\n mode = 'interp ')\r\n\r\noutput = result['out_table']", + "description": "This is the script expression for the usage of Savitzky-Golay Filter, with the same setting to the VA example model. ", + "context": "python" + } + ] +} diff --git a/function/python/brightics/function/transform/meta/split_data.json b/function/python/brightics/function/transform/meta/split_data.json index 6275b5bac..36fab6980 100644 --- a/function/python/brightics/function/transform/meta/split_data.json +++ b/function/python/brightics/function/transform/meta/split_data.json @@ -95,7 +95,7 @@ "tutorialLink": "https://www.brightics.ai/docs/ai/v3.6/tutorials/10_sampling?type=insight", "summary": "Split data into random train and test sets." }, - "md": "# Split Data\nSplit data into random train and test sets.\n\n## Description\nSplit Data is a method of splitting a dataset into a training set and a test set. A training dataset is a dataset used for learning, and a test dataset is a dataset that is independent of the training dataset. Therefore, a test set is used in to assess the performance (i.e. generalization) of a fitted model. \n\nReference\n- \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data in a table. \n#### OUTPUT\n1. **train_table**: (Table) Training dataset in a table. \n2. **test_table**: (Table) Test dataset in a table. \n#### PARAMETER\n1. **Train Ratio**: Train ratio for splitting a dataset. It should be numeric type. \n\n\t* Default value: \n2. **Test Ratio**: Test ratio for splitting a dataset. It should be numeric type. \n\n3. **Seed**: The seed used by the random number generator.\n\n4. **Group By**: Columns to group by\n\n\n### Python\n#### USAGE\n\n```\nsplit_data(table, train_ratio = 7.0, test_ratio = 3.0, random_state = None, group_by = None)\n```\n\n#### INPUT\n1. **table***: (Table) Data in a table. \n#### OUTPUT\n1. **train_table**: (Table) Training dataset in a table. \n2. **test_table**: (Table) Test dataset in a table. \n#### PARAMETER\n1. **train_ratio**: Train ratio for splitting a dataset. It should be numeric type. \n\t* Type: *double*\n\t* Default value: 7.0\n\n2. **test_ratio**: Test ratio for splitting a dataset. It should be numeric type. \n\t* Type: *double*\n\t* Default value: 3.0\n\n3. **random_state**: The seed used by the random number generator.\n\t* Type: *int*\n\n4. **group_by**: Columns to group by\n\t* Type: *list[str]*\n\n## Example\n### VA\n**[Related Tutorial]**\n\n\n**[Sample Model]**\n\n\n\n\nIn this tutorial workflow, sample_iris data is used for Split Data. The generated model shows the result of Split Data for a given train ratio 7.0 and test ratio 3.0. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Train Ratio**: 7.0\n2. **Test Ratio**: 3.0\n3. **Seed**: None\n4. **Group By**: None\n\n\n### Python\n\n```\nfrom brightics.function.transform import split_data\ninput_table = inputs[0]\nres = split_data(table = input_table, \n\t\t train_ratio = 7.0, \n\t\t test_ratio = 3.0, \n\t\t random_state = None, \n\t\t group_by = None)\noutput1 = res['train_table']\noutput2 = res['test_table']\n```\n\nIn this python script, Split Data function divides the input data into train data and test data according to a given train ratio and test ratio.", + "md": "# Split Data\nSplit data into random train and test sets.\n\n## Description\nSplit Data is a method of splitting a dataset into a training set and a test set. A training dataset is a dataset used for learning, and a test dataset is a dataset that is independent of the training dataset. Therefore, a test set is used in to assess the performance (i.e. generalization) of a fitted model. \n\nReference\n- \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data in a table. \n#### OUTPUT\n1. **train_table**: (Table) Training dataset in a table. \n2. **test_table**: (Table) Test dataset in a table. \n#### PARAMETER\n1. **Train Ratio**: Train ratio for splitting a dataset. It should be numeric type. \n\n\t* Default value: 7.0 \n2. **Test Ratio**: Test ratio for splitting a dataset. It should be numeric type. \n\n\t* Default value: 3.0 \n\n3. **Seed**: The seed used by the random number generator.\n\n4. **Group By**: Columns to group by\n\n\n### Python\n#### USAGE\n\n```\nsplit_data(table, train_ratio = 7.0, test_ratio = 3.0, random_state = None, group_by = None)\n```\n\n#### INPUT\n1. **table***: (Table) Data in a table. \n#### OUTPUT\n1. **train_table**: (Table) Training dataset in a table. \n2. **test_table**: (Table) Test dataset in a table. \n#### PARAMETER\n1. **train_ratio**: Train ratio for splitting a dataset. It should be numeric type. \n\t* Type: *double*\n\t* Default value: 7.0\n\n2. **test_ratio**: Test ratio for splitting a dataset. It should be numeric type. \n\t* Type: *double*\n\t* Default value: 3.0\n\n3. **random_state**: The seed used by the random number generator.\n\t* Type: *int*\n\n4. **group_by**: Columns to group by\n\t* Type: *list[str]*\n\n## Example\n### VA\n**[Related Tutorial]**\n\n\n**[Sample Model]**\n\n\n\n\nIn this tutorial workflow, sample_iris data is used for Split Data. The generated model shows the result of Split Data for a given train ratio 7.0 and test ratio 3.0. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Train Ratio**: 7.0\n2. **Test Ratio**: 3.0\n3. **Seed**: None\n4. **Group By**: None\n\n\n### Python\n\n```\nfrom brightics.function.transform import split_data\ninput_table = inputs[0]\nres = split_data(table = input_table, \n\t\t train_ratio = 7.0, \n\t\t test_ratio = 3.0, \n\t\t random_state = None, \n\t\t group_by = None)\noutput1 = res['train_table']\noutput2 = res['test_table']\n```\n\nIn this python script, Split Data function divides the input data into train data and test data according to a given train ratio and test ratio.", "exampleModels": [ { "fileName": "Split Data.json", diff --git a/function/python/brightics/function/transform/meta/svd.json b/function/python/brightics/function/transform/meta/svd.json index e0780023a..e20a2cae7 100644 --- a/function/python/brightics/function/transform/meta/svd.json +++ b/function/python/brightics/function/transform/meta/svd.json @@ -4,6 +4,7 @@ "content": "" }, "specJson": { + "deprecated": true, "category": "transform", "func": "brightics.function.transform$svd88688", "name": "brightics.function.transform$svd", diff --git a/function/python/brightics/function/transform/meta/svd2.json b/function/python/brightics/function/transform/meta/svd2.json new file mode 100644 index 000000000..083c9a5c4 --- /dev/null +++ b/function/python/brightics/function/transform/meta/svd2.json @@ -0,0 +1,920 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "transform", + "func": "brightics.function.transform$svd2", + "name": "brightics.function.transform$svd2", + "context": "python", + "label": "SVD", + "description": "The singular value decomposition(SVD) is a decomposition of a matrix, which generalizes the eigendecomposition of a square matrix to any m * n matrix. More specifically, for a given matrix A of size m * n, the SVD of A is a factorization of the form A=USV, where U and V are orthonormal matrices of size m * m and n * n, respectively and S is an m * n rectangular matrix with non-negative real numbers on the diagonal. The diagonal entries of S are known as the singular values of A. \nIn the case of the compact singular value decomposition, U and V are matrices of size m * k and k * n, respectively and S is a diagonal matrix of size k * k, where k=min{m, n}. ", + "tags": [], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "out_table1": "", + "out_table2": "", + "out_table3": "", + "out_table4": "", + "model": "" + }, + "meta": { + "table": { + "type": "table", + "desc": "Data in a Table. ", + "optional": false + }, + "out_table1": { + "type": "table", + "desc": "Output table whose columns are left-singular vectors of an input table. " + }, + "out_table2": { + "type": "table", + "desc": "Output table that returns the array of singular values." + }, + "out_table3": { + "type": "table", + "desc": "Output table whose columns are right-singular vectors of an input table. " + }, + "out_table4": { + "type": "table", + "desc": "Output table that projects the orginal dataset onto the singular vector subspace. " + }, + "model": { + "type": "model", + "desc": "Output model." + } + }, + "params": [ + { + "id": "input_cols", + "label": "Input Columns", + "description": "Choose columns of the input data to make a matrix A, where this function performs the singular value decomposition. 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", + "visibleOption": [], + "control": "BooleanRadio", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "defaultValue": false + }, + { + "id": "new_column_name", + "label": "New Column Name", + "description": "Name of new column(s)", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "type": "String", + "placeHolder": "projected_" + }, + { + "id": "group_by", + "label": "Group By", + "description": "Columns to group by.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": false, + "items": [], + "columnType": [], + "multiple": true + } + ], + "summary": "This function is for the singular value decomposition of a given dataset, which generalizes the eigendecomposition. " + }, + "md": { + "en": "# SVD\nThis function is for the singular value decomposition of a given dataset, which generalizes the eigendecomposition. \n\n## Description\nThe singular value decomposition(SVD) is a decomposition of a matrix, which generalizes the eigendecomposition of a square matrix to any m * n matrix. More specifically, for a given matrix A of size m * n, the SVD of A is a factorization of the form A=USV, where U and V are orthonormal matrices of size m * m and n * n, respectively and S is an m * n rectangular matrix with non-negative real numbers on the diagonal. The diagonal entries of S are known as the singular values of A. \nIn the case of the compact singular value decomposition, U and V are matrices of size m * k and k * n, respectively and S is a diagonal matrix of size k * k, where k=min{m, n}. \n\nReference\n- \n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data in a Table. \n#### OUTPUT\n1. **out_table1**: (Table) Output table whose columns are left-singular vectors of an input table. \n2. **out_table2**: (Table) Output table that returns the array of singular values.\n3. **out_table3**: (Table) Output table whose columns are right-singular vectors of an input table. \n4. **out_table4**: (Table) Output table that projects the orginal dataset onto the singular vector subspace. \n5. **model**: (Model) Output model.\n#### PARAMETER\n1. **Input Columns***: Choose columns of the input data to make a matrix A, where this function performs the singular value decomposition. 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Otherwise, it returns the full singular value decomposition. \n\n\t* Default value: false \n3. **New Column Name**: Name of new column(s)\n\n4. **Group By**: Columns to group by.\n\n\n### Python\n#### USAGE\n\n```\nsvd2(table = , input_cols = , full_matrices = false, new_column_name = , group_by = )\n```\n\n#### INPUT\n1. **table***: (Table) Data in a Table. \n#### OUTPUT\n1. **out_table1**: (Table) Output table whose columns are left-singular vectors of an input table. \n2. **out_table2**: (Table) Output table that returns the array of singular values.\n3. **out_table3**: (Table) Output table whose columns are right-singular vectors of an input table. \n4. **out_table4**: (Table) Output table that projects the orginal dataset onto the singular vector subspace. \n5. **model**: (Model) Output model.\n#### PARAMETER\n1. **input_cols***: Choose columns of the input data to make a matrix A, where this function performs the singular value decomposition. If there is no input, all columns of number types(Double, Long, Integer) are selected.\n\t* Type: *list[str]*\n2. **full_matrices**: If `False`, it returns the compact singular value decomposition. Otherwise, it returns the full singular value decomposition. \n\t* Type: *bool*\n\t* Default value: `False`\n3. **new_column_name**: Name of new column(s)\n\t* Type: *str*\n4. **group_by**: Columns to group by.\n\t* Type: *list[str]*\n\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, sample_iris data is used for SVD. The generated model shows the result of SVD of the given columns. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Input Columns***: sepal_length, sepal_width, petal_length, petal_width\n2. **Calculate Full Matrix**: False\n3. **New Column Name**: projected_\n4. **Group By**: None\n\n\n\n### Python\n\n```\nfrom brightics.function.transform import svd2\ninput_table = inputs[0]\nres = svd2(table = input_table, \n\t input_cols = [\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\"], \n\t full_matrices = False, \n\t group_by = None)\noutput1 = res['out_table1']\noutput2 = res['out_table2']\noutput3 = res['out_table3']\noutput4 = res['out_table4']\nmodel = res['model']\n```\nIn this python script, sample_iris data is used for SVD. 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The generated model shows the result of SVD of the given columns. The parameter settings used in the function are shown below.", + "parameter": "1. **Input Columns***: sepal_length, sepal_width, petal_length, petal_width\n2. **Calculate Full Matrix**: False\n3. **New Column Name**: projected_\n4. **Group By**: None\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.transform import svd2\r\ninput_table = inputs[0]\r\nres = svd2(table = input_table, \r\n\t input_cols = [\"sepal_length\", \"sepal_width\", \"petal_length\", \"petal_width\"], \r\n\t full_matrices = False, \r\n\t group_by = None)\r\noutput1 = res['out_table1']\r\noutput2 = res['out_table2']\r\noutput3 = res['out_table3']\r\noutput4 = res['out_table4']\r\nmodel = res['model']", + "description": "In this tutorial workflow, sample_iris data is used for SVD. The generated model shows the result of SVD of the given columns. ", + "context": "python" + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/transform/meta/svd_model.json b/function/python/brightics/function/transform/meta/svd_model.json new file mode 100644 index 000000000..f905572c3 --- /dev/null +++ b/function/python/brightics/function/transform/meta/svd_model.json @@ -0,0 +1,1793 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "transform", + "func": "brightics.function.transform$svd_model", + "name": "brightics.function.transform$svd_model", + "context": "python", + "label": "SVD Model", + "description": "This function transforms the features using the result of SVD.", + "tags": [ + "model", + "preprocessing", + "svd" + ], + "version": "3.6", + "inputs": { + "table": "", + "model": "" + }, + "outputs": { + "out_table": "" + }, + "meta": { + "table": { + "type": "table", + "desc": "Table including data to be transformed.", + "optional": false + }, + "out_table": { + "type": "table", + "desc": "Output table." + }, + "model": { + "type": "model", + "desc": "SVD Model from SVD function.", + "optional": false + } + }, + "params": [ + { + "id": "new_column_name", + "label": "New Column Name", + "description": "New column name", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "placeHolder": "projected_", + "type": "String" + } + ] + }, + "md": { + "en": "# SVD Model\n\n\n## Description\nThis function transforms the features using the result of SVD.\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Table including data to be transformed.\n2. **model***: (Model) SVD Model from SVD function.\n#### OUTPUT\n1. **out_table**: (Table) Output table.\n#### PARAMETER\n1. **New Column Name**: New column name\n\n\n### Python\n#### USAGE\n\n```\nsvd_model(table = inputs[0], model = models[0], new_column_name = 'projected_')\n```\n\n#### INPUT\n1. **table***: (Table) Table including data to be transformed.\n2. **model***: (Model) SVD Model from SVD function.\n#### OUTPUT\n1. **out_table**: (Table) Output table.\n#### PARAMETER\n1. **new_column_name**: New column name\n\t* Type: *str*\n\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, sample_iris data loaded and split into train data set and test data set. Then the singular value decomposition is performed to the training data. 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+ "description": "In this tutorial workflow, sample_iris data loaded and split into train data set and test data set. Then the singular value decomposition is performed to the training data. The SVD result is finally applied to the test dataset to transform the features. ", + "parameter": "1. **New Column Name**: projected_", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.transform.svd import svd_model\r\ninput_table = inputs[5]\r\ninput_model = models[0]\r\nres = svd_model(table = input_table, model = input_model)\r\noutput = res['out_table']", + "context": "python", + "description": "In this example code, the result of SVD function(models[0]) is applied to the input dataset(inputs[5]) to transform the features." + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/transform/meta/tsne.json b/function/python/brightics/function/transform/meta/tsne.json new file mode 100644 index 000000000..18ec71c3a --- /dev/null +++ b/function/python/brightics/function/transform/meta/tsne.json @@ -0,0 +1,1102 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "transform", + "func": "brightics.function.transform$tsne92932", + "name": "brightics.function.transform$tsne", + "context": "python", + "label": "t-SNE", + "description": "t-SNE is a tool to visualize high-dimensional data. It converts similarities between data points to joint probabilities and tries to minimize the Kullback-Leibler divergence between the joint probabilities of the low-dimensional embedding and the high-dimensional data. t-SNE has a cost function that is not convex, i.e. with different initializations we can get different results.\n", + "tags": [ + "dimension", + "preprocessing", + "add columns", + "add", + "column", + "Stochastic Neighbor Embedding" + ], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "out_table": "" + }, + "meta": { + "table": { + "type": "table" + }, + "out_table": { + "type": "table" + } + }, + "params": [ + { + "id": "input_cols", + "label": "Input Columns", + "description": "", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "globalVariable": false, + "columnType": [ + "Double", + "Float", + "Integer", + "Long" + ], + "multiple": true + }, + { + "id": "n_components", + "label": "Number of Components", + "description": "Dimension of the embedded space.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Integer", + "min": 1, + "placeHolder": "2 (value >= 1)" + }, + { + "id": "perplexity", + "label": "Perplexity", + "description": "The perplexity is related to the number of nearest neighbors that is used in other manifold learning algorithms.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Double", + "min": 1, + "placeHolder": "30.0" + }, + { + "id": "early_exaggeration", + "label": "Early Exaggeration", + "description": "Controls how tight natural clusters in the original space are in the embedded space and how much space will be between them.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Double", + "min": 0, + "placeHolder": "12.0" + }, + { + "id": "new_column_name", + "label": "New Column Name", + "description": "Name of new column(s)", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "String", + "placeHolder": "projected_" + }, + { + "id": "learning_rate", + "label": "Learning Rate", + "description": "The learning rate for t-SNE is usually in the range [10.0, 1000.0]", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Double", + "min": 0, + "placeHolder": "200.0" + }, + { + "id": "n_iter", + "label": "Maximum Number of Iterations", + "description": "Maximum number of iterations for the optimization.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Integer", + "min": 1, + "placeHolder": "1000 (value >= 250)" + }, + { + "id": "n_iter_without_progress", + "label": "Maximum Number of Iterations Without Progress", + "description": "Maximum number of iterations without progress before we abort the optimization, used after 250 initial iterations with early exaggeration", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Integer", + "min": 1, + "placeHolder": "300" + }, + { + "id": "min_grad_norm", + "label": "Minimum Gradient Norm", + "description": "If the gradient norm is below this threshold, the optimization will be stopped.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Double", + "min": 0, + "placeHolder": "1e-7" + }, + { + "id": "metric", + "label": "Metric", + "description": "The metric to use when calculating distance between instances in a feature array", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "String", + "placeHolder": "euclidean" + }, + { + "id": "init", + "label": "Initialization of Embedding", + "description": "Initialization of embedding", + "visibleOption": [], + "control": "RadioButton", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "Random", + "value": "random", + "default": true + }, + { + "label": "PCA", + "value": "pca", + "default": false + } + ], + "globalVariable": false, + "columnType": [], + "targetTable": [] + }, + { + "id": 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"columnType": [], + "targetTable": [] + }, + { + "id": "angle", + "label": "Angle", + "description": "Angular size of a distant node as measured from a point", + "visibleOption": [ + { + "id": "method", + "value": "barnes_hut" + } + ], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Double", + "placeHolder": "0.5" + }, + { + "id": "group_by", + "label": "Group By", + "description": "Columns to group by", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "multiple": true + } + ] + }, + "md": { + "en": "# t-SNE\n\n\n## Description\nt-SNE is a tool to visualize high-dimensional data. It converts similarities between data points to joint probabilities and tries to minimize the Kullback-Leibler divergence between the joint probabilities of the low-dimensional embedding and the high-dimensional data. t-SNE has a cost function that is not convex, i.e. with different initializations we can get different results.\n\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) \n#### OUTPUT\n1. **out_table**: (Table) \n#### PARAMETER\n1. **Input Columns***: Input columns.\n\n2. **Number of Components**: Dimension of the embedded space.\n\n3. **Perplexity**: The perplexity is related to the number of nearest neighbors that is used in other manifold learning algorithms.\n\n4. **Early Exaggeration**: Controls how tight natural clusters in the original space are in the embedded space and how much space will be between them.\n\n5. **New Column Name**: Name of new column(s)\n\n6. **Learning Rate**: The learning rate for t-SNE is usually in the range [10.0, 1000.0]\n\n7. **Maximum Number of Iterations**: Maximum number of iterations for the optimization.\n\n8. **Maximum Number of Iterations Without Progress**: Maximum number of iterations without progress before we abort the optimization, used after 250 initial iterations with early exaggeration\n\n9. **Minimum Gradient Norm**: If the gradient norm is below this threshold, the optimization will be stopped.\n\n10. **Metric**: The metric to use when calculating distance between instances in a feature array\n\n11. **Initialization of Embedding**: Initialization of embedding\n\n12. **Verbosity level**: Verbosity level\n\n13. **Seed**: The seed used by the random number generator.\n\n14. **Method**: Gradient calculation algorithm approximation method.\n\n15. **Angle**: Angular size of a distant node as measured from a point\n\n16. **Group By**: Columns to group by\n\n\n### Python\n#### USAGE\n\n```\ntsne(table = , input_cols = , n_components = , perplexity = , early_exaggeration = , new_column_name = , learning_rate = , n_iter = , n_iter_without_progress = , min_grad_norm = , metric = , init = , verbose = , seed = , method = , angle = , group_by = )\n```\n\n#### INPUT\n1. **table***: (Table) \n#### OUTPUT\n1. **out_table**: (Table) \n#### PARAMETER\n1. **input_cols***: Input columns.\n\t* Type: *list[str]*\n2. **n_components**: Dimension of the embedded space.\n\t* Type: *int*\n\t* Default / Range: 2 (value >= 1)\n3. **perplexity**: The perplexity is related to the number of nearest neighbors that is used in other manifold learning algorithms.\n\t* Type: *double*\n\t* Default / Range: 30.0\n4. **early_exaggeration**: Controls how tight natural clusters in the original space are in the embedded space and how much space will be between them.\n\t* Type: *double*\n\t* Default / Range: 12.0\n5. **new_column_name**: Name of new column(s)\n\t* Type: *str*\n6. **learning_rate**: The learning rate for t-SNE is usually in the range [10.0, 1000.0]\n\t* Type: *double*\n\t* Default / Range: 200.0\n7. **n_iter**: Maximum number of iterations for the optimization.\n\t* Type: *int*\n\t* Default / Range: 1000 (value >= 250)\n8. **n_iter_without_progress**: Maximum number of iterations without progress before we abort the optimization, used after 250 initial iterations with early exaggeration\n\t* Type: *int*\n\t* Default / Range: 300\n9. **min_grad_norm**: If the gradient norm is below this threshold, the optimization will be stopped.\n\t* Type: *double*\n\t* Default / Range: 1e-7\n10. **metric**: The metric to use when calculating distance between instances in a feature array\n\t* Type: *str*\n11. **init**: Initialization of embedding\n\t* Type: *str*\n\t* Default / Range: random ( random | pca )\n12. **verbose**: Verbosity level\n\t* Type: *int*\n\n13. **seed**: The seed used by the random number generator.\n\t* Type: *int*\n\n14. **method**: Gradient calculation algorithm approximation method.\n\t* Type: *str*\n\t* Default / Range: barnes_hut ( barnes_hut | exact )\n15. **angle**: Angular size of a distant node as measured from a point\n\t* Type: *double*\n\n16. **group_by**: Columns to group by\n\t* Type: *list[str]*\n\n\n## Example\n### VA\n\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, t-Stochastic Neighbor Embedding(t-SNE) is applied to sample iris data. Input columns are 'sepal_length', 'sepal_width', 'petal_length' and 'petal_width'.\n\n++Parameters++\n1. **Input Columns***: sepal_length, petal_length, sepal_width, petal_width\n2. **Number of Components**: 2\n3. **Perplexity**: 30\n4. **Early Exaggeration**: 12\n5. **New Column Name**: projected_\n6. **Learning Rate**: 200\n7. **Maximum Number of Iterations**: 1000\n8. **Maximum Number of Iterations Without Progress**: 300\n9. **Minimum Gradient Norm**: 1e-7\n10. **Metric**: euclidean\n11. **Initialization of Embedding**: Random\n12. **Verbosity level**: 0\n13. **Seed**: None\n14. **Method**: Barnes-Hut\n15. **Angle**: 0.5\n16. **Group By**: None\n\n\n\n### Python\n\n```\nfrom brightics.function.transform import tsne\nres = tsne(table = inputs[0], input_cols = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'])\nout_table = res['out_table']\n```\nIn this python script, t-Stochastic Neighbor Embedding is applied to sample iris data. 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", + "description": "In this tutorial workflow, t-Stochastic Neighbor Embedding(t-SNE) is applied to sample iris data. Input columns are 'sepal_length', 'sepal_width', 'petal_length' and 'petal_width'.", + "parameter": "1. **Input Columns***: sepal_length, petal_length, sepal_width, petal_width\n2. **Number of Components**: 2\n3. **Perplexity**: 30\n4. **Early Exaggeration**: 12\n5. **New Column Name**: projected_\n6. **Learning Rate**: 200\n7. **Maximum Number of Iterations**: 1000\n8. **Maximum Number of Iterations Without Progress**: 300\n9. **Minimum Gradient Norm**: 1e-7\n10. **Metric**: euclidean\n11. **Initialization of Embedding**: Random\n12. **Verbosity level**: 0\n13. **Seed**: None\n14. **Method**: Barnes-Hut\n15. **Angle**: 0.5\n16. **Group By**: None\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.transform import tsne\r\nres = tsne(table = inputs[0], input_cols = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'])\r\nout_table = res['out_table']", + "context": "python", + "description": "In this python script, t-Stochastic Neighbor Embedding is applied to sample iris data. This script yields the same result of the VA sample model, if the t-SNE function is altered by Python Script function with the script." + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/transform/meta/under_sampling.json b/function/python/brightics/function/transform/meta/under_sampling.json new file mode 100644 index 000000000..d20095c42 --- /dev/null +++ b/function/python/brightics/function/transform/meta/under_sampling.json @@ -0,0 +1,1488 @@ +{ + "script": { + "type": "", + "content": "" + }, + "specJson": { + "category": "transform", + "func": "brightics.function.transform$under_sampling", + "name": "brightics.function.transform$under_sampling", + "context": "python", + "label": "Under Sampling (Cluster Centroids)", + "description": "Method that under samples a specific class C by replacing a cluster of samples corresponding to the class C by the cluster centroid of a KMeans algorithm. This algorithm keeps N samples corresponding to the class C by fitting the KMeans algorithm with N cluster to the class C and using the coordinates of the N cluster centroids as the new samples corresponding to the class C.\n\nReferences:\nhttps://en.wikipedia.org/wiki/Oversampling_and_undersampling_in_data_analysis", + "tags": [ + "sampling", + "transform" + ], + "version": "3.6", + "inputs": { + "table": "" + }, + "outputs": { + "out_table": "" + }, + "meta": { + "table": { + "type": "table", + "desc": "Data in a table.", + "optional": false + }, + "out_table": { + "type": "table", + "desc": "Undersampled data." + } + }, + "params": [ + { + "id": "label_col", + "label": "Label Column", + "description": "The column designated for the labels of data.", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": true, + "items": [], + "globalVariable": false, + "columnType": [ + "Integer", + "Long", + "Float", + "Double", + "String" + ], + "multiple": false + }, + { + "id": "sampling_strategy", + "label": "Sampling Strategy", + "description": "Sampling information to resample the data set.", + "visibleOption": [], + "control": "RadioButton", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "Majority", + "value": "majority", + "default": false + }, + { + "label": "Not Minority", + "value": "not minority", + "default": true + }, + { + "label": "Not Majority", + "value": "not majority", + "default": false + }, + { + "label": "All", + "value": "all", + "default": false + } + ], + "globalVariable": false, + "columnType": [], + "targetTable": [] + }, + { + "id": "seed", + "label": "Seed", + "description": "Control the randomization of the algorithm.", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Integer" + }, + { + "id": "estimator", + "label": "Estimator", + "description": "Estimator for cluster centroids.", + "visibleOption": [], + "control": "RadioButton", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "KMeans", + "value": "KMeans", + "default": true + }, + { + "label": "None", + "value": "None", + "default": false + } + ], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "String" + }, + { + "id": "n_clusters", + "label": "N Clusters", + "description": "The number of clusters.", + "visibleOption": [ + { + "id": "estimator", + "value": "KMeans" + } + ], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Integer", + "min": 1, + "defaultValue": 8 + }, + { + "id": "voting", + "label": "Voting", + "description": "Voting strategy to generate the new samples", + "visibleOption": [], + "control": "RadioButton", + "validation": [], + "mandatory": false, + "items": [ + { + "label": "auto", + "value": "auto", + "default": true + }, + { + "label": "hard", + "value": "hard", + "default": false + }, + { + "label": "soft", + "value": "soft", + "default": false + } + ], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "String" + }, + { + "id": "n_jobs", + "label": "N Jobs", + "description": "", + "visibleOption": [], + "control": "InputBox", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "targetTable": [], + "type": "Integer", + "min": 1, + "defaultValue": 1 + }, + { + "id": "group_by", + "label": "Group By", + "description": "Columns to group by", + "visibleOption": [], + "control": "ColumnSelector", + "validation": [], + "mandatory": false, + "items": [], + "globalVariable": false, + "columnType": [], + "multiple": true, + "rowCount": 5 + } + ], + "summary": "This function performs under-sampling by generating centroids based on clustering methods.", + "tutorialLink": "https://www.brightics.ai/kr/docs/ai/v3.7/tutorials/121_class_imbalance?type=insight" + }, + "md": { + "en": "# Under Sampling (Cluster Centroids)\nThis function performs under-sampling by generating centroids based on clustering methods.\n\n## Description\nMethod that under samples a specific class C by replacing a cluster of samples corresponding to the class C by the cluster centroid of a KMeans algorithm. This algorithm keeps N samples corresponding to the class C by fitting the KMeans algorithm with N cluster to the class C and using the coordinates of the N cluster centroids as the new samples corresponding to the class C.\n\n+ References:\nhttps://en.wikipedia.org/wiki/Oversampling_and_undersampling_in_data_analysis\n\n## Properties\n### VA\n#### INPUT\n1. **table***: (Table) Data in a table.\n#### OUTPUT\n1. **out_table**: (Table) Undersampled data.\n#### PARAMETER\n1. **Label Column***: The column designated for the labels of data.\n\n2. **Sampling Strategy**: Sampling information to resample the data set.\n\n3. **Seed**: Control the randomization of the algorithm.\n\n4. **Estimator**: Estimator for cluster centroids.\n\n5. **N Clusters**: The number of clusters.\n\n\t* Default value: 8 \n6. **Voting**: Voting strategy to generate the new samples\n\n7. **N Jobs**: \n\n\t* Default value: 1 \n8. **Group By**: Columns to group by\n\n\n### Python\n#### USAGE\n\n```\nunder_sampling(table, label_col, sampling_strategy = \"majority\", seed, estimator = \"KMeans\", n_clusters = 8, voting = \"auto\", n_jobs = 1, group_by = None)\n```\n\n#### INPUT\n1. **table***: (Table) Data in a table.\n#### OUTPUT\n1. **out_table**: (Table) Undersampled data.\n#### PARAMETER\n1. **label_col***: The column designated for the labels of data.\n\t* Type: *str*\n2. **sampling_strategy**: Sampling information to resample the data set.\n\t* Type: *str*\n\t* Default / Range: ( majoritynot minority | not minority | not majority | all )\n3. **seed**: Control the randomization of the algorithm.\n\t* Type: *int*\n\n4. **estimator**: Estimator for cluster centroids.\n\t* Type: *str*\n\t* Default / Range: KMeans ( KMeans | None )\n5. **n_clusters**: The number of clusters.\n\t* Type: *int*\n\t* Default / Range: \n\t* Default value: 8 \n6. **voting**: Voting strategy to generate the new samples\n\t* Type: *str*\n\t* Default / Range: auto ( auto | hard | soft )\n7. **n_jobs**: \n\t* Type: *int*\n\t* Default / Range: \n\t* Default value: 1 \n8. **group_by**: Columns to group by\n\t* Type: *list[str]*\n\n\n## Example\n### VA\n**[Related Tutorial]**\n\n\n**[Sample Model]**\n\n\n\nIn this tutorial workflow, modified sample_iris data is used for undersampling the majority class. The parameter settings used in the function are shown below.\n\n++Parameters++\n1. **Label Column***: \"isVirginica\"\n2. **Sampling Strategy**: \"majority\"\n3. **Seed**: None\n4. **Estimator**: \"KMeans\" \n5. **N Clusters**: 8\n6. **Voting**: \"auto\"\n7. **N Jobs**: 1\n8. **Group By**: None\n\n\n\n### Python\n\n```\nfrom brightics.function.transform import under_sampling\nres = under_sampling(table = inputs[0], label_col = \"isVirginica\", sampling_strategy = \"majority\", estimator = \"KMeans\", n_clusters = 8, voting = \"auto\", n_jobs = 1)\noutput = res['out_table']\n```\nIn this python script, modified sample_iris data is used for undersampling the majority class. \n\n\n", + "kr": "" + }, + "exampleModels": [ + { + "fileName": "transform_under_sampling.json", + "label": "under_sampling", + "contents": { + "type": "files", + "data": [ + { + "type": "file", + "data": { + "id": "m7pvgutpkdcx94ma", + "project_id": "p5dmzu78qb564crk", + "label": "under_sampling", + "contents": { + 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+ "description": "In this tutorial workflow, modified sample_iris data is used for undersampling the majority class. The parameter settings used in the function are shown below.", + "parameter": "1. **Label Column***: \"isVirginica\"\n2. **Sampling Strategy**: \"majority\"\n3. **Seed**: None\n4. **Estimator**: \"KMeans\" \n5. **N Clusters**: 8\n6. **Voting**: \"auto\"\n7. **N Jobs**: 1\n8. **Group By**: None\n", + "index": 0 + } + ], + "scriptExamples": [ + { + "index": 1, + "code": "from brightics.function.transform import under_sampling\nres = under_sampling(table = inputs[0], label_col = \"isVirginica\", sampling_strategy = \"majority\", estimator = \"KMeans\", n_clusters = 8, voting = \"auto\", n_jobs = 1)\noutput = res['out_table']", + "description": "In this python script, modified sample_iris data is used for undersampling the majority class. ", + "context": "python" + } + ] +} \ No newline at end of file diff --git a/function/python/brightics/function/transform/missing_data.py b/function/python/brightics/function/transform/missing_data.py index cfccc0139..733fff4d3 100644 --- a/function/python/brightics/function/transform/missing_data.py +++ b/function/python/brightics/function/transform/missing_data.py @@ -14,20 +14,28 @@ limitations under the License. """ -from brightics.common.validation import validate, greater_than_or_equal_to +from brightics.common.utils import check_required_parameters -def delete_missing_data(table, input_cols, how='any', **params): - - _table = table.copy() - - if 'thresh' in params: - validate(greater_than_or_equal_to(params, 1, 'thresh')) - thresh = params['thresh'] - thresh = len(input_cols) - thresh + 1 +def delete_missing_data(table, **params): + check_required_parameters(_delete_missing_data, params, ['table']) + return _delete_missing_data(table, **params) + + +def _delete_missing_data(table, input_cols, how='any', row_or_column="index", missing_ratio=0): + if row_or_column == 'index': + subset = input_cols + length = len(input_cols) else: - thresh = None - - _out_table = _table.dropna(subset=input_cols, how=how, axis='index', thresh=thresh) - - return {'out_table':_out_table} + subset = None + length = table.shape[0] + if missing_ratio != 0: + max_na = missing_ratio * length + if max_na != int(max_na): + thresh = length - int(max_na) + else: + thresh = length - int(max_na) + 1 + else: + thresh = length + out_table = table.dropna(subset=subset, how=how, axis=row_or_column, thresh=thresh) + return {'out_table': out_table} diff --git a/function/python/brightics/function/transform/reshaping.py b/function/python/brightics/function/transform/reshaping.py index 58779f8ca..022e01514 100644 --- a/function/python/brightics/function/transform/reshaping.py +++ b/function/python/brightics/function/transform/reshaping.py @@ -16,6 +16,7 @@ import pandas as pd import numpy as np +from string import punctuation from brightics.common.groupby import _function_by_group from brightics.common.validation import raise_runtime_error @@ -175,6 +176,86 @@ def _distinct(x): return list(set(x)) out_table.columns = index + name_list return {'out_table':out_table} + +def pivot3(table, values, aggfunc, index=None, columns=None): # TODO + + if index is None and columns is None: + # TODO: assign an error code. + raise_runtime_error('Group key value is required: Index or Columns.') + + def _count(x): return len(x) + + def _sum(x): return brtc_stat.sum(x) + + def _mean(x): return brtc_stat.mean(x) + + def _std(x): return brtc_stat.std(x) + + def _var(x): return brtc_stat.var_samp(x) + + def _max(x): return brtc_stat.max(x) + + def _min(x): return brtc_stat.min(x) + + def _q1(x): return brtc_stat.percentile(x, 25) + + def _median(x): return brtc_stat.median(x) + + def _q3(x): return brtc_stat.percentile(x, 75) + + def _distinct(x): return list(set(x)) + + func_list = [] + for func_name in aggfunc: + if func_name == 'count': + func_list.append(_count) + elif func_name == 'mean': + func_list.append(_mean) + elif func_name == 'std': + func_list.append(_std) + elif func_name == 'var': + func_list.append(_var) + elif func_name == 'min': + func_list.append(_min) + elif func_name == 'q1': + func_list.append(_q1) + elif func_name == 'median': + func_list.append(_median) + elif func_name == 'q3': + func_list.append(_q3) + elif func_name == 'max': + func_list.append(_max) + elif func_name == 'sum': + func_list.append(_sum) + elif func_name == 'distinct': + func_list.append(_distinct) + + pivoted = pd.pivot_table(table, values=values, index=index, columns=columns, aggfunc=func_list, fill_value=None, margins=False, margins_name='All') + out_table = pd.concat([pivoted.index.to_frame(), pivoted], axis=1) + if index is None: + out_table = out_table.rename(index=str, columns={0: "values"}) + name_list = [] + if len(columns) == 1: + for name in out_table.columns.tolist()[1:]: + name_list.append(name[0][1:] + "_" + str(name[1])) + out_table.columns = out_table.columns.tolist()[:1] + name_list + else: + for name in out_table.columns.tolist()[len(columns) + 1:]: + name_list.append(name[1:]) + out_table.columns = out_table.columns.tolist()[:len(columns) + 1] + name_list + elif index is not None: + name_list = [] + for name in out_table.columns.tolist()[len(index):]: + name_list.append(name[0][1:] + "_" + "_".join(str(i) for i in name[1:])) + out_table.columns = index + name_list + + special_char = str(punctuation) + ' ' + + for char in special_char: + out_table.columns = out_table.columns.str.replace(char, '_') + + return {'out_table':out_table} + def transpose(table, group_by=None, **params): check_required_parameters(_transpose, params, ['table']) diff --git a/function/python/brightics/function/transform/savgol_filter.py b/function/python/brightics/function/transform/savgol_filter.py new file mode 100644 index 000000000..e17d20e8b --- /dev/null +++ b/function/python/brightics/function/transform/savgol_filter.py @@ -0,0 +1,50 @@ +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +import pandas as pd +import numpy as np +import statsmodels.tsa.filters.filtertools as sm +from brightics.common.groupby import _function_by_group +from brightics.common.utils import check_required_parameters +from brightics.common.utils import get_default_from_parameters_if_required +from brightics.common.validation import validate +from brightics.common.validation import greater_than_or_equal_to +from brightics.common.validation import from_to + +from scipy.signal import savgol_filter as savitzky_golay_filter + + +def savgol_filter(table, group_by=None, **params): + check_required_parameters(_savgol_filter, params, ['table']) + params = get_default_from_parameters_if_required(params, _savgol_filter) + param_validation_check = [greater_than_or_equal_to(params, 1, 'window_length')] + validate(*param_validation_check) + if group_by is not None: + return _function_by_group(_savgol_filter, table, group_by=group_by, **params) + else: + return _savgol_filter(table, **params) + + +def _savgol_filter(table, input_cols, window_length=1, polyorder=0, deriv=0, delta=1.0, axis=-1, mode='interp', + cval=0.0): + df = pd.DataFrame(table, columns=input_cols) + sf_filter = df[input_cols].apply( + lambda cols_data: savitzky_golay_filter(cols_data, window_length=window_length, polyorder=polyorder, + deriv=deriv, delta=delta, axis=axis, mode=mode, cval=cval), axis=0) + sf_filter.columns = sf_filter.columns + '_sf' + out_table = pd.concat([table, sf_filter], axis=1) + + return {'out_table': out_table} diff --git a/function/python/brightics/function/transform/sql/functions.py b/function/python/brightics/function/transform/sql/functions.py index 1919b77c1..efbc90d08 100644 --- a/function/python/brightics/function/transform/sql/functions.py +++ b/function/python/brightics/function/transform/sql/functions.py @@ -21,7 +21,8 @@ from .serializer import _serialize from .serializer import _deserialize import re - +from brightics.common.validation import greater_than_or_equal_to +from brightics.common.validation import validate """ constants """ @@ -116,7 +117,64 @@ def _is_empty(tup): return re.search(pattern, subject).group(1) else: return re.search(pattern, subject).group(index[0]) - + +# NOTE: String index start from 1 in SQL and 0 in Python +def _validate_params(params): + position = 1 + occurrence = 1 + return_option = 0 + param_validation_check = [] + # validate value of return_option + if len(params) >= 3: + return_option = params[2] + param_validation_check.append(greater_than_or_equal_to(params, 0, 2)) + # validate value of occurrence + if len(params) >= 2: + occurrence = params[1] + param_validation_check.append(greater_than_or_equal_to(params, 1, 1)) + # validate value of position + if len(params) >= 1: + position = params[0] + param_validation_check.append(greater_than_or_equal_to(params, 1, 0)) + validate(*param_validation_check) + return position-1, occurrence-1, return_option + + +def regexp_like(subject, pattern): + if re.search(pattern, subject) is None: + return False + return True + + +def regexp_count(subject, pattern, *params): + position, _, _ = _validate_params(params) + return len(re.findall(pattern, subject[position:])) + + +def regexp_substr(subject, pattern, *params): + position, occurrence, _ = _validate_params(params) + start, end = _regexp_common(subject, pattern, position, occurrence) + return subject[start:end] + + +def regexp_instr(subject, pattern, *params): + position, occurrence, return_option = _validate_params(params) + if return_option == 0: + return _regexp_common(subject, pattern, position, occurrence)[0] + 1 + return _regexp_common(subject, pattern, position, occurrence)[1] + 1 + + +def _regexp_common(subject, pattern, position, occurrence): + start = -1 + end = -1 + ret = re.finditer(pattern, subject[position:]) + for ind, r in enumerate(ret): + if (ind == occurrence): + start = r.start() + end = r.end() + break + return start, end + """ datetime related functions """ diff --git a/function/python/brightics/function/transform/svd.py b/function/python/brightics/function/transform/svd.py index 8f3b34cb5..dafca9c31 100644 --- a/function/python/brightics/function/transform/svd.py +++ b/function/python/brightics/function/transform/svd.py @@ -16,8 +16,12 @@ import numpy as np import pandas as pd +from sklearn.utils.extmath import safe_sparse_dot + +from brightics.common.repr import BrtcReprBuilder, strip_margin, pandasDF2MD, plt2MD, dict2MD from brightics.common.groupby import _function_by_group from brightics.common.utils import check_required_parameters +from brightics.function.utils import _model_dict def svd(table, group_by=None, **params): @@ -58,3 +62,97 @@ def _svd(table, input_cols, full_matrices=False): column_name_projection += ['proj%d' % (i + 1)] return {'out_table1':pd.DataFrame(u, columns=column_name_u), 'out_table2':pd.DataFrame(s, columns=column_name_s), 'out_table3':pd.DataFrame(v, columns=column_name_v), 'out_table4':pd.DataFrame(projection, columns=column_name_projection)} + +def svd2(table, group_by=None, **params): + check_required_parameters(_svd2, params, ['table']) + if group_by is not None: + return _function_by_group(_svd2, table, group_by=group_by, **params) + else: + return _svd2(table, **params) + + +def _svd2(table, input_cols, new_column_name='projected_', full_matrices=False): + A = table[input_cols] + + u, s, vh = np.linalg.svd(A, full_matrices=full_matrices) + projection = [] + for i in range(len(s)): + projection += [(u.T[i] * s[i])] + projection = np.array(projection).T + s_normal = [] + for i in range(len(s)): + if i == 0: + s_normal += [s[i] / s.sum()] + else: + s_normal += [s[i] / s.sum() + s_normal[i - 1]] + s = [s] + [s_normal] + s = np.array(s) + v = vh.T + column_name_u = [] + column_name_s = [] + column_name_v = [] + column_name_projection = [] + for i in range(u.shape[1]): + column_name_u += ['u%d' % (i + 1)] + for i in range(s.shape[1]): + column_name_s += ['s%d' % (i + 1)] + for i in range(v.shape[1]): + column_name_v += ['v%d' % (i + 1)] + for i in range(s.shape[1]): + column_name_projection += [new_column_name + '%d' % (i + 1)] + + out_table4 = pd.DataFrame(data=projection, columns=[column_name_projection]) + out_table4 = pd.concat([table.reset_index(drop=True), out_table4], axis=1) + out_table4.columns = table.columns.values.tolist() + column_name_projection + + res_param1 = {} + res_param1['Input Columns'] = input_cols + res_param1['full_matrices'] = full_matrices + + res_param2 = {} + res_param2['u'] = u.shape + res_param2['s'] = s.shape + res_param2['v'] = v.shape + res_param2['Projected Matrix'] = projection.shape + + rb = BrtcReprBuilder() + rb.addMD(strip_margin(""" + | ## SVD Result + | + | ### Dimensions of Matrices + | {parameter2} + | + | ### Parameters + | {parameter1} + """.format(parameter1=dict2MD(res_param1), + parameter2=dict2MD(res_param2) + ))) + + model = _model_dict('svd') + model['right_singular_vectors'] = pd.DataFrame(v, columns=column_name_v) + model['input_cols'] = input_cols + model['parameters'] = res_param1 + model['_repr_brtc_'] = rb.get() + + return {'out_table1':pd.DataFrame(u, columns=column_name_u), 'out_table2':pd.DataFrame(s, columns=column_name_s), 'out_table3':pd.DataFrame(v, columns=column_name_v), 'out_table4':out_table4, 'model':model} + +def svd_model(table, model, **params): + check_required_parameters(_svd_model, params, ['table', 'model']) + return _svd_model(table, model, **params) + + +def _svd_model(table, model, new_column_name='projected_'): + right_singular_vectors = model['right_singular_vectors'] + input_cols = model['input_cols'] + + _, col = right_singular_vectors.shape + + new_col_names = [] + for i in range(col): + new_col_names.append(new_column_name + str(i)) + + svd_result = safe_sparse_dot(table[input_cols], right_singular_vectors) + out_table = pd.concat([table.reset_index(drop=True), pd.DataFrame(data=svd_result, columns=[new_col_names])], axis=1) + out_table.columns = table.columns.values.tolist() + new_col_names + + return {'out_table' : out_table} \ No newline at end of file diff --git a/function/python/brightics/function/transform/tsne.py b/function/python/brightics/function/transform/tsne.py new file mode 100644 index 000000000..a6d513cdb --- /dev/null +++ b/function/python/brightics/function/transform/tsne.py @@ -0,0 +1,72 @@ +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +from sklearn.manifold import TSNE as tSNE +import pandas as pd +import matplotlib.pyplot as plt +from brightics.common.repr import BrtcReprBuilder, strip_margin, pandasDF2MD, plt2MD, dict2MD +from brightics.function.utils import _model_dict +from brightics.common.groupby import _function_by_group +from brightics.common.utils import check_required_parameters +from brightics.common.utils import get_default_from_parameters_if_required +from brightics.common.validation import validate +from brightics.common.validation import from_to +import seaborn as sns +import numpy as np +import matplotlib.cm as cm +from matplotlib.patches import Patch +from brightics.common.validation import validate, greater_than_or_equal_to + + +def tsne(table, group_by=None, **params): + check_required_parameters(_tsne, params, ['table']) + params = get_default_from_parameters_if_required(params, _tsne) + param_validation_check = [from_to(params, 1, len(params['input_cols']), 'n_components')] + validate(*param_validation_check) + if group_by is not None: + grouped_model = _function_by_group(_tsne, table, group_by=group_by, **params) + return grouped_model + else: + return _tsne(table, **params) + + +def _tsne(table, input_cols, new_column_name='projected_', n_components=2, perplexity=30.0, + early_exaggeration=12.0, learning_rate=200.0, n_iter=1000, + n_iter_without_progress=300, min_grad_norm=1e-7, + metric="euclidean", init="random", verbose=0, + seed=None, method='barnes_hut', angle=0.5): + + num_feature_cols = len(input_cols) + if n_components is None: + n_components = num_feature_cols + + tsne = tSNE(n_components=n_components, perplexity=perplexity, early_exaggeration=early_exaggeration, + learning_rate=learning_rate, n_iter=n_iter, n_iter_without_progress=n_iter_without_progress, + min_grad_norm=min_grad_norm, metric=metric, init=init, verbose=verbose, + random_state=seed, method=method, angle=angle) + + tsne_result = tsne.fit_transform(table[input_cols]) + + column_names = [] + for i in range(0, n_components): + column_names.append(new_column_name + str(i)) + + out_df = pd.DataFrame(data=tsne_result[:, :n_components], columns=[column_names]) + + out_df = pd.concat([table.reset_index(drop=True), out_df], axis=1) + out_df.columns = table.columns.values.tolist() + column_names + + return {'out_table': out_df} \ No newline at end of file diff --git a/function/python/brightics/function/transform/under_sampling.py b/function/python/brightics/function/transform/under_sampling.py new file mode 100644 index 000000000..deda1d0e4 --- /dev/null +++ b/function/python/brightics/function/transform/under_sampling.py @@ -0,0 +1,99 @@ +""" + Copyright 2019 Samsung SDS + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +from brightics.common.groupby import _function_by_group +from brightics.common.utils import check_required_parameters +from brightics.common.utils import get_default_from_parameters_if_required +from brightics.common.validation import validate +from brightics.common.validation import greater_than_or_equal_to +from brightics.common.validation import raise_error +from brightics.common.classify_input_type import check_col_type + +import sklearn.utils as sklearn_utils +from sklearn import svm +from sklearn import preprocessing +from sklearn.cluster import KMeans +from imblearn.over_sampling import SMOTE as SMOTE_LIB +from imblearn.under_sampling import ClusterCentroids +import pandas as pd +import numpy as np + + +def under_sampling(table, group_by=None, **params): + check_required_parameters(_under_sampling, params, ['table']) + params = get_default_from_parameters_if_required(params, _under_sampling) + + if group_by is not None: + return _function_by_group(_under_sampling, table, group_by=group_by, **params) + else: + return _under_sampling(table, **params) + + +def _under_sampling(table, label_col, sampling_strategy='not majority', seed=None, estimator='KMeans', + n_clusters=8, voting='auto', n_jobs=1): + + # Separate features and label + features = table.drop([label_col], axis=1) + y = table[label_col] + + if(sklearn_utils.multiclass.type_of_target(y) == 'continuous'): + raise_error('0718', 'label_col') + + # Initialization label encoder + lab_encoder = preprocessing.LabelEncoder() + + # Filter out categorical columns in features + categorical_cols = [col for col in features.columns if features[col].dtypes == 'object'] + + # Transform categorical columns and add to the original features + for cate_col in categorical_cols: + features_encoder = lab_encoder.fit_transform(features[cate_col]) + features[cate_col] = features_encoder + + # Transform label column with object type + if (y.dtypes == 'object'): + y_encoder = lab_encoder.fit_transform(y) + else: + y_encoder = y + + if (estimator == 'Kmeans'): + estimator_model = KMeans(n_clusters=n_clusters) + else: + estimator_model = None + + # Process under sampling + sm = ClusterCentroids(sampling_strategy=sampling_strategy, random_state=seed, + estimator=estimator_model, voting=voting, n_jobs=n_jobs) + + X_res, y_res = sm.fit_resample(features, y_encoder) + + # Invert to original data + if (y.dtypes == 'object'): + y_decoder = lab_encoder.inverse_transform(y_res) + else: + y_decoder = y_res + + df = pd.DataFrame(data=X_res, columns=features.columns) + + for cate_col in categorical_cols: + df[cate_col] = lab_encoder.inverse_transform(df[cate_col].astype('int32')) + + df1 = pd.DataFrame(data=y_decoder, columns=[label_col]) + + # Output result + out_table = df.join(df1) + + return {'out_table' : out_table} diff --git a/lib/nltk_data/taggers/averaged_perceptron_tagger/averaged_perceptron_tagger.pickle b/lib/nltk_data/taggers/averaged_perceptron_tagger/averaged_perceptron_tagger.pickle new file mode 100644 index 000000000..28fd11a25 Binary files /dev/null and b/lib/nltk_data/taggers/averaged_perceptron_tagger/averaged_perceptron_tagger.pickle differ diff --git a/lib/nltk_data/tokenizers/punkt/PY3/english.pickle b/lib/nltk_data/tokenizers/punkt/PY3/english.pickle new file mode 100644 index 000000000..ca5a7ccd0 Binary files /dev/null and b/lib/nltk_data/tokenizers/punkt/PY3/english.pickle differ diff --git a/lib/nltk_data/tokenizers/punkt/english.pickle b/lib/nltk_data/tokenizers/punkt/english.pickle new file mode 100644 index 000000000..648cb75b3 --- /dev/null +++ b/lib/nltk_data/tokenizers/punkt/english.pickle @@ -0,0 +1,61702 @@ +ccopy_reg +_reconstructor +p0 +(cnltk.tokenize.punkt +PunktSentenceTokenizer +p1 +c__builtin__ +object +p2 +Ntp3 +Rp4 +(dp5 +S'_Token' +p6 +cnltk.tokenize.punkt +PunktToken +p7 +sS'_lang_vars' +p8 +g0 +(cnltk.tokenize.punkt +PunktLanguageVars +p9 +g2 +Ntp10 +Rp11 +I1 +bsS'_params' +p12 +g0 +(cnltk.tokenize.punkt +PunktParameters +p13 +g2 +Ntp14 +Rp15 +(dp16 +S'sent_starters' +p17 +c__builtin__ +set +p18 +((lp19 +Vamong +p20 +aVsince +p21 +aVthey +p22 +aVindeed +p23 +aVsome +p24 +aVsales +p25 +aVin +p26 +aVmoreover +p27 +aVyet +p28 +aVif +p29 +aVeven +p30 +aVthese +p31 +aVnevertheless +p32 +aVsimilarly +p33 +aVthere +p34 +aVwhen +p35 +aVunder +p36 +aVinstead +p37 +aVmeanwhile +p38 +aVthus +p39 +aVhowever +p40 +aVbut +p41 +aVit +p42 +aVmost +p43 +aValthough +p44 +aVdespite +p45 +aVnor +p46 +aVhe +p47 +aVboth 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@@ asn1crypto==0.24.0 boto==2.49.0 boto3==1.9.67 botocore==1.12.67 +bs4==0.0.1 cached-property==1.5.1 certifi==2018.11.29 cffi==1.11.5 @@ -35,7 +36,7 @@ lightgbm==2.2.2 MarkupSafe==1.1.1 matplotlib==3.0.2 networkx==2.2 -nltk==3.4.3 +nltk==3.4.5 numexpr==2.6.2 numpy==1.18.1 opencv-python-headless==4.0.0.21 @@ -58,7 +59,7 @@ PyMySQL==0.9.3 pyparsing==2.3.0 python-dateutil==2.7.5 pytz==2018.7 -PyYAML==3.13 +PyYAML==5.1 redis==2.10.6 requests==2.20.1 s3transfer==0.1.13 @@ -75,7 +76,7 @@ statsmodels==0.9.0 texttable==0.9.1 tqdm==4.28.1 twkorean==0.1.5 -urllib3==1.24.1 +urllib3==1.24.2 websocket-client==0.55.0 xgboost==0.80 xlrd==1.2.0 diff --git a/pom.xml b/pom.xml index 151d8aada..2ae33b3fe 100644 --- a/pom.xml +++ b/pom.xml @@ -261,7 +261,7 @@ mysql mysql-connector-java - 5.1.41 + 8.0.16 org.postgresql @@ -292,7 +292,7 @@ com.google.guava guava - 20.0 + 24.1.1-jre joda-time @@ -346,7 +346,7 @@ org.quartz-scheduler quartz - 2.2.1 + 2.3.2 c3p0 @@ -426,17 +426,17 @@ com.fasterxml.jackson.core jackson-databind - 2.9.10 + 2.11.1 com.fasterxml.jackson.core jackson-core - 2.9.9 + 2.11.1 com.fasterxml.jackson.core jackson-annotations - 2.9.9 + 2.11.1 org.reflections @@ -461,7 +461,7 @@ com.fasterxml.jackson.module jackson-module-scala_2.11 - 2.9.5 + 2.11.1 org.apache.commons @@ -561,7 +561,7 @@ io.netty netty-all - 4.1.17.Final + 4.1.42.Final io.netty diff --git a/va-server/visual-analytics/app.js b/va-server/visual-analytics/app.js index 39a43d292..770314df2 100644 --- a/va-server/visual-analytics/app.js +++ b/va-server/visual-analytics/app.js @@ -111,6 +111,7 @@ app.use(__REQ_express.static(__REQ_path.join(__dirname, 'public'))); app.use(subPathUrl + '*/js/plugins/echarts', __REQ_express.static(__dirname + '/node_modules/echarts/dist/')); app.use(subPathUrl + '*/js/plugins/echarts-stat', __REQ_express.static(__dirname + '/node_modules/echarts-stat/dist/')); +app.use(subPathUrl + '*/js/plugins/echarts-wordcloud', __REQ_express.static(__dirname + '/node_modules/echarts-wordcloud/dist/')); app.use(subPathUrl + '*/js/plugins/d3', __REQ_express.static(__dirname + '/node_modules/d3/build/')); app.use(subPathUrl + '*/js/plugins/d3-tip', __REQ_express.static(__dirname + '/node_modules/d3-tip/')); diff --git a/va-server/visual-analytics/package.json b/va-server/visual-analytics/package.json index 1f8a0b872..f32d7268f 100644 --- a/va-server/visual-analytics/package.json +++ b/va-server/visual-analytics/package.json @@ -32,6 +32,7 @@ "dotenv": "2.0.0", "echarts": "4.1.0", "echarts-stat": "1.1.0", + "echarts-wordcloud": "^1.1.3", "ejs": "2.5.7", "element-ui": "2.4.2", "es6-promise": "4.2.4", diff --git a/va-server/visual-analytics/public/css/plugins/bcharts/bcharts-adonis-all.css b/va-server/visual-analytics/public/css/plugins/bcharts/bcharts-adonis-all.css index 404c0859d..8b4ad877d 100644 --- a/va-server/visual-analytics/public/css/plugins/bcharts/bcharts-adonis-all.css +++ b/va-server/visual-analytics/public/css/plugins/bcharts/bcharts-adonis-all.css @@ -1,4 +1,4 @@ -/* Brightics 2.0 Chart Build 190718 */ +/* Brightics 2.0 Chart Build 200703 */ .bcharts-adonis { display: flex; } @@ -15,29 +15,6 @@ flex-direction: column; } -.bcharts-adonis .jqx-tabs, .bo-container .jqx-tabs { - border: none; -} - -.bcharts-adonis .jqx-tabs-headerWrapper, .bo-container .jqx-tabs-headerWrapper { - border: none; -} - -.bcharts-adonis li.jqx-tabs-title, .bo-container li.jqx-tabs-title{ - border: 1px solid #DBDDE2; - padding-bottom: 7px; - margin-right: 0; - width: 76px; -} - -.bcharts-adonis li.jqx-tabs-title-selected-top, .bo-container li.jqx-tabs-title-selected-top { - border-top: 2px solid #6C79E9; - color: rgba(0, 0, 0, 0.8); - background-color: #FFFFFF; - padding-bottom: 6px; - border-bottom: 1px solid #FFF; -} - .bcharts-adonis-left { width: 260px; height: 100%; @@ -59,7 +36,7 @@ height: 100%; box-sizing: border-box; border: solid 1px #DBDDE2; - background: #f7f7f7; + background: #ffffff; margin-left: 8px; } @@ -70,11 +47,14 @@ } .bcharts-adonis-viewpart-header { - height: 54px; + height: 24px; box-sizing: border-box; - line-height: 54px; - text-align: center; + text-align: left; font-weight: bold; + color: #333333; + line-height: 24px; + font-size: 16px; + margin: 14px 0 14px 20px; } .bcharts-adonis-viewpart::before @@ -104,7 +84,7 @@ /*chart type*/ .bcharts-adonis-chart-selector { - padding-left: 30px; + padding-left: 40px; box-sizing: border-box; line-height: 30px; cursor: pointer; @@ -119,169 +99,167 @@ .bcharts-adonis-chart-selector::before { content: ''; - width: 30px; - height: 30px; - background-image: url('images/bcharts.png'); - background-position-y: -30px; + width: 40px; + height: 40px; position: absolute; top: 0; left: 0; } .bcharts-adonis-chart-selector[chart="scatter"]::before { - background-position-x: -60px; + background-image: url('images/chart-26/scatter.svg'); } .bcharts-adonis-chart-selector[chart="line"]::before { - background-position-x: -90px; + background-image: url('images/chart-26/line.svg'); } .bcharts-adonis-chart-selector[chart="column"]::before { - background-position-x: -120px; + background-image: url('images/chart-26/column.svg'); } .bcharts-adonis-chart-selector[chart="column-stacked"]::before { - background-position-x: -150px; + background-image: url('images/chart-26/column-stacked.svg'); } .bcharts-adonis-chart-selector[chart="column-stacked-100"]::before { - background-position-x: -180px; + background-image: url('images/chart-26/column-stacked-100.svg'); } .bcharts-adonis-chart-selector[chart="bar"]::before { - background-position-x: -210px; + background-image: url('images/chart-26/bar.svg'); } .bcharts-adonis-chart-selector[chart="bar-stacked"]::before { - background-position-x: -240px; + background-image: url('images/chart-26/bar-stacked.svg'); } .bcharts-adonis-chart-selector[chart="bar-stacked-100"]::before { - background-position-x: -270px; + background-image: url('images/chart-26/bar-stacked-100.svg'); } .bcharts-adonis-chart-selector[chart="area"]::before { - background-position-x: -300px; + background-image: url('images/chart-26/area.svg'); } .bcharts-adonis-chart-selector[chart="area-stacked"]::before { - background-position-x: -330px; + background-image: url('images/chart-26/area-stacked.svg'); } .bcharts-adonis-chart-selector[chart="area-stacked-100"]::before { - background-position-x: -360px; + background-image: url('images/chart-26/area-stacked-100.svg'); } .bcharts-adonis-chart-selector[chart="boxplot"]::before { - background-position-x: -390px; + background-image: url('images/chart-26/boxplot.svg'); } .bcharts-adonis-chart-selector[chart="heatmap"]::before { - background-position-x: -420px; + background-image: url('images/chart-26/heatmap.svg'); } .bcharts-adonis-chart-selector[chart="pie"]::before { - background-position-x: -450px; + background-image: url('images/chart-26/pie.svg'); } .bcharts-adonis-chart-selector[chart="network"]::before { - background-position-x: -480px; + background-image: url('images/chart-26/network.svg'); } .bcharts-adonis-chart-selector[chart="treemap"]::before { - background-position-x: -510px; + background-image: url('images/chart-26/treemap.svg'); } .bcharts-adonis-chart-selector[chart="decisionTree"]::before { - background-position-x: -540px; + background-image: url('images/chart-26/decisionTree.svg'); } .bcharts-adonis-chart-selector[chart="decisionTreeForBrightics"]::before { - background-position-x: -540px; + background-image: url('images/chart-26/decisionTree.svg'); } .bcharts-adonis-chart-selector[chart="table"]::before { - background-position-x: -570px; + background-image: url('images/chart-26/table.svg'); } .bcharts-adonis-chart-selector[chart="mcc"]::before { - background-position-x: -600px; + background-image: url('images/chart-26/mcc.svg'); } .bcharts-adonis-chart-selector[chart="dendrogram"]::before { - background-position-x: -630px; + background-image: url('images/chart-26/dendrogram.svg'); } .bcharts-adonis-chart-selector[chart="roccurve"]::before { - background-position-x: -660px; + background-image: url('images/chart-26/roccurve.svg'); } .bcharts-adonis-chart-selector[chart="correlation-matrix"]::before { - background-position-x: -690px; + background-image: url('images/chart-26/correlation-matrix.svg'); } .bcharts-adonis-chart-selector[chart="map"]::before { - background-position-x: -720px; + background-image: url('images/chart-26/map.svg'); } .bcharts-adonis-chart-selector[chart="biplot"]::before { - background-position-x: -750px; + background-image: url('images/chart-26/biplot.svg'); } .bcharts-adonis-chart-selector[chart="pos-neg-bar"]::before { - background-position-x: -780px; + background-image: url('images/chart-26/pos-neg-bar.svg'); } .bcharts-adonis-chart-selector[chart="radar"]::before { - background-position-x: -810px; + background-image: url('images/chart-26/radar.svg'); } .bcharts-adonis-chart-selector[chart="wordcloud"]::before { - background-position-x: -840px; + background-image: url('images/chart-26/wordcloud.svg'); } .bcharts-adonis-chart-selector[chart="complex"]::before { - background-position-x: -870px; + background-image: url('images/chart-26/complex.svg'); } .bcharts-adonis-chart-selector[chart="histogram"]::before { - background-position-x: -900px; + background-image: url('images/chart-26/histogram.svg'); } .bcharts-adonis-chart-selector[chart="bubble"]::before { - background-position-x: -930px; + background-image: url('images/chart-26/bubble.svg'); } .bcharts-adonis-chart-selector[chart="heatmap-matrix"]::before { - background-position-x: -960px; + background-image: url('images/chart-26/heatmap-matrix.svg'); } .bcharts-adonis-chart-selector[chart="qqplot"]::before { - background-position-x: -990px; + background-image: url('images/chart-26/qqplot.svg'); } .bcharts-adonis-chart-selector[chart="pairwise-scatter"]::before { - background-position-x: -1020px; + background-image: url('images/chart-26/pairwise-scatter.svg'); } .bcharts-adonis-chart-selector[chart="card"]::before { - background-position-x: -1050px; + background-image: url('images/chart-26/card.svg'); } .bcharts-adonis-chart-selector[chart="donut"]::before { - background-position-x: -1080px; + background-image: url('images/chart-26/donut.svg'); } .bcharts-adonis-chart-selector[chart="image-grid"]::before { - background-position-x: -1110px; + background-image: url('images/chart-26/image-grid.svg'); } -.selected .bcharts-adonis-chart-selector::before { - background-position-y: -60px; +.bcharts-adonis-chart-selector[chart="wordCloud"]::before { + background-image: url('images/chart-26/wordcloud.svg'); } .bcharts-adonis-chart-selector-16 { - padding: 0 2px 0 18px; + padding: 0 2px 0 25px; box-sizing: border-box; line-height: 16px; cursor: pointer; @@ -293,156 +271,158 @@ content: ''; width: 16px; height: 16px; - background-image: url('images/bcharts.png'); - background-position-y: -238px; position: absolute; top: 0; left: 0; } .bcharts-adonis-chart-selector-16[chart="column"]::before { - background-position-x: -60px; + background-image: url('images/chart-16/column.svg'); } .bcharts-adonis-chart-selector-16[chart="bar"]::before { - background-position-x: -76px; + background-image: url('images/chart-16/bar.svg'); } .bcharts-adonis-chart-selector-16[chart="line"]::before { - background-position-x: -92px; + background-image: url('images/chart-16/line.svg'); } .bcharts-adonis-chart-selector-16[chart="boxplot"]::before { - background-position-x: -108px; + background-image: url('images/chart-16/boxplot.svg'); } .bcharts-adonis-chart-selector-16[chart="scatter"]::before { - background-position-x: -124px; + background-image: url('images/chart-16/scatter.svg'); } .bcharts-adonis-chart-selector-16[chart="pie"]::before { - background-position-x: -140px; + background-image: url('images/chart-16/pie.svg'); } .bcharts-adonis-chart-selector-16[chart="table"]::before { - background-position-x: -156px; + background-image: url('images/chart-16/table.svg'); } .bcharts-adonis-chart-selector-16[chart="treemap"]::before { - background-position-x: -172px; + background-image: url('images/chart-16/treemap.svg'); } .bcharts-adonis-chart-selector-16[chart="network"]::before { - background-position-x: -188px; + background-image: url('images/chart-16/network.svg'); } .bcharts-adonis-chart-selector-16[chart="decisionTree"]::before { - background-position-x: -204px; + background-image: url('images/chart-16/decisionTree.svg'); } .bcharts-adonis-chart-selector-16[chart="decisionTreeForBrightics"]::before { - background-position-x: -204px; + background-image: url('images/chart-16/decisionTree.svg'); } .bcharts-adonis-chart-selector-16[chart="dendrogram"]::before { - background-position-x: -220px; + background-image: url('images/chart-16/dendrogram.svg'); } /*FIXME: correlation matrix img ->heatmap image*/ .bcharts-adonis-chart-selector-16[chart="heatmap"]::before { - background-position-x: -236px; + background-image: url('images/chart-16/heatmap.svg'); } .bcharts-adonis-chart-selector-16[chart="mcc"]::before { - background-position-x: -252px; + background-image: url('images/chart-16/mcc.svg'); } .bcharts-adonis-chart-selector-16[chart="roccurve"]::before { - background-position-x: -268px; + background-image: url('images/chart-16/roccurve.svg'); } .bcharts-adonis-chart-selector-16[chart="map"]::before { - background-position-x: -284px; + background-image: url('images/chart-16/map.svg'); } .bcharts-adonis-chart-selector-16[chart="biplot"]::before { - background-position-x: -300px; + background-image: url('images/chart-16/biplot.svg'); } .bcharts-adonis-chart-selector-16[chart="pos-neg-bar"]::before { - background-position-x: -316px; + background-image: url('images/chart-16/pos-neg-bar.svg'); } .bcharts-adonis-chart-selector-16[chart="bar-stacked"]::before { - background-position-x: -332px; + background-image: url('images/chart-16/bar-stacked.svg'); } .bcharts-adonis-chart-selector-16[chart="bar-stacked-100"]::before { - background-position-x: -348px; + background-image: url('images/chart-16/bar-stacked-100.svg'); } .bcharts-adonis-chart-selector-16[chart="column-stacked"]::before { - background-position-x: -364px; + background-image: url('images/chart-16/column-stacked.svg'); } .bcharts-adonis-chart-selector-16[chart="column-stacked-100"]::before { - background-position-x: -380px; + background-image: url('images/chart-16/column-stacked-100.svg'); } .bcharts-adonis-chart-selector-16[chart="area"]::before { - background-position-x: -396px; + background-image: url('images/chart-16/area.svg'); } .bcharts-adonis-chart-selector-16[chart="area-stacked"]::before { - background-position-x: -412px; + background-image: url('images/chart-16/area-stacked.svg'); } .bcharts-adonis-chart-selector-16[chart="area-stacked-100"]::before { - background-position-x: -428px; + background-image: url('images/chart-16/area-stacked-100.svg'); } .bcharts-adonis-chart-selector-16[chart="radar"]::before { - background-position-x: -444px; + background-image: url('images/chart-16/radar.svg'); } .bcharts-adonis-chart-selector-16[chart="wordcloud"]::before { - background-position-x: -460px; + background-image: url('images/chart-16/wordcloud.svg'); } .bcharts-adonis-chart-selector-16[chart="complex"]::before { - background-position-x: -476px; + background-image: url('images/chart-16/complex.svg'); } .bcharts-adonis-chart-selector-16[chart="histogram"]::before { - background-position-x: -492px; + background-image: url('images/chart-16/histogram.svg'); } .bcharts-adonis-chart-selector-16[chart="bubble"]::before { - background-position-x: -508px; + background-image: url('images/chart-16/bubble.svg'); } .bcharts-adonis-chart-selector-16[chart="heatmap-matrix"]::before { - background-position-x: -524px; + background-image: url('images/chart-16/heatmap-matrix.svg'); } .bcharts-adonis-chart-selector-16[chart="qqplot"]::before { - background-position-x: -540px; + background-image: url('images/chart-16/qqplot.svg'); } .bcharts-adonis-chart-selector-16[chart="pairwise-scatter"]::before { - background-position-x: -556px; + background-image: url('images/chart-16/pairwise-scatter.svg'); } .bcharts-adonis-chart-selector-16[chart="card"]::before { - background-position-x: -572px; + background-image: url('images/chart-16/card.svg'); } .bcharts-adonis-chart-selector-16[chart="donut"]::before { - background-position-x: -588px; + background-image: url('images/chart-16/donut.svg'); } .bcharts-adonis-chart-selector-16[chart="image-grid"]::before { - background-position-x: -604px; + background-image: url('images/chart-16/image-grid.svg'); +} + +.bcharts-adonis-chart-selector-16[chart="wordCloud"]::before { + background-image: url('images/chart-16/wordcloud.svg'); } .bos-col-1 { @@ -510,9 +490,8 @@ .bos-display-flex { display: flex; - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; + padding-top: 2px; + padding-bottom: 2px; } .bos-display-flex-center { @@ -533,6 +512,10 @@ justify-content: space-between; } +.bos-flex-row { + flex-direction: row; +} + .bos-flex-space-evenly { justify-content: space-evenly; } @@ -541,6 +524,10 @@ justify-content: center; } +.bos-flex-left { + justify-content: left; +} + .bos-flex-align-center { align-items: center; } @@ -565,24 +552,55 @@ flex: 4; } +.bos-flex-5 { + flex: 5; +} + +.bos-flex-6 { + flex: 6; +} + .bos-display-flex .bos-widget-header { - width: 70px; + width: 100%; display: flex; -webkit-box-sizing: border-box; -moz-box-sizing: border-box; box-sizing: border-box; /*justify-content: center;*/ align-items: center; + font-size: 14px; + line-height: 30px; + /*color: #767676;*/ + /*padding-bottom: 8px;*/ } .bos-display-flex .bos-widget-header.half { - width: 35px; + width: 50%; +} + +.bos-display-flex .bos-widget-multi-label { + display: flex; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; + align-items: center; + font-size: 14px; + line-height: 20px; + /*color: #767676;*/ +} + +.bos-display-flex .bos-widget-multi-label.half { + width: 50%; } .bos-text-overflow-hidden { overflow: hidden; text-overflow: ellipsis; white-space: nowrap; + font-size: 12px; + line-height: 18px; + color: #767676; + text-align: left; /*white-space: normal;*/ /*display: -webkit-box;*/ /*-webkit-line-clamp: 1;*/ @@ -597,7 +615,6 @@ .bos-flex-direction-column .bos-widget-header, .bos-widget-contents { flex-grow: 1; - padding: 2.5px 0; } .bos-border-only-bottom { @@ -615,6 +632,7 @@ height: 20px; box-sizing: border-box; flex-shrink: 0; + margin-right: 15px; } .bos-simple-circle { @@ -639,11 +657,21 @@ /*widget css START*/ +.bos-widget-header-label { + text-align: left; + justify-content: left; + display: left; + align-items: left; + width: 35px; +} + .bos-widget-label { text-align: center; justify-content: center; display: flex; align-items: center; + line-height: 20px; + color: #767676; } .bos-widget-inputbox { @@ -725,44 +753,41 @@ } .bos-border-color-red { - border-color: rgba(194, 6, 17, .65) !important; - border: 1px solid; + outline: 1px solid rgba(194, 6, 17, .65) !important; } .bos-border-color-blue { - border-color: rgba(98, 111, 219, 1.0) !important; - border: 1px solid; + outline: 1px solid #3420c3 !important; } .bos-dialogs-header { - font-family: Arial, helvetica, sans-serif; + font-family: 'NotoSansCJKkr', Arial, helvetica, sans-serif; font-size: 16px; font-weight: bold; - - width: calc(100% - 20px) !important; - margin-left: 10px; - padding: 10px 0 5px 0; - + width: calc(100% - 4px) !important; + height: 34px !important; + margin: 6px 2px 6px 2px; + padding: 0; border-color: #d9d9d9; background-color: #fff; } -.bos-dialogs-header .jqx-window-close-button { - position: absolute; - right: 0; - top: 0 !important; - width: 18px !important; - height: 18px !important; - background-image: url(images/tool-close-18.png); - background-repeat: no-repeat; - background-position: 0 0; +.bos-dialogs-header .left{ + line-height: 34px; + margin-left: 10px; +} + +.bos-dialogs-header .right{ + padding-top: 7px; + padding-right: 15px; } .bos-dialogs-body { - font-family: Arial, helvetica, sans-serif; + font-family: 'NotoSansCJKkr', Arial, helvetica, sans-serif; width: 100%; height: 100%; position: relative; + padding: 0 !important; } .bos-font-bold { @@ -801,11 +826,11 @@ } .bos-widget-column-contents { - width: calc(100% - 70px); + width: 100%; } .bos-widget-column-unit { - font-family: Arial, helvetica, sans-serif; + font-family: 'NotoSansCJKkr', Arial, helvetica, sans-serif; font-size: 11px; position: relative; float: left; @@ -814,11 +839,7 @@ flex: 1; line-height: 34px; height: 100%; - background-color: white; - - border: 1px solid #CCCCCC; - border-radius: 17.5px; box-sizing: border-box; overflow: hidden; cursor: pointer; @@ -844,32 +865,66 @@ -moz-box-sizing: border-box; box-sizing: border-box; align-items: center; - height: 35px; - margin: 2px 0; + height: 30px; + margin-bottom: 5px; +} + +.bos-widget-column-unit-label { + width: 100%; + height: 30px; + box-sizing: border-box; + font-size: 14px; + font-family: 'NotoSansCJKkr', Arial; + display: block; + align-items: center; + justify-content: center; + text-align: left; + line-height: 30px; + padding: 0 10px; + + overflow: hidden; + white-space: nowrap; + text-overflow: ellipsis; + + border: 1px solid #cccccc; + background-color: #ffffff; + color: #959595; } -.bos-widget-column-unit-label, .bos-widget-datasource-unit-label { + width: 100%; + height: 30px; + box-sizing: border-box; + font-size: 14px; + font-family: 'NotoSansCJKkr', Arial; align-items: center; + justify-content: left; + line-height: 30px; + padding: 0 10px; + overflow: hidden; white-space: nowrap; text-overflow: ellipsis; - padding: 0 20px 0 8px; + border: 1px solid #cccccc; + background-color: #ffffff; + color: #959595; } .bos-column-unit-type { - min-width: 65px; - width: 65px; - height: 100%; + width: 40%; + height: 30px; box-sizing: border-box; - background-color: #E6E9FF; - color: #454758; - font-size: 12px; - font-family: Arial; + font-size: 14px; + font-family: 'NotoSansCJKkr', Arial; display: flex; align-items: center; justify-content: center; + line-height: 30px; + + border: 1px solid #3420c3; + background-color: #ebe9f9; + color: #3420c3; } .bos-widget-disable { @@ -894,22 +949,6 @@ margin: 3px 0 3px 1px; } -.bos-widget-button-group-container .jqx-button { - height: 30px; - background-color: #ffffff; -} - -.bos-widget-button-group-container .jqx-button:not(:first-child) { - border-left: none; - margin-left: 0 !important; -} - -.bos-widget-button-group-container .jqx-button.jqx-fill-state-pressed-office { - background-color: #8290FF; - border-color: #d9d9d9; - color: #ffffff; -} - .bos-aggregation-list-box { display: flex; -webkit-box-sizing: border-box; @@ -934,7 +973,12 @@ } .bo-control-container-contents .bos-widget-chart-selector { - max-width: calc(100% - 70px); + width: 100%; + max-width: 100%; + border-left: 0; + border-right: 0; + border-top: 0; + border-bottom: 1px solid #cccccc; } .bos-widget-chart-selector.bos-widget-chart-selector-readonly, @@ -945,7 +989,6 @@ .bos-widget-item-selector { height: 35px; - border-radius: 17.5px; padding: 3px 0; margin: 2px 0; } @@ -972,9 +1015,9 @@ box-sizing: border-box; flex-direction: column; flex: 1; - max-width: 270px; - margin: 3px 10px 0px 10px; + margin: 3px 0px 0px 0px; position: relative; + height: calc(100% - 20px); } .bos-widget-column-selector-list { @@ -986,51 +1029,44 @@ display: flex; flex-direction: row; align-items: center; - height: 25px; + height: 32px; cursor: pointer; - margin-right: 10px; overflow: hidden; + padding: 0 20px; } .bos-dialog-column-item-type { - width: 70px; - min-width: 70px; height: 21px; box-sizing: border-box; - margin-top: 2px; - margin-left: 2px; - border-radius: 9.5px; - background-color: #e6e9ff; - border: solid 1px #bdc0d6; display: flex; - justify-content: center; - align-items: center; - font-size: 11px; - text-align: center; -} - -.bos-dialog-column-item-type.bos-dialog-column-item-type-schema { - background-color: #828282; - color: #ffffff; + justify-content: right; + align-items: right; + font-size: 14px; + text-align: right; + color: #767676; } .bos-dialog-column-item-wrapper.selected { - background-color: #8190ff; + background-color: #ebe9f9; } .bos-dialog-column-item-label { - font-size: 12px; - padding-left: 10px; + font-size: 14px; + justify-content: left; + align-items: left; + text-align: left; + color: #333333; + line-height: 20px; + margin-right: auto; + text-overflow: ellipsis; + white-space: nowrap; + overflow: hidden; } .bos-dialog-column-item-label.has-type { max-width: calc(100% - 80px); } -.selected .bos-dialog-column-item-label { - color: #ffffff; -} - .bos-aggregation-list-box .bos-dialog-column-item-wrapper { display: flex; align-items: center; @@ -1038,32 +1074,12 @@ font-size: 12px; } -.bos-aggregation-list-box .bos-dialog-column-item-wrapper.selected { - color: #ffffff; - -} - .bos-dialog-filter-container { box-sizing: border-box; - width: 100%; - padding: 5px 10px 5px 10px; -} - -input[type=search].jqx-input-office { - padding-right: 30px; - background: url(images/search-28.png) no-repeat; - background-position: center right; - font-size: 13px; - box-sizing: border-box; - min-width: 16px; - min-height: 16px; - margin: 0; - padding: 0; - padding-right: 10px; - padding-left: 10px; - border-width: 1px; - border-style: solid; - width: 100%; + width: calc(100% - 40px); + padding: 5px 0px; + margin: 0 20px; + border-bottom: 1px solid #cccccc; } .bos-dialog-column-item { @@ -1082,7 +1098,10 @@ input[type=search].jqx-input-office { .bos-widget-selector { box-sizing: border-box; - border: 1px solid #d9d9d9; + border-top: 0; + border-left: 0; + border-right: 0; + border-bottom: 1px solid #cccccc; padding-left: 0.1px; background-color: #ffffff; } @@ -1124,14 +1143,6 @@ input[type=search].jqx-input-office { margin-right: 2px; } -.bos-container .bos-control-container-expander-expanded .jqx-expander-header-content { - color: rgba(0, 0, 0, 1); -} - -.bos-container .bos-control-container-expander-collapsed .jqx-expander-header-content { - color: rgba(0, 0, 0, 0.8); -} - /*chart option control START*/ .bos-container .bos-control-container-expander { position: relative; @@ -1176,7 +1187,7 @@ input[type=search].jqx-input-office { .bos-control-container-label { border: none; - background-color: #f7f7f7; + background-color: #ffffff; display: flex; align-items: center; padding: 0 0 0 20px; @@ -1184,13 +1195,17 @@ input[type=search].jqx-input-office { font-size: 14px !important; height: 40px !important; position: relative; + line-height: 20px; + color: #333333; + font-weight: 500; + padding-left: 0; } -.bos-control-container-label.jqx-fill-state-pressed { +.bos-control-container-label.brtc-toggle-button__check--checked { background-color: #ffffff; } -.bos-control-container-label.jqx-fill-state-pressed:after { +.bos-control-container-label.brtc-toggle-button__check--checked:after { content: ''; position: absolute; top: 39px; @@ -1267,6 +1282,10 @@ input[type=search].jqx-input-office { margin: 5px 0 6px auto; } +.bos-widget-onoff-switch.bos-widget-button-disable{ + opacity: .55; +} + .bos-widget-onoff-switch input { display: none; } @@ -1292,11 +1311,11 @@ input[type=search].jqx-input-office { } .bos-widget-onoff-switch input:checked + .bos-widget-onoff-switch-slider { - background-color: #8290FF; + background-color: #3420c3; } .bos-widget-onoff-switch input:hover + .bos-widget-onoff-switch-slider { - box-shadow: 0 0 2px #8290FF; + box-shadow: 0 0 2px #3420c3; } .bos-widget-onoff-switch input:checked + .bos-widget-onoff-switch-slider::before { @@ -1305,6 +1324,13 @@ input[type=search].jqx-input-office { transform: translateX(22px); } + +.bos-widget-onoff-switch-slider.bos-widget-button-disable { + cursor: default; + pointer-events: none; + color: rgba(0, 0, 0, 0.8); +} + .bos-widget-onoff-switch-inner { display: inline-block; line-height: 28px; @@ -1323,30 +1349,8 @@ input[type=search].jqx-input-office { margin-right: 4px; } -.bos-control-container-label .jqx-expander-arrow { - width: 12px; - height: 6px; - margin: 0 !important; - left: 20px; - top: 17px; -} - -.bos-control-container-label .jqx-expander-arrow.jqx-expander-arrow-top { - background-image: url('images/icon/icon-arrow-down.png'); -} - -.bos-control-container-label .jqx-expander-arrow.jqx-expander-arrow-bottom { - background-image: url('images/icon/icon-arrow-up.png'); -} - -.bos-control-container-label .jqx-expander-header-content { - padding: 0 !important; - margin: 0 24px !important; - flex-grow: 1; -} - .bos-control-container-contents { - padding: 0 20px; + padding: 0 16px; } .bos-control { @@ -1361,13 +1365,13 @@ input[type=search].jqx-input-office { } .bos-control-contents { - background-color: #f7f7f7; + background-color: #ffffff; padding: 8px; margin-bottom: 5px; } .bos-control-subcontents { - background-color: #eaeaea; + background-color: #ffffff; padding: 10px; margin-bottom: 5px; } @@ -1386,6 +1390,23 @@ input[type=search].jqx-input-office { cursor: pointer; } +.bos-control-header-add-button-box-style { + width: 64px; + height: 32px; + border-radius: 2px; + border: 1px solid #cccccc; + color: #5a5a5a; + line-height: 20px; + font-size: 14px; + background: #ffffff; + display: inline-block; + cursor: pointer; +} + +.bos-control-header-add-button-box-style:focus { + outline: 0; +} + .bos-control-header-add-button:hover { background-position: -17px 0; } @@ -1399,6 +1420,7 @@ input[type=search].jqx-input-office { display: inline-block; background: url('images/tool-close-18.png') no-repeat; background-position: -4px -5px; + cursor: pointer; } .bos-middle-button { @@ -1409,6 +1431,7 @@ input[type=search].jqx-input-office { .bos-deletable-component { position: relative; padding-top: 17px; + border: 1px solid #eaeaea; } .bos-fit-contents { @@ -1420,15 +1443,6 @@ input[type=search].jqx-input-office { justify-content: space-between; } -/*modify jqx widget css */ -.jqx-fill-state-pressed-office { - /*background-color: #ffffff;*/ -} - -.jqx-expander-header-content-office { - margin-left: 12px; -} - .bos-icon-help { float: right; @@ -1443,6 +1457,14 @@ input[type=search].jqx-input-office { /************************ * Color Picker CSS Start ************************/ +.bo-widget-color-picker-wrapper { + justify-content: flex-start; +} + +.bo-component-style-font { + margin-right: 2px; +} + .bos-widget-color-picker-container { position: relative; display: flex; @@ -1451,9 +1473,10 @@ input[type=search].jqx-input-office { box-sizing: border-box; width: 100%; height: 100%; - border: 1px solid #d9d9d9; + outline: 1px solid #eaeaea; + border-radius: 30px; background-color: #ffffff; - margin: 2.5px 0; + cursor: pointer; /* javascript code로 width: 50px; height: 30px; 넣고 있음. 2017. 05. 30 ColorPickerWidget.prototype._configureMainControlSize = function () { this.$mainControl.css('width', (this.options.width) ? this.options.width : '50px'); @@ -1462,8 +1485,25 @@ input[type=search].jqx-input-office { */ } +.bos-widget-color-palette-container .bos-widget-color-picker-container { + outline: none; +} + +.bos-widget-color-picker-icon-container { + position: relative; + display: flex; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; + width: 28px; + height: 28px; + cursor: pointer; + background-image: url('images/icon-color-picker.png'); +} + .bos-widget-color-picker-container.bos-widget-disable .bos-widget-disable-cover { background-color: rgba(245, 245, 245, 0.5); + cursor: default; } .bos-color-picker-box-wrapper { @@ -1531,15 +1571,15 @@ input[type=search].jqx-input-office { height: 12px; } -.bos-font-style-bold.jqx-fill-state-pressed { +.bos-font-style-bold.brtc-toggle-button__check--checked { background-image: url('images/icon/icon-font-heavy-white.png'); } -.bos-font-style-italic.jqx-fill-state-pressed { +.bos-font-style-italic.brtc-toggle-button__check--checked { background-image: url('images/icon/icon-font-slope-white.png'); } -.bos-font-style-underline.jqx-fill-state-pressed { +.bos-font-style-underline.brtc-toggle-button__check--checked { background-image: url('images/icon/icon-font-underline-white.png'); } @@ -1562,15 +1602,15 @@ input[type=search].jqx-input-office { background-image: url('images/align-right.png'); } -.bos-horizontal-align-left.jqx-fill-state-pressed { +.bos-horizontal-align-left.brtc-toggle-button__check--checked { background-image: url('images/align-left-white.png'); } -.bos-horizontal-align-center.jqx-fill-state-pressed { +.bos-horizontal-align-center.brtc-toggle-button__check--checked { background-image: url('images/align-center-white.png'); } -.bos-horizontal-align-right.jqx-fill-state-pressed { +.bos-horizontal-align-right.brtc-toggle-button__check--checked { background-image: url('images/align-right-white.png'); } @@ -1596,16 +1636,16 @@ input[type=search].jqx-input-office { background-position-y: 80%; } -.bos-vertical-align-up.jqx-fill-state-pressed { +.bos-vertical-align-up.brtc-toggle-button__check--checked { background-image: url('images/align-up-white.png'); background-position-y: 20%; } -.bos-vertical-align-middle.jqx-fill-state-pressed { +.bos-vertical-align-middle.brtc-toggle-button__check--checked { background-image: url('images/align-middle-white.png'); } -.bos-vertical-align-down.jqx-fill-state-pressed { +.bos-vertical-align-down.brtc-toggle-button__check--checked { background-image: url('images/align-down-white.png'); background-position-y: 80%; } @@ -1618,11 +1658,11 @@ input[type=search].jqx-input-office { background-image: url('images/align-vertical.png'); } -.bos-direction-align-horizontal.jqx-fill-state-pressed { +.bos-direction-align-horizontal.brtc-toggle-button__check--checked { background-image: url('images/align-horizontal-white.png'); } -.bos-direction-align-vertical.jqx-fill-state-pressed { +.bos-direction-align-vertical.brtc-toggle-button__check--checked { background-image: url('images/align-vertical-white.png'); } @@ -1649,6 +1689,68 @@ input[type=search].jqx-input-office { .bos-row-multi-div { display: inline-flex; + padding-right: 5px; + padding-left: 5px; +} + +.bos-component-legend-set-btn { + display: block; + margin: 2px; + width: 100%; + height: 30px; + background-color: #3420c3; + border-color: #d9d9d9; + color: #ffffff; + text-align: center; + line-height: 30px; + cursor: pointer; +} + +.bos-custom-legend-label-control { + padding: 0 0 0 22px; +} + +.custom-legend-label-table { + height: 235px; + width: auto; +} + +.custom-legend-label-table-set-btn { + display: flex; + width: 100%; + margin-left: -11px; + margin-top: 5px; +} + +.custom-legend-label-table-set-btn > div { + display: block; + margin: 2px; + width: 100%; + height: 28px; + background-color: #3420c3; + border-color: #d9d9d9; + color: #ffffff; + text-align: center; + line-height: 28px; + cursor: pointer; +} + +.bo-component-style-column-selector { + height: 40px; +} + +.bo-component-style-width-selector { + height: 40px; + padding: 0; +} +.brtc-toggle-button__check--disabled { + opacity: 0.5; +} + +.bo-component-chart-position-contents .bo-widget-position-top, .bo-component-chart-position-contents .bo-widget-position-bottom{ + width: 105px; + margin-left: auto; + margin-right: auto; } /*chart option control END*/ @@ -1657,12 +1759,10 @@ input[type=search].jqx-input-office { position: relative; } -/* jqxTab에 가려짐. */ .bcharts-ds-panel .ps-scrollbar-x-rail { z-index: 999999; } -/* jqxTab에 가려짐. */ .bcharts-ds-panel .ps-scrollbar-y-rail { z-index: 999999; } @@ -1801,54 +1901,19 @@ input[type=search].jqx-input-office { visibility: hidden; } -.bcharts-ds-contents { -} - -.bcharts-ds .jqx-tabs-headerWrapper.jqx-tabs-header { - padding: 0 !important; - margin: 0 20px; - height: 32px !important; -} - -.bcharts-ds ul.jqx-tabs-title-container.jqx-tabs-title-container-office { - width: 100% !important; - height: 32px !important; -} - -.bcharts-ds li.jqx-tabs-title { - width: 50% !important; /* 다른 css가 .jqx-tabs-title을 바꿀 수 도 있어서 important */ - background-color: #fff; - box-sizing: border-box; - - height: 32px !important; - line-height: 26px !important; - padding: 0 !important; - display: flex; - justify-content: center; - border: none; /* 모든 border를 제거한 후 bottom 만 붙인다. */ - border-bottom: 1px solid #D9D9D9; -} - -.bcharts-ds li.jqx-tabs-title-selected-top { - color: #8190FF; - border-bottom: 2px solid #8190FF; -} - -.bcharts-ds .jqx-tabs-content { - padding: 0 !important; /* 다른 css가 .jqx-tabs-content을 바꿀 수 도 있어서 important */ - margin: 15px 0 10px 19px; +.bcharts-ds-columns-container { + padding: 15px; } .bcharts-ds-columns-util-tools-area { - width: calc(100% - 21px); + width: 100%; } .bcharts-ds-columns { box-sizing: border-box; - padding: 0 12px 0 0; max-height: 378px; position: relative; - width: calc(100% - 9px); + width: 100%; } .bcharts-ds-column { @@ -1871,7 +1936,7 @@ input[type=search].jqx-input-office { background-color: #E6E9FF; color: #454758; font-size: 12px; - font-family: Arial; + font-family: 'NotoSansCJKkr', Arial; display: flex; align-items: center; justify-content: center; @@ -1906,70 +1971,6 @@ input[type=search].jqx-input-office { box-sizing: content-box; } -.bcharts-ds-columns-searcharea input[type=search].jqx-input-office { - width: 100%; - height: 31px; -} - -input[type=search].jqx-input-office { - padding-right: 30px; - - background: url('images/search-28.png') no-repeat; - background-position: center right; -} - -input[type=search].jqx-input-office::-webkit-search-cancel-button { - position: relative; - right: -3px !important; - - width: 17px; - height: 17px; - - background: url('images/icon/icon_palette.png') no-repeat; - background-position: -27px 0; - - -webkit-appearance: none; -} - -input[type=search].jqx-input-office::-webkit-search-cancel-button:hover { - position: relative; - right: -3px !important; - - width: 17px; - height: 17px; - - background: url('images/icon/icon_palette.png') no-repeat; - background-position: -54px 0; - - -webkit-appearance: none; -} - -input[type=search].jqx-input-office::-ms-clear { - width: 17px; - height: 17px; - margin-right: 20px; - - color: #fff; - background: url('images/icon/icon_palette.png') no-repeat; - background-position: -27px 0; -} - -input[type=search].jqx-input-office::-ms-clear:hover { - width: 17px; - height: 17px; - margin-right: 20px; - - color: #fff; - background: url('images/icon/icon_palette.png') no-repeat; - background-position: -54px 0; -} - -.bcharts-ds .jqx-tabs-titleContentWrapper { - display: flex !important; - float: none !important; - margin-top: 0 !important; -} - .bcharts-ds .bcharts-ds-contents-tab-columns { margin-right: 3px; } @@ -1990,14 +1991,6 @@ input[type=search].jqx-input-office::-ms-clear:hover { position: relative; } -.bfs-container .bfs-control-container-expander-expanded .jqx-expander-header-content { - color: rgba(0, 0, 0, 1); -} - -.bfs-container .bfs-control-container-expander-collapsed .jqx-expander-header-content { - color: rgba(0, 0, 0, 0.8); -} - .bfs-control-container-header-label { flex-grow: 1; line-height: 18px; @@ -2034,11 +2027,11 @@ input[type=search].jqx-input-office::-ms-clear:hover { line-height: 100%; } -.bfs-container .bfs-control-container-expander { +/*.bfs-container .bfs-control-container-expander { position: relative; box-sizing: border-box; -} - +}*/ +/* .bfs-container .bfs-control-container-expander::before { content: ''; position: absolute; @@ -2067,15 +2060,6 @@ input[type=search].jqx-input-office::-ms-clear:hover { z-index: 2; } -.bfs-control-container-expander.bfs-control-container-expander-expanded::after { - content: ''; - position: absolute; - width: 100%; - height: 1px; - background-color: #AEAEAE; - z-index: 2; -} - .bfs-control-container-label { border: none; background-color: #f7f7f7; @@ -2088,47 +2072,10 @@ input[type=search].jqx-input-office::-ms-clear:hover { position: relative; } -.bfs-control-container-label.jqx-fill-state-pressed { - background-color: #ffffff; -} - -.bfs-control-container-label.jqx-fill-state-pressed:after { - content: ''; - position: absolute; - top: 39px; - left: 20px; - - box-sizing: border-box; - width: calc(100% - 40px); - height: 1px; - background-color: #d9d9d9; -} - -.bfs-control-container-label .jqx-expander-arrow { - width: 12px; - height: 6px; - margin: 0 !important; -} - -.bfs-control-container-label .jqx-expander-arrow.jqx-expander-arrow-top { - background-image: url('images/icon/icon-arrow-down.png'); -} - -.bfs-control-container-label .jqx-expander-arrow.jqx-expander-arrow-bottom { - background-image: url('images/icon/icon-arrow-up.png'); -} - -.bfs-control-container-label .jqx-expander-header-content { - padding: 0 !important; - margin: 0 24px !important; - flex-grow: 1; - width: calc(100% - 48px); -} - .bfs-control-container-contents { padding: 0 20px; } - +*/ .bfs-control { padding: 5px 0; } @@ -2187,8 +2134,12 @@ input[type=search].jqx-input-office::-ms-clear:hover { margin-top: 10px; } -.bfs-column-filter-control .jqx-checkbox-office { - margin-bottom: 4px; +.bfs-column-filter-checkbox > div { + margin-bottom: 5px; +} + +.bfs-column-filter-listbox > div > table > tbody > tr > td { + padding: 3px 5px; } .bfs-column-filter-slider { @@ -2267,6 +2218,7 @@ input[type=search].jqx-input-office::-ms-clear:hover { } .bfs-widget-button { + font-family: 'NotoSansCJKkr', arial,sans-serif; line-height: 30px; height: 30px; background-color: #f7f7f7; @@ -2279,6 +2231,10 @@ input[type=search].jqx-input-office::-ms-clear:hover { background-color: #e7e7e7; } +.bfs-widget-button-disable { + pointer-events: none; + color: #DBDDE2; +} .bcharts-layout { width: 100%; @@ -2299,17 +2255,6 @@ input[type=search].jqx-input-office::-ms-clear:hover { height: 100%; } -.bcharts-layout .jqx-splitter-collapse-button-horizontal, .bcharts-layout .jqx-splitter-collapse-button-vertical { - display: none; -} - -.bcharts-layout .jqx-splitter-splitbar-horizontal, .bcharts-layout .jqx-splitter-splitbar-vertical { - border-color: transparent; -} - -.bcharts-layout-splitter > .jqx-splitter-splitbar-vertical:last-child, .bcharts-layout-splitter > .jqx-splitter-splitbar-horizontal:last-child { - background-color: #e5e5e5; -} .bcharts-ws-panel { display: block; width: calc(100% - 1px); @@ -2452,10 +2397,6 @@ input[type=search].jqx-input-office::-ms-clear:hover { padding: 0 7px 7px 7px; } -.bcharts-ws-panel-contents .bcharts-chart.jqx-grid { - box-sizing: border-box; -} - .bcharts-ws-panel.ui-selected { border-color: #656eea; } @@ -2563,7 +2504,3 @@ input[type=search].jqx-input-office::-ms-clear:hover { animation-duration: 0.1s; animation-iteration-count: 1; } - -.bcharts-layout-splitter, .bcharts-layout-splitter .jqx-splitter-panel { - overflow: visible; -} diff --git a/va-server/visual-analytics/public/css/plugins/bcharts/bcharts-adonis-all.min.css b/va-server/visual-analytics/public/css/plugins/bcharts/bcharts-adonis-all.min.css index 66f120d9a..71f8708ad 100644 --- a/va-server/visual-analytics/public/css/plugins/bcharts/bcharts-adonis-all.min.css +++ b/va-server/visual-analytics/public/css/plugins/bcharts/bcharts-adonis-all.min.css @@ -1,5 +1,5 @@ -/* Brightics 2.0 Chart Build 190718 */ -/* Brightics 2.0 Chart Build 190718 */ +/* Brightics 2.0 Chart Build 200703 */ +/* Brightics 2.0 Chart Build 200703 */ .bcharts-adonis { display: flex; } @@ -16,29 +16,6 @@ flex-direction: column; } -.bcharts-adonis .jqx-tabs, .bo-container .jqx-tabs { - border: none; -} - -.bcharts-adonis .jqx-tabs-headerWrapper, .bo-container .jqx-tabs-headerWrapper { - border: none; -} - -.bcharts-adonis li.jqx-tabs-title, .bo-container li.jqx-tabs-title{ - border: 1px solid #DBDDE2; - padding-bottom: 7px; - margin-right: 0; - width: 76px; -} - -.bcharts-adonis li.jqx-tabs-title-selected-top, .bo-container li.jqx-tabs-title-selected-top { - border-top: 2px solid #6C79E9; - color: rgba(0, 0, 0, 0.8); - background-color: #FFFFFF; - padding-bottom: 6px; - border-bottom: 1px solid #FFF; -} - .bcharts-adonis-left { width: 260px; height: 100%; @@ -60,7 +37,7 @@ height: 100%; box-sizing: border-box; border: solid 1px #DBDDE2; - background: #f7f7f7; + background: #ffffff; margin-left: 8px; } @@ -71,11 +48,14 @@ } .bcharts-adonis-viewpart-header { - height: 54px; + height: 24px; box-sizing: border-box; - line-height: 54px; - text-align: center; + text-align: left; font-weight: bold; + color: #333333; + line-height: 24px; + font-size: 16px; + margin: 14px 0 14px 20px; } .bcharts-adonis-viewpart::before @@ -105,7 +85,7 @@ /*chart type*/ .bcharts-adonis-chart-selector { - padding-left: 30px; + padding-left: 40px; box-sizing: border-box; line-height: 30px; cursor: pointer; @@ -120,169 +100,167 @@ .bcharts-adonis-chart-selector::before { content: ''; - width: 30px; - height: 30px; - background-image: url('images/bcharts.png'); - background-position-y: -30px; + width: 40px; + height: 40px; position: absolute; top: 0; left: 0; } .bcharts-adonis-chart-selector[chart="scatter"]::before { - background-position-x: -60px; + background-image: url('images/chart-26/scatter.svg'); } .bcharts-adonis-chart-selector[chart="line"]::before { - background-position-x: -90px; + background-image: url('images/chart-26/line.svg'); } .bcharts-adonis-chart-selector[chart="column"]::before { - background-position-x: -120px; + background-image: url('images/chart-26/column.svg'); } .bcharts-adonis-chart-selector[chart="column-stacked"]::before { - background-position-x: -150px; + background-image: url('images/chart-26/column-stacked.svg'); } .bcharts-adonis-chart-selector[chart="column-stacked-100"]::before { - background-position-x: -180px; + background-image: url('images/chart-26/column-stacked-100.svg'); } .bcharts-adonis-chart-selector[chart="bar"]::before { - background-position-x: -210px; + background-image: url('images/chart-26/bar.svg'); } .bcharts-adonis-chart-selector[chart="bar-stacked"]::before { - background-position-x: -240px; + background-image: url('images/chart-26/bar-stacked.svg'); } .bcharts-adonis-chart-selector[chart="bar-stacked-100"]::before { - background-position-x: -270px; + background-image: url('images/chart-26/bar-stacked-100.svg'); } .bcharts-adonis-chart-selector[chart="area"]::before { - background-position-x: -300px; + background-image: url('images/chart-26/area.svg'); } .bcharts-adonis-chart-selector[chart="area-stacked"]::before { - background-position-x: -330px; + background-image: url('images/chart-26/area-stacked.svg'); } .bcharts-adonis-chart-selector[chart="area-stacked-100"]::before { - background-position-x: -360px; + background-image: url('images/chart-26/area-stacked-100.svg'); } .bcharts-adonis-chart-selector[chart="boxplot"]::before { - background-position-x: -390px; + background-image: url('images/chart-26/boxplot.svg'); } .bcharts-adonis-chart-selector[chart="heatmap"]::before { - background-position-x: -420px; + background-image: url('images/chart-26/heatmap.svg'); } .bcharts-adonis-chart-selector[chart="pie"]::before { - background-position-x: -450px; + background-image: url('images/chart-26/pie.svg'); } .bcharts-adonis-chart-selector[chart="network"]::before { - background-position-x: -480px; + background-image: url('images/chart-26/network.svg'); } .bcharts-adonis-chart-selector[chart="treemap"]::before { - background-position-x: -510px; + background-image: url('images/chart-26/treemap.svg'); } .bcharts-adonis-chart-selector[chart="decisionTree"]::before { - background-position-x: -540px; + background-image: url('images/chart-26/decisionTree.svg'); } .bcharts-adonis-chart-selector[chart="decisionTreeForBrightics"]::before { - background-position-x: -540px; + background-image: url('images/chart-26/decisionTree.svg'); } .bcharts-adonis-chart-selector[chart="table"]::before { - background-position-x: -570px; + background-image: url('images/chart-26/table.svg'); } .bcharts-adonis-chart-selector[chart="mcc"]::before { - background-position-x: -600px; + background-image: url('images/chart-26/mcc.svg'); } .bcharts-adonis-chart-selector[chart="dendrogram"]::before { - background-position-x: -630px; + background-image: url('images/chart-26/dendrogram.svg'); } .bcharts-adonis-chart-selector[chart="roccurve"]::before { - background-position-x: -660px; + background-image: url('images/chart-26/roccurve.svg'); } .bcharts-adonis-chart-selector[chart="correlation-matrix"]::before { - background-position-x: -690px; + background-image: url('images/chart-26/correlation-matrix.svg'); } .bcharts-adonis-chart-selector[chart="map"]::before { - background-position-x: -720px; + background-image: url('images/chart-26/map.svg'); } .bcharts-adonis-chart-selector[chart="biplot"]::before { - background-position-x: -750px; + background-image: url('images/chart-26/biplot.svg'); } .bcharts-adonis-chart-selector[chart="pos-neg-bar"]::before { - background-position-x: -780px; + background-image: url('images/chart-26/pos-neg-bar.svg'); } .bcharts-adonis-chart-selector[chart="radar"]::before { - background-position-x: -810px; + background-image: url('images/chart-26/radar.svg'); } .bcharts-adonis-chart-selector[chart="wordcloud"]::before { - background-position-x: -840px; + background-image: url('images/chart-26/wordcloud.svg'); } .bcharts-adonis-chart-selector[chart="complex"]::before { - background-position-x: -870px; + background-image: url('images/chart-26/complex.svg'); } .bcharts-adonis-chart-selector[chart="histogram"]::before { - background-position-x: -900px; + background-image: url('images/chart-26/histogram.svg'); } .bcharts-adonis-chart-selector[chart="bubble"]::before { - background-position-x: -930px; + background-image: url('images/chart-26/bubble.svg'); } .bcharts-adonis-chart-selector[chart="heatmap-matrix"]::before { - background-position-x: -960px; + background-image: url('images/chart-26/heatmap-matrix.svg'); } .bcharts-adonis-chart-selector[chart="qqplot"]::before { - background-position-x: -990px; + background-image: url('images/chart-26/qqplot.svg'); } .bcharts-adonis-chart-selector[chart="pairwise-scatter"]::before { - background-position-x: -1020px; + background-image: url('images/chart-26/pairwise-scatter.svg'); } .bcharts-adonis-chart-selector[chart="card"]::before { - background-position-x: -1050px; + background-image: url('images/chart-26/card.svg'); } .bcharts-adonis-chart-selector[chart="donut"]::before { - background-position-x: -1080px; + background-image: url('images/chart-26/donut.svg'); } .bcharts-adonis-chart-selector[chart="image-grid"]::before { - background-position-x: -1110px; + background-image: url('images/chart-26/image-grid.svg'); } -.selected .bcharts-adonis-chart-selector::before { - background-position-y: -60px; +.bcharts-adonis-chart-selector[chart="wordCloud"]::before { + background-image: url('images/chart-26/wordcloud.svg'); } .bcharts-adonis-chart-selector-16 { - padding: 0 2px 0 18px; + padding: 0 2px 0 25px; box-sizing: border-box; line-height: 16px; cursor: pointer; @@ -294,156 +272,158 @@ content: ''; width: 16px; height: 16px; - background-image: url('images/bcharts.png'); - background-position-y: -238px; position: absolute; top: 0; left: 0; } .bcharts-adonis-chart-selector-16[chart="column"]::before { - background-position-x: -60px; + background-image: url('images/chart-16/column.svg'); } .bcharts-adonis-chart-selector-16[chart="bar"]::before { - background-position-x: -76px; + background-image: url('images/chart-16/bar.svg'); } .bcharts-adonis-chart-selector-16[chart="line"]::before { - background-position-x: -92px; + background-image: url('images/chart-16/line.svg'); } .bcharts-adonis-chart-selector-16[chart="boxplot"]::before { - background-position-x: -108px; + background-image: url('images/chart-16/boxplot.svg'); } .bcharts-adonis-chart-selector-16[chart="scatter"]::before { - background-position-x: -124px; + background-image: url('images/chart-16/scatter.svg'); } .bcharts-adonis-chart-selector-16[chart="pie"]::before { - background-position-x: -140px; + background-image: url('images/chart-16/pie.svg'); } .bcharts-adonis-chart-selector-16[chart="table"]::before { - background-position-x: -156px; + background-image: url('images/chart-16/table.svg'); } .bcharts-adonis-chart-selector-16[chart="treemap"]::before { - background-position-x: -172px; + background-image: url('images/chart-16/treemap.svg'); } .bcharts-adonis-chart-selector-16[chart="network"]::before { - background-position-x: -188px; + background-image: url('images/chart-16/network.svg'); } .bcharts-adonis-chart-selector-16[chart="decisionTree"]::before { - background-position-x: -204px; + background-image: url('images/chart-16/decisionTree.svg'); } .bcharts-adonis-chart-selector-16[chart="decisionTreeForBrightics"]::before { - background-position-x: -204px; + background-image: url('images/chart-16/decisionTree.svg'); } .bcharts-adonis-chart-selector-16[chart="dendrogram"]::before { - background-position-x: -220px; + background-image: url('images/chart-16/dendrogram.svg'); } /*FIXME: correlation matrix img ->heatmap image*/ .bcharts-adonis-chart-selector-16[chart="heatmap"]::before { - background-position-x: -236px; + background-image: url('images/chart-16/heatmap.svg'); } .bcharts-adonis-chart-selector-16[chart="mcc"]::before { - background-position-x: -252px; + background-image: url('images/chart-16/mcc.svg'); } .bcharts-adonis-chart-selector-16[chart="roccurve"]::before { - background-position-x: -268px; + background-image: url('images/chart-16/roccurve.svg'); } .bcharts-adonis-chart-selector-16[chart="map"]::before { - background-position-x: -284px; + background-image: url('images/chart-16/map.svg'); } .bcharts-adonis-chart-selector-16[chart="biplot"]::before { - background-position-x: -300px; + background-image: url('images/chart-16/biplot.svg'); } .bcharts-adonis-chart-selector-16[chart="pos-neg-bar"]::before { - background-position-x: -316px; + background-image: url('images/chart-16/pos-neg-bar.svg'); } .bcharts-adonis-chart-selector-16[chart="bar-stacked"]::before { - background-position-x: -332px; + background-image: url('images/chart-16/bar-stacked.svg'); } .bcharts-adonis-chart-selector-16[chart="bar-stacked-100"]::before { - background-position-x: -348px; + background-image: url('images/chart-16/bar-stacked-100.svg'); } .bcharts-adonis-chart-selector-16[chart="column-stacked"]::before { - background-position-x: -364px; + background-image: url('images/chart-16/column-stacked.svg'); } .bcharts-adonis-chart-selector-16[chart="column-stacked-100"]::before { - background-position-x: -380px; + background-image: url('images/chart-16/column-stacked-100.svg'); } .bcharts-adonis-chart-selector-16[chart="area"]::before { - background-position-x: -396px; + background-image: url('images/chart-16/area.svg'); } .bcharts-adonis-chart-selector-16[chart="area-stacked"]::before { - background-position-x: -412px; + background-image: url('images/chart-16/area-stacked.svg'); } .bcharts-adonis-chart-selector-16[chart="area-stacked-100"]::before { - background-position-x: -428px; + background-image: url('images/chart-16/area-stacked-100.svg'); } .bcharts-adonis-chart-selector-16[chart="radar"]::before { - background-position-x: -444px; + background-image: url('images/chart-16/radar.svg'); } .bcharts-adonis-chart-selector-16[chart="wordcloud"]::before { - background-position-x: -460px; + background-image: url('images/chart-16/wordcloud.svg'); } .bcharts-adonis-chart-selector-16[chart="complex"]::before { - background-position-x: -476px; + background-image: url('images/chart-16/complex.svg'); } .bcharts-adonis-chart-selector-16[chart="histogram"]::before { - background-position-x: -492px; + background-image: url('images/chart-16/histogram.svg'); } .bcharts-adonis-chart-selector-16[chart="bubble"]::before { - background-position-x: -508px; + background-image: url('images/chart-16/bubble.svg'); } .bcharts-adonis-chart-selector-16[chart="heatmap-matrix"]::before { - background-position-x: -524px; + background-image: url('images/chart-16/heatmap-matrix.svg'); } .bcharts-adonis-chart-selector-16[chart="qqplot"]::before { - background-position-x: -540px; + background-image: url('images/chart-16/qqplot.svg'); } .bcharts-adonis-chart-selector-16[chart="pairwise-scatter"]::before { - background-position-x: -556px; + background-image: url('images/chart-16/pairwise-scatter.svg'); } .bcharts-adonis-chart-selector-16[chart="card"]::before { - background-position-x: -572px; + background-image: url('images/chart-16/card.svg'); } .bcharts-adonis-chart-selector-16[chart="donut"]::before { - background-position-x: -588px; + background-image: url('images/chart-16/donut.svg'); } .bcharts-adonis-chart-selector-16[chart="image-grid"]::before { - background-position-x: -604px; + background-image: url('images/chart-16/image-grid.svg'); +} + +.bcharts-adonis-chart-selector-16[chart="wordCloud"]::before { + background-image: url('images/chart-16/wordcloud.svg'); } .bos-col-1 { @@ -511,9 +491,8 @@ .bos-display-flex { display: flex; - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; + padding-top: 2px; + padding-bottom: 2px; } .bos-display-flex-center { @@ -534,6 +513,10 @@ justify-content: space-between; } +.bos-flex-row { + flex-direction: row; +} + .bos-flex-space-evenly { justify-content: space-evenly; } @@ -542,6 +525,10 @@ justify-content: center; } +.bos-flex-left { + justify-content: left; +} + .bos-flex-align-center { align-items: center; } @@ -566,24 +553,55 @@ flex: 4; } +.bos-flex-5 { + flex: 5; +} + +.bos-flex-6 { + flex: 6; +} + .bos-display-flex .bos-widget-header { - width: 70px; + width: 100%; display: flex; -webkit-box-sizing: border-box; -moz-box-sizing: border-box; box-sizing: border-box; /*justify-content: center;*/ align-items: center; + font-size: 14px; + line-height: 30px; + /*color: #767676;*/ + /*padding-bottom: 8px;*/ } .bos-display-flex .bos-widget-header.half { - width: 35px; + width: 50%; +} + +.bos-display-flex .bos-widget-multi-label { + display: flex; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; + align-items: center; + font-size: 14px; + line-height: 20px; + /*color: #767676;*/ +} + +.bos-display-flex .bos-widget-multi-label.half { + width: 50%; } .bos-text-overflow-hidden { overflow: hidden; text-overflow: ellipsis; white-space: nowrap; + font-size: 12px; + line-height: 18px; + color: #767676; + text-align: left; /*white-space: normal;*/ /*display: -webkit-box;*/ /*-webkit-line-clamp: 1;*/ @@ -598,7 +616,6 @@ .bos-flex-direction-column .bos-widget-header, .bos-widget-contents { flex-grow: 1; - padding: 2.5px 0; } .bos-border-only-bottom { @@ -616,6 +633,7 @@ height: 20px; box-sizing: border-box; flex-shrink: 0; + margin-right: 15px; } .bos-simple-circle { @@ -640,11 +658,21 @@ /*widget css START*/ +.bos-widget-header-label { + text-align: left; + justify-content: left; + display: left; + align-items: left; + width: 35px; +} + .bos-widget-label { text-align: center; justify-content: center; display: flex; align-items: center; + line-height: 20px; + color: #767676; } .bos-widget-inputbox { @@ -726,44 +754,41 @@ } .bos-border-color-red { - border-color: rgba(194, 6, 17, .65) !important; - border: 1px solid; + outline: 1px solid rgba(194, 6, 17, .65) !important; } .bos-border-color-blue { - border-color: rgba(98, 111, 219, 1.0) !important; - border: 1px solid; + outline: 1px solid #3420c3 !important; } .bos-dialogs-header { - font-family: Arial, helvetica, sans-serif; + font-family: 'NotoSansCJKkr', Arial, helvetica, sans-serif; font-size: 16px; font-weight: bold; - - width: calc(100% - 20px) !important; - margin-left: 10px; - padding: 10px 0 5px 0; - + width: calc(100% - 4px) !important; + height: 34px !important; + margin: 6px 2px 6px 2px; + padding: 0; border-color: #d9d9d9; background-color: #fff; } -.bos-dialogs-header .jqx-window-close-button { - position: absolute; - right: 0; - top: 0 !important; - width: 18px !important; - height: 18px !important; - background-image: url(images/tool-close-18.png); - background-repeat: no-repeat; - background-position: 0 0; +.bos-dialogs-header .left{ + line-height: 34px; + margin-left: 10px; +} + +.bos-dialogs-header .right{ + padding-top: 7px; + padding-right: 15px; } .bos-dialogs-body { - font-family: Arial, helvetica, sans-serif; + font-family: 'NotoSansCJKkr', Arial, helvetica, sans-serif; width: 100%; height: 100%; position: relative; + padding: 0 !important; } .bos-font-bold { @@ -802,11 +827,11 @@ } .bos-widget-column-contents { - width: calc(100% - 70px); + width: 100%; } .bos-widget-column-unit { - font-family: Arial, helvetica, sans-serif; + font-family: 'NotoSansCJKkr', Arial, helvetica, sans-serif; font-size: 11px; position: relative; float: left; @@ -815,11 +840,7 @@ flex: 1; line-height: 34px; height: 100%; - background-color: white; - - border: 1px solid #CCCCCC; - border-radius: 17.5px; box-sizing: border-box; overflow: hidden; cursor: pointer; @@ -845,32 +866,66 @@ -moz-box-sizing: border-box; box-sizing: border-box; align-items: center; - height: 35px; - margin: 2px 0; + height: 30px; + margin-bottom: 5px; +} + +.bos-widget-column-unit-label { + width: 100%; + height: 30px; + box-sizing: border-box; + font-size: 14px; + font-family: 'NotoSansCJKkr', Arial; + display: block; + align-items: center; + justify-content: center; + text-align: left; + line-height: 30px; + padding: 0 10px; + + overflow: hidden; + white-space: nowrap; + text-overflow: ellipsis; + + border: 1px solid #cccccc; + background-color: #ffffff; + color: #959595; } -.bos-widget-column-unit-label, .bos-widget-datasource-unit-label { + width: 100%; + height: 30px; + box-sizing: border-box; + font-size: 14px; + font-family: 'NotoSansCJKkr', Arial; align-items: center; + justify-content: left; + line-height: 30px; + padding: 0 10px; + overflow: hidden; white-space: nowrap; text-overflow: ellipsis; - padding: 0 20px 0 8px; + border: 1px solid #cccccc; + background-color: #ffffff; + color: #959595; } .bos-column-unit-type { - min-width: 65px; - width: 65px; - height: 100%; + width: 40%; + height: 30px; box-sizing: border-box; - background-color: #E6E9FF; - color: #454758; - font-size: 12px; - font-family: Arial; + font-size: 14px; + font-family: 'NotoSansCJKkr', Arial; display: flex; align-items: center; justify-content: center; + line-height: 30px; + + border: 1px solid #3420c3; + background-color: #ebe9f9; + color: #3420c3; } .bos-widget-disable { @@ -895,22 +950,6 @@ margin: 3px 0 3px 1px; } -.bos-widget-button-group-container .jqx-button { - height: 30px; - background-color: #ffffff; -} - -.bos-widget-button-group-container .jqx-button:not(:first-child) { - border-left: none; - margin-left: 0 !important; -} - -.bos-widget-button-group-container .jqx-button.jqx-fill-state-pressed-office { - background-color: #8290FF; - border-color: #d9d9d9; - color: #ffffff; -} - .bos-aggregation-list-box { display: flex; -webkit-box-sizing: border-box; @@ -935,7 +974,12 @@ } .bo-control-container-contents .bos-widget-chart-selector { - max-width: calc(100% - 70px); + width: 100%; + max-width: 100%; + border-left: 0; + border-right: 0; + border-top: 0; + border-bottom: 1px solid #cccccc; } .bos-widget-chart-selector.bos-widget-chart-selector-readonly, @@ -946,7 +990,6 @@ .bos-widget-item-selector { height: 35px; - border-radius: 17.5px; padding: 3px 0; margin: 2px 0; } @@ -973,9 +1016,9 @@ box-sizing: border-box; flex-direction: column; flex: 1; - max-width: 270px; - margin: 3px 10px 0px 10px; + margin: 3px 0px 0px 0px; position: relative; + height: calc(100% - 20px); } .bos-widget-column-selector-list { @@ -987,51 +1030,44 @@ display: flex; flex-direction: row; align-items: center; - height: 25px; + height: 32px; cursor: pointer; - margin-right: 10px; overflow: hidden; + padding: 0 20px; } .bos-dialog-column-item-type { - width: 70px; - min-width: 70px; height: 21px; box-sizing: border-box; - margin-top: 2px; - margin-left: 2px; - border-radius: 9.5px; - background-color: #e6e9ff; - border: solid 1px #bdc0d6; display: flex; - justify-content: center; - align-items: center; - font-size: 11px; - text-align: center; -} - -.bos-dialog-column-item-type.bos-dialog-column-item-type-schema { - background-color: #828282; - color: #ffffff; + justify-content: right; + align-items: right; + font-size: 14px; + text-align: right; + color: #767676; } .bos-dialog-column-item-wrapper.selected { - background-color: #8190ff; + background-color: #ebe9f9; } .bos-dialog-column-item-label { - font-size: 12px; - padding-left: 10px; + font-size: 14px; + justify-content: left; + align-items: left; + text-align: left; + color: #333333; + line-height: 20px; + margin-right: auto; + text-overflow: ellipsis; + white-space: nowrap; + overflow: hidden; } .bos-dialog-column-item-label.has-type { max-width: calc(100% - 80px); } -.selected .bos-dialog-column-item-label { - color: #ffffff; -} - .bos-aggregation-list-box .bos-dialog-column-item-wrapper { display: flex; align-items: center; @@ -1039,32 +1075,12 @@ font-size: 12px; } -.bos-aggregation-list-box .bos-dialog-column-item-wrapper.selected { - color: #ffffff; - -} - .bos-dialog-filter-container { box-sizing: border-box; - width: 100%; - padding: 5px 10px 5px 10px; -} - -input[type=search].jqx-input-office { - padding-right: 30px; - background: url(images/search-28.png) no-repeat; - background-position: center right; - font-size: 13px; - box-sizing: border-box; - min-width: 16px; - min-height: 16px; - margin: 0; - padding: 0; - padding-right: 10px; - padding-left: 10px; - border-width: 1px; - border-style: solid; - width: 100%; + width: calc(100% - 40px); + padding: 5px 0px; + margin: 0 20px; + border-bottom: 1px solid #cccccc; } .bos-dialog-column-item { @@ -1083,7 +1099,10 @@ input[type=search].jqx-input-office { .bos-widget-selector { box-sizing: border-box; - border: 1px solid #d9d9d9; + border-top: 0; + border-left: 0; + border-right: 0; + border-bottom: 1px solid #cccccc; padding-left: 0.1px; background-color: #ffffff; } @@ -1125,14 +1144,6 @@ input[type=search].jqx-input-office { margin-right: 2px; } -.bos-container .bos-control-container-expander-expanded .jqx-expander-header-content { - color: rgba(0, 0, 0, 1); -} - -.bos-container .bos-control-container-expander-collapsed .jqx-expander-header-content { - color: rgba(0, 0, 0, 0.8); -} - /*chart option control START*/ .bos-container .bos-control-container-expander { position: relative; @@ -1177,7 +1188,7 @@ input[type=search].jqx-input-office { .bos-control-container-label { border: none; - background-color: #f7f7f7; + background-color: #ffffff; display: flex; align-items: center; padding: 0 0 0 20px; @@ -1185,13 +1196,17 @@ input[type=search].jqx-input-office { font-size: 14px !important; height: 40px !important; position: relative; + line-height: 20px; + color: #333333; + font-weight: 500; + padding-left: 0; } -.bos-control-container-label.jqx-fill-state-pressed { +.bos-control-container-label.brtc-toggle-button__check--checked { background-color: #ffffff; } -.bos-control-container-label.jqx-fill-state-pressed:after { +.bos-control-container-label.brtc-toggle-button__check--checked:after { content: ''; position: absolute; top: 39px; @@ -1268,6 +1283,10 @@ input[type=search].jqx-input-office { margin: 5px 0 6px auto; } +.bos-widget-onoff-switch.bos-widget-button-disable{ + opacity: .55; +} + .bos-widget-onoff-switch input { display: none; } @@ -1293,11 +1312,11 @@ input[type=search].jqx-input-office { } .bos-widget-onoff-switch input:checked + .bos-widget-onoff-switch-slider { - background-color: #8290FF; + background-color: #3420c3; } .bos-widget-onoff-switch input:hover + .bos-widget-onoff-switch-slider { - box-shadow: 0 0 2px #8290FF; + box-shadow: 0 0 2px #3420c3; } .bos-widget-onoff-switch input:checked + .bos-widget-onoff-switch-slider::before { @@ -1306,6 +1325,13 @@ input[type=search].jqx-input-office { transform: translateX(22px); } + +.bos-widget-onoff-switch-slider.bos-widget-button-disable { + cursor: default; + pointer-events: none; + color: rgba(0, 0, 0, 0.8); +} + .bos-widget-onoff-switch-inner { display: inline-block; line-height: 28px; @@ -1324,30 +1350,8 @@ input[type=search].jqx-input-office { margin-right: 4px; } -.bos-control-container-label .jqx-expander-arrow { - width: 12px; - height: 6px; - margin: 0 !important; - left: 20px; - top: 17px; -} - -.bos-control-container-label .jqx-expander-arrow.jqx-expander-arrow-top { - background-image: url('images/icon/icon-arrow-down.png'); -} - -.bos-control-container-label .jqx-expander-arrow.jqx-expander-arrow-bottom { - background-image: url('images/icon/icon-arrow-up.png'); -} - -.bos-control-container-label .jqx-expander-header-content { - padding: 0 !important; - margin: 0 24px !important; - flex-grow: 1; -} - .bos-control-container-contents { - padding: 0 20px; + padding: 0 16px; } .bos-control { @@ -1362,13 +1366,13 @@ input[type=search].jqx-input-office { } .bos-control-contents { - background-color: #f7f7f7; + background-color: #ffffff; padding: 8px; margin-bottom: 5px; } .bos-control-subcontents { - background-color: #eaeaea; + background-color: #ffffff; padding: 10px; margin-bottom: 5px; } @@ -1387,6 +1391,23 @@ input[type=search].jqx-input-office { cursor: pointer; } +.bos-control-header-add-button-box-style { + width: 64px; + height: 32px; + border-radius: 2px; + border: 1px solid #cccccc; + color: #5a5a5a; + line-height: 20px; + font-size: 14px; + background: #ffffff; + display: inline-block; + cursor: pointer; +} + +.bos-control-header-add-button-box-style:focus { + outline: 0; +} + .bos-control-header-add-button:hover { background-position: -17px 0; } @@ -1400,6 +1421,7 @@ input[type=search].jqx-input-office { display: inline-block; background: url('images/tool-close-18.png') no-repeat; background-position: -4px -5px; + cursor: pointer; } .bos-middle-button { @@ -1410,6 +1432,7 @@ input[type=search].jqx-input-office { .bos-deletable-component { position: relative; padding-top: 17px; + border: 1px solid #eaeaea; } .bos-fit-contents { @@ -1421,15 +1444,6 @@ input[type=search].jqx-input-office { justify-content: space-between; } -/*modify jqx widget css */ -.jqx-fill-state-pressed-office { - /*background-color: #ffffff;*/ -} - -.jqx-expander-header-content-office { - margin-left: 12px; -} - .bos-icon-help { float: right; @@ -1444,6 +1458,14 @@ input[type=search].jqx-input-office { /************************ * Color Picker CSS Start ************************/ +.bo-widget-color-picker-wrapper { + justify-content: flex-start; +} + +.bo-component-style-font { + margin-right: 2px; +} + .bos-widget-color-picker-container { position: relative; display: flex; @@ -1452,9 +1474,10 @@ input[type=search].jqx-input-office { box-sizing: border-box; width: 100%; height: 100%; - border: 1px solid #d9d9d9; + outline: 1px solid #eaeaea; + border-radius: 30px; background-color: #ffffff; - margin: 2.5px 0; + cursor: pointer; /* javascript code로 width: 50px; height: 30px; 넣고 있음. 2017. 05. 30 ColorPickerWidget.prototype._configureMainControlSize = function () { this.$mainControl.css('width', (this.options.width) ? this.options.width : '50px'); @@ -1463,8 +1486,25 @@ input[type=search].jqx-input-office { */ } +.bos-widget-color-palette-container .bos-widget-color-picker-container { + outline: none; +} + +.bos-widget-color-picker-icon-container { + position: relative; + display: flex; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; + width: 28px; + height: 28px; + cursor: pointer; + background-image: url('images/icon-color-picker.png'); +} + .bos-widget-color-picker-container.bos-widget-disable .bos-widget-disable-cover { background-color: rgba(245, 245, 245, 0.5); + cursor: default; } .bos-color-picker-box-wrapper { @@ -1532,15 +1572,15 @@ input[type=search].jqx-input-office { height: 12px; } -.bos-font-style-bold.jqx-fill-state-pressed { +.bos-font-style-bold.brtc-toggle-button__check--checked { background-image: url('images/icon/icon-font-heavy-white.png'); } -.bos-font-style-italic.jqx-fill-state-pressed { +.bos-font-style-italic.brtc-toggle-button__check--checked { background-image: url('images/icon/icon-font-slope-white.png'); } -.bos-font-style-underline.jqx-fill-state-pressed { +.bos-font-style-underline.brtc-toggle-button__check--checked { background-image: url('images/icon/icon-font-underline-white.png'); } @@ -1563,15 +1603,15 @@ input[type=search].jqx-input-office { background-image: url('images/align-right.png'); } -.bos-horizontal-align-left.jqx-fill-state-pressed { +.bos-horizontal-align-left.brtc-toggle-button__check--checked { background-image: url('images/align-left-white.png'); } -.bos-horizontal-align-center.jqx-fill-state-pressed { +.bos-horizontal-align-center.brtc-toggle-button__check--checked { background-image: url('images/align-center-white.png'); } -.bos-horizontal-align-right.jqx-fill-state-pressed { +.bos-horizontal-align-right.brtc-toggle-button__check--checked { background-image: url('images/align-right-white.png'); } @@ -1597,16 +1637,16 @@ input[type=search].jqx-input-office { background-position-y: 80%; } -.bos-vertical-align-up.jqx-fill-state-pressed { +.bos-vertical-align-up.brtc-toggle-button__check--checked { background-image: url('images/align-up-white.png'); background-position-y: 20%; } -.bos-vertical-align-middle.jqx-fill-state-pressed { +.bos-vertical-align-middle.brtc-toggle-button__check--checked { background-image: url('images/align-middle-white.png'); } -.bos-vertical-align-down.jqx-fill-state-pressed { +.bos-vertical-align-down.brtc-toggle-button__check--checked { background-image: url('images/align-down-white.png'); background-position-y: 80%; } @@ -1619,11 +1659,11 @@ input[type=search].jqx-input-office { background-image: url('images/align-vertical.png'); } -.bos-direction-align-horizontal.jqx-fill-state-pressed { +.bos-direction-align-horizontal.brtc-toggle-button__check--checked { background-image: url('images/align-horizontal-white.png'); } -.bos-direction-align-vertical.jqx-fill-state-pressed { +.bos-direction-align-vertical.brtc-toggle-button__check--checked { background-image: url('images/align-vertical-white.png'); } @@ -1650,6 +1690,68 @@ input[type=search].jqx-input-office { .bos-row-multi-div { display: inline-flex; + padding-right: 5px; + padding-left: 5px; +} + +.bos-component-legend-set-btn { + display: block; + margin: 2px; + width: 100%; + height: 30px; + background-color: #3420c3; + border-color: #d9d9d9; + color: #ffffff; + text-align: center; + line-height: 30px; + cursor: pointer; +} + +.bos-custom-legend-label-control { + padding: 0 0 0 22px; +} + +.custom-legend-label-table { + height: 235px; + width: auto; +} + +.custom-legend-label-table-set-btn { + display: flex; + width: 100%; + margin-left: -11px; + margin-top: 5px; +} + +.custom-legend-label-table-set-btn > div { + display: block; + margin: 2px; + width: 100%; + height: 28px; + background-color: #3420c3; + border-color: #d9d9d9; + color: #ffffff; + text-align: center; + line-height: 28px; + cursor: pointer; +} + +.bo-component-style-column-selector { + height: 40px; +} + +.bo-component-style-width-selector { + height: 40px; + padding: 0; +} +.brtc-toggle-button__check--disabled { + opacity: 0.5; +} + +.bo-component-chart-position-contents .bo-widget-position-top, .bo-component-chart-position-contents .bo-widget-position-bottom{ + width: 105px; + margin-left: auto; + margin-right: auto; } /*chart option control END*/ @@ -1658,12 +1760,10 @@ input[type=search].jqx-input-office { position: relative; } -/* jqxTab에 가려짐. */ .bcharts-ds-panel .ps-scrollbar-x-rail { z-index: 999999; } -/* jqxTab에 가려짐. */ .bcharts-ds-panel .ps-scrollbar-y-rail { z-index: 999999; } @@ -1802,54 +1902,19 @@ input[type=search].jqx-input-office { visibility: hidden; } -.bcharts-ds-contents { -} - -.bcharts-ds .jqx-tabs-headerWrapper.jqx-tabs-header { - padding: 0 !important; - margin: 0 20px; - height: 32px !important; -} - -.bcharts-ds ul.jqx-tabs-title-container.jqx-tabs-title-container-office { - width: 100% !important; - height: 32px !important; -} - -.bcharts-ds li.jqx-tabs-title { - width: 50% !important; /* 다른 css가 .jqx-tabs-title을 바꿀 수 도 있어서 important */ - background-color: #fff; - box-sizing: border-box; - - height: 32px !important; - line-height: 26px !important; - padding: 0 !important; - display: flex; - justify-content: center; - border: none; /* 모든 border를 제거한 후 bottom 만 붙인다. */ - border-bottom: 1px solid #D9D9D9; -} - -.bcharts-ds li.jqx-tabs-title-selected-top { - color: #8190FF; - border-bottom: 2px solid #8190FF; -} - -.bcharts-ds .jqx-tabs-content { - padding: 0 !important; /* 다른 css가 .jqx-tabs-content을 바꿀 수 도 있어서 important */ - margin: 15px 0 10px 19px; +.bcharts-ds-columns-container { + padding: 15px; } .bcharts-ds-columns-util-tools-area { - width: calc(100% - 21px); + width: 100%; } .bcharts-ds-columns { box-sizing: border-box; - padding: 0 12px 0 0; max-height: 378px; position: relative; - width: calc(100% - 9px); + width: 100%; } .bcharts-ds-column { @@ -1872,7 +1937,7 @@ input[type=search].jqx-input-office { background-color: #E6E9FF; color: #454758; font-size: 12px; - font-family: Arial; + font-family: 'NotoSansCJKkr', Arial; display: flex; align-items: center; justify-content: center; @@ -1907,70 +1972,6 @@ input[type=search].jqx-input-office { box-sizing: content-box; } -.bcharts-ds-columns-searcharea input[type=search].jqx-input-office { - width: 100%; - height: 31px; -} - -input[type=search].jqx-input-office { - padding-right: 30px; - - background: url('images/search-28.png') no-repeat; - background-position: center right; -} - -input[type=search].jqx-input-office::-webkit-search-cancel-button { - position: relative; - right: -3px !important; - - width: 17px; - height: 17px; - - background: url('images/icon/icon_palette.png') no-repeat; - background-position: -27px 0; - - -webkit-appearance: none; -} - -input[type=search].jqx-input-office::-webkit-search-cancel-button:hover { - position: relative; - right: -3px !important; - - width: 17px; - height: 17px; - - background: url('images/icon/icon_palette.png') no-repeat; - background-position: -54px 0; - - -webkit-appearance: none; -} - -input[type=search].jqx-input-office::-ms-clear { - width: 17px; - height: 17px; - margin-right: 20px; - - color: #fff; - background: url('images/icon/icon_palette.png') no-repeat; - background-position: -27px 0; -} - -input[type=search].jqx-input-office::-ms-clear:hover { - width: 17px; - height: 17px; - margin-right: 20px; - - color: #fff; - background: url('images/icon/icon_palette.png') no-repeat; - background-position: -54px 0; -} - -.bcharts-ds .jqx-tabs-titleContentWrapper { - display: flex !important; - float: none !important; - margin-top: 0 !important; -} - .bcharts-ds .bcharts-ds-contents-tab-columns { margin-right: 3px; } @@ -1991,14 +1992,6 @@ input[type=search].jqx-input-office::-ms-clear:hover { position: relative; } -.bfs-container .bfs-control-container-expander-expanded .jqx-expander-header-content { - color: rgba(0, 0, 0, 1); -} - -.bfs-container .bfs-control-container-expander-collapsed .jqx-expander-header-content { - color: rgba(0, 0, 0, 0.8); -} - .bfs-control-container-header-label { flex-grow: 1; line-height: 18px; @@ -2035,11 +2028,11 @@ input[type=search].jqx-input-office::-ms-clear:hover { line-height: 100%; } -.bfs-container .bfs-control-container-expander { +/*.bfs-container .bfs-control-container-expander { position: relative; box-sizing: border-box; -} - +}*/ +/* .bfs-container .bfs-control-container-expander::before { content: ''; position: absolute; @@ -2068,15 +2061,6 @@ input[type=search].jqx-input-office::-ms-clear:hover { z-index: 2; } -.bfs-control-container-expander.bfs-control-container-expander-expanded::after { - content: ''; - position: absolute; - width: 100%; - height: 1px; - background-color: #AEAEAE; - z-index: 2; -} - .bfs-control-container-label { border: none; background-color: #f7f7f7; @@ -2089,47 +2073,10 @@ input[type=search].jqx-input-office::-ms-clear:hover { position: relative; } -.bfs-control-container-label.jqx-fill-state-pressed { - background-color: #ffffff; -} - -.bfs-control-container-label.jqx-fill-state-pressed:after { - content: ''; - position: absolute; - top: 39px; - left: 20px; - - box-sizing: border-box; - width: calc(100% - 40px); - height: 1px; - background-color: #d9d9d9; -} - -.bfs-control-container-label .jqx-expander-arrow { - width: 12px; - height: 6px; - margin: 0 !important; -} - -.bfs-control-container-label .jqx-expander-arrow.jqx-expander-arrow-top { - background-image: url('images/icon/icon-arrow-down.png'); -} - -.bfs-control-container-label .jqx-expander-arrow.jqx-expander-arrow-bottom { - background-image: url('images/icon/icon-arrow-up.png'); -} - -.bfs-control-container-label .jqx-expander-header-content { - padding: 0 !important; - margin: 0 24px !important; - flex-grow: 1; - width: calc(100% - 48px); -} - .bfs-control-container-contents { padding: 0 20px; } - +*/ .bfs-control { padding: 5px 0; } @@ -2188,8 +2135,12 @@ input[type=search].jqx-input-office::-ms-clear:hover { margin-top: 10px; } -.bfs-column-filter-control .jqx-checkbox-office { - margin-bottom: 4px; +.bfs-column-filter-checkbox > div { + margin-bottom: 5px; +} + +.bfs-column-filter-listbox > div > table > tbody > tr > td { + padding: 3px 5px; } .bfs-column-filter-slider { @@ -2268,6 +2219,7 @@ input[type=search].jqx-input-office::-ms-clear:hover { } .bfs-widget-button { + font-family: 'NotoSansCJKkr', arial,sans-serif; line-height: 30px; height: 30px; background-color: #f7f7f7; @@ -2280,6 +2232,10 @@ input[type=search].jqx-input-office::-ms-clear:hover { background-color: #e7e7e7; } +.bfs-widget-button-disable { + pointer-events: none; + color: #DBDDE2; +} .bcharts-layout { width: 100%; @@ -2300,17 +2256,6 @@ input[type=search].jqx-input-office::-ms-clear:hover { height: 100%; } -.bcharts-layout .jqx-splitter-collapse-button-horizontal, .bcharts-layout .jqx-splitter-collapse-button-vertical { - display: none; -} - -.bcharts-layout .jqx-splitter-splitbar-horizontal, .bcharts-layout .jqx-splitter-splitbar-vertical { - border-color: transparent; -} - -.bcharts-layout-splitter > .jqx-splitter-splitbar-vertical:last-child, .bcharts-layout-splitter > .jqx-splitter-splitbar-horizontal:last-child { - background-color: #e5e5e5; -} .bcharts-ws-panel { display: block; width: calc(100% - 1px); @@ -2453,10 +2398,6 @@ input[type=search].jqx-input-office::-ms-clear:hover { padding: 0 7px 7px 7px; } -.bcharts-ws-panel-contents .bcharts-chart.jqx-grid { - box-sizing: border-box; -} - .bcharts-ws-panel.ui-selected { border-color: #656eea; } @@ -2564,7 +2505,3 @@ input[type=search].jqx-input-office::-ms-clear:hover { animation-duration: 0.1s; animation-iteration-count: 1; } - -.bcharts-layout-splitter, .bcharts-layout-splitter .jqx-splitter-panel { - overflow: visible; -} diff --git a/va-server/visual-analytics/public/css/plugins/bcharts/bcharts-all.css b/va-server/visual-analytics/public/css/plugins/bcharts/bcharts-all.css index 5e2485b46..7904408e9 100644 --- a/va-server/visual-analytics/public/css/plugins/bcharts/bcharts-all.css +++ b/va-server/visual-analytics/public/css/plugins/bcharts/bcharts-all.css @@ -1,321 +1,4 @@ -/* Brightics 2.0 Chart Build 190718 */ -.jqx-widget { - font-family: Arial, Dotum, Tahoma, sans-serif; - font-size: 12px; - - color: rgba(0, 0, 0, .8); -} - -.jqx-widget-header-office { - font-family: 'Arial', Dotum, Tahoma, sans-serif; - color: rgba(0, 0, 0, .7); -} - -.jqx-widget-content-office { - font-family: 'Arial', Dotum, Tahoma, sans-serif; - color: rgba(0, 0, 0, .7); -} - -.jqx-widget-office .jqx-grid-column-header-office, -.jqx-grid-cell-office, -.jqx-widget-office .jqx-grid-cell-office, -.jqx-widget-office .jqx-grid-group-cell-office, -.jqx-grid-group-cell-office { - font-family: 'Arial', Dotum, Tahoma, sans-serif; -} - -.jqx-fill-state-hover-office { - border-color: #ccc; - background: #e5e5e5; -} - -.jqx-grid-cell-hover-office { - color: #626fdb !important; - background: transparent; -} - -.jqx-fill-state-focus-office, -.jqx-combobox-content-focus-office, -.jqx-numberinput-focus, -.jqx-datetimeinput-office .jqx-fill-state-hover-office, -.jqx-datetimeinput-office .jqx-fill-state-pressed-office { - border-color: #626fdb; -} - -.jqx-fill-state-pressed-office { - background: #eff1fd; -} - -.jqx-grid-cell { - white-space: pre; -} - -.jqx-grid-content-office .jqx-fill-state-pressed-office { - color: #626fdb !important; -} - -.jqx-expander-header-content-office { - font-size: 13px; - - margin-left: 12px !important; -} - -.jqx-button-office { - cursor: pointer; -} - -.jqx-radiobutton-office { - line-height: 18px; -} - -.jqx-radiobutton-default-office { - display: inline-block; - - width: 18px !important; - height: 18px !important; - margin: 0 7px 0 0; - margin-top: 0 !important; - - vertical-align: middle; - - border: none; - -webkit-border-radius: 0 !important; - -moz-border-radius: 0 !important; - border-radius: 0 !important; - background: url('images/icon/radio_check.png') no-repeat; - background-position: 0 0; - - -o-border-radius: 0 !important; -} - -.jqx-radiobutton-check-checked-office { - top: 0 !important; - left: 0 !important; - - width: 18px !important; - height: 18px !important; - margin-top: 0 !important; - margin-left: 0 !important; - - border: none; - -webkit-border-radius: 0 !important; - -moz-border-radius: 0 !important; - border-radius: 0 !important; - background: url('images/icon/radio_check.png') no-repeat; - background-position: 0 -56px; - - -o-border-radius: 0 !important; -} - -.jqx-radiobutton-hover-office.jqx-fill-state-hover-office { - border-color: #626fdb; -} - -.jqx-checkbox-office { - line-height: 19px; -} - -.jqx-checkbox-default-office { - margin-right: 7px; -} - -.jqx-checkbox-default-office.jqx-fill-state-hover-office { - border-color: #626fdb; - background: transparent; -} - -.jqx-checkbox-default-office > div:first-child { - width: 17px !important; - height: 17px !important; -} - -.jqx-checkbox-check-checked-office { - top: 0 !important; - left: 0 !important; - - width: 17px !important; - height: 17px !important; - - background: url('images/icon/radio_check.png') no-repeat; - background-position: -38px -56px; -} - -.jqx-checkbox-check-indeterminate.jqx-checkbox-check-indeterminate-office { - top: 0 !important; - left: 0 !important; - - width: 17px !important; - height: 17px !important; - - background: url('images/icon/radio_check.png') no-repeat; - background-position: -38px -140px; -} - -.jqx-popover { - box-shadow: none; -} - -.jqx-popover-title-office { - font-family: Arial, helvetica, sans-serif; - font-size: 16px; - font-weight: bold; - line-height: 60px; - - height: 60px; - padding: 5px 30px; - - background: #fff; -} - -.jqx-popover-content-office { - padding: 7px; -} - -.jqx-window-close-button-background > .jqx-window-close-button { - position: absolute; - top: 9px; - right: 0; - - width: 18px !important; - height: 18px !important; - - background-image: url('images/tool-close-18.png'); - background-repeat: no-repeat; - background-position: 0 0; -} - -.jqx-window-close-button-background > .jqx-window-close-button-hover-office { - background-position-x: -18px; -} - -.jqx-popover-title-office > .jqx-window-close-button-background > .jqx-window-close-button { - top: 25px; - right: 25px; -} - -.jqx-menu-wrapper { - outline: none; -} - -.jqx-menu-office { - height: auto !important; -} - -.jqx-menu li, -.jqx-menu-dropdown li { - line-height: 30px !important; - - height: 30px; -} - -.jqx-menu li[type=separator] { - height: 1px !important; -} - -.jqx-menu-item-top-hover-office { - border-color: #f7f7f7; - background: #f7f7f7; -} - -.jqx-menu-item-hover-office { - border-color: #f7f7f7; - background: #f7f7f7; -} - -.jqx-menu-item-top-selected-office { - border-color: #f7f7f7; - background: #f7f7f7; -} - -.jqx-menu-item-top > .jqx-icon-arrow-right { - margin-top: 8px; -} - -.jqx-notification-office { - background: rgba(0, 0, 0, .95) !important; -} - -.jqx-notification-close-button { - top: 50%; - right: 0; - - background-image: url('images/tool-close-white-18.png'); -} - -.jqx-progressbar-value.jqx-fill-state-pressed { - background: #58bd7c; -} - -input[type=text].jqx-input-office, -input[type=password].jqx-input-office, -input[type=search].jqx-input-office { - font-size: 13px; - - box-sizing: border-box; - min-width: 16px; - min-height: 16px; - margin: 0; - padding: 0; - padding-right: 10px; - padding-left: 10px; - - border-width: 1px; - border-style: solid; -} - -input[type=search].jqx-input-office { - padding-right: 30px; - - background: url('images/search-28.png') no-repeat; - background-position: center right; -} - -input[type=search].jqx-input-office::-webkit-search-cancel-button { - position: relative; - right: -3px !important; - - width: 17px; - height: 17px; - - background: url('images/icon/icon_palette.png') no-repeat; - background-position: -27px 0; - - -webkit-appearance: none; -} - -input[type=search].jqx-input-office::-webkit-search-cancel-button:hover { - position: relative; - right: -3px !important; - - width: 17px; - height: 17px; - - background: url('images/icon/icon_palette.png') no-repeat; - background-position: -54px 0; - - -webkit-appearance: none; -} - -input[type=search].jqx-input-office::-ms-clear { - width: 17px; - height: 17px; - margin-right: 20px; - - color: #fff; - background: url('images/icon/icon_palette.png') no-repeat; - background-position: -27px 0; -} - -input[type=search].jqx-input-office::-ms-clear:hover { - width: 17px; - height: 17px; - margin-right: 20px; - - color: #fff; - background: url('images/icon/icon_palette.png') no-repeat; - background-position: -54px 0; -} - +/* Brightics 2.0 Chart Build 200703 */ .bcharts-container { width: 100%; height: 100%; @@ -347,12 +30,13 @@ input[type=search].jqx-input-office::-ms-clear:hover { .bcharts-chart[chart-vendor="echarts"] > div:nth-child(2) { border-radius: 0 !important; background-color: rgba(0, 0, 0, 0.8) !important; - font-family: Arial, helvetica, sans-serif !important; + font-family: 'NotoSansCJKkr', Arial, helvetica, sans-serif !important; font-size: 12px !important; max-width: 300px; max-height: 200px; overflow: auto; text-overflow: ellipsis; + text-align: left; /* z-index < 1000 : this should be located under minimap*/ z-index: 900 !important; } @@ -556,11 +240,6 @@ input[type=search].jqx-input-office::-ms-clear:hover { background-position: -2px -3px; } -.bcharts-chart.jqx-grid, -.bcharts-chart.jqx-grid .jqx-grid-content { - background-color: transparent; -} - .hidden { display: none; } @@ -590,7 +269,7 @@ input[type=search].jqx-input-office::-ms-clear:hover { padding: 5px; border-radius: 0 !important; background-color: rgba(0, 0, 0, 0.8) !important; - font-family: Arial, helvetica, sans-serif !important; + font-family: 'NotoSansCJKkr', Arial, helvetica, sans-serif !important; font-size: 12px !important; max-width: 300px; max-height: 200px; @@ -761,37 +440,7 @@ input[type=search].jqx-input-office::-ms-clear:hover { .bcharts-dialog .bcharts-dialog-contents.cellviewer{ position: relative; white-space: pre; - width: 100%; - height: 100%; -} - -.bcharts-dialog .bcharts-dialog-header > div:first-child { - line-height: 25px; - height: 25px; -} - -.bcharts-dialog .bcharts-dialog-header > .jqx-window-close-button-background { - margin: 0 !important; - width: 20px !important; - height: 20px !important; - right: 8px !important; - top: 8px !important; -} - -.bcharts-dialog .bcharts-dialog-header > .jqx-window-close-button-background > .jqx-window-close-button -{ - position: absolute; - width: 18px !important; - height: 18px !important; - background-image: url('images/tool-close-18.png'); - background-repeat: no-repeat; - background-position: 0 0; - top: 1px; - right: 1px; -} - -.bcharts-dialog .bcharts-dialog-header > .jqx-window-close-button-background > .jqx-window-close-button:hover { - background-position-x: -18px + padding: 10px 20px; } .bcharts-pagination { height: 30px; @@ -801,6 +450,7 @@ input[type=search].jqx-input-office::-ms-clear:hover { .bcharts-pagination-navigator { display: inline-flex; + align-items: center; min-width: 160px; } @@ -826,6 +476,10 @@ input[type=search].jqx-input-office::-ms-clear:hover { pointer-events: none; } +.bcharts-pagination-navigator-page > input{ + text-align: center; +} + .bcharts-pagination-navigator-button.bcharts-non-editable { opacity: .3; pointer-events: none; @@ -833,6 +487,7 @@ input[type=search].jqx-input-office::-ms-clear:hover { .bcharts-pagination-size { display: inline-flex; + align-items: center; } .bcharts-pagination-size > div { @@ -840,36 +495,11 @@ input[type=search].jqx-input-office::-ms-clear:hover { margin-right: 6px; } -.bcharts-pagination-navigator-page { - margin-top: 2px; -} - -.bcharts-pagination-size-input { - width: 80px; - height: 24px; - border: solid 1px #d3d3d3; - justify-content: center; - line-height: 26px; - background-image: url('images/icon-up-17.png'); - background-repeat: no-repeat; - background-position: right center; - background-color: #fff; - cursor: pointer; - padding-right: 8px; - margin-top: 2px; -} - .bcharts-pagination-size-input.disabled { cursor: default; background-color: #eee; } -.bcharts-pagination-size-input-menu.jqx-menu li { - height: 26px; - line-height: 16px !important; - box-sizing: border-box; -} - .bcharts-pagination-showing { display: inline-flex; float: right; diff --git a/va-server/visual-analytics/public/css/plugins/bcharts/bcharts-all.min.css b/va-server/visual-analytics/public/css/plugins/bcharts/bcharts-all.min.css index 1e5fbf5fb..bfcb84c0f 100644 --- a/va-server/visual-analytics/public/css/plugins/bcharts/bcharts-all.min.css +++ b/va-server/visual-analytics/public/css/plugins/bcharts/bcharts-all.min.css @@ -1,322 +1,5 @@ -/* Brightics 2.0 Chart Build 190718 */ -/* Brightics 2.0 Chart Build 190718 */ -.jqx-widget { - font-family: Arial, Dotum, Tahoma, sans-serif; - font-size: 12px; - - color: rgba(0, 0, 0, .8); -} - -.jqx-widget-header-office { - font-family: 'Arial', Dotum, Tahoma, sans-serif; - color: rgba(0, 0, 0, .7); -} - -.jqx-widget-content-office { - font-family: 'Arial', Dotum, Tahoma, sans-serif; - color: rgba(0, 0, 0, .7); -} - -.jqx-widget-office .jqx-grid-column-header-office, -.jqx-grid-cell-office, -.jqx-widget-office .jqx-grid-cell-office, -.jqx-widget-office .jqx-grid-group-cell-office, -.jqx-grid-group-cell-office { - font-family: 'Arial', Dotum, Tahoma, sans-serif; -} - -.jqx-fill-state-hover-office { - border-color: #ccc; - background: #e5e5e5; -} - -.jqx-grid-cell-hover-office { - color: #626fdb !important; - background: transparent; -} - -.jqx-fill-state-focus-office, -.jqx-combobox-content-focus-office, -.jqx-numberinput-focus, -.jqx-datetimeinput-office .jqx-fill-state-hover-office, -.jqx-datetimeinput-office .jqx-fill-state-pressed-office { - border-color: #626fdb; -} - -.jqx-fill-state-pressed-office { - background: #eff1fd; -} - -.jqx-grid-cell { - white-space: pre; -} - -.jqx-grid-content-office .jqx-fill-state-pressed-office { - color: #626fdb !important; -} - -.jqx-expander-header-content-office { - font-size: 13px; - - margin-left: 12px !important; -} - -.jqx-button-office { - cursor: pointer; -} - -.jqx-radiobutton-office { - line-height: 18px; -} - -.jqx-radiobutton-default-office { - display: inline-block; - - width: 18px !important; - height: 18px !important; - margin: 0 7px 0 0; - margin-top: 0 !important; - - vertical-align: middle; - - border: none; - -webkit-border-radius: 0 !important; - -moz-border-radius: 0 !important; - border-radius: 0 !important; - background: url('images/icon/radio_check.png') no-repeat; - background-position: 0 0; - - -o-border-radius: 0 !important; -} - -.jqx-radiobutton-check-checked-office { - top: 0 !important; - left: 0 !important; - - width: 18px !important; - height: 18px !important; - margin-top: 0 !important; - margin-left: 0 !important; - - border: none; - -webkit-border-radius: 0 !important; - -moz-border-radius: 0 !important; - border-radius: 0 !important; - background: url('images/icon/radio_check.png') no-repeat; - background-position: 0 -56px; - - -o-border-radius: 0 !important; -} - -.jqx-radiobutton-hover-office.jqx-fill-state-hover-office { - border-color: #626fdb; -} - -.jqx-checkbox-office { - line-height: 19px; -} - -.jqx-checkbox-default-office { - margin-right: 7px; -} - -.jqx-checkbox-default-office.jqx-fill-state-hover-office { - border-color: #626fdb; - background: transparent; -} - -.jqx-checkbox-default-office > div:first-child { - width: 17px !important; - height: 17px !important; -} - -.jqx-checkbox-check-checked-office { - top: 0 !important; - left: 0 !important; - - width: 17px !important; - height: 17px !important; - - background: url('images/icon/radio_check.png') no-repeat; - background-position: -38px -56px; -} - -.jqx-checkbox-check-indeterminate.jqx-checkbox-check-indeterminate-office { - top: 0 !important; - left: 0 !important; - - width: 17px !important; - height: 17px !important; - - background: url('images/icon/radio_check.png') no-repeat; - background-position: -38px -140px; -} - -.jqx-popover { - box-shadow: none; -} - -.jqx-popover-title-office { - font-family: Arial, helvetica, sans-serif; - font-size: 16px; - font-weight: bold; - line-height: 60px; - - height: 60px; - padding: 5px 30px; - - background: #fff; -} - -.jqx-popover-content-office { - padding: 7px; -} - -.jqx-window-close-button-background > .jqx-window-close-button { - position: absolute; - top: 9px; - right: 0; - - width: 18px !important; - height: 18px !important; - - background-image: url('images/tool-close-18.png'); - background-repeat: no-repeat; - background-position: 0 0; -} - -.jqx-window-close-button-background > .jqx-window-close-button-hover-office { - background-position-x: -18px; -} - -.jqx-popover-title-office > .jqx-window-close-button-background > .jqx-window-close-button { - top: 25px; - right: 25px; -} - -.jqx-menu-wrapper { - outline: none; -} - -.jqx-menu-office { - height: auto !important; -} - -.jqx-menu li, -.jqx-menu-dropdown li { - line-height: 30px !important; - - height: 30px; -} - -.jqx-menu li[type=separator] { - height: 1px !important; -} - -.jqx-menu-item-top-hover-office { - border-color: #f7f7f7; - background: #f7f7f7; -} - -.jqx-menu-item-hover-office { - border-color: #f7f7f7; - background: #f7f7f7; -} - -.jqx-menu-item-top-selected-office { - border-color: #f7f7f7; - background: #f7f7f7; -} - -.jqx-menu-item-top > .jqx-icon-arrow-right { - margin-top: 8px; -} - -.jqx-notification-office { - background: rgba(0, 0, 0, .95) !important; -} - -.jqx-notification-close-button { - top: 50%; - right: 0; - - background-image: url('images/tool-close-white-18.png'); -} - -.jqx-progressbar-value.jqx-fill-state-pressed { - background: #58bd7c; -} - -input[type=text].jqx-input-office, -input[type=password].jqx-input-office, -input[type=search].jqx-input-office { - font-size: 13px; - - box-sizing: border-box; - min-width: 16px; - min-height: 16px; - margin: 0; - padding: 0; - padding-right: 10px; - padding-left: 10px; - - border-width: 1px; - border-style: solid; -} - -input[type=search].jqx-input-office { - padding-right: 30px; - - background: url('images/search-28.png') no-repeat; - background-position: center right; -} - -input[type=search].jqx-input-office::-webkit-search-cancel-button { - position: relative; - right: -3px !important; - - width: 17px; - height: 17px; - - background: url('images/icon/icon_palette.png') no-repeat; - background-position: -27px 0; - - -webkit-appearance: none; -} - -input[type=search].jqx-input-office::-webkit-search-cancel-button:hover { - position: relative; - right: -3px !important; - - width: 17px; - height: 17px; - - background: url('images/icon/icon_palette.png') no-repeat; - background-position: -54px 0; - - -webkit-appearance: none; -} - -input[type=search].jqx-input-office::-ms-clear { - width: 17px; - height: 17px; - margin-right: 20px; - - color: #fff; - background: url('images/icon/icon_palette.png') no-repeat; - background-position: -27px 0; -} - -input[type=search].jqx-input-office::-ms-clear:hover { - width: 17px; - height: 17px; - margin-right: 20px; - - color: #fff; - background: url('images/icon/icon_palette.png') no-repeat; - background-position: -54px 0; -} - +/* Brightics 2.0 Chart Build 200703 */ +/* Brightics 2.0 Chart Build 200703 */ .bcharts-container { width: 100%; height: 100%; @@ -348,12 +31,13 @@ input[type=search].jqx-input-office::-ms-clear:hover { .bcharts-chart[chart-vendor="echarts"] > div:nth-child(2) { border-radius: 0 !important; background-color: rgba(0, 0, 0, 0.8) !important; - font-family: Arial, helvetica, sans-serif !important; + font-family: 'NotoSansCJKkr', Arial, helvetica, sans-serif !important; font-size: 12px !important; max-width: 300px; max-height: 200px; overflow: auto; text-overflow: ellipsis; + text-align: left; /* z-index < 1000 : this should be located under minimap*/ z-index: 900 !important; } @@ -557,11 +241,6 @@ input[type=search].jqx-input-office::-ms-clear:hover { background-position: -2px -3px; } -.bcharts-chart.jqx-grid, -.bcharts-chart.jqx-grid .jqx-grid-content { - background-color: transparent; -} - .hidden { display: none; } @@ -591,7 +270,7 @@ input[type=search].jqx-input-office::-ms-clear:hover { padding: 5px; border-radius: 0 !important; background-color: rgba(0, 0, 0, 0.8) !important; - font-family: Arial, helvetica, sans-serif !important; + font-family: 'NotoSansCJKkr', Arial, helvetica, sans-serif !important; font-size: 12px !important; max-width: 300px; max-height: 200px; @@ -762,37 +441,7 @@ input[type=search].jqx-input-office::-ms-clear:hover { .bcharts-dialog .bcharts-dialog-contents.cellviewer{ position: relative; white-space: pre; - width: 100%; - height: 100%; -} - -.bcharts-dialog .bcharts-dialog-header > div:first-child { - line-height: 25px; - height: 25px; -} - -.bcharts-dialog .bcharts-dialog-header > .jqx-window-close-button-background { - margin: 0 !important; - width: 20px !important; - height: 20px !important; - right: 8px !important; - top: 8px !important; -} - -.bcharts-dialog .bcharts-dialog-header > .jqx-window-close-button-background > .jqx-window-close-button -{ - position: absolute; - width: 18px !important; - height: 18px !important; - background-image: url('images/tool-close-18.png'); - background-repeat: no-repeat; - background-position: 0 0; - top: 1px; - right: 1px; -} - -.bcharts-dialog .bcharts-dialog-header > .jqx-window-close-button-background > .jqx-window-close-button:hover { - background-position-x: -18px + padding: 10px 20px; } .bcharts-pagination { height: 30px; @@ -802,6 +451,7 @@ input[type=search].jqx-input-office::-ms-clear:hover { .bcharts-pagination-navigator { display: inline-flex; + align-items: center; min-width: 160px; } @@ -827,6 +477,10 @@ input[type=search].jqx-input-office::-ms-clear:hover { pointer-events: none; } +.bcharts-pagination-navigator-page > input{ + text-align: center; +} + .bcharts-pagination-navigator-button.bcharts-non-editable { opacity: .3; pointer-events: none; @@ -834,6 +488,7 @@ input[type=search].jqx-input-office::-ms-clear:hover { .bcharts-pagination-size { display: inline-flex; + align-items: center; } .bcharts-pagination-size > div { @@ -841,36 +496,11 @@ input[type=search].jqx-input-office::-ms-clear:hover { margin-right: 6px; } -.bcharts-pagination-navigator-page { - margin-top: 2px; -} - -.bcharts-pagination-size-input { - width: 80px; - height: 24px; - border: solid 1px #d3d3d3; - justify-content: center; - line-height: 26px; - background-image: url('images/icon-up-17.png'); - background-repeat: no-repeat; - background-position: right center; - background-color: #fff; - cursor: pointer; - padding-right: 8px; - margin-top: 2px; -} - .bcharts-pagination-size-input.disabled { cursor: default; background-color: #eee; } -.bcharts-pagination-size-input-menu.jqx-menu li { - height: 26px; - line-height: 16px !important; - box-sizing: border-box; -} - .bcharts-pagination-showing { display: inline-flex; float: right; diff --git a/va-server/visual-analytics/public/css/plugins/bcharts/bcharts-tethys-all.css b/va-server/visual-analytics/public/css/plugins/bcharts/bcharts-tethys-all.css index 455caacea..b1cf1e4dc 100644 --- a/va-server/visual-analytics/public/css/plugins/bcharts/bcharts-tethys-all.css +++ b/va-server/visual-analytics/public/css/plugins/bcharts/bcharts-tethys-all.css @@ -1,4 +1,4 @@ -/* Brightics 2.0 Chart Build 190718 */ +/* Brightics 2.0 Chart Build 200703 */ .bcharts-tethys { } diff --git a/va-server/visual-analytics/public/css/plugins/bcharts/bcharts-tethys-all.min.css b/va-server/visual-analytics/public/css/plugins/bcharts/bcharts-tethys-all.min.css index de8b135f7..bd0713007 100644 --- a/va-server/visual-analytics/public/css/plugins/bcharts/bcharts-tethys-all.min.css +++ b/va-server/visual-analytics/public/css/plugins/bcharts/bcharts-tethys-all.min.css @@ -1,5 +1,5 @@ -/* Brightics 2.0 Chart Build 190718 */ -/* Brightics 2.0 Chart Build 190718 */ +/* Brightics 2.0 Chart Build 200703 */ +/* Brightics 2.0 Chart Build 200703 */ .bcharts-tethys { } diff --git a/va-server/visual-analytics/public/css/plugins/bcharts/images/bcharts.png b/va-server/visual-analytics/public/css/plugins/bcharts/images/bcharts.png index d6f81afda..8fa154eb7 100644 Binary files a/va-server/visual-analytics/public/css/plugins/bcharts/images/bcharts.png and b/va-server/visual-analytics/public/css/plugins/bcharts/images/bcharts.png differ diff --git a/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/area-stacked-100.svg b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/area-stacked-100.svg new file mode 100644 index 000000000..ea4280d91 --- /dev/null +++ b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/area-stacked-100.svg @@ -0,0 +1,11 @@ + + + + + + + + + diff --git a/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/area-stacked.svg b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/area-stacked.svg new file mode 100644 index 000000000..ae31f96df --- /dev/null +++ b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/area-stacked.svg @@ -0,0 +1,14 @@ + + + + + + + + + + + + diff --git a/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/area.svg b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/area.svg new file mode 100644 index 000000000..c538b1fe0 --- /dev/null +++ b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/area.svg @@ -0,0 +1,14 @@ + + + + + + + + + + + + diff --git a/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/bar-stacked-100.svg b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/bar-stacked-100.svg new file mode 100644 index 000000000..df6dd62c8 --- /dev/null +++ b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/bar-stacked-100.svg @@ -0,0 +1,11 @@ + + + + + + + + + diff --git a/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/bar-stacked.svg b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/bar-stacked.svg new file mode 100644 index 000000000..64ea7a58b --- /dev/null +++ b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/bar-stacked.svg @@ -0,0 +1,11 @@ + + + + + + + + + diff --git a/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/bar.svg b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/bar.svg new file mode 100644 index 000000000..601c7ae37 --- /dev/null +++ b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/bar.svg @@ -0,0 +1,11 @@ + + + + + + + + + diff --git a/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/biplot.svg b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/biplot.svg new file mode 100644 index 000000000..62cd8fd4d --- 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b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/card.svg new file mode 100644 index 000000000..707f10b68 --- /dev/null +++ b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/card.svg @@ -0,0 +1,16 @@ + + + + + + + + + + + + + + diff --git a/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/column-stacked-100.svg b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/column-stacked-100.svg new file mode 100644 index 000000000..6f3a6432d --- /dev/null +++ b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/column-stacked-100.svg @@ -0,0 +1,11 @@ + + + + + + + + + diff --git a/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/column-stacked.svg b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/column-stacked.svg new file mode 100644 index 000000000..a146eca49 --- /dev/null +++ b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/column-stacked.svg @@ -0,0 +1,11 @@ + + + + + + + + + diff --git a/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/column.svg b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/column.svg new file mode 100644 index 000000000..fc1ba499a --- /dev/null +++ b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/column.svg @@ -0,0 +1,11 @@ + + + + + + + + + diff --git a/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/complex.svg b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/complex.svg new file mode 100644 index 000000000..b5a718620 --- /dev/null +++ b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/complex.svg @@ -0,0 +1,11 @@ + + + + + + + + + diff --git a/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/correlation-matrix.svg b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/correlation-matrix.svg new file mode 100644 index 000000000..1acee413d --- /dev/null +++ b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/correlation-matrix.svg @@ -0,0 +1,11 @@ + + + + + + + + + diff --git a/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/decisionTree.svg b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/decisionTree.svg new file mode 100644 index 000000000..8650fafd8 --- /dev/null +++ b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/decisionTree.svg @@ -0,0 +1,11 @@ + + + + + + + + + diff --git a/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/dendrogram.svg b/va-server/visual-analytics/public/css/plugins/bcharts/images/chart-16/dendrogram.svg new file mode 100644 index 000000000..42b2212cc --- /dev/null +++ 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sans-serif;font-size:1em}.brtc-display-flex{display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.brtc-display-flex-center{display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;justify-content:center;align-items:center;height:100%}.brtc-flex-direction-column{flex-direction:column}.brtc-flex-direction-row{flex-direction:row}.bo-control-container-expander{background-color:white}.dropdown.jui{display:block;top:0;left:0;position:absolute} +.brtc-display-flex{display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.brtc-display-flex-center{display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;align-items:center;justify-content:center;height:100%}.brtc-flex-direction-column{flex-direction:column}.brtc-flex-direction-row{flex-direction:row} +.brtc-state-normal{border:1px solid #d4d4d4;color:#444444;background-color:#fff}.brtc-widget.brtc-state-disabled,.brtc-widget.brtc-state-disabled *,.brtc-state-disabled,.brtc-state-disabled *{cursor:default;opacity:.55;user-select:none}.brtc-state-error{border:1px solid rgba(194,6,17,0.65)}.brtc-state-focus{border:1px solid #626fdb}.brtc-state-pressed{background-color:#eff1fd}.brtc-state-empty{border:1px solid #626fdb} +.jui { + /* Icon Base */ + /* Icon Animations */ + /* Icon List */ +} +.jui .clearfix { + *zoom: 1; +} +.jui .clearfix:before, +.jui .clearfix:after { + display: table; + content: ""; + line-height: 0; +} +.jui .clearfix:after { + clear: both; +} +.jui .hide-text { + font: 0/0 a; + color: transparent; + text-shadow: none; + background-color: transparent; + border: 0; +} +.jui label, +.jui input, +.jui button, +.jui select, +.jui textarea { + font-weight: normal; +} +.jui select, +.jui button, +.jui input[type="button"], +.jui input[type="reset"], +.jui input[type="submit"], +.jui input[type="radio"], +.jui input[type="checkbox"] { + cursor: pointer; +} +.jui button, +.jui html input[type="button"], +.jui input[type="reset"], +.jui input[type="submit"] { + cursor: pointer; + -webkit-appearance: button; +} +.jui button, +.jui input { + line-height: normal; +} +.jui button, +.jui input, +.jui select, +.jui textarea { + margin: 0; + font-size: 100%; + vertical-align: middle; +} +.jui select[disabled], +.jui textarea[disabled], +.jui select[readonly], +.jui textarea[readonly] { + cursor: not-allowed; +} +.jui [class^="icon-"], +.jui [class*=" icon-"] { + font-family: "jennifer"; + speak: none; + font-style: normal; + font-weight: normal; + font-variant: normal; + text-transform: none; + line-height: 1; + display: inline-block; + /* Better Font Rendering =========== */ + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} +.jui [class^="icon-"].spin, +.jui [class*=" icon-"].spin { + -webkit-animation: spin 2s infinite linear; + -moz-animation: spin 2s infinite linear; + -o-animation: spin 2s infinite linear; + animation: spin 2s infinite linear; + /* SPIN */ + /* Flash */ + /* BOUNCE */ + /* FLOAT */ + /* PULSE */ +} +@-moz-keyframes spin { + 0% { + -moz-transform: rotate(0deg); + } + 100% { + -moz-transform: rotate(359deg); + } +} +@-webkit-keyframes spin { + 0% { + -webkit-transform: rotate(0deg); + } + 100% { + -webkit-transform: rotate(359deg); + } +} +@-ms-keyframes spin { + 0% { + -ms-transform: rotate(0deg); + } + 100% { + -ms-transform: rotate(359deg); + } +} +@keyframes spin { + 0% { + transform: rotate(0deg); + } + 100% { + transform: rotate(359deg); + } +} +@-moz-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-webkit-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-ms-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-moz-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -moz-transform: translateY(0); + } + 40% { + -moz-transform: translateY(-10px); + } + 60% { + -moz-transform: translateY(-10px); + } +} +@-webkit-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -webkit-transform: translateY(0); + } + 40% { + -webkit-transform: translateY(-10px); + } + 60% { + -webkit-transform: translateY(-10px); + } +} +@-ms-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -ms-transform: translateY(0); + } + 40% { + -ms-transform: translateY(-10px); + } + 60% { + -ms-transform: translateY(-10px); + } +} +@keyframes bounce { + 0%, + 10%, + 20%, + 50%, + 80% { + transform: translateY(0); + } + 40% { + transform: translateY(-10px); + } + 60% { + transform: translateY(-10px); + } +} +@-moz-keyframes float { + 0% { + -moz-transform: translateY(0); + } + 50% { + -moz-transform: translateY(-6px); + } + 100% { + -moz-transform: translateY(0); + } +} +@-webkit-keyframes float { + 0% { + -webkit-transform: translateY(0); + } + 50% { + -webkit-transform: translateY(-6px); + } + 100% { + -webkit-transform: translateY(0); + } +} +@-ms-keyframes float { + 0% { + -ms-transform: translateY(0); + } + 50% { + -ms-transform: translateY(-6px); + } + 100% { + -ms-transform: translateY(0); + } +} +@keyframes float { + 0% { + transform: translateY(0); + } + 50% { + transform: translateY(-6px); + } + 100% { + transform: translateY(0); + } +} +@-moz-keyframes pulse { + 0% { + -moz-transform: scale(1.1); + } + 50% { + -moz-transform: scale(0.8); + } + 100% { + -moz-transform: scale(1); + } +} +@-webkit-keyframes pulse { + 0% { + -webkit-transform: scale(1); + } + 50% { + -webkit-transform: scale(0.8); + } + 100% { + -webkit-transform: scale(1); + } +} +@-ms-keyframes pulse { + 0% { + -ms-transform: scale(1.1); + } + 50% { + -ms-transform: scale(0.8); + } + 100% { + -ms-transform: scale(1); + } +} +@keyframes pulse { + 0% { + transform: scale(1.1); + } + 50% { + transform: scale(0.8); + } + 100% { + transform: scale(1); + } +} +.jui [class^="icon-"].flash, +.jui [class*=" icon-"].flash { + -webkit-animation: flash 2s infinite ease; + -moz-animation: flash 2s infinite ease; + -o-animation: flash 2s infinite ease; + animation: flash 2s infinite ease; + /* SPIN */ + /* Flash */ + /* BOUNCE */ + /* FLOAT */ + /* PULSE */ +} +@-moz-keyframes spin { + 0% { + -moz-transform: rotate(0deg); + } + 100% { + -moz-transform: rotate(359deg); + } +} +@-webkit-keyframes spin { + 0% { + -webkit-transform: rotate(0deg); + } + 100% { + -webkit-transform: rotate(359deg); + } +} +@-ms-keyframes spin { + 0% { + -ms-transform: rotate(0deg); + } + 100% { + -ms-transform: rotate(359deg); + } +} +@keyframes spin { + 0% { + transform: rotate(0deg); + } + 100% { + transform: rotate(359deg); + } +} +@-moz-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-webkit-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-ms-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-moz-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -moz-transform: translateY(0); + } + 40% { + -moz-transform: translateY(-10px); + } + 60% { + -moz-transform: translateY(-10px); + } +} +@-webkit-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -webkit-transform: translateY(0); + } + 40% { + -webkit-transform: translateY(-10px); + } + 60% { + -webkit-transform: translateY(-10px); + } +} +@-ms-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -ms-transform: translateY(0); + } + 40% { + -ms-transform: translateY(-10px); + } + 60% { + -ms-transform: translateY(-10px); + } +} +@keyframes bounce { + 0%, + 10%, + 20%, + 50%, + 80% { + transform: translateY(0); + } + 40% { + transform: translateY(-10px); + } + 60% { + transform: translateY(-10px); + } +} +@-moz-keyframes float { + 0% { + -moz-transform: translateY(0); + } + 50% { + -moz-transform: translateY(-6px); + } + 100% { + -moz-transform: translateY(0); + } +} +@-webkit-keyframes float { + 0% { + -webkit-transform: translateY(0); + } + 50% { + -webkit-transform: translateY(-6px); + } + 100% { + -webkit-transform: translateY(0); + } +} +@-ms-keyframes float { + 0% { + -ms-transform: translateY(0); + } + 50% { + -ms-transform: translateY(-6px); + } + 100% { + -ms-transform: translateY(0); + } +} +@keyframes float { + 0% { + transform: translateY(0); + } + 50% { + transform: translateY(-6px); + } + 100% { + transform: translateY(0); + } +} +@-moz-keyframes pulse { + 0% { + -moz-transform: scale(1.1); + } + 50% { + -moz-transform: scale(0.8); + } + 100% { + -moz-transform: scale(1); + } +} +@-webkit-keyframes pulse { + 0% { + -webkit-transform: scale(1); + } + 50% { + -webkit-transform: scale(0.8); + } + 100% { + -webkit-transform: scale(1); + } +} +@-ms-keyframes pulse { + 0% { + -ms-transform: scale(1.1); + } + 50% { + -ms-transform: scale(0.8); + } + 100% { + -ms-transform: scale(1); + } +} +@keyframes pulse { + 0% { + transform: scale(1.1); + } + 50% { + transform: scale(0.8); + } + 100% { + transform: scale(1); + } +} +.jui [class^="icon-"].bounce, +.jui [class*=" icon-"].bounce { + -webkit-animation: bounce 2s infinite ease; + -moz-animation: bounce 2s infinite ease; + -o-animation: bounce 2s infinite ease; + animation: bounce 2s infinite ease; + /* SPIN */ + /* Flash */ + /* BOUNCE */ + /* FLOAT */ + /* PULSE */ +} +@-moz-keyframes spin { + 0% { + -moz-transform: rotate(0deg); + } + 100% { + -moz-transform: rotate(359deg); + } +} +@-webkit-keyframes spin { + 0% { + -webkit-transform: rotate(0deg); + } + 100% { + -webkit-transform: rotate(359deg); + } +} +@-ms-keyframes spin { + 0% { + -ms-transform: rotate(0deg); + } + 100% { + -ms-transform: rotate(359deg); + } +} +@keyframes spin { + 0% { + transform: rotate(0deg); + } + 100% { + transform: rotate(359deg); + } +} +@-moz-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-webkit-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-ms-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-moz-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -moz-transform: translateY(0); + } + 40% { + -moz-transform: translateY(-10px); + } + 60% { + -moz-transform: translateY(-10px); + } +} +@-webkit-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -webkit-transform: translateY(0); + } + 40% { + -webkit-transform: translateY(-10px); + } + 60% { + -webkit-transform: translateY(-10px); + } +} +@-ms-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -ms-transform: translateY(0); + } + 40% { + -ms-transform: translateY(-10px); + } + 60% { + -ms-transform: translateY(-10px); + } +} +@keyframes bounce { + 0%, + 10%, + 20%, + 50%, + 80% { + transform: translateY(0); + } + 40% { + transform: translateY(-10px); + } + 60% { + transform: translateY(-10px); + } +} +@-moz-keyframes float { + 0% { + -moz-transform: translateY(0); + } + 50% { + -moz-transform: translateY(-6px); + } + 100% { + -moz-transform: translateY(0); + } +} +@-webkit-keyframes float { + 0% { + -webkit-transform: translateY(0); + } + 50% { + -webkit-transform: translateY(-6px); + } + 100% { + -webkit-transform: translateY(0); + } +} +@-ms-keyframes float { + 0% { + -ms-transform: translateY(0); + } + 50% { + -ms-transform: translateY(-6px); + } + 100% { + -ms-transform: translateY(0); + } +} +@keyframes float { + 0% { + transform: translateY(0); + } + 50% { + transform: translateY(-6px); + } + 100% { + transform: translateY(0); + } +} +@-moz-keyframes pulse { + 0% { + -moz-transform: scale(1.1); + } + 50% { + -moz-transform: scale(0.8); + } + 100% { + -moz-transform: scale(1); + } +} +@-webkit-keyframes pulse { + 0% { + -webkit-transform: scale(1); + } + 50% { + -webkit-transform: scale(0.8); + } + 100% { + -webkit-transform: scale(1); + } +} +@-ms-keyframes pulse { + 0% { + -ms-transform: scale(1.1); + } + 50% { + -ms-transform: scale(0.8); + } + 100% { + -ms-transform: scale(1); + } +} +@keyframes pulse { + 0% { + transform: scale(1.1); + } + 50% { + transform: scale(0.8); + } + 100% { + transform: scale(1); + } +} +.jui [class^="icon-"].float, +.jui [class*=" icon-"].float { + -webkit-animation: float 2s infinite linear; + -moz-animation: float 2s infinite linear; + -o-animation: float 2s infinite linear; + animation: float 2s infinite linear; + /* SPIN */ + /* Flash */ + /* BOUNCE */ + /* FLOAT */ + /* PULSE */ +} +@-moz-keyframes spin { + 0% { + -moz-transform: rotate(0deg); + } + 100% { + -moz-transform: rotate(359deg); + } +} +@-webkit-keyframes spin { + 0% { + -webkit-transform: rotate(0deg); + } + 100% { + -webkit-transform: rotate(359deg); + } +} +@-ms-keyframes spin { + 0% { + -ms-transform: rotate(0deg); + } + 100% { + -ms-transform: rotate(359deg); + } +} +@keyframes spin { + 0% { + transform: rotate(0deg); + } + 100% { + transform: rotate(359deg); + } +} +@-moz-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-webkit-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-ms-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-moz-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -moz-transform: translateY(0); + } + 40% { + -moz-transform: translateY(-10px); + } + 60% { + -moz-transform: translateY(-10px); + } +} +@-webkit-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -webkit-transform: translateY(0); + } + 40% { + -webkit-transform: translateY(-10px); + } + 60% { + -webkit-transform: translateY(-10px); + } +} +@-ms-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -ms-transform: translateY(0); + } + 40% { + -ms-transform: translateY(-10px); + } + 60% { + -ms-transform: translateY(-10px); + } +} +@keyframes bounce { + 0%, + 10%, + 20%, + 50%, + 80% { + transform: translateY(0); + } + 40% { + transform: translateY(-10px); + } + 60% { + transform: translateY(-10px); + } +} +@-moz-keyframes float { + 0% { + -moz-transform: translateY(0); + } + 50% { + -moz-transform: translateY(-6px); + } + 100% { + -moz-transform: translateY(0); + } +} +@-webkit-keyframes float { + 0% { + -webkit-transform: translateY(0); + } + 50% { + -webkit-transform: translateY(-6px); + } + 100% { + -webkit-transform: translateY(0); + } +} +@-ms-keyframes float { + 0% { + -ms-transform: translateY(0); + } + 50% { + -ms-transform: translateY(-6px); + } + 100% { + -ms-transform: translateY(0); + } +} +@keyframes float { + 0% { + transform: translateY(0); + } + 50% { + transform: translateY(-6px); + } + 100% { + transform: translateY(0); + } +} +@-moz-keyframes pulse { + 0% { + -moz-transform: scale(1.1); + } + 50% { + -moz-transform: scale(0.8); + } + 100% { + -moz-transform: scale(1); + } +} +@-webkit-keyframes pulse { + 0% { + -webkit-transform: scale(1); + } + 50% { + -webkit-transform: scale(0.8); + } + 100% { + -webkit-transform: scale(1); + } +} +@-ms-keyframes pulse { + 0% { + -ms-transform: scale(1.1); + } + 50% { + -ms-transform: scale(0.8); + } + 100% { + -ms-transform: scale(1); + } +} +@keyframes pulse { + 0% { + transform: scale(1.1); + } + 50% { + transform: scale(0.8); + } + 100% { + transform: scale(1); + } +} +.jui [class^="icon-"].pulse, +.jui [class*=" icon-"].pulse { + -webkit-animation: pulse 2s infinite linear; + -moz-animation: pulse 2s infinite linear; + -o-animation: pulse 2s infinite linear; + animation: pulse 2s infinite linear; + /* SPIN */ + /* Flash */ + /* BOUNCE */ + /* FLOAT */ + /* PULSE */ +} +@-moz-keyframes spin { + 0% { + -moz-transform: rotate(0deg); + } + 100% { + -moz-transform: rotate(359deg); + } +} +@-webkit-keyframes spin { + 0% { + -webkit-transform: rotate(0deg); + } + 100% { + -webkit-transform: rotate(359deg); + } +} +@-ms-keyframes spin { + 0% { + -ms-transform: rotate(0deg); + } + 100% { + -ms-transform: rotate(359deg); + } +} +@keyframes spin { + 0% { + transform: rotate(0deg); + } + 100% { + transform: rotate(359deg); + } +} +@-moz-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-webkit-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-ms-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-moz-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -moz-transform: translateY(0); + } + 40% { + -moz-transform: translateY(-10px); + } + 60% { + -moz-transform: translateY(-10px); + } +} +@-webkit-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -webkit-transform: translateY(0); + } + 40% { + -webkit-transform: translateY(-10px); + } + 60% { + -webkit-transform: translateY(-10px); + } +} +@-ms-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -ms-transform: translateY(0); + } + 40% { + -ms-transform: translateY(-10px); + } + 60% { + -ms-transform: translateY(-10px); + } +} +@keyframes bounce { + 0%, + 10%, + 20%, + 50%, + 80% { + transform: translateY(0); + } + 40% { + transform: translateY(-10px); + } + 60% { + transform: translateY(-10px); + } +} +@-moz-keyframes float { + 0% { + -moz-transform: translateY(0); + } + 50% { + -moz-transform: translateY(-6px); + } + 100% { + -moz-transform: translateY(0); + } +} +@-webkit-keyframes float { + 0% { + -webkit-transform: translateY(0); + } + 50% { + -webkit-transform: translateY(-6px); + } + 100% { + -webkit-transform: translateY(0); + } +} +@-ms-keyframes float { + 0% { + -ms-transform: translateY(0); + } + 50% { + -ms-transform: translateY(-6px); + } + 100% { + -ms-transform: translateY(0); + } +} +@keyframes float { + 0% { + transform: translateY(0); + } + 50% { + transform: translateY(-6px); + } + 100% { + transform: translateY(0); + } +} +@-moz-keyframes pulse { + 0% { + -moz-transform: scale(1.1); + } + 50% { + -moz-transform: scale(0.8); + } + 100% { + -moz-transform: scale(1); + } +} +@-webkit-keyframes pulse { + 0% { + -webkit-transform: scale(1); + } + 50% { + -webkit-transform: scale(0.8); + } + 100% { + -webkit-transform: scale(1); + } +} +@-ms-keyframes pulse { + 0% { + -ms-transform: scale(1.1); + } + 50% { + -ms-transform: scale(0.8); + } + 100% { + -ms-transform: scale(1); + } +} +@keyframes pulse { + 0% { + transform: scale(1.1); + } + 50% { + transform: scale(0.8); + } + 100% { + transform: scale(1); + } +} +.jui .icon-chevron-left:before { + content: "\e90e"; +} +.jui .icon-iframe:before { + content: "\e9be"; +} +.jui .icon-textbox:before { + content: "\e9bf"; +} +.jui .icon-arrow5:before { + content: "\e905"; +} +.jui .icon-arrow7:before { + content: "\e900"; +} +.jui .icon-loveit2:before { + content: "\e901"; +} +.jui .icon-favorites2:before { + content: "\e902"; +} +.jui .icon-favorites1:before { + content: "\e903"; +} +.jui .icon-arrow6:before { + content: "\e904"; +} +.jui .icon-happy2:before { + content: "\e906"; +} +.jui .icon-unhappy2:before { + content: "\e907"; +} +.jui .icon-happy:before { + content: "\e908"; +} +.jui .icon-gear2:before { + content: "\e909"; +} +.jui .icon-expand:before { + content: "\e90a"; +} +.jui .icon-export:before { + content: "\e90b"; +} +.jui .icon-back:before { + content: "\e90c"; +} +.jui .icon-report-link:before { + content: "\e90d"; +} +.jui .icon-chevron-right:before { + content: "\e90f"; +} +.jui .icon-dot:before { + content: "\e910"; +} +.jui .icon-list1:before { + content: "\e911"; +} +.jui .icon-pause:before { + content: "\e912"; +} +.jui .icon-minus:before { + content: "\e913"; +} +.jui .icon-plus:before { + content: "\e914"; +} +.jui .icon-close:before { + content: "\e915"; +} +.jui .icon-tool2:before { + content: "\e916"; +} +.jui .icon-time:before { + content: "\e917"; +} +.jui .icon-check:before { + content: "\e918"; +} +.jui .icon-download:before { + content: "\e919"; +} +.jui .icon-upload:before { + content: "\e91a"; +} +.jui .icon-layout3:before { + content: "\e91b"; +} +.jui .icon-tool:before { + content: "\e91c"; +} +.jui .icon-screenshot:before { + content: "\e91d"; +} +.jui .icon-server:before { + content: "\e91e"; +} +.jui .icon-slider:before { + content: "\e91f"; +} +.jui .icon-statistics:before { + content: "\e920"; +} +.jui .icon-themes:before { + content: "\e921"; +} +.jui .icon-was:before { + content: "\e922"; +} +.jui .icon-realtime:before { + content: "\e923"; +} +.jui .icon-report2:before { + content: "\e924"; +} +.jui .icon-resize:before { + content: "\e925"; +} +.jui .icon-return:before { + content: "\e926"; +} +.jui .icon-label:before { + content: "\e927"; +} +.jui .icon-mail:before { + content: "\e928"; +} +.jui .icon-message:before { + content: "\e929"; +} +.jui .icon-monitoring:before { + content: "\e92a"; +} +.jui .icon-grip1:before { + content: "\e92b"; +} +.jui .icon-grip2:before { + content: "\e92c"; +} +.jui .icon-grip3:before { + content: "\e92d"; +} +.jui .icon-dashboard:before { + content: "\e92e"; +} +.jui .icon-domain:before { + content: "\e92f"; +} +.jui .icon-edit:before { + content: "\e930"; +} +.jui .icon-etc:before { + content: "\e931"; +} +.jui .icon-chart-candle:before { + content: "\e932"; +} +.jui .icon-chart-gauge:before { + content: "\e933"; +} +.jui .icon-arrow2:before { + content: "\e934"; +} +.jui .icon-arrow4:before { + content: "\e935"; +} +.jui .icon-bell:before { + content: "\e936"; +} +.jui .icon-info:before { + content: "\e937"; +} +.jui .icon-tumblr:before { + content: "\e938"; +} +.jui .icon-kakao:before { + content: "\e939"; +} +.jui .icon-googleplus2:before { + content: "\e93a"; +} +.jui .icon-close2:before { + content: "\e93b"; +} +.jui .icon-facebook2:before { + content: "\e93c"; +} +.jui .icon-github2:before { + content: "\e93d"; +} +.jui .icon-hierarchy:before { + content: "\e93e"; +} +.jui .icon-loveit:before { + content: "\e93f"; +} +.jui .icon-return2:before { + content: "\e940"; +} +.jui .icon-theme:before { + content: "\e941"; +} +.jui .icon-line-merge:before { + content: "\e942"; +} +.jui .icon-line-separate:before { + content: "\e943"; +} +.jui .icon-enter:before { + content: "\e944"; +} +.jui .icon-ws:before { + content: "\e945"; +} +.jui .icon-clip:before { + content: "\e946"; +} +.jui .icon-scissors:before { + content: "\e947"; +} +.jui .icon-topology:before { + content: "\e948"; +} +.jui .icon-equalizer:before { + content: "\e949"; +} +.jui .icon-idea:before { + content: "\e94a"; +} +.jui .icon-tag:before { + content: "\e94b"; +} +.jui .icon-all:before { + content: "\e94c"; +} +.jui .icon-cursor1:before { + content: "\e94d"; +} +.jui .icon-roundsquare:before { + content: "\e94e"; +} +.jui .icon-paintbucket:before { + content: "\e94f"; +} +.jui .icon-square:before { + content: "\e950"; +} +.jui .icon-circle:before { + content: "\e951"; +} +.jui .icon-eraser:before { + content: "\e952"; +} +.jui .icon-paintbrush:before { + content: "\e953"; +} +.jui .icon-pen:before { + content: "\e954"; +} +.jui .icon-eyedropper:before { + content: "\e955"; +} +.jui .icon-x-mark:before { + content: "\e956"; +} +.jui .icon-server2:before { + content: "\e957"; +} +.jui .icon-server1:before { + content: "\e958"; +} +.jui .icon-unhappy:before { + content: "\e959"; +} +.jui .icon-layout2:before { + content: "\e95a"; +} +.jui .icon-layout1:before { + content: "\e95b"; +} +.jui .icon-d:before { + content: "\e95c"; +} +.jui .icon-cursor:before { + content: "\e95d"; +} +.jui .icon-feed:before { + content: "\e95e"; +} +.jui .icon-filter:before { + content: "\e95f"; +} +.jui .icon-column:before { + content: "\e960"; +} +.jui .icon-analysis:before { + content: "\e961"; +} +.jui .icon-wireless:before { + content: "\e962"; +} +.jui .icon-network:before { + content: "\e963"; +} +.jui .icon-cloud:before { + content: "\e964"; +} +.jui .icon-checkbox:before { + content: "\e965"; +} +.jui .icon-checkbox2:before { + content: "\e966"; +} +.jui .icon-stop:before { + content: "\e967"; +} +.jui .icon-link2:before { + content: "\e968"; +} +.jui .icon-minus2:before { + content: "\e969"; +} +.jui .icon-more:before { + content: "\e96a"; +} +.jui .icon-zip:before { + content: "\e96b"; +} +.jui .icon-mobile:before { + content: "\e96c"; +} +.jui .icon-tablet:before { + content: "\e96d"; +} +.jui .icon-share2:before { + content: "\e96e"; +} +.jui .icon-caution3:before { + content: "\e96f"; +} +.jui .icon-lock:before { + content: "\e970"; +} +.jui .icon-script:before { + content: "\e971"; +} +.jui .icon-business:before { + content: "\e972"; +} +.jui .icon-build:before { + content: "\e973"; +} +.jui .icon-themes2:before { + content: "\e974"; +} +.jui .icon-pin:before { + content: "\e975"; +} +.jui .icon-template:before { + content: "\e976"; +} +.jui .icon-line-height:before { + content: "\e977"; +} +.jui .icon-outdent:before { + content: "\e978"; +} +.jui .icon-indent:before { + content: "\e979"; +} +.jui .icon-like:before { + content: "\e97a"; +} +.jui .icon-blogger:before { + content: "\e97b"; +} +.jui .icon-github:before { + content: "\e97c"; +} +.jui .icon-facebook:before { + content: "\e97d"; +} +.jui .icon-googleplus:before { + content: "\e97e"; +} +.jui .icon-share:before { + content: "\e97f"; +} +.jui .icon-twitter:before { + content: "\e980"; +} +.jui .icon-image:before { + content: "\e981"; +} +.jui .icon-refresh2:before { + content: "\e982"; +} +.jui .icon-connection:before { + content: "\e983"; +} +.jui .icon-analysis2:before { + content: "\e984"; +} +.jui .icon-chart-scatter:before { + content: "\e985"; +} +.jui .icon-chart-radar:before { + content: "\e986"; +} +.jui .icon-chart-area:before { + content: "\e987"; +} +.jui .icon-chart-column:before { + content: "\e988"; +} +.jui .icon-chart-bar:before { + content: "\e989"; +} +.jui .icon-chart-line:before { + content: "\e98a"; +} +.jui .icon-info-message:before { + content: "\e98b"; +} +.jui .icon-report:before { + content: "\e98c"; +} +.jui .icon-menu:before { + content: "\e98d"; +} +.jui .icon-report-build:before { + content: "\e98e"; +} +.jui .icon-jennifer-server:before { + content: "\e98f"; +} +.jui .icon-user:before { + content: "\e990"; +} +.jui .icon-rule:before { + content: "\e991"; +} +.jui .icon-profile:before { + content: "\e992"; +} +.jui .icon-device:before { + content: "\e993"; +} +.jui .icon-caution2:before { + content: "\e994"; +} +.jui .icon-db:before { + content: "\e995"; +} +.jui .icon-checkmark:before { + content: "\e996"; +} +.jui .icon-stoppage:before { + content: "\e997"; +} +.jui .icon-align-right:before { + content: "\e998"; +} +.jui .icon-caution:before { + content: "\e999"; +} +.jui .icon-loading:before { + content: "\e99a"; +} +.jui .icon-play:before { + content: "\e99b"; +} +.jui .icon-right:before { + content: "\e99c"; +} +.jui .icon-left:before { + content: "\e99d"; +} +.jui .icon-bold:before { + content: "\e99e"; +} +.jui .icon-chart:before { + content: "\e99f"; +} +.jui .icon-document:before { + content: "\e9a0"; +} +.jui .icon-link:before { + content: "\e9a1"; +} +.jui .icon-arrow3:before { + content: "\e9a2"; +} +.jui .icon-arrow1:before { + content: "\e9a3"; +} +.jui .icon-textcolor:before { + content: "\e9a4"; +} +.jui .icon-text:before { + content: "\e9a5"; +} +.jui .icon-refresh:before { + content: "\e9a6"; +} +.jui .icon-align-center:before { + content: "\e9a7"; +} +.jui .icon-align-left:before { + content: "\e9a8"; +} +.jui .icon-preview:before { + content: "\e9a9"; +} +.jui .icon-exit:before { + content: "\e9aa"; +} +.jui .icon-dashboardlist:before { + content: "\e9ab"; +} +.jui .icon-add-dir:before { + content: "\e9ac"; +} +.jui .icon-add-dir2:before { + content: "\e9ad"; +} +.jui .icon-calendar:before { + content: "\e9ae"; +} +.jui .icon-gear:before { + content: "\e9af"; +} +.jui .icon-help:before { + content: "\e9b0"; +} +.jui .icon-hide:before { + content: "\e9b1"; +} +.jui .icon-home:before { + content: "\e9b2"; +} +.jui .icon-html:before { + content: "\e9b3"; +} +.jui .icon-italic:before { + content: "\e9b4"; +} +.jui .icon-new-window:before { + content: "\e9b5"; +} +.jui .icon-orderedlist:before { + content: "\e9b6"; +} +.jui .icon-printer:before { + content: "\e9b7"; +} +.jui .icon-save:before { + content: "\e9b8"; +} +.jui .icon-search:before { + content: "\e9b9"; +} +.jui .icon-table:before { + content: "\e9ba"; +} +.jui .icon-trashcan:before { + content: "\e9bb"; +} +.jui .icon-underline:before { + content: "\e9bc"; +} +.jui .icon-unorderedlist:before { + content: "\e9bd"; +} +.jui .colorpicker { + position: relative; + width: 226px; +} +.jui .colorpicker > .color { + position: relative; + height: 145px; + overflow: hidden; + cursor: pointer; +} +.jui .colorpicker > .color > .saturation { + position: relative; + width: 100%; + height: 100%; +} +.jui .colorpicker > .color > .saturation > .value { + position: relative; + width: 100%; + height: 100%; +} +.jui .colorpicker > .color > .saturation > .value > .drag-pointer { + position: absolute; + width: 10px; + height: 10px; + -webkit-border-radius: 50%; + -moz-border-radius: 50%; + border-radius: 50%; + left: -5px; + top: -5px; +} +.jui .colorpicker > .control { + position: relative; + padding: 18px 0px 20px 0px; + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + -o-user-select: none; + user-select: none; +} +.jui .colorpicker > .control > .color, +.jui .colorpicker > .control > .empty { + position: absolute; + left: 11px; + top: 24px; + width: 30px; + height: 30px; + -webkit-border-radius: 50%; + -moz-border-radius: 50%; + border-radius: 50%; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; +} +.jui .colorpicker > .control > .hue { + position: relative; + padding: 8px 12px; + margin: 0px 0px 0px 45px; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; + cursor: pointer; +} +.jui .colorpicker > .control > .hue > .container { + position: relative; + width: 157px; + height: 6px; + -webkit-border-radius: 5px; + -moz-border-radius: 5px; + border-radius: 5px; +} +.jui .colorpicker > .control > .opacity { + position: relative; + width: 143px; + padding: 8px 12px; + margin: 0px 0px 0px 45px; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; + cursor: pointer; +} +.jui .colorpicker > .control > .opacity > .container { + position: relative; + width: 100%; + height: 3px; + z-index: 2; + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; +} +.jui .colorpicker > .control > .input { + position: absolute; + font-size: 10px; + right: 10px; + bottom: 20px; + width: 30px; + height: 20px; + padding: 0 0 0 2px; +} +.jui .colorpicker > .control .drag-bar, +.jui .colorpicker > .control .drag-bar2 { + position: absolute; + cursor: pointer; + top: 50% !important; + margin-top: -7px !important; + left: -3px; + width: 12px; + height: 12px; + -webkit-border-radius: 50px; + -moz-border-radius: 50px; + border-radius: 50px; +} +.jui .colorpicker > .information { + position: relative; + height: 73px; + -webkit-box-sizing: padding-box; + -moz-box-sizing: padding-box; + box-sizing: padding-box; +} +.jui .colorpicker > .information > .input { + position: absolute; + font-size: 10px; + height: 20px; + bottom: 20px; + padding: 0 0 0 2px; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; + -webkit-user-select: text; + -moz-user-select: text; + -ms-user-select: text; + -o-user-select: text; + user-select: text; +} +.jui .colorpicker > .information > .input:nth-child(1) { + width: 93px; + left: 10px; +} +.jui .colorpicker > .information > .input:nth-child(2) { + width: 30px; + right: 74px; +} +.jui .colorpicker > .information > .input:nth-child(3) { + width: 30px; + right: 42px; +} +.jui .colorpicker > .information > .input:nth-child(4) { + width: 30px; + right: 10px; +} +.jui .colorpicker > .information > .title { + position: absolute; + font-size: 11px; + top: 15px; +} +.jui .colorpicker > .information > .title:nth-child(5) { + left: 10px; +} +.jui .colorpicker > .information > .title:nth-child(6) { + left: 123px; +} +.jui .colorpicker > .information > .title:nth-child(7) { + left: 155px; +} +.jui .colorpicker > .information > .title:nth-child(8) { + left: 187px; +} +.jui .window { + position: absolute; + margin: 0 auto; + z-index: 2000; + min-width: 200px; + min-height: 100px; + font-size: 13px; + -webkit-border-radius: 5px; + -moz-border-radius: 5px; + border-radius: 5px; +} +.jui .window > .head { + position: relative; + height: 32px; + -webkit-border-radius: 5px 5px 0 0; + -moz-border-radius: 5px 5px 0 0; + border-radius: 5px 5px 0 0; +} +.jui .window > .head > .left { + left: 10px; + right: 40px; + font-weight: bold; +} +.jui .window > .head > .left > .title { + display: inline-block; +} +.jui .window > .head > .right { + min-width: 20px; + right: 5px; +} +.jui .window > .head > .right > .close { + cursor: pointer; + right: 0px; +} +.jui .window > .head > * { + position: absolute; + padding: 8px 8px 6px 8px; +} +.jui .window > .head > * > a { + text-decoration: none; + color: inherit; +} +.jui .window > .body { + position: relative; + overflow: auto; + padding: 15px; + word-break: break-all; +} +.jui .window > .body.has-property { + padding: 0px; +} +.jui .window > .foot { + position: absolute; + left: 0px; + right: 0px; + bottom: 0px; + height: 47px; + text-align: center; + -webkit-border-radius: 0 0 5px 5px; + -moz-border-radius: 0 0 5px 5px; + border-radius: 0 0 5px 5px; +} +.jui .window > .foot > .btn { + margin-top: 9px; +} +.jui .window > .resize { + position: absolute; + right: 0px; + bottom: 0px; + cursor: nw-resize; +} +.jui label { + max-width: none; +} +.brtc-button-default{float:right;width:100px;height:32px;cursor:pointer;color:#656eea;border:1px solid #656eea;background-color:#fff}.brtc-button-primary{float:right;width:100px;height:32px;cursor:pointer;color:#656eea;border:1px solid #656eea;background-color:#fff;color:#fff;background-color:#656eea} +.brtc-radio-button-wrapper{background-color:transparent;border:none}.brtc-radio-button-wrapper input[type="radio"]{width:0;height:0;margin:-1px}.brtc-radio-button-wrapper input[type="radio"]+label{display:inline-block;position:relative}.brtc-radio-button-wrapper input[type="radio"]+label:before{float:left;padding:0;margin:0 7px 0 0;width:18px;height:18px;line-height:19px;content:close-quote;background:url(data:image/png;base64,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) no-repeat;background-position:0 0}.brtc-radio-button-wrapper 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input[type="checkbox"]:hover+label:before{background:url(data:image/png;base64,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) no-repeat;background-position:-38px -28px}.brtc-chekckbox-wrapper input[type="checkbox"]:checked+label:before{background:url(data:image/png;base64,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) no-repeat;background-position:0px 0px} +.brtc-input{width:100%;height:25px;box-sizing:border-box;min-width:16px;min-height:16px;margin:0;padding:0;padding-right:10px;padding-left:10px;border-width:1px;border-style:solid}.brtc-input.number{padding-right:10px;padding-left:10px;text-align:right;word-wrap:normal} +.brtc-text-area{height:80px;width:100%} +.brtc-display-flex{display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.brtc-display-flex-center{display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;align-items:center;justify-content:center;height:100%}.brtc-flex-direction-column{flex-direction:column}.brtc-flex-direction-row{flex-direction:row}.bos-datepicker-calendar{font-family:"NotoSansCJKkr",Verdana,Arial,sans-serif;font-size:13px;background-color:#fff;border:1px solid #555;display:none;position:absolute;top:100px;left:700px;width:200px;height:205px;z-index:9999999;outline:none}.bos-datepicker-calendar--visible{display:block}.bos-datepicker-calendar tr{display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;flex:1 1 0}.bos-datepicker-calendar__title{width:100px;text-align:center}.bos-datepicker-calendar__title:hover{cursor:pointer}.bos-datepicker-calendar__head{display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;align-items:center;justify-content:center;background-color:#e8e8e8;height:10%}.bos-datepicker-calendar__body{width:100%;height:90%}.bos-datepicker-calendar tbody{display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;flex-direction:column;height:100%}.bos-datepicker-calendar__prev{border-radius:6px;display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;align-items:center;justify-content:center;justify-content:center;align-items:center;width:20px;height:20px}.bos-datepicker-calendar__prev:hover{cursor:pointer}.bos-datepicker-calendar__next{border-radius:6px;display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;align-items:center;justify-content:center;justify-content:center;align-items:center;width:20px;height:20px}.bos-datepicker-calendar__next:hover{cursor:pointer}.bos-datepicker-calendar__row{display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.bos-datepicker-calendar__date{background-color:rgba(0,0,0,0);border:1px solid rgba(0,0,0,0);flex:1 1 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sans-serif;font-size:1em}.brtc-display-flex{display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.brtc-display-flex-center{display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;justify-content:center;align-items:center;height:100%}.brtc-flex-direction-column{flex-direction:column}.brtc-flex-direction-row{flex-direction:row}.bo-control-container-expander{background-color:white}.dropdown.jui{display:block;top:0;left:0;position:absolute}.brtc-widget-tree{padding:5px;background-color:white;border:1px solid black;overflow:hidden}.brtc-widget-tree ul{margin-block-start:0;margin-block-end:0;padding-inline-start:16px}.brtc-widget-tree__item--hidden{display:none}.brtc-widget-tree__item-wrapper{-webkit-touch-callout:none;-webkit-user-select:none;-khtml-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;display:inline-block}.brtc-widget-tree__item-wrapper:hover{cursor:pointer;background-color:#e5e5e5}.brtc-widget-tree__item-wrapper--selected{background-color:#dee9ef;border:1px solid #ccc}.brtc-widget-tree__item-wrapper--selected:hover{background-color:#dee9ef}.brtc-widget-tree__empty{float:left;width:12px}.brtc-widget-tree__arrow{float:left;width:12px}.brtc-widget-tree__arrow:hover{cursor:pointer} diff --git a/va-server/visual-analytics/public/css/plugins/brightics/brtc-widgets-all.min.css b/va-server/visual-analytics/public/css/plugins/brightics/brtc-widgets-all.min.css new file mode 100644 index 000000000..e4ae20950 --- /dev/null +++ b/va-server/visual-analytics/public/css/plugins/brightics/brtc-widgets-all.min.css @@ -0,0 +1,2096 @@ +.brtc-widget{font-family:'NotoSansCJKkr', Arial, Helvetica, sans-serif;font-size:1em}.brtc-display-flex{display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.brtc-display-flex-center{display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;justify-content:center;align-items:center;height:100%}.brtc-flex-direction-column{flex-direction:column}.brtc-flex-direction-row{flex-direction:row}.bo-control-container-expander{background-color:white}.dropdown.jui{display:block;top:0;left:0;position:absolute} +.brtc-display-flex{display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.brtc-display-flex-center{display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;align-items:center;justify-content:center;height:100%}.brtc-flex-direction-column{flex-direction:column}.brtc-flex-direction-row{flex-direction:row} +.brtc-state-normal{border:1px solid #d4d4d4;color:#444444;background-color:#fff}.brtc-widget.brtc-state-disabled,.brtc-widget.brtc-state-disabled *,.brtc-state-disabled,.brtc-state-disabled *{cursor:default;opacity:.55;user-select:none}.brtc-state-error{border:1px solid rgba(194,6,17,0.65)}.brtc-state-focus{border:1px solid #626fdb}.brtc-state-pressed{background-color:#eff1fd}.brtc-state-empty{border:1px solid #626fdb} +.jui { + /* Icon Base */ + /* Icon Animations */ + /* Icon List */ +} +.jui .clearfix { + *zoom: 1; +} +.jui .clearfix:before, +.jui .clearfix:after { + display: table; + content: ""; + line-height: 0; +} +.jui .clearfix:after { + clear: both; +} +.jui .hide-text { + font: 0/0 a; + color: transparent; + text-shadow: none; + background-color: transparent; + border: 0; +} +.jui label, +.jui input, +.jui button, +.jui select, +.jui textarea { + font-weight: normal; +} +.jui select, +.jui button, +.jui input[type="button"], +.jui input[type="reset"], +.jui input[type="submit"], +.jui input[type="radio"], +.jui input[type="checkbox"] { + cursor: pointer; +} +.jui button, +.jui html input[type="button"], +.jui input[type="reset"], +.jui input[type="submit"] { + cursor: pointer; + -webkit-appearance: button; +} +.jui button, +.jui input { + line-height: normal; +} +.jui button, +.jui input, +.jui select, +.jui textarea { + margin: 0; + font-size: 100%; + vertical-align: middle; +} +.jui select[disabled], +.jui textarea[disabled], +.jui select[readonly], +.jui textarea[readonly] { + cursor: not-allowed; +} +.jui [class^="icon-"], +.jui [class*=" icon-"] { + font-family: "jennifer"; + speak: none; + font-style: normal; + font-weight: normal; + font-variant: normal; + text-transform: none; + line-height: 1; + display: inline-block; + /* Better Font Rendering =========== */ + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} +.jui [class^="icon-"].spin, +.jui [class*=" icon-"].spin { + -webkit-animation: spin 2s infinite linear; + -moz-animation: spin 2s infinite linear; + -o-animation: spin 2s infinite linear; + animation: spin 2s infinite linear; + /* SPIN */ + /* Flash */ + /* BOUNCE */ + /* FLOAT */ + /* PULSE */ +} +@-moz-keyframes spin { + 0% { + -moz-transform: rotate(0deg); + } + 100% { + -moz-transform: rotate(359deg); + } +} +@-webkit-keyframes spin { + 0% { + -webkit-transform: rotate(0deg); + } + 100% { + -webkit-transform: rotate(359deg); + } +} +@-ms-keyframes spin { + 0% { + -ms-transform: rotate(0deg); + } + 100% { + -ms-transform: rotate(359deg); + } +} +@keyframes spin { + 0% { + transform: rotate(0deg); + } + 100% { + transform: rotate(359deg); + } +} +@-moz-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-webkit-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-ms-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-moz-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -moz-transform: translateY(0); + } + 40% { + -moz-transform: translateY(-10px); + } + 60% { + -moz-transform: translateY(-10px); + } +} +@-webkit-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -webkit-transform: translateY(0); + } + 40% { + -webkit-transform: translateY(-10px); + } + 60% { + -webkit-transform: translateY(-10px); + } +} +@-ms-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -ms-transform: translateY(0); + } + 40% { + -ms-transform: translateY(-10px); + } + 60% { + -ms-transform: translateY(-10px); + } +} +@keyframes bounce { + 0%, + 10%, + 20%, + 50%, + 80% { + transform: translateY(0); + } + 40% { + transform: translateY(-10px); + } + 60% { + transform: translateY(-10px); + } +} +@-moz-keyframes float { + 0% { + -moz-transform: translateY(0); + } + 50% { + -moz-transform: translateY(-6px); + } + 100% { + -moz-transform: translateY(0); + } +} +@-webkit-keyframes float { + 0% { + -webkit-transform: translateY(0); + } + 50% { + -webkit-transform: translateY(-6px); + } + 100% { + -webkit-transform: translateY(0); + } +} +@-ms-keyframes float { + 0% { + -ms-transform: translateY(0); + } + 50% { + -ms-transform: translateY(-6px); + } + 100% { + -ms-transform: translateY(0); + } +} +@keyframes float { + 0% { + transform: translateY(0); + } + 50% { + transform: translateY(-6px); + } + 100% { + transform: translateY(0); + } +} +@-moz-keyframes pulse { + 0% { + -moz-transform: scale(1.1); + } + 50% { + -moz-transform: scale(0.8); + } + 100% { + -moz-transform: scale(1); + } +} +@-webkit-keyframes pulse { + 0% { + -webkit-transform: scale(1); + } + 50% { + -webkit-transform: scale(0.8); + } + 100% { + -webkit-transform: scale(1); + } +} +@-ms-keyframes pulse { + 0% { + -ms-transform: scale(1.1); + } + 50% { + -ms-transform: scale(0.8); + } + 100% { + -ms-transform: scale(1); + } +} +@keyframes pulse { + 0% { + transform: scale(1.1); + } + 50% { + transform: scale(0.8); + } + 100% { + transform: scale(1); + } +} +.jui [class^="icon-"].flash, +.jui [class*=" icon-"].flash { + -webkit-animation: flash 2s infinite ease; + -moz-animation: flash 2s infinite ease; + -o-animation: flash 2s infinite ease; + animation: flash 2s infinite ease; + /* SPIN */ + /* Flash */ + /* BOUNCE */ + /* FLOAT */ + /* PULSE */ +} +@-moz-keyframes spin { + 0% { + -moz-transform: rotate(0deg); + } + 100% { + -moz-transform: rotate(359deg); + } +} +@-webkit-keyframes spin { + 0% { + -webkit-transform: rotate(0deg); + } + 100% { + -webkit-transform: rotate(359deg); + } +} +@-ms-keyframes spin { + 0% { + -ms-transform: rotate(0deg); + } + 100% { + -ms-transform: rotate(359deg); + } +} +@keyframes spin { + 0% { + transform: rotate(0deg); + } + 100% { + transform: rotate(359deg); + } +} +@-moz-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-webkit-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-ms-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-moz-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -moz-transform: translateY(0); + } + 40% { + -moz-transform: translateY(-10px); + } + 60% { + -moz-transform: translateY(-10px); + } +} +@-webkit-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -webkit-transform: translateY(0); + } + 40% { + -webkit-transform: translateY(-10px); + } + 60% { + -webkit-transform: translateY(-10px); + } +} +@-ms-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -ms-transform: translateY(0); + } + 40% { + -ms-transform: translateY(-10px); + } + 60% { + -ms-transform: translateY(-10px); + } +} +@keyframes bounce { + 0%, + 10%, + 20%, + 50%, + 80% { + transform: translateY(0); + } + 40% { + transform: translateY(-10px); + } + 60% { + transform: translateY(-10px); + } +} +@-moz-keyframes float { + 0% { + -moz-transform: translateY(0); + } + 50% { + -moz-transform: translateY(-6px); + } + 100% { + -moz-transform: translateY(0); + } +} +@-webkit-keyframes float { + 0% { + -webkit-transform: translateY(0); + } + 50% { + -webkit-transform: translateY(-6px); + } + 100% { + -webkit-transform: translateY(0); + } +} +@-ms-keyframes float { + 0% { + -ms-transform: translateY(0); + } + 50% { + -ms-transform: translateY(-6px); + } + 100% { + -ms-transform: translateY(0); + } +} +@keyframes float { + 0% { + transform: translateY(0); + } + 50% { + transform: translateY(-6px); + } + 100% { + transform: translateY(0); + } +} +@-moz-keyframes pulse { + 0% { + -moz-transform: scale(1.1); + } + 50% { + -moz-transform: scale(0.8); + } + 100% { + -moz-transform: scale(1); + } +} +@-webkit-keyframes pulse { + 0% { + -webkit-transform: scale(1); + } + 50% { + -webkit-transform: scale(0.8); + } + 100% { + -webkit-transform: scale(1); + } +} +@-ms-keyframes pulse { + 0% { + -ms-transform: scale(1.1); + } + 50% { + -ms-transform: scale(0.8); + } + 100% { + -ms-transform: scale(1); + } +} +@keyframes pulse { + 0% { + transform: scale(1.1); + } + 50% { + transform: scale(0.8); + } + 100% { + transform: scale(1); + } +} +.jui [class^="icon-"].bounce, +.jui [class*=" icon-"].bounce { + -webkit-animation: bounce 2s infinite ease; + -moz-animation: bounce 2s infinite ease; + -o-animation: bounce 2s infinite ease; + animation: bounce 2s infinite ease; + /* SPIN */ + /* Flash */ + /* BOUNCE */ + /* FLOAT */ + /* PULSE */ +} +@-moz-keyframes spin { + 0% { + -moz-transform: rotate(0deg); + } + 100% { + -moz-transform: rotate(359deg); + } +} +@-webkit-keyframes spin { + 0% { + -webkit-transform: rotate(0deg); + } + 100% { + -webkit-transform: rotate(359deg); + } +} +@-ms-keyframes spin { + 0% { + -ms-transform: rotate(0deg); + } + 100% { + -ms-transform: rotate(359deg); + } +} +@keyframes spin { + 0% { + transform: rotate(0deg); + } + 100% { + transform: rotate(359deg); + } +} +@-moz-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-webkit-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-ms-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-moz-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -moz-transform: translateY(0); + } + 40% { + -moz-transform: translateY(-10px); + } + 60% { + -moz-transform: translateY(-10px); + } +} +@-webkit-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -webkit-transform: translateY(0); + } + 40% { + -webkit-transform: translateY(-10px); + } + 60% { + -webkit-transform: translateY(-10px); + } +} +@-ms-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -ms-transform: translateY(0); + } + 40% { + -ms-transform: translateY(-10px); + } + 60% { + -ms-transform: translateY(-10px); + } +} +@keyframes bounce { + 0%, + 10%, + 20%, + 50%, + 80% { + transform: translateY(0); + } + 40% { + transform: translateY(-10px); + } + 60% { + transform: translateY(-10px); + } +} +@-moz-keyframes float { + 0% { + -moz-transform: translateY(0); + } + 50% { + -moz-transform: translateY(-6px); + } + 100% { + -moz-transform: translateY(0); + } +} +@-webkit-keyframes float { + 0% { + -webkit-transform: translateY(0); + } + 50% { + -webkit-transform: translateY(-6px); + } + 100% { + -webkit-transform: translateY(0); + } +} +@-ms-keyframes float { + 0% { + -ms-transform: translateY(0); + } + 50% { + -ms-transform: translateY(-6px); + } + 100% { + -ms-transform: translateY(0); + } +} +@keyframes float { + 0% { + transform: translateY(0); + } + 50% { + transform: translateY(-6px); + } + 100% { + transform: translateY(0); + } +} +@-moz-keyframes pulse { + 0% { + -moz-transform: scale(1.1); + } + 50% { + -moz-transform: scale(0.8); + } + 100% { + -moz-transform: scale(1); + } +} +@-webkit-keyframes pulse { + 0% { + -webkit-transform: scale(1); + } + 50% { + -webkit-transform: scale(0.8); + } + 100% { + -webkit-transform: scale(1); + } +} +@-ms-keyframes pulse { + 0% { + -ms-transform: scale(1.1); + } + 50% { + -ms-transform: scale(0.8); + } + 100% { + -ms-transform: scale(1); + } +} +@keyframes pulse { + 0% { + transform: scale(1.1); + } + 50% { + transform: scale(0.8); + } + 100% { + transform: scale(1); + } +} +.jui [class^="icon-"].float, +.jui [class*=" icon-"].float { + -webkit-animation: float 2s infinite linear; + -moz-animation: float 2s infinite linear; + -o-animation: float 2s infinite linear; + animation: float 2s infinite linear; + /* SPIN */ + /* Flash */ + /* BOUNCE */ + /* FLOAT */ + /* PULSE */ +} +@-moz-keyframes spin { + 0% { + -moz-transform: rotate(0deg); + } + 100% { + -moz-transform: rotate(359deg); + } +} +@-webkit-keyframes spin { + 0% { + -webkit-transform: rotate(0deg); + } + 100% { + -webkit-transform: rotate(359deg); + } +} +@-ms-keyframes spin { + 0% { + -ms-transform: rotate(0deg); + } + 100% { + -ms-transform: rotate(359deg); + } +} +@keyframes spin { + 0% { + transform: rotate(0deg); + } + 100% { + transform: rotate(359deg); + } +} +@-moz-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-webkit-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-ms-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-moz-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -moz-transform: translateY(0); + } + 40% { + -moz-transform: translateY(-10px); + } + 60% { + -moz-transform: translateY(-10px); + } +} +@-webkit-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -webkit-transform: translateY(0); + } + 40% { + -webkit-transform: translateY(-10px); + } + 60% { + -webkit-transform: translateY(-10px); + } +} +@-ms-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -ms-transform: translateY(0); + } + 40% { + -ms-transform: translateY(-10px); + } + 60% { + -ms-transform: translateY(-10px); + } +} +@keyframes bounce { + 0%, + 10%, + 20%, + 50%, + 80% { + transform: translateY(0); + } + 40% { + transform: translateY(-10px); + } + 60% { + transform: translateY(-10px); + } +} +@-moz-keyframes float { + 0% { + -moz-transform: translateY(0); + } + 50% { + -moz-transform: translateY(-6px); + } + 100% { + -moz-transform: translateY(0); + } +} +@-webkit-keyframes float { + 0% { + -webkit-transform: translateY(0); + } + 50% { + -webkit-transform: translateY(-6px); + } + 100% { + -webkit-transform: translateY(0); + } +} +@-ms-keyframes float { + 0% { + -ms-transform: translateY(0); + } + 50% { + -ms-transform: translateY(-6px); + } + 100% { + -ms-transform: translateY(0); + } +} +@keyframes float { + 0% { + transform: translateY(0); + } + 50% { + transform: translateY(-6px); + } + 100% { + transform: translateY(0); + } +} +@-moz-keyframes pulse { + 0% { + -moz-transform: scale(1.1); + } + 50% { + -moz-transform: scale(0.8); + } + 100% { + -moz-transform: scale(1); + } +} +@-webkit-keyframes pulse { + 0% { + -webkit-transform: scale(1); + } + 50% { + -webkit-transform: scale(0.8); + } + 100% { + -webkit-transform: scale(1); + } +} +@-ms-keyframes pulse { + 0% { + -ms-transform: scale(1.1); + } + 50% { + -ms-transform: scale(0.8); + } + 100% { + -ms-transform: scale(1); + } +} +@keyframes pulse { + 0% { + transform: scale(1.1); + } + 50% { + transform: scale(0.8); + } + 100% { + transform: scale(1); + } +} +.jui [class^="icon-"].pulse, +.jui [class*=" icon-"].pulse { + -webkit-animation: pulse 2s infinite linear; + -moz-animation: pulse 2s infinite linear; + -o-animation: pulse 2s infinite linear; + animation: pulse 2s infinite linear; + /* SPIN */ + /* Flash */ + /* BOUNCE */ + /* FLOAT */ + /* PULSE */ +} +@-moz-keyframes spin { + 0% { + -moz-transform: rotate(0deg); + } + 100% { + -moz-transform: rotate(359deg); + } +} +@-webkit-keyframes spin { + 0% { + -webkit-transform: rotate(0deg); + } + 100% { + -webkit-transform: rotate(359deg); + } +} +@-ms-keyframes spin { + 0% { + -ms-transform: rotate(0deg); + } + 100% { + -ms-transform: rotate(359deg); + } +} +@keyframes spin { + 0% { + transform: rotate(0deg); + } + 100% { + transform: rotate(359deg); + } +} +@-moz-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-webkit-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-ms-keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@keyframes flash { + 0%, + 100%, + 50% { + opacity: 1; + } + 25%, + 75% { + opacity: 0; + } +} +@-moz-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -moz-transform: translateY(0); + } + 40% { + -moz-transform: translateY(-10px); + } + 60% { + -moz-transform: translateY(-10px); + } +} +@-webkit-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -webkit-transform: translateY(0); + } + 40% { + -webkit-transform: translateY(-10px); + } + 60% { + -webkit-transform: translateY(-10px); + } +} +@-ms-keyframes bounce { + 0%, + 100%, + 20%, + 50%, + 80% { + -ms-transform: translateY(0); + } + 40% { + -ms-transform: translateY(-10px); + } + 60% { + -ms-transform: translateY(-10px); + } +} +@keyframes bounce { + 0%, + 10%, + 20%, + 50%, + 80% { + transform: translateY(0); + } + 40% { + transform: translateY(-10px); + } + 60% { + transform: translateY(-10px); + } +} +@-moz-keyframes float { + 0% { + -moz-transform: translateY(0); + } + 50% { + -moz-transform: translateY(-6px); + } + 100% { + -moz-transform: translateY(0); + } +} +@-webkit-keyframes float { + 0% { + -webkit-transform: translateY(0); + } + 50% { + -webkit-transform: translateY(-6px); + } + 100% { + -webkit-transform: translateY(0); + } +} +@-ms-keyframes float { + 0% { + -ms-transform: translateY(0); + } + 50% { + -ms-transform: translateY(-6px); + } + 100% { + -ms-transform: translateY(0); + } +} +@keyframes float { + 0% { + transform: translateY(0); + } + 50% { + transform: translateY(-6px); + } + 100% { + transform: translateY(0); + } +} +@-moz-keyframes pulse { + 0% { + -moz-transform: scale(1.1); + } + 50% { + -moz-transform: scale(0.8); + } + 100% { + -moz-transform: scale(1); + } +} +@-webkit-keyframes pulse { + 0% { + -webkit-transform: scale(1); + } + 50% { + -webkit-transform: scale(0.8); + } + 100% { + -webkit-transform: scale(1); + } +} +@-ms-keyframes pulse { + 0% { + -ms-transform: scale(1.1); + } + 50% { + -ms-transform: scale(0.8); + } + 100% { + -ms-transform: scale(1); + } +} +@keyframes pulse { + 0% { + transform: scale(1.1); + } + 50% { + transform: scale(0.8); + } + 100% { + transform: scale(1); + } +} +.jui .icon-chevron-left:before { + content: "\e90e"; +} +.jui .icon-iframe:before { + content: "\e9be"; +} +.jui .icon-textbox:before { + content: "\e9bf"; +} +.jui .icon-arrow5:before { + content: "\e905"; +} +.jui .icon-arrow7:before { + content: "\e900"; +} +.jui .icon-loveit2:before { + content: "\e901"; +} +.jui .icon-favorites2:before { + content: "\e902"; +} +.jui .icon-favorites1:before { + content: "\e903"; +} +.jui .icon-arrow6:before { + content: "\e904"; +} +.jui .icon-happy2:before { + content: "\e906"; +} +.jui .icon-unhappy2:before { + content: "\e907"; +} +.jui .icon-happy:before { + content: "\e908"; +} +.jui .icon-gear2:before { + content: "\e909"; +} +.jui .icon-expand:before { + content: "\e90a"; +} +.jui .icon-export:before { + content: "\e90b"; +} +.jui .icon-back:before { + content: "\e90c"; +} +.jui .icon-report-link:before { + content: "\e90d"; +} +.jui .icon-chevron-right:before { + content: "\e90f"; +} +.jui .icon-dot:before { + content: "\e910"; +} +.jui .icon-list1:before { + content: "\e911"; +} +.jui .icon-pause:before { + content: "\e912"; +} +.jui .icon-minus:before { + content: "\e913"; +} +.jui .icon-plus:before { + content: "\e914"; +} +.jui .icon-close:before { + content: "\e915"; +} +.jui .icon-tool2:before { + content: "\e916"; +} +.jui .icon-time:before { + content: "\e917"; +} +.jui .icon-check:before { + content: "\e918"; +} +.jui .icon-download:before { + content: "\e919"; +} +.jui .icon-upload:before { + content: "\e91a"; +} +.jui .icon-layout3:before { + content: "\e91b"; +} +.jui .icon-tool:before { + content: "\e91c"; +} +.jui .icon-screenshot:before { + content: "\e91d"; +} +.jui .icon-server:before { + content: "\e91e"; +} +.jui .icon-slider:before { + content: "\e91f"; +} +.jui .icon-statistics:before { + content: "\e920"; +} +.jui .icon-themes:before { + content: "\e921"; +} +.jui .icon-was:before { + content: "\e922"; +} +.jui .icon-realtime:before { + content: "\e923"; +} +.jui .icon-report2:before { + content: "\e924"; +} +.jui .icon-resize:before { + content: "\e925"; +} +.jui .icon-return:before { + content: "\e926"; +} +.jui .icon-label:before { + content: "\e927"; +} +.jui .icon-mail:before { + content: "\e928"; +} +.jui .icon-message:before { + content: "\e929"; +} +.jui .icon-monitoring:before { + content: "\e92a"; +} +.jui .icon-grip1:before { + content: "\e92b"; +} +.jui .icon-grip2:before { + content: "\e92c"; +} +.jui .icon-grip3:before { + content: "\e92d"; +} +.jui .icon-dashboard:before { + content: "\e92e"; +} +.jui .icon-domain:before { + content: "\e92f"; +} +.jui .icon-edit:before { + content: "\e930"; +} +.jui .icon-etc:before { + content: "\e931"; +} +.jui .icon-chart-candle:before { + content: "\e932"; +} +.jui .icon-chart-gauge:before { + content: "\e933"; +} +.jui .icon-arrow2:before { + content: "\e934"; +} +.jui .icon-arrow4:before { + content: "\e935"; +} +.jui .icon-bell:before { + content: "\e936"; +} +.jui .icon-info:before { + content: "\e937"; +} +.jui .icon-tumblr:before { + content: "\e938"; +} +.jui .icon-kakao:before { + content: "\e939"; +} +.jui .icon-googleplus2:before { + content: "\e93a"; +} +.jui .icon-close2:before { + content: "\e93b"; +} +.jui .icon-facebook2:before { + content: "\e93c"; +} +.jui .icon-github2:before { + content: "\e93d"; +} +.jui .icon-hierarchy:before { + content: "\e93e"; +} +.jui .icon-loveit:before { + content: "\e93f"; +} +.jui .icon-return2:before { + content: "\e940"; +} +.jui .icon-theme:before { + content: "\e941"; +} +.jui .icon-line-merge:before { + content: "\e942"; +} +.jui .icon-line-separate:before { + content: "\e943"; +} +.jui .icon-enter:before { + content: "\e944"; +} +.jui .icon-ws:before { + content: "\e945"; +} +.jui .icon-clip:before { + content: "\e946"; +} +.jui .icon-scissors:before { + content: "\e947"; +} +.jui .icon-topology:before { + content: "\e948"; +} +.jui .icon-equalizer:before { + content: "\e949"; +} +.jui .icon-idea:before { + content: "\e94a"; +} +.jui .icon-tag:before { + content: "\e94b"; +} +.jui .icon-all:before { + content: "\e94c"; +} +.jui .icon-cursor1:before { + content: "\e94d"; +} +.jui .icon-roundsquare:before { + content: "\e94e"; +} +.jui .icon-paintbucket:before { + content: "\e94f"; +} +.jui .icon-square:before { + content: "\e950"; +} +.jui .icon-circle:before { + content: "\e951"; +} +.jui .icon-eraser:before { + content: "\e952"; +} +.jui .icon-paintbrush:before { + content: "\e953"; +} +.jui .icon-pen:before { + content: "\e954"; +} +.jui .icon-eyedropper:before { + content: "\e955"; +} +.jui .icon-x-mark:before { + content: "\e956"; +} +.jui .icon-server2:before { + content: "\e957"; +} +.jui .icon-server1:before { + content: "\e958"; +} +.jui .icon-unhappy:before { + content: "\e959"; +} +.jui .icon-layout2:before { + content: "\e95a"; +} +.jui .icon-layout1:before { + content: "\e95b"; +} +.jui .icon-d:before { + content: "\e95c"; +} +.jui .icon-cursor:before { + content: "\e95d"; +} +.jui .icon-feed:before { + content: "\e95e"; +} +.jui .icon-filter:before { + content: "\e95f"; +} +.jui .icon-column:before { + content: "\e960"; +} +.jui .icon-analysis:before { + content: "\e961"; +} +.jui .icon-wireless:before { + content: "\e962"; +} +.jui .icon-network:before { + content: "\e963"; +} +.jui .icon-cloud:before { + content: "\e964"; +} +.jui .icon-checkbox:before { + content: "\e965"; +} +.jui .icon-checkbox2:before { + content: "\e966"; +} +.jui .icon-stop:before { + content: "\e967"; +} +.jui .icon-link2:before { + content: "\e968"; +} +.jui .icon-minus2:before { + content: "\e969"; +} +.jui .icon-more:before { + content: "\e96a"; +} +.jui .icon-zip:before { + content: "\e96b"; +} +.jui .icon-mobile:before { + content: "\e96c"; +} +.jui .icon-tablet:before { + content: "\e96d"; +} +.jui .icon-share2:before { + content: "\e96e"; +} +.jui .icon-caution3:before { + content: "\e96f"; +} +.jui .icon-lock:before { + content: "\e970"; +} +.jui .icon-script:before { + content: "\e971"; +} +.jui .icon-business:before { + content: "\e972"; +} +.jui .icon-build:before { + content: "\e973"; +} +.jui .icon-themes2:before { + content: "\e974"; +} +.jui .icon-pin:before { + content: "\e975"; +} +.jui .icon-template:before { + content: "\e976"; +} +.jui .icon-line-height:before { + content: "\e977"; +} +.jui .icon-outdent:before { + content: "\e978"; +} +.jui .icon-indent:before { + content: "\e979"; +} +.jui .icon-like:before { + content: "\e97a"; +} +.jui .icon-blogger:before { + content: "\e97b"; +} +.jui .icon-github:before { + content: "\e97c"; +} +.jui .icon-facebook:before { + content: "\e97d"; +} +.jui .icon-googleplus:before { + content: "\e97e"; +} +.jui .icon-share:before { + content: "\e97f"; +} +.jui .icon-twitter:before { + content: "\e980"; +} +.jui .icon-image:before { + content: "\e981"; +} +.jui .icon-refresh2:before { + content: "\e982"; +} +.jui .icon-connection:before { + content: "\e983"; +} +.jui .icon-analysis2:before { + content: "\e984"; +} +.jui .icon-chart-scatter:before { + content: "\e985"; +} +.jui .icon-chart-radar:before { + content: "\e986"; +} +.jui .icon-chart-area:before { + content: "\e987"; +} +.jui .icon-chart-column:before { + content: "\e988"; +} +.jui .icon-chart-bar:before { + content: "\e989"; +} +.jui .icon-chart-line:before { + content: "\e98a"; +} +.jui .icon-info-message:before { + content: "\e98b"; +} +.jui .icon-report:before { + content: "\e98c"; +} +.jui .icon-menu:before { + content: "\e98d"; +} +.jui .icon-report-build:before { + content: "\e98e"; +} +.jui .icon-jennifer-server:before { + content: "\e98f"; +} +.jui .icon-user:before { + content: "\e990"; +} +.jui .icon-rule:before { + content: "\e991"; +} +.jui .icon-profile:before { + content: "\e992"; +} +.jui .icon-device:before { + content: "\e993"; +} +.jui .icon-caution2:before { + content: "\e994"; +} +.jui .icon-db:before { + content: "\e995"; +} +.jui .icon-checkmark:before { + content: "\e996"; +} +.jui .icon-stoppage:before { + content: "\e997"; +} +.jui .icon-align-right:before { + content: "\e998"; +} +.jui .icon-caution:before { + content: "\e999"; +} +.jui .icon-loading:before { + content: "\e99a"; +} +.jui .icon-play:before { + content: "\e99b"; +} +.jui .icon-right:before { + content: "\e99c"; +} +.jui .icon-left:before { + content: "\e99d"; +} +.jui .icon-bold:before { + content: "\e99e"; +} +.jui .icon-chart:before { + content: "\e99f"; +} +.jui .icon-document:before { + content: "\e9a0"; +} +.jui .icon-link:before { + content: "\e9a1"; +} +.jui .icon-arrow3:before { + content: "\e9a2"; +} +.jui .icon-arrow1:before { + content: "\e9a3"; +} +.jui .icon-textcolor:before { + content: "\e9a4"; +} +.jui .icon-text:before { + content: "\e9a5"; +} +.jui .icon-refresh:before { + content: "\e9a6"; +} +.jui .icon-align-center:before { + content: "\e9a7"; +} +.jui .icon-align-left:before { + content: "\e9a8"; +} +.jui .icon-preview:before { + content: "\e9a9"; +} +.jui .icon-exit:before { + content: "\e9aa"; +} +.jui .icon-dashboardlist:before { + content: "\e9ab"; +} +.jui .icon-add-dir:before { + content: "\e9ac"; +} +.jui .icon-add-dir2:before { + content: "\e9ad"; +} +.jui .icon-calendar:before { + content: "\e9ae"; +} +.jui .icon-gear:before { + content: "\e9af"; +} +.jui .icon-help:before { + content: "\e9b0"; +} +.jui .icon-hide:before { + content: "\e9b1"; +} +.jui .icon-home:before { + content: "\e9b2"; +} +.jui .icon-html:before { + content: "\e9b3"; +} +.jui .icon-italic:before { + content: "\e9b4"; +} +.jui .icon-new-window:before { + content: "\e9b5"; +} +.jui .icon-orderedlist:before { + content: "\e9b6"; +} +.jui .icon-printer:before { + content: "\e9b7"; +} +.jui .icon-save:before { + content: "\e9b8"; +} +.jui .icon-search:before { + content: "\e9b9"; +} +.jui .icon-table:before { + content: "\e9ba"; +} +.jui .icon-trashcan:before { + content: "\e9bb"; +} +.jui .icon-underline:before { + content: "\e9bc"; +} +.jui .icon-unorderedlist:before { + content: "\e9bd"; +} +.jui .colorpicker { + position: relative; + width: 226px; +} +.jui .colorpicker > .color { + position: relative; + height: 145px; + overflow: hidden; + cursor: pointer; +} +.jui .colorpicker > .color > .saturation { + position: relative; + width: 100%; + height: 100%; +} +.jui .colorpicker > .color > .saturation > .value { + position: relative; + width: 100%; + height: 100%; +} +.jui .colorpicker > .color > .saturation > .value > .drag-pointer { + position: absolute; + width: 10px; + height: 10px; + -webkit-border-radius: 50%; + -moz-border-radius: 50%; + border-radius: 50%; + left: -5px; + top: -5px; +} +.jui .colorpicker > .control { + position: relative; + padding: 18px 0px 20px 0px; + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + -o-user-select: none; + user-select: none; +} +.jui .colorpicker > .control > .color, +.jui .colorpicker > .control > .empty { + position: absolute; + left: 11px; + top: 24px; + width: 30px; + height: 30px; + -webkit-border-radius: 50%; + -moz-border-radius: 50%; + border-radius: 50%; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; +} +.jui .colorpicker > .control > .hue { + position: relative; + padding: 8px 12px; + margin: 0px 0px 0px 45px; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; + cursor: pointer; +} +.jui .colorpicker > .control > .hue > .container { + position: relative; + width: 157px; + height: 6px; + -webkit-border-radius: 5px; + -moz-border-radius: 5px; + border-radius: 5px; +} +.jui .colorpicker > .control > .opacity { + position: relative; + width: 143px; + padding: 8px 12px; + margin: 0px 0px 0px 45px; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; + cursor: pointer; +} +.jui .colorpicker > .control > .opacity > .container { + position: relative; + width: 100%; + height: 3px; + z-index: 2; + -webkit-border-radius: 3px; + -moz-border-radius: 3px; + border-radius: 3px; +} +.jui .colorpicker > .control > .input { + position: absolute; + font-size: 10px; + right: 10px; + bottom: 20px; + width: 30px; + height: 20px; + padding: 0 0 0 2px; +} +.jui .colorpicker > .control .drag-bar, +.jui .colorpicker > .control .drag-bar2 { + position: absolute; + cursor: pointer; + top: 50% !important; + margin-top: -7px !important; + left: -3px; + width: 12px; + height: 12px; + -webkit-border-radius: 50px; + -moz-border-radius: 50px; + border-radius: 50px; +} +.jui .colorpicker > .information { + position: relative; + height: 73px; + -webkit-box-sizing: padding-box; + -moz-box-sizing: padding-box; + box-sizing: padding-box; +} +.jui .colorpicker > .information > .input { + position: absolute; + font-size: 10px; + height: 20px; + bottom: 20px; + padding: 0 0 0 2px; + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; + -webkit-user-select: text; + -moz-user-select: text; + -ms-user-select: text; + -o-user-select: text; + user-select: text; +} +.jui .colorpicker > .information > .input:nth-child(1) { + width: 93px; + left: 10px; +} +.jui .colorpicker > .information > .input:nth-child(2) { + width: 30px; + right: 74px; +} +.jui .colorpicker > .information > .input:nth-child(3) { + width: 30px; + right: 42px; +} +.jui .colorpicker > .information > .input:nth-child(4) { + width: 30px; + right: 10px; +} +.jui .colorpicker > .information > .title { + position: absolute; + font-size: 11px; + top: 15px; +} +.jui .colorpicker > .information > .title:nth-child(5) { + left: 10px; +} +.jui .colorpicker > .information > .title:nth-child(6) { + left: 123px; +} +.jui .colorpicker > .information > .title:nth-child(7) { + left: 155px; +} +.jui .colorpicker > .information > .title:nth-child(8) { + left: 187px; +} +.jui .window { + position: absolute; + margin: 0 auto; + z-index: 2000; + min-width: 200px; + min-height: 100px; + font-size: 13px; + -webkit-border-radius: 5px; + -moz-border-radius: 5px; + border-radius: 5px; +} +.jui .window > .head { + position: relative; + height: 32px; + -webkit-border-radius: 5px 5px 0 0; + -moz-border-radius: 5px 5px 0 0; + border-radius: 5px 5px 0 0; +} +.jui .window > .head > .left { + left: 10px; + right: 40px; + font-weight: bold; +} +.jui .window > .head > .left > .title { + display: inline-block; +} +.jui .window > .head > .right { + min-width: 20px; + right: 5px; +} +.jui .window > .head > .right > .close { + cursor: pointer; + right: 0px; +} +.jui .window > .head > * { + position: absolute; + padding: 8px 8px 6px 8px; +} +.jui .window > .head > * > a { + text-decoration: none; + color: inherit; +} +.jui .window > .body { + position: relative; + overflow: auto; + padding: 15px; + word-break: break-all; +} +.jui .window > .body.has-property { + padding: 0px; +} +.jui .window > .foot { + position: absolute; + left: 0px; + right: 0px; + bottom: 0px; + height: 47px; + text-align: center; + -webkit-border-radius: 0 0 5px 5px; + -moz-border-radius: 0 0 5px 5px; + border-radius: 0 0 5px 5px; +} +.jui .window > .foot > .btn { + margin-top: 9px; +} +.jui .window > .resize { + position: absolute; + right: 0px; + bottom: 0px; + cursor: nw-resize; +} +.jui label { + max-width: none; +} +.brtc-button-default{float:right;width:100px;height:32px;cursor:pointer;color:#656eea;border:1px solid #656eea;background-color:#fff}.brtc-button-primary{float:right;width:100px;height:32px;cursor:pointer;color:#656eea;border:1px solid #656eea;background-color:#fff;color:#fff;background-color:#656eea} +.brtc-radio-button-wrapper{background-color:transparent;border:none}.brtc-radio-button-wrapper input[type="radio"]{width:0;height:0;margin:-1px}.brtc-radio-button-wrapper input[type="radio"]+label{display:inline-block;position:relative}.brtc-radio-button-wrapper input[type="radio"]+label:before{float:left;padding:0;margin:0 7px 0 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input[type="checkbox"]:hover+label:before{background:url(data:image/png;base64,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) no-repeat;background-position:-38px -28px}.brtc-chekckbox-wrapper input[type="checkbox"]:checked+label:before{background:url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAYAAACNiR0NAAAAAXNSR0IArs4c6QAAAUFJREFUOBFjNFE4/J+BioCJimaBjRrmBvLwMWOEGNlellfiZFi/34QhMkkKxVCyDFRS5WKYs1qPQUCIlYGNHdUIFhTjieCADJsNNIyPn4VhStcDhoXTn6DoQjGekZGBwcCUD0UBMkddm5thzhqIYbMnPsIwDKQWxcCKFhWGGct0GawdBZHNAbO19HgYZi7XY+DlY2FYAHTVrAmPMNSABFAM/PrlLwMzCyND7ywtFENBhk0HWsTNyww2bCrQq7gAs5RAUgNM8uSRD2CmqZUAg6u3KMP1K18YBIEBP3WJLgMXN2HDQJoZseXlhEwZhuwyBYa/fxkYfv38x8DJxUTQZTBHoXgZJggKo4lt9xmYgekWZNiyec8Y8HkTpg9E40w2S2Y/BbtOWp6Tob/5HrIevGysXsarg4AkVi8T0INXegQaCADwSFOCVRRIJgAAAABJRU5ErkJggg==) no-repeat;background-position:0px 0px} +.brtc-input{width:100%;height:25px;box-sizing:border-box;min-width:16px;min-height:16px;margin:0;padding:0;padding-right:10px;padding-left:10px;border-width:1px;border-style:solid}.brtc-input.number{padding-right:10px;padding-left:10px;text-align:right;word-wrap:normal} +.brtc-text-area{height:80px;width:100%} +.brtc-display-flex{display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.brtc-display-flex-center{display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;align-items:center;justify-content:center;height:100%}.brtc-flex-direction-column{flex-direction:column}.brtc-flex-direction-row{flex-direction:row}.bos-datepicker-calendar{font-family:"NotoSansCJKkr",Verdana,Arial,sans-serif;font-size:13px;background-color:#fff;border:1px solid #555;display:none;position:absolute;top:100px;left:700px;width:200px;height:205px;z-index:9999999;outline:none}.bos-datepicker-calendar--visible{display:block}.bos-datepicker-calendar tr{display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;flex:1 1 0}.bos-datepicker-calendar__title{width:100px;text-align:center}.bos-datepicker-calendar__title:hover{cursor:pointer}.bos-datepicker-calendar__head{display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;align-items:center;justify-content:center;background-color:#e8e8e8;height:10%}.bos-datepicker-calendar__body{width:100%;height:90%}.bos-datepicker-calendar tbody{display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;flex-direction:column;height:100%}.bos-datepicker-calendar__prev{border-radius:6px;display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;align-items:center;justify-content:center;justify-content:center;align-items:center;width:20px;height:20px}.bos-datepicker-calendar__prev:hover{cursor:pointer}.bos-datepicker-calendar__next{border-radius:6px;display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;align-items:center;justify-content:center;justify-content:center;align-items:center;width:20px;height:20px}.bos-datepicker-calendar__next:hover{cursor:pointer}.bos-datepicker-calendar__row{display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.bos-datepicker-calendar__date{background-color:rgba(0,0,0,0);border:1px solid rgba(0,0,0,0);flex:1 1 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15px}.jui.colorpicker{position:relative;width:223px;box-sizing:content-box}.jui.colorpicker>.color{position:relative;height:145px;overflow:hidden;cursor:pointer}.jui.colorpicker>.color>.saturation{position:relative;width:100%;height:100%}.jui.colorpicker>.color>.saturation>.value{position:relative;width:100%;height:100%}.jui.colorpicker>.color>.saturation>.value>.drag-pointer{position:absolute;width:10px;height:10px;-webkit-border-radius:5px;-moz-border-radius:5px;border-radius:5px;left:-5px;top:-5px}.jui.colorpicker>.control{position:relative;padding:5px 0 20px 0}.jui.colorpicker>.control>.color,.jui.colorpicker>.control>.empty{position:absolute;left:11px;top:15px;width:30px;height:30px;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.jui.colorpicker>.control>.hue{position:relative;padding:8px 12px;margin:10px 0px 0px 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.color>.saturation{background-color:rgba(204,154,129,0);background-image:-moz-linear-gradient(left, #fff, rgba(204,154,129,0));background-image:-webkit-gradient(linear, 0 0, 100% 0, from(#fff), to(rgba(204,154,129,0)));background-image:-webkit-linear-gradient(left, #fff, rgba(204,154,129,0));background-image:-o-linear-gradient(left, #fff, rgba(204,154,129,0));background-image:linear-gradient(to right, #fff, rgba(204,154,129,0));background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#00cc9a81', GradientType=1)}.colorpicker .color>.saturation>.value{background-image:-webkit-gradient(linear, 0 100%, 0 0, from(#000), to(rgba(204,154,129,0)));background-image:-webkit-linear-gradient(bottom, #000, rgba(204,154,129,0));background-image:-moz-linear-gradient(bottom, #000, rgba(204,154,129,0));background-image:-o-linear-gradient(bottom, #000, rgba(204,154,129,0));background-image:-ms-linear-gradient(bottom, #000, 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17%, lime 33%, cyan 50%, blue 67%, #f0f 83%, red 100%)}.colorpicker .control>.input{color:#333}.colorpicker .control>.empty{background:url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAICAYAAADED76LAAAAJElEQVQYV2NctWrVfwYkEBYWxojMZ6SDAmT7QGx0K1EcRBsFAADeG/3M/HteAAAAAElFTkSuQmCC") repeat}.colorpicker .control .drag-bar,.colorpicker .control .drag-bar2{border:1px solid rgba(0,0,0,0.1);-webkit-box-shadow:0 0 2px 0 rgba(0,0,0,0.05);-moz-box-shadow:0 0 2px 0 rgba(0,0,0,0.05);box-shadow:0 0 2px 0 rgba(0,0,0,0.05);background-color:#FFFFFF}.colorpicker .information{border-top:1px solid #e8e8e8}.colorpicker .information>.title{color:#a3a3a3}.colorpicker .information>.input{color:#333} +div.brtc-layout-splitter-container{position:relative;width:100%;height:100%}div.brtc-layout-splitter-container div.ui-splitter-vertical,div.brtc-layout-splitter-container div.ui-splitter-horizontal{z-index:1249} +.brtc-widget{font-family:'NotoSansCJKkr', Arial, Helvetica, sans-serif;font-size:1em}.brtc-display-flex{display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.brtc-display-flex-center{display:flex;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;justify-content:center;align-items:center;height:100%}.brtc-flex-direction-column{flex-direction:column}.brtc-flex-direction-row{flex-direction:row}.bo-control-container-expander{background-color:white}.dropdown.jui{display:block;top:0;left:0;position:absolute}.brtc-widget-tree{padding:5px;background-color:white;border:1px solid black;overflow:hidden}.brtc-widget-tree ul{margin-block-start:0;margin-block-end:0;padding-inline-start:16px}.brtc-widget-tree__item--hidden{display:none}.brtc-widget-tree__item-wrapper{-webkit-touch-callout:none;-webkit-user-select:none;-khtml-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;display:inline-block}.brtc-widget-tree__item-wrapper:hover{cursor:pointer;background-color:#e5e5e5}.brtc-widget-tree__item-wrapper--selected{background-color:#dee9ef;border:1px solid #ccc}.brtc-widget-tree__item-wrapper--selected:hover{background-color:#dee9ef}.brtc-widget-tree__empty{float:left;width:12px}.brtc-widget-tree__arrow{float:left;width:12px}.brtc-widget-tree__arrow:hover{cursor:pointer} diff --git a/va-server/visual-analytics/public/css/va/brightics-va.css b/va-server/visual-analytics/public/css/va/brightics-va.css index 5ba30c245..df8fb3a89 100644 --- a/va-server/visual-analytics/public/css/va/brightics-va.css +++ b/va-server/visual-analytics/public/css/va/brightics-va.css @@ -1,4 +1,4 @@ -/* Brightics VA Build 191105 */ +/* Brightics VA Build 200629 */ * { -webkit-box-sizing: content-box; -moz-box-sizing: content-box; @@ -15593,6 +15593,7 @@ div.brtc-va-refine-step-validation-error { .brtc-va-editors-sheet-fnunitviewer { background-color: #f4f6f8; + display: flex; } .brtc-va-editors-sheet-fnunitviewer .brtc-va-progress > div { @@ -19483,24 +19484,28 @@ div.brtc-va-refine-step-validation-error { } .brtc-style-views-palette-fnunit { - font-family: Arial, helvetica, sans-serif; - font-size: 11px; + font-family: 'Noto Sans KR', helvetica, sans-serif; + font-size: 10.5px; position: relative; z-index: 5100; display: flex !important; float: left; box-sizing: border-box; width: 130px; - height: 32px; - margin: 0 3px 3px 0; + height: 38px; + margin: 0 3px 4px 0; cursor: move; color: #667185; border: 1px solid #dcdcdc; - border-radius: 16px; + border-radius: 18px; background: #fff; align-items: center; } +.brtc-va-dialogs-selectfnunit .brtc-style-views-palette-fnunit { + width: 146px; +} + .brtc-va-editors-sheet-panels-basepanel-contents-area .brtc-va-views-palette-fnunit { z-index: 0; } @@ -19524,9 +19529,9 @@ div.brtc-va-refine-step-validation-error { display: -webkit-box; overflow: hidden; max-width: calc(100% - 45px); - max-height: calc(1em * 1.2 * 2); + max-height: calc(1em * 1.2 * 3); text-overflow: ellipsis; - -webkit-line-clamp: 2; + -webkit-line-clamp: 3; -webkit-box-orient: vertical; } diff --git a/va-server/visual-analytics/public/js/plugins/bcharts/bcharts-adonis-all.js b/va-server/visual-analytics/public/js/plugins/bcharts/bcharts-adonis-all.js index 08a4bc3a3..d2873a04a 100644 --- a/va-server/visual-analytics/public/js/plugins/bcharts/bcharts-adonis-all.js +++ b/va-server/visual-analytics/public/js/plugins/bcharts/bcharts-adonis-all.js @@ -60,7 +60,7 @@ /******/ __webpack_require__.p = ""; /******/ /******/ // Load entry module and return exports -/******/ return __webpack_require__(__webpack_require__.s = 559); +/******/ return __webpack_require__(__webpack_require__.s = 581); /******/ }) /************************************************************************/ /******/ ([ @@ -122,8 +122,16 @@ exports.createCenterPositionNumberWidget = createCenterPositionNumberWidget; exports.createToolTipTriggerRadioButtonWidget = createToolTipTriggerRadioButtonWidget; exports.createEdgeTypeSelectorWidget = createEdgeTypeSelectorWidget; exports.createLabelWidget = createLabelWidget; +exports.createColumnWidthsWidget = createColumnWidthsWidget; +exports.createMarkerLabelPositionSelectorWidget = createMarkerLabelPositionSelectorWidget; +exports.createBaseSliderWidget = createBaseSliderWidget; +exports.createSortRadioButtonWidget = createSortRadioButtonWidget; +exports.createSortAxisValueRadioButtonWidget = createSortAxisValueRadioButtonWidget; +exports.createSortLegendValueRadioButtonWidget = createSortLegendValueRadioButtonWidget; +exports.createPieLabelPositionSelectorWidget = createPieLabelPositionSelectorWidget; +exports.createColumnListWidget = createColumnListWidget; -var _widgetIndex = __webpack_require__(564); +var _widgetIndex = __webpack_require__(587); var Widgets = _interopRequireWildcard(_widgetIndex); @@ -314,10 +322,209 @@ function createLabelWidget(parentId, options) { return new Widgets.LabelWidget(parentId, options); } +function createColumnWidthsWidget(parentId, options) { + return new Widgets.ColumnWidthsWidget(parentId, options); +} + +function createMarkerLabelPositionSelectorWidget(parentId, options) { + return new Widgets.MarkerLabelPositionWidget(parentId, options); +} + +function createBaseSliderWidget(parentId, options) { + return new Widgets.BaseSliderWidget(parentId, options); +} + +function createSortRadioButtonWidget(parentId, options) { + return new Widgets.SortRuleRadioButtonWidget(parentId, options); +} + +function createSortAxisValueRadioButtonWidget(parentId, options) { + return new Widgets.SortAxisValueRadioButtonWidget(parentId, options); +} + +function createSortLegendValueRadioButtonWidget(parentId, options) { + return new Widgets.SortLegendValueRadioButtonWidget(parentId, options); +} + +function createPieLabelPositionSelectorWidget(parentId, options) { + return new Widgets.PieLabelPositionWidget(parentId, options); +} + +function createColumnListWidget(parentId, options) { + return new Widgets.ColumnListWidget(parentId, options); +} + /***/ }), /* 4 */, -/* 5 */, -/* 6 */ +/* 5 */ +/***/ (function(module, exports, __webpack_require__) { + +"use strict"; + + +Object.defineProperty(exports, "__esModule", { + value: true +}); + +var _widgetFactory = __webpack_require__(3); + +var WidgetFactory = _interopRequireWildcard(_widgetFactory); + +var _controlContainerPreview = __webpack_require__(126); + +var ControlContainerPreview = _interopRequireWildcard(_controlContainerPreview); + +function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } } + +/** + * Created by mk90.kim on 2017-05-10. + */ + +var ChartWidget = Brightics.Chart.Widget; + + +function BaseControl(parentId, options, headerKey) { + if (headerKey) { + this.headerKey = headerKey; + } + + ChartWidget.call(this, parentId, options); + this.renderProblem(); + this.renderWarning(); +} + +BaseControl.prototype = Object.create(ChartWidget.prototype); +BaseControl.prototype.constructor = BaseControl; + +BaseControl.prototype._init = function () { + ChartWidget.prototype._init.call(this); + this._widgetList = []; +}; + +BaseControl.prototype._createContents = function ($parent) { + if (this.$controlMain) { + this.destroy(); + } + this.$controlMain = $('
'); + $parent.append(this.$controlMain); +}; + +/** + var headerOption = { + label: 'View Range', + showBtn: { + defaultVal: false, + clickfunc: function () { + console.log('show btn clicked'); + } + }, + showBtn: { + clickfunc: function () { + console.log('show btn clicked'); + } + } +}; + */ +BaseControl.prototype.createComponentHeader = function (headerOption) { + var _this = this; + + var $controlHeader = $('
'); + + this.$controlMain.append($controlHeader); + + if (!headerOption) { + return; + } + if (headerOption.additionalType) { + $controlHeader.attr('type', headerOption.additionalType); + } + + if (headerOption.label) { + var $controlHeaderLabel = $('
'); + $controlHeader.append($controlHeaderLabel); + $controlHeaderLabel.text(headerOption.label); + } + + if (headerOption.showBtn) { + this.headerShowBtn = WidgetFactory.createShowButtonWidget($controlHeader, headerOption.showBtn); + } + + if (headerOption.addIcon) { + if (!headerOption.label && !headerOption.showBtn) { + $controlHeader.removeClass('bos-flex-space-between').addClass('bos-flex-center'); + } + var $panelHeaderAddBtn = $('
'); + $controlHeader.append($panelHeaderAddBtn); + if (typeof headerOption.addIcon.clickfunc === 'function') { + $panelHeaderAddBtn.click(headerOption.addIcon.clickfunc); + } else { + console.error('Header [' + headerOption.label + '] add button: click event function is not existed.'); + } + } else if (headerOption.addBtn) { + if (!headerOption.label && !headerOption.showBtn) { + $controlHeader.removeClass('bos-flex-space-between').addClass('bos-flex-left'); + } + var panelHeaderAddBtn = $(''); + $controlHeader.append(panelHeaderAddBtn); + if (typeof headerOption.addBtn.clickfunc === 'function') { + panelHeaderAddBtn.click(headerOption.addBtn.clickfunc); + } else { + console.error('Header [' + headerOption.label + '] add button: click event function is not existed.'); + } + } +}; + +BaseControl.prototype.createComponentContents = function () { + this.$controlContents = $('
'); + this.$controlMain.append(this.$controlContents); +}; + +BaseControl.setTemplate = function (template, refData) { + var copiedData = $.extend(true, {}, refData); + $.extend(true, refData, template, copiedData); +}; + +BaseControl.prototype.setComponentShow = function () { + if (this.headerShowBtn && !this.headerShowBtn.getValue()) { + this.toggleDisableComponent(false); + } +}; + +BaseControl.prototype.toggleDisableComponent = function (checkedVal) { + this._widgetList.forEach(function (widget) { + widget.toggleDisable(!checkedVal); + }); +}; + +BaseControl.prototype.render = function () {}; + +BaseControl.prototype.destroy = function () { + this._widgetList.forEach(function (widget) { + widget.destroy(); + }); + this.$controlMain.remove(); +}; + +BaseControl.prototype.close = function () { + this._widgetList.forEach(function (widget) { + widget.close(); + }); +}; + +BaseControl.prototype.renderProblem = function () {}; + +BaseControl.prototype.renderWarning = function (warningList) {}; + +BaseControl.prototype.setExpanderPreview = function () { + var $previewTarget = this.$parent.closest('.brtc-expander-wrapper').find('.bo-control-container-header-preview'); + this.options.expanderCallBack(ControlContainerPreview[this.headerKey].call(this), $previewTarget); +}; + +exports.default = BaseControl; + +/***/ }), +/* 6 */, +/* 7 */ /***/ (function(module, exports, __webpack_require__) { "use strict"; @@ -327,11 +534,11 @@ Object.defineProperty(exports, "__esModule", { value: true }); -var _controlContainerPreview = __webpack_require__(119); +var _controlContainerPreview = __webpack_require__(126); var ControlContainerPreview = _interopRequireWildcard(_controlContainerPreview); -var _controlContainerFactory = __webpack_require__(272); +var _controlContainerFactory = __webpack_require__(279); var ControlContainerFactory = _interopRequireWildcard(_controlContainerFactory); @@ -521,9 +728,20 @@ ChartOptionBase.prototype._configureSwitchConf = function (switchSetting) { exports.default = ChartOptionBase; /***/ }), -/* 7 */, /* 8 */, -/* 9 */ +/* 9 */, +/* 10 */, +/* 11 */, +/* 12 */, +/* 13 */, +/* 14 */, +/* 15 */, +/* 16 */, +/* 17 */, +/* 18 */, +/* 19 */, +/* 20 */, +/* 21 */ /***/ (function(module, exports, __webpack_require__) { "use strict"; @@ -532,242 +750,79 @@ exports.default = ChartOptionBase; Object.defineProperty(exports, "__esModule", { value: true }); +/** + * Source: chart-option-const + * Created by SDS on 2018-05-29 + */ -var _widgetFactory = __webpack_require__(3); +var ChartOptionConst = { + Theme: 'office', + EXTRA_AXIS_TYPE: 'SCHEMA', + EXTRA_AXIS_TYPE_LIST: [{ + name: '(Column Names)', + type: 'SCHEMA', + value: 'byColumnNames' + }, { + name: '(Row Index)', + type: 'SCHEMA', + value: 'byRowIndex' + }], + AggregationMap: { + NONE: { label: "(None)", value: "none" }, + SUM: { label: "Sum", value: "sum" }, + AVG: { label: "Average", value: "average" }, + COUNT: { label: "Count", value: "count" }, + UNIQUE_COUNT: { label: "Unique Count", value: "unique_count" }, + MIN: { label: "Min", value: "min" }, + MAX: { label: "Max", value: "max" } + }, + LineStyleList: [{ label: 'Solid', value: 'solid' }, { label: 'Dashed', value: 'dashed' }, { label: 'Dotted', value: 'dotted' }, { label: 'Double', value: 'double' }, + // {label: 'Groove', value: 'groove'}, + // {label: 'Inset', value: 'inset'}, + // {label: 'Outset', value: 'outset'}, + // {label: 'Ridge', value: 'ridge'}, + { label: 'None', value: 'none' }], + OpacityList: [{ label: '0%', value: 0 }, { label: '10%', value: 0.1 }, { label: '20%', value: 0.2 }, { label: '30%', value: 0.3 }, { label: '40%', value: 0.4 }, { label: '50%', value: 0.5 }, { label: '60%', value: 0.6 }, { label: '70%', value: 0.7 }, { label: '80%', value: 0.8 }, { label: '90%', value: 0.9 }, { label: '100%', value: 1 }], + FontList: [{ label: 'Arial', value: 'Arial' }, { label: 'Arial Black', value: 'Arial Black' }, { label: 'Comic Sans MS', value: 'Comic Sans MS' }, { label: 'Courier New', value: 'Courier New' }, { label: 'Impact', value: 'Impact' }, { label: 'Tahoma', value: 'Tahoma' }, { label: 'Times New Roman', value: 'Times New Roman' }, { label: 'Verdana', value: 'Verdana' }], + FormatList: [{ label: 'Numbers', value: 'number' }, + // {label: 'Time', value: 'time'}, + { label: 'Exponential', value: 'exponential' }], + // @deprecated + ColorPaletteList: { + Brightics: ['#FFC14A', '#20BAC2', '#6952DB', '#1E5FCB', '#0EBB59', '#D7C7FE', '#FD9C94', '#5C0931', '#8EDDCF', '#166440', '#E6E6E6', '#7394FF', '#8B21A6', '#BDBDBD', '#F56813', '#EBE3FE', '#FECDC9', '#AE8498', '#C6EEE7', '#8BB2A0', '#F2F2F2', '#B9C9FF', '#C590D2', '#DEDEDE', '#FAB489'], + RainBow: ['#FF0000', '#FF9D00', '#EEFF00', '#34B545', '#3E6DC4', '#863DCC'] + }, + edgeStyle: [{ label: 'Arrow', value: 'arrow' }, { label: 'Triangle', value: 'triangle' }, { label: 'Circle', value: 'circle' }, { label: 'Rect', value: 'rect' }, { label: 'None', value: 'none' }], + chartLineStyleList: [{ label: 'Solid', value: 'solid' }, { label: 'Dashed', value: 'dashed' }, { label: 'Dotted', value: 'dotted' }, { label: 'Double', value: 'double' }] +}; -var WidgetFactory = _interopRequireWildcard(_widgetFactory); +exports.default = ChartOptionConst; -var _controlContainerPreview = __webpack_require__(119); +/***/ }), +/* 22 */ +/***/ (function(module, exports, __webpack_require__) { -var ControlContainerPreview = _interopRequireWildcard(_controlContainerPreview); +"use strict"; -function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } } +Object.defineProperty(exports, "__esModule", { + value: true +}); /** * Created by mk90.kim on 2017-05-10. */ var ChartWidget = Brightics.Chart.Widget; - -function BaseControl(parentId, options, headerKey) { - if (headerKey) { - this.headerKey = headerKey; - } - +function BaseWidget(parentId, options) { ChartWidget.call(this, parentId, options); - this.renderProblem(); - this.renderWarning(); } -BaseControl.prototype = Object.create(ChartWidget.prototype); -BaseControl.prototype.constructor = BaseControl; +BaseWidget.prototype = Object.create(ChartWidget.prototype); +BaseWidget.prototype.constructor = BaseWidget; -BaseControl.prototype._init = function () { - ChartWidget.prototype._init.call(this); - this._widgetList = []; -}; - -BaseControl.prototype._createContents = function ($parent) { - if (this.$controlMain) { - this.destroy(); - } - this.$controlMain = $('
'); - $parent.append(this.$controlMain); -}; - -/** - var headerOption = { - label: 'View Range', - showBtn: { - defaultVal: false, - clickfunc: function () { - console.log('show btn clicked'); - } - }, - showBtn: { - clickfunc: function () { - console.log('show btn clicked'); - } - } -}; - */ -BaseControl.prototype.createComponentHeader = function (headerOption) { - var _this = this; - - var $controlHeader = $('
'); - - this.$controlMain.append($controlHeader); - - if (!headerOption) { - return; - } - if (headerOption.additionalType) { - $controlHeader.attr('type', headerOption.additionalType); - } - - if (headerOption.label) { - var $controlHeaderLabel = $('
'); - $controlHeader.append($controlHeaderLabel); - $controlHeaderLabel.text(headerOption.label); - } - - if (headerOption.showBtn) { - this.headerShowBtn = WidgetFactory.createShowButtonWidget($controlHeader, headerOption.showBtn); - } - - if (headerOption.addBtn) { - if (!headerOption.label && !headerOption.showBtn) { - $controlHeader.removeClass('bos-flex-space-between').addClass('bos-flex-center'); - } - var $panelHeaderAddBtn = $('
'); - $controlHeader.append($panelHeaderAddBtn); - if (typeof headerOption.addBtn.clickfunc === 'function') { - $panelHeaderAddBtn.click(headerOption.addBtn.clickfunc); - } else { - console.error('Header [' + headerOption.label + '] add button: click event function is not existed.'); - } - } -}; - -BaseControl.prototype.createComponentContents = function () { - this.$controlContents = $('
'); - this.$controlMain.append(this.$controlContents); -}; - -BaseControl.setTemplate = function (template, refData) { - var copiedData = $.extend(true, {}, refData); - $.extend(true, refData, template, copiedData); -}; - -BaseControl.prototype.setComponentShow = function () { - if (this.headerShowBtn && !this.headerShowBtn.getValue()) { - this.toggleDisableComponent(false); - } -}; - -BaseControl.prototype.toggleDisableComponent = function (checkedVal) { - this._widgetList.forEach(function (widget) { - widget.toggleDisable(!checkedVal); - }); -}; - -BaseControl.prototype.render = function () {}; - -BaseControl.prototype.destroy = function () { - this._widgetList.forEach(function (widget) { - widget.destroy(); - }); - this.$controlMain.remove(); -}; - -BaseControl.prototype.close = function () { - this._widgetList.forEach(function (widget) { - widget.close(); - }); -}; - -BaseControl.prototype.renderProblem = function () {}; - -BaseControl.prototype.renderWarning = function (warningList) {}; - -BaseControl.prototype.setExpanderPreview = function () { - var $previewTarget = this.$parent.closest('.bo-control-container-expander').find('.bo-control-container-header-preview'); - this.options.expanderCallBack(ControlContainerPreview[this.headerKey].call(this), $previewTarget); -}; - -exports.default = BaseControl; - -/***/ }), -/* 10 */, -/* 11 */, -/* 12 */, -/* 13 */, -/* 14 */, -/* 15 */, -/* 16 */, -/* 17 */ -/***/ (function(module, exports, __webpack_require__) { - -"use strict"; - - -Object.defineProperty(exports, "__esModule", { - value: true -}); -/** - * Source: chart-option-const - * Created by SDS on 2018-05-29 - */ - -var ChartOptionConst = { - Theme: 'office', - EXTRA_AXIS_TYPE: 'SCHEMA', - EXTRA_AXIS_TYPE_LIST: [{ - name: '(Column Names)', - type: 'SCHEMA', - value: 'byColumnNames' - }, { - name: '(Row Index)', - type: 'SCHEMA', - value: 'byRowIndex' - }], - AggregationMap: { - NONE: { label: "(None)", value: "none" }, - SUM: { label: "Sum", value: "sum" }, - AVG: { label: "Average", value: "average" }, - COUNT: { label: "Count", value: "count" }, - UNIQUE_COUNT: { label: "Unique Count", value: "unique_count" }, - MIN: { label: "Min", value: "min" }, - MAX: { label: "Max", value: "max" } - }, - LineStyleList: [{ label: 'Solid', value: 'solid' }, { label: 'Dashed', value: 'dashed' }, { label: 'Dotted', value: 'dotted' }, { label: 'None', value: 'none' }], - OpacityList: [{ label: '0%', value: 0 }, { label: '10%', value: 0.1 }, { label: '20%', value: 0.2 }, { label: '30%', value: 0.3 }, { label: '40%', value: 0.4 }, { label: '50%', value: 0.5 }, { label: '60%', value: 0.6 }, { label: '70%', value: 0.7 }, { label: '80%', value: 0.8 }, { label: '90%', value: 0.9 }, { label: '100%', value: 1 }], - FontList: [{ label: 'Arial', value: 'Arial' }, { label: 'Arial Black', value: 'Arial Black' }, { label: 'Comic Sans MS', value: 'Comic Sans MS' }, { label: 'Courier New', value: 'Courier New' }, { label: 'Impact', value: 'Impact' }, { label: 'Tahoma', value: 'Tahoma' }, { label: 'Times New Roman', value: 'Times New Roman' }, { label: 'Verdana', value: 'Verdana' }], - FormatList: [{ label: 'Numbers', value: 'number' }, - // {label: 'Time', value: 'time'}, - { label: 'Exponential', value: 'exponential' }], - // @deprecated - ColorPaletteList: { - Brightics: ['#FD026C', '#4682B8', '#A5D221', '#F5CC0A', '#FE8C01', '#6B9494', '#B97C46', '#84ACD0', '#C2E173', '#F9DD5B', '#FE569D', '#FEB356', '#9CB8B8', '#D0A884', '#2E6072', '#6D8C1E', '#A48806', '#A90148', '#A95E01', '#476363', '#7B532F'], - RainBow: ['#FF0000', '#FF9D00', '#EEFF00', '#34B545', '#3E6DC4', '#863DCC'] - }, - edgeStyle: [{ label: 'Arrow', value: 'arrow' }, { label: 'Triangle', value: 'triangle' }, { label: 'Circle', value: 'circle' }, { label: 'Rect', value: 'rect' }, { label: 'None', value: 'none' }], - chartLineStyleList: [{ label: 'Solid', value: 'solid' }, { label: 'Dashed', value: 'dashed' }, { label: 'Dotted', value: 'dotted' }] -}; - -exports.default = ChartOptionConst; - -/***/ }), -/* 18 */, -/* 19 */, -/* 20 */, -/* 21 */, -/* 22 */, -/* 23 */ -/***/ (function(module, exports, __webpack_require__) { - -"use strict"; - - -Object.defineProperty(exports, "__esModule", { - value: true -}); -/** - * Created by mk90.kim on 2017-05-10. - */ - -var ChartWidget = Brightics.Chart.Widget; - -function BaseWidget(parentId, options) { - ChartWidget.call(this, parentId, options); -} - -BaseWidget.prototype = Object.create(ChartWidget.prototype); -BaseWidget.prototype.constructor = BaseWidget; - -BaseWidget.prototype._fireEvent = function (eventName, eventParam) { - this.$mainControl.closest('.bo-container').trigger(eventName, eventParam); +BaseWidget.prototype._fireEvent = function (eventName, eventParam) { + this.$mainControl.closest('.bo-container').trigger(eventName, eventParam); }; BaseWidget.prototype._setPreValue = function (preVal) { @@ -794,13 +849,10 @@ BaseWidget.prototype.close = function () {}; exports.default = BaseWidget; /***/ }), +/* 23 */, /* 24 */, /* 25 */, -/* 26 */, -/* 27 */, -/* 28 */, -/* 29 */, -/* 30 */ +/* 26 */ /***/ (function(module, exports, __webpack_require__) { "use strict"; @@ -815,7 +867,7 @@ exports.getAllColumnList = getAllColumnList; exports.isEmpty = isEmpty; exports.mergeChartOption = mergeChartOption; -var _chartOptionConst = __webpack_require__(17); +var _chartOptionConst = __webpack_require__(21); var _chartOptionConst2 = _interopRequireDefault(_chartOptionConst); @@ -835,9 +887,19 @@ function getColumnInternalType(column, columnList) { return ''; } var internalList = _chartOptionConst2.default.EXTRA_AXIS_TYPE_LIST.concat(columnList); - var foundColumn = internalList.find(function (columnObj) { - return columnObj.name === column.name; - }); + var foundColumn; + if (typeof column === 'string') { + foundColumn = internalList.find(function (columnObj) { + return columnObj.name === column; + }); + } else { + foundColumn = internalList.find(function (columnObj) { + return columnObj.name === column.name; + }); + } + // var foundColumn = internalList.find(function (columnObj) { + // return columnObj.name === column.name; + // }); if (foundColumn) { return foundColumn.internalType || foundColumn.type; } else { @@ -883,9 +945,10 @@ function mergeChartOption(a, b) { } /***/ }), -/* 31 */, -/* 32 */, -/* 33 */ +/* 27 */, +/* 28 */, +/* 29 */, +/* 30 */ /***/ (function(module, exports, __webpack_require__) { "use strict"; @@ -895,11 +958,11 @@ Object.defineProperty(exports, "__esModule", { value: true }); -var _baseWidget = __webpack_require__(23); +var _baseWidget = __webpack_require__(22); var _baseWidget2 = _interopRequireDefault(_baseWidget); -var _chartOptionConst = __webpack_require__(17); +var _chartOptionConst = __webpack_require__(21); var _chartOptionConst2 = _interopRequireDefault(_chartOptionConst); @@ -915,6 +978,7 @@ function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { de * onChanged: function(){] * } */ +/* global brtcWidget */ /** * Created by mk90.kim on 2017-05-10. */ @@ -937,87 +1001,65 @@ BaseSelectorWidget.prototype._createContents = function ($parent) { BaseSelectorWidget.prototype._createIconArea = function () {}; BaseSelectorWidget.prototype._createSelectorArea = function ($parent) { - this.$selectorControl = $('
'); //bos-border-only-bottom - $parent.append(this.$selectorControl); var source = this._createListSource(); var option = { - width: this.options.width || '100%', - height: this.options.height || '30px', - scrollBarSize: 8, + width: this.options.width, + height: this.options.height, source: source, - placeHolder: this.options.placeHolder || ' ', - animationType: 'none', - enableBrowserBoundsDetection: true, - autoDropDownHeight: true, - theme: _chartOptionConst2.default.Theme, - disabled: this.options.disabled || false + type: this.options.type, + value: this.options.value + // onChange: this.options.onChanged }; - var selectorOption = this._createSelectorOption(); - $.extend(true, option, selectorOption); - this.$selectorControl.jqxDropDownList(option); + $.extend(true, option, this._createSelectorOption()); - this._bindCallbackFunc(); + this.selectorControl = brtcWidget.dropDownList($parent, option); + this.selectorControl.subscribe('change', this.options.onChanged); }; BaseSelectorWidget.prototype._createListSource = function () { return []; }; -BaseSelectorWidget.prototype._createSelectorOption = function () { - if (this.options.type && this.options.type === 'number') { - this.options.value = Number(this.options.value); - } - var selectedIdx = this._getItemIndexInSource(this.options.value); - return { selectedIndex: selectedIdx }; -}; - BaseSelectorWidget.prototype._getItemIndexInSource = function (item) { return $.inArray(item, this._createListSource()); }; -BaseSelectorWidget.prototype._bindCallbackFunc = function () { - if (typeof this.options.onChanged === 'function') { - var _this = this; - var callbackFunc = this.options.onChanged; - if (this.options.type && this.options.type === 'number') { - this.$selectorControl.on('change', function (event) { - callbackFunc.bind(_this)(Number(event.args.item.value)); - }); - } else { - this.$selectorControl.on('change', function (event) { - callbackFunc.bind(_this)(event.args.item.value); - }); - } - } +BaseSelectorWidget.prototype._bindRenderer = function () { + var selectorOption = this._createSelectorOption(); + this.selectorControl.setOption(selectorOption); +}; + +BaseSelectorWidget.prototype._createSelectorOption = function () { + return {}; }; BaseSelectorWidget.prototype.clearSelection = function () { - this.$selectorControl.jqxDropDownList('clearSelection'); + this.selectorControl.setOption('clearSelection'); }; BaseSelectorWidget.prototype.toggleDisable = function (disabled) { - this.$selectorControl.jqxDropDownList({ disabled: disabled }); + this.selectorControl.setOption({ disabled: disabled }); }; BaseSelectorWidget.prototype.render = function (changedValue) { - var selectedIdx = this._getItemIndexInSource(changedValue); - this.$selectorControl.jqxDropDownList({ selectedIndex: selectedIdx }); + this.selectorControl.setOption({ value: changedValue }); }; BaseSelectorWidget.prototype.close = function () { - this.$selectorControl.jqxDropDownList('close'); + this.selectorControl.execute('close'); }; BaseSelectorWidget.prototype.destroy = function () { - this.$selectorControl.jqxDropDownList('destroy'); + this.selectorControl.execute('destroy'); }; exports.default = BaseSelectorWidget; /***/ }), -/* 34 */, -/* 35 */, -/* 36 */ +/* 31 */, +/* 32 */, +/* 33 */, +/* 34 */ /***/ (function(module, exports, __webpack_require__) { "use strict"; @@ -1027,7 +1069,7 @@ Object.defineProperty(exports, "__esModule", { value: true }); -var _baseWidget = __webpack_require__(23); +var _baseWidget = __webpack_require__(22); var _baseWidget2 = _interopRequireDefault(_baseWidget); @@ -1087,12 +1129,7 @@ BaseComplexWidget.prototype.destroy = function () { exports.default = BaseComplexWidget; /***/ }), -/* 37 */, -/* 38 */, -/* 39 */, -/* 40 */, -/* 41 */, -/* 42 */ +/* 35 */ /***/ (function(module, exports, __webpack_require__) { "use strict"; @@ -1126,13 +1163,16 @@ Dialog.prototype._init = function () { }; Dialog.prototype._createContents = function () { - this.$mainControl = $('' + '
' + '
' + '
' + '
' + '
' + '
' + '
'); + var _this = this; + this.$mainControl = $('' + '
' + '
' + '
' + '
' + '
' + '
' + '
'); + this.$parent.append(this.$mainControl); this._setDialogsHeader(); var defaults = this._getDefaultWindowOption(); var opts = $.extend(defaults, this.options.window); - this.$mainControl.jqxWindow(opts); + + this.controlWindow = brtcWidget.window(this.$mainControl, opts); this._createDialogsBody(); }; @@ -1152,20 +1192,25 @@ Dialog.prototype._getTitle = function () { Dialog.prototype._createDialogsBody = function () { this._setFocus(); this._createCloseHandler(); - this.$mainControl.on('close', this._destroy.bind(this)); + + this.controlWindow.subscribe('close', this._destroyWithOutHideEvent.bind(this)); }; Dialog.prototype._getDefaultWindowOption = function () { var _this = this; + var width = 600; + var height = 500; + var position = _this._configurePosition(width, height, _this.options.windowPosition); + return { - theme: ChartOptionConst.Theme, - width: '600px', - height: '500px', - maxWidth: '600px', - maxHeight: '500px', - resizable: false, + modal: false, + width: width, + height: height, + left: position ? position.left : 'auto', + top: position ? position.top : 'auto', + move: true, + resize: false, initContent: function initContent() { - _this._configurePosition(_this.options.windowPosition); _this._createDialogContentsArea(_this.$mainControl.find('.bo-dialogs-contents')); } }; @@ -1175,13 +1220,13 @@ Dialog.prototype._createCloseHandler = function () { var _this = this; this._closeHandler = function (event) { if (!event || _this.$mainControl.has(event.target).length === 0) { - _this.$mainControl.jqxWindow('close'); + _this.controlWindow.execute('close'); } }; $(window).on('mousedown', this._closeHandler); }; -Dialog.prototype._configurePosition = function (position, isSwitch) { +Dialog.prototype._configurePosition = function (windowWidth, windowHeight, position, isSwitch) { position = position || this.options.windowPosition; if (!position) return; var $window = this.$mainControl; @@ -1189,12 +1234,13 @@ Dialog.prototype._configurePosition = function (position, isSwitch) { anchorWidth = this.$parent.width(), anchorHeight = this.$parent.height(), windowOffset = this.$mainControl.offset(), - windowWidth = this.$mainControl.width(), - windowHeight = this.$mainControl.height(), windowLeft, windowTop, isChanged = false; + if (!windowWidth) windowWidth = $window.width(); + if (!windowHeight) windowHeight = $window.height(); + switch (position) { case 'left': windowLeft = anchorOffset.left - windowWidth - 3; @@ -1228,21 +1274,22 @@ Dialog.prototype._configurePosition = function (position, isSwitch) { if (windowTop < 0) windowTop = 5, isChanged = true; if (isChanged === true && this.options.switchPosition && isSwitch !== true) { - this._configurePosition(this.options.switchPosition, true); + return this._configurePosition(windowWidth, windowHeight, this.options.switchPosition, true); } else { $window.css('left', windowLeft); $window.css('top', windowTop); + return { 'left': windowLeft, 'top': windowTop }; } }; Dialog.prototype._setPosition = function (posititon) {}; Dialog.prototype._setFocus = function () { - this.$mainControl.jqxWindow('focus'); + this.controlWindow.execute('focus'); }; Dialog.prototype._close = function () { - this.$mainControl.jqxWindow('close'); + this.controlWindow.execute('close'); }; Dialog.prototype._destroy = function () { @@ -1250,8 +1297,17 @@ Dialog.prototype._destroy = function () { this.options.close(this._getDialogResult()); } - $(window).off('mousedown', this._closeHandler); - this.$mainControl.jqxWindow('destroy'); + $(window).off('mousedown'); + this.controlWindow.execute('hide'); + this.$mainControl.remove(); +}; + +Dialog.prototype._destroyWithOutHideEvent = function () { + if (typeof this.options.close === 'function') { + this.options.close(this._getDialogResult()); + } + + $(window).off('mousedown'); this.$mainControl.remove(); }; @@ -1266,22 +1322,150 @@ Dialog.prototype._createDialogContentsArea = function ($parent) {}; exports.default = Dialog; /***/ }), -/* 43 */, -/* 44 */, -/* 45 */, -/* 46 */, -/* 47 */, -/* 48 */, -/* 49 */, -/* 50 */, -/* 51 */, -/* 52 */, -/* 53 */, -/* 54 */, -/* 55 */, -/* 56 */, -/* 57 */, -/* 58 */ +/* 36 */, +/* 37 */, +/* 38 */, +/* 39 */, +/* 40 */, +/* 41 */, +/* 42 */ +/***/ (function(module, exports, __webpack_require__) { + +"use strict"; + + +Object.defineProperty(exports, "__esModule", { + value: true +}); + +var _baseWidget = __webpack_require__(22); + +var _baseWidget2 = _interopRequireDefault(_baseWidget); + +var _chartOptionConst = __webpack_require__(21); + +var _chartOptionConst2 = _interopRequireDefault(_chartOptionConst); + +var _chartOptionUtil = __webpack_require__(26); + +var ChartOptionUtil = _interopRequireWildcard(_chartOptionUtil); + +function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } } + +function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + +/** + * options = { + * width: % or px default 100%, + * height:% or px default 30px, + * value: '' + * onChanged: function(){] + * } + * @param parentId + * @param options + * @constructor + */ +function BaseRadioButtonGroupWidget(parentId, options) { + _baseWidget2.default.call(this, parentId, options); +} /* global brtcWidget */ +/** + * Created by mk90.kim on 2017-05-10. + */ + +BaseRadioButtonGroupWidget.prototype = Object.create(_baseWidget2.default.prototype); +BaseRadioButtonGroupWidget.prototype.constructor = BaseRadioButtonGroupWidget; + +BaseRadioButtonGroupWidget.prototype._init = function () { + _baseWidget2.default.prototype._init.call(this); + this._buttonList = []; + this._setRadioButtonList(); + this.$buttonObjList = []; +}; + +BaseRadioButtonGroupWidget.prototype._setRadioButtonList = function () { + //implement +}; + +BaseRadioButtonGroupWidget.prototype._createContents = function ($parent) { + this._isRendered = false; + this.$radioButtonControl = $('' + '
' + ''); + $parent.append(this.$radioButtonControl); + this._createRadioBtnGrpUnits(); + this._renderButtonSelected(this.options.value); + this._setPreValue(this.options.value); + this._isRendered = true; +}; + +BaseRadioButtonGroupWidget.prototype._renderButtonSelected = function (selectedValue) { + var selectedBtn = this.$buttonObjList.find(function (btn) { + return btn.getOption('value') === selectedValue; + }); + if (selectedBtn) { + selectedBtn.setOption({ check: true }); + } else { + this.$buttonObjList.forEach(function ($el) { + return $el.setOption({ check: false }); + }); + } +}; + +BaseRadioButtonGroupWidget.prototype._createRadioBtnGrpUnits = function () { + var btnSetList = this._buttonList; + var _this = this; + var clickFunc = function clickFunc(btnId) { + _this.$buttonObjList.forEach(function (btn) { + var clickedBtn = btn.getOption('value'); + if (clickedBtn !== btnId) { + btn.execute('uncheck'); + } else { + btn.execute('check'); + } + }); + if (ChartOptionUtil.isEmpty(_this.options.onChanged) || _this._isRendered === false) return; + if (!_this._isChanged(btnId)) return; + _this.options.onChanged(btnId); + }; + + for (var btnSetIdx = 0; btnSetIdx < btnSetList.length; btnSetIdx++) { + var button = brtcWidget.toggleButton(this.$radioButtonControl, { + height: this.options.height || '30px', + label: btnSetList[btnSetIdx].label, + imageClass: btnSetList[btnSetIdx].imageClass, + value: btnSetList[btnSetIdx].value + }); + + button.subscribe('click', clickFunc); + this.$buttonObjList.push(button); + } +}; + +BaseRadioButtonGroupWidget.prototype.toggleDisable = function (disabledVal) { + this.$buttonObjList.forEach(function ($btn) { + $btn.setOption({ disabled: disabledVal }); + }); +}; + +BaseRadioButtonGroupWidget.prototype.unselect = function () { + this.options.value = null; + this._setPreValue(null); + this.render(null); +}; + +BaseRadioButtonGroupWidget.prototype.render = function (changedValue) { + this._renderButtonSelected(changedValue); +}; + +exports.default = BaseRadioButtonGroupWidget; + +/***/ }), +/* 43 */, +/* 44 */, +/* 45 */, +/* 46 */, +/* 47 */, +/* 48 */, +/* 49 */, +/* 50 */ /***/ (function(module, exports, __webpack_require__) { "use strict"; @@ -1291,15 +1475,15 @@ Object.defineProperty(exports, "__esModule", { value: true }); -var _baseWidget = __webpack_require__(23); +var _baseWidget = __webpack_require__(22); var _baseWidget2 = _interopRequireDefault(_baseWidget); -var _chartOptionUtil = __webpack_require__(30); +var _chartOptionUtil = __webpack_require__(26); var ChartOptionUtil = _interopRequireWildcard(_chartOptionUtil); -var _columnSelectorDialog = __webpack_require__(120); +var _columnSelectorDialog = __webpack_require__(127); var _columnSelectorDialog2 = _interopRequireDefault(_columnSelectorDialog); @@ -1347,7 +1531,7 @@ ColumnSelectorWidget.prototype._init = function () { }; ColumnSelectorWidget.prototype._createContents = function ($parent) { - this.$mainControl = $('' + '
' + '
' + '
' + '
'); + this.$mainControl = $('' + '
' + '
' + '
' + '
'); $parent.append(this.$mainControl); if (this.options.hideLabel) { @@ -1470,9 +1654,9 @@ ColumnSelectorWidget.prototype._createNewUnitWrapper = function () { ColumnSelectorWidget.prototype._createNewColumnUnit = function () { if (this.hasType) { - return $('
' + '
' + '
' + this.defaultMsg + '
' + '
' + '
'); + return $('
' + '
' + '
' + this.defaultMsg + '
' + '
' + '
'); } else { - return $('' + '
' + '
' + this.defaultMsg + '
' + '
'); + return $('' + '
' + '
' + this.defaultMsg + '
' + '
'); } }; @@ -1511,8 +1695,10 @@ ColumnSelectorWidget.prototype._renderColumnControlUnit = function (index, input $unit.find('.bo-widget-column-unit-type').text(columnInternalType); } - $unit.find('.bo-widget-column-unit-label').text(this._getColumnLabel(targetColumn) || ''); - $unit.find('.bo-widget-column-unit-label').attr('title', this._getColumnLabel(targetColumn)); + var columnLabel = this._getColumnLabel(targetColumn) || ''; + $unit.find('.bo-widget-column-unit-label').css({ width: this.defaultMsg == columnLabel ? '100%' : '60%' }); + $unit.find('.bo-widget-column-unit-label').text(columnLabel); + $unit.find('.bo-widget-column-unit-label').attr('title', columnLabel); }; ColumnSelectorWidget.prototype._getColumnLabel = function (selectedColumn) { @@ -1588,7 +1774,7 @@ ColumnSelectorWidget.prototype.close = function () { exports.default = ColumnSelectorWidget; /***/ }), -/* 59 */ +/* 51 */ /***/ (function(module, exports, __webpack_require__) { "use strict"; @@ -1598,143 +1784,106 @@ Object.defineProperty(exports, "__esModule", { value: true }); -var _baseWidget = __webpack_require__(23); +var _dialog = __webpack_require__(35); -var _baseWidget2 = _interopRequireDefault(_baseWidget); +var _dialog2 = _interopRequireDefault(_dialog); -var _chartOptionConst = __webpack_require__(17); +var _chartOptionConst = __webpack_require__(21); var _chartOptionConst2 = _interopRequireDefault(_chartOptionConst); -var _chartOptionUtil = __webpack_require__(30); - -var ChartOptionUtil = _interopRequireWildcard(_chartOptionUtil); - -function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } } - function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } +var ChartWidget = Brightics.Chart.Widget; /** + * Created by sds on 2018-04-09. + */ + /** - * options = { - * width: % or px default 100%, - * height:% or px default 30px, - * value: '' + * options = { + * value: '', + * chartTypeList: [], * onChanged: function(){] - * } - * @param parentId - * @param options - * @constructor + * } + * */ -function BaseRadioButtonGroupWidget(parentId, options) { - _baseWidget2.default.call(this, parentId, options); -} /** - * Created by mk90.kim on 2017-05-10. - */ - -BaseRadioButtonGroupWidget.prototype = Object.create(_baseWidget2.default.prototype); -BaseRadioButtonGroupWidget.prototype.constructor = BaseRadioButtonGroupWidget; - -BaseRadioButtonGroupWidget.prototype._init = function () { - _baseWidget2.default.prototype._init.call(this); - this._buttonList = []; - this._setRadioButtonList(); - this.$buttonObjList = []; -}; -BaseRadioButtonGroupWidget.prototype._setRadioButtonList = function () { - //implement -}; +function FormatHelperDialog(parentId, options) { + _dialog2.default.call(this, parentId, options); +} -BaseRadioButtonGroupWidget.prototype._createContents = function ($parent) { - this._isRendered = false; - this.$radioButtonControl = $('' + '
' + ''); - $parent.append(this.$radioButtonControl); - this._createRadioBtnGrpUnits(); - this._renderButtonSelected(this.options.value); - this._setPreValue(this.options.value); - this._isRendered = true; -}; +FormatHelperDialog.prototype = Object.create(_dialog2.default.prototype); +FormatHelperDialog.prototype.constructor = FormatHelperDialog; -BaseRadioButtonGroupWidget.prototype._renderButtonSelected = function (selectedValue) { - var selectedBtn = this.$buttonObjList.find(function (btn) { - return btn.attr('id') === selectedValue; - }); - if (selectedBtn) { - selectedBtn.jqxToggleButton('check'); - } else { - this.$buttonObjList.forEach(function ($el) { - return $el.jqxToggleButton('unCheck'); - }); - } +FormatHelperDialog.prototype._init = function () { + ChartWidget.prototype._init.call(this); + this.options.showHeader = false; }; -BaseRadioButtonGroupWidget.prototype._createRadioBtnGrpUnits = function () { - var btnSetList = this._buttonList; - - for (var btnSetIdx = 0; btnSetIdx < btnSetList.length; btnSetIdx++) { - var $radioButton = $('
'); - this.$radioButtonControl.append($radioButton); +// FormatHelperDialog.prototype._getTitle = function () { +// return 'Format Example'; +// }; - if (btnSetList[btnSetIdx].imageClass) { - $radioButton.addClass(btnSetList[btnSetIdx].imageClass); - } +FormatHelperDialog.prototype._getDefaultWindowOption = function () { + var _this = this; + var width = 170; + var height = 160; + var position = _this._configurePosition(width, height, _this.options.windowPosition); - if (btnSetList[btnSetIdx].label) { - $radioButton.attr('value', btnSetList[btnSetIdx].label); + return { + modal: false, + width: width, + height: height, + left: position ? position.left : 'auto', + top: position ? position.top : 'auto', + move: true, + resize: false, + initContent: function initContent() { + _this._createDialogContentsArea(_this.$mainControl.find('.bo-dialogs-contents')); } + }; +}; - $radioButton.attr('id', btnSetList[btnSetIdx].value); - - $radioButton.jqxToggleButton({ - theme: _chartOptionConst2.default.Theme, - height: this.options.height || '30px' - }); +FormatHelperDialog.prototype._createDialogContentsArea = function ($parent) { + var $exampleText = $('
' + '
All Types
' + '
${value}: 13.25 > $13.25
' + '
Number Type
' + '
' + ' {0,0}: 145000 > 145,000
' + ' {0.00}: 130.4721 > 130.47
' + '
' + '
'); - this.$buttonObjList.push($radioButton); - this._createClickEvent($radioButton); - } + $exampleText.css('line-height', '1.6'); + $parent.append($exampleText); }; -BaseRadioButtonGroupWidget.prototype._createClickEvent = function ($radioButton) { - var _this = this; - $radioButton.on('click', function (event) { - var selectedBtnId = $(event.target).attr('id'); - //set another buttons disabled. - _this.$buttonObjList.forEach(function (btn) { - if (btn.attr('id') !== selectedBtnId) { - btn.jqxToggleButton('unCheck'); - } else { - btn.jqxToggleButton('check'); - } - }); - - //call onChanged function - if (ChartOptionUtil.isEmpty(_this.options.onChanged) || _this._isRendered === false) return; - if (!_this._isChanged(selectedBtnId)) return; - _this.options.onChanged(selectedBtnId); - }); +FormatHelperDialog.prototype._destroy = function () { + $(window).off('mousedown', this._closeHandler); + this.controlWindow.execute('hide'); + this.$mainControl.remove(); }; -BaseRadioButtonGroupWidget.prototype.toggleDisable = function (disabledVal) { - this.$buttonObjList.forEach(function ($btn) { - $btn.jqxToggleButton({ disabled: disabledVal }); - $btn.addClass('jqx-fill-state-disabled jqx-fill-state-disabled-office'); - }); +FormatHelperDialog.prototype._destroyWithOutHideEvent = function () { + $(window).off('mousedown', this._closeHandler); + this.$mainControl.remove(); }; -BaseRadioButtonGroupWidget.prototype.unselect = function () { - this.options.value = null; - this._setPreValue(null); - this.render(null); +FormatHelperDialog.prototype._getItemList = function () { + if (this.options.itemList) { + return this.options.itemList; + } else { + return []; + } }; -BaseRadioButtonGroupWidget.prototype.render = function (changedValue) { - this._renderButtonSelected(changedValue); +FormatHelperDialog.prototype._getDialogResult = function () { + return this.$itemListContainer.find('.selected .bo-widget-item-selector-item').attr('item-value'); }; -exports.default = BaseRadioButtonGroupWidget; +exports.default = FormatHelperDialog; /***/ }), +/* 52 */, +/* 53 */, +/* 54 */, +/* 55 */, +/* 56 */, +/* 57 */, +/* 58 */, +/* 59 */, /* 60 */, /* 61 */, /* 62 */, @@ -1750,7 +1899,8 @@ exports.default = BaseRadioButtonGroupWidget; /* 72 */, /* 73 */, /* 74 */, -/* 75 */ +/* 75 */, +/* 76 */ /***/ (function(module, exports, __webpack_require__) { "use strict"; @@ -1759,91 +1909,37 @@ exports.default = BaseRadioButtonGroupWidget; Object.defineProperty(exports, "__esModule", { value: true }); +exports.createFilter = createFilter; +exports.getFilterTypes = getFilterTypes; -var _dialog = __webpack_require__(42); +var _filterControlBase = __webpack_require__(77); -var _dialog2 = _interopRequireDefault(_dialog); +var _filterControlBase2 = _interopRequireDefault(_filterControlBase); -var _chartOptionConst = __webpack_require__(17); +var _filterControlIndex = __webpack_require__(717); -var _chartOptionConst2 = _interopRequireDefault(_chartOptionConst); +var FilterControl = _interopRequireWildcard(_filterControlIndex); -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } +function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } } -var ChartWidget = Brightics.Chart.Widget; /** - * Created by sds on 2018-04-09. - */ +function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } /** - * options = { - * value: '', - * chartTypeList: [], - * onChanged: function(){] - * } - * + * Source: filter-control-factory.js + * Created by ji_sung.park on 2017-09-25. */ -function FormatHelperDialog(parentId, options) { - _dialog2.default.call(this, parentId, options); +function createFilter(parentId, type, options) { + var types = this.getFilterTypes(); + if (types.indexOf(type) === -1) return new _filterControlBase2.default(parentId, options);else return new FilterControl[type](parentId, options, type); } -FormatHelperDialog.prototype = Object.create(_dialog2.default.prototype); -FormatHelperDialog.prototype.constructor = FormatHelperDialog; - -FormatHelperDialog.prototype._init = function () { - ChartWidget.prototype._init.call(this); - this.options.showHeader = false; -}; - -// FormatHelperDialog.prototype._getTitle = function () { -// return 'Format Example'; -// }; - -FormatHelperDialog.prototype._getDefaultWindowOption = function () { - var _this = this; - - return { - theme: _chartOptionConst2.default.Theme, - width: '170px', - height: '110px', - maxWidth: '200px', - resizable: false, - initContent: function initContent() { - _this._configurePosition(_this.options.windowPosition); - _this._createDialogContentsArea(_this.$mainControl.find('.bo-dialogs-contents')); - } - }; -}; - -FormatHelperDialog.prototype._createDialogContentsArea = function ($parent) { - var $exampleText = $('
' + '
All Types
' + '
${value}: 13.25 > $13.25
' + '
Number Type
' + '
' + ' {0,0}: 145000 > 145,000
' + ' {0.00}: 130.4721 > 130.47
' + '
' + '
'); - - $exampleText.css('line-height', '1.6'); - $parent.append($exampleText); -}; - -FormatHelperDialog.prototype._destroy = function () { - $(window).off('mousedown', this._closeHandler); - this.$mainControl.jqxWindow('destroy'); - this.$mainControl.remove(); -}; - -FormatHelperDialog.prototype._getItemList = function () { - if (this.options.itemList) { - return this.options.itemList; - } else { - return []; - } -}; - -FormatHelperDialog.prototype._getDialogResult = function () { - return this.$itemListContainer.find('.selected .bo-widget-item-selector-item').attr('item-value'); -}; - -exports.default = FormatHelperDialog; +function getFilterTypes() { + return Object.keys(FilterControl); +} /***/ }), -/* 76 */ +/* 77 */ /***/ (function(module, exports, __webpack_require__) { "use strict"; @@ -1853,15 +1949,16 @@ Object.defineProperty(exports, "__esModule", { value: true }); -var _filterControlFactory = __webpack_require__(123); +var _filterControlFactory = __webpack_require__(76); var FilterControlFactory = _interopRequireWildcard(_filterControlFactory); function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } } -function FilterControl(parentId, options) { +function FilterControl(parentId, options, type) { this.parentId = parentId; this.options = options; + this.type = type; this.retrieveParent(); this.createContents(); } /** @@ -1874,73 +1971,42 @@ FilterControl.prototype.retrieveParent = function () { }; FilterControl.prototype.createContents = function () { - this.$mainContent = $('' + '
' + '
' + '
Condition
' + '
' + '
' + '
' + '
' + ''); + this.$mainContent = $('' + '
' + '
' + '
' + '
' + '
' + ''); this.$parent.append(this.$mainContent); - this.$changeFilterButton = this.$mainContent.find('.bf-control-change-filter-button'); - this.$controlArea = this.$mainContent.find('.bf-control-content-control-area'); + var filterTypes = FilterControlFactory.getFilterTypes(); + var isContinuous = this._isContinuous(); + var optionLength = this.options.values.length; + var defaultType = this.type; + + var menuItems = filterTypes.filter(function (type) { + return !(type == 'RangeSlider' && (optionLength < 2 || !isContinuous)); + }); - this.bindChangeFilterButtonClickEvent(this.$changeFilterButton); + var $wrapper = this.$mainContent.find('.bf-control-content-header-area'); + var _this = this; + this.selectorControl = brtcWidget.dropDownList($wrapper, { + width: '100%', + source: menuItems, + value: defaultType + }); + + this.selectorControl.subscribe('change', function (val) { + _this.options.switchFilter(val); + }); + + this.$controlArea = this.$mainContent.find('.bf-control-content-control-area'); this.createControlArea(this.$controlArea); if (this.options.getFilteredValues()) this.setSelected(this.options.getFilteredValues()); }; FilterControl.prototype.createControlArea = function ($parent) {}; -FilterControl.prototype.bindChangeFilterButtonClickEvent = function ($parent) { +FilterControl.prototype.bindChangeFilterButtonClickEvent = function ($menu) { var _this = this; - $parent.on('click', function (event) { - var filterId = _this.options.filterId; - - var $contextMenu = $("#bf-context-menu"), - point, - menuPoint; - - $contextMenu.jqxMenu('close'); - var scrollTop = $(window).scrollTop(); - var scrollLeft = $(window).scrollLeft(); - point = { - x: parseInt(event.clientX) + scrollLeft, - y: parseInt(event.clientY) + scrollTop - }; - - if (point) { - $.each(FilterControlFactory.getFilterTypes(), function (index, type) { - var id = 'bfs-context-menu-switch-item-' + type; - $contextMenu.jqxMenu('disable', id, _this.getType() === type); - }); - - var isContinuous = _this._isContinuous(); - - if (_this.options.values.length < 2 || !isContinuous) { - var id = 'bfs-context-menu-switch-item-RangeSlider'; - $contextMenu.jqxMenu('disable', id, true); - } else if (_this.options.values.length > 5) { - var id = 'bfs-context-menu-switch-item-CheckBox'; - $contextMenu.jqxMenu('disable', id, true); - } else if (_this.options.values.length > 50) { - var id = 'bfs-context-menu-switch-item-ListBox'; - $contextMenu.jqxMenu('disable', id, true); - } - - menuPoint = _this.getContextMenuPoint($contextMenu.width(), $contextMenu.height(), point.x, point.y); - $contextMenu.data('filterId', _this.options.filterId); - $contextMenu.data('chartId', _this.options.chartId); - $contextMenu.jqxMenu('open', menuPoint.x, menuPoint.y); - $contextMenu.on('shown', function () { - $.each($('div.jqx-menu-popup.jqx-menu-popup'), function (index, menuDiv) { - if ($(menuDiv).css('display') == 'block') { - if (menuDiv.offsetHeight + menuDiv.offsetTop > $(window).height()) { - menuDiv.style.top = $(window).height() - menuDiv.offsetHeight + 'px'; - } - if (menuDiv.offsetLeft + menuDiv.offsetWidth > $(window).width()) { - menuDiv.style.left = menuDiv.offsetLeft - menuDiv.offsetWidth - $contextMenu.width() + 5 + 'px'; - } - } - }); - }); - } + this.selectorControl.subscribe('change', function (val) { + _this.options.switchFilter(val); }); }; @@ -1993,7 +2059,6 @@ FilterControl.prototype.destroy = function () { exports.default = FilterControl; /***/ }), -/* 77 */, /* 78 */, /* 79 */, /* 80 */, @@ -2002,3833 +2067,6436 @@ exports.default = FilterControl; /* 83 */, /* 84 */, /* 85 */, -/* 86 */, -/* 87 */, -/* 88 */, -/* 89 */, -/* 90 */, -/* 91 */, -/* 92 */, -/* 93 */, -/* 94 */, -/* 95 */, -/* 96 */, -/* 97 */, -/* 98 */, -/* 99 */, -/* 100 */, -/* 101 */, -/* 102 */, -/* 103 */, -/* 104 */, -/* 105 */, -/* 106 */, -/* 107 */, -/* 108 */, -/* 109 */, -/* 110 */, -/* 111 */, -/* 112 */, -/* 113 */, -/* 114 */, -/* 115 */, -/* 116 */ +/* 86 */ +/***/ (function(module, exports) { + +/******/ (function(modules) { // webpackBootstrap +/******/ // The module cache +/******/ var installedModules = {}; +/******/ +/******/ // The require function +/******/ function __webpack_require__(moduleId) { +/******/ +/******/ // Check if module is in cache +/******/ if(installedModules[moduleId]) { +/******/ return installedModules[moduleId].exports; +/******/ } +/******/ // Create a new module (and put it into the cache) +/******/ var module = installedModules[moduleId] = { +/******/ i: moduleId, +/******/ l: false, +/******/ exports: {} +/******/ }; +/******/ +/******/ // Execute the module function +/******/ modules[moduleId].call(module.exports, module, module.exports, __webpack_require__); +/******/ +/******/ // Flag the module as loaded +/******/ module.l = true; +/******/ +/******/ // Return the exports of the module +/******/ return module.exports; +/******/ } +/******/ +/******/ +/******/ // expose the modules object (__webpack_modules__) +/******/ __webpack_require__.m = modules; +/******/ +/******/ // expose the module cache +/******/ __webpack_require__.c = installedModules; +/******/ +/******/ // define getter function for harmony exports +/******/ __webpack_require__.d = function(exports, name, getter) { +/******/ if(!__webpack_require__.o(exports, name)) { +/******/ Object.defineProperty(exports, name, { +/******/ configurable: false, +/******/ enumerable: true, +/******/ get: getter +/******/ }); +/******/ } +/******/ }; +/******/ +/******/ // getDefaultExport function for compatibility with non-harmony modules +/******/ __webpack_require__.n = function(module) { +/******/ var getter = module && module.__esModule ? +/******/ function getDefault() { return module['default']; } : +/******/ function getModuleExports() { return module; }; +/******/ __webpack_require__.d(getter, 'a', getter); +/******/ return getter; +/******/ }; +/******/ +/******/ // Object.prototype.hasOwnProperty.call +/******/ __webpack_require__.o = function(object, property) { return Object.prototype.hasOwnProperty.call(object, property); }; +/******/ +/******/ // __webpack_public_path__ +/******/ __webpack_require__.p = ""; +/******/ +/******/ // Load entry module and return exports +/******/ return __webpack_require__(__webpack_require__.s = 228); +/******/ }) +/************************************************************************/ +/******/ ([ +/* 0 */ /***/ (function(module, exports, __webpack_require__) { -"use strict"; +/* WEBPACK VAR INJECTION */(function(module) {var require;//! moment.js +//! version : 2.27.0 +//! authors : Tim Wood, Iskren Chernev, Moment.js contributors +//! license : MIT +//! momentjs.com +;(function (global, factory) { + true ? module.exports = factory() : + typeof define === 'function' && define.amd ? define(factory) : + global.moment = factory() +}(this, (function () { 'use strict'; -Object.defineProperty(exports, "__esModule", { - value: true -}); + var hookCallback; -var _chartOptionRegister = __webpack_require__(117); + function hooks() { + return hookCallback.apply(null, arguments); + } -var ChartOptionRegistry = _interopRequireWildcard(_chartOptionRegister); + // This is done to register the method called with moment() + // without creating circular dependencies. + function setHookCallback(callback) { + hookCallback = callback; + } -var _chartOptionUtil = __webpack_require__(30); + function isArray(input) { + return ( + input instanceof Array || + Object.prototype.toString.call(input) === '[object Array]' + ); + } -var _autoOptionSelector = __webpack_require__(295); + function isObject(input) { + // IE8 will treat undefined and null as object if it wasn't for + // input != null + return ( + input != null && + Object.prototype.toString.call(input) === '[object Object]' + ); + } -function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } } + function hasOwnProp(a, b) { + return Object.prototype.hasOwnProperty.call(a, b); + } -/** - * Created by SDS on 2017-05-10. - */ + function isObjectEmpty(obj) { + if (Object.getOwnPropertyNames) { + return Object.getOwnPropertyNames(obj).length === 0; + } else { + var k; + for (k in obj) { + if (hasOwnProp(obj, k)) { + return false; + } + } + return true; + } + } -var ChartWidget = Brightics.Chart.Widget; + function isUndefined(input) { + return input === void 0; + } + function isNumber(input) { + return ( + typeof input === 'number' || + Object.prototype.toString.call(input) === '[object Number]' + ); + } -/** - * - * @param parentId - * @param options = { - chartOption: { - chart: { - type: 'area', - ... - }, - ... - }, - onChanged: function (chartOption) { - }, - dataSource: { - selectable: true (default: false), - getDataSourceList: function(){ - } - getDataSourceColumnList: function(dataSourceId){ - } - }, - chartTypeSelectable: false (default: true), - getControlContainerList: function(defaultControls){ - return defaultControls; - } - } - */ + function isDate(input) { + return ( + input instanceof Date || + Object.prototype.toString.call(input) === '[object Date]' + ); + } -/** - * @constructor - */ -function ChartOption(parentId, options) { - var defaultChartOptions = Brightics.Chart.getChartAttr(options.chartOption.chart.type).DefaultOptions; + function map(arr, fn) { + var res = [], + i; + for (i = 0; i < arr.length; ++i) { + res.push(fn(arr[i], i)); + } + return res; + } - var chartOption = (0, _chartOptionUtil.mergeChartOption)(defaultChartOptions, options.chartOption); + function extend(a, b) { + for (var i in b) { + if (hasOwnProp(b, i)) { + a[i] = b[i]; + } + } - var opt = $.extend(true, {}, { - dataSource: {}, - setting: {}, - chartTypeSelectable: true, - chartOptionAPI: { - setChartOptions: this.setChartOptions.bind(this), - reloadColumnSelectorSetting: this.reloadColumnSelectorSetting.bind(this) + if (hasOwnProp(b, 'toString')) { + a.toString = b.toString; } - }, options); - opt.chartOption = chartOption; - ChartWidget.call(this, parentId, opt); -} -ChartOption.prototype = Object.create(ChartWidget.prototype); -ChartOption.prototype.constructor = ChartOption; + if (hasOwnProp(b, 'valueOf')) { + a.valueOf = b.valueOf; + } -ChartOption.prototype._init = function () { - ChartWidget.prototype._init.call(this); - this.options.onChanged = this._onChanged.bind(this); - this._validateChartOption(); -}; + return a; + } -ChartOption.prototype._validateChartOption = function ($parent) { - if (this.options.dataSource.selectable) { - if (!(this.options.dataSource.getDataSourceList && this.options.dataSource.getDataSourceColumnList)) { - this._setDataSourceOption(); + function createUTC(input, format, locale, strict) { + return createLocalOrUTC(input, format, locale, strict, true).utc(); + } + + function defaultParsingFlags() { + // We need to deep clone this object. + return { + empty: false, + unusedTokens: [], + unusedInput: [], + overflow: -2, + charsLeftOver: 0, + nullInput: false, + invalidEra: null, + invalidMonth: null, + invalidFormat: false, + userInvalidated: false, + iso: false, + parsedDateParts: [], + era: null, + meridiem: null, + rfc2822: false, + weekdayMismatch: false, + }; + } + + function getParsingFlags(m) { + if (m._pf == null) { + m._pf = defaultParsingFlags(); } + return m._pf; } -}; -ChartOption.prototype._setDataSourceOption = function () { - var sourceList; - var dataSrcOption = this.options.dataSource; - if (this.options.chartOption.source.dataType === 'lazy') { - sourceList = this.options.chartOption.source.lazyData; + var some; + if (Array.prototype.some) { + some = Array.prototype.some; } else { - sourceList = this.options.chartOption.source.localData; + some = function (fun) { + var t = Object(this), + len = t.length >>> 0, + i; + + for (i = 0; i < len; i++) { + if (i in t && fun.call(this, t[i], i, t)) { + return true; + } + } + + return false; + }; } - dataSrcOption.getDataSourceList = function () { - var dataSourceList = []; - for (var dataIdx = 0; dataIdx < sourceList.length; dataIdx++) { - dataSourceList.push({ - value: sourceList[dataIdx].id.value, - label: sourceList[dataIdx].id.label, - dataSourceIndex: dataIdx - }); - } - return dataSourceList; - }; - dataSrcOption.getDataSourceColumnList = function (id) { - var dataSrcColumn = []; - return sourceList.find(function (dataSrc) { - return dataSrc.id.value == id; - }).columns(); - }; -}; -ChartOption.prototype._createContents = function ($parent) { - this.$mainControl = $('
'); - $parent.append(this.$mainControl); + function isValid(m) { + if (m._isValid == null) { + var flags = getParsingFlags(m), + parsedParts = some.call(flags.parsedDateParts, function (i) { + return i != null; + }), + isNowValid = + !isNaN(m._d.getTime()) && + flags.overflow < 0 && + !flags.empty && + !flags.invalidEra && + !flags.invalidMonth && + !flags.invalidWeekday && + !flags.weekdayMismatch && + !flags.nullInput && + !flags.invalidFormat && + !flags.userInvalidated && + (!flags.meridiem || (flags.meridiem && parsedParts)); + + if (m._strict) { + isNowValid = + isNowValid && + flags.charsLeftOver === 0 && + flags.unusedTokens.length === 0 && + flags.bigHour === undefined; + } - this.$controlWrapper = $('
'); - this.$mainControl.append(this.$controlWrapper); + if (Object.isFrozen == null || !Object.isFrozen(m)) { + m._isValid = isNowValid; + } else { + return isNowValid; + } + } + return m._isValid; + } - try { - this.chartOptionControl = ChartOptionRegistry.createChartOptionControl(this.options.chartOption.chart.type, this.$controlWrapper, this.options); - } catch (err) { - this.chartOptionControl = null; - this._createErrContents(); - console.error(err); + function createInvalid(flags) { + var m = createUTC(NaN); + if (flags != null) { + extend(getParsingFlags(m), flags); + } else { + getParsingFlags(m).userInvalidated = true; + } + + return m; } - this.$mainControl.perfectScrollbar(); - var _this = this; + // Plugins that add properties should also add the key here (null value), + // so we can properly clone ourselves. + var momentProperties = (hooks.momentProperties = []), + updateInProgress = false; - this.$mainControl.parents('.ps-container').on('scroll', function () { - if (_this.chartOptionControl != null) { - _this.chartOptionControl.close(); + function copyConfig(to, from) { + var i, prop, val; + + if (!isUndefined(from._isAMomentObject)) { + to._isAMomentObject = from._isAMomentObject; } - }); - this.$mainControl.on('scroll', function () { - if (_this.chartOptionControl != null) { - _this.chartOptionControl.close(); + if (!isUndefined(from._i)) { + to._i = from._i; } - }); - this.resizeHandler = function () { - if (_this.chartOptionControl != null) { - _this.chartOptionControl.close(); + if (!isUndefined(from._f)) { + to._f = from._f; + } + if (!isUndefined(from._l)) { + to._l = from._l; + } + if (!isUndefined(from._strict)) { + to._strict = from._strict; + } + if (!isUndefined(from._tzm)) { + to._tzm = from._tzm; + } + if (!isUndefined(from._isUTC)) { + to._isUTC = from._isUTC; + } + if (!isUndefined(from._offset)) { + to._offset = from._offset; + } + if (!isUndefined(from._pf)) { + to._pf = getParsingFlags(from); + } + if (!isUndefined(from._locale)) { + to._locale = from._locale; } - }; - $(window).resize(this.resizeHandler); -}; -ChartOption.prototype._createErrContents = function () {}; + if (momentProperties.length > 0) { + for (i = 0; i < momentProperties.length; i++) { + prop = momentProperties[i]; + val = from[prop]; + if (!isUndefined(val)) { + to[prop] = val; + } + } + } -ChartOption.prototype._onChanged = function (chartOptionAPI, param, forced) { - if (!this.options.callBack) return; + return to; + } - if (typeof this.options.callBack[chartOptionAPI] === 'function') { - this.options.callBack[chartOptionAPI](param || this.options.chartOption, forced); + // Moment prototype object + function Moment(config) { + copyConfig(this, config); + this._d = new Date(config._d != null ? config._d.getTime() : NaN); + if (!this.isValid()) { + this._d = new Date(NaN); + } + // Prevent infinite loop in case updateOffset creates new moment + // objects. + if (updateInProgress === false) { + updateInProgress = true; + hooks.updateOffset(this); + updateInProgress = false; + } } - //FIXME: 수정필요 - if (chartOptionAPI !== 'onChartTypeChanged') { - this.chartOptionControl.doValidate(); + + function isMoment(obj) { + return ( + obj instanceof Moment || (obj != null && obj._isAMomentObject != null) + ); } - this.chartOptionControl.renderProblem(); - this.chartOptionControl.renderWarning(); -}; -ChartOption.prototype.destroy = function () { - ChartWidget.prototype.destroy.call(this); + function warn(msg) { + if ( + hooks.suppressDeprecationWarnings === false && + typeof console !== 'undefined' && + console.warn + ) { + console.warn('Deprecation warning: ' + msg); + } + } - if (this.resizeHandler) { - $(window).off('resize', this.resizeHandler); - this.resizeHandler = null; + function deprecate(msg, fn) { + var firstTime = true; + + return extend(function () { + if (hooks.deprecationHandler != null) { + hooks.deprecationHandler(null, msg); + } + if (firstTime) { + var args = [], + arg, + i, + key; + for (i = 0; i < arguments.length; i++) { + arg = ''; + if (typeof arguments[i] === 'object') { + arg += '\n[' + i + '] '; + for (key in arguments[0]) { + if (hasOwnProp(arguments[0], key)) { + arg += key + ': ' + arguments[0][key] + ', '; + } + } + arg = arg.slice(0, -2); // Remove trailing comma and space + } else { + arg = arguments[i]; + } + args.push(arg); + } + warn( + msg + + '\nArguments: ' + + Array.prototype.slice.call(args).join('') + + '\n' + + new Error().stack + ); + firstTime = false; + } + return fn.apply(this, arguments); + }, fn); } - this.chartOptionControl.destroy(); - this.chartOptionControl = null; - this.$mainControl.remove(); -}; -/* - * @param options = { - chart: { - type: 'area', - ... - }, - } - */ -//Control Container 전체 render -ChartOption.prototype.setChartOptions = function (options) { - // if (options.chart.type && options.chart.type !== this.options.chartOption.chart.type) { - // this.chartOptionControl.destroy(); - // this.chartOptionControl = ChartOptionRegistry.createChartOptionControl(options.chart.type, this.$controlWrapper, this.options); - // } - var defaultChartOptions = Brightics.Chart.getChartAttr(options.chart.type).DefaultOptions; - var colorSet = (options.colorSet || defaultChartOptions.colorSet).slice(); - this.options.chartOption = $.extend(true, {}, defaultChartOptions, { source: this.options.chartOption.source }, options); - this.options.chartOption.colorSet = colorSet; - // this.chartOptionControl.render(); + var deprecations = {}; - //FIXME: 무조건 destroy 후 redraw(render완성되면 삭제하고 위 코드 사용) - if (this.chartOptionControl) this.chartOptionControl.destroy(); - this.chartOptionControl = ChartOptionRegistry.createChartOptionControl(options.chart.type, this.$controlWrapper, this.options); + function deprecateSimple(name, msg) { + if (hooks.deprecationHandler != null) { + hooks.deprecationHandler(name, msg); + } + if (!deprecations[name]) { + warn(msg); + deprecations[name] = true; + } + } - return this.options.chartOption; //FIXME: getChartOptions로 구분해야할듯 -}; + hooks.suppressDeprecationWarnings = false; + hooks.deprecationHandler = null; -ChartOption.prototype.show = function () { - ChartWidget.prototype.show.call(this); - this.$mainControl.perfectScrollbar('update'); -}; + function isFunction(input) { + return ( + (typeof Function !== 'undefined' && input instanceof Function) || + Object.prototype.toString.call(input) === '[object Function]' + ); + } -ChartOption.prototype.hide = function () { - ChartWidget.prototype.hide.call(this); -}; + function set(config) { + var prop, i; + for (i in config) { + if (hasOwnProp(config, i)) { + prop = config[i]; + if (isFunction(prop)) { + this[i] = prop; + } else { + this['_' + i] = prop; + } + } + } + this._config = config; + // Lenient ordinal parsing accepts just a number in addition to + // number + (possibly) stuff coming from _dayOfMonthOrdinalParse. + // TODO: Remove "ordinalParse" fallback in next major release. + this._dayOfMonthOrdinalParseLenient = new RegExp( + (this._dayOfMonthOrdinalParse.source || this._ordinalParse.source) + + '|' + + /\d{1,2}/.source + ); + } -//특정 Control Container만 render -ChartOption.prototype.reloadColumnSelectorSetting = function () { - this.chartOptionControl.reloadColumnSelectorSetting(); -}; + function mergeConfigs(parentConfig, childConfig) { + var res = extend({}, parentConfig), + prop; + for (prop in childConfig) { + if (hasOwnProp(childConfig, prop)) { + if (isObject(parentConfig[prop]) && isObject(childConfig[prop])) { + res[prop] = {}; + extend(res[prop], parentConfig[prop]); + extend(res[prop], childConfig[prop]); + } else if (childConfig[prop] != null) { + res[prop] = childConfig[prop]; + } else { + delete res[prop]; + } + } + } + for (prop in parentConfig) { + if ( + hasOwnProp(parentConfig, prop) && + !hasOwnProp(childConfig, prop) && + isObject(parentConfig[prop]) + ) { + // make sure changes to properties don't modify parent config + res[prop] = extend({}, res[prop]); + } + } + return res; + } -ChartOption.prototype.getColumnSelectorSetting = function () { - //TODO MAP Chart인 경우 처리 어떻게 해야 하는지 확인 필요 - if (this.chartOptionControl.options.setting.columnSelector.constructor !== Array) { - return []; + function Locale(config) { + if (config != null) { + this.set(config); + } } - return this.chartOptionControl.options.setting.columnSelector; -}; -ChartOption.prototype.getOptions = function () { - return this.options; -}; + var keys; -exports.default = ChartOption; + if (Object.keys) { + keys = Object.keys; + } else { + keys = function (obj) { + var i, + res = []; + for (i in obj) { + if (hasOwnProp(obj, i)) { + res.push(i); + } + } + return res; + }; + } -/***/ }), -/* 117 */ -/***/ (function(module, exports, __webpack_require__) { + var defaultCalendar = { + sameDay: '[Today at] LT', + nextDay: '[Tomorrow at] LT', + nextWeek: 'dddd [at] LT', + lastDay: '[Yesterday at] LT', + lastWeek: '[Last] dddd [at] LT', + sameElse: 'L', + }; -"use strict"; + function calendar(key, mom, now) { + var output = this._calendar[key] || this._calendar['sameElse']; + return isFunction(output) ? output.call(mom, now) : output; + } + function zeroFill(number, targetLength, forceSign) { + var absNumber = '' + Math.abs(number), + zerosToFill = targetLength - absNumber.length, + sign = number >= 0; + return ( + (sign ? (forceSign ? '+' : '') : '-') + + Math.pow(10, Math.max(0, zerosToFill)).toString().substr(1) + + absNumber + ); + } -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.registerChartOptionControl = registerChartOptionControl; -exports.getChartOptionControlList = getChartOptionControlList; -exports.getChartOptionControl = getChartOptionControl; -exports.createChartOptionControl = createChartOptionControl; + var formattingTokens = /(\[[^\[]*\])|(\\)?([Hh]mm(ss)?|Mo|MM?M?M?|Do|DDDo|DD?D?D?|ddd?d?|do?|w[o|w]?|W[o|W]?|Qo?|N{1,5}|YYYYYY|YYYYY|YYYY|YY|y{2,4}|yo?|gg(ggg?)?|GG(GGG?)?|e|E|a|A|hh?|HH?|kk?|mm?|ss?|S{1,9}|x|X|zz?|ZZ?|.)/g, + localFormattingTokens = /(\[[^\[]*\])|(\\)?(LTS|LT|LL?L?L?|l{1,4})/g, + formatFunctions = {}, + formatTokenFunctions = {}; + + // token: 'M' + // padded: ['MM', 2] + // ordinal: 'Mo' + // callback: function () { this.month() + 1 } + function addFormatToken(token, padded, ordinal, callback) { + var func = callback; + if (typeof callback === 'string') { + func = function () { + return this[callback](); + }; + } + if (token) { + formatTokenFunctions[token] = func; + } + if (padded) { + formatTokenFunctions[padded[0]] = function () { + return zeroFill(func.apply(this, arguments), padded[1], padded[2]); + }; + } + if (ordinal) { + formatTokenFunctions[ordinal] = function () { + return this.localeData().ordinal( + func.apply(this, arguments), + token + ); + }; + } + } -var _chartOptionIndex = __webpack_require__(562); + function removeFormattingTokens(input) { + if (input.match(/\[[\s\S]/)) { + return input.replace(/^\[|\]$/g, ''); + } + return input.replace(/\\/g, ''); + } -var _chartOptionIndex2 = _interopRequireDefault(_chartOptionIndex); + function makeFormatFunction(format) { + var array = format.match(formattingTokens), + i, + length; -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + for (i = 0, length = array.length; i < length; i++) { + if (formatTokenFunctions[array[i]]) { + array[i] = formatTokenFunctions[array[i]]; + } else { + array[i] = removeFormattingTokens(array[i]); + } + } -var chartOption = {}; /** - * Source: chart-option-register.js - * Created by ji_sung.park on 2018-05-30 - */ + return function (mom) { + var output = '', + i; + for (i = 0; i < length; i++) { + output += isFunction(array[i]) + ? array[i].call(mom, format) + : array[i]; + } + return output; + }; + } -_init(); + // format date using native date object + function formatMoment(m, format) { + if (!m.isValid()) { + return m.localeData().invalidDate(); + } -function _init() { - var chartTypeList = Object.keys(_chartOptionIndex2.default); - chartTypeList.forEach(function (chartType) { - chartOption[chartType] = _chartOptionIndex2.default[chartType]; - }); -} + format = expandFormat(format, m.localeData()); + formatFunctions[format] = + formatFunctions[format] || makeFormatFunction(format); -exports.default = chartOption; -function registerChartOptionControl(option) { - if ($.isEmptyObject(chartOption)) { - console.log('chart option register is empty'); + return formatFunctions[format](m); } - chartOption[option.Key] = option.Func; -} -function getChartOptionControlList() { - return chartOption; -} + function expandFormat(format, locale) { + var i = 5; -function getChartOptionControl(chartType) { - return chartOption[chartType]; -} + function replaceLongDateFormatTokens(input) { + return locale.longDateFormat(input) || input; + } -function createChartOptionControl(chartType, parentId, options) { - if (!chartOption[chartType] || !parentId || !options) { - throw new Error('Cannot create chart option control. ' + chartType); + localFormattingTokens.lastIndex = 0; + while (i >= 0 && localFormattingTokens.test(format)) { + format = format.replace( + localFormattingTokens, + replaceLongDateFormatTokens + ); + localFormattingTokens.lastIndex = 0; + i -= 1; + } + + return format; } - return new chartOption[chartType](parentId, options); -} -/***/ }), -/* 118 */ -/***/ (function(module, exports, __webpack_require__) { + var defaultLongDateFormat = { + LTS: 'h:mm:ss A', + LT: 'h:mm A', + L: 'MM/DD/YYYY', + LL: 'MMMM D, YYYY', + LLL: 'MMMM D, YYYY h:mm A', + LLLL: 'dddd, MMMM D, YYYY h:mm A', + }; -"use strict"; + function longDateFormat(key) { + var format = this._longDateFormat[key], + formatUpper = this._longDateFormat[key.toUpperCase()]; + if (format || !formatUpper) { + return format; + } -Object.defineProperty(exports, "__esModule", { - value: true -}); + this._longDateFormat[key] = formatUpper + .match(formattingTokens) + .map(function (tok) { + if ( + tok === 'MMMM' || + tok === 'MM' || + tok === 'DD' || + tok === 'dddd' + ) { + return tok.slice(1); + } + return tok; + }) + .join(''); -var _chartOptionBase = __webpack_require__(6); + return this._longDateFormat[key]; + } -var _chartOptionBase2 = _interopRequireDefault(_chartOptionBase); + var defaultInvalidDate = 'Invalid date'; -var _chartOptionAreaStacked = __webpack_require__(291); + function invalidDate() { + return this._invalidDate; + } -var _chartOptionAreaStacked2 = _interopRequireDefault(_chartOptionAreaStacked); + var defaultOrdinal = '%d', + defaultDayOfMonthOrdinalParse = /\d{1,2}/; -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + function ordinal(number) { + return this._ordinal.replace('%d', number); + } -/** - * Created by SDS on 2017-05-10. - */ + var defaultRelativeTime = { + future: 'in %s', + past: '%s ago', + s: 'a few seconds', + ss: '%d seconds', + m: 'a minute', + mm: '%d minutes', + h: 'an hour', + hh: '%d hours', + d: 'a day', + dd: '%d days', + w: 'a week', + ww: '%d weeks', + M: 'a month', + MM: '%d months', + y: 'a year', + yy: '%d years', + }; -function AreaChartOption(parentId, options) { - _chartOptionBase2.default.call(this, parentId, options); -} + function relativeTime(number, withoutSuffix, string, isFuture) { + var output = this._relativeTime[string]; + return isFunction(output) + ? output(number, withoutSuffix, string, isFuture) + : output.replace(/%d/i, number); + } -AreaChartOption.prototype = Object.create(_chartOptionBase2.default.prototype); -AreaChartOption.prototype.constructor = AreaChartOption; + function pastFuture(diff, output) { + var format = this._relativeTime[diff > 0 ? 'future' : 'past']; + return isFunction(format) ? format(output) : format.replace(/%s/i, output); + } -AreaChartOption.prototype._init = function () { - _chartOptionBase2.default.prototype._init.call(this); - var _this = this; - var columnSelectorSetting = [[{ - key: 'xAxis', - ref: _this.options.chartOption.xAxis[0] - }, { - key: 'yAxis', - ref: _this.options.chartOption.yAxis[0] - }, { - key: 'colorBy', - ref: _this.options.chartOption.colorBy[0] - }]]; + var aliases = {}; - columnSelectorSetting = this._configureColumnConf(columnSelectorSetting); + function addUnitAlias(unit, shorthand) { + var lowerCase = unit.toLowerCase(); + aliases[lowerCase] = aliases[lowerCase + 's'] = aliases[shorthand] = unit; + } - this.options.setting.columnSelector = columnSelectorSetting; - this.options.setting.marker = this.options.chartOption.plotOptions.area.marker; - this.options.setting.stripLine = this.options.chartOption.plotOptions.area.stripLine; - this.options.setting.tooltip = this.options.chartOption.plotOptions.area.tooltip; - this.options.setting.showSymbol = this.options.chartOption.plotOptions.area.showSymbol; -}; + function normalizeUnits(units) { + return typeof units === 'string' + ? aliases[units] || aliases[units.toLowerCase()] + : undefined; + } -AreaChartOption.prototype.getDefaultControlContainerList = function () { - return ['Data', 'ToolTipTrigger', 'Title', 'Axis', 'Legend', 'Marker', 'Lines', 'Frame']; -}; + function normalizeObjectUnits(inputObject) { + var normalizedInput = {}, + normalizedProp, + prop; -exports.default = AreaChartOption; + for (prop in inputObject) { + if (hasOwnProp(inputObject, prop)) { + normalizedProp = normalizeUnits(prop); + if (normalizedProp) { + normalizedInput[normalizedProp] = inputObject[prop]; + } + } + } -/***/ }), -/* 119 */ -/***/ (function(module, exports, __webpack_require__) { + return normalizedInput; + } -"use strict"; + var priorities = {}; + function addUnitPriority(unit, priority) { + priorities[unit] = priority; + } -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.Data = Data; -exports.ComplexData = ComplexData; -exports.Format = Format; -exports.FormatCard = FormatCard; -exports.Title = Title; -exports.Axis = Axis; -exports.AxisR = AxisR; -exports.AxisEach = AxisEach; -exports.AxisRadar = AxisRadar; -exports.Legend = Legend; -exports.CustomLegend = CustomLegend; -exports.VisualMap = VisualMap; -exports.Marker = Marker; -exports.MarkerLine = MarkerLine; -exports.Lines = Lines; -exports.LinesWithTrend = LinesWithTrend; -exports.Frame = Frame; -exports.FrameStyle = FrameStyle; -exports.FramePie = FramePie; -exports.FrameDonut = FrameDonut; -exports.ToolTip = ToolTip; -exports.Label = Label; -exports.Figure = Figure; -exports.MapData = MapData; -exports.MapStyle = MapStyle; -exports.Layers = Layers; -exports.ToolTipTrigger = ToolTipTrigger; -exports.Grid = Grid; -/** - * Source: control-container-preview.js - * Created by SDS on 2018-05-30 - */ + function getPrioritizedUnits(unitsObj) { + var units = [], + u; + for (u in unitsObj) { + if (hasOwnProp(unitsObj, u)) { + units.push({ unit: u, priority: priorities[u] }); + } + } + units.sort(function (a, b) { + return a.priority - b.priority; + }); + return units; + } -var _axisTypePreview = function _axisTypePreview() { - var preViewText = '', - axisAttrUsed = false; - if (this.options.chartOption.xAxis[0].min || this.options.chartOption.xAxis[0].max || this.options.chartOption.yAxis[0].min || this.options.chartOption.yAxis[0].max) { - preViewText += 'Range'; - axisAttrUsed = true; + function isLeapYear(year) { + return (year % 4 === 0 && year % 100 !== 0) || year % 400 === 0; } - if (this.options.chartOption.xAxis[0].title.show || this.options.chartOption.yAxis[0].title.show) { - preViewText = preViewText === '' ? 'Title' : preViewText += ', Title'; - axisAttrUsed = true; + + function absFloor(number) { + if (number < 0) { + // -0 -> 0 + return Math.ceil(number) || 0; + } else { + return Math.floor(number); + } } - if (this.options.chartOption.xAxis[0].axisLabel.show || this.options.chartOption.yAxis[0].axisLabel.show) { - preViewText = preViewText === '' ? 'Label' : preViewText += ', Label'; - axisAttrUsed = true; + + function toInt(argumentForCoercion) { + var coercedNumber = +argumentForCoercion, + value = 0; + + if (coercedNumber !== 0 && isFinite(coercedNumber)) { + value = absFloor(coercedNumber); + } + + return value; } - return axisAttrUsed ? preViewText + ' ON' : 'OFF'; -}; -function Data() { - //TODO: multichart 고려안됨 - var preViewText = ''; - var columnList = this.options.setting.columnSelector[0]; - if (columnList && columnList.length > 0) { - columnList.forEach(function (columnInfo) { - if (columnInfo.ref.selected && columnInfo.ref.selected.length > 0 && columnInfo.ref.selected[0] !== null) { - preViewText = preViewText === '' ? columnInfo.label : preViewText += ', ' + columnInfo.label; + function makeGetSet(unit, keepTime) { + return function (value) { + if (value != null) { + set$1(this, unit, value); + hooks.updateOffset(this, keepTime); + return this; + } else { + return get(this, unit); } - }); + }; } - var itemSelector = this.options.setting.itemSelector; - if (itemSelector && itemSelector.length > 0) { - itemSelector[0].forEach(function (itemInfo) { - if (itemInfo.ref.selected && itemInfo.ref.selected !== '') { - preViewText = preViewText === '' ? itemInfo.label : preViewText += ', ' + itemInfo.label; - } - }); + function get(mom, unit) { + return mom.isValid() + ? mom._d['get' + (mom._isUTC ? 'UTC' : '') + unit]() + : NaN; } - var onOffSwitch = this.options.setting.onOffSwitch; - if (onOffSwitch && onOffSwitch.length > 0) { - onOffSwitch[0].forEach(function (switchInfo) { - if (switchInfo.ref && switchInfo.ref !== '') { - preViewText = preViewText === '' ? switchInfo.label : preViewText += ', ' + switchInfo.label; + function set$1(mom, unit, value) { + if (mom.isValid() && !isNaN(value)) { + if ( + unit === 'FullYear' && + isLeapYear(mom.year()) && + mom.month() === 1 && + mom.date() === 29 + ) { + value = toInt(value); + mom._d['set' + (mom._isUTC ? 'UTC' : '') + unit]( + value, + mom.month(), + daysInMonth(value, mom.month()) + ); + } else { + mom._d['set' + (mom._isUTC ? 'UTC' : '') + unit](value); } - }); + } } - return preViewText; -} + // MOMENTS -function ComplexData() { - //TODO: multichart 고려안됨 - var preViewText = ''; - var columnList = this.options.setting.columnSelector[0]; - if (columnList && columnList.length > 0) { - columnList.forEach(function (columnInfo) { - if (columnInfo.ref.selected && columnInfo.ref.selected.length > 0 && columnInfo.ref.selected[0] !== null) { - preViewText = preViewText === '' ? columnInfo.label : preViewText += ', ' + columnInfo.label; - } - }); + function stringGet(units) { + units = normalizeUnits(units); + if (isFunction(this[units])) { + return this[units](); + } + return this; } - var itemSelector = this.options.setting.itemSelector; - if (itemSelector && itemSelector.length > 0) { - itemSelector[0].forEach(function (itemInfo) { - if (itemInfo.ref.selected && itemInfo.ref.selected !== '') { - preViewText = preViewText === '' ? itemInfo.label : preViewText += ', ' + itemInfo.label; + function stringSet(units, value) { + if (typeof units === 'object') { + units = normalizeObjectUnits(units); + var prioritized = getPrioritizedUnits(units), + i; + for (i = 0; i < prioritized.length; i++) { + this[prioritized[i].unit](units[prioritized[i].unit]); } - }); + } else { + units = normalizeUnits(units); + if (isFunction(this[units])) { + return this[units](value); + } + } + return this; } - return preViewText; -} - -function Format() { - return ''; -} -function FormatCard() { - return ''; -} + var match1 = /\d/, // 0 - 9 + match2 = /\d\d/, // 00 - 99 + match3 = /\d{3}/, // 000 - 999 + match4 = /\d{4}/, // 0000 - 9999 + match6 = /[+-]?\d{6}/, // -999999 - 999999 + match1to2 = /\d\d?/, // 0 - 99 + match3to4 = /\d\d\d\d?/, // 999 - 9999 + match5to6 = /\d\d\d\d\d\d?/, // 99999 - 999999 + match1to3 = /\d{1,3}/, // 0 - 999 + match1to4 = /\d{1,4}/, // 0 - 9999 + match1to6 = /[+-]?\d{1,6}/, // -999999 - 999999 + matchUnsigned = /\d+/, // 0 - inf + matchSigned = /[+-]?\d+/, // -inf - inf + matchOffset = /Z|[+-]\d\d:?\d\d/gi, // +00:00 -00:00 +0000 -0000 or Z + matchShortOffset = /Z|[+-]\d\d(?::?\d\d)?/gi, // +00 -00 +00:00 -00:00 +0000 -0000 or Z + matchTimestamp = /[+-]?\d+(\.\d{1,3})?/, // 123456789 123456789.123 + // any word (or two) characters or numbers including two/three word month in arabic. + // includes scottish gaelic two word and hyphenated months + matchWord = /[0-9]{0,256}['a-z\u00A0-\u05FF\u0700-\uD7FF\uF900-\uFDCF\uFDF0-\uFF07\uFF10-\uFFEF]{1,256}|[\u0600-\u06FF\/]{1,256}(\s*?[\u0600-\u06FF]{1,256}){1,2}/i, + regexes; + + regexes = {}; + + function addRegexToken(token, regex, strictRegex) { + regexes[token] = isFunction(regex) + ? regex + : function (isStrict, localeData) { + return isStrict && strictRegex ? strictRegex : regex; + }; + } -function Title() { - var preViewText = ''; - if (this.options.chartOption.title.show) { - preViewText = preViewText + this.options.chartOption.title.text; - if (this.options.chartOption.title.subtext !== '') { - preViewText = preViewText + ' / ' + this.options.chartOption.title.subtext; + function getParseRegexForToken(token, config) { + if (!hasOwnProp(regexes, token)) { + return new RegExp(unescapeFormat(token)); } + + return regexes[token](config._strict, config._locale); } - return preViewText && preViewText !== '' ? preViewText : 'OFF'; -} -function Axis() { - return _axisTypePreview.call(this); -} + // Code from http://stackoverflow.com/questions/3561493/is-there-a-regexp-escape-function-in-javascript + function unescapeFormat(s) { + return regexEscape( + s + .replace('\\', '') + .replace(/\\(\[)|\\(\])|\[([^\]\[]*)\]|\\(.)/g, function ( + matched, + p1, + p2, + p3, + p4 + ) { + return p1 || p2 || p3 || p4; + }) + ); + } -function AxisR() { - return _axisTypePreview.call(this); -} + function regexEscape(s) { + return s.replace(/[-\/\\^$*+?.()|[\]{}]/g, '\\$&'); + } -function AxisEach() { - return _axisTypePreview.call(this); -} + var tokens = {}; -function AxisRadar() { - var preViewText = []; - var axisAttrUsed = false; - var min = this.options.chartOption.plotOptions.radar.min; - var max = this.options.chartOption.plotOptions.radar.max; - var show = this.options.chartOption.plotOptions.radar.name.show; + function addParseToken(token, callback) { + var i, + func = callback; + if (typeof token === 'string') { + token = [token]; + } + if (isNumber(callback)) { + func = function (input, array) { + array[callback] = toInt(input); + }; + } + for (i = 0; i < token.length; i++) { + tokens[token[i]] = func; + } + } - if (min || max) { - preViewText.push('Range'); - axisAttrUsed = true; + function addWeekParseToken(token, callback) { + addParseToken(token, function (input, array, config, token) { + config._w = config._w || {}; + callback(input, config._w, config, token); + }); } - if (show) { - preViewText.push('Label'); - axisAttrUsed = true; + + function addTimeToArrayFromToken(token, input, config) { + if (input != null && hasOwnProp(tokens, token)) { + tokens[token](input, config._a, config, token); + } } - return axisAttrUsed ? preViewText.join(', ') + ' ON' : 'OFF'; -} -function Legend() { - return this.options.chartOption.legend.show ? 'ON' : 'OFF'; -} + var YEAR = 0, + MONTH = 1, + DATE = 2, + HOUR = 3, + MINUTE = 4, + SECOND = 5, + MILLISECOND = 6, + WEEK = 7, + WEEKDAY = 8; + + function mod(n, x) { + return ((n % x) + x) % x; + } -function CustomLegend() { - return Legend.call(this); -} + var indexOf; -function VisualMap() { - return this.options.chartOption.legend.show ? 'ON' : 'OFF'; -} + if (Array.prototype.indexOf) { + indexOf = Array.prototype.indexOf; + } else { + indexOf = function (o) { + // I know + var i; + for (i = 0; i < this.length; ++i) { + if (this[i] === o) { + return i; + } + } + return -1; + }; + } -function Marker() { - var $previewDiv = $('
'); - var border = '1px solid rgba(0, 0, 0, 0.6)'; - $previewDiv.css({ - border: border - }); - for (var colorSetIdx = 0; colorSetIdx < 4; colorSetIdx++) { - var $miniDiv = $('
'); - if (colorSetIdx === 0) { - $miniDiv.css({ - 'border-right': border, - 'border-bottom': border - }); - } else if (colorSetIdx === 1) { - $miniDiv.css({ - 'border-bottom': border - }); - } else if (colorSetIdx === 2) { - $miniDiv.css({ - 'border-right': border - }); + function daysInMonth(year, month) { + if (isNaN(year) || isNaN(month)) { + return NaN; } - $miniDiv.css({ - width: '8.5px', - height: '8.5px', - background: this.options.chartOption.colorSet[colorSetIdx] || 'transparent' - }); - $previewDiv.append($miniDiv); + var modMonth = mod(month, 12); + year += (month - modMonth) / 12; + return modMonth === 1 + ? isLeapYear(year) + ? 29 + : 28 + : 31 - ((modMonth % 7) % 2); } - return $previewDiv; -} -function MarkerLine() { - var $previewDiv = $('
'); - var border = '1px solid rgba(0, 0, 0, 0.6)'; - $previewDiv.css({ - border: border + // FORMATTING + + addFormatToken('M', ['MM', 2], 'Mo', function () { + return this.month() + 1; }); - for (var colorSetIdx = 0; colorSetIdx < 4; colorSetIdx++) { - var $miniDiv = $('
'); - if (colorSetIdx === 0) { - $miniDiv.css({ - 'border-right': border, - 'border-bottom': border - }); - } else if (colorSetIdx === 1) { - $miniDiv.css({ - 'border-bottom': border - }); - } else if (colorSetIdx === 2) { - $miniDiv.css({ - 'border-right': border - }); + + addFormatToken('MMM', 0, 0, function (format) { + return this.localeData().monthsShort(this, format); + }); + + addFormatToken('MMMM', 0, 0, function (format) { + return this.localeData().months(this, format); + }); + + // ALIASES + + addUnitAlias('month', 'M'); + + // PRIORITY + + addUnitPriority('month', 8); + + // PARSING + + addRegexToken('M', match1to2); + addRegexToken('MM', match1to2, match2); + addRegexToken('MMM', function (isStrict, locale) { + return locale.monthsShortRegex(isStrict); + }); + addRegexToken('MMMM', function (isStrict, locale) { + return locale.monthsRegex(isStrict); + }); + + addParseToken(['M', 'MM'], function (input, array) { + array[MONTH] = toInt(input) - 1; + }); + + addParseToken(['MMM', 'MMMM'], function (input, array, config, token) { + var month = config._locale.monthsParse(input, token, config._strict); + // if we didn't find a month name, mark the date as invalid. + if (month != null) { + array[MONTH] = month; + } else { + getParsingFlags(config).invalidMonth = input; } - $miniDiv.css({ - width: '8.5px', - height: '8.5px', - background: this.options.chartOption.colorSet[colorSetIdx] || 'transparent' - }); - $previewDiv.append($miniDiv); + }); + + // LOCALES + + var defaultLocaleMonths = 'January_February_March_April_May_June_July_August_September_October_November_December'.split( + '_' + ), + defaultLocaleMonthsShort = 'Jan_Feb_Mar_Apr_May_Jun_Jul_Aug_Sep_Oct_Nov_Dec'.split( + '_' + ), + MONTHS_IN_FORMAT = /D[oD]?(\[[^\[\]]*\]|\s)+MMMM?/, + defaultMonthsShortRegex = matchWord, + defaultMonthsRegex = matchWord; + + function localeMonths(m, format) { + if (!m) { + return isArray(this._months) + ? this._months + : this._months['standalone']; + } + return isArray(this._months) + ? this._months[m.month()] + : this._months[ + (this._months.isFormat || MONTHS_IN_FORMAT).test(format) + ? 'format' + : 'standalone' + ][m.month()]; } - return $previewDiv; -} -function Lines() { - var $previeDivParent = $('
'); - var stripLineList = this.options.setting.stripLine.data[0]; - var defaultLineStyle = this.options.setting.stripLine.lineStyle || { - 'normal': { - 'color': '#000000', - 'type': 'solid' + function localeMonthsShort(m, format) { + if (!m) { + return isArray(this._monthsShort) + ? this._monthsShort + : this._monthsShort['standalone']; } - }; - var maxLinePreviewCnt = 6, - totalCnt = 0; - for (var axisKey in stripLineList) { - if (stripLineList[axisKey] && stripLineList[axisKey].length > 0) { - for (var linePreviewCnt = 0; linePreviewCnt < stripLineList[axisKey].length; linePreviewCnt++) { - var lineInfo = stripLineList[axisKey][linePreviewCnt]; - if ($.isEmptyObject(lineInfo) || lineInfo.display == false) continue; - var $previeDiv = $('
'); - var slashDiv = $('
'); - $previeDiv.css({ - border: '1px solid rgba(0, 0, 0, 0.6)', - 'margin-right': '2px' - }); - var borderStyle = '2px' + (lineInfo.lineStyle && lineInfo.lineStyle.normal.color ? ' ' + lineInfo.lineStyle.normal.color : ' ' + defaultLineStyle.normal.color) + (lineInfo.lineStyle && lineInfo.lineStyle.normal.type ? ' ' + lineInfo.lineStyle.normal.type : ' ' + defaultLineStyle.normal.type); - slashDiv.css({ - 'border-top': borderStyle - }); - $previeDiv.append(slashDiv); - $previeDivParent.append($previeDiv); - totalCnt++; + return isArray(this._monthsShort) + ? this._monthsShort[m.month()] + : this._monthsShort[ + MONTHS_IN_FORMAT.test(format) ? 'format' : 'standalone' + ][m.month()]; + } + + function handleStrictParse(monthName, format, strict) { + var i, + ii, + mom, + llc = monthName.toLocaleLowerCase(); + if (!this._monthsParse) { + // this is not used + this._monthsParse = []; + this._longMonthsParse = []; + this._shortMonthsParse = []; + for (i = 0; i < 12; ++i) { + mom = createUTC([2000, i]); + this._shortMonthsParse[i] = this.monthsShort( + mom, + '' + ).toLocaleLowerCase(); + this._longMonthsParse[i] = this.months(mom, '').toLocaleLowerCase(); } } - } - if (totalCnt === 0) { - return 'OFF'; - } else { - return $previeDivParent.children(); + if (strict) { + if (format === 'MMM') { + ii = indexOf.call(this._shortMonthsParse, llc); + return ii !== -1 ? ii : null; + } else { + ii = indexOf.call(this._longMonthsParse, llc); + return ii !== -1 ? ii : null; + } + } else { + if (format === 'MMM') { + ii = indexOf.call(this._shortMonthsParse, llc); + if (ii !== -1) { + return ii; + } + ii = indexOf.call(this._longMonthsParse, llc); + return ii !== -1 ? ii : null; + } else { + ii = indexOf.call(this._longMonthsParse, llc); + if (ii !== -1) { + return ii; + } + ii = indexOf.call(this._shortMonthsParse, llc); + return ii !== -1 ? ii : null; + } + } } -} -function LinesWithTrend() { - var $previeDivParent = $('
'); - var stripLineList = this.options.setting.stripLine.data[0]; - var defaultLineStyle = this.options.setting.stripLine.lineStyle || { - 'normal': { - 'color': '#000000', - 'type': 'solid' + function localeMonthsParse(monthName, format, strict) { + var i, mom, regex; + + if (this._monthsParseExact) { + return handleStrictParse.call(this, monthName, format, strict); } - }; - var maxLinePreviewCnt = 6, - totalCnt = 0; - for (var axisKey in stripLineList) { - if (stripLineList[axisKey] && stripLineList[axisKey].length > 0) { - for (var linePreviewCnt = 0; linePreviewCnt < stripLineList[axisKey].length; linePreviewCnt++) { - var lineInfo = stripLineList[axisKey][linePreviewCnt]; - if ($.isEmptyObject(lineInfo) || lineInfo.display == false) continue; - var $previeDiv = $('
'); - var slashDiv = $('
'); - $previeDiv.css({ - border: '1px solid rgba(0, 0, 0, 0.6)', - 'margin-right': '2px' - }); - var borderStyle = '2px' + (lineInfo.lineStyle && lineInfo.lineStyle.normal.color ? ' ' + lineInfo.lineStyle.normal.color : ' ' + defaultLineStyle.normal.color) + (lineInfo.lineStyle && lineInfo.lineStyle.normal.type ? ' ' + lineInfo.lineStyle.normal.type : ' ' + defaultLineStyle.normal.type); - slashDiv.css({ - 'border-top': borderStyle - }); - $previeDiv.append(slashDiv); - $previeDivParent.append($previeDiv); - totalCnt++; + + if (!this._monthsParse) { + this._monthsParse = []; + this._longMonthsParse = []; + this._shortMonthsParse = []; + } + + // TODO: add sorting + // Sorting makes sure if one month (or abbr) is a prefix of another + // see sorting in computeMonthsParse + for (i = 0; i < 12; i++) { + // make the regex if we don't have it already + mom = createUTC([2000, i]); + if (strict && !this._longMonthsParse[i]) { + this._longMonthsParse[i] = new RegExp( + '^' + this.months(mom, '').replace('.', '') + '$', + 'i' + ); + this._shortMonthsParse[i] = new RegExp( + '^' + this.monthsShort(mom, '').replace('.', '') + '$', + 'i' + ); + } + if (!strict && !this._monthsParse[i]) { + regex = + '^' + this.months(mom, '') + '|^' + this.monthsShort(mom, ''); + this._monthsParse[i] = new RegExp(regex.replace('.', ''), 'i'); + } + // test the regex + if ( + strict && + format === 'MMMM' && + this._longMonthsParse[i].test(monthName) + ) { + return i; + } else if ( + strict && + format === 'MMM' && + this._shortMonthsParse[i].test(monthName) + ) { + return i; + } else if (!strict && this._monthsParse[i].test(monthName)) { + return i; } } } - if (this.options.setting.trendLine && this.options.setting.trendLine.data[0]) { - var trendLineList = this.options.setting.trendLine.data[0]; - for (var axisKey in trendLineList) { - if (trendLineList[axisKey] && trendLineList[axisKey].length > 0) { - for (var linePreviewCnt = 0; linePreviewCnt < trendLineList[axisKey].length; linePreviewCnt++) { - var lineInfo = trendLineList[axisKey][linePreviewCnt]; - if ($.isEmptyObject(lineInfo) || lineInfo.display == false) continue; - var $previeDiv = $('
'); - var slashDiv = $('
'); - $previeDiv.css({ - border: '1px solid rgba(0, 0, 0, 0.6)', - 'margin-right': '2px' - }); - var borderStyle = '2px' + (lineInfo.lineStyle && lineInfo.lineStyle.normal.color ? ' ' + lineInfo.lineStyle.normal.color : ' ' + defaultLineStyle.normal.color) + (lineInfo.lineStyle && lineInfo.lineStyle.normal.type ? ' ' + lineInfo.lineStyle.normal.type : ' ' + defaultLineStyle.normal.type); - slashDiv.css({ - 'border-top': borderStyle - }); - $previeDiv.append(slashDiv); - $previeDivParent.append($previeDiv); - totalCnt++; + + // MOMENTS + + function setMonth(mom, value) { + var dayOfMonth; + + if (!mom.isValid()) { + // No op + return mom; + } + + if (typeof value === 'string') { + if (/^\d+$/.test(value)) { + value = toInt(value); + } else { + value = mom.localeData().monthsParse(value); + // TODO: Another silent failure? + if (!isNumber(value)) { + return mom; } } } + + dayOfMonth = Math.min(mom.date(), daysInMonth(mom.year(), value)); + mom._d['set' + (mom._isUTC ? 'UTC' : '') + 'Month'](value, dayOfMonth); + return mom; } - if (totalCnt === 0) { - return 'OFF'; - } else { - return $previeDivParent.children(); + function getSetMonth(value) { + if (value != null) { + setMonth(this, value); + hooks.updateOffset(this, true); + return this; + } else { + return get(this, 'Month'); + } } -} -function Frame() { - var $previewDiv = $('
'); - $previewDiv.css({ - border: this.options.chartOption.chart.border, - background: this.options.chartOption.chart.background - }); - return $previewDiv; -} + function getDaysInMonth() { + return daysInMonth(this.year(), this.month()); + } -function FrameStyle() { - var $previewDiv = $('
'); - $previewDiv.css({ - border: this.options.chartOption.chart.border, - background: this.options.chartOption.chart.background - }); - return $previewDiv; -} + function monthsShortRegex(isStrict) { + if (this._monthsParseExact) { + if (!hasOwnProp(this, '_monthsRegex')) { + computeMonthsParse.call(this); + } + if (isStrict) { + return this._monthsShortStrictRegex; + } else { + return this._monthsShortRegex; + } + } else { + if (!hasOwnProp(this, '_monthsShortRegex')) { + this._monthsShortRegex = defaultMonthsShortRegex; + } + return this._monthsShortStrictRegex && isStrict + ? this._monthsShortStrictRegex + : this._monthsShortRegex; + } + } -function FramePie() { - var $previewDiv = $('
'); - $previewDiv.css({ - border: this.options.chartOption.chart.border, - background: this.options.chartOption.chart.background - }); - return $previewDiv; -} + function monthsRegex(isStrict) { + if (this._monthsParseExact) { + if (!hasOwnProp(this, '_monthsRegex')) { + computeMonthsParse.call(this); + } + if (isStrict) { + return this._monthsStrictRegex; + } else { + return this._monthsRegex; + } + } else { + if (!hasOwnProp(this, '_monthsRegex')) { + this._monthsRegex = defaultMonthsRegex; + } + return this._monthsStrictRegex && isStrict + ? this._monthsStrictRegex + : this._monthsRegex; + } + } -function FrameDonut() { - var $previewDiv = $('
'); - $previewDiv.css({ - border: this.options.chartOption.chart.border, - background: this.options.chartOption.chart.background - }); - return $previewDiv; -} + function computeMonthsParse() { + function cmpLenRev(a, b) { + return b.length - a.length; + } -function ToolTip() { - var preViewText = ''; - if (this.options.chartOption.tooltip.triggerOn === 'click') preViewText = 'Click';else preViewText = 'Mouse Move'; - return preViewText; -} + var shortPieces = [], + longPieces = [], + mixedPieces = [], + i, + mom; + for (i = 0; i < 12; i++) { + // make the regex if we don't have it already + mom = createUTC([2000, i]); + shortPieces.push(this.monthsShort(mom, '')); + longPieces.push(this.months(mom, '')); + mixedPieces.push(this.months(mom, '')); + mixedPieces.push(this.monthsShort(mom, '')); + } + // Sorting makes sure if one month (or abbr) is a prefix of another it + // will match the longer piece. + shortPieces.sort(cmpLenRev); + longPieces.sort(cmpLenRev); + mixedPieces.sort(cmpLenRev); + for (i = 0; i < 12; i++) { + shortPieces[i] = regexEscape(shortPieces[i]); + longPieces[i] = regexEscape(longPieces[i]); + } + for (i = 0; i < 24; i++) { + mixedPieces[i] = regexEscape(mixedPieces[i]); + } -function Label() { - var labelShow = this.options.setting.labelSettings.some(function (setting) { - return setting.ref.show; + this._monthsRegex = new RegExp('^(' + mixedPieces.join('|') + ')', 'i'); + this._monthsShortRegex = this._monthsRegex; + this._monthsStrictRegex = new RegExp( + '^(' + longPieces.join('|') + ')', + 'i' + ); + this._monthsShortStrictRegex = new RegExp( + '^(' + shortPieces.join('|') + ')', + 'i' + ); + } + + // FORMATTING + + addFormatToken('Y', 0, 0, function () { + var y = this.year(); + return y <= 9999 ? zeroFill(y, 4) : '+' + y; }); - return labelShow ? 'ON' : 'OFF'; -} -function Figure() { - if (this.options.chartOption.chart.type != 'decisionTree') { - return; - } - var $previeDiv = $('
'); - var border = '3px solid ' + this.options.chartOption.plotOptions.decisionTree.style.link.color[0]; - $previeDiv.css({ - border: border, - 'background-color': this.options.chartOption.plotOptions.decisionTree.style.node.color[0] + addFormatToken(0, ['YY', 2], 0, function () { + return this.year() % 100; }); - return $previeDiv; -} -function MapData() { - var preViewText = this.options.chartOption.plotOptions.map.mapName || ''; - return preViewText; -} + addFormatToken(0, ['YYYY', 4], 0, 'year'); + addFormatToken(0, ['YYYYY', 5], 0, 'year'); + addFormatToken(0, ['YYYYYY', 6, true], 0, 'year'); -function MapStyle() { - var $previewbackDiv = $('
'); - $previewbackDiv.css({ - border: this.options.chartOption.chart.border, - background: this.options.chartOption.chart.background + // ALIASES + + addUnitAlias('year', 'y'); + + // PRIORITIES + + addUnitPriority('year', 1); + + // PARSING + + addRegexToken('Y', matchSigned); + addRegexToken('YY', match1to2, match2); + addRegexToken('YYYY', match1to4, match4); + addRegexToken('YYYYY', match1to6, match6); + addRegexToken('YYYYYY', match1to6, match6); + + addParseToken(['YYYYY', 'YYYYYY'], YEAR); + addParseToken('YYYY', function (input, array) { + array[YEAR] = + input.length === 2 ? hooks.parseTwoDigitYear(input) : toInt(input); }); - var $previewareaDiv = $('
'); - $previewareaDiv.css({ - border: this.options.chartOption.plotOptions.map.mapStyle.itemStyle.normal.borderColor, - background: this.options.chartOption.plotOptions.map.mapStyle.itemStyle.normal.areaColor + addParseToken('YY', function (input, array) { + array[YEAR] = hooks.parseTwoDigitYear(input); + }); + addParseToken('Y', function (input, array) { + array[YEAR] = parseInt(input, 10); }); - return $previewareaDiv; //$previewbackDiv;//+$previewareaDiv; -} -function Layers() { - var cntScatter = 0; - var cntLine = 0; - if (this.options.chartOption.plotOptions.map.layers.length > 0) { - for (var ele in this.options.chartOption.plotOptions.map.layers) { - if (this.options.chartOption.plotOptions.map.layers[ele].type == 'lines') { - cntLine++; - } else { - cntScatter++; - } - } - var previewText = ''; - if (cntScatter > 0) previewText += 'Point: ' + cntScatter; - if (cntLine > 0) previewText += ' Line: ' + cntLine; - return previewText; - } else return ''; -} + // HELPERS -function ToolTipTrigger() { - var preViewText = ''; - if (this.options.chartOption.tooltip.triggerOn === 'click') { - preViewText += 'Click'; - } else { - preViewText += 'Mouse Move'; - } - if (this.options.setting.tooltip.trigger === 'axis') { - preViewText += ', Axis'; - } else { - preViewText += ', Item'; + function daysInYear(year) { + return isLeapYear(year) ? 366 : 365; } - return preViewText; -} -function Grid() { - return ''; -} + // HOOKS -/***/ }), -/* 120 */ -/***/ (function(module, exports, __webpack_require__) { + hooks.parseTwoDigitYear = function (input) { + return toInt(input) + (toInt(input) > 68 ? 1900 : 2000); + }; -"use strict"; + // MOMENTS + var getSetYear = makeGetSet('FullYear', true); -Object.defineProperty(exports, "__esModule", { - value: true -}); + function getIsLeapYear() { + return isLeapYear(this.year()); + } -var _dialog = __webpack_require__(42); + function createDate(y, m, d, h, M, s, ms) { + // can't just apply() to create a date: + // https://stackoverflow.com/q/181348 + var date; + // the date constructor remaps years 0-99 to 1900-1999 + if (y < 100 && y >= 0) { + // preserve leap years using a full 400 year cycle, then reset + date = new Date(y + 400, m, d, h, M, s, ms); + if (isFinite(date.getFullYear())) { + date.setFullYear(y); + } + } else { + date = new Date(y, m, d, h, M, s, ms); + } -var _dialog2 = _interopRequireDefault(_dialog); + return date; + } -var _chartOptionConst = __webpack_require__(17); + function createUTCDate(y) { + var date, args; + // the Date.UTC function remaps years 0-99 to 1900-1999 + if (y < 100 && y >= 0) { + args = Array.prototype.slice.call(arguments); + // preserve leap years using a full 400 year cycle, then reset + args[0] = y + 400; + date = new Date(Date.UTC.apply(null, args)); + if (isFinite(date.getUTCFullYear())) { + date.setUTCFullYear(y); + } + } else { + date = new Date(Date.UTC.apply(null, arguments)); + } -var _chartOptionConst2 = _interopRequireDefault(_chartOptionConst); + return date; + } -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + // start-of-first-week - start-of-year + function firstWeekOffset(year, dow, doy) { + var // first-week day -- which january is always in the first week (4 for iso, 1 for other) + fwd = 7 + dow - doy, + // first-week day local weekday -- which local weekday is fwd + fwdlw = (7 + createUTCDate(year, 0, fwd).getUTCDay() - dow) % 7; -/** - * Created by mk90.kim on 2016-08-20. - */ + return -fwdlw + fwd - 1; + } -var selectableColumnType = Brightics.Chart.Validator.selectableColumnType; + // https://en.wikipedia.org/wiki/ISO_week_date#Calculating_a_date_given_the_year.2C_week_number_and_weekday + function dayOfYearFromWeeks(year, week, weekday, dow, doy) { + var localWeekday = (7 + weekday - dow) % 7, + weekOffset = firstWeekOffset(year, dow, doy), + dayOfYear = 1 + 7 * (week - 1) + localWeekday + weekOffset, + resYear, + resDayOfYear; + + if (dayOfYear <= 0) { + resYear = year - 1; + resDayOfYear = daysInYear(resYear) + dayOfYear; + } else if (dayOfYear > daysInYear(year)) { + resYear = year + 1; + resDayOfYear = dayOfYear - daysInYear(year); + } else { + resYear = year; + resDayOfYear = dayOfYear; + } -/** - * options = { - * column : [ - {name:'SepalLength','type':'number'}, - {name:'SepalWidth','type':'number'}, - {name:'PetalLength','type':'number'}, - {name:'PetalWidth','type':'number'}, - {name:'Species','type':'string'}, - {name:'Class','type':'string'}, - ], - selected: [{name: 'SepalLength', aggregation: 'none' }], - selectedIndex: 0, - unique: true, (default: false) - aggregation: true (default : false), - aggregationMandatory: false (aggregation이 true 일 경우만 사용 가능 default : false), - label: 'X-axis' (mandantory), - onChanged: function(){], - close: function(){} - * } - */ -var aggregationMap = _chartOptionConst2.default.AggregationMap; - -function ColumnSelectorDialog(parentId, options) { - _dialog2.default.call(this, parentId, options); -} + return { + year: resYear, + dayOfYear: resDayOfYear, + }; + } -ColumnSelectorDialog.prototype = Object.create(_dialog2.default.prototype); -ColumnSelectorDialog.prototype.constructor = ColumnSelectorDialog; + function weekOfYear(mom, dow, doy) { + var weekOffset = firstWeekOffset(mom.year(), dow, doy), + week = Math.floor((mom.dayOfYear() - weekOffset - 1) / 7) + 1, + resWeek, + resYear; + + if (week < 1) { + resYear = mom.year() - 1; + resWeek = week + weeksInYear(resYear, dow, doy); + } else if (week > weeksInYear(mom.year(), dow, doy)) { + resWeek = week - weeksInYear(mom.year(), dow, doy); + resYear = mom.year() + 1; + } else { + resYear = mom.year(); + resWeek = week; + } -ColumnSelectorDialog.prototype._getTitle = function () { - return 'Select Column' + (this.options.label ? '(' + this.options.label + ')' : ''); -}; + return { + week: resWeek, + year: resYear, + }; + } -ColumnSelectorDialog.prototype._getDefaultWindowOption = function () { - var _this = this; - var defaultOption = { - theme: _chartOptionConst2.default.Theme, - width: '300px', - height: '400px', - maxWidth: '300px', - maxHeight: '400px', - resizable: false, - initContent: function initContent() { - _this._configurePosition(); - _this._createDialogContentsArea(_this.$mainControl.find('.bo-dialogs-contents')); - } - }; - if (this.options.aggregationEnabled === true) { - defaultOption.width = '600px'; - defaultOption.maxWidth = '600px'; + function weeksInYear(year, dow, doy) { + var weekOffset = firstWeekOffset(year, dow, doy), + weekOffsetNext = firstWeekOffset(year + 1, dow, doy); + return (daysInYear(year) - weekOffset + weekOffsetNext) / 7; } - return defaultOption; -}; -ColumnSelectorDialog.prototype._createDialogContentsArea = function ($parent) { - this.$mainContainer = $('
'); - this.$columnArea = $('
'); - $parent.append(this.$mainContainer); - this.$mainContainer.append(this.$columnArea); + // FORMATTING - this._createFilterContents(this.$columnArea); - this._createColumnListContents(this.$columnArea); - this._createAggregationListContents(this.$mainContainer); - this._render(); -}; + addFormatToken('w', ['ww', 2], 'wo', 'week'); + addFormatToken('W', ['WW', 2], 'Wo', 'isoWeek'); -ColumnSelectorDialog.prototype._createFilterContents = function ($parent) { - var _this = this; - var $container = $('
'); - $parent.append($container); + // ALIASES - this.$filterControl = $(''); - $container.append(this.$filterControl); + addUnitAlias('week', 'w'); + addUnitAlias('isoWeek', 'W'); - this.$filterControl.jqxInput({ - theme: _chartOptionConst2.default.Theme, - placeHolder: "Search Column", - height: 31, - minLength: 1 - }); + // PRIORITIES - this.$filterControl.on('keyup search', function (event) { - var colList = _this.$columnListBox.find('.bo-dialog-column-item-wrapper'); - var searchText = _this.$filterControl.val().toLowerCase(); - for (var i = 0; i < colList.length; i++) { - var colName = $(colList[i]).attr('value').toLowerCase(); - if (colName.indexOf(searchText) < 0) { - $(colList[i]).hide(); - } else { - $(colList[i]).show(); - } - } + addUnitPriority('week', 5); + addUnitPriority('isoWeek', 5); + + // PARSING + + addRegexToken('w', match1to2); + addRegexToken('ww', match1to2, match2); + addRegexToken('W', match1to2); + addRegexToken('WW', match1to2, match2); + + addWeekParseToken(['w', 'ww', 'W', 'WW'], function ( + input, + week, + config, + token + ) { + week[token.substr(0, 1)] = toInt(input); }); -}; -ColumnSelectorDialog.prototype._createColumnListContents = function ($parent) { - var _this = this; - var $container = $('
'); - this.$columnListBox = $('
'); - $parent.append($container); - $container.append(this.$columnListBox); + // HELPERS - var columns = this._getColumns() || []; + // LOCALES - // this._createAxisTypeList(this.$columnListBox); + function localeWeek(mom) { + return weekOfYear(mom, this._week.dow, this._week.doy).week; + } - for (var i = 0; i < columns.length; i++) { - var columnType = this._getColumnType(columns, columns[i].name); - this._createColumnItem(columnType, columns[i]); + var defaultLocaleWeek = { + dow: 0, // Sunday is the first day of the week. + doy: 6, // The week that contains Jan 6th is the first week of the year. + }; + + function localeFirstDayOfWeek() { + return this._week.dow; } - this.$columnListBox.perfectScrollbar(); -}; -ColumnSelectorDialog.prototype._createColumnItem = function (columnType, column) { - var _this = this; - var $column; + function localeFirstDayOfYear() { + return this._week.doy; + } - if (columnType === 'SCHEMA') { - $column = $('
' + '
' + columnType + '
' + '
' + '
'); - } else { - $column = $('
' + '
' + columnType + '
' + '
' + '
'); + // MOMENTS + + function getSetWeek(input) { + var week = this.localeData().week(this); + return input == null ? week : this.add((input - week) * 7, 'd'); } - $column.find('.bos-dialog-column-item-label').text(column.name); - this.$columnListBox.append($column); + function getSetISOWeek(input) { + var week = weekOfYear(this, 1, 4).week; + return input == null ? week : this.add((input - week) * 7, 'd'); + } - $column.click(function () { - var selected = _this.options.selected, - selectedIndex = _this.options.selectedIndex; - selected[selectedIndex] = selected[selectedIndex] || {}; - selected[selectedIndex].value = column.name; - selected[selectedIndex].name = column.name; //$(this).attr('value'); - selected[selectedIndex].type = $(this).attr('type'); + // FORMATTING - _this._fillAggregationList(); - _this._setAggregation(); - _this._render(); - _this._bindColumnSelectOnChanged(); + addFormatToken('d', 0, 'do', 'day'); + + addFormatToken('dd', 0, 0, function (format) { + return this.localeData().weekdaysMin(this, format); }); -}; -ColumnSelectorDialog.prototype._getColumnType = function (columnList, columnName) { - var columnType = ''; - columnList.forEach(function (column) { - if (columnName === column.name) { - columnType = column.internalType || column.type; + addFormatToken('ddd', 0, 0, function (format) { + return this.localeData().weekdaysShort(this, format); + }); + + addFormatToken('dddd', 0, 0, function (format) { + return this.localeData().weekdays(this, format); + }); + + addFormatToken('e', 0, 0, 'weekday'); + addFormatToken('E', 0, 0, 'isoWeekday'); + + // ALIASES + + addUnitAlias('day', 'd'); + addUnitAlias('weekday', 'e'); + addUnitAlias('isoWeekday', 'E'); + + // PRIORITY + addUnitPriority('day', 11); + addUnitPriority('weekday', 11); + addUnitPriority('isoWeekday', 11); + + // PARSING + + addRegexToken('d', match1to2); + addRegexToken('e', match1to2); + addRegexToken('E', match1to2); + addRegexToken('dd', function (isStrict, locale) { + return locale.weekdaysMinRegex(isStrict); + }); + addRegexToken('ddd', function (isStrict, locale) { + return locale.weekdaysShortRegex(isStrict); + }); + addRegexToken('dddd', function (isStrict, locale) { + return locale.weekdaysRegex(isStrict); + }); + + addWeekParseToken(['dd', 'ddd', 'dddd'], function (input, week, config, token) { + var weekday = config._locale.weekdaysParse(input, token, config._strict); + // if we didn't get a weekday name, mark the date as invalid + if (weekday != null) { + week.d = weekday; + } else { + getParsingFlags(config).invalidWeekday = input; } }); - return columnType; -}; -ColumnSelectorDialog.prototype._bindColumnSelectOnChanged = function () { - if (typeof this.options.onChanged === 'function') { - this.options.onChanged(this.options.selected[this.options.selectedIndex]); + addWeekParseToken(['d', 'e', 'E'], function (input, week, config, token) { + week[token] = toInt(input); + }); + + // HELPERS + + function parseWeekday(input, locale) { + if (typeof input !== 'string') { + return input; + } + + if (!isNaN(input)) { + return parseInt(input, 10); + } + + input = locale.weekdaysParse(input); + if (typeof input === 'number') { + return input; + } + + return null; } -}; -ColumnSelectorDialog.prototype._createAggregationListContents = function ($parent) { - if (this.options.aggregationEnabled !== true) return; - this.$aggregationArea = $('
'); - $parent.append(this.$aggregationArea); - this._fillAggregationList(); -}; + function parseIsoWeekday(input, locale) { + if (typeof input === 'string') { + return locale.weekdaysParse(input) % 7 || 7; + } + return isNaN(input) ? null : input; + } -ColumnSelectorDialog.prototype._getCurrentColumnType = function () { - var selected = this.options.selected, - selectedIndex = this.options.selectedIndex; - var colName = selected[selectedIndex].name; - var columns = this._getColumns(); + // LOCALES + function shiftWeekdays(ws, n) { + return ws.slice(n, 7).concat(ws.slice(0, n)); + } - var foundCol = columns.find(function (columnObj) { - return columnObj.name === colName; - }); + var defaultLocaleWeekdays = 'Sunday_Monday_Tuesday_Wednesday_Thursday_Friday_Saturday'.split( + '_' + ), + defaultLocaleWeekdaysShort = 'Sun_Mon_Tue_Wed_Thu_Fri_Sat'.split('_'), + defaultLocaleWeekdaysMin = 'Su_Mo_Tu_We_Th_Fr_Sa'.split('_'), + defaultWeekdaysRegex = matchWord, + defaultWeekdaysShortRegex = matchWord, + defaultWeekdaysMinRegex = matchWord; + + function localeWeekdays(m, format) { + var weekdays = isArray(this._weekdays) + ? this._weekdays + : this._weekdays[ + m && m !== true && this._weekdays.isFormat.test(format) + ? 'format' + : 'standalone' + ]; + return m === true + ? shiftWeekdays(weekdays, this._week.dow) + : m + ? weekdays[m.day()] + : weekdays; + } - if (!foundCol || $.inArray(foundCol.type, selectableColumnType) < 0) { - return; - } else { - return foundCol.type; + function localeWeekdaysShort(m) { + return m === true + ? shiftWeekdays(this._weekdaysShort, this._week.dow) + : m + ? this._weekdaysShort[m.day()] + : this._weekdaysShort; } -}; -ColumnSelectorDialog.prototype._getColumns = function () { - if (typeof this.options.getColumns === 'function') { - return this.options.getColumns(); - } else { - return this.options.getColumns; + function localeWeekdaysMin(m) { + return m === true + ? shiftWeekdays(this._weekdaysMin, this._week.dow) + : m + ? this._weekdaysMin[m.day()] + : this._weekdaysMin; } -}; -ColumnSelectorDialog.prototype._fillAggregationList = function () { - var _this = this; - if (!this.$aggregationArea) return; - var selected = this.options.selected, - selectedIndex = this.options.selectedIndex, - aggregationList = []; - this.$aggregationArea.empty(); - if ($.isEmptyObject(selected[selectedIndex])) { - aggregationList = this.options.aggregationMap.number; - this.$aggregationArea.attr('selectable', 'false'); - } else { + function handleStrictParse$1(weekdayName, format, strict) { + var i, + ii, + mom, + llc = weekdayName.toLocaleLowerCase(); + if (!this._weekdaysParse) { + this._weekdaysParse = []; + this._shortWeekdaysParse = []; + this._minWeekdaysParse = []; + + for (i = 0; i < 7; ++i) { + mom = createUTC([2000, 1]).day(i); + this._minWeekdaysParse[i] = this.weekdaysMin( + mom, + '' + ).toLocaleLowerCase(); + this._shortWeekdaysParse[i] = this.weekdaysShort( + mom, + '' + ).toLocaleLowerCase(); + this._weekdaysParse[i] = this.weekdays(mom, '').toLocaleLowerCase(); + } + } - var colType = this._getCurrentColumnType(); - if ($.isEmptyObject(colType)) { - this.$aggregationArea.attr('selectable', 'false'); + if (strict) { + if (format === 'dddd') { + ii = indexOf.call(this._weekdaysParse, llc); + return ii !== -1 ? ii : null; + } else if (format === 'ddd') { + ii = indexOf.call(this._shortWeekdaysParse, llc); + return ii !== -1 ? ii : null; + } else { + ii = indexOf.call(this._minWeekdaysParse, llc); + return ii !== -1 ? ii : null; + } } else { - aggregationList = this.options.aggregationMap[colType]; - this.$aggregationArea.attr('selectable', 'true'); + if (format === 'dddd') { + ii = indexOf.call(this._weekdaysParse, llc); + if (ii !== -1) { + return ii; + } + ii = indexOf.call(this._shortWeekdaysParse, llc); + if (ii !== -1) { + return ii; + } + ii = indexOf.call(this._minWeekdaysParse, llc); + return ii !== -1 ? ii : null; + } else if (format === 'ddd') { + ii = indexOf.call(this._shortWeekdaysParse, llc); + if (ii !== -1) { + return ii; + } + ii = indexOf.call(this._weekdaysParse, llc); + if (ii !== -1) { + return ii; + } + ii = indexOf.call(this._minWeekdaysParse, llc); + return ii !== -1 ? ii : null; + } else { + ii = indexOf.call(this._minWeekdaysParse, llc); + if (ii !== -1) { + return ii; + } + ii = indexOf.call(this._weekdaysParse, llc); + if (ii !== -1) { + return ii; + } + ii = indexOf.call(this._shortWeekdaysParse, llc); + return ii !== -1 ? ii : null; + } } } - for (var i = 0; i < aggregationList.length; i++) { - var aggrObj = aggregationList[i]; - var $column = $('
' + aggrObj.label + '
'); - this.$aggregationArea.append($column); - $column.click(function () { - var selected = _this.options.selected, - selectedIndex = _this.options.selectedIndex; - selected[selectedIndex] = selected[selectedIndex] || {}; - selected[selectedIndex].aggregation = $(this).attr('value'); - _this._renderAggregationList(); - _this._bindColumnSelectOnChanged(); - }); + function localeWeekdaysParse(weekdayName, format, strict) { + var i, mom, regex; + + if (this._weekdaysParseExact) { + return handleStrictParse$1.call(this, weekdayName, format, strict); + } + + if (!this._weekdaysParse) { + this._weekdaysParse = []; + this._minWeekdaysParse = []; + this._shortWeekdaysParse = []; + this._fullWeekdaysParse = []; + } + + for (i = 0; i < 7; i++) { + // make the regex if we don't have it already + + mom = createUTC([2000, 1]).day(i); + if (strict && !this._fullWeekdaysParse[i]) { + this._fullWeekdaysParse[i] = new RegExp( + '^' + this.weekdays(mom, '').replace('.', '\\.?') + '$', + 'i' + ); + this._shortWeekdaysParse[i] = new RegExp( + '^' + this.weekdaysShort(mom, '').replace('.', '\\.?') + '$', + 'i' + ); + this._minWeekdaysParse[i] = new RegExp( + '^' + this.weekdaysMin(mom, '').replace('.', '\\.?') + '$', + 'i' + ); + } + if (!this._weekdaysParse[i]) { + regex = + '^' + + this.weekdays(mom, '') + + '|^' + + this.weekdaysShort(mom, '') + + '|^' + + this.weekdaysMin(mom, ''); + this._weekdaysParse[i] = new RegExp(regex.replace('.', ''), 'i'); + } + // test the regex + if ( + strict && + format === 'dddd' && + this._fullWeekdaysParse[i].test(weekdayName) + ) { + return i; + } else if ( + strict && + format === 'ddd' && + this._shortWeekdaysParse[i].test(weekdayName) + ) { + return i; + } else if ( + strict && + format === 'dd' && + this._minWeekdaysParse[i].test(weekdayName) + ) { + return i; + } else if (!strict && this._weekdaysParse[i].test(weekdayName)) { + return i; + } + } } - this._setAggregation(); -}; + // MOMENTS -ColumnSelectorDialog.prototype._getCurrentAggregationList = function () { - if (!this.$aggregationArea) return; - var aggregationItemList = this.$aggregationArea.find('.bo-dialog-column-item-wrapper'); - var currentAggregationList = []; - for (var i = 0; i < aggregationItemList.length; i++) { - currentAggregationList.push($(aggregationItemList[i]).attr('value')); + function getSetDayOfWeek(input) { + if (!this.isValid()) { + return input != null ? this : NaN; + } + var day = this._isUTC ? this._d.getUTCDay() : this._d.getDay(); + if (input != null) { + input = parseWeekday(input, this.localeData()); + return this.add(input - day, 'd'); + } else { + return day; + } } - return currentAggregationList; -}; -ColumnSelectorDialog.prototype._setAggregation = function () { - if (!this.$aggregationArea) return; - var selected = this.options.selected, - selectedIndex = this.options.selectedIndex; - if ($.isEmptyObject(selected[selectedIndex])) return; - var currentAggregationList = this._getCurrentAggregationList(); - if ($.isEmptyObject(selected[selectedIndex].aggregation) || currentAggregationList.indexOf(selected[selectedIndex].aggregation) < 0) { - selected[selectedIndex].aggregation = currentAggregationList[0]; + function getSetLocaleDayOfWeek(input) { + if (!this.isValid()) { + return input != null ? this : NaN; + } + var weekday = (this.day() + 7 - this.localeData()._week.dow) % 7; + return input == null ? weekday : this.add(input - weekday, 'd'); } -}; -ColumnSelectorDialog.prototype._render = function () { - this._renderColumnList(); - this._renderAggregationList(); -}; + function getSetISODayOfWeek(input) { + if (!this.isValid()) { + return input != null ? this : NaN; + } -ColumnSelectorDialog.prototype._renderColumnList = function () { - var selected = this.options.selected, - selectedIndex = this.options.selectedIndex; - if ($.isEmptyObject(selected[selectedIndex]) || $.isEmptyObject(selected[selectedIndex].name)) return; + // behaves the same as moment#day except + // as a getter, returns 7 instead of 0 (1-7 range instead of 0-6) + // as a setter, sunday should belong to the previous week. - this.$columnListBox.find('.selected').removeClass('selected'); - this.$columnListBox.find('.bo-dialog-column-item-wrapper[value="' + selected[selectedIndex].name + '"]').addClass('selected'); -}; -ColumnSelectorDialog.prototype._renderAggregationList = function () { - var selected = this.options.selected, - selectedIndex = this.options.selectedIndex; - if (!this.$aggregationArea) return; - if ($.isEmptyObject(selected[selectedIndex]) || $.isEmptyObject(selected[selectedIndex].aggregation)) return; - this.$aggregationArea.find('.selected').removeClass('selected'); - this.$aggregationArea.find('.bo-dialog-column-item-wrapper[value="' + selected[selectedIndex].aggregation + '"]').addClass('selected'); -}; - -ColumnSelectorDialog.prototype._getDialogResult = function () { - return this.options.selected; -}; - -exports.default = ColumnSelectorDialog; + if (input != null) { + var weekday = parseIsoWeekday(input, this.localeData()); + return this.day(this.day() % 7 ? weekday : weekday - 7); + } else { + return this.day() || 7; + } + } -/***/ }), -/* 121 */ -/***/ (function(module, exports, __webpack_require__) { + function weekdaysRegex(isStrict) { + if (this._weekdaysParseExact) { + if (!hasOwnProp(this, '_weekdaysRegex')) { + computeWeekdaysParse.call(this); + } + if (isStrict) { + return this._weekdaysStrictRegex; + } else { + return this._weekdaysRegex; + } + } else { + if (!hasOwnProp(this, '_weekdaysRegex')) { + this._weekdaysRegex = defaultWeekdaysRegex; + } + return this._weekdaysStrictRegex && isStrict + ? this._weekdaysStrictRegex + : this._weekdaysRegex; + } + } -"use strict"; + function weekdaysShortRegex(isStrict) { + if (this._weekdaysParseExact) { + if (!hasOwnProp(this, '_weekdaysRegex')) { + computeWeekdaysParse.call(this); + } + if (isStrict) { + return this._weekdaysShortStrictRegex; + } else { + return this._weekdaysShortRegex; + } + } else { + if (!hasOwnProp(this, '_weekdaysShortRegex')) { + this._weekdaysShortRegex = defaultWeekdaysShortRegex; + } + return this._weekdaysShortStrictRegex && isStrict + ? this._weekdaysShortStrictRegex + : this._weekdaysShortRegex; + } + } + function weekdaysMinRegex(isStrict) { + if (this._weekdaysParseExact) { + if (!hasOwnProp(this, '_weekdaysRegex')) { + computeWeekdaysParse.call(this); + } + if (isStrict) { + return this._weekdaysMinStrictRegex; + } else { + return this._weekdaysMinRegex; + } + } else { + if (!hasOwnProp(this, '_weekdaysMinRegex')) { + this._weekdaysMinRegex = defaultWeekdaysMinRegex; + } + return this._weekdaysMinStrictRegex && isStrict + ? this._weekdaysMinStrictRegex + : this._weekdaysMinRegex; + } + } -Object.defineProperty(exports, "__esModule", { - value: true -}); + function computeWeekdaysParse() { + function cmpLenRev(a, b) { + return b.length - a.length; + } -var _baseComplexWidget = __webpack_require__(36); + var minPieces = [], + shortPieces = [], + longPieces = [], + mixedPieces = [], + i, + mom, + minp, + shortp, + longp; + for (i = 0; i < 7; i++) { + // make the regex if we don't have it already + mom = createUTC([2000, 1]).day(i); + minp = regexEscape(this.weekdaysMin(mom, '')); + shortp = regexEscape(this.weekdaysShort(mom, '')); + longp = regexEscape(this.weekdays(mom, '')); + minPieces.push(minp); + shortPieces.push(shortp); + longPieces.push(longp); + mixedPieces.push(minp); + mixedPieces.push(shortp); + mixedPieces.push(longp); + } + // Sorting makes sure if one weekday (or abbr) is a prefix of another it + // will match the longer piece. + minPieces.sort(cmpLenRev); + shortPieces.sort(cmpLenRev); + longPieces.sort(cmpLenRev); + mixedPieces.sort(cmpLenRev); + + this._weekdaysRegex = new RegExp('^(' + mixedPieces.join('|') + ')', 'i'); + this._weekdaysShortRegex = this._weekdaysRegex; + this._weekdaysMinRegex = this._weekdaysRegex; + + this._weekdaysStrictRegex = new RegExp( + '^(' + longPieces.join('|') + ')', + 'i' + ); + this._weekdaysShortStrictRegex = new RegExp( + '^(' + shortPieces.join('|') + ')', + 'i' + ); + this._weekdaysMinStrictRegex = new RegExp( + '^(' + minPieces.join('|') + ')', + 'i' + ); + } -var _baseComplexWidget2 = _interopRequireDefault(_baseComplexWidget); + // FORMATTING -var _widgetFactory = __webpack_require__(3); + function hFormat() { + return this.hours() % 12 || 12; + } -var WidgetFactory = _interopRequireWildcard(_widgetFactory); + function kFormat() { + return this.hours() || 24; + } -function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } } + addFormatToken('H', ['HH', 2], 0, 'hour'); + addFormatToken('h', ['hh', 2], 0, hFormat); + addFormatToken('k', ['kk', 2], 0, kFormat); -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + addFormatToken('hmm', 0, 0, function () { + return '' + hFormat.apply(this) + zeroFill(this.minutes(), 2); + }); -/** - * options = { - * width: % or px default 100%, - * height:% or px default 100%, - * value: - * { - * left: 'px', - * right: 'px', - * top: 'px', - * bottom: 'px', - * }, (all value is px) - * position: ['left', 'top', 'right', 'bottom'], - * onChanged: function(){] - * } - * @param parentId - * @param options - * @constructor - */ -/** - * Created by mk90 on 2017-05-27. - */ + addFormatToken('hmmss', 0, 0, function () { + return ( + '' + + hFormat.apply(this) + + zeroFill(this.minutes(), 2) + + zeroFill(this.seconds(), 2) + ); + }); -function CenterPositionWidget(parentId, options) { - _baseComplexWidget2.default.call(this, parentId, options); - this.renderProblem(); -} + addFormatToken('Hmm', 0, 0, function () { + return '' + this.hours() + zeroFill(this.minutes(), 2); + }); -CenterPositionWidget.prototype = Object.create(_baseComplexWidget2.default.prototype); -CenterPositionWidget.prototype.constructor = CenterPositionWidget; + addFormatToken('Hmmss', 0, 0, function () { + return ( + '' + + this.hours() + + zeroFill(this.minutes(), 2) + + zeroFill(this.seconds(), 2) + ); + }); -CenterPositionWidget.prototype._init = function () { - _baseComplexWidget2.default.prototype._init.call(this); - this.baseIconAppended = false; - this.inputValue = {}; - this.selectedUnit = {}; - if (typeof this.options.getValue === 'function') { - for (var i in this.options.value) { - this.options.value[i] = this.options.getValue(this.options.value[i]); - } + function meridiem(token, lowercase) { + addFormatToken(token, 0, 0, function () { + return this.localeData().meridiem( + this.hours(), + this.minutes(), + lowercase + ); + }); } -}; - -CenterPositionWidget.prototype._createContents = function ($parent) { - this.$mainControl = $('' + '
' + '
' + '
' + '
'); - $parent.append(this.$mainControl); + meridiem('a', true); + meridiem('A', false); - var _this = this; - this.options.position.forEach(function (position) { - _this._createPositionContent(position); - }); -}; + // ALIASES -CenterPositionWidget.prototype._createPositionContent = function (position) { - var $targetArea, positionInputControl, defaultValue, tempDom; + addUnitAlias('hour', 'h'); - tempDom = $('
'); - this.$mainControl.find('.bo-component-frame-center').append(tempDom); - this._createPositionInputContent(tempDom, position); -}; + // PRIORITY + addUnitPriority('hour', 13); -CenterPositionWidget.prototype._createPositionInputContent = function ($parent, position) { - var value = this.options.value && this.options.value[position] ? this.options.value[position] + '' : ''; + // PARSING - var postFix = value.indexOf('%') >= 0 ? '%' : 'px'; - var numberValue = value.replace(postFix, ''); + function matchMeridiem(isStrict, locale) { + return locale._meridiemParse; + } - var _this = this; - this.inputValue[position] = numberValue; - this.selectedUnit[position] = postFix; - var numberInput = WidgetFactory.createNumberInputWidget($parent, { - labelPosition: 'row', - label: position.toLocaleString(), - labelWidth: '40px', - width: this.options.width || '50px', - height: this.options.height || '25px', - value: numberValue, - problemKeyList: this.options.problemKeyList, - onChanged: function onChanged(value) { - _this.inputValue[position] = value; - _this.options.onChanged[position](value || value === 0 ? value + _this.selectedUnit[position] : ''); - } + addRegexToken('a', matchMeridiem); + addRegexToken('A', matchMeridiem); + addRegexToken('H', match1to2); + addRegexToken('h', match1to2); + addRegexToken('k', match1to2); + addRegexToken('HH', match1to2, match2); + addRegexToken('hh', match1to2, match2); + addRegexToken('kk', match1to2, match2); + + addRegexToken('hmm', match3to4); + addRegexToken('hmmss', match5to6); + addRegexToken('Hmm', match3to4); + addRegexToken('Hmmss', match5to6); + + addParseToken(['H', 'HH'], HOUR); + addParseToken(['k', 'kk'], function (input, array, config) { + var kInput = toInt(input); + array[HOUR] = kInput === 24 ? 0 : kInput; }); - - var unitSwitchButton = WidgetFactory.createSwitchButtonWidget($parent, { - value: postFix, - width: '20px', - height: this.options.height || '25px', - margin: '2.5px 20px 2.5px 0px', - itemList: this.options.itemList || [{ value: 'px', label: 'Px' }, { value: '%', label: '%' }], - onChanged: function onChanged(changedUnit) { - _this.selectedUnit[position] = changedUnit; - _this.options.onChanged[position](_this.inputValue[position] ? _this.inputValue[position] + changedUnit : ''); - } + addParseToken(['a', 'A'], function (input, array, config) { + config._isPm = config._locale.isPM(input); + config._meridiem = input; }); - unitSwitchButton.addCSSInMainControl({ - 'border-left': 'none' + addParseToken(['h', 'hh'], function (input, array, config) { + array[HOUR] = toInt(input); + getParsingFlags(config).bigHour = true; }); - - this._widgetList.push(numberInput); - this._widgetList.push(unitSwitchButton); -}; - -CenterPositionWidget.prototype.renderProblem = function (problems) { - var _this = this; - this._widgetList.forEach(function (widget) { - widget.renderProblem(problems); + addParseToken('hmm', function (input, array, config) { + var pos = input.length - 2; + array[HOUR] = toInt(input.substr(0, pos)); + array[MINUTE] = toInt(input.substr(pos)); + getParsingFlags(config).bigHour = true; + }); + addParseToken('hmmss', function (input, array, config) { + var pos1 = input.length - 4, + pos2 = input.length - 2; + array[HOUR] = toInt(input.substr(0, pos1)); + array[MINUTE] = toInt(input.substr(pos1, 2)); + array[SECOND] = toInt(input.substr(pos2)); + getParsingFlags(config).bigHour = true; + }); + addParseToken('Hmm', function (input, array, config) { + var pos = input.length - 2; + array[HOUR] = toInt(input.substr(0, pos)); + array[MINUTE] = toInt(input.substr(pos)); + }); + addParseToken('Hmmss', function (input, array, config) { + var pos1 = input.length - 4, + pos2 = input.length - 2; + array[HOUR] = toInt(input.substr(0, pos1)); + array[MINUTE] = toInt(input.substr(pos1, 2)); + array[SECOND] = toInt(input.substr(pos2)); }); -}; -exports.default = CenterPositionWidget; + // LOCALES -/***/ }), -/* 122 */ -/***/ (function(module, exports, __webpack_require__) { + function localeIsPM(input) { + // IE8 Quirks Mode & IE7 Standards Mode do not allow accessing strings like arrays + // Using charAt should be more compatible. + return (input + '').toLowerCase().charAt(0) === 'p'; + } -"use strict"; + var defaultLocaleMeridiemParse = /[ap]\.?m?\.?/i, + // Setting the hour should keep the time, because the user explicitly + // specified which hour they want. So trying to maintain the same hour (in + // a new timezone) makes sense. Adding/subtracting hours does not follow + // this rule. + getSetHour = makeGetSet('Hours', true); + function localeMeridiem(hours, minutes, isLower) { + if (hours > 11) { + return isLower ? 'pm' : 'PM'; + } else { + return isLower ? 'am' : 'AM'; + } + } -Object.defineProperty(exports, "__esModule", { - value: true -}); + var baseConfig = { + calendar: defaultCalendar, + longDateFormat: defaultLongDateFormat, + invalidDate: defaultInvalidDate, + ordinal: defaultOrdinal, + dayOfMonthOrdinalParse: defaultDayOfMonthOrdinalParse, + relativeTime: defaultRelativeTime, -var _chartOptionBase = __webpack_require__(6); + months: defaultLocaleMonths, + monthsShort: defaultLocaleMonthsShort, -var _chartOptionBase2 = _interopRequireDefault(_chartOptionBase); + week: defaultLocaleWeek, -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + weekdays: defaultLocaleWeekdays, + weekdaysMin: defaultLocaleWeekdaysMin, + weekdaysShort: defaultLocaleWeekdaysShort, -function ColumnChartOption(parentId, options) { - _chartOptionBase2.default.call(this, parentId, options); -} /** - * Created by SDS on 2017-05-10. - */ + meridiemParse: defaultLocaleMeridiemParse, + }; -ColumnChartOption.prototype = Object.create(_chartOptionBase2.default.prototype); -ColumnChartOption.prototype.constructor = ColumnChartOption; + // internal storage for locale config files + var locales = {}, + localeFamilies = {}, + globalLocale; + + function commonPrefix(arr1, arr2) { + var i, + minl = Math.min(arr1.length, arr2.length); + for (i = 0; i < minl; i += 1) { + if (arr1[i] !== arr2[i]) { + return i; + } + } + return minl; + } -ColumnChartOption.prototype._init = function () { - _chartOptionBase2.default.prototype._init.call(this); - var columnSelectorSetting = this._createColumnSelectorSetting(); - columnSelectorSetting = this._configureColumnConf(columnSelectorSetting); - this.options.setting.columnSelector = columnSelectorSetting; - this.options.setting.stripLine = this.options.chartOption.plotOptions.column.stripLine; - this.options.setting.tooltip = this.options.chartOption.plotOptions.column.tooltip; - this.options.setting.scaleOpt = ['yaxis']; -}; + function normalizeLocale(key) { + return key ? key.toLowerCase().replace('_', '-') : key; + } -ColumnChartOption.prototype._createColumnSelectorSetting = function () { - return [[{ - key: 'xAxis', - ref: this.options.chartOption.xAxis[0] - }, { - key: 'yAxis', - ref: this.options.chartOption.yAxis[0] - }, { - key: 'colorBy', - ref: this.options.chartOption.colorBy[0] - }]]; -}; + // pick the locale from the array + // try ['en-au', 'en-gb'] as 'en-au', 'en-gb', 'en', as in move through the list trying each + // substring from most specific to least, but move to the next array item if it's a more specific variant than the current root + function chooseLocale(names) { + var i = 0, + j, + next, + locale, + split; + + while (i < names.length) { + split = normalizeLocale(names[i]).split('-'); + j = split.length; + next = normalizeLocale(names[i + 1]); + next = next ? next.split('-') : null; + while (j > 0) { + locale = loadLocale(split.slice(0, j).join('-')); + if (locale) { + return locale; + } + if ( + next && + next.length >= j && + commonPrefix(split, next) >= j - 1 + ) { + //the next array item is better than a shallower substring of this one + break; + } + j--; + } + i++; + } + return globalLocale; + } -ColumnChartOption.prototype.getDefaultControlContainerList = function () { - return ['Data', 'ToolTipTrigger', 'Title', 'Axis', 'Legend', 'Marker', 'Lines', 'Frame']; -}; + function loadLocale(name) { + var oldLocale = null, + aliasedRequire; + // TODO: Find a better way to register and load all the locales in Node + if ( + locales[name] === undefined && + typeof module !== 'undefined' && + module && + module.exports + ) { + try { + oldLocale = globalLocale._abbr; + aliasedRequire = require; + __webpack_require__(253)("./" + name); + getSetGlobalLocale(oldLocale); + } catch (e) { + // mark as not found to avoid repeating expensive file require call causing high CPU + // when trying to find en-US, en_US, en-us for every format call + locales[name] = null; // null means not found + } + } + return locales[name]; + } -exports.default = ColumnChartOption; + // This function will load locale and then set the global locale. If + // no arguments are passed in, it will simply return the current global + // locale key. + function getSetGlobalLocale(key, values) { + var data; + if (key) { + if (isUndefined(values)) { + data = getLocale(key); + } else { + data = defineLocale(key, values); + } -/***/ }), -/* 123 */ -/***/ (function(module, exports, __webpack_require__) { + if (data) { + // moment.duration._locale = moment._locale = data; + globalLocale = data; + } else { + if (typeof console !== 'undefined' && console.warn) { + //warn user if arguments are passed but the locale could not be set + console.warn( + 'Locale ' + key + ' not found. Did you forget to load it?' + ); + } + } + } -"use strict"; + return globalLocale._abbr; + } + function defineLocale(name, config) { + if (config !== null) { + var locale, + parentConfig = baseConfig; + config.abbr = name; + if (locales[name] != null) { + deprecateSimple( + 'defineLocaleOverride', + 'use moment.updateLocale(localeName, config) to change ' + + 'an existing locale. moment.defineLocale(localeName, ' + + 'config) should only be used for creating a new locale ' + + 'See http://momentjs.com/guides/#/warnings/define-locale/ for more info.' + ); + parentConfig = locales[name]._config; + } else if (config.parentLocale != null) { + if (locales[config.parentLocale] != null) { + parentConfig = locales[config.parentLocale]._config; + } else { + locale = loadLocale(config.parentLocale); + if (locale != null) { + parentConfig = locale._config; + } else { + if (!localeFamilies[config.parentLocale]) { + localeFamilies[config.parentLocale] = []; + } + localeFamilies[config.parentLocale].push({ + name: name, + config: config, + }); + return null; + } + } + } + locales[name] = new Locale(mergeConfigs(parentConfig, config)); -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.createFilter = createFilter; -exports.getFilterTypes = getFilterTypes; + if (localeFamilies[name]) { + localeFamilies[name].forEach(function (x) { + defineLocale(x.name, x.config); + }); + } -var _filterControlBase = __webpack_require__(76); + // backwards compat for now: also set the locale + // make sure we set the locale AFTER all child locales have been + // created, so we won't end up with the child locale set. + getSetGlobalLocale(name); -var _filterControlBase2 = _interopRequireDefault(_filterControlBase); + return locales[name]; + } else { + // useful for testing + delete locales[name]; + return null; + } + } -var _filterControlIndex = __webpack_require__(298); + function updateLocale(name, config) { + if (config != null) { + var locale, + tmpLocale, + parentConfig = baseConfig; -var FilterControl = _interopRequireWildcard(_filterControlIndex); + if (locales[name] != null && locales[name].parentLocale != null) { + // Update existing child locale in-place to avoid memory-leaks + locales[name].set(mergeConfigs(locales[name]._config, config)); + } else { + // MERGE + tmpLocale = loadLocale(name); + if (tmpLocale != null) { + parentConfig = tmpLocale._config; + } + config = mergeConfigs(parentConfig, config); + if (tmpLocale == null) { + // updateLocale is called for creating a new locale + // Set abbr so it will have a name (getters return + // undefined otherwise). + config.abbr = name; + } + locale = new Locale(config); + locale.parentLocale = locales[name]; + locales[name] = locale; + } -function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } } + // backwards compat for now: also set the locale + getSetGlobalLocale(name); + } else { + // pass null for config to unupdate, useful for tests + if (locales[name] != null) { + if (locales[name].parentLocale != null) { + locales[name] = locales[name].parentLocale; + if (name === getSetGlobalLocale()) { + getSetGlobalLocale(name); + } + } else if (locales[name] != null) { + delete locales[name]; + } + } + } + return locales[name]; + } -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + // returns locale data + function getLocale(key) { + var locale; -/** - * Source: filter-control-factory.js - * Created by ji_sung.park on 2017-09-25. - */ + if (key && key._locale && key._locale._abbr) { + key = key._locale._abbr; + } -function createFilter(parentId, type, options) { - var types = this.getFilterTypes(); - if (types.indexOf(type) === -1) return new _filterControlBase2.default(parentId, options);else return new FilterControl[type](parentId, options); -} + if (!key) { + return globalLocale; + } -function getFilterTypes() { - return Object.keys(FilterControl); -} + if (!isArray(key)) { + //short-circuit everything else + locale = loadLocale(key); + if (locale) { + return locale; + } + key = [key]; + } -/***/ }), -/* 124 */, -/* 125 */, -/* 126 */, -/* 127 */, -/* 128 */, -/* 129 */, -/* 130 */, -/* 131 */, -/* 132 */, -/* 133 */, -/* 134 */, -/* 135 */, -/* 136 */, -/* 137 */, -/* 138 */, -/* 139 */, -/* 140 */, -/* 141 */, -/* 142 */, -/* 143 */, -/* 144 */, -/* 145 */, -/* 146 */, -/* 147 */, -/* 148 */, -/* 149 */, -/* 150 */, -/* 151 */, -/* 152 */, -/* 153 */, -/* 154 */, -/* 155 */, -/* 156 */, -/* 157 */, -/* 158 */, -/* 159 */, -/* 160 */, -/* 161 */, -/* 162 */, -/* 163 */, -/* 164 */, -/* 165 */, -/* 166 */, -/* 167 */, -/* 168 */, -/* 169 */, -/* 170 */, -/* 171 */, -/* 172 */, -/* 173 */, -/* 174 */, -/* 175 */, -/* 176 */, -/* 177 */, -/* 178 */, -/* 179 */, -/* 180 */, -/* 181 */, -/* 182 */, -/* 183 */, -/* 184 */, -/* 185 */, -/* 186 */, -/* 187 */, -/* 188 */, -/* 189 */, -/* 190 */, -/* 191 */, -/* 192 */, -/* 193 */, -/* 194 */, -/* 195 */, -/* 196 */, -/* 197 */, -/* 198 */, -/* 199 */, -/* 200 */, -/* 201 */, -/* 202 */, -/* 203 */, -/* 204 */, -/* 205 */, -/* 206 */, -/* 207 */, -/* 208 */, -/* 209 */, -/* 210 */, -/* 211 */, -/* 212 */, -/* 213 */, -/* 214 */, -/* 215 */, -/* 216 */, -/* 217 */, -/* 218 */, -/* 219 */, -/* 220 */, -/* 221 */, -/* 222 */, -/* 223 */, -/* 224 */, -/* 225 */, -/* 226 */, -/* 227 */, -/* 228 */, -/* 229 */, -/* 230 */, -/* 231 */, -/* 232 */, -/* 233 */, -/* 234 */, -/* 235 */, -/* 236 */, -/* 237 */, -/* 238 */, -/* 239 */, -/* 240 */, -/* 241 */, -/* 242 */, -/* 243 */, -/* 244 */, -/* 245 */, -/* 246 */, -/* 247 */, -/* 248 */, -/* 249 */, -/* 250 */, -/* 251 */, -/* 252 */, -/* 253 */, -/* 254 */, -/* 255 */, -/* 256 */, -/* 257 */, -/* 258 */, -/* 259 */, -/* 260 */, -/* 261 */, -/* 262 */, -/* 263 */, -/* 264 */, -/* 265 */, -/* 266 */, -/* 267 */, -/* 268 */, -/* 269 */, -/* 270 */, -/* 271 */, -/* 272 */ -/***/ (function(module, exports, __webpack_require__) { + return chooseLocale(key); + } -"use strict"; + function listLocales() { + return keys(locales); + } + function checkOverflow(m) { + var overflow, + a = m._a; + + if (a && getParsingFlags(m).overflow === -2) { + overflow = + a[MONTH] < 0 || a[MONTH] > 11 + ? MONTH + : a[DATE] < 1 || a[DATE] > daysInMonth(a[YEAR], a[MONTH]) + ? DATE + : a[HOUR] < 0 || + a[HOUR] > 24 || + (a[HOUR] === 24 && + (a[MINUTE] !== 0 || + a[SECOND] !== 0 || + a[MILLISECOND] !== 0)) + ? HOUR + : a[MINUTE] < 0 || a[MINUTE] > 59 + ? MINUTE + : a[SECOND] < 0 || a[SECOND] > 59 + ? SECOND + : a[MILLISECOND] < 0 || a[MILLISECOND] > 999 + ? MILLISECOND + : -1; + + if ( + getParsingFlags(m)._overflowDayOfYear && + (overflow < YEAR || overflow > DATE) + ) { + overflow = DATE; + } + if (getParsingFlags(m)._overflowWeeks && overflow === -1) { + overflow = WEEK; + } + if (getParsingFlags(m)._overflowWeekday && overflow === -1) { + overflow = WEEKDAY; + } -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.Data = Data; -exports.ComplexData = ComplexData; -exports.Format = Format; -exports.FormatCard = FormatCard; -exports.Title = Title; -exports.Axis = Axis; -exports.AxisR = AxisR; -exports.AxisEach = AxisEach; -exports.AxisRadar = AxisRadar; -exports.Legend = Legend; -exports.VisualMap = VisualMap; -exports.Marker = Marker; -exports.MarkerLine = MarkerLine; -exports.Lines = Lines; -exports.LinesWithTrend = LinesWithTrend; -exports.Frame = Frame; -exports.FrameStyle = FrameStyle; -exports.FramePie = FramePie; -exports.FrameDonut = FrameDonut; -exports.ToolTip = ToolTip; -exports.Label = Label; -exports.Figure = Figure; -exports.MapData = MapData; -exports.Layers = Layers; -exports.MapStyle = MapStyle; -exports.ToolTipTrigger = ToolTipTrigger; -exports.Grid = Grid; + getParsingFlags(m).overflow = overflow; + } -var _controlContainer = __webpack_require__(273); + return m; + } -var _controlContainer2 = _interopRequireDefault(_controlContainer); + // iso 8601 regex + // 0000-00-00 0000-W00 or 0000-W00-0 + T + 00 or 00:00 or 00:00:00 or 00:00:00.000 + +00:00 or +0000 or +00) + var extendedIsoRegex = /^\s*((?:[+-]\d{6}|\d{4})-(?:\d\d-\d\d|W\d\d-\d|W\d\d|\d\d\d|\d\d))(?:(T| )(\d\d(?::\d\d(?::\d\d(?:[.,]\d+)?)?)?)([+-]\d\d(?::?\d\d)?|\s*Z)?)?$/, + basicIsoRegex = /^\s*((?:[+-]\d{6}|\d{4})(?:\d\d\d\d|W\d\d\d|W\d\d|\d\d\d|\d\d|))(?:(T| )(\d\d(?:\d\d(?:\d\d(?:[.,]\d+)?)?)?)([+-]\d\d(?::?\d\d)?|\s*Z)?)?$/, + tzRegex = /Z|[+-]\d\d(?::?\d\d)?/, + isoDates = [ + ['YYYYYY-MM-DD', /[+-]\d{6}-\d\d-\d\d/], + ['YYYY-MM-DD', /\d{4}-\d\d-\d\d/], + ['GGGG-[W]WW-E', /\d{4}-W\d\d-\d/], + ['GGGG-[W]WW', /\d{4}-W\d\d/, false], + ['YYYY-DDD', /\d{4}-\d{3}/], + ['YYYY-MM', /\d{4}-\d\d/, false], + ['YYYYYYMMDD', /[+-]\d{10}/], + ['YYYYMMDD', /\d{8}/], + ['GGGG[W]WWE', /\d{4}W\d{3}/], + ['GGGG[W]WW', /\d{4}W\d{2}/, false], + ['YYYYDDD', /\d{7}/], + ['YYYYMM', /\d{6}/, false], + ['YYYY', /\d{4}/, false], + ], + // iso time formats and regexes + isoTimes = [ + ['HH:mm:ss.SSSS', /\d\d:\d\d:\d\d\.\d+/], + ['HH:mm:ss,SSSS', /\d\d:\d\d:\d\d,\d+/], + ['HH:mm:ss', /\d\d:\d\d:\d\d/], + ['HH:mm', /\d\d:\d\d/], + ['HHmmss.SSSS', /\d\d\d\d\d\d\.\d+/], + ['HHmmss,SSSS', /\d\d\d\d\d\d,\d+/], + ['HHmmss', /\d\d\d\d\d\d/], + ['HHmm', /\d\d\d\d/], + ['HH', /\d\d/], + ], + aspNetJsonRegex = /^\/?Date\((-?\d+)/i, + // RFC 2822 regex: For details see https://tools.ietf.org/html/rfc2822#section-3.3 + rfc2822 = /^(?:(Mon|Tue|Wed|Thu|Fri|Sat|Sun),?\s)?(\d{1,2})\s(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)\s(\d{2,4})\s(\d\d):(\d\d)(?::(\d\d))?\s(?:(UT|GMT|[ECMP][SD]T)|([Zz])|([+-]\d{4}))$/, + obsOffsets = { + UT: 0, + GMT: 0, + EDT: -4 * 60, + EST: -5 * 60, + CDT: -5 * 60, + CST: -6 * 60, + MDT: -6 * 60, + MST: -7 * 60, + PDT: -7 * 60, + PST: -8 * 60, + }; -var _controlFactory = __webpack_require__(563); + // date from iso format + function configFromISO(config) { + var i, + l, + string = config._i, + match = extendedIsoRegex.exec(string) || basicIsoRegex.exec(string), + allowTime, + dateFormat, + timeFormat, + tzFormat; + + if (match) { + getParsingFlags(config).iso = true; + + for (i = 0, l = isoDates.length; i < l; i++) { + if (isoDates[i][1].exec(match[1])) { + dateFormat = isoDates[i][0]; + allowTime = isoDates[i][2] !== false; + break; + } + } + if (dateFormat == null) { + config._isValid = false; + return; + } + if (match[3]) { + for (i = 0, l = isoTimes.length; i < l; i++) { + if (isoTimes[i][1].exec(match[3])) { + // match[2] should be 'T' or space + timeFormat = (match[2] || ' ') + isoTimes[i][0]; + break; + } + } + if (timeFormat == null) { + config._isValid = false; + return; + } + } + if (!allowTime && timeFormat != null) { + config._isValid = false; + return; + } + if (match[4]) { + if (tzRegex.exec(match[4])) { + tzFormat = 'Z'; + } else { + config._isValid = false; + return; + } + } + config._f = dateFormat + (timeFormat || '') + (tzFormat || ''); + configFromStringAndFormat(config); + } else { + config._isValid = false; + } + } -var ControlFactory = _interopRequireWildcard(_controlFactory); + function extractFromRFC2822Strings( + yearStr, + monthStr, + dayStr, + hourStr, + minuteStr, + secondStr + ) { + var result = [ + untruncateYear(yearStr), + defaultLocaleMonthsShort.indexOf(monthStr), + parseInt(dayStr, 10), + parseInt(hourStr, 10), + parseInt(minuteStr, 10), + ]; + + if (secondStr) { + result.push(parseInt(secondStr, 10)); + } -function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } } + return result; + } -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + function untruncateYear(yearStr) { + var year = parseInt(yearStr, 10); + if (year <= 49) { + return 2000 + year; + } else if (year <= 999) { + return 1900 + year; + } + return year; + } -/** - * Created by SDS on 2017-05-10. - */ + function preprocessRFC2822(s) { + // Remove comments and folding whitespace and replace multiple-spaces with a single space + return s + .replace(/\([^)]*\)|[\n\t]/g, ' ') + .replace(/(\s\s+)/g, ' ') + .replace(/^\s\s*/, '') + .replace(/\s\s*$/, ''); + } -var expanderStatus = {}; + function checkWeekday(weekdayStr, parsedInput, config) { + if (weekdayStr) { + // TODO: Replace the vanilla JS Date object with an independent day-of-week check. + var weekdayProvided = defaultLocaleWeekdaysShort.indexOf(weekdayStr), + weekdayActual = new Date( + parsedInput[0], + parsedInput[1], + parsedInput[2] + ).getDay(); + if (weekdayProvided !== weekdayActual) { + getParsingFlags(config).weekdayMismatch = true; + config._isValid = false; + return false; + } + } + return true; + } -function Data(previewText) { - return new _controlContainer2.default({ - headerKey: 'Data', - headerLabel: 'Data', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - controlList.push(ControlFactory.createColumnSelectControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} + function calculateOffset(obsOffset, militaryOffset, numOffset) { + if (obsOffset) { + return obsOffsets[obsOffset]; + } else if (militaryOffset) { + // the only allowed military tz is Z + return 0; + } else { + var hm = parseInt(numOffset, 10), + m = hm % 100, + h = (hm - m) / 100; + return h * 60 + m; + } + } -function ComplexData(previewText) { - return new _controlContainer2.default({ - headerKey: 'Data', - headerLabel: 'Chart Type', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - controlList.push(ControlFactory.createColumnSelectControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} + // date and time from ref 2822 format + function configFromRFC2822(config) { + var match = rfc2822.exec(preprocessRFC2822(config._i)), + parsedArray; + if (match) { + parsedArray = extractFromRFC2822Strings( + match[4], + match[3], + match[2], + match[5], + match[6], + match[7] + ); + if (!checkWeekday(match[1], parsedArray, config)) { + return; + } -function Format(previewText) { - return new _controlContainer2.default({ - headerKey: 'Format', - headerLabel: 'Formatter', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - controlList.push(ControlFactory.createFormatControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} + config._a = parsedArray; + config._tzm = calculateOffset(match[8], match[9], match[10]); -function FormatCard(previewText) { - return new _controlContainer2.default({ - headerKey: 'FormatCard', - headerLabel: 'Formatter', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - controlList.push(ControlFactory.createFormatCardControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} + config._d = createUTCDate.apply(null, config._a); + config._d.setUTCMinutes(config._d.getUTCMinutes() - config._tzm); -function Title(previewText) { - return new _controlContainer2.default({ - headerKey: 'Title', - headerLabel: 'Title', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - controlList.push(ControlFactory.createTitleControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} + getParsingFlags(config).rfc2822 = true; + } else { + config._isValid = false; + } + } -function Axis(previewText) { - var _this = this; - return new _controlContainer2.default({ - headerKey: 'Axis', - headerLabel: 'Axis Label', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - //TODO: chart-validator-base()에 _validateAxisViewRange 주석도 같이해제할것. - //TODO: 2017 하반기 PVR 기능 검증 문제로 주석처리함 - // controlList.push(ControlFactory.createAxisViewRangeControl($contentsArea, option, headerKey)); - controlList.push(ControlFactory.createAxisTitleControl($contentsArea, option, headerKey)); - controlList.push(ControlFactory.createAxisLabelControl($contentsArea, option, headerKey)); - controlList.push(ControlFactory.createAxisScaleControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} + // date from 1) ASP.NET, 2) ISO, 3) RFC 2822 formats, or 4) optional fallback if parsing isn't strict + function configFromString(config) { + var matched = aspNetJsonRegex.exec(config._i); + if (matched !== null) { + config._d = new Date(+matched[1]); + return; + } -function AxisR(previewText) { - return new _controlContainer2.default({ - headerKey: 'Axis', - headerLabel: 'Axis Label', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - controlList.push(ControlFactory.createAxisViewRangeControl($contentsArea, option, headerKey)); - controlList.push(ControlFactory.createAxisTitleControl($contentsArea, option, headerKey)); - controlList.push(ControlFactory.createAxisLabelControl($contentsArea, option, headerKey)); - controlList.push(ControlFactory.createAxisScaleControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} + configFromISO(config); + if (config._isValid === false) { + delete config._isValid; + } else { + return; + } -function AxisEach(previewText) { - var _this = this; - return new _controlContainer2.default({ - headerKey: 'Axis', - headerLabel: 'Axis Label', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - //TODO: chart-validator-base()에 _validateAxisViewRange 주석도 같이해제할것. - //TODO: 2017 하반기 PVR 기능 검증 문제로 주석처리함 - // controlList.push(ControlFactory.createAxisViewRangeControl($contentsArea, option, headerKey)); - controlList.push(ControlFactory.createAxisTitleSeparatedControl($contentsArea, option, headerKey)); - controlList.push(ControlFactory.createAxisLabelSeparatedControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} + configFromRFC2822(config); + if (config._isValid === false) { + delete config._isValid; + } else { + return; + } -function AxisRadar(previewText) { - return new _controlContainer2.default({ - headerKey: 'AxisRadar', - headerLabel: 'Axis Label', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - controlList.push(ControlFactory.createAxisRadarViewRangeControl($contentsArea, option, headerKey)); - controlList.push(ControlFactory.createAxisRadarLabelControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} + if (config._strict) { + config._isValid = false; + } else { + // Final attempt, use Input Fallback + hooks.createFromInputFallback(config); + } + } -function Legend(previewText) { - return new _controlContainer2.default({ - headerKey: 'Legend', - headerLabel: 'Legend', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - controlList.push(ControlFactory.createCustomLegendControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} + hooks.createFromInputFallback = deprecate( + 'value provided is not in a recognized RFC2822 or ISO format. moment construction falls back to js Date(), ' + + 'which is not reliable across all browsers and versions. Non RFC2822/ISO date formats are ' + + 'discouraged and will be removed in an upcoming major release. Please refer to ' + + 'http://momentjs.com/guides/#/warnings/js-date/ for more info.', + function (config) { + config._d = new Date(config._i + (config._useUTC ? ' UTC' : '')); + } + ); -function VisualMap(previewText) { - return new _controlContainer2.default({ - headerKey: 'VisualMap', - headerLabel: 'Legend', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - // controlList.push(ControlFactory.createVisualMapControl($contentsArea, option, headerKey)); - controlList.push(ControlFactory.createVisualMapAlignControl($contentsArea, option, headerKey)); - controlList.push(ControlFactory.createVisualMapValueControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} + // Pick the first defined of two or three arguments. + function defaults(a, b, c) { + if (a != null) { + return a; + } + if (b != null) { + return b; + } + return c; + } -function Marker(previewText) { - return new _controlContainer2.default({ - headerKey: 'Marker', - headerLabel: 'Style', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - controlList.push(ControlFactory.createMarkerControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} + function currentDateArray(config) { + // hooks is actually the exported moment object + var nowValue = new Date(hooks.now()); + if (config._useUTC) { + return [ + nowValue.getUTCFullYear(), + nowValue.getUTCMonth(), + nowValue.getUTCDate(), + ]; + } + return [nowValue.getFullYear(), nowValue.getMonth(), nowValue.getDate()]; + } -function MarkerLine(previewText) { - return new _controlContainer2.default({ - headerKey: 'MarkerLine', - headerLabel: 'Style', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - controlList.push(ControlFactory.createMarkerLineControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} + // convert an array to a date. + // the array should mirror the parameters below + // note: all values past the year are optional and will default to the lowest possible value. + // [year, month, day , hour, minute, second, millisecond] + function configFromArray(config) { + var i, + date, + input = [], + currentDate, + expectedWeekday, + yearToUse; + + if (config._d) { + return; + } -function Lines(previewText) { - return new _controlContainer2.default({ - headerKey: 'Lines', - headerLabel: 'Lines', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - controlList.push(ControlFactory.createStripLineControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} + currentDate = currentDateArray(config); -function LinesWithTrend(previewText) { - return new _controlContainer2.default({ - headerKey: 'LinesWithTrend', - headerLabel: 'Lines', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - controlList.push(ControlFactory.createStripLineControl($contentsArea, option, headerKey)); - controlList.push(ControlFactory.createTrendLineControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} + //compute day of the year from weeks and weekdays + if (config._w && config._a[DATE] == null && config._a[MONTH] == null) { + dayOfYearFromWeekInfo(config); + } -function Frame(previewText) { - return new _controlContainer2.default({ - headerKey: 'Frame', - headerLabel: 'Frame', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - controlList.push(ControlFactory.createFrameControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} + //if the day of the year is set, figure out what it is + if (config._dayOfYear != null) { + yearToUse = defaults(config._a[YEAR], currentDate[YEAR]); -function FrameStyle(previewText) { - return new _controlContainer2.default({ - headerKey: 'Frame', - headerLabel: 'Frame', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - controlList.push(ControlFactory.createFrameStyleControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} + if ( + config._dayOfYear > daysInYear(yearToUse) || + config._dayOfYear === 0 + ) { + getParsingFlags(config)._overflowDayOfYear = true; + } -function FramePie(previewText) { - return new _controlContainer2.default({ - headerKey: 'Frame', - headerLabel: 'Frame', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - controlList.push(ControlFactory.createFramePieControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} + date = createUTCDate(yearToUse, 0, config._dayOfYear); + config._a[MONTH] = date.getUTCMonth(); + config._a[DATE] = date.getUTCDate(); + } -function FrameDonut(previewText) { - return new _controlContainer2.default({ - headerKey: 'Frame', - headerLabel: 'Frame', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - controlList.push(ControlFactory.createFrameDonutControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} - -function ToolTip(previewText) { - return new _controlContainer2.default({ - headerKey: 'ToolTip', - headerLabel: 'ToolTip', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - controlList.push(ControlFactory.createToolTipControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} - -function Label(previewText) { - return new _controlContainer2.default({ - headerKey: 'Label', - headerLabel: 'Label', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - controlList.push(ControlFactory.createLabelControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} - -function Figure(previewText) { - return new _controlContainer2.default({ - headerKey: 'Figure', - headerLabel: 'Style', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - controlList.push(ControlFactory.createFigureControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} + // Default to current date. + // * if no year, month, day of month are given, default to today + // * if day of month is given, default month and year + // * if month is given, default only year + // * if year is given, don't default anything + for (i = 0; i < 3 && config._a[i] == null; ++i) { + config._a[i] = input[i] = currentDate[i]; + } -function MapData(previewText) { - return new _controlContainer2.default({ - headerKey: 'Data', - headerLabel: 'Data', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - controlList.push(ControlFactory.createMapSelectControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} + // Zero out whatever was not defaulted, including time + for (; i < 7; i++) { + config._a[i] = input[i] = + config._a[i] == null ? (i === 2 ? 1 : 0) : config._a[i]; + } -function Layers(previewText) { - return new _controlContainer2.default({ - headerKey: 'Layers', - headerLabel: 'Layers', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - controlList.push(ControlFactory.createLayersControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} + // Check for 24:00:00.000 + if ( + config._a[HOUR] === 24 && + config._a[MINUTE] === 0 && + config._a[SECOND] === 0 && + config._a[MILLISECOND] === 0 + ) { + config._nextDay = true; + config._a[HOUR] = 0; + } -function MapStyle(previewText) { - return new _controlContainer2.default({ - headerKey: 'MapStyle', - headerLabel: 'Map Style', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - //TODO: make new Control about headerKey('MapStyle') - controlList.push(ControlFactory.createFrameMapControl($contentsArea, option, headerKey)); - controlList.push(ControlFactory.createAreaControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} + config._d = (config._useUTC ? createUTCDate : createDate).apply( + null, + input + ); + expectedWeekday = config._useUTC + ? config._d.getUTCDay() + : config._d.getDay(); + + // Apply timezone offset from input. The actual utcOffset can be changed + // with parseZone. + if (config._tzm != null) { + config._d.setUTCMinutes(config._d.getUTCMinutes() - config._tzm); + } -function ToolTipTrigger(previewText) { - return new _controlContainer2.default({ - headerKey: 'ToolTipTrigger', - headerLabel: 'ToolTip', - headerPreviewText: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - controlList.push(ControlFactory.createToolTipTriggerControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} + if (config._nextDay) { + config._a[HOUR] = 24; + } -function Grid(previewText) { - return new _controlContainer2.default({ - headerKey: 'Grid', - headerLabel: 'Grid', - headerPreviewTExt: previewText, - $parent: this.$parent, - contentInit: function contentInit($contentsArea, headerKey, option, controlList) { - controlList.push(ControlFactory.createGridControl($contentsArea, option, headerKey)); - }, - optionRef: this.options, - expanderStatus: expanderStatus - }); -} + // check for mismatching day of week + if ( + config._w && + typeof config._w.d !== 'undefined' && + config._w.d !== expectedWeekday + ) { + getParsingFlags(config).weekdayMismatch = true; + } + } -/***/ }), -/* 273 */ -/***/ (function(module, exports, __webpack_require__) { + function dayOfYearFromWeekInfo(config) { + var w, weekYear, week, weekday, dow, doy, temp, weekdayOverflow, curWeek; + + w = config._w; + if (w.GG != null || w.W != null || w.E != null) { + dow = 1; + doy = 4; + + // TODO: We need to take the current isoWeekYear, but that depends on + // how we interpret now (local, utc, fixed offset). So create + // a now version of current config (take local/utc/offset flags, and + // create now). + weekYear = defaults( + w.GG, + config._a[YEAR], + weekOfYear(createLocal(), 1, 4).year + ); + week = defaults(w.W, 1); + weekday = defaults(w.E, 1); + if (weekday < 1 || weekday > 7) { + weekdayOverflow = true; + } + } else { + dow = config._locale._week.dow; + doy = config._locale._week.doy; -"use strict"; + curWeek = weekOfYear(createLocal(), dow, doy); + weekYear = defaults(w.gg, config._a[YEAR], curWeek.year); -Object.defineProperty(exports, "__esModule", { - value: true -}); + // Default to current week. + week = defaults(w.w, curWeek.week); -var _chartOptionConst = __webpack_require__(17); + if (w.d != null) { + // weekday -- low day numbers are considered next week + weekday = w.d; + if (weekday < 0 || weekday > 6) { + weekdayOverflow = true; + } + } else if (w.e != null) { + // local weekday -- counting starts from beginning of week + weekday = w.e + dow; + if (w.e < 0 || w.e > 6) { + weekdayOverflow = true; + } + } else { + // default to beginning of week + weekday = dow; + } + } + if (week < 1 || week > weeksInYear(weekYear, dow, doy)) { + getParsingFlags(config)._overflowWeeks = true; + } else if (weekdayOverflow != null) { + getParsingFlags(config)._overflowWeekday = true; + } else { + temp = dayOfYearFromWeeks(weekYear, week, weekday, dow, doy); + config._a[YEAR] = temp.year; + config._dayOfYear = temp.dayOfYear; + } + } -var _chartOptionConst2 = _interopRequireDefault(_chartOptionConst); + // constant that refers to the ISO standard + hooks.ISO_8601 = function () {}; -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + // constant that refers to the RFC 2822 form + hooks.RFC_2822 = function () {}; -// var containerOption = { -// headerLabel: '', //expander header label -// headerPreviewText: '', //expander header preview text -// $parent: '', -// contentInitCallback: function () {}, // create control function -// optionRef: this.options // content parameter when init(ex.chart option) -// }; -function ControlContainer(containerOption) { - this._init(); - this._createExpander(containerOption); -} /** - * Created by SDS on 2017-05-10. - */ + // date from string and format string + function configFromStringAndFormat(config) { + // TODO: Move this to another part of the creation flow to prevent circular deps + if (config._f === hooks.ISO_8601) { + configFromISO(config); + return; + } + if (config._f === hooks.RFC_2822) { + configFromRFC2822(config); + return; + } + config._a = []; + getParsingFlags(config).empty = true; + + // This array is used to make a Date, either with `new Date` or `Date.UTC` + var string = '' + config._i, + i, + parsedInput, + tokens, + token, + skipped, + stringLength = string.length, + totalParsedInputLength = 0, + era; + + tokens = + expandFormat(config._f, config._locale).match(formattingTokens) || []; + + for (i = 0; i < tokens.length; i++) { + token = tokens[i]; + parsedInput = (string.match(getParseRegexForToken(token, config)) || + [])[0]; + if (parsedInput) { + skipped = string.substr(0, string.indexOf(parsedInput)); + if (skipped.length > 0) { + getParsingFlags(config).unusedInput.push(skipped); + } + string = string.slice( + string.indexOf(parsedInput) + parsedInput.length + ); + totalParsedInputLength += parsedInput.length; + } + // don't parse if it's not a known token + if (formatTokenFunctions[token]) { + if (parsedInput) { + getParsingFlags(config).empty = false; + } else { + getParsingFlags(config).unusedTokens.push(token); + } + addTimeToArrayFromToken(token, parsedInput, config); + } else if (config._strict && !parsedInput) { + getParsingFlags(config).unusedTokens.push(token); + } + } -ControlContainer.prototype._init = function () { - this._controlList = []; //render -}; + // add remaining unparsed input length to the string + getParsingFlags(config).charsLeftOver = + stringLength - totalParsedInputLength; + if (string.length > 0) { + getParsingFlags(config).unusedInput.push(string); + } -ControlContainer.prototype._createExpander = function (containerOption) { - var _this = this; + // clear _12h flag if hour is <= 12 + if ( + config._a[HOUR] <= 12 && + getParsingFlags(config).bigHour === true && + config._a[HOUR] > 0 + ) { + getParsingFlags(config).bigHour = undefined; + } - this.$propertyControl = $('' + '
' + '
' + '
' + '
'); + getParsingFlags(config).parsedDateParts = config._a.slice(0); + getParsingFlags(config).meridiem = config._meridiem; + // handle meridiem + config._a[HOUR] = meridiemFixWrap( + config._locale, + config._a[HOUR], + config._meridiem + ); + + // handle era + era = getParsingFlags(config).era; + if (era !== null) { + config._a[YEAR] = config._locale.erasConvertYear(era, config._a[YEAR]); + } - var $contentsArea = this.$propertyControl.find('.bo-control-container-contents'); - containerOption.$parent.append(this.$propertyControl); + configFromArray(config); + checkOverflow(config); + } - var callback = containerOption.contentInit; - var chartOption = containerOption.optionRef; - chartOption.expanderCallBack = _this.headerContentPreviewChanged.bind(_this); + function meridiemFixWrap(locale, hour, meridiem) { + var isPm; - this.$propertyControl.jqxExpander({ - theme: _chartOptionConst2.default.Theme, - animationType: 'none', - arrowPosition: "left", - expanded: false, - initContent: function initContent() { - if (typeof callback === 'function') { - $contentsArea.css('height', 'auto'); - callback.call(_this, $contentsArea, containerOption.headerKey, chartOption, _this._controlList); - containerOption.$parent.parent().perfectScrollbar('update'); + if (meridiem == null) { + // nothing to do + return hour; + } + if (locale.meridiemHour != null) { + return locale.meridiemHour(hour, meridiem); + } else if (locale.isPM != null) { + // Fallback + isPm = locale.isPM(meridiem); + if (isPm && hour < 12) { + hour += 12; + } + if (!isPm && hour === 12) { + hour = 0; } + return hour; + } else { + // this is not supposed to happen + return hour; } - }); + } - this.$propertyControl.jqxExpander('setHeaderContent', '
'); - this._createHeaderContent(this.$propertyControl.find('.bo-control-container-head-contents'), containerOption.headerLabel, containerOption.headerPreviewText); + // date from string and array of format strings + function configFromStringAndArray(config) { + var tempConfig, + bestMoment, + scoreToBeat, + i, + currentScore, + validFormatFound, + bestFormatIsValid = false; + + if (config._f.length === 0) { + getParsingFlags(config).invalidFormat = true; + config._d = new Date(NaN); + return; + } - this.$propertyControl.jqxExpander({ - expanded: containerOption.expanderStatus[containerOption.headerLabel] || chartOption.expanded || false - }); - this.$propertyControl.on('collapsed', function () { - $(this).removeClass('bos-control-container-expander-expanded').addClass('bos-control-container-expander-collapsed'); - containerOption.$parent.parent().perfectScrollbar('update'); - containerOption.expanderStatus[containerOption.headerLabel] = false; - }); - this.$propertyControl.on('expanded', function () { - $(this).removeClass('bos-control-container-expander-collapsed').addClass('bos-control-container-expander-expanded'); - containerOption.$parent.parent().perfectScrollbar('update'); - containerOption.expanderStatus[containerOption.headerLabel] = true; - }); -}; + for (i = 0; i < config._f.length; i++) { + currentScore = 0; + validFormatFound = false; + tempConfig = copyConfig({}, config); + if (config._useUTC != null) { + tempConfig._useUTC = config._useUTC; + } + tempConfig._f = config._f[i]; + configFromStringAndFormat(tempConfig); -ControlContainer.prototype._createHeaderContent = function ($target, label, previewValue) { - $target.append($('
' + label + '
')); - $target.append($('
')); + if (isValid(tempConfig)) { + validFormatFound = true; + } - this.$headerPreview = $target.find('.bo-control-container-header-preview'); - this.$headerPreview.css({ 'max-width': '160px' }); + // if there is any input that was not parsed add a penalty for that format + currentScore += getParsingFlags(tempConfig).charsLeftOver; - this.headerContentPreviewChanged(previewValue, this.$headerPreview); -}; + //or tokens + currentScore += getParsingFlags(tempConfig).unusedTokens.length * 10; -ControlContainer.prototype.headerContentPreviewChanged = function (previewValue, $previewTarget) { - // this.setHeaderContentPreviewSize(); - if ((previewValue || previewValue === '') && typeof previewValue === "string") { - $previewTarget.addClass('bos-text-overflow-hidden'); - $previewTarget.removeClass('bos-display-flex'); - $previewTarget.attr('title', previewValue); - $previewTarget.text(previewValue); - } else if (previewValue instanceof jQuery) { - $previewTarget.addClass('bos-display-flex'); - $previewTarget.removeClass('bos-text-overflow-hidden'); - $previewTarget.text(''); - $previewTarget.removeAttr('title'); - $previewTarget.children().remove(); - $previewTarget.append(previewValue); - } -}; + getParsingFlags(tempConfig).score = currentScore; -// ControlContainer.prototype.setHeaderContentPreviewSize = function () { -// overflow bug 해결되면 추가해야함. 지금은 그냥 고정해야겠다 -// if (this.$propertyControl.width() > 0 && !this.$headerPreview.css('max-width')) { -// this.$headerPreview.css({'max-width': this.$propertyControl.width() / 2}); -// } -// }; + if (!bestFormatIsValid) { + if ( + scoreToBeat == null || + currentScore < scoreToBeat || + validFormatFound + ) { + scoreToBeat = currentScore; + bestMoment = tempConfig; + if (validFormatFound) { + bestFormatIsValid = true; + } + } + } else { + if (currentScore < scoreToBeat) { + scoreToBeat = currentScore; + bestMoment = tempConfig; + } + } + } + extend(config, bestMoment || tempConfig); + } -ControlContainer.prototype.getControlList = function () { - return this._controlList; -}; + function configFromObject(config) { + if (config._d) { + return; + } -ControlContainer.prototype.render = function () { - if (!this._controlList) { - return; - } - this._controlList.forEach(function (control) { - control.render(); - }); -}; + var i = normalizeObjectUnits(config._i), + dayOrDate = i.day === undefined ? i.date : i.day; + config._a = map( + [i.year, i.month, dayOrDate, i.hour, i.minute, i.second, i.millisecond], + function (obj) { + return obj && parseInt(obj, 10); + } + ); -ControlContainer.prototype.close = function () { - if (!this._controlList) { - return; + configFromArray(config); } - this._controlList.forEach(function (control) { - control.close(); - }); -}; -ControlContainer.prototype.destroy = function () { - this._controlList.forEach(function (control) { - control.destroy(); - }); - this.$propertyControl.remove(); //껍데기도 지워줘야함 ㅜㅜ -}; + function createFromConfig(config) { + var res = new Moment(checkOverflow(prepareConfig(config))); + if (res._nextDay) { + // Adding is smart enough around DST + res.add(1, 'd'); + res._nextDay = undefined; + } -exports.default = ControlContainer; + return res; + } -/***/ }), -/* 274 */ -/***/ (function(module, exports, __webpack_require__) { + function prepareConfig(config) { + var input = config._i, + format = config._f; -"use strict"; + config._locale = config._locale || getLocale(config._l); + if (input === null || (format === undefined && input === '')) { + return createInvalid({ nullInput: true }); + } -Object.defineProperty(exports, "__esModule", { - value: true -}); + if (typeof input === 'string') { + config._i = input = config._locale.preparse(input); + } -var _baseControl = __webpack_require__(9); + if (isMoment(input)) { + return new Moment(checkOverflow(input)); + } else if (isDate(input)) { + config._d = input; + } else if (isArray(format)) { + configFromStringAndArray(config); + } else if (format) { + configFromStringAndFormat(config); + } else { + configFromInput(config); + } -var _baseControl2 = _interopRequireDefault(_baseControl); + if (!isValid(config)) { + config._d = null; + } -var _widgetFactory = __webpack_require__(3); + return config; + } -var WidgetFactory = _interopRequireWildcard(_widgetFactory); + function configFromInput(config) { + var input = config._i; + if (isUndefined(input)) { + config._d = new Date(hooks.now()); + } else if (isDate(input)) { + config._d = new Date(input.valueOf()); + } else if (typeof input === 'string') { + configFromString(config); + } else if (isArray(input)) { + config._a = map(input.slice(0), function (obj) { + return parseInt(obj, 10); + }); + configFromArray(config); + } else if (isObject(input)) { + configFromObject(config); + } else if (isNumber(input)) { + // from milliseconds + config._d = new Date(input); + } else { + hooks.createFromInputFallback(config); + } + } -var _chartOptionConst = __webpack_require__(17); + function createLocalOrUTC(input, format, locale, strict, isUTC) { + var c = {}; -var _chartOptionConst2 = _interopRequireDefault(_chartOptionConst); + if (format === true || format === false) { + strict = format; + format = undefined; + } -var _chartOptionUtil = __webpack_require__(30); + if (locale === true || locale === false) { + strict = locale; + locale = undefined; + } -var ChartOptionUtil = _interopRequireWildcard(_chartOptionUtil); + if ( + (isObject(input) && isObjectEmpty(input)) || + (isArray(input) && input.length === 0) + ) { + input = undefined; + } + // object construction must be done this way. + // https://github.com/moment/moment/issues/1423 + c._isAMomentObject = true; + c._useUTC = c._isUTC = isUTC; + c._l = locale; + c._i = input; + c._f = format; + c._strict = strict; + + return createFromConfig(c); + } -var _columnSelectorWidget = __webpack_require__(58); + function createLocal(input, format, locale, strict) { + return createLocalOrUTC(input, format, locale, strict, false); + } -var _columnSelectorWidget2 = _interopRequireDefault(_columnSelectorWidget); + var prototypeMin = deprecate( + 'moment().min is deprecated, use moment.max instead. http://momentjs.com/guides/#/warnings/min-max/', + function () { + var other = createLocal.apply(null, arguments); + if (this.isValid() && other.isValid()) { + return other < this ? this : other; + } else { + return createInvalid(); + } + } + ), + prototypeMax = deprecate( + 'moment().max is deprecated, use moment.min instead. http://momentjs.com/guides/#/warnings/min-max/', + function () { + var other = createLocal.apply(null, arguments); + if (this.isValid() && other.isValid()) { + return other > this ? this : other; + } else { + return createInvalid(); + } + } + ); -function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } } + // Pick a moment m from moments so that m[fn](other) is true for all + // other. This relies on the function fn to be transitive. + // + // moments should either be an array of moment objects or an array, whose + // first element is an array of moment objects. + function pickBy(fn, moments) { + var res, i; + if (moments.length === 1 && isArray(moments[0])) { + moments = moments[0]; + } + if (!moments.length) { + return createLocal(); + } + res = moments[0]; + for (i = 1; i < moments.length; ++i) { + if (!moments[i].isValid() || moments[i][fn](res)) { + res = moments[i]; + } + } + return res; + } -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + // TODO: Use [].sort instead? + function min() { + var args = [].slice.call(arguments, 0); -function ColumnSelectControl(parentId, options, headerKey) { - _baseControl2.default.call(this, parentId, options, headerKey); -} /** - * Created by mk90.kim on 2017-05-10. - */ + return pickBy('isBefore', args); + } -ColumnSelectControl.prototype = Object.create(_baseControl2.default.prototype); -ColumnSelectControl.prototype.constructor = ColumnSelectControl; + function max() { + var args = [].slice.call(arguments, 0); -ColumnSelectControl.prototype._init = function () { - _baseControl2.default.prototype._init.call(this); - this._widgetList = []; -}; + return pickBy('isAfter', args); + } -ColumnSelectControl.prototype._createContents = function ($parent) { - _baseControl2.default.prototype._createContents.call(this, $parent); - var selectorSettings = {}; - selectorSettings.columnSelector = this.options.setting.columnSelector; - selectorSettings.datasourceSelector = this.options.setting.datasourceSelector; - selectorSettings.itemSelector = this.options.setting.itemSelector; - selectorSettings.onOffSwitch = this.options.setting.onOffSwitch; - this.createComponentContents(selectorSettings); -}; + var now = function () { + return Date.now ? Date.now() : +new Date(); + }; -ColumnSelectControl.prototype.createComponentContents = function (contentsOption) { - _baseControl2.default.prototype.createComponentContents.call(this); - var _this = this; - this._createChartTypeSelectorContent(); + var ordering = [ + 'year', + 'quarter', + 'month', + 'week', + 'day', + 'hour', + 'minute', + 'second', + 'millisecond', + ]; + + function isDurationValid(m) { + var key, + unitHasDecimal = false, + i; + for (key in m) { + if ( + hasOwnProp(m, key) && + !( + indexOf.call(ordering, key) !== -1 && + (m[key] == null || !isNaN(m[key])) + ) + ) { + return false; + } + } - if (!contentsOption || !contentsOption.columnSelector) { - return; - } - for (var chartIdx = 0; chartIdx < contentsOption.columnSelector.length; chartIdx++) { - this._createDataSourceSelectorContent(contentsOption.datasourceSelector[chartIdx], chartIdx); - if (contentsOption.columnSelector[chartIdx].length > 0 && (this.options.dataSource.selectable || this.options.chartTypeSelectable)) { - this.$controlContents.append($('
')); + for (i = 0; i < ordering.length; ++i) { + if (m[ordering[i]]) { + if (unitHasDecimal) { + return false; // only allow non-integers for smallest unit + } + if (parseFloat(m[ordering[i]]) !== toInt(m[ordering[i]])) { + unitHasDecimal = true; + } + } } - contentsOption.columnSelector[chartIdx].forEach(function (columnSettings) { - if (!columnSettings.ref.selected && !columnSettings.ref.axisType) return; - // _this._configureDefaultSelectedAggregation(columnSettings, chartIdx); - _this._createColumnSelectorContent(columnSettings, chartIdx); - }); + return true; + } - if (contentsOption.itemSelector) { - contentsOption.itemSelector[chartIdx].forEach(function (itemSettings) { - _this._createItemSelectorContent(itemSettings, chartIdx); - }); - } + function isValid$1() { + return this._isValid; + } - if (contentsOption.onOffSwitch) { - contentsOption.onOffSwitch[chartIdx].forEach(function (switchSettings) { - _this._createOnOffSwitchContent(switchSettings, chartIdx); - }); - } + function createInvalid$1() { + return createDuration(NaN); } -}; -ColumnSelectControl.prototype._createChartTypeSelectorContent = function () { - var _this = this; - var chartTypeSelectorWidget = WidgetFactory.createChartTypeSelectorWidget(_this.$controlContents, { - label: 'Chart Type', - value: this.options.chartOption.chart.type, - chartTypeList: this.options.chartTypeList, - chartTypeSelectable: this.options.chartTypeSelectable, - onChanged: function onChanged(inputVal) { - _this.options.chartOption.chart.type = inputVal; - var newOptions = _this.options.chartOptionAPI.setChartOptions(_this.options.chartOption); - _this.options.onChanged('onChartTypeChanged', newOptions); - } - }); - this._widgetList.push(chartTypeSelectorWidget); -}; + function Duration(duration) { + var normalizedInput = normalizeObjectUnits(duration), + years = normalizedInput.year || 0, + quarters = normalizedInput.quarter || 0, + months = normalizedInput.month || 0, + weeks = normalizedInput.week || normalizedInput.isoWeek || 0, + days = normalizedInput.day || 0, + hours = normalizedInput.hour || 0, + minutes = normalizedInput.minute || 0, + seconds = normalizedInput.second || 0, + milliseconds = normalizedInput.millisecond || 0; + + this._isValid = isDurationValid(normalizedInput); + + // representation for dateAddRemove + this._milliseconds = + +milliseconds + + seconds * 1e3 + // 1000 + minutes * 6e4 + // 1000 * 60 + hours * 1000 * 60 * 60; //using 1000 * 60 * 60 instead of 36e5 to avoid floating point rounding errors https://github.com/moment/moment/issues/2978 + // Because of dateAddRemove treats 24 hours as different from a + // day when working around DST, we need to store them separately + this._days = +days + weeks * 7; + // It is impossible to translate months into days without knowing + // which months you are are talking about, so we have to store + // it separately. + this._months = +months + quarters * 3 + years * 12; + + this._data = {}; + + this._locale = getLocale(); + + this._bubble(); + } -ColumnSelectControl.prototype._createDataSourceSelectorContent = function (setting, chartIdx) { + function isDuration(obj) { + return obj instanceof Duration; + } - if (this.options.dataSource.selectable) { - this.$controlContents.append($('
')); - this._createDataSourceContent(this.$controlContents, this._createDataSourceSelectorContentOption(setting, chartIdx)); + function absRound(number) { + if (number < 0) { + return Math.round(-1 * number) * -1; + } else { + return Math.round(number); + } } -}; -ColumnSelectControl.prototype._createDataSourceSelectorContentOption = function (setting, chartIdx) { - var _this = this; - var dataSourceList = this._getDataSourceList(); - var dataSourceOptions = { - dataSource: this.options.dataSource, - id: dataSourceList[chartIdx] ? dataSourceList[chartIdx].id : null, - label: setting.label, - problemKeyList: ['datasource-001', 'datasource-002', 'datasource-002-01', 'datasource-003', 'datasource-003-01'], - onChanged: function onChanged(changedDataSource) { - var callBackParam = {}; - if (typeof _this.options.dataSource.onDataSourceChangedCallBack === 'function') { - var dataSourceValueList = _this._getDataSourceValueList(); - dataSourceValueList[chartIdx] = changedDataSource.value; - callBackParam.dataSourceValueList = dataSourceValueList; - _this.options.onChanged('onDataSourceChanged', callBackParam); - } else if (typeof _this.options.dataSource.getDataSourceColumnList === 'function') { - var newColumnList = _this.options.dataSource.getDataSourceColumnList(changedDataSource.value); - callBackParam.changedDataSource = changedDataSource; - callBackParam.changedDataSource.targetIdx = chartIdx; - callBackParam.newColumnList = newColumnList; - // _this._updateChangedDatasource(chartIdx, changedDataSource, newColumnList); - _this._updateChangedDatasource(callBackParam); + // compare two arrays, return the number of differences + function compareArrays(array1, array2, dontConvert) { + var len = Math.min(array1.length, array2.length), + lengthDiff = Math.abs(array1.length - array2.length), + diffs = 0, + i; + for (i = 0; i < len; i++) { + if ( + (dontConvert && array1[i] !== array2[i]) || + (!dontConvert && toInt(array1[i]) !== toInt(array2[i])) + ) { + diffs++; } } - }; - return dataSourceOptions; -}; - -ColumnSelectControl.prototype._updateChangedDatasource = function (dataSource) { - var chartIdx = dataSource.changedDataSource.targetIdx, - changedDataSource = dataSource.changedDataSource, - newColumnList = dataSource.newColumnList; - - this._initDataSource(chartIdx); - this._setColumnList(chartIdx, newColumnList); - this._setDataSource(chartIdx, changedDataSource); - - this.options.onChanged('onDataSourceChanged', changedDataSource); - this.setExpanderPreview.call(this); -}; - -ColumnSelectControl.prototype._getSelectedColumns = function (columnSettings) { - if (columnSettings.ref.axisType && columnSettings.axisTypeList) { - return [_chartOptionConst2.default.EXTRA_AXIS_TYPE_LIST.find(function (column) { - return column.value == columnSettings.ref.axisType; - })]; - } else { - return columnSettings.ref.selected; + return diffs + lengthDiff; } -}; -ColumnSelectControl.prototype._createColumnSelectorContent = function (columnSettings, sourceIndex) { - var _this = this; - var widgetOptions = { - selected: this._getSelectedColumns(columnSettings), //[{name: 'SepalLength', aggregation: 'none' }], - multiple: columnSettings.multiple ? columnSettings.multiple : false, - multipleMaxCnt: columnSettings.multipleMaxCnt, - aggregationEnabled: columnSettings.aggregationEnabled ? columnSettings.aggregationEnabled : false, - aggregationMap: columnSettings.aggregationMap ? columnSettings.aggregationMap : {}, - label: columnSettings.label, - getColumns: this._getColumnList.bind(this, columnSettings, sourceIndex), - getAllColumns: this._getAllColumnList.bind(this, sourceIndex), - problemKeyList: ['axis-001', 'axis-002', 'axis-003', 'axis-004', 'axis-005'], - chartOption: this.options.chartOption, - onChanged: function onChanged(changedColumnInfo) { - if (changedColumnInfo[0] && changedColumnInfo[0].type == _chartOptionConst2.default.EXTRA_AXIS_TYPE) { - columnSettings.ref.axisType = _chartOptionConst2.default.EXTRA_AXIS_TYPE_LIST.find(function (column) { - return column.name == changedColumnInfo[0].name; - }).value; - columnSettings.ref.selected = []; - } else { - columnSettings.ref.selected = changedColumnInfo; - columnSettings.ref.axisType = null; - } + // FORMATTING - _this.setExpanderPreview.call(_this); - var callbackParam; - if (typeof columnSettings.getColumnChangedOption === 'function') callbackParam = columnSettings.getColumnChangedOption(changedColumnInfo);else { - callbackParam = {}; - callbackParam[columnSettings.key] = _this.options.chartOption[columnSettings.key]; - callbackParam[columnSettings.key][sourceIndex] = columnSettings.ref; + function offset(token, separator) { + addFormatToken(token, 0, 0, function () { + var offset = this.utcOffset(), + sign = '+'; + if (offset < 0) { + offset = -offset; + sign = '-'; } - _this.options.onChanged('onChartOptionChanged', callbackParam); + return ( + sign + + zeroFill(~~(offset / 60), 2) + + separator + + zeroFill(~~offset % 60, 2) + ); + }); + } - if (columnSettings.axisTypeList) { - _this.options.chartOptionAPI.reloadColumnSelectorSetting(); - _this._createContents(_this.$parent); - _this.renderProblem(); - } - } - }; - var columnSelectorWidget = WidgetFactory.createColumnSelectorWidget(this.$controlContents, widgetOptions); - this._widgetList.push(columnSelectorWidget); -}; + offset('Z', ':'); + offset('ZZ', ''); -ColumnSelectControl.prototype._getDataSourceList = function () { - var sourceList; - if (this.options.chartOption.source.dataType === 'lazy') { - sourceList = this.options.chartOption.source.lazyData; - } else { - sourceList = this.options.chartOption.source.localData; - } - return sourceList; -}; + // PARSING -ColumnSelectControl.prototype._getDataSourceValueList = function () { - var sourceValueList = []; - var dataSourceList = this._getDataSourceList(); - dataSourceList.forEach(function (dataSource) { - sourceValueList.push(dataSource.id.value); + addRegexToken('Z', matchShortOffset); + addRegexToken('ZZ', matchShortOffset); + addParseToken(['Z', 'ZZ'], function (input, array, config) { + config._useUTC = true; + config._tzm = offsetFromString(matchShortOffset, input); }); - return sourceValueList; -}; + // HELPERS -ColumnSelectControl.prototype._createDataSourceContent = function ($parent, options) { - var dataSourceSelectorWidget = WidgetFactory.createDataSourceSelectorWidget($parent, options); - this._widgetList.push(dataSourceSelectorWidget); -}; + // timezone chunker + // '+10:00' > ['10', '00'] + // '-1530' > ['-15', '30'] + var chunkOffset = /([\+\-]|\d\d)/gi; -ColumnSelectControl.prototype._initDataSource = function (chartIdx) { - if (this.options.chartOption.source.dataType === 'lazy') { - this.options.chartOption.source.lazyData[chartIdx] = this.options.chartOption.source.lazyData[chartIdx] || { id: {} }; - } else { - this.options.chartOption.source.localData[chartIdx] = this.options.chartOption.source.localData[chartIdx] || { id: {} }; - } -}; + function offsetFromString(matcher, string) { + var matches = (string || '').match(matcher), + chunk, + parts, + minutes; -ColumnSelectControl.prototype._setColumnList = function (chartIdx, newColumnList) { - if (this.options.chartOption.source.dataType === 'lazy') { - this.options.chartOption.source.lazyData[chartIdx].columns = newColumnList; - } else { - this.options.chartOption.source.localData[chartIdx].columns = newColumnList; + if (matches === null) { + return null; + } + + chunk = matches[matches.length - 1] || []; + parts = (chunk + '').match(chunkOffset) || ['-', 0, 0]; + minutes = +(parts[1] * 60) + toInt(parts[2]); + + return minutes === 0 ? 0 : parts[0] === '+' ? minutes : -minutes; } - for (var i in this._widgetList) { - var widget = this._widgetList[i]; - if (widget instanceof _columnSelectorWidget2.default) { - widget._fillColumnControlUnit(); + // Return a moment from input, that is local/utc/zone equivalent to model. + function cloneWithOffset(input, model) { + var res, diff; + if (model._isUTC) { + res = model.clone(); + diff = + (isMoment(input) || isDate(input) + ? input.valueOf() + : createLocal(input).valueOf()) - res.valueOf(); + // Use low-level api, because this fn is low-level api. + res._d.setTime(res._d.valueOf() + diff); + hooks.updateOffset(res, false); + return res; + } else { + return createLocal(input).local(); } } -}; -ColumnSelectControl.prototype._setDataSource = function (chartIdx, dataSource) { - if (this.options.chartOption.source.dataType === 'lazy') { - this.options.chartOption.source.lazyData[chartIdx].id.value = dataSource.value; - this.options.chartOption.source.lazyData[chartIdx].id.label = dataSource.label; - } else { - this.options.chartOption.source.localData[chartIdx].id.value = dataSource.value; - this.options.chartOption.source.localData[chartIdx].id.label = dataSource.label; + function getDateOffset(m) { + // On Firefox.24 Date#getTimezoneOffset returns a floating point. + // https://github.com/moment/moment/pull/1871 + return -Math.round(m._d.getTimezoneOffset()); } -}; -//column list filtered by type -ColumnSelectControl.prototype._getColumnList = function (columnSettings, sourceIndex) { - var columnList = this._getAllColumnList(sourceIndex); + // HOOKS - if (columnList && columnSettings.columnType) { - columnList = columnList.filter(function (column) { - return columnSettings.columnType.indexOf(column.type) > -1; - }); - } + // This function will be called whenever a moment is mutated. + // It is intended to keep the offset in sync with the timezone. + hooks.updateOffset = function () {}; - if (columnSettings.axisTypeList && $.isArray(columnSettings.axisTypeList)) { - columnList = $.merge(_chartOptionConst2.default.EXTRA_AXIS_TYPE_LIST.filter(function (defAxisType) { - return $.inArray(defAxisType.value, columnSettings.axisTypeList) > -1; - }), columnList); - } + // MOMENTS - return columnList; -}; + // keepLocalTime = true means only change the timezone, without + // affecting the local hour. So 5:31:26 +0300 --[utcOffset(2, true)]--> + // 5:31:26 +0200 It is possible that 5:31:26 doesn't exist with offset + // +0200, so we adjust the time as needed, to be valid. + // + // Keeping the time actually adds/subtracts (one hour) + // from the actual represented time. That is why we call updateOffset + // a second time. In case it wants us to change the offset again + // _changeInProgress == true case, then we have to adjust, because + // there is no such time in the given timezone. + function getSetOffset(input, keepLocalTime, keepMinutes) { + var offset = this._offset || 0, + localAdjust; + if (!this.isValid()) { + return input != null ? this : NaN; + } + if (input != null) { + if (typeof input === 'string') { + input = offsetFromString(matchShortOffset, input); + if (input === null) { + return this; + } + } else if (Math.abs(input) < 16 && !keepMinutes) { + input = input * 60; + } + if (!this._isUTC && keepLocalTime) { + localAdjust = getDateOffset(this); + } + this._offset = input; + this._isUTC = true; + if (localAdjust != null) { + this.add(localAdjust, 'm'); + } + if (offset !== input) { + if (!keepLocalTime || this._changeInProgress) { + addSubtract( + this, + createDuration(input - offset, 'm'), + 1, + false + ); + } else if (!this._changeInProgress) { + this._changeInProgress = true; + hooks.updateOffset(this, true); + this._changeInProgress = null; + } + } + return this; + } else { + return this._isUTC ? offset : getDateOffset(this); + } + } -ColumnSelectControl.prototype._getAllColumnList = function (sourceIndex) { - return ChartOptionUtil.getAllColumnList(this.options.chartOption, sourceIndex); -}; + function getSetZone(input, keepLocalTime) { + if (input != null) { + if (typeof input !== 'string') { + input = -input; + } -ColumnSelectControl.prototype._createItemSelectorContent = function (itemSettings, sourceIndex) { - var _this = this; - var options = { - label: itemSettings.label, - value: itemSettings.ref.selected, - source: itemSettings.source, - placeHolder: 'Select ' + itemSettings.label, - problemKeyList: ['value-001', 'value-002', 'value-003', 'value-004', 'value-005'], - onChanged: function onChanged(inputVal) { - itemSettings.ref.selected = inputVal; - _this.setExpanderPreview.call(_this); - var callbackParam; - if (typeof itemSettings.getItemChangedOption === 'function') callbackParam = itemSettings.getItemChangedOption(inputVal); - _this.options.onChanged('onChartOptionChanged', callbackParam); - } - }; - var itemSelectorWidget = WidgetFactory.createItemSelectorWidget(this.$controlContents, options); - this._widgetList.push(itemSelectorWidget); -}; + this.utcOffset(input, keepLocalTime); -ColumnSelectControl.prototype._createOnOffSwitchContent = function (switchSettings, sourceIndex) { - var _this = this; - var options = { - label: switchSettings.label, - value: switchSettings.ref, - onChanged: function onChanged(value) { - switchSettings.ref = value; - _this.setExpanderPreview.call(_this); - var callbackParam; - if (typeof switchSettings.getValueChangedOption === 'function') callbackParam = switchSettings.getValueChangedOption(value); - _this.options.onChanged('onChartOptionChanged', callbackParam); + return this; + } else { + return -this.utcOffset(); } - }; - var onOffSwitchWidget = WidgetFactory.createOnOffSwitchWidget(this.$controlContents, options); - this._widgetList.push(onOffSwitchWidget); -}; - -ColumnSelectControl.prototype.renderProblem = function (problems) { - var _this = this; - this._widgetList.forEach(function (widget) { - widget.renderProblem(problems || _this.options.problemList); - }); -}; + } -exports.default = ColumnSelectControl; + function setOffsetToUTC(keepLocalTime) { + return this.utcOffset(0, keepLocalTime); + } -/***/ }), -/* 275 */ -/***/ (function(module, exports, __webpack_require__) { + function setOffsetToLocal(keepLocalTime) { + if (this._isUTC) { + this.utcOffset(0, keepLocalTime); + this._isUTC = false; -"use strict"; + if (keepLocalTime) { + this.subtract(getDateOffset(this), 'm'); + } + } + return this; + } + function setOffsetToParsedOffset() { + if (this._tzm != null) { + this.utcOffset(this._tzm, false, true); + } else if (typeof this._i === 'string') { + var tZone = offsetFromString(matchOffset, this._i); + if (tZone != null) { + this.utcOffset(tZone); + } else { + this.utcOffset(0, true); + } + } + return this; + } -Object.defineProperty(exports, "__esModule", { - value: true -}); + function hasAlignedHourOffset(input) { + if (!this.isValid()) { + return false; + } + input = input ? createLocal(input).utcOffset() : 0; -var _chartOptionConst = __webpack_require__(17); + return (this.utcOffset() - input) % 60 === 0; + } -var _chartOptionConst2 = _interopRequireDefault(_chartOptionConst); + function isDaylightSavingTime() { + return ( + this.utcOffset() > this.clone().month(0).utcOffset() || + this.utcOffset() > this.clone().month(5).utcOffset() + ); + } -var _baseWidget = __webpack_require__(23); + function isDaylightSavingTimeShifted() { + if (!isUndefined(this._isDSTShifted)) { + return this._isDSTShifted; + } -var _baseWidget2 = _interopRequireDefault(_baseWidget); + var c = {}, + other; -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + copyConfig(c, this); + c = prepareConfig(c); -/** - * options = { - * label : String ( default: Value ) - * labelPosition : row or column, (default: row) - * internalLabel: (default: label) //use as validation target. - * inputStyle : line or box, - * placeHolder: '', - * value: '' // default value - * onChanged: function(){] - * } - * @param parentId - * @param options - * @constructor - */ -/** - * Created by mk90.kim on 2017-05-10. - */ + if (c._a) { + other = c._isUTC ? createUTC(c._a) : createLocal(c._a); + this._isDSTShifted = + this.isValid() && compareArrays(c._a, other.toArray()) > 0; + } else { + this._isDSTShifted = false; + } -function InputWidget(parentId, options) { - _baseWidget2.default.call(this, parentId, options); -} + return this._isDSTShifted; + } -InputWidget.prototype = Object.create(_baseWidget2.default.prototype); -InputWidget.prototype.constructor = InputWidget; + function isLocal() { + return this.isValid() ? !this._isUTC : false; + } -InputWidget.prototype._createContents = function ($parent) { - this.$mainControl = $('' + '
' + '
' + '
' + '
'); + function isUtcOffset() { + return this.isValid() ? this._isUTC : false; + } - $parent.append(this.$mainControl); - if (this.options.labelPosition === 'column') { - this.$mainControl.addClass('bos-flex-direction-column'); + function isUtc() { + return this.isValid() ? this._isUTC && this._offset === 0 : false; } - this._createHeader(this.$mainControl.find('.bo-widget-header')); + // ASP.NET json date format regex + var aspNetRegex = /^(-|\+)?(?:(\d*)[. ])?(\d+):(\d+)(?::(\d+)(\.\d*)?)?$/, + // from http://docs.closure-library.googlecode.com/git/closure_goog_date_date.js.source.html + // somewhat more in line with 4.4.3.2 2004 spec, but allows decimal anywhere + // and further modified to allow for strings containing both week and day + isoRegex = /^(-|\+)?P(?:([-+]?[0-9,.]*)Y)?(?:([-+]?[0-9,.]*)M)?(?:([-+]?[0-9,.]*)W)?(?:([-+]?[0-9,.]*)D)?(?:T(?:([-+]?[0-9,.]*)H)?(?:([-+]?[0-9,.]*)M)?(?:([-+]?[0-9,.]*)S)?)?$/; + + function createDuration(input, key) { + var duration = input, + // matching against regexp is expensive, do it on demand + match = null, + sign, + ret, + diffRes; + + if (isDuration(input)) { + duration = { + ms: input._milliseconds, + d: input._days, + M: input._months, + }; + } else if (isNumber(input) || !isNaN(+input)) { + duration = {}; + if (key) { + duration[key] = +input; + } else { + duration.milliseconds = +input; + } + } else if ((match = aspNetRegex.exec(input))) { + sign = match[1] === '-' ? -1 : 1; + duration = { + y: 0, + d: toInt(match[DATE]) * sign, + h: toInt(match[HOUR]) * sign, + m: toInt(match[MINUTE]) * sign, + s: toInt(match[SECOND]) * sign, + ms: toInt(absRound(match[MILLISECOND] * 1000)) * sign, // the millisecond decimal point is included in the match + }; + } else if ((match = isoRegex.exec(input))) { + sign = match[1] === '-' ? -1 : 1; + duration = { + y: parseIso(match[2], sign), + M: parseIso(match[3], sign), + w: parseIso(match[4], sign), + d: parseIso(match[5], sign), + h: parseIso(match[6], sign), + m: parseIso(match[7], sign), + s: parseIso(match[8], sign), + }; + } else if (duration == null) { + // checks for null or undefined + duration = {}; + } else if ( + typeof duration === 'object' && + ('from' in duration || 'to' in duration) + ) { + diffRes = momentsDifference( + createLocal(duration.from), + createLocal(duration.to) + ); + + duration = {}; + duration.ms = diffRes.milliseconds; + duration.M = diffRes.months; + } - this._createInputBox(this.$mainControl.find('.bo-widget-contents')); + ret = new Duration(duration); - this._setPreValue(this.options.value || ''); -}; + if (isDuration(input) && hasOwnProp(input, '_locale')) { + ret._locale = input._locale; + } -InputWidget.prototype._createHeader = function ($parent) { - if (!this.options.label) $parent.remove();else { - var label = this.options.label || 'Value'; - $parent.text(label); - if (this.options.labelWidth) { - $parent.css({ width: this.options.labelWidth }); + if (isDuration(input) && hasOwnProp(input, '_isValid')) { + ret._isValid = input._isValid; } + + return ret; } -}; -InputWidget.prototype._createInputBox = function ($parent) { - this.$inputControl = $(''); - $parent.append(this.$inputControl); - var options = { - theme: _chartOptionConst2.default.Theme, - height: this.options.height || '25px', - placeHolder: this.options.placeHolder || '', - value: this.options.value || this.options.value == 0 ? this.options.value : '' - }; - this.$inputControl.jqxInput(options); - this._setDefaultStatus(this.$inputControl, options.value); + createDuration.fn = Duration.prototype; + createDuration.invalid = createInvalid$1; - if (this.options.inputStyle === 'line') { - this.$inputControl.addClass('bos-widget-line-inputbox'); + function parseIso(inp, sign) { + // We'd normally use ~~inp for this, but unfortunately it also + // converts floats to ints. + // inp may be undefined, so careful calling replace on it. + var res = inp && parseFloat(inp.replace(',', '.')); + // apply sign while we're at it + return (isNaN(res) ? 0 : res) * sign; } - this.$inputControl.css('width', this.options.width ? this.options.width : '100%'); + function positiveMomentsDifference(base, other) { + var res = {}; - this._bindOnChangedFunc(); -}; + res.months = + other.month() - base.month() + (other.year() - base.year()) * 12; + if (base.clone().add(res.months, 'M').isAfter(other)) { + --res.months; + } -InputWidget.prototype._bindOnChangedFunc = function ($parent) { - var _this = this; - if (typeof this.options.onChanged === 'function') { - var callbackFunc = this.options.onChanged; - this.$inputControl.on('blur', function () { - if (!_this._isChanged(_this.$inputControl.val())) return; - callbackFunc.bind(_this)(_this.$inputControl.val()); - }); - this.$inputControl.keydown(function _OnNumericInputKeyDown(e) { - var key = e.which || e.keyCode; - if (key === 13) { - if (!_this._isChanged(_this.$inputControl.val())) return; - callbackFunc.bind(_this)(_this.$inputControl.val()); - } - }); - } -}; + res.milliseconds = +other - +base.clone().add(res.months, 'M'); -//default value가 function type일 경우 disable처리 -//TODO: function type일 경우 dialog 띄워서 수정가능하게 변경해야함.(ex. axis label formatter) -InputWidget.prototype._setDefaultStatus = function (target, defaultValue) { - if (typeof defaultValue === 'function') { - target.jqxInput({ disabled: true, value: '[Custom function]' }); + return res; } -}; -InputWidget.prototype.toggleDisable = function (disabled) { - this.$inputControl.jqxInput({ disabled: disabled }); -}; + function momentsDifference(base, other) { + var res; + if (!(base.isValid() && other.isValid())) { + return { milliseconds: 0, months: 0 }; + } -InputWidget.prototype.render = function (changedValue) { - this.$inputControl.jqxInput({ value: changedValue }); -}; + other = cloneWithOffset(other, base); + if (base.isBefore(other)) { + res = positiveMomentsDifference(base, other); + } else { + res = positiveMomentsDifference(other, base); + res.milliseconds = -res.milliseconds; + res.months = -res.months; + } -InputWidget.prototype.renderProblem = function (problems) { - var _this = this; - if (!problems || !this.options.problemKeyList) return; + return res; + } - var targetLabel = _this.options.internalLabel || _this.options.label; - this.$mainControl.find('input').removeClass('bos-border-color-red'); - problems.forEach(function (problem) { - _this.options.problemKeyList.forEach(function (problemKey) { - if (problem.key === problemKey && problem.target === targetLabel) { - _this._renderProblem(); + // TODO: remove 'name' arg after deprecation is removed + function createAdder(direction, name) { + return function (val, period) { + var dur, tmp; + //invert the arguments, but complain about it + if (period !== null && !isNaN(+period)) { + deprecateSimple( + name, + 'moment().' + + name + + '(period, number) is deprecated. Please use moment().' + + name + + '(number, period). ' + + 'See http://momentjs.com/guides/#/warnings/add-inverted-param/ for more info.' + ); + tmp = val; + val = period; + period = tmp; } - }); - }); -}; - -InputWidget.prototype._renderProblem = function () { - this.$mainControl.find('input').addClass('bos-border-color-red'); -}; -exports.default = InputWidget; + dur = createDuration(val, period); + addSubtract(this, dur, direction); + return this; + }; + } -/***/ }), -/* 276 */ -/***/ (function(module, exports, __webpack_require__) { + function addSubtract(mom, duration, isAdding, updateOffset) { + var milliseconds = duration._milliseconds, + days = absRound(duration._days), + months = absRound(duration._months); -"use strict"; + if (!mom.isValid()) { + // No op + return; + } + updateOffset = updateOffset == null ? true : updateOffset; -Object.defineProperty(exports, "__esModule", { - value: true -}); + if (months) { + setMonth(mom, get(mom, 'Month') + months * isAdding); + } + if (days) { + set$1(mom, 'Date', get(mom, 'Date') + days * isAdding); + } + if (milliseconds) { + mom._d.setTime(mom._d.valueOf() + milliseconds * isAdding); + } + if (updateOffset) { + hooks.updateOffset(mom, days || months); + } + } -var _baseWidget = __webpack_require__(23); + var add = createAdder(1, 'add'), + subtract = createAdder(-1, 'subtract'); -var _baseWidget2 = _interopRequireDefault(_baseWidget); + function isString(input) { + return typeof input === 'string' || input instanceof String; + } -var _widgetFactory = __webpack_require__(3); + // type MomentInput = Moment | Date | string | number | (number | string)[] | MomentInputObject | void; // null | undefined + function isMomentInput(input) { + return ( + isMoment(input) || + isDate(input) || + isString(input) || + isNumber(input) || + isNumberOrStringArray(input) || + isMomentInputObject(input) || + input === null || + input === undefined + ); + } -var WidgetFactory = _interopRequireWildcard(_widgetFactory); + function isMomentInputObject(input) { + var objectTest = isObject(input) && !isObjectEmpty(input), + propertyTest = false, + properties = [ + 'years', + 'year', + 'y', + 'months', + 'month', + 'M', + 'days', + 'day', + 'd', + 'dates', + 'date', + 'D', + 'hours', + 'hour', + 'h', + 'minutes', + 'minute', + 'm', + 'seconds', + 'second', + 's', + 'milliseconds', + 'millisecond', + 'ms', + ], + i, + property; + + for (i = 0; i < properties.length; i += 1) { + property = properties[i]; + propertyTest = propertyTest || hasOwnProp(input, property); + } -function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } } + return objectTest && propertyTest; + } -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + function isNumberOrStringArray(input) { + var arrayTest = isArray(input), + dataTypeTest = false; + if (arrayTest) { + dataTypeTest = + input.filter(function (item) { + return !isNumber(item) && isString(input); + }).length === 0; + } + return arrayTest && dataTypeTest; + } -/** - * options = { - * label : String ( default: Value ) - * labelPosition : row or column - * inputStyle : line or box, - * placeHolder: ['','',''], - * value: ['','',''], - * numberOfInput: 3 (default: 2) - * onChanged: [function(){}, function(){}, function(){}] - * } - * @param parentId - * @param options - * @constructor - */ -/** - * Created by mk90.kim on 2017-05-10. - */ + function isCalendarSpec(input) { + var objectTest = isObject(input) && !isObjectEmpty(input), + propertyTest = false, + properties = [ + 'sameDay', + 'nextDay', + 'lastDay', + 'nextWeek', + 'lastWeek', + 'sameElse', + ], + i, + property; + + for (i = 0; i < properties.length; i += 1) { + property = properties[i]; + propertyTest = propertyTest || hasOwnProp(input, property); + } -function MultiInputWidget(parentId, options) { - _baseWidget2.default.call(this, parentId, options); -} + return objectTest && propertyTest; + } -MultiInputWidget.prototype = Object.create(_baseWidget2.default.prototype); -MultiInputWidget.prototype.constructor = MultiInputWidget; + function getCalendarFormat(myMoment, now) { + var diff = myMoment.diff(now, 'days', true); + return diff < -6 + ? 'sameElse' + : diff < -1 + ? 'lastWeek' + : diff < 0 + ? 'lastDay' + : diff < 1 + ? 'sameDay' + : diff < 2 + ? 'nextDay' + : diff < 7 + ? 'nextWeek' + : 'sameElse'; + } -MultiInputWidget.prototype._init = function () { - _baseWidget2.default.prototype._init.call(this); - this.inputBoxIdx = 0; - this.inputControlList = []; -}; + function calendar$1(time, formats) { + // Support for single parameter, formats only overload to the calendar function + if (arguments.length === 1) { + if (isMomentInput(arguments[0])) { + time = arguments[0]; + formats = undefined; + } else if (isCalendarSpec(arguments[0])) { + formats = arguments[0]; + time = undefined; + } + } + // We want to compare the start of today, vs this. + // Getting start-of-today depends on whether we're local/utc/offset or not. + var now = time || createLocal(), + sod = cloneWithOffset(now, this).startOf('day'), + format = hooks.calendarFormat(this, sod) || 'sameElse', + output = + formats && + (isFunction(formats[format]) + ? formats[format].call(this, now) + : formats[format]); + + return this.format( + output || this.localeData().calendar(format, this, createLocal(now)) + ); + } -MultiInputWidget.prototype._createContents = function ($parent) { - this.$mainControl = $('' + '
' + '
' + '
' + '
'); + function clone() { + return new Moment(this); + } - $parent.append(this.$mainControl); - if (this.options.labelPosition === 'column') { - this.$mainControl.addClass('bos-flex-direction-column'); + function isAfter(input, units) { + var localInput = isMoment(input) ? input : createLocal(input); + if (!(this.isValid() && localInput.isValid())) { + return false; + } + units = normalizeUnits(units) || 'millisecond'; + if (units === 'millisecond') { + return this.valueOf() > localInput.valueOf(); + } else { + return localInput.valueOf() < this.clone().startOf(units).valueOf(); + } } - this._createHeader(this.$mainControl.find('.bo-widget-header')); - this._createMultiInput(this.$mainControl.find('.bo-widget-contents')); + function isBefore(input, units) { + var localInput = isMoment(input) ? input : createLocal(input); + if (!(this.isValid() && localInput.isValid())) { + return false; + } + units = normalizeUnits(units) || 'millisecond'; + if (units === 'millisecond') { + return this.valueOf() < localInput.valueOf(); + } else { + return this.clone().endOf(units).valueOf() < localInput.valueOf(); + } + } - this._setPreValue(this.options.value); -}; + function isBetween(from, to, units, inclusivity) { + var localFrom = isMoment(from) ? from : createLocal(from), + localTo = isMoment(to) ? to : createLocal(to); + if (!(this.isValid() && localFrom.isValid() && localTo.isValid())) { + return false; + } + inclusivity = inclusivity || '()'; + return ( + (inclusivity[0] === '(' + ? this.isAfter(localFrom, units) + : !this.isBefore(localFrom, units)) && + (inclusivity[1] === ')' + ? this.isBefore(localTo, units) + : !this.isAfter(localTo, units)) + ); + } -MultiInputWidget.prototype._createHeader = function ($parent) { - if (!this.options.label) $parent.remove();else { - var label = this.options.label || 'Value'; - $parent.text(label); + function isSame(input, units) { + var localInput = isMoment(input) ? input : createLocal(input), + inputMs; + if (!(this.isValid() && localInput.isValid())) { + return false; + } + units = normalizeUnits(units) || 'millisecond'; + if (units === 'millisecond') { + return this.valueOf() === localInput.valueOf(); + } else { + inputMs = localInput.valueOf(); + return ( + this.clone().startOf(units).valueOf() <= inputMs && + inputMs <= this.clone().endOf(units).valueOf() + ); + } } - $parent.addClass('bos-widget-column-separator'); -}; -MultiInputWidget.prototype._createMultiInput = function ($parent) { - var numberOfInput = this.options.numOfComponent ? this.options.numOfComponent : 2; - var callbackList = $.extend({}, this.options.onChanged); - var Widget, defaultValue; - for (var idx = 0; idx < numberOfInput; idx++) { - this.inputBoxIdx = idx; + function isSameOrAfter(input, units) { + return this.isSame(input, units) || this.isAfter(input, units); + } - if (this.options.inputType && this.options.inputType[idx] === 'number') { - Widget = WidgetFactory.createNumberInputWidget; - defaultValue = this.options.value && this.options.value[idx] !== 0 ? this.options.value[idx] : ''; - } else { - Widget = WidgetFactory.createInputWidget; - defaultValue = this.options.value && this.options.value[idx] ? this.options.value[idx] : ''; - } + function isSameOrBefore(input, units) { + return this.isSame(input, units) || this.isBefore(input, units); + } - var inputControl = Widget($parent, { - labelPosition: this.options.labelPosition, - inputStyle: this.options.inputStyle, - placeHolder: this.options.placeHolder && this.options.placeHolder[idx] ? this.options.placeHolder[idx] : '', - value: defaultValue, - onChanged: this.options.onChanged[idx] - }); - inputControl.$mainControl.css({ 'flex-grow': 1 }); - if (idx != numberOfInput - 1) { - inputControl.$mainControl.addClass('bos-widget-column-separator'); + function diff(input, units, asFloat) { + var that, zoneDelta, output; + + if (!this.isValid()) { + return NaN; } - this.inputControlList.push(inputControl); - } -}; -MultiInputWidget.prototype.toggleDisable = function (disabled) { - this.inputControlList.forEach(function (inputControl) { - // $inputControl.jqxInput({disabled: disabled}); - inputControl.toggleDisable(disabled); - }); -}; + that = cloneWithOffset(input, this); -MultiInputWidget.prototype.renderProblem = function (problems) { - var _this = this; + if (!that.isValid()) { + return NaN; + } - this.$mainControl.find('input').removeClass('bos-border-color-red'); - problems.forEach(function (problem) { - _this.options.problemKeyList.forEach(function (problemKey) { - if (problem.key === problemKey && problem.target === _this.options.label) { - _this._renderProblem(); - } - }); - }); -}; + zoneDelta = (that.utcOffset() - this.utcOffset()) * 6e4; -MultiInputWidget.prototype._renderProblem = function () { - this.$mainControl.find('input').addClass('bos-border-color-red'); -}; + units = normalizeUnits(units); -exports.default = MultiInputWidget; + switch (units) { + case 'year': + output = monthDiff(this, that) / 12; + break; + case 'month': + output = monthDiff(this, that); + break; + case 'quarter': + output = monthDiff(this, that) / 3; + break; + case 'second': + output = (this - that) / 1e3; + break; // 1000 + case 'minute': + output = (this - that) / 6e4; + break; // 1000 * 60 + case 'hour': + output = (this - that) / 36e5; + break; // 1000 * 60 * 60 + case 'day': + output = (this - that - zoneDelta) / 864e5; + break; // 1000 * 60 * 60 * 24, negate dst + case 'week': + output = (this - that - zoneDelta) / 6048e5; + break; // 1000 * 60 * 60 * 24 * 7, negate dst + default: + output = this - that; + } -/***/ }), -/* 277 */ -/***/ (function(module, exports, __webpack_require__) { + return asFloat ? output : absFloor(output); + } -"use strict"; + function monthDiff(a, b) { + if (a.date() < b.date()) { + // end-of-month calculations work correct when the start month has more + // days than the end month. + return -monthDiff(b, a); + } + // difference in months + var wholeMonthDiff = (b.year() - a.year()) * 12 + (b.month() - a.month()), + // b is in (anchor - 1 month, anchor + 1 month) + anchor = a.clone().add(wholeMonthDiff, 'months'), + anchor2, + adjust; + + if (b - anchor < 0) { + anchor2 = a.clone().add(wholeMonthDiff - 1, 'months'); + // linear across the month + adjust = (b - anchor) / (anchor - anchor2); + } else { + anchor2 = a.clone().add(wholeMonthDiff + 1, 'months'); + // linear across the month + adjust = (b - anchor) / (anchor2 - anchor); + } + //check for negative zero, return zero if negative zero + return -(wholeMonthDiff + adjust) || 0; + } -Object.defineProperty(exports, "__esModule", { - value: true -}); + hooks.defaultFormat = 'YYYY-MM-DDTHH:mm:ssZ'; + hooks.defaultFormatUtc = 'YYYY-MM-DDTHH:mm:ss[Z]'; -var _dialog = __webpack_require__(42); + function toString() { + return this.clone().locale('en').format('ddd MMM DD YYYY HH:mm:ss [GMT]ZZ'); + } -var _dialog2 = _interopRequireDefault(_dialog); + function toISOString(keepOffset) { + if (!this.isValid()) { + return null; + } + var utc = keepOffset !== true, + m = utc ? this.clone().utc() : this; + if (m.year() < 0 || m.year() > 9999) { + return formatMoment( + m, + utc + ? 'YYYYYY-MM-DD[T]HH:mm:ss.SSS[Z]' + : 'YYYYYY-MM-DD[T]HH:mm:ss.SSSZ' + ); + } + if (isFunction(Date.prototype.toISOString)) { + // native implementation is ~50x faster, use it when we can + if (utc) { + return this.toDate().toISOString(); + } else { + return new Date(this.valueOf() + this.utcOffset() * 60 * 1000) + .toISOString() + .replace('Z', formatMoment(m, 'Z')); + } + } + return formatMoment( + m, + utc ? 'YYYY-MM-DD[T]HH:mm:ss.SSS[Z]' : 'YYYY-MM-DD[T]HH:mm:ss.SSSZ' + ); + } -var _chartOptionConst = __webpack_require__(17); + /** + * Return a human readable representation of a moment that can + * also be evaluated to get a new moment which is the same + * + * @link https://nodejs.org/dist/latest/docs/api/util.html#util_custom_inspect_function_on_objects + */ + function inspect() { + if (!this.isValid()) { + return 'moment.invalid(/* ' + this._i + ' */)'; + } + var func = 'moment', + zone = '', + prefix, + year, + datetime, + suffix; + if (!this.isLocal()) { + func = this.utcOffset() === 0 ? 'moment.utc' : 'moment.parseZone'; + zone = 'Z'; + } + prefix = '[' + func + '("]'; + year = 0 <= this.year() && this.year() <= 9999 ? 'YYYY' : 'YYYYYY'; + datetime = '-MM-DD[T]HH:mm:ss.SSS'; + suffix = zone + '[")]'; -var _chartOptionConst2 = _interopRequireDefault(_chartOptionConst); + return this.format(prefix + year + datetime + suffix); + } -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + function format(inputString) { + if (!inputString) { + inputString = this.isUtc() + ? hooks.defaultFormatUtc + : hooks.defaultFormat; + } + var output = formatMoment(this, inputString); + return this.localeData().postformat(output); + } -/** - * options = { - * close: function(color){ - * // color: {} - * }, - * onChanged: function(){] - * } - * - */ + function from(time, withoutSuffix) { + if ( + this.isValid() && + ((isMoment(time) && time.isValid()) || createLocal(time).isValid()) + ) { + return createDuration({ to: this, from: time }) + .locale(this.locale()) + .humanize(!withoutSuffix); + } else { + return this.localeData().invalidDate(); + } + } -/** - * Created by daewon77.park on 2016-08-20. - */ + function fromNow(withoutSuffix) { + return this.from(createLocal(), withoutSuffix); + } -function ColorPickerDialog(parentId, options) { - _dialog2.default.call(this, parentId, options); -} + function to(time, withoutSuffix) { + if ( + this.isValid() && + ((isMoment(time) && time.isValid()) || createLocal(time).isValid()) + ) { + return createDuration({ from: this, to: time }) + .locale(this.locale()) + .humanize(!withoutSuffix); + } else { + return this.localeData().invalidDate(); + } + } -ColorPickerDialog.prototype = Object.create(_dialog2.default.prototype); -ColorPickerDialog.prototype.constructor = ColorPickerDialog; + function toNow(withoutSuffix) { + return this.to(createLocal(), withoutSuffix); + } -ColorPickerDialog.prototype._getTitle = function () { - return 'Color Picker'; -}; + // If passed a locale key, it will set the locale for this + // instance. Otherwise, it will return the locale configuration + // variables for this instance. + function locale(key) { + var newLocaleData; -ColorPickerDialog.prototype._getDefaultWindowOption = function () { - var _this = this; - return { - theme: _chartOptionConst2.default.Theme, - width: '300px', - height: '320px', - maxWidth: '300px', - maxHeight: '320px', - resizable: false, - initContent: function initContent() { - _this._configurePosition(); - _this._createDialogContentsArea(_this.$mainControl.find('.bo-dialogs-contents')); + if (key === undefined) { + return this._locale._abbr; + } else { + newLocaleData = getLocale(key); + if (newLocaleData != null) { + this._locale = newLocaleData; + } + return this; } - }; -}; + } -ColorPickerDialog.prototype._createDialogContentsArea = function ($parent) { + var lang = deprecate( + 'moment().lang() is deprecated. Instead, use moment().localeData() to get the language configuration. Use moment().locale() to change languages.', + function (key) { + if (key === undefined) { + return this.localeData(); + } else { + return this.locale(key); + } + } + ); - var _this = this; + function localeData() { + return this._locale; + } - this.$colorPickerControl = $('
'); - $parent.append(this.$colorPickerControl); + var MS_PER_SECOND = 1000, + MS_PER_MINUTE = 60 * MS_PER_SECOND, + MS_PER_HOUR = 60 * MS_PER_MINUTE, + MS_PER_400_YEARS = (365 * 400 + 97) * 24 * MS_PER_HOUR; - this.$colorPickerControl.jqxColorPicker({ - width: 275, - height: 230, - colorMode: 'hue' - }); + // actual modulo - handles negative numbers (for dates before 1970): + function mod$1(dividend, divisor) { + return ((dividend % divisor) + divisor) % divisor; + } - if (this.options.color) { - this.$colorPickerControl.jqxColorPicker('setColor', this.options.color); - var backupColor = this.options.color; + function localStartOfDate(y, m, d) { + // the date constructor remaps years 0-99 to 1900-1999 + if (y < 100 && y >= 0) { + // preserve leap years using a full 400 year cycle, then reset + return new Date(y + 400, m, d) - MS_PER_400_YEARS; + } else { + return new Date(y, m, d).valueOf(); + } } - if (typeof this.options.onChanged === 'function') { - // var _this = this; - this.$colorPickerControl.bind('colorchange', function (event) { - _this.options.onChanged('#' + event.args.color.hex); - }); + function utcStartOfDate(y, m, d) { + // Date.UTC remaps years 0-99 to 1900-1999 + if (y < 100 && y >= 0) { + // preserve leap years using a full 400 year cycle, then reset + return Date.UTC(y + 400, m, d) - MS_PER_400_YEARS; + } else { + return Date.UTC(y, m, d); + } } - this.$bottomArea = $('' + '
' + '
Reset
' + '
'); + function startOf(units) { + var time, startOfDate; + units = normalizeUnits(units); + if (units === undefined || units === 'millisecond' || !this.isValid()) { + return this; + } - $parent.append(this.$bottomArea); + startOfDate = this._isUTC ? utcStartOfDate : localStartOfDate; - this.$bottomArea.on('click', function () { - _this.$colorPickerControl.jqxColorPicker('setColor', backupColor); - }); -}; + switch (units) { + case 'year': + time = startOfDate(this.year(), 0, 1); + break; + case 'quarter': + time = startOfDate( + this.year(), + this.month() - (this.month() % 3), + 1 + ); + break; + case 'month': + time = startOfDate(this.year(), this.month(), 1); + break; + case 'week': + time = startOfDate( + this.year(), + this.month(), + this.date() - this.weekday() + ); + break; + case 'isoWeek': + time = startOfDate( + this.year(), + this.month(), + this.date() - (this.isoWeekday() - 1) + ); + break; + case 'day': + case 'date': + time = startOfDate(this.year(), this.month(), this.date()); + break; + case 'hour': + time = this._d.valueOf(); + time -= mod$1( + time + (this._isUTC ? 0 : this.utcOffset() * MS_PER_MINUTE), + MS_PER_HOUR + ); + break; + case 'minute': + time = this._d.valueOf(); + time -= mod$1(time, MS_PER_MINUTE); + break; + case 'second': + time = this._d.valueOf(); + time -= mod$1(time, MS_PER_SECOND); + break; + } -ColorPickerDialog.prototype._getDialogResult = function () { - return this.$colorPickerControl.jqxColorPicker('getColor'); -}; + this._d.setTime(time); + hooks.updateOffset(this, true); + return this; + } -exports.default = ColorPickerDialog; + function endOf(units) { + var time, startOfDate; + units = normalizeUnits(units); + if (units === undefined || units === 'millisecond' || !this.isValid()) { + return this; + } -/***/ }), -/* 278 */ -/***/ (function(module, exports, __webpack_require__) { + startOfDate = this._isUTC ? utcStartOfDate : localStartOfDate; -"use strict"; + switch (units) { + case 'year': + time = startOfDate(this.year() + 1, 0, 1) - 1; + break; + case 'quarter': + time = + startOfDate( + this.year(), + this.month() - (this.month() % 3) + 3, + 1 + ) - 1; + break; + case 'month': + time = startOfDate(this.year(), this.month() + 1, 1) - 1; + break; + case 'week': + time = + startOfDate( + this.year(), + this.month(), + this.date() - this.weekday() + 7 + ) - 1; + break; + case 'isoWeek': + time = + startOfDate( + this.year(), + this.month(), + this.date() - (this.isoWeekday() - 1) + 7 + ) - 1; + break; + case 'day': + case 'date': + time = startOfDate(this.year(), this.month(), this.date() + 1) - 1; + break; + case 'hour': + time = this._d.valueOf(); + time += + MS_PER_HOUR - + mod$1( + time + (this._isUTC ? 0 : this.utcOffset() * MS_PER_MINUTE), + MS_PER_HOUR + ) - + 1; + break; + case 'minute': + time = this._d.valueOf(); + time += MS_PER_MINUTE - mod$1(time, MS_PER_MINUTE) - 1; + break; + case 'second': + time = this._d.valueOf(); + time += MS_PER_SECOND - mod$1(time, MS_PER_SECOND) - 1; + break; + } + this._d.setTime(time); + hooks.updateOffset(this, true); + return this; + } -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.getColorPalette = exports.setColorPalette = undefined; + function valueOf() { + return this._d.valueOf() - (this._offset || 0) * 60000; + } -var _defaultPreference = __webpack_require__(574); + function unix() { + return Math.floor(this.valueOf() / 1000); + } -var _defaultPreference2 = _interopRequireDefault(_defaultPreference); + function toDate() { + return new Date(this.valueOf()); + } -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + function toArray() { + var m = this; + return [ + m.year(), + m.month(), + m.date(), + m.hour(), + m.minute(), + m.second(), + m.millisecond(), + ]; + } -var selected = undefined; /* ----------------------------------------------------- - * preference.js - * Created by hyunseok.oh@samsung.com on 2018-09-10. - * ---------------------------------------------------- */ + function toObject() { + var m = this; + return { + years: m.year(), + months: m.month(), + date: m.date(), + hours: m.hours(), + minutes: m.minutes(), + seconds: m.seconds(), + milliseconds: m.milliseconds(), + }; + } -var colorSet = _defaultPreference2.default.colorPalette; + function toJSON() { + // new Date(NaN).toJSON() === null + return this.isValid() ? this.toISOString() : null; + } -function setColorPalette(_selected, _colorSet) { - selected = _selected; - colorSet = _colorSet.slice(); -} + function isValid$2() { + return isValid(this); + } -function getColorPalette() { - return colorSet; -} + function parsingFlags() { + return extend({}, getParsingFlags(this)); + } -exports.setColorPalette = setColorPalette; -exports.getColorPalette = getColorPalette; + function invalidAt() { + return getParsingFlags(this).overflow; + } -/***/ }), -/* 279 */ -/***/ (function(module, exports, __webpack_require__) { + function creationData() { + return { + input: this._i, + format: this._f, + locale: this._locale, + isUTC: this._isUTC, + strict: this._strict, + }; + } -"use strict"; + addFormatToken('N', 0, 0, 'eraAbbr'); + addFormatToken('NN', 0, 0, 'eraAbbr'); + addFormatToken('NNN', 0, 0, 'eraAbbr'); + addFormatToken('NNNN', 0, 0, 'eraName'); + addFormatToken('NNNNN', 0, 0, 'eraNarrow'); + + addFormatToken('y', ['y', 1], 'yo', 'eraYear'); + addFormatToken('y', ['yy', 2], 0, 'eraYear'); + addFormatToken('y', ['yyy', 3], 0, 'eraYear'); + addFormatToken('y', ['yyyy', 4], 0, 'eraYear'); + + addRegexToken('N', matchEraAbbr); + addRegexToken('NN', matchEraAbbr); + addRegexToken('NNN', matchEraAbbr); + addRegexToken('NNNN', matchEraName); + addRegexToken('NNNNN', matchEraNarrow); + + addParseToken(['N', 'NN', 'NNN', 'NNNN', 'NNNNN'], function ( + input, + array, + config, + token + ) { + var era = config._locale.erasParse(input, token, config._strict); + if (era) { + getParsingFlags(config).era = era; + } else { + getParsingFlags(config).invalidEra = input; + } + }); + addRegexToken('y', matchUnsigned); + addRegexToken('yy', matchUnsigned); + addRegexToken('yyy', matchUnsigned); + addRegexToken('yyyy', matchUnsigned); + addRegexToken('yo', matchEraYearOrdinal); + + addParseToken(['y', 'yy', 'yyy', 'yyyy'], YEAR); + addParseToken(['yo'], function (input, array, config, token) { + var match; + if (config._locale._eraYearOrdinalRegex) { + match = input.match(config._locale._eraYearOrdinalRegex); + } -Object.defineProperty(exports, "__esModule", { - value: true -}); + if (config._locale.eraYearOrdinalParse) { + array[YEAR] = config._locale.eraYearOrdinalParse(input, match); + } else { + array[YEAR] = parseInt(input, 10); + } + }); -var _baseRadioButtonGroupWidget = __webpack_require__(59); + function localeEras(m, format) { + var i, + l, + date, + eras = this._eras || getLocale('en')._eras; + for (i = 0, l = eras.length; i < l; ++i) { + switch (typeof eras[i].since) { + case 'string': + // truncate time + date = hooks(eras[i].since).startOf('day'); + eras[i].since = date.valueOf(); + break; + } -var _baseRadioButtonGroupWidget2 = _interopRequireDefault(_baseRadioButtonGroupWidget); + switch (typeof eras[i].until) { + case 'undefined': + eras[i].until = +Infinity; + break; + case 'string': + // truncate time + date = hooks(eras[i].until).startOf('day').valueOf(); + eras[i].until = date.valueOf(); + break; + } + } + return eras; + } -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + function localeErasParse(eraName, format, strict) { + var i, + l, + eras = this.eras(), + name, + abbr, + narrow; + eraName = eraName.toUpperCase(); + + for (i = 0, l = eras.length; i < l; ++i) { + name = eras[i].name.toUpperCase(); + abbr = eras[i].abbr.toUpperCase(); + narrow = eras[i].narrow.toUpperCase(); + + if (strict) { + switch (format) { + case 'N': + case 'NN': + case 'NNN': + if (abbr === eraName) { + return eras[i]; + } + break; -function HorizontalAlignRadioButtonWidget(parentId, options) { - _baseRadioButtonGroupWidget2.default.call(this, parentId, options); -} /** - * Created by mk90.kim on 2017-05-10. - */ + case 'NNNN': + if (name === eraName) { + return eras[i]; + } + break; -HorizontalAlignRadioButtonWidget.prototype = Object.create(_baseRadioButtonGroupWidget2.default.prototype); -HorizontalAlignRadioButtonWidget.prototype.constructor = HorizontalAlignRadioButtonWidget; + case 'NNNNN': + if (narrow === eraName) { + return eras[i]; + } + break; + } + } else if ([name, abbr, narrow].indexOf(eraName) >= 0) { + return eras[i]; + } + } + } -HorizontalAlignRadioButtonWidget.prototype._setRadioButtonList = function () { - this._buttonList = [{ - imageClass: 'bos-horizontal-align-left', - value: 'left' - }, { - imageClass: 'bos-horizontal-align-center', - value: 'center' - }, { - imageClass: 'bos-horizontal-align-right', - value: 'right' - }]; -}; - -exports.default = HorizontalAlignRadioButtonWidget; + function localeErasConvertYear(era, year) { + var dir = era.since <= era.until ? +1 : -1; + if (year === undefined) { + return hooks(era.since).year(); + } else { + return hooks(era.since).year() + (year - era.offset) * dir; + } + } -/***/ }), -/* 280 */ -/***/ (function(module, exports, __webpack_require__) { + function getEraName() { + var i, + l, + val, + eras = this.localeData().eras(); + for (i = 0, l = eras.length; i < l; ++i) { + // truncate time + val = this.startOf('day').valueOf(); + + if (eras[i].since <= val && val <= eras[i].until) { + return eras[i].name; + } + if (eras[i].until <= val && val <= eras[i].since) { + return eras[i].name; + } + } -"use strict"; + return ''; + } + function getEraNarrow() { + var i, + l, + val, + eras = this.localeData().eras(); + for (i = 0, l = eras.length; i < l; ++i) { + // truncate time + val = this.startOf('day').valueOf(); + + if (eras[i].since <= val && val <= eras[i].until) { + return eras[i].narrow; + } + if (eras[i].until <= val && val <= eras[i].since) { + return eras[i].narrow; + } + } -Object.defineProperty(exports, "__esModule", { - value: true -}); + return ''; + } -var _baseRadioButtonGroupWidget = __webpack_require__(59); + function getEraAbbr() { + var i, + l, + val, + eras = this.localeData().eras(); + for (i = 0, l = eras.length; i < l; ++i) { + // truncate time + val = this.startOf('day').valueOf(); + + if (eras[i].since <= val && val <= eras[i].until) { + return eras[i].abbr; + } + if (eras[i].until <= val && val <= eras[i].since) { + return eras[i].abbr; + } + } -var _baseRadioButtonGroupWidget2 = _interopRequireDefault(_baseRadioButtonGroupWidget); + return ''; + } -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + function getEraYear() { + var i, + l, + dir, + val, + eras = this.localeData().eras(); + for (i = 0, l = eras.length; i < l; ++i) { + dir = eras[i].since <= eras[i].until ? +1 : -1; + + // truncate time + val = this.startOf('day').valueOf(); + + if ( + (eras[i].since <= val && val <= eras[i].until) || + (eras[i].until <= val && val <= eras[i].since) + ) { + return ( + (this.year() - hooks(eras[i].since).year()) * dir + + eras[i].offset + ); + } + } -function VerticalAlignRadioButtonWidget(parentId, options) { - _baseRadioButtonGroupWidget2.default.call(this, parentId, options); -} /** - * Created by mk90.kim on 2017-05-10. - */ + return this.year(); + } -VerticalAlignRadioButtonWidget.prototype = Object.create(_baseRadioButtonGroupWidget2.default.prototype); -VerticalAlignRadioButtonWidget.prototype.constructor = VerticalAlignRadioButtonWidget; + function erasNameRegex(isStrict) { + if (!hasOwnProp(this, '_erasNameRegex')) { + computeErasParse.call(this); + } + return isStrict ? this._erasNameRegex : this._erasRegex; + } -VerticalAlignRadioButtonWidget.prototype._setRadioButtonList = function () { - this._buttonList = [{ - imageClass: 'bos-vertical-align-up', - value: 'top' - }, { - imageClass: 'bos-vertical-align-middle', - value: 'center' - }, { - imageClass: 'bos-vertical-align-down', - value: 'bottom' - }]; -}; + function erasAbbrRegex(isStrict) { + if (!hasOwnProp(this, '_erasAbbrRegex')) { + computeErasParse.call(this); + } + return isStrict ? this._erasAbbrRegex : this._erasRegex; + } -exports.default = VerticalAlignRadioButtonWidget; + function erasNarrowRegex(isStrict) { + if (!hasOwnProp(this, '_erasNarrowRegex')) { + computeErasParse.call(this); + } + return isStrict ? this._erasNarrowRegex : this._erasRegex; + } -/***/ }), -/* 281 */ -/***/ (function(module, exports, __webpack_require__) { + function matchEraAbbr(isStrict, locale) { + return locale.erasAbbrRegex(isStrict); + } -"use strict"; + function matchEraName(isStrict, locale) { + return locale.erasNameRegex(isStrict); + } + function matchEraNarrow(isStrict, locale) { + return locale.erasNarrowRegex(isStrict); + } -Object.defineProperty(exports, "__esModule", { - value: true -}); + function matchEraYearOrdinal(isStrict, locale) { + return locale._eraYearOrdinalRegex || matchUnsigned; + } -var _baseWidget = __webpack_require__(23); + function computeErasParse() { + var abbrPieces = [], + namePieces = [], + narrowPieces = [], + mixedPieces = [], + i, + l, + eras = this.eras(); + + for (i = 0, l = eras.length; i < l; ++i) { + namePieces.push(regexEscape(eras[i].name)); + abbrPieces.push(regexEscape(eras[i].abbr)); + narrowPieces.push(regexEscape(eras[i].narrow)); + + mixedPieces.push(regexEscape(eras[i].name)); + mixedPieces.push(regexEscape(eras[i].abbr)); + mixedPieces.push(regexEscape(eras[i].narrow)); + } -var _baseWidget2 = _interopRequireDefault(_baseWidget); + this._erasRegex = new RegExp('^(' + mixedPieces.join('|') + ')', 'i'); + this._erasNameRegex = new RegExp('^(' + namePieces.join('|') + ')', 'i'); + this._erasAbbrRegex = new RegExp('^(' + abbrPieces.join('|') + ')', 'i'); + this._erasNarrowRegex = new RegExp( + '^(' + narrowPieces.join('|') + ')', + 'i' + ); + } -var _chartOptionConst = __webpack_require__(17); + // FORMATTING -var _chartOptionConst2 = _interopRequireDefault(_chartOptionConst); + addFormatToken(0, ['gg', 2], 0, function () { + return this.weekYear() % 100; + }); -var _chartOptionUtil = __webpack_require__(30); + addFormatToken(0, ['GG', 2], 0, function () { + return this.isoWeekYear() % 100; + }); -var ChartOptionUtil = _interopRequireWildcard(_chartOptionUtil); + function addWeekYearFormatToken(token, getter) { + addFormatToken(0, [token, token.length], 0, getter); + } -function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } } + addWeekYearFormatToken('gggg', 'weekYear'); + addWeekYearFormatToken('ggggg', 'weekYear'); + addWeekYearFormatToken('GGGG', 'isoWeekYear'); + addWeekYearFormatToken('GGGGG', 'isoWeekYear'); -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + // ALIASES -/** - * options = { - * width: % or px default 100%, - * height:% or px default 30px, - * value: '' - * onChanged: function(){] - * } - * @param parentId - * @param options - * @constructor - */ -function BaseCheckButtonGroupWidget(parentId, options) { - _baseWidget2.default.call(this, parentId, options); -} /** - * Created by mk90.kim on 2017-05-10. - */ + addUnitAlias('weekYear', 'gg'); + addUnitAlias('isoWeekYear', 'GG'); -BaseCheckButtonGroupWidget.prototype = Object.create(_baseWidget2.default.prototype); -BaseCheckButtonGroupWidget.prototype.constructor = BaseCheckButtonGroupWidget; + // PRIORITY -BaseCheckButtonGroupWidget.prototype._init = function () { - _baseWidget2.default.prototype._init.call(this); - this._buttonList = []; - this._setCheckBtnList(); - this.$buttonObjList = []; -}; + addUnitPriority('weekYear', 1); + addUnitPriority('isoWeekYear', 1); -BaseCheckButtonGroupWidget.prototype._setCheckBtnList = function () { - //implement -}; + // PARSING -BaseCheckButtonGroupWidget.prototype._createContents = function ($parent) { - this._isRendered = false; - this.$checkButtonGroupControl = $('' + '
' + ''); - $parent.append(this.$checkButtonGroupControl); - this._createChkBtnGrpUnits(); - this._renderButtonSelected(this.options.value); - this._setPreValue(this.options.value); - this._isRendered = true; -}; + addRegexToken('G', matchSigned); + addRegexToken('g', matchSigned); + addRegexToken('GG', match1to2, match2); + addRegexToken('gg', match1to2, match2); + addRegexToken('GGGG', match1to4, match4); + addRegexToken('gggg', match1to4, match4); + addRegexToken('GGGGG', match1to6, match6); + addRegexToken('ggggg', match1to6, match6); -BaseCheckButtonGroupWidget.prototype._renderButtonSelected = function (selectedValueList) { - this.$buttonObjList.forEach(function (btn) { - if ($.inArray(btn.attr('id'), selectedValueList) > -1) { - btn.jqxToggleButton('check'); - } + addWeekParseToken(['gggg', 'ggggg', 'GGGG', 'GGGGG'], function ( + input, + week, + config, + token + ) { + week[token.substr(0, 2)] = toInt(input); }); -}; -BaseCheckButtonGroupWidget.prototype._createChkBtnGrpUnits = function () { - var btnSetList = this._buttonList; + addWeekParseToken(['gg', 'GG'], function (input, week, config, token) { + week[token] = hooks.parseTwoDigitYear(input); + }); - for (var btnSetIdx = 0; btnSetIdx < btnSetList.length; btnSetIdx++) { - var $checkButton = $('
'); - this.$checkButtonGroupControl.append($checkButton); + // MOMENTS + + function getSetWeekYear(input) { + return getSetWeekYearHelper.call( + this, + input, + this.week(), + this.weekday(), + this.localeData()._week.dow, + this.localeData()._week.doy + ); + } - if (btnSetList[btnSetIdx].imageClass) { - $checkButton.addClass(btnSetList[btnSetIdx].imageClass); - } + function getSetISOWeekYear(input) { + return getSetWeekYearHelper.call( + this, + input, + this.isoWeek(), + this.isoWeekday(), + 1, + 4 + ); + } - if (btnSetList[btnSetIdx].label) { - $checkButton.attr('value', btnSetList[btnSetIdx].label); - } + function getISOWeeksInYear() { + return weeksInYear(this.year(), 1, 4); + } - $checkButton.attr('id', btnSetList[btnSetIdx].value); + function getISOWeeksInISOWeekYear() { + return weeksInYear(this.isoWeekYear(), 1, 4); + } - $checkButton.jqxToggleButton({ - theme: _chartOptionConst2.default.Theme, - height: this.options.height || '30px' - }); + function getWeeksInYear() { + var weekInfo = this.localeData()._week; + return weeksInYear(this.year(), weekInfo.dow, weekInfo.doy); + } - this.$buttonObjList.push($checkButton); - this._createClickEvent($checkButton); + function getWeeksInWeekYear() { + var weekInfo = this.localeData()._week; + return weeksInYear(this.weekYear(), weekInfo.dow, weekInfo.doy); } -}; -BaseCheckButtonGroupWidget.prototype._createClickEvent = function ($checkButton) { - var _this = this; - $checkButton.on('click', function (event) { - var selectedBtnId = $(event.target).attr('id'); - //get selected button list - var selectedValue = []; - _this.$buttonObjList.forEach(function (btn) { - if (btn.jqxToggleButton('toggled')) { - selectedValue.push(btn.attr('id')); + function getSetWeekYearHelper(input, week, weekday, dow, doy) { + var weeksTarget; + if (input == null) { + return weekOfYear(this, dow, doy).year; + } else { + weeksTarget = weeksInYear(input, dow, doy); + if (week > weeksTarget) { + week = weeksTarget; } - }); + return setWeekAll.call(this, input, week, weekday, dow, doy); + } + } - //call onChanged function - if (ChartOptionUtil.isEmpty(_this.options.onChanged) || _this._isRendered === false) return; - if (!_this._isChanged(selectedValue)) return; - _this.options.onChanged(selectedValue); - }); -}; + function setWeekAll(weekYear, week, weekday, dow, doy) { + var dayOfYearData = dayOfYearFromWeeks(weekYear, week, weekday, dow, doy), + date = createUTCDate(dayOfYearData.year, 0, dayOfYearData.dayOfYear); -BaseCheckButtonGroupWidget.prototype.toggleDisable = function (disabledVal) { - this.$buttonObjList.forEach(function ($btn) { - $btn.jqxToggleButton({ disabled: disabledVal }); - //TODO: jqx bug인지 직접 add class 해주지않으면 disabled가 안먹힘 - $btn.addClass('jqx-fill-state-disabled jqx-fill-state-disabled-' + _chartOptionConst2.default.Theme); + this.year(date.getUTCFullYear()); + this.month(date.getUTCMonth()); + this.date(date.getUTCDate()); + return this; + } + + // FORMATTING + + addFormatToken('Q', 0, 'Qo', 'quarter'); + + // ALIASES + + addUnitAlias('quarter', 'Q'); + + // PRIORITY + + addUnitPriority('quarter', 7); + + // PARSING + + addRegexToken('Q', match1); + addParseToken('Q', function (input, array) { + array[MONTH] = (toInt(input) - 1) * 3; }); -}; -BaseCheckButtonGroupWidget.prototype.render = function (changedValue) { - this._renderButtonSelected(changedValue); -}; + // MOMENTS -exports.default = BaseCheckButtonGroupWidget; + function getSetQuarter(input) { + return input == null + ? Math.ceil((this.month() + 1) / 3) + : this.month((input - 1) * 3 + (this.month() % 3)); + } -/***/ }), -/* 282 */ -/***/ (function(module, exports, __webpack_require__) { + // FORMATTING -"use strict"; + addFormatToken('D', ['DD', 2], 'Do', 'date'); + // ALIASES -Object.defineProperty(exports, "__esModule", { - value: true -}); + addUnitAlias('date', 'D'); -var _dialog = __webpack_require__(42); + // PRIORITY + addUnitPriority('date', 9); -var _dialog2 = _interopRequireDefault(_dialog); + // PARSING -var _chartOptionConst = __webpack_require__(17); + addRegexToken('D', match1to2); + addRegexToken('DD', match1to2, match2); + addRegexToken('Do', function (isStrict, locale) { + // TODO: Remove "ordinalParse" fallback in next major release. + return isStrict + ? locale._dayOfMonthOrdinalParse || locale._ordinalParse + : locale._dayOfMonthOrdinalParseLenient; + }); -var _chartOptionConst2 = _interopRequireDefault(_chartOptionConst); + addParseToken(['D', 'DD'], DATE); + addParseToken('Do', function (input, array) { + array[DATE] = toInt(input.match(match1to2)[0]); + }); -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + // MOMENTS -/** - * Created by mk90.kim on 2016-08-20. - */ + var getSetDayOfMonth = makeGetSet('Date', true); -function DataSourceSelectorDialog(parentId, options) { - _dialog2.default.call(this, parentId, options); -} + // FORMATTING -DataSourceSelectorDialog.prototype = Object.create(_dialog2.default.prototype); -DataSourceSelectorDialog.prototype.constructor = DataSourceSelectorDialog; + addFormatToken('DDD', ['DDDD', 3], 'DDDo', 'dayOfYear'); -DataSourceSelectorDialog.prototype._getTitle = function () { - return 'Select DataSource'; -}; + // ALIASES -DataSourceSelectorDialog.prototype._getDefaultWindowOption = function () { - var _this = this; - var defaultOption = { - theme: _chartOptionConst2.default.Theme, - width: '300px', - height: '400px', - maxWidth: '300px', - maxHeight: '400px', - resizable: false, - initContent: function initContent() { - _this._configurePosition(); - _this._dataSourceList = _this._getDataSourceList() || []; - _this._createDialogContentsArea(_this.$mainControl.find('.bo-dialogs-contents')); - _this.dialogResult = _this.options.id; - } - }; - return defaultOption; -}; + addUnitAlias('dayOfYear', 'DDD'); -DataSourceSelectorDialog.prototype._getDataSourceList = function () { - if (typeof this.options.dataSource.getDataSourceList === 'function') { - return this.options.dataSource.getDataSourceList(); + // PRIORITY + addUnitPriority('dayOfYear', 4); + + // PARSING + + addRegexToken('DDD', match1to3); + addRegexToken('DDDD', match3); + addParseToken(['DDD', 'DDDD'], function (input, array, config) { + config._dayOfYear = toInt(input); + }); + + // HELPERS + + // MOMENTS + + function getSetDayOfYear(input) { + var dayOfYear = + Math.round( + (this.clone().startOf('day') - this.clone().startOf('year')) / 864e5 + ) + 1; + return input == null ? dayOfYear : this.add(input - dayOfYear, 'd'); } -}; -DataSourceSelectorDialog.prototype._createDialogContentsArea = function ($parent) { - this.$mainContainer = $('
'); - this.$datasourceArea = $('
'); - $parent.append(this.$mainContainer); - this.$mainContainer.append(this.$datasourceArea); + // FORMATTING - this._createColumnListContents(this.$datasourceArea); -}; + addFormatToken('m', ['mm', 2], 0, 'minute'); -DataSourceSelectorDialog.prototype._createColumnListContents = function ($parent) { - var _this = this; - var $container = $('
'); - this.$datasourceListBox = $('
'); - $parent.append($container); - $container.append(this.$datasourceListBox); + // ALIASES - for (var i = 0; i < this._dataSourceList.length; i++) { + addUnitAlias('minute', 'm'); - var $datasource = $('' + '
' + '
' + '
'); - $datasource.attr('value', this._dataSourceList[i].value); - $datasource.attr('label', this._dataSourceList[i].label); - $datasource.attr('dsIdx', this._dataSourceList[i].dataSourceIndex); - $datasource.find('.bo-dialog-column-item-label').text(this._dataSourceList[i].label); - this.$datasourceListBox.append($datasource); + // PRIORITY - if (this.options.id && this.options.id.value === this._dataSourceList[i].value) { - $datasource.addClass('selected'); - } + addUnitPriority('minute', 14); - $datasource.click(function () { - _this.$datasourceListBox.find('.selected').removeClass('selected'); - $(this).addClass('selected'); - _this.dialogResult = { - value: $(this).attr('value'), - label: $(this).attr('label'), - dataSourceIndex: Number($(this).attr('dsIdx')) - }; - }); - } -}; + // PARSING -exports.default = DataSourceSelectorDialog; + addRegexToken('m', match1to2); + addRegexToken('mm', match1to2, match2); + addParseToken(['m', 'mm'], MINUTE); -/***/ }), -/* 283 */ -/***/ (function(module, exports, __webpack_require__) { + // MOMENTS -"use strict"; + var getSetMinute = makeGetSet('Minutes', false); + // FORMATTING -Object.defineProperty(exports, "__esModule", { - value: true -}); + addFormatToken('s', ['ss', 2], 0, 'second'); -var _dialog = __webpack_require__(42); + // ALIASES -var _dialog2 = _interopRequireDefault(_dialog); + addUnitAlias('second', 's'); -var _chartOptionConst = __webpack_require__(17); + // PRIORITY -var _chartOptionConst2 = _interopRequireDefault(_chartOptionConst); + addUnitPriority('second', 15); -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + // PARSING -/** - * options = { - * value: '', - * chartTypeList: [], - * onChanged: function(){] - * } - * - */ - -/** - * Created by daewon77.park on 2016-08-20. - */ - -function ChartTypeSelectorDialog(parentId, options) { - _dialog2.default.call(this, parentId, options); -} + addRegexToken('s', match1to2); + addRegexToken('ss', match1to2, match2); + addParseToken(['s', 'ss'], SECOND); -ChartTypeSelectorDialog.prototype = Object.create(_dialog2.default.prototype); -ChartTypeSelectorDialog.prototype.constructor = ChartTypeSelectorDialog; + // MOMENTS -ChartTypeSelectorDialog.prototype._init = function () { - _dialog2.default.prototype._init.call(this); - this.options.showHeader = false; -}; + var getSetSecond = makeGetSet('Seconds', false); -ChartTypeSelectorDialog.prototype._createDialogsBody = function () { - this._setFocus(); - this._createCloseHandler(); - this.$mainControl.on('close', this._destroy.bind(this)); + // FORMATTING - var $body = this.$mainControl.find('.bo-dialogs-body'); + addFormatToken('S', 0, 0, function () { + return ~~(this.millisecond() / 100); + }); - $body.css({ - width: '100%', - height: '100%', - 'box-sizing': 'border-box' + addFormatToken(0, ['SS', 2], 0, function () { + return ~~(this.millisecond() / 10); }); - $body.perfectScrollbar(); -}; -ChartTypeSelectorDialog.prototype._getDefaultWindowOption = function () { - var _this = this; - var windowHeight = (parseInt(this._getChartTypeList().length / 4) + 1) * 66 + 10; + addFormatToken(0, ['SSS', 3], 0, 'millisecond'); + addFormatToken(0, ['SSSS', 4], 0, function () { + return this.millisecond() * 10; + }); + addFormatToken(0, ['SSSSS', 5], 0, function () { + return this.millisecond() * 100; + }); + addFormatToken(0, ['SSSSSS', 6], 0, function () { + return this.millisecond() * 1000; + }); + addFormatToken(0, ['SSSSSSS', 7], 0, function () { + return this.millisecond() * 10000; + }); + addFormatToken(0, ['SSSSSSSS', 8], 0, function () { + return this.millisecond() * 100000; + }); + addFormatToken(0, ['SSSSSSSSS', 9], 0, function () { + return this.millisecond() * 1000000; + }); - return { - theme: _chartOptionConst2.default.Theme, - width: '282px', - height: windowHeight + 'px', - maxWidth: '300px', - maxHeight: '345px', - resizable: false, - initContent: function initContent() { - _this._configurePosition(); - _this._createDialogContentsArea(_this.$mainControl.find('.bo-dialogs-contents')); - } - }; -}; + // ALIASES -ChartTypeSelectorDialog.prototype._createDialogContentsArea = function ($parent) { - var _this = this; - this.$chartListContainer = $('
'); - $parent.append(this.$chartListContainer); + addUnitAlias('millisecond', 'ms'); - var chartList = this._getChartTypeList().sort(function (a, b) { - return Brightics.Chart.getChartAttr(a).Order - Brightics.Chart.getChartAttr(b).Order; - }); - // if(chartList.length < 8){ - this.$chartListContainer.closest('div[role="dialog"]').css({ height: '144px' }); - // } + // PRIORITY + addUnitPriority('millisecond', 16); - for (var i = 0; i < chartList.length; i++) { - var chartLabel = Brightics.Chart.getChartAttr(chartList[i]).Label; - var $addChart = $('' + '
' + '
' + '
' + chartLabel + '
' + '
'); + // PARSING - if (this.options.value === chartList[i]) { - $addChart.addClass('selected'); - } + addRegexToken('S', match1to3, match1); + addRegexToken('SS', match1to3, match2); + addRegexToken('SSS', match1to3, match3); - this.$chartListContainer.append($addChart); + var token, getSetMillisecond; + for (token = 'SSSS'; token.length <= 9; token += 'S') { + addRegexToken(token, matchUnsigned); + } - $addChart.click(function () { - _this.$chartListContainer.find('.selected').removeClass('selected'); - $(this).addClass('selected'); - _this._close(); + function parseMs(input, array) { + array[MILLISECOND] = toInt(('0.' + input) * 1000); + } - if (typeof _this.options.close === 'function') { - _this.options.close(_this._getDialogResult()); - } - }); + for (token = 'S'; token.length <= 9; token += 'S') { + addParseToken(token, parseMs); } -}; -ChartTypeSelectorDialog.prototype._destroy = function () { - $(window).off('mousedown', this._closeHandler); - this.$mainControl.jqxWindow('destroy'); - this.$mainControl.remove(); -}; + getSetMillisecond = makeGetSet('Milliseconds', false); -ChartTypeSelectorDialog.prototype._getChartTypeList = function () { - if (this.options.chartTypeList) { - return this.options.chartTypeList; - } else { - return Brightics.Chart.getChartTypeList(); - } -}; + // FORMATTING -ChartTypeSelectorDialog.prototype._getDialogResult = function () { - return this.$chartListContainer.find('.selected .bcharts-adonis-chart-selector').attr('chart'); -}; + addFormatToken('z', 0, 0, 'zoneAbbr'); + addFormatToken('zz', 0, 0, 'zoneName'); -exports.default = ChartTypeSelectorDialog; + // MOMENTS -/***/ }), -/* 284 */ -/***/ (function(module, exports, __webpack_require__) { + function getZoneAbbr() { + return this._isUTC ? 'UTC' : ''; + } -"use strict"; + function getZoneName() { + return this._isUTC ? 'Coordinated Universal Time' : ''; + } + var proto = Moment.prototype; + + proto.add = add; + proto.calendar = calendar$1; + proto.clone = clone; + proto.diff = diff; + proto.endOf = endOf; + proto.format = format; + proto.from = from; + proto.fromNow = fromNow; + proto.to = to; + proto.toNow = toNow; + proto.get = stringGet; + proto.invalidAt = invalidAt; + proto.isAfter = isAfter; + proto.isBefore = isBefore; + proto.isBetween = isBetween; + proto.isSame = isSame; + proto.isSameOrAfter = isSameOrAfter; + proto.isSameOrBefore = isSameOrBefore; + proto.isValid = isValid$2; + proto.lang = lang; + proto.locale = locale; + proto.localeData = localeData; + proto.max = prototypeMax; + proto.min = prototypeMin; + proto.parsingFlags = parsingFlags; + proto.set = stringSet; + proto.startOf = startOf; + proto.subtract = subtract; + proto.toArray = toArray; + proto.toObject = toObject; + proto.toDate = toDate; + proto.toISOString = toISOString; + proto.inspect = inspect; + if (typeof Symbol !== 'undefined' && Symbol.for != null) { + proto[Symbol.for('nodejs.util.inspect.custom')] = function () { + return 'Moment<' + this.format() + '>'; + }; + } + proto.toJSON = toJSON; + proto.toString = toString; + proto.unix = unix; + proto.valueOf = valueOf; + proto.creationData = creationData; + proto.eraName = getEraName; + proto.eraNarrow = getEraNarrow; + proto.eraAbbr = getEraAbbr; + proto.eraYear = getEraYear; + proto.year = getSetYear; + proto.isLeapYear = getIsLeapYear; + proto.weekYear = getSetWeekYear; + proto.isoWeekYear = getSetISOWeekYear; + proto.quarter = proto.quarters = getSetQuarter; + proto.month = getSetMonth; + proto.daysInMonth = getDaysInMonth; + proto.week = proto.weeks = getSetWeek; + proto.isoWeek = proto.isoWeeks = getSetISOWeek; + proto.weeksInYear = getWeeksInYear; + proto.weeksInWeekYear = getWeeksInWeekYear; + proto.isoWeeksInYear = getISOWeeksInYear; + proto.isoWeeksInISOWeekYear = getISOWeeksInISOWeekYear; + proto.date = getSetDayOfMonth; + proto.day = proto.days = getSetDayOfWeek; + proto.weekday = getSetLocaleDayOfWeek; + proto.isoWeekday = getSetISODayOfWeek; + proto.dayOfYear = getSetDayOfYear; + proto.hour = proto.hours = getSetHour; + proto.minute = proto.minutes = getSetMinute; + proto.second = proto.seconds = getSetSecond; + proto.millisecond = proto.milliseconds = getSetMillisecond; + proto.utcOffset = getSetOffset; + proto.utc = setOffsetToUTC; + proto.local = setOffsetToLocal; + proto.parseZone = setOffsetToParsedOffset; + proto.hasAlignedHourOffset = hasAlignedHourOffset; + proto.isDST = isDaylightSavingTime; + proto.isLocal = isLocal; + proto.isUtcOffset = isUtcOffset; + proto.isUtc = isUtc; + proto.isUTC = isUtc; + proto.zoneAbbr = getZoneAbbr; + proto.zoneName = getZoneName; + proto.dates = deprecate( + 'dates accessor is deprecated. Use date instead.', + getSetDayOfMonth + ); + proto.months = deprecate( + 'months accessor is deprecated. Use month instead', + getSetMonth + ); + proto.years = deprecate( + 'years accessor is deprecated. Use year instead', + getSetYear + ); + proto.zone = deprecate( + 'moment().zone is deprecated, use moment().utcOffset instead. http://momentjs.com/guides/#/warnings/zone/', + getSetZone + ); + proto.isDSTShifted = deprecate( + 'isDSTShifted is deprecated. See http://momentjs.com/guides/#/warnings/dst-shifted/ for more information', + isDaylightSavingTimeShifted + ); + + function createUnix(input) { + return createLocal(input * 1000); + } -Object.defineProperty(exports, "__esModule", { - value: true -}); + function createInZone() { + return createLocal.apply(null, arguments).parseZone(); + } -var _baseComplexWidget = __webpack_require__(36); + function preParsePostFormat(string) { + return string; + } -var _baseComplexWidget2 = _interopRequireDefault(_baseComplexWidget); + var proto$1 = Locale.prototype; + + proto$1.calendar = calendar; + proto$1.longDateFormat = longDateFormat; + proto$1.invalidDate = invalidDate; + proto$1.ordinal = ordinal; + proto$1.preparse = preParsePostFormat; + proto$1.postformat = preParsePostFormat; + proto$1.relativeTime = relativeTime; + proto$1.pastFuture = pastFuture; + proto$1.set = set; + proto$1.eras = localeEras; + proto$1.erasParse = localeErasParse; + proto$1.erasConvertYear = localeErasConvertYear; + proto$1.erasAbbrRegex = erasAbbrRegex; + proto$1.erasNameRegex = erasNameRegex; + proto$1.erasNarrowRegex = erasNarrowRegex; + + proto$1.months = localeMonths; + proto$1.monthsShort = localeMonthsShort; + proto$1.monthsParse = localeMonthsParse; + proto$1.monthsRegex = monthsRegex; + proto$1.monthsShortRegex = monthsShortRegex; + proto$1.week = localeWeek; + proto$1.firstDayOfYear = localeFirstDayOfYear; + proto$1.firstDayOfWeek = localeFirstDayOfWeek; + + proto$1.weekdays = localeWeekdays; + proto$1.weekdaysMin = localeWeekdaysMin; + proto$1.weekdaysShort = localeWeekdaysShort; + proto$1.weekdaysParse = localeWeekdaysParse; + + proto$1.weekdaysRegex = weekdaysRegex; + proto$1.weekdaysShortRegex = weekdaysShortRegex; + proto$1.weekdaysMinRegex = weekdaysMinRegex; + + proto$1.isPM = localeIsPM; + proto$1.meridiem = localeMeridiem; + + function get$1(format, index, field, setter) { + var locale = getLocale(), + utc = createUTC().set(setter, index); + return locale[field](utc, format); + } -var _widgetFactory = __webpack_require__(3); + function listMonthsImpl(format, index, field) { + if (isNumber(format)) { + index = format; + format = undefined; + } -var WidgetFactory = _interopRequireWildcard(_widgetFactory); + format = format || ''; -function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } } + if (index != null) { + return get$1(format, index, field, 'month'); + } -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + var i, + out = []; + for (i = 0; i < 12; i++) { + out[i] = get$1(format, i, field, 'month'); + } + return out; + } -/** - * options = { - * width: % or px default 100%, - * height:% or px default 100%, - * value: - * { - * left: 'px', - * right: 'px', - * top: 'px', - * bottom: 'px', - * }, (all value is px) - * position: ['left', 'top', 'right', 'bottom'], - * onChanged: function(){] - * } - * @param parentId - * @param options - * @constructor - */ -/** - * Created by mk90 on 2017-05-27. - */ + // () + // (5) + // (fmt, 5) + // (fmt) + // (true) + // (true, 5) + // (true, fmt, 5) + // (true, fmt) + function listWeekdaysImpl(localeSorted, format, index, field) { + if (typeof localeSorted === 'boolean') { + if (isNumber(format)) { + index = format; + format = undefined; + } -function PositionWidget(parentId, options) { - _baseComplexWidget2.default.call(this, parentId, options); - this.renderProblem(); -} + format = format || ''; + } else { + format = localeSorted; + index = format; + localeSorted = false; -PositionWidget.prototype = Object.create(_baseComplexWidget2.default.prototype); -PositionWidget.prototype.constructor = PositionWidget; + if (isNumber(format)) { + index = format; + format = undefined; + } -PositionWidget.prototype._init = function () { - _baseComplexWidget2.default.prototype._init.call(this); - this.baseIconAppended = false; - this.inputValue = {}; - this.selectedUnit = {}; - if (typeof this.options.getValue === 'function') { - for (var i in this.options.value) { - this.options.value[i] = this.options.getValue(this.options.value[i]); + format = format || ''; } - } -}; - -PositionWidget.prototype._createContents = function ($parent) { - this.$mainControl = $('' + '
' + '
' + '
' + '
' + '
'); - $parent.append(this.$mainControl); + var locale = getLocale(), + shift = localeSorted ? locale._week.dow : 0, + i, + out = []; - var _this = this; - this.options.position.forEach(function (position) { - _this._createPositionContent(position); - }); -}; + if (index != null) { + return get$1(format, (index + shift) % 7, field, 'day'); + } -PositionWidget.prototype._createPositionContent = function (position) { - var $targetArea, positionInputControl, defaultValue; - if (position === 'left') { - $targetArea = this.$mainControl.find('.bo-widget-position-middle'); - this._createPositionInputContent($targetArea, position); - this._createBaseIcon(); - } - if (position === 'right') { - $targetArea = this.$mainControl.find('.bo-widget-position-middle'); - this._createPositionInputContent($targetArea, position); - } - if (position === 'top') { - $targetArea = this.$mainControl.find('.bo-widget-position-top'); - this._createPositionInputContent($targetArea, position); - this._createBaseIcon(); + for (i = 0; i < 7; i++) { + out[i] = get$1(format, (i + shift) % 7, field, 'day'); + } + return out; } - if (position === 'bottom') { - $targetArea = this.$mainControl.find('.bo-widget-position-bottom'); - this._createBaseIcon(); - this._createPositionInputContent($targetArea, position); + + function listMonths(format, index) { + return listMonthsImpl(format, index, 'months'); } -}; -PositionWidget.prototype._createBaseIcon = function () { - if (!this.baseIconAppended) { - var $targetArea = this.$mainControl.find('.bo-widget-position-middle'); - var $baseIcon = $('
'); - $baseIcon.css({ - border: 'dashed 1px #000000', - background: '#d3d3d3', - // width: '30px', - // height: '20px', - margin: '0 5px' - }); + function listMonthsShort(format, index) { + return listMonthsImpl(format, index, 'monthsShort'); + } - $targetArea.append($baseIcon); - this.baseIconAppended = true; + function listWeekdays(localeSorted, format, index) { + return listWeekdaysImpl(localeSorted, format, index, 'weekdays'); } -}; -PositionWidget.prototype._createPositionInputContent = function ($parent, position) { - var value = this.options.value && this.options.value[position] ? this.options.value[position] + '' : ''; + function listWeekdaysShort(localeSorted, format, index) { + return listWeekdaysImpl(localeSorted, format, index, 'weekdaysShort'); + } - var postFix = value.indexOf('%') >= 0 ? '%' : 'px'; - var numberValue = value.replace(postFix, ''); + function listWeekdaysMin(localeSorted, format, index) { + return listWeekdaysImpl(localeSorted, format, index, 'weekdaysMin'); + } - var _this = this; - this.inputValue[position] = numberValue; - this.selectedUnit[position] = postFix; - var numberInput = WidgetFactory.createNumberInputWidget($parent, { - width: '80px', - value: numberValue, - internalLabel: position, - problemKeyList: this.options.problemKeyList, - onChanged: function onChanged(value) { - _this.inputValue[position] = value; - _this.options.onChanged[position](value || value === 0 ? value + _this.selectedUnit[position] : ''); - } + getSetGlobalLocale('en', { + eras: [ + { + since: '0001-01-01', + until: +Infinity, + offset: 1, + name: 'Anno Domini', + narrow: 'AD', + abbr: 'AD', + }, + { + since: '0000-12-31', + until: -Infinity, + offset: 1, + name: 'Before Christ', + narrow: 'BC', + abbr: 'BC', + }, + ], + dayOfMonthOrdinalParse: /\d{1,2}(th|st|nd|rd)/, + ordinal: function (number) { + var b = number % 10, + output = + toInt((number % 100) / 10) === 1 + ? 'th' + : b === 1 + ? 'st' + : b === 2 + ? 'nd' + : b === 3 + ? 'rd' + : 'th'; + return number + output; + }, }); - var unitSwitchButton = WidgetFactory.createSwitchButtonWidget($parent, { - value: postFix, - width: '20px', - height: '27px', - itemList: this.options.itemList || [{ value: 'px', label: 'Px' }, { value: '%', label: '%' }], - onChanged: function onChanged(changedUnit) { - _this.selectedUnit[position] = changedUnit; - _this.options.onChanged[position](_this.inputValue[position] ? _this.inputValue[position] + changedUnit : ''); - } - }); - unitSwitchButton.addCSSInMainControl({ - 'border-left': 'none' - }); + // Side effect imports - this._widgetList.push(numberInput); - this._widgetList.push(unitSwitchButton); -}; + hooks.lang = deprecate( + 'moment.lang is deprecated. Use moment.locale instead.', + getSetGlobalLocale + ); + hooks.langData = deprecate( + 'moment.langData is deprecated. Use moment.localeData instead.', + getLocale + ); -PositionWidget.prototype.renderProblem = function (problems) { - var _this = this; - this._widgetList.forEach(function (widget) { - widget.renderProblem(problems); - }); -}; + var mathAbs = Math.abs; -exports.default = PositionWidget; + function abs() { + var data = this._data; -/***/ }), -/* 285 */ -/***/ (function(module, exports, __webpack_require__) { + this._milliseconds = mathAbs(this._milliseconds); + this._days = mathAbs(this._days); + this._months = mathAbs(this._months); -"use strict"; + data.milliseconds = mathAbs(data.milliseconds); + data.seconds = mathAbs(data.seconds); + data.minutes = mathAbs(data.minutes); + data.hours = mathAbs(data.hours); + data.months = mathAbs(data.months); + data.years = mathAbs(data.years); + return this; + } -Object.defineProperty(exports, "__esModule", { - value: true -}); + function addSubtract$1(duration, input, value, direction) { + var other = createDuration(input, value); -var _dialog = __webpack_require__(42); + duration._milliseconds += direction * other._milliseconds; + duration._days += direction * other._days; + duration._months += direction * other._months; -var _dialog2 = _interopRequireDefault(_dialog); + return duration._bubble(); + } -var _chartOptionConst = __webpack_require__(17); + // supports only 2.0-style add(1, 's') or add(duration) + function add$1(input, value) { + return addSubtract$1(this, input, value, 1); + } -var _chartOptionConst2 = _interopRequireDefault(_chartOptionConst); + // supports only 2.0-style subtract(1, 's') or subtract(duration) + function subtract$1(input, value) { + return addSubtract$1(this, input, value, -1); + } -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + function absCeil(number) { + if (number < 0) { + return Math.floor(number); + } else { + return Math.ceil(number); + } + } -/** - * options = { - * value: '', - * chartTypeList: [], - * onChanged: function(){] - * } - * - */ + function bubble() { + var milliseconds = this._milliseconds, + days = this._days, + months = this._months, + data = this._data, + seconds, + minutes, + hours, + years, + monthsFromDays; + + // if we have a mix of positive and negative values, bubble down first + // check: https://github.com/moment/moment/issues/2166 + if ( + !( + (milliseconds >= 0 && days >= 0 && months >= 0) || + (milliseconds <= 0 && days <= 0 && months <= 0) + ) + ) { + milliseconds += absCeil(monthsToDays(months) + days) * 864e5; + days = 0; + months = 0; + } -/** - * Created by daewon77.park on 2016-08-20. - */ + // The following code bubbles up values, see the tests for + // examples of what that means. + data.milliseconds = milliseconds % 1000; -function ItemSelectorDialog(parentId, options) { - _dialog2.default.call(this, parentId, options); -} + seconds = absFloor(milliseconds / 1000); + data.seconds = seconds % 60; -ItemSelectorDialog.prototype = Object.create(_dialog2.default.prototype); -ItemSelectorDialog.prototype.constructor = ItemSelectorDialog; + minutes = absFloor(seconds / 60); + data.minutes = minutes % 60; -ItemSelectorDialog.prototype._init = function () { - _dialog2.default.prototype._init.call(this); - this.options.showHeader = false; -}; + hours = absFloor(minutes / 60); + data.hours = hours % 24; -ItemSelectorDialog.prototype._createDialogsBody = function () { - this._setFocus(); - this._createCloseHandler(); - this.$mainControl.on('close', this._destroy.bind(this)); + days += absFloor(hours / 24); - var $body = this.$mainControl.find('.bo-dialogs-body'); + // convert days to months + monthsFromDays = absFloor(daysToMonths(days)); + months += monthsFromDays; + days -= absCeil(monthsToDays(monthsFromDays)); - $body.css({ - width: '100%', - height: '100%', - 'box-sizing': 'border-box' - }); - $body.perfectScrollbar(); -}; + // 12 months -> 1 year + years = absFloor(months / 12); + months %= 12; -ItemSelectorDialog.prototype._getDefaultWindowOption = function () { - var _this = this; + data.days = days; + data.months = months; + data.years = years; - var windowHeight = this.options.itemList.length * 22 + 10; + return this; + } - return { - theme: _chartOptionConst2.default.Theme, - width: this.options.width || '285px', - height: windowHeight + 'px', - minHeight: '32px', - maxWidth: '300px', - maxHeight: '120px', - resizable: false, - initContent: function initContent() { - _this._configurePosition(); - _this._createDialogContentsArea(_this.$mainControl.find('.bo-dialogs-contents')); + function daysToMonths(days) { + // 400 years have 146097 days (taking into account leap year rules) + // 400 years have 12 months === 4800 + return (days * 4800) / 146097; + } + + function monthsToDays(months) { + // the reverse of daysToMonths + return (months * 146097) / 4800; + } + + function as(units) { + if (!this.isValid()) { + return NaN; } - }; -}; + var days, + months, + milliseconds = this._milliseconds; + + units = normalizeUnits(units); + + if (units === 'month' || units === 'quarter' || units === 'year') { + days = this._days + milliseconds / 864e5; + months = this._months + daysToMonths(days); + switch (units) { + case 'month': + return months; + case 'quarter': + return months / 3; + case 'year': + return months / 12; + } + } else { + // handle milliseconds separately because of floating point math errors (issue #1867) + days = this._days + Math.round(monthsToDays(this._months)); + switch (units) { + case 'week': + return days / 7 + milliseconds / 6048e5; + case 'day': + return days + milliseconds / 864e5; + case 'hour': + return days * 24 + milliseconds / 36e5; + case 'minute': + return days * 1440 + milliseconds / 6e4; + case 'second': + return days * 86400 + milliseconds / 1000; + // Math.floor prevents floating point math errors here + case 'millisecond': + return Math.floor(days * 864e5) + milliseconds; + default: + throw new Error('Unknown unit ' + units); + } + } + } -ItemSelectorDialog.prototype._configurePosition = function (position, isSwitch) { - position = position || this.options.windowPosition; - if (!position) return; - var $window = this.$mainControl; - var anchorOffset = this.$parent.offset(), - anchorWidth = this.$parent.width(), - anchorHeight = this.$parent.height(), - windowOffset = this.$mainControl.offset(), - windowWidth = this.$mainControl.width(), - windowHeight = this.$mainControl.height(), - windowLeft, - windowTop, - isChanged = false; + // TODO: Use this.as('ms')? + function valueOf$1() { + if (!this.isValid()) { + return NaN; + } + return ( + this._milliseconds + + this._days * 864e5 + + (this._months % 12) * 2592e6 + + toInt(this._months / 12) * 31536e6 + ); + } - switch (position) { - case 'left': - windowLeft = anchorOffset.left - windowWidth - 3; - windowTop = anchorOffset.top; - break; - case 'right': - windowLeft = anchorOffset.left + anchorWidth + 3; - windowTop = anchorOffset.top; - break; - case 'top': - windowLeft = anchorOffset.left; - windowTop = anchorOffset.top - windowHeight; - break; - case 'bottom': - windowLeft = anchorOffset.left; - windowTop = anchorOffset.top + anchorHeight - 1; - break; - case 'bottom-left': - windowLeft = anchorOffset.left - (windowWidth - anchorWidth) - 2; - windowTop = anchorOffset.top + anchorHeight - 1; - break; + function makeAs(alias) { + return function () { + return this.as(alias); + }; } - if (windowLeft + windowWidth > window.innerWidth) windowLeft = window.innerWidth - windowWidth - 5, isChanged = true; - if (windowTop + windowHeight > window.innerHeight) windowTop = window.innerHeight - windowHeight - 5, isChanged = true; - if (windowLeft < 0) windowLeft = 5, isChanged = true; - if (windowTop < 0) windowTop = 5, isChanged = true; + var asMilliseconds = makeAs('ms'), + asSeconds = makeAs('s'), + asMinutes = makeAs('m'), + asHours = makeAs('h'), + asDays = makeAs('d'), + asWeeks = makeAs('w'), + asMonths = makeAs('M'), + asQuarters = makeAs('Q'), + asYears = makeAs('y'); + + function clone$1() { + return createDuration(this); + } - if (isChanged === true && this.options.switchPosition && isSwitch !== true) { - this._configurePosition(this.options.switchPosition, true); - } else { - $window.css('left', windowLeft); - $window.css('top', windowTop); + function get$2(units) { + units = normalizeUnits(units); + return this.isValid() ? this[units + 's']() : NaN; } -}; -ItemSelectorDialog.prototype._createDialogContentsArea = function ($parent) { - var _this = this; - this.$itemListContainer = $('
'); - $parent.append(this.$itemListContainer); + function makeGetter(name) { + return function () { + return this.isValid() ? this._data[name] : NaN; + }; + } - var itemList = this._getItemList(); - // if(chartList.length < 8){ - this.$itemListContainer.closest('div[role="dialog"]').css({ height: '144px' }); - // } + var milliseconds = makeGetter('milliseconds'), + seconds = makeGetter('seconds'), + minutes = makeGetter('minutes'), + hours = makeGetter('hours'), + days = makeGetter('days'), + months = makeGetter('months'), + years = makeGetter('years'); + function weeks() { + return absFloor(this.days() / 7); + } - for (var i = 0; i < itemList.length; i++) { - var itemLabel = itemList[i].label; - var $addItem = $('
' + '
' + itemLabel + '
' + '
'); + var round = Math.round, + thresholds = { + ss: 44, // a few seconds to seconds + s: 45, // seconds to minute + m: 45, // minutes to hour + h: 22, // hours to day + d: 26, // days to month/week + w: null, // weeks to month + M: 11, // months to year + }; - if (this.options.value === itemList[i].value) { - $addItem.addClass('selected'); + // helper function for moment.fn.from, moment.fn.fromNow, and moment.duration.fn.humanize + function substituteTimeAgo(string, number, withoutSuffix, isFuture, locale) { + return locale.relativeTime(number || 1, !!withoutSuffix, string, isFuture); + } + + function relativeTime$1(posNegDuration, withoutSuffix, thresholds, locale) { + var duration = createDuration(posNegDuration).abs(), + seconds = round(duration.as('s')), + minutes = round(duration.as('m')), + hours = round(duration.as('h')), + days = round(duration.as('d')), + months = round(duration.as('M')), + weeks = round(duration.as('w')), + years = round(duration.as('y')), + a = + (seconds <= thresholds.ss && ['s', seconds]) || + (seconds < thresholds.s && ['ss', seconds]) || + (minutes <= 1 && ['m']) || + (minutes < thresholds.m && ['mm', minutes]) || + (hours <= 1 && ['h']) || + (hours < thresholds.h && ['hh', hours]) || + (days <= 1 && ['d']) || + (days < thresholds.d && ['dd', days]); + + if (thresholds.w != null) { + a = + a || + (weeks <= 1 && ['w']) || + (weeks < thresholds.w && ['ww', weeks]); } + a = a || + (months <= 1 && ['M']) || + (months < thresholds.M && ['MM', months]) || + (years <= 1 && ['y']) || ['yy', years]; + + a[2] = withoutSuffix; + a[3] = +posNegDuration > 0; + a[4] = locale; + return substituteTimeAgo.apply(null, a); + } - this.$itemListContainer.append($addItem); + // This function allows you to set the rounding function for relative time strings + function getSetRelativeTimeRounding(roundingFunction) { + if (roundingFunction === undefined) { + return round; + } + if (typeof roundingFunction === 'function') { + round = roundingFunction; + return true; + } + return false; + } - $addItem.click(function () { - _this.$itemListContainer.find('.selected').removeClass('selected'); - $(this).addClass('selected'); - _this._close(); + // This function allows you to set a threshold for relative time strings + function getSetRelativeTimeThreshold(threshold, limit) { + if (thresholds[threshold] === undefined) { + return false; + } + if (limit === undefined) { + return thresholds[threshold]; + } + thresholds[threshold] = limit; + if (threshold === 's') { + thresholds.ss = limit - 1; + } + return true; + } - if (typeof _this.options.close === 'function') { - _this.options.close(_this._getDialogResult()); + function humanize(argWithSuffix, argThresholds) { + if (!this.isValid()) { + return this.localeData().invalidDate(); + } + + var withSuffix = false, + th = thresholds, + locale, + output; + + if (typeof argWithSuffix === 'object') { + argThresholds = argWithSuffix; + argWithSuffix = false; + } + if (typeof argWithSuffix === 'boolean') { + withSuffix = argWithSuffix; + } + if (typeof argThresholds === 'object') { + th = Object.assign({}, thresholds, argThresholds); + if (argThresholds.s != null && argThresholds.ss == null) { + th.ss = argThresholds.s - 1; } - }); - } -}; + } -ItemSelectorDialog.prototype._destroy = function () { - $(window).off('mousedown', this._closeHandler); - this.$mainControl.jqxWindow('destroy'); - this.$mainControl.remove(); -}; + locale = this.localeData(); + output = relativeTime$1(this, !withSuffix, th, locale); -ItemSelectorDialog.prototype._getItemList = function () { - if (this.options.itemList) { - return this.options.itemList; - } else { - return []; + if (withSuffix) { + output = locale.pastFuture(+this, output); + } + + return locale.postformat(output); } -}; -ItemSelectorDialog.prototype._getDialogResult = function () { - return this.$itemListContainer.find('.selected .bo-widget-item-selector-item').attr('item-value'); -}; + var abs$1 = Math.abs; -exports.default = ItemSelectorDialog; + function sign(x) { + return (x > 0) - (x < 0) || +x; + } -/***/ }), -/* 286 */ -/***/ (function(module, exports, __webpack_require__) { + function toISOString$1() { + // for ISO strings we do not use the normal bubbling rules: + // * milliseconds bubble up until they become hours + // * days do not bubble at all + // * months bubble up until they become years + // This is because there is no context-free conversion between hours and days + // (think of clock changes) + // and also not between days and months (28-31 days per month) + if (!this.isValid()) { + return this.localeData().invalidDate(); + } -"use strict"; + var seconds = abs$1(this._milliseconds) / 1000, + days = abs$1(this._days), + months = abs$1(this._months), + minutes, + hours, + years, + s, + total = this.asSeconds(), + totalSign, + ymSign, + daysSign, + hmsSign; + + if (!total) { + // this is the same as C#'s (Noda) and python (isodate)... + // but not other JS (goog.date) + return 'P0D'; + } + // 3600 seconds -> 60 minutes -> 1 hour + minutes = absFloor(seconds / 60); + hours = absFloor(minutes / 60); + seconds %= 60; + minutes %= 60; + + // 12 months -> 1 year + years = absFloor(months / 12); + months %= 12; + + // inspired by https://github.com/dordille/moment-isoduration/blob/master/moment.isoduration.js + s = seconds ? seconds.toFixed(3).replace(/\.?0+$/, '') : ''; + + totalSign = total < 0 ? '-' : ''; + ymSign = sign(this._months) !== sign(total) ? '-' : ''; + daysSign = sign(this._days) !== sign(total) ? '-' : ''; + hmsSign = sign(this._milliseconds) !== sign(total) ? '-' : ''; + + return ( + totalSign + + 'P' + + (years ? ymSign + years + 'Y' : '') + + (months ? ymSign + months + 'M' : '') + + (days ? daysSign + days + 'D' : '') + + (hours || minutes || seconds ? 'T' : '') + + (hours ? hmsSign + hours + 'H' : '') + + (minutes ? hmsSign + minutes + 'M' : '') + + (seconds ? hmsSign + s + 'S' : '') + ); + } -Object.defineProperty(exports, "__esModule", { - value: true -}); + var proto$2 = Duration.prototype; + + proto$2.isValid = isValid$1; + proto$2.abs = abs; + proto$2.add = add$1; + proto$2.subtract = subtract$1; + proto$2.as = as; + proto$2.asMilliseconds = asMilliseconds; + proto$2.asSeconds = asSeconds; + proto$2.asMinutes = asMinutes; + proto$2.asHours = asHours; + proto$2.asDays = asDays; + proto$2.asWeeks = asWeeks; + proto$2.asMonths = asMonths; + proto$2.asQuarters = asQuarters; + proto$2.asYears = asYears; + proto$2.valueOf = valueOf$1; + proto$2._bubble = bubble; + proto$2.clone = clone$1; + proto$2.get = get$2; + proto$2.milliseconds = milliseconds; + proto$2.seconds = seconds; + proto$2.minutes = minutes; + proto$2.hours = hours; + proto$2.days = days; + proto$2.weeks = weeks; + proto$2.months = months; + proto$2.years = years; + proto$2.humanize = humanize; + proto$2.toISOString = toISOString$1; + proto$2.toString = toISOString$1; + proto$2.toJSON = toISOString$1; + proto$2.locale = locale; + proto$2.localeData = localeData; + + proto$2.toIsoString = deprecate( + 'toIsoString() is deprecated. Please use toISOString() instead (notice the capitals)', + toISOString$1 + ); + proto$2.lang = lang; + + // FORMATTING + + addFormatToken('X', 0, 0, 'unix'); + addFormatToken('x', 0, 0, 'valueOf'); + + // PARSING + + addRegexToken('x', matchSigned); + addRegexToken('X', matchTimestamp); + addParseToken('X', function (input, array, config) { + config._d = new Date(parseFloat(input) * 1000); + }); + addParseToken('x', function (input, array, config) { + config._d = new Date(toInt(input)); + }); -var _baseWidget = __webpack_require__(23); + //! moment.js + + hooks.version = '2.27.0'; + + setHookCallback(createLocal); + + hooks.fn = proto; + hooks.min = min; + hooks.max = max; + hooks.now = now; + hooks.utc = createUTC; + hooks.unix = createUnix; + hooks.months = listMonths; + hooks.isDate = isDate; + hooks.locale = getSetGlobalLocale; + hooks.invalid = createInvalid; + hooks.duration = createDuration; + hooks.isMoment = isMoment; + hooks.weekdays = listWeekdays; + hooks.parseZone = createInZone; + hooks.localeData = getLocale; + hooks.isDuration = isDuration; + hooks.monthsShort = listMonthsShort; + hooks.weekdaysMin = listWeekdaysMin; + hooks.defineLocale = defineLocale; + hooks.updateLocale = updateLocale; + hooks.locales = listLocales; + hooks.weekdaysShort = listWeekdaysShort; + hooks.normalizeUnits = normalizeUnits; + hooks.relativeTimeRounding = getSetRelativeTimeRounding; + hooks.relativeTimeThreshold = getSetRelativeTimeThreshold; + hooks.calendarFormat = getCalendarFormat; + hooks.prototype = proto; + + // currently HTML5 input type only supports 24-hour formats + hooks.HTML5_FMT = { + DATETIME_LOCAL: 'YYYY-MM-DDTHH:mm', // + DATETIME_LOCAL_SECONDS: 'YYYY-MM-DDTHH:mm:ss', // + DATETIME_LOCAL_MS: 'YYYY-MM-DDTHH:mm:ss.SSS', // + DATE: 'YYYY-MM-DD', // + TIME: 'HH:mm', // + TIME_SECONDS: 'HH:mm:ss', // + TIME_MS: 'HH:mm:ss.SSS', // + WEEK: 'GGGG-[W]WW', // + MONTH: 'YYYY-MM', // + }; -var _baseWidget2 = _interopRequireDefault(_baseWidget); + return hooks; -var _centerPositionWidget = __webpack_require__(121); +}))); -var _centerPositionWidget2 = _interopRequireDefault(_centerPositionWidget); +/* WEBPACK VAR INJECTION */}.call(exports, __webpack_require__(252)(module))) -var _chartOptionConst = __webpack_require__(17); +/***/ }), +/* 1 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { -var _chartOptionConst2 = _interopRequireDefault(_chartOptionConst); +"use strict"; +/* harmony export (immutable) */ __webpack_exports__["a"] = __extends; +/* unused harmony export __assign */ +/* unused harmony export __rest */ +/* unused harmony export __decorate */ +/* unused harmony export __param */ +/* unused harmony export __metadata */ +/* unused harmony export __awaiter */ +/* unused harmony export __generator */ +/* unused harmony export __createBinding */ +/* unused harmony export __exportStar */ +/* unused harmony export __values */ +/* unused harmony export __read */ +/* unused harmony export __spread */ +/* unused harmony export __spreadArrays */ +/* unused harmony export __await */ +/* unused harmony export __asyncGenerator */ +/* unused harmony export __asyncDelegator */ +/* unused harmony export __asyncValues */ +/* unused harmony export __makeTemplateObject */ +/* unused harmony export __importStar */ +/* unused harmony export __importDefault */ +/* unused harmony export __classPrivateFieldGet */ +/* unused harmony export __classPrivateFieldSet */ +/*! ***************************************************************************** +Copyright (c) Microsoft Corporation. + +Permission to use, copy, modify, and/or distribute this software for any +purpose with or without fee is hereby granted. + +THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH +REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY +AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, +INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM +LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR +OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR +PERFORMANCE OF THIS SOFTWARE. +***************************************************************************** */ +/* global Reflect, Promise */ + +var extendStatics = function(d, b) { + extendStatics = Object.setPrototypeOf || + ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) || + function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; }; + return extendStatics(d, b); +}; + +function __extends(d, b) { + extendStatics(d, b); + function __() { this.constructor = d; } + d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __()); +} -var _chartOptionUtil = __webpack_require__(30); +var __assign = function() { + __assign = Object.assign || function __assign(t) { + for (var s, i = 1, n = arguments.length; i < n; i++) { + s = arguments[i]; + for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p]; + } + return t; + } + return __assign.apply(this, arguments); +} -var ChartOptionUtil = _interopRequireWildcard(_chartOptionUtil); +function __rest(s, e) { + var t = {}; + for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0) + t[p] = s[p]; + if (s != null && typeof Object.getOwnPropertySymbols === "function") + for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) { + if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i])) + t[p[i]] = s[p[i]]; + } + return t; +} -var _customAlignInputWidget = __webpack_require__(602); +function __decorate(decorators, target, key, desc) { + var c = arguments.length, r = c < 3 ? target : desc === null ? desc = Object.getOwnPropertyDescriptor(target, key) : desc, d; + if (typeof Reflect === "object" && typeof Reflect.decorate === "function") r = Reflect.decorate(decorators, target, key, desc); + else for (var i = decorators.length - 1; i >= 0; i--) if (d = decorators[i]) r = (c < 3 ? d(r) : c > 3 ? d(target, key, r) : d(target, key)) || r; + return c > 3 && r && Object.defineProperty(target, key, r), r; +} -var _customAlignInputWidget2 = _interopRequireDefault(_customAlignInputWidget); +function __param(paramIndex, decorator) { + return function (target, key) { decorator(target, key, paramIndex); } +} -var _columnSelectorDialog = __webpack_require__(120); +function __metadata(metadataKey, metadataValue) { + if (typeof Reflect === "object" && typeof Reflect.metadata === "function") return Reflect.metadata(metadataKey, metadataValue); +} -var _columnSelectorDialog2 = _interopRequireDefault(_columnSelectorDialog); +function __awaiter(thisArg, _arguments, P, generator) { + function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); } + return new (P || (P = Promise))(function (resolve, reject) { + function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } } + function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } } + function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); } + step((generator = generator.apply(thisArg, _arguments || [])).next()); + }); +} -function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } } +function __generator(thisArg, body) { + var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g; + return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g; + function verb(n) { return function (v) { return step([n, v]); }; } + function step(op) { + if (f) throw new TypeError("Generator is already executing."); + while (_) try { + if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t; + if (y = 0, t) op = [op[0] & 2, t.value]; + switch (op[0]) { + case 0: case 1: t = op; break; + case 4: _.label++; return { value: op[1], done: false }; + case 5: _.label++; y = op[1]; op = [0]; continue; + case 7: op = _.ops.pop(); _.trys.pop(); continue; + default: + if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; } + if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; } + if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; } + if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; } + if (t[2]) _.ops.pop(); + _.trys.pop(); continue; + } + op = body.call(thisArg, _); + } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; } + if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true }; + } +} -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } +function __createBinding(o, m, k, k2) { + if (k2 === undefined) k2 = k; + o[k2] = m[k]; +} -/** - * options = { - * width: % or px default 100%, - * height:% or px default 30px, - * value: '' - * onChanged: function(){] - * } - * @param parentId - * @param options - * @constructor - */ -/** - * Created by mk90.kim on 2017-05-10. - */ +function __exportStar(m, exports) { + for (var p in m) if (p !== "default" && !exports.hasOwnProperty(p)) exports[p] = m[p]; +} -function CustomAlignRadioButtonWidget(parentId, options) { - _baseWidget2.default.call(this, parentId, options); +function __values(o) { + var s = typeof Symbol === "function" && Symbol.iterator, m = s && o[s], i = 0; + if (m) return m.call(o); + if (o && typeof o.length === "number") return { + next: function () { + if (o && i >= o.length) o = void 0; + return { value: o && o[i++], done: !o }; + } + }; + throw new TypeError(s ? "Object is not iterable." : "Symbol.iterator is not defined."); } -CustomAlignRadioButtonWidget.prototype = Object.create(_baseWidget2.default.prototype); -CustomAlignRadioButtonWidget.prototype.constructor = CustomAlignRadioButtonWidget; +function __read(o, n) { + var m = typeof Symbol === "function" && o[Symbol.iterator]; + if (!m) return o; + var i = m.call(o), r, ar = [], e; + try { + while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value); + } + catch (error) { e = { error: error }; } + finally { + try { + if (r && !r.done && (m = i["return"])) m.call(i); + } + finally { if (e) throw e.error; } + } + return ar; +} -CustomAlignRadioButtonWidget.prototype._init = function () { - _baseWidget2.default.prototype._init.call(this); -}; +function __spread() { + for (var ar = [], i = 0; i < arguments.length; i++) + ar = ar.concat(__read(arguments[i])); + return ar; +} -CustomAlignRadioButtonWidget.prototype._createAlignWidget = function ($el, options) { - throw new Error('not implemented'); +function __spreadArrays() { + for (var s = 0, i = 0, il = arguments.length; i < il; i++) s += arguments[i].length; + for (var r = Array(s), k = 0, i = 0; i < il; i++) + for (var a = arguments[i], j = 0, jl = a.length; j < jl; j++, k++) + r[k] = a[j]; + return r; }; -CustomAlignRadioButtonWidget.prototype._createContents = function ($parent) { - this._isRendered = false; - this.$widgetWrapper = $('\n
\n
\n
\n
\n '); +function __await(v) { + return this instanceof __await ? (this.v = v, this) : new __await(v); +} - $parent.append(this.$widgetWrapper); +function __asyncGenerator(thisArg, _arguments, generator) { + if (!Symbol.asyncIterator) throw new TypeError("Symbol.asyncIterator is not defined."); + var g = generator.apply(thisArg, _arguments || []), i, q = []; + return i = {}, verb("next"), verb("throw"), verb("return"), i[Symbol.asyncIterator] = function () { return this; }, i; + function verb(n) { if (g[n]) i[n] = function (v) { return new Promise(function (a, b) { q.push([n, v, a, b]) > 1 || resume(n, v); }); }; } + function resume(n, v) { try { step(g[n](v)); } catch (e) { settle(q[0][3], e); } } + function step(r) { r.value instanceof __await ? Promise.resolve(r.value.v).then(fulfill, reject) : settle(q[0][2], r); } + function fulfill(value) { resume("next", value); } + function reject(value) { resume("throw", value); } + function settle(f, v) { if (f(v), q.shift(), q.length) resume(q[0][0], q[0][1]); } +} - this.widgets = []; - this._createWidgetGrpUnits(); - this.render(this.options.value); - this._setPreValue(this.options.value); - this._isRendered = true; -}; +function __asyncDelegator(o) { + var i, p; + return i = {}, verb("next"), verb("throw", function (e) { throw e; }), verb("return"), i[Symbol.iterator] = function () { return this; }, i; + function verb(n, f) { i[n] = o[n] ? function (v) { return (p = !p) ? { value: __await(o[n](v)), done: n === "return" } : f ? f(v) : v; } : f; } +} -CustomAlignRadioButtonWidget.prototype.toggleDisable = function (disabledVal) { - this.align.toggleDisable(disabledVal); - this.input.toggleDisable(disabledVal); +function __asyncValues(o) { + if (!Symbol.asyncIterator) throw new TypeError("Symbol.asyncIterator is not defined."); + var m = o[Symbol.asyncIterator], i; + return m ? m.call(o) : (o = typeof __values === "function" ? __values(o) : o[Symbol.iterator](), i = {}, verb("next"), verb("throw"), verb("return"), i[Symbol.asyncIterator] = function () { return this; }, i); + function verb(n) { i[n] = o[n] && function (v) { return new Promise(function (resolve, reject) { v = o[n](v), settle(resolve, reject, v.done, v.value); }); }; } + function settle(resolve, reject, d, v) { Promise.resolve(v).then(function(v) { resolve({ value: v, done: d }); }, reject); } +} + +function __makeTemplateObject(cooked, raw) { + if (Object.defineProperty) { Object.defineProperty(cooked, "raw", { value: raw }); } else { cooked.raw = raw; } + return cooked; }; -CustomAlignRadioButtonWidget.prototype._createWidgetGrpUnits = function () { - var _this = this; +function __importStar(mod) { + if (mod && mod.__esModule) return mod; + var result = {}; + if (mod != null) for (var k in mod) if (Object.hasOwnProperty.call(mod, k)) result[k] = mod[k]; + result.default = mod; + return result; +} - var $alignComponent = this.$widgetWrapper.find('.bos-widget-button-group-container__row[id="1"]'); - var $inputComponent = this.$widgetWrapper.find('.bos-widget-button-group-container__row[id="2"]'); +function __importDefault(mod) { + return (mod && mod.__esModule) ? mod : { default: mod }; +} - this.$alignComponent = $alignComponent; - this.$inputComponent = $inputComponent; +function __classPrivateFieldGet(receiver, privateMap) { + if (!privateMap.has(receiver)) { + throw new TypeError("attempted to get private field on non-instance"); + } + return privateMap.get(receiver); +} - this.align = this._createAlignWidget($alignComponent, { - // value: !this.options.value.custom && this.options.value.align, - value: this.options.value.align, - onChanged: function onChanged(selectedValue) { - _this.options.value.align = selectedValue; - _this.render(_this.options.value); - _this._triggerChangeEvent(_this.options.value); +function __classPrivateFieldSet(receiver, privateMap, value) { + if (!privateMap.has(receiver)) { + throw new TypeError("attempted to set private field on non-instance"); + } + privateMap.set(receiver, value); + return value; +} + + +/***/ }), +/* 2 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return Subscriber; }); +/* unused harmony export SafeSubscriber */ +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0_tslib__ = __webpack_require__(1); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__util_isFunction__ = __webpack_require__(22); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_2__Observer__ = __webpack_require__(199); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_3__Subscription__ = __webpack_require__(7); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_4__internal_symbol_rxSubscriber__ = __webpack_require__(48); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_5__config__ = __webpack_require__(33); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_6__util_hostReportError__ = __webpack_require__(46); +/** PURE_IMPORTS_START tslib,_util_isFunction,_Observer,_Subscription,_internal_symbol_rxSubscriber,_config,_util_hostReportError PURE_IMPORTS_END */ + + + + + + + +var Subscriber = /*@__PURE__*/ (function (_super) { + __WEBPACK_IMPORTED_MODULE_0_tslib__["a" /* __extends */](Subscriber, _super); + function Subscriber(destinationOrNext, error, complete) { + var _this = _super.call(this) || this; + _this.syncErrorValue = null; + _this.syncErrorThrown = false; + _this.syncErrorThrowable = false; + _this.isStopped = false; + switch (arguments.length) { + case 0: + _this.destination = __WEBPACK_IMPORTED_MODULE_2__Observer__["a" /* empty */]; + break; + case 1: + if (!destinationOrNext) { + _this.destination = __WEBPACK_IMPORTED_MODULE_2__Observer__["a" /* empty */]; + break; + } + if (typeof destinationOrNext === 'object') { + if (destinationOrNext instanceof Subscriber) { + _this.syncErrorThrowable = destinationOrNext.syncErrorThrowable; + _this.destination = destinationOrNext; + destinationOrNext.add(_this); + } + else { + _this.syncErrorThrowable = true; + _this.destination = new SafeSubscriber(_this, destinationOrNext); + } + break; + } + default: + _this.syncErrorThrowable = true; + _this.destination = new SafeSubscriber(_this, destinationOrNext, error, complete); + break; } - }); + return _this; + } + Subscriber.prototype[__WEBPACK_IMPORTED_MODULE_4__internal_symbol_rxSubscriber__["a" /* rxSubscriber */]] = function () { return this; }; + Subscriber.create = function (next, error, complete) { + var subscriber = new Subscriber(next, error, complete); + subscriber.syncErrorThrowable = false; + return subscriber; + }; + Subscriber.prototype.next = function (value) { + if (!this.isStopped) { + this._next(value); + } + }; + Subscriber.prototype.error = function (err) { + if (!this.isStopped) { + this.isStopped = true; + this._error(err); + } + }; + Subscriber.prototype.complete = function () { + if (!this.isStopped) { + this.isStopped = true; + this._complete(); + } + }; + Subscriber.prototype.unsubscribe = function () { + if (this.closed) { + return; + } + this.isStopped = true; + _super.prototype.unsubscribe.call(this); + }; + Subscriber.prototype._next = function (value) { + this.destination.next(value); + }; + Subscriber.prototype._error = function (err) { + this.destination.error(err); + this.unsubscribe(); + }; + Subscriber.prototype._complete = function () { + this.destination.complete(); + this.unsubscribe(); + }; + Subscriber.prototype._unsubscribeAndRecycle = function () { + var _parentOrParents = this._parentOrParents; + this._parentOrParents = null; + this.unsubscribe(); + this.closed = false; + this.isStopped = false; + this._parentOrParents = _parentOrParents; + return this; + }; + return Subscriber; +}(__WEBPACK_IMPORTED_MODULE_3__Subscription__["a" /* Subscription */])); + +var SafeSubscriber = /*@__PURE__*/ (function (_super) { + __WEBPACK_IMPORTED_MODULE_0_tslib__["a" /* __extends */](SafeSubscriber, _super); + function SafeSubscriber(_parentSubscriber, observerOrNext, error, complete) { + var _this = _super.call(this) || this; + _this._parentSubscriber = _parentSubscriber; + var next; + var context = _this; + if (Object(__WEBPACK_IMPORTED_MODULE_1__util_isFunction__["a" /* isFunction */])(observerOrNext)) { + next = observerOrNext; + } + else if (observerOrNext) { + next = observerOrNext.next; + error = observerOrNext.error; + complete = observerOrNext.complete; + if (observerOrNext !== __WEBPACK_IMPORTED_MODULE_2__Observer__["a" /* empty */]) { + context = Object.create(observerOrNext); + if (Object(__WEBPACK_IMPORTED_MODULE_1__util_isFunction__["a" /* isFunction */])(context.unsubscribe)) { + _this.add(context.unsubscribe.bind(context)); + } + context.unsubscribe = _this.unsubscribe.bind(_this); + } + } + _this._context = context; + _this._next = next; + _this._error = error; + _this._complete = complete; + return _this; + } + SafeSubscriber.prototype.next = function (value) { + if (!this.isStopped && this._next) { + var _parentSubscriber = this._parentSubscriber; + if (!__WEBPACK_IMPORTED_MODULE_5__config__["a" /* config */].useDeprecatedSynchronousErrorHandling || !_parentSubscriber.syncErrorThrowable) { + this.__tryOrUnsub(this._next, value); + } + else if (this.__tryOrSetError(_parentSubscriber, this._next, value)) { + this.unsubscribe(); + } + } + }; + SafeSubscriber.prototype.error = function (err) { + if (!this.isStopped) { + var _parentSubscriber = this._parentSubscriber; + var useDeprecatedSynchronousErrorHandling = __WEBPACK_IMPORTED_MODULE_5__config__["a" /* config */].useDeprecatedSynchronousErrorHandling; + if (this._error) { + if (!useDeprecatedSynchronousErrorHandling || !_parentSubscriber.syncErrorThrowable) { + this.__tryOrUnsub(this._error, err); + this.unsubscribe(); + } + else { + this.__tryOrSetError(_parentSubscriber, this._error, err); + this.unsubscribe(); + } + } + else if (!_parentSubscriber.syncErrorThrowable) { + this.unsubscribe(); + if (useDeprecatedSynchronousErrorHandling) { + throw err; + } + Object(__WEBPACK_IMPORTED_MODULE_6__util_hostReportError__["a" /* hostReportError */])(err); + } + else { + if (useDeprecatedSynchronousErrorHandling) { + _parentSubscriber.syncErrorValue = err; + _parentSubscriber.syncErrorThrown = true; + } + else { + Object(__WEBPACK_IMPORTED_MODULE_6__util_hostReportError__["a" /* hostReportError */])(err); + } + this.unsubscribe(); + } + } + }; + SafeSubscriber.prototype.complete = function () { + var _this = this; + if (!this.isStopped) { + var _parentSubscriber = this._parentSubscriber; + if (this._complete) { + var wrappedComplete = function () { return _this._complete.call(_this._context); }; + if (!__WEBPACK_IMPORTED_MODULE_5__config__["a" /* config */].useDeprecatedSynchronousErrorHandling || !_parentSubscriber.syncErrorThrowable) { + this.__tryOrUnsub(wrappedComplete); + this.unsubscribe(); + } + else { + this.__tryOrSetError(_parentSubscriber, wrappedComplete); + this.unsubscribe(); + } + } + else { + this.unsubscribe(); + } + } + }; + SafeSubscriber.prototype.__tryOrUnsub = function (fn, value) { + try { + fn.call(this._context, value); + } + catch (err) { + this.unsubscribe(); + if (__WEBPACK_IMPORTED_MODULE_5__config__["a" /* config */].useDeprecatedSynchronousErrorHandling) { + throw err; + } + else { + Object(__WEBPACK_IMPORTED_MODULE_6__util_hostReportError__["a" /* hostReportError */])(err); + } + } + }; + SafeSubscriber.prototype.__tryOrSetError = function (parent, fn, value) { + if (!__WEBPACK_IMPORTED_MODULE_5__config__["a" /* config */].useDeprecatedSynchronousErrorHandling) { + throw new Error('bad call'); + } + try { + fn.call(this._context, value); + } + catch (err) { + if (__WEBPACK_IMPORTED_MODULE_5__config__["a" /* config */].useDeprecatedSynchronousErrorHandling) { + parent.syncErrorValue = err; + parent.syncErrorThrown = true; + return true; + } + else { + Object(__WEBPACK_IMPORTED_MODULE_6__util_hostReportError__["a" /* hostReportError */])(err); + return true; + } + } + return false; + }; + SafeSubscriber.prototype._unsubscribe = function () { + var _parentSubscriber = this._parentSubscriber; + this._context = null; + this._parentSubscriber = null; + _parentSubscriber.unsubscribe(); + }; + return SafeSubscriber; +}(Subscriber)); - this.input = new _customAlignInputWidget2.default($inputComponent, { - value: { - custom: !!this.options.value.custom, - value: this.options.value.value - }, - label: this._getLabel(), - onChanged: function onChanged(value) { - _this.options.value.value = value.value; - _this._triggerChangeEvent(_this.options.value); +//# sourceMappingURL=Subscriber.js.map + + +/***/ }), +/* 3 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return Observable; }); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__util_canReportError__ = __webpack_require__(45); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__util_toSubscriber__ = __webpack_require__(255); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_2__symbol_observable__ = __webpack_require__(19); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_3__util_pipe__ = __webpack_require__(49); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_4__config__ = __webpack_require__(33); +/** PURE_IMPORTS_START _util_canReportError,_util_toSubscriber,_symbol_observable,_util_pipe,_config PURE_IMPORTS_END */ + + + + + +var Observable = /*@__PURE__*/ (function () { + function Observable(subscribe) { + this._isScalar = false; + if (subscribe) { + this._subscribe = subscribe; } - }); -}; + } + Observable.prototype.lift = function (operator) { + var observable = new Observable(); + observable.source = this; + observable.operator = operator; + return observable; + }; + Observable.prototype.subscribe = function (observerOrNext, error, complete) { + var operator = this.operator; + var sink = Object(__WEBPACK_IMPORTED_MODULE_1__util_toSubscriber__["a" /* toSubscriber */])(observerOrNext, error, complete); + if (operator) { + sink.add(operator.call(sink, this.source)); + } + else { + sink.add(this.source || (__WEBPACK_IMPORTED_MODULE_4__config__["a" /* config */].useDeprecatedSynchronousErrorHandling && !sink.syncErrorThrowable) ? + this._subscribe(sink) : + this._trySubscribe(sink)); + } + if (__WEBPACK_IMPORTED_MODULE_4__config__["a" /* config */].useDeprecatedSynchronousErrorHandling) { + if (sink.syncErrorThrowable) { + sink.syncErrorThrowable = false; + if (sink.syncErrorThrown) { + throw sink.syncErrorValue; + } + } + } + return sink; + }; + Observable.prototype._trySubscribe = function (sink) { + try { + return this._subscribe(sink); + } + catch (err) { + if (__WEBPACK_IMPORTED_MODULE_4__config__["a" /* config */].useDeprecatedSynchronousErrorHandling) { + sink.syncErrorThrown = true; + sink.syncErrorValue = err; + } + if (Object(__WEBPACK_IMPORTED_MODULE_0__util_canReportError__["a" /* canReportError */])(sink)) { + sink.error(err); + } + else { + console.warn(err); + } + } + }; + Observable.prototype.forEach = function (next, promiseCtor) { + var _this = this; + promiseCtor = getPromiseCtor(promiseCtor); + return new promiseCtor(function (resolve, reject) { + var subscription; + subscription = _this.subscribe(function (value) { + try { + next(value); + } + catch (err) { + reject(err); + if (subscription) { + subscription.unsubscribe(); + } + } + }, reject, resolve); + }); + }; + Observable.prototype._subscribe = function (subscriber) { + var source = this.source; + return source && source.subscribe(subscriber); + }; + Observable.prototype[__WEBPACK_IMPORTED_MODULE_2__symbol_observable__["a" /* observable */]] = function () { + return this; + }; + Observable.prototype.pipe = function () { + var operations = []; + for (var _i = 0; _i < arguments.length; _i++) { + operations[_i] = arguments[_i]; + } + if (operations.length === 0) { + return this; + } + return Object(__WEBPACK_IMPORTED_MODULE_3__util_pipe__["b" /* pipeFromArray */])(operations)(this); + }; + Observable.prototype.toPromise = function (promiseCtor) { + var _this = this; + promiseCtor = getPromiseCtor(promiseCtor); + return new promiseCtor(function (resolve, reject) { + var value; + _this.subscribe(function (x) { return value = x; }, function (err) { return reject(err); }, function () { return resolve(value); }); + }); + }; + Observable.create = function (subscribe) { + return new Observable(subscribe); + }; + return Observable; +}()); -CustomAlignRadioButtonWidget.prototype._triggerChangeEvent = function (value) { - if (typeof this.options.onChanged === 'function') { - this.options.onChanged(value); - } else { - console.warn('CustomAlignRadioButtonWidget: onChanged is not specified'); +function getPromiseCtor(promiseCtor) { + if (!promiseCtor) { + promiseCtor = __WEBPACK_IMPORTED_MODULE_4__config__["a" /* config */].Promise || Promise; } -}; + if (!promiseCtor) { + throw new Error('no Promise impl found'); + } + return promiseCtor; +} +//# sourceMappingURL=Observable.js.map -CustomAlignRadioButtonWidget.prototype.render = function (value) { - this.align.render(value.align); - this.input.render({ value: value.value }); - this.input.toggleDisable(value.align === 'center'); -}; -exports.default = CustomAlignRadioButtonWidget; +/***/ }), +/* 4 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return OuterSubscriber; }); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0_tslib__ = __webpack_require__(1); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__Subscriber__ = __webpack_require__(2); +/** PURE_IMPORTS_START tslib,_Subscriber PURE_IMPORTS_END */ + + +var OuterSubscriber = /*@__PURE__*/ (function (_super) { + __WEBPACK_IMPORTED_MODULE_0_tslib__["a" /* __extends */](OuterSubscriber, _super); + function OuterSubscriber() { + return _super !== null && _super.apply(this, arguments) || this; + } + OuterSubscriber.prototype.notifyNext = function (outerValue, innerValue, outerIndex, innerIndex, innerSub) { + this.destination.next(innerValue); + }; + OuterSubscriber.prototype.notifyError = function (error, innerSub) { + this.destination.error(error); + }; + OuterSubscriber.prototype.notifyComplete = function (innerSub) { + this.destination.complete(); + }; + return OuterSubscriber; +}(__WEBPACK_IMPORTED_MODULE_1__Subscriber__["a" /* Subscriber */])); + +//# sourceMappingURL=OuterSubscriber.js.map + /***/ }), -/* 287 */ +/* 5 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (immutable) */ __webpack_exports__["a"] = subscribeToResult; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__InnerSubscriber__ = __webpack_require__(17); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__subscribeTo__ = __webpack_require__(56); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_2__Observable__ = __webpack_require__(3); +/** PURE_IMPORTS_START _InnerSubscriber,_subscribeTo,_Observable PURE_IMPORTS_END */ + + + +function subscribeToResult(outerSubscriber, result, outerValue, outerIndex, innerSubscriber) { + if (innerSubscriber === void 0) { + innerSubscriber = new __WEBPACK_IMPORTED_MODULE_0__InnerSubscriber__["a" /* InnerSubscriber */](outerSubscriber, outerValue, outerIndex); + } + if (innerSubscriber.closed) { + return undefined; + } + if (result instanceof __WEBPACK_IMPORTED_MODULE_2__Observable__["a" /* Observable */]) { + return result.subscribe(innerSubscriber); + } + return Object(__WEBPACK_IMPORTED_MODULE_1__subscribeTo__["a" /* subscribeTo */])(result)(innerSubscriber); +} +//# sourceMappingURL=subscribeToResult.js.map + + +/***/ }), +/* 6 */ /***/ (function(module, exports, __webpack_require__) { "use strict"; @@ -5838,1865 +8506,64135 @@ Object.defineProperty(exports, "__esModule", { value: true }); -var _baseControl = __webpack_require__(9); +var _slicedToArray = function () { function sliceIterator(arr, i) { var _arr = []; var _n = true; var _d = false; var _e = undefined; try { for (var _i = arr[Symbol.iterator](), _s; !(_n = (_s = _i.next()).done); _n = true) { _arr.push(_s.value); if (i && _arr.length === i) break; } } catch (err) { _d = true; _e = err; } finally { try { if (!_n && _i["return"]) _i["return"](); } finally { if (_d) throw _e; } } return _arr; } return function (arr, i) { if (Array.isArray(arr)) { return arr; } else if (Symbol.iterator in Object(arr)) { return sliceIterator(arr, i); } else { throw new TypeError("Invalid attempt to destructure non-iterable instance"); } }; }(); -var _baseControl2 = _interopRequireDefault(_baseControl); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); -var _widgetFactory = __webpack_require__(3); +__webpack_require__(232); -var WidgetFactory = _interopRequireWildcard(_widgetFactory); +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } -function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } } +var disableClass = 'brtc-state-disabled'; +var errorFunction = function errorFunction(key) { + return console.error(key + ' is undefined function'); +}; -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } +var Base = function () { + function Base($el, options) { + _classCallCheck(this, Base); -/** - * Created by mk90.kim on 2017-05-10. - */ + this.create($el, options); + } -function AxisTitleControl(parentId, options, headerKey) { - _baseControl2.default.call(this, parentId, options, headerKey); -} + _createClass(Base, [{ + key: 'create', + value: function create($el, opt) { + this.$elem = $el; + } -AxisTitleControl.prototype = Object.create(_baseControl2.default.prototype); -AxisTitleControl.prototype.constructor = AxisTitleControl; + // Option get -AxisTitleControl.prototype._init = function () { - _baseControl2.default.prototype._init.call(this); - this.xAxisOption = this.options.chartOption.xAxis; - this.yAxisOption = this.options.chartOption.yAxis; -}; + }, { + key: 'getOption', + value: function getOption(optNm) { + for (var _len = arguments.length, args = Array(_len > 1 ? _len - 1 : 0), _key = 1; _key < _len; _key++) { + args[_key - 1] = arguments[_key]; + } -AxisTitleControl.prototype._createContents = function ($parent) { - _baseControl2.default.prototype._createContents.call(this, $parent); + return { + 'disabled': this._getDisabled, + 'value': this._getValue + }[optNm].apply(this, args); + } - var _this = this; - var headerOption = { - label: 'Title', - showBtn: { - //POLICY: xAxis값을 기준으로 함 - defaultVal: this.xAxisOption[0].title.show | this.yAxisOption[0].title.show, - clickfunc: function clickfunc(event) { - var checkedVal = event.args.checked; - _this.toggleDisableComponent(checkedVal); - _this.xAxisOption[0].title.show = checkedVal; - _this.yAxisOption[0].title.show = checkedVal; - _this.setExpanderPreview.call(_this); - _this.options.onChanged('onChartOptionChanged', { - xAxis: _this.xAxisOption, - yAxis: _this.yAxisOption - }); - } + // Option setting + + }, { + key: 'setOption', + value: function setOption(opts) { + var _this = this; + + this.lazyExecuteList = this.lazyExecuteList ? this.lazyExecuteList : []; + this.lazyExecuteQueue = []; + this.applyOpt = this._getForcePropertyList(); + + Object.entries(opts).map(function (_ref) { + var _ref2 = _slicedToArray(_ref, 2), + key = _ref2[0], + value = _ref2[1]; + + return [key, _this._getPropertyList(key), value, _this.lazyExecuteList.some(function (lazy) { + return lazy === key; + })]; + }).filter(function (_ref3) { + var _ref4 = _slicedToArray(_ref3, 2), + key = _ref4[0], + func = _ref4[1]; + + return !func ? errorFunction(key) && false : !!func; + }).filter(function (_ref5) { + var _ref6 = _slicedToArray(_ref5, 4), + func = _ref6[1], + value = _ref6[2], + lazy = _ref6[3]; + + return lazy ? _this.lazyExecuteQueue.push(function () { + return func.call(_this, value); + }) && false : !lazy; + }).forEach(function (_ref7) { + var _ref8 = _slicedToArray(_ref7, 3), + key = _ref8[0], + func = _ref8[1], + value = _ref8[2]; + + func.call(_this, value); + }); + this._applyOption(this.applyOpt); + this.lazyExecuteQueue.forEach(function (func) { + return func.call(); + }); } - }; - this.createComponentHeader(headerOption); - this.createComponentContents(); - this.setComponentShow(); -}; -AxisTitleControl.prototype.createComponentContents = function (contentsOption) { - _baseControl2.default.prototype.createComponentContents.call(this, contentsOption); + // Method call - this._createValueComponent(); - this._createStyleComponent(); -}; + }, { + key: 'execute', + value: function execute(name) { + for (var _len2 = arguments.length, args = Array(_len2 > 1 ? _len2 - 1 : 0), _key2 = 1; _key2 < _len2; _key2++) { + args[_key2 - 1] = arguments[_key2]; + } -AxisTitleControl.prototype._createValueComponent = function () { + return this._getMethodList(name).apply(this, args); + } - var $valueComponent = $('
'); - this.$controlContents.append($valueComponent); + // EventCallback binding - var multiLabelWidget = WidgetFactory.createMultiLabelWidget($valueComponent, { - label: 'Axis', - numOfComponent: 1, - value: ['Value'] - }); + }, { + key: 'subscribe', + value: function subscribe(name) { + var func = this._getEventList(name) || errorFunction(name); - var _this = this; - this.xAxisValueInput = WidgetFactory.createCheckedMultiInputWidget($valueComponent, { - label: 'X Axis', - labelPosition: 'row', - labelWidth: '40px', - inputStyle: 'box', - numOfComponent: 1, - value: [this.xAxisOption[0].title.text], - inputType: [''], - onChanged: [function (inputVal) { - _this.xAxisOption[0].title.text = inputVal; - _this.options.onChanged('onChartOptionChanged', { xAxis: _this.xAxisOption }); - }], - showBtn: { - defaultVal: this.xAxisOption[0].title.show, - clickfunc: function clickfunc(event) { - _this.xAxisOption[0].title.show = event.args.checked; - _this.xAxisValueInput.toggleDisable(!event.args.checked, true); - _this.setExpanderPreview.call(_this); - _this.options.onChanged('onChartOptionChanged', { xAxis: _this.xAxisOption }); + for (var _len3 = arguments.length, args = Array(_len3 > 1 ? _len3 - 1 : 0), _key3 = 1; _key3 < _len3; _key3++) { + args[_key3 - 1] = arguments[_key3]; + } + + func.apply(this, args); + } + }, { + key: '_setPreValue', + value: function _setPreValue(preVal) { + this._preValue = JSON.stringify(preVal); + } + }, { + key: '_isChanged', + value: function _isChanged(currentVal) { + if (this._preValue !== JSON.stringify(currentVal)) { + this._setPreValue(currentVal); + return true; + } else { + return false; + } + } + }, { + key: '_setOnChange', + value: function _setOnChange(func) { + if (typeof func === 'function') { + this.$elem.on('change', function (evt) { + // eslint-disable-next-line no-invalid-this + func.bind(this)(evt); + }); + } + } + }, { + key: '_setDisabled', + value: function _setDisabled(val) { + if (val === true) { + this.$elem.addClass(disableClass); + } else { + this.$elem.removeClass(disableClass); } } - }); - this._widgetList.push(this.xAxisValueInput); + // Base.prototype._getDisabled = function () { + // return getDisable(this.$elem) + // }; - this.yAxisValueInput = WidgetFactory.createCheckedMultiInputWidget($valueComponent, { - label: 'Y Axis', - labelPosition: 'row', - labelWidth: '40px', - inputStyle: 'box', - numOfComponent: 1, - value: [this.yAxisOption[0].title.text], - inputType: [''], - placeHolder: [''], - onChanged: [function (inputVal) { - _this.yAxisOption[0].title.text = inputVal; - _this.options.onChanged('onChartOptionChanged', { yAxis: _this.yAxisOption }); - }], - showBtn: { - defaultVal: this.yAxisOption[0].title.show, - clickfunc: function clickfunc(event) { - _this.yAxisOption[0].title.show = event.args.checked; - _this.yAxisValueInput.toggleDisable(!event.args.checked, true); - _this.setExpanderPreview.call(_this); - _this.options.onChanged('onChartOptionChanged', { yAxis: _this.yAxisOption }); - } + }, { + key: '_setValue', + value: function _setValue(val) {} + }, { + key: '_getValue', + value: function _getValue() {} + }, { + key: '_addEvent', + value: function _addEvent(evtNm, callback) { + this.$elem.on(evtNm, callback); } - }); + }, { + key: '_getPropertyList', + value: function _getPropertyList(attrNm) { + var setters = {}; + return setters[attrNm]; + } + }, { + key: '_getEventList', + value: function _getEventList(name) { + return {}[name]; + } + }, { + key: '_getMethodList', + value: function _getMethodList(name) { + return {}[name]; + } + }, { + key: '_getForcePropertyList', + value: function _getForcePropertyList() { + return {}; + } + }, { + key: '_applyOption', + value: function _applyOption() {} + }]); - this._widgetList.push(this.yAxisValueInput); -}; + return Base; +}(); -AxisTitleControl.prototype._createStyleComponent = function () { - var _this = this; +exports.default = Base; - //POLICY: xAxis값을 기준으로 함 - this.fontStyle = WidgetFactory.createFontStyleWidget(this.$controlContents, { - value: [this.xAxisOption[0].title.textStyle.fontFamily, this.xAxisOption[0].title.textStyle.fontSize, this.xAxisOption[0].title.textStyle.color, [this.xAxisOption[0].title.textStyle.fontWeight, this.xAxisOption[0].title.textStyle.fontStyle, this.xAxisOption[0].title.textStyle.textDecoration]], - onChanged: [function (inputVal) { - _this.xAxisOption[0].title.textStyle.fontFamily = inputVal; - _this.yAxisOption[0].title.textStyle.fontFamily = inputVal; - _this.options.onChanged('onChartOptionChanged', { - xAxis: _this.xAxisOption, - yAxis: _this.yAxisOption - }); - }, function (inputVal) { - _this.xAxisOption[0].title.textStyle.fontSize = inputVal; - _this.yAxisOption[0].title.textStyle.fontSize = inputVal; - _this.options.onChanged('onChartOptionChanged', { - xAxis: _this.xAxisOption, - yAxis: _this.yAxisOption - }); - }, function (inputVal) { - _this.xAxisOption[0].title.textStyle.color = inputVal; - _this.yAxisOption[0].title.textStyle.color = inputVal; - _this.options.onChanged('onChartOptionChanged', { - xAxis: _this.xAxisOption, - yAxis: _this.yAxisOption - }); - }, function (selectedVals) { - _this.xAxisOption[0].title.textStyle.fontWeight = $.inArray('bold', selectedVals) > -1 ? 'bold' : 'normal'; - _this.xAxisOption[0].title.textStyle.fontStyle = $.inArray('italic', selectedVals) > -1 ? 'italic' : 'normal'; - _this.xAxisOption[0].title.textStyle.textDecoration = $.inArray('underline', selectedVals) > -1 ? 'underline' : 'none'; - _this.yAxisOption[0].title.textStyle.fontWeight = $.inArray('bold', selectedVals) > -1 ? 'bold' : 'normal'; - _this.yAxisOption[0].title.textStyle.fontStyle = $.inArray('italic', selectedVals) > -1 ? 'italic' : 'normal'; - _this.yAxisOption[0].title.textStyle.textDecoration = $.inArray('underline', selectedVals) > -1 ? 'underline' : 'none'; - _this.options.onChanged('onChartOptionChanged', { - xAxis: _this.xAxisOption, - yAxis: _this.yAxisOption - }); - }] - }); +/***/ }), +/* 7 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { - this._widgetList.push(this.fontStyle); -}; +"use strict"; +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return Subscription; }); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__util_isArray__ = __webpack_require__(11); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__util_isObject__ = __webpack_require__(47); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_2__util_isFunction__ = __webpack_require__(22); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_3__util_UnsubscriptionError__ = __webpack_require__(200); +/** PURE_IMPORTS_START _util_isArray,_util_isObject,_util_isFunction,_util_UnsubscriptionError PURE_IMPORTS_END */ + + + + +var Subscription = /*@__PURE__*/ (function () { + function Subscription(unsubscribe) { + this.closed = false; + this._parentOrParents = null; + this._subscriptions = null; + if (unsubscribe) { + this._unsubscribe = unsubscribe; + } + } + Subscription.prototype.unsubscribe = function () { + var errors; + if (this.closed) { + return; + } + var _a = this, _parentOrParents = _a._parentOrParents, _unsubscribe = _a._unsubscribe, _subscriptions = _a._subscriptions; + this.closed = true; + this._parentOrParents = null; + this._subscriptions = null; + if (_parentOrParents instanceof Subscription) { + _parentOrParents.remove(this); + } + else if (_parentOrParents !== null) { + for (var index = 0; index < _parentOrParents.length; ++index) { + var parent_1 = _parentOrParents[index]; + parent_1.remove(this); + } + } + if (Object(__WEBPACK_IMPORTED_MODULE_2__util_isFunction__["a" /* isFunction */])(_unsubscribe)) { + try { + _unsubscribe.call(this); + } + catch (e) { + errors = e instanceof __WEBPACK_IMPORTED_MODULE_3__util_UnsubscriptionError__["a" /* UnsubscriptionError */] ? flattenUnsubscriptionErrors(e.errors) : [e]; + } + } + if (Object(__WEBPACK_IMPORTED_MODULE_0__util_isArray__["a" /* isArray */])(_subscriptions)) { + var index = -1; + var len = _subscriptions.length; + while (++index < len) { + var sub = _subscriptions[index]; + if (Object(__WEBPACK_IMPORTED_MODULE_1__util_isObject__["a" /* isObject */])(sub)) { + try { + sub.unsubscribe(); + } + catch (e) { + errors = errors || []; + if (e instanceof __WEBPACK_IMPORTED_MODULE_3__util_UnsubscriptionError__["a" /* UnsubscriptionError */]) { + errors = errors.concat(flattenUnsubscriptionErrors(e.errors)); + } + else { + errors.push(e); + } + } + } + } + } + if (errors) { + throw new __WEBPACK_IMPORTED_MODULE_3__util_UnsubscriptionError__["a" /* UnsubscriptionError */](errors); + } + }; + Subscription.prototype.add = function (teardown) { + var subscription = teardown; + if (!teardown) { + return Subscription.EMPTY; + } + switch (typeof teardown) { + case 'function': + subscription = new Subscription(teardown); + case 'object': + if (subscription === this || subscription.closed || typeof subscription.unsubscribe !== 'function') { + return subscription; + } + else if (this.closed) { + subscription.unsubscribe(); + return subscription; + } + else if (!(subscription instanceof Subscription)) { + var tmp = subscription; + subscription = new Subscription(); + subscription._subscriptions = [tmp]; + } + break; + default: { + throw new Error('unrecognized teardown ' + teardown + ' added to Subscription.'); + } + } + var _parentOrParents = subscription._parentOrParents; + if (_parentOrParents === null) { + subscription._parentOrParents = this; + } + else if (_parentOrParents instanceof Subscription) { + if (_parentOrParents === this) { + return subscription; + } + subscription._parentOrParents = [_parentOrParents, this]; + } + else if (_parentOrParents.indexOf(this) === -1) { + _parentOrParents.push(this); + } + else { + return subscription; + } + var subscriptions = this._subscriptions; + if (subscriptions === null) { + this._subscriptions = [subscription]; + } + else { + subscriptions.push(subscription); + } + return subscription; + }; + Subscription.prototype.remove = function (subscription) { + var subscriptions = this._subscriptions; + if (subscriptions) { + var subscriptionIndex = subscriptions.indexOf(subscription); + if (subscriptionIndex !== -1) { + subscriptions.splice(subscriptionIndex, 1); + } + } + }; + Subscription.EMPTY = (function (empty) { + empty.closed = true; + return empty; + }(new Subscription())); + return Subscription; +}()); + +function flattenUnsubscriptionErrors(errors) { + return errors.reduce(function (errs, err) { return errs.concat((err instanceof __WEBPACK_IMPORTED_MODULE_3__util_UnsubscriptionError__["a" /* UnsubscriptionError */]) ? err.errors : err); }, []); +} +//# sourceMappingURL=Subscription.js.map -exports.default = AxisTitleControl; /***/ }), -/* 288 */ +/* 8 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +Object.defineProperty(__webpack_exports__, "__esModule", { value: true }); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0_juijs__ = __webpack_require__(250); + +/* harmony default export */ __webpack_exports__["default"] = (__WEBPACK_IMPORTED_MODULE_0_juijs__["a" /* default */]); + +/***/ }), +/* 9 */ /***/ (function(module, exports, __webpack_require__) { +var __WEBPACK_AMD_DEFINE_ARRAY__, __WEBPACK_AMD_DEFINE_RESULT__;/*! + * jQuery JavaScript Library v3.5.1 + * https://jquery.com/ + * + * Includes Sizzle.js + * https://sizzlejs.com/ + * + * Copyright JS Foundation and other contributors + * Released under the MIT license + * https://jquery.org/license + * + * Date: 2020-05-04T22:49Z + */ +( function( global, factory ) { + + "use strict"; + + if ( typeof module === "object" && typeof module.exports === "object" ) { + + // For CommonJS and CommonJS-like environments where a proper `window` + // is present, execute the factory and get jQuery. + // For environments that do not have a `window` with a `document` + // (such as Node.js), expose a factory as module.exports. + // This accentuates the need for the creation of a real `window`. + // e.g. var jQuery = require("jquery")(window); + // See ticket #14549 for more info. + module.exports = global.document ? + factory( global, true ) : + function( w ) { + if ( !w.document ) { + throw new Error( "jQuery requires a window with a document" ); + } + return factory( w ); + }; + } else { + factory( global ); + } + +// Pass this if window is not defined yet +} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { + +// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 +// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode +// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common +// enough that all such attempts are guarded in a try block. "use strict"; +var arr = []; -Object.defineProperty(exports, "__esModule", { - value: true -}); +var getProto = Object.getPrototypeOf; -var _baseControl = __webpack_require__(9); +var slice = arr.slice; -var _baseControl2 = _interopRequireDefault(_baseControl); +var flat = arr.flat ? function( array ) { + return arr.flat.call( array ); +} : function( array ) { + return arr.concat.apply( [], array ); +}; -var _widgetFactory = __webpack_require__(3); -var WidgetFactory = _interopRequireWildcard(_widgetFactory); +var push = arr.push; -var _formatHelperDialog = __webpack_require__(75); +var indexOf = arr.indexOf; -var _formatHelperDialog2 = _interopRequireDefault(_formatHelperDialog); +var class2type = {}; -function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } } +var toString = class2type.toString; -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } +var hasOwn = class2type.hasOwnProperty; -function AxisLabelControl(parentId, options, headerKey) { - _baseControl2.default.call(this, parentId, options, headerKey); -} /** - * Created by mk90.kim on 2017-05-10. - */ +var fnToString = hasOwn.toString; -AxisLabelControl.prototype = Object.create(_baseControl2.default.prototype); -AxisLabelControl.prototype.constructor = AxisLabelControl; +var ObjectFunctionString = fnToString.call( Object ); -AxisLabelControl.prototype._init = function () { - _baseControl2.default.prototype._init.call(this); - this.xAxisOption = this.options.chartOption.xAxis; - this.yAxisOption = this.options.chartOption.yAxis; -}; +var support = {}; -AxisLabelControl.prototype._createContents = function ($parent) { - _baseControl2.default.prototype._createContents.call(this, $parent); +var isFunction = function isFunction( obj ) { - var _this = this; - var headerOption = { - label: 'Label', - showBtn: { - defaultVal: this.xAxisOption[0].axisLabel.show | this.yAxisOption[0].axisLabel.show, - clickfunc: function clickfunc(event) { - var checkedVal = event.args.checked; - _this.toggleDisableComponent(checkedVal); - _this.xAxisOption[0].axisLabel.show = checkedVal; - _this.yAxisOption[0].axisLabel.show = checkedVal; - _this.setExpanderPreview.call(_this); - _this.options.onChanged('onChartOptionChanged', { - xAxis: _this.xAxisOption, - yAxis: _this.yAxisOption - }); - } - } - }; - this.createComponentHeader(headerOption); - this.createComponentContents(); - this.setComponentShow(); -}; + // Support: Chrome <=57, Firefox <=52 + // In some browsers, typeof returns "function" for HTML elements + // (i.e., `typeof document.createElement( "object" ) === "function"`). + // We don't want to classify *any* DOM node as a function. + return typeof obj === "function" && typeof obj.nodeType !== "number"; + }; -AxisLabelControl.prototype.createComponentContents = function (contentsOption) { - _baseControl2.default.prototype.createComponentContents.call(this, contentsOption); - this._createValueComponent(); - this._createFontStyleComponent(); -}; +var isWindow = function isWindow( obj ) { + return obj != null && obj === obj.window; + }; -AxisLabelControl.prototype._createValueComponent = function () { - var $valueComponent = $('
'); - this.$controlContents.append($valueComponent); - var _this = this; +var document = window.document; - var multiLabelWidget = WidgetFactory.createMultiLabelWidget($valueComponent, { - label: 'Axis', - numOfComponent: 2, - value: ['Formatter', 'Angle'], - helper: [_formatHelperDialog2.default, false] - }); - this._widgetList.push(multiLabelWidget); - this.xAxisFormatInput = WidgetFactory.createCheckedMultiInputWidget($valueComponent, { - label: 'X Axis', - labelPosition: 'row', - labelWidth: '40px', - inputStyle: 'box', - numOfComponent: 2, - placeHolder: ['{value}', ''], - value: [this.xAxisOption[0].axisLabel.formatter, this.xAxisOption[0].axisLabel.rotate], - inputType: ['', 'number'], - onChanged: [function (inputVal) { - _this.xAxisOption[0].axisLabel.formatter = inputVal ? inputVal : null; - _this.options.onChanged('onChartOptionChanged', { xAxis: _this.xAxisOption }); - }, function (inputVal) { - _this.xAxisOption[0].axisLabel.rotate = inputVal; - _this.options.onChanged('onChartOptionChanged', { xAxis: _this.xAxisOption }); - }], - showBtn: { - defaultVal: this.xAxisOption[0].axisLabel.show, - clickfunc: function clickfunc(event) { - _this.xAxisOption[0].axisLabel.show = event.args.checked; - _this.xAxisFormatInput.toggleDisable(!event.args.checked, true); - _this.setExpanderPreview.call(_this); - _this.options.onChanged('onChartOptionChanged', { xAxis: _this.xAxisOption }); - } - } - }); + var preservedScriptAttributes = { + type: true, + src: true, + nonce: true, + noModule: true + }; - this._widgetList.push(this.xAxisFormatInput); + function DOMEval( code, node, doc ) { + doc = doc || document; - this.yAxisFormatInput = WidgetFactory.createCheckedMultiInputWidget($valueComponent, { - label: 'Y Axis', - labelPosition: 'row', - labelWidth: '40px', - inputStyle: 'box', - numOfComponent: 2, - placeHolder: ['{value}', ''], - value: [this.yAxisOption[0].axisLabel.formatter, this.yAxisOption[0].axisLabel.rotate], - inputType: ['', 'number'], - onChanged: [function (inputVal) { - _this.yAxisOption[0].axisLabel.formatter = inputVal ? inputVal : null; - _this.options.onChanged('onChartOptionChanged', { yAxis: _this.yAxisOption }); - }, function (inputVal) { - _this.yAxisOption[0].axisLabel.rotate = inputVal; - _this.options.onChanged('onChartOptionChanged', { yAxis: _this.yAxisOption }); - }], - showBtn: { - defaultVal: this.yAxisOption[0].axisLabel.show, - clickfunc: function clickfunc(event) { - _this.yAxisOption[0].axisLabel.show = event.args.checked; - _this.yAxisFormatInput.toggleDisable(!event.args.checked, true); - _this.setExpanderPreview.call(_this); - _this.options.onChanged('onChartOptionChanged', { yAxis: _this.yAxisOption }); - } - } - }); + var i, val, + script = doc.createElement( "script" ); - this._widgetList.push(this.yAxisFormatInput); -}; + script.text = code; + if ( node ) { + for ( i in preservedScriptAttributes ) { -AxisLabelControl.prototype._createFontStyleComponent = function () { - var _this = this; - this.fontStyle = WidgetFactory.createFontStyleWidget(this.$controlContents, { - fontStyleBtnList: ['bold', 'italic'], - value: [this.xAxisOption[0].axisLabel.textStyle.fontFamily, this.xAxisOption[0].axisLabel.textStyle.fontSize, this.xAxisOption[0].axisLabel.textStyle.color, [this.xAxisOption[0].axisLabel.textStyle.fontWeight, this.xAxisOption[0].axisLabel.textStyle.fontStyle]], - onChanged: [function (inputVal) { - _this.xAxisOption[0].axisLabel.textStyle.fontFamily = inputVal; - _this.yAxisOption[0].axisLabel.textStyle.fontFamily = inputVal; - _this.options.onChanged('onChartOptionChanged', { - xAxis: _this.xAxisOption, - yAxis: _this.yAxisOption - }); - }, function (inputVal) { - _this.xAxisOption[0].axisLabel.textStyle.fontSize = inputVal; - _this.yAxisOption[0].axisLabel.textStyle.fontSize = inputVal; - _this.options.onChanged('onChartOptionChanged', { - xAxis: _this.xAxisOption, - yAxis: _this.yAxisOption - }); - }, function (inputVal) { - _this.xAxisOption[0].axisLabel.textStyle.color = inputVal; - _this.yAxisOption[0].axisLabel.textStyle.color = inputVal; - _this.options.onChanged('onChartOptionChanged', { - xAxis: _this.xAxisOption, - yAxis: _this.yAxisOption - }); - }, function (selectedVals) { - _this.xAxisOption[0].axisLabel.textStyle.fontWeight = $.inArray('bold', selectedVals) > -1 ? 'bold' : 'normal'; - _this.xAxisOption[0].axisLabel.textStyle.fontStyle = $.inArray('italic', selectedVals) > -1 ? 'italic' : 'normal'; - _this.yAxisOption[0].axisLabel.textStyle.fontWeight = $.inArray('bold', selectedVals) > -1 ? 'bold' : 'normal'; - _this.yAxisOption[0].axisLabel.textStyle.fontStyle = $.inArray('italic', selectedVals) > -1 ? 'italic' : 'normal'; - _this.options.onChanged('onChartOptionChanged', { - xAxis: _this.xAxisOption, - yAxis: _this.yAxisOption - }); - }] - }); + // Support: Firefox 64+, Edge 18+ + // Some browsers don't support the "nonce" property on scripts. + // On the other hand, just using `getAttribute` is not enough as + // the `nonce` attribute is reset to an empty string whenever it + // becomes browsing-context connected. + // See https://github.com/whatwg/html/issues/2369 + // See https://html.spec.whatwg.org/#nonce-attributes + // The `node.getAttribute` check was added for the sake of + // `jQuery.globalEval` so that it can fake a nonce-containing node + // via an object. + val = node[ i ] || node.getAttribute && node.getAttribute( i ); + if ( val ) { + script.setAttribute( i, val ); + } + } + } + doc.head.appendChild( script ).parentNode.removeChild( script ); + } - this._widgetList.push(this.fontStyle); -}; -exports.default = AxisLabelControl; +function toType( obj ) { + if ( obj == null ) { + return obj + ""; + } -/***/ }), -/* 289 */ -/***/ (function(module, exports, __webpack_require__) { + // Support: Android <=2.3 only (functionish RegExp) + return typeof obj === "object" || typeof obj === "function" ? + class2type[ toString.call( obj ) ] || "object" : + typeof obj; +} +/* global Symbol */ +// Defining this global in .eslintrc.json would create a danger of using the global +// unguarded in another place, it seems safer to define global only for this module -"use strict"; -Object.defineProperty(exports, "__esModule", { - value: true -}); +var + version = "3.5.1", -var _baseControl = __webpack_require__(9); + // Define a local copy of jQuery + jQuery = function( selector, context ) { -var _baseControl2 = _interopRequireDefault(_baseControl); + // The jQuery object is actually just the init constructor 'enhanced' + // Need init if jQuery is called (just allow error to be thrown if not included) + return new jQuery.fn.init( selector, context ); + }; -var _widgetFactory = __webpack_require__(3); +jQuery.fn = jQuery.prototype = { -var WidgetFactory = _interopRequireWildcard(_widgetFactory); + // The current version of jQuery being used + jquery: version, -function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } } + constructor: jQuery, -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + // The default length of a jQuery object is 0 + length: 0, -/** - * Created by mk90.kim on 2017-05-10. - */ + toArray: function() { + return slice.call( this ); + }, -function MarkerControl(parentId, options, headerKey) { - _baseControl2.default.call(this, parentId, options, headerKey); -} + // Get the Nth element in the matched element set OR + // Get the whole matched element set as a clean array + get: function( num ) { -MarkerControl.prototype = Object.create(_baseControl2.default.prototype); -MarkerControl.prototype.constructor = MarkerControl; + // Return all the elements in a clean array + if ( num == null ) { + return slice.call( this ); + } -MarkerControl.prototype._init = function () { - _baseControl2.default.prototype._init.call(this); - this.markerOption = this.options.setting.marker; - this.showSymbol = this.options.setting.showSymbol; -}; + // Return just the one element from the set + return num < 0 ? this[ num + this.length ] : this[ num ]; + }, -MarkerControl.prototype._createContents = function ($parent) { - _baseControl2.default.prototype._createContents.call(this, $parent); - this.createComponentContents(); -}; + // Take an array of elements and push it onto the stack + // (returning the new matched element set) + pushStack: function( elems ) { -MarkerControl.prototype.createComponentContents = function (contentsOption) { - _baseControl2.default.prototype.createComponentContents.call(this, contentsOption); - this._createSizeOpacityComponent(); - this._createColorPaletteComponent(); -}; + // Build a new jQuery matched element set + var ret = jQuery.merge( this.constructor(), elems ); -MarkerControl.prototype._createSizeOpacityComponent = function () { - if (!this.markerOption) return; + // Add the old object onto the stack (as a reference) + ret.prevObject = this; - var $sizeOpacityComponent; - if (this.showSymbol) { - $sizeOpacityComponent = $('' + '
' + '
Size / Opacity / Show All
' + '
' + '
' + '
' + '
' + '
' + '
'); - } else { - $sizeOpacityComponent = $('' + '
' + '
Size / Opacity
' + '
' + '
' + '
' + '
' + '
'); - } + // Return the newly-formed element set + return ret; + }, - this.$controlContents.append($sizeOpacityComponent); - var $sizeOpacityComponentContents = $sizeOpacityComponent.find('.bo-component-marker-contents'); + // Execute a callback for every element in the matched set. + each: function( callback ) { + return jQuery.each( this, callback ); + }, + + map: function( callback ) { + return this.pushStack( jQuery.map( this, function( elem, i ) { + return callback.call( elem, i, elem ); + } ) ); + }, + + slice: function() { + return this.pushStack( slice.apply( this, arguments ) ); + }, + + first: function() { + return this.eq( 0 ); + }, + + last: function() { + return this.eq( -1 ); + }, + + even: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return ( i + 1 ) % 2; + } ) ); + }, - var _this = this; - var markerSizeSelectorWidget = WidgetFactory.createMarkerSizeSelectorWidget($sizeOpacityComponentContents.find('.bo-component-marker-size'), { - type: 'number', - value: this.markerOption.symbolSize, - onChanged: function onChanged(inputVal) { - _this.markerOption.symbolSize = inputVal; - _this.options.onChanged('onChartOptionChanged', { plotOptions: _this.options.chartOption.plotOptions }); - } - }); - this._widgetList.push(markerSizeSelectorWidget); + odd: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return i % 2; + } ) ); + }, + + eq: function( i ) { + var len = this.length, + j = +i + ( i < 0 ? len : 0 ); + return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); + }, - var opacitySelectorWidget = WidgetFactory.createOpacitySelectorWidget($sizeOpacityComponentContents.find('.bo-component-marker-opacity'), { - value: this.markerOption.itemStyle.normal.opacity, - onChanged: function onChanged(inputVal) { - _this.markerOption.itemStyle.normal.opacity = inputVal; - _this.options.onChanged('onChartOptionChanged', { plotOptions: _this.options.chartOption.plotOptions }); - } - }); - this._widgetList.push(opacitySelectorWidget); + end: function() { + return this.prevObject || this.constructor(); + }, - if (this.showSymbol) { - var showAllSelectorWidget = WidgetFactory.createMarkerShowSelectorWidget($sizeOpacityComponentContents.find('.bo-component-marker-show'), { - value: this.showSymbol.all, - onChanged: function onChanged(inputVal) { - _this.showSymbol.all = inputVal; - _this.options.onChanged('onChartOptionChanged', { plotOptions: _this.options.chartOption.plotOptions }); - } - }); - this._widgetList.push(showAllSelectorWidget); - } -}; + // For internal use only. + // Behaves like an Array's method, not like a jQuery method. + push: push, + sort: arr.sort, + splice: arr.splice +}; + +jQuery.extend = jQuery.fn.extend = function() { + var options, name, src, copy, copyIsArray, clone, + target = arguments[ 0 ] || {}, + i = 1, + length = arguments.length, + deep = false; + + // Handle a deep copy situation + if ( typeof target === "boolean" ) { + deep = target; + + // Skip the boolean and the target + target = arguments[ i ] || {}; + i++; + } + + // Handle case when target is a string or something (possible in deep copy) + if ( typeof target !== "object" && !isFunction( target ) ) { + target = {}; + } + + // Extend jQuery itself if only one argument is passed + if ( i === length ) { + target = this; + i--; + } + + for ( ; i < length; i++ ) { + + // Only deal with non-null/undefined values + if ( ( options = arguments[ i ] ) != null ) { + + // Extend the base object + for ( name in options ) { + copy = options[ name ]; + + // Prevent Object.prototype pollution + // Prevent never-ending loop + if ( name === "__proto__" || target === copy ) { + continue; + } + + // Recurse if we're merging plain objects or arrays + if ( deep && copy && ( jQuery.isPlainObject( copy ) || + ( copyIsArray = Array.isArray( copy ) ) ) ) { + src = target[ name ]; + + // Ensure proper type for the source value + if ( copyIsArray && !Array.isArray( src ) ) { + clone = []; + } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { + clone = {}; + } else { + clone = src; + } + copyIsArray = false; + + // Never move original objects, clone them + target[ name ] = jQuery.extend( deep, clone, copy ); + + // Don't bring in undefined values + } else if ( copy !== undefined ) { + target[ name ] = copy; + } + } + } + } + + // Return the modified object + return target; +}; + +jQuery.extend( { + + // Unique for each copy of jQuery on the page + expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), + + // Assume jQuery is ready without the ready module + isReady: true, + + error: function( msg ) { + throw new Error( msg ); + }, + + noop: function() {}, + + isPlainObject: function( obj ) { + var proto, Ctor; + + // Detect obvious negatives + // Use toString instead of jQuery.type to catch host objects + if ( !obj || toString.call( obj ) !== "[object Object]" ) { + return false; + } + + proto = getProto( obj ); + + // Objects with no prototype (e.g., `Object.create( null )`) are plain + if ( !proto ) { + return true; + } + + // Objects with prototype are plain iff they were constructed by a global Object function + Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; + return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; + }, + + isEmptyObject: function( obj ) { + var name; + + for ( name in obj ) { + return false; + } + return true; + }, + + // Evaluates a script in a provided context; falls back to the global one + // if not specified. + globalEval: function( code, options, doc ) { + DOMEval( code, { nonce: options && options.nonce }, doc ); + }, + + each: function( obj, callback ) { + var length, i = 0; + + if ( isArrayLike( obj ) ) { + length = obj.length; + for ( ; i < length; i++ ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } else { + for ( i in obj ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } + + return obj; + }, + + // results is for internal usage only + makeArray: function( arr, results ) { + var ret = results || []; + + if ( arr != null ) { + if ( isArrayLike( Object( arr ) ) ) { + jQuery.merge( ret, + typeof arr === "string" ? + [ arr ] : arr + ); + } else { + push.call( ret, arr ); + } + } + + return ret; + }, + + inArray: function( elem, arr, i ) { + return arr == null ? -1 : indexOf.call( arr, elem, i ); + }, + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + merge: function( first, second ) { + var len = +second.length, + j = 0, + i = first.length; + + for ( ; j < len; j++ ) { + first[ i++ ] = second[ j ]; + } + + first.length = i; + + return first; + }, + + grep: function( elems, callback, invert ) { + var callbackInverse, + matches = [], + i = 0, + length = elems.length, + callbackExpect = !invert; + + // Go through the array, only saving the items + // that pass the validator function + for ( ; i < length; i++ ) { + callbackInverse = !callback( elems[ i ], i ); + if ( callbackInverse !== callbackExpect ) { + matches.push( elems[ i ] ); + } + } + + return matches; + }, + + // arg is for internal usage only + map: function( elems, callback, arg ) { + var length, value, + i = 0, + ret = []; + + // Go through the array, translating each of the items to their new values + if ( isArrayLike( elems ) ) { + length = elems.length; + for ( ; i < length; i++ ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + + // Go through every key on the object, + } else { + for ( i in elems ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + } + + // Flatten any nested arrays + return flat( ret ); + }, + + // A global GUID counter for objects + guid: 1, + + // jQuery.support is not used in Core but other projects attach their + // properties to it so it needs to exist. + support: support +} ); + +if ( typeof Symbol === "function" ) { + jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; +} -MarkerControl.prototype._createColorPaletteComponent = function () { +// Populate the class2type map +jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), +function( _i, name ) { + class2type[ "[object " + name + "]" ] = name.toLowerCase(); +} ); - var $colorPaletteComponent = $('
Color Palette
'); - this.$controlContents.append($colorPaletteComponent); +function isArrayLike( obj ) { - var _this = this; - var colorPaletteWidget = WidgetFactory.createColorPaletteWidget($colorPaletteComponent, { - value: this.options.chartOption.colorSet, - onChanged: function onChanged(inputVal) { - _this.options.chartOption.colorSet = inputVal; - _this.setExpanderPreview.call(_this); - _this.options.onChanged('onChartOptionChanged', { colorSet: _this.options.chartOption.colorSet }); - } - }); - this._widgetList.push(colorPaletteWidget); -}; + // Support: real iOS 8.2 only (not reproducible in simulator) + // `in` check used to prevent JIT error (gh-2145) + // hasOwn isn't used here due to false negatives + // regarding Nodelist length in IE + var length = !!obj && "length" in obj && obj.length, + type = toType( obj ); -exports.default = MarkerControl; + if ( isFunction( obj ) || isWindow( obj ) ) { + return false; + } -/***/ }), -/* 290 */ -/***/ (function(module, exports, __webpack_require__) { + return type === "array" || length === 0 || + typeof length === "number" && length > 0 && ( length - 1 ) in obj; +} +var Sizzle = +/*! + * Sizzle CSS Selector Engine v2.3.5 + * https://sizzlejs.com/ + * + * Copyright JS Foundation and other contributors + * Released under the MIT license + * https://js.foundation/ + * + * Date: 2020-03-14 + */ +( function( window ) { +var i, + support, + Expr, + getText, + isXML, + tokenize, + compile, + select, + outermostContext, + sortInput, + hasDuplicate, + + // Local document vars + setDocument, + document, + docElem, + documentIsHTML, + rbuggyQSA, + rbuggyMatches, + matches, + contains, + + // Instance-specific data + expando = "sizzle" + 1 * new Date(), + preferredDoc = window.document, + dirruns = 0, + done = 0, + classCache = createCache(), + tokenCache = createCache(), + compilerCache = createCache(), + nonnativeSelectorCache = createCache(), + sortOrder = function( a, b ) { + if ( a === b ) { + hasDuplicate = true; + } + return 0; + }, + + // Instance methods + hasOwn = ( {} ).hasOwnProperty, + arr = [], + pop = arr.pop, + pushNative = arr.push, + push = arr.push, + slice = arr.slice, + + // Use a stripped-down indexOf as it's faster than native + // https://jsperf.com/thor-indexof-vs-for/5 + indexOf = function( list, elem ) { + var i = 0, + len = list.length; + for ( ; i < len; i++ ) { + if ( list[ i ] === elem ) { + return i; + } + } + return -1; + }, + + booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + + "ismap|loop|multiple|open|readonly|required|scoped", + + // Regular expressions + + // http://www.w3.org/TR/css3-selectors/#whitespace + whitespace = "[\\x20\\t\\r\\n\\f]", + + // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram + identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + + "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", + + // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors + attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + + + // Operator (capture 2) + "*([*^$|!~]?=)" + whitespace + + + // "Attribute values must be CSS identifiers [capture 5] + // or strings [capture 3 or capture 4]" + "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + + whitespace + "*\\]", + + pseudos = ":(" + identifier + ")(?:\\((" + + + // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: + // 1. quoted (capture 3; capture 4 or capture 5) + "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + + + // 2. simple (capture 6) + "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + + + // 3. anything else (capture 2) + ".*" + + ")\\)|)", + + // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter + rwhitespace = new RegExp( whitespace + "+", "g" ), + rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + + whitespace + "+$", "g" ), + + rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), + rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + + "*" ), + rdescend = new RegExp( whitespace + "|>" ), + + rpseudo = new RegExp( pseudos ), + ridentifier = new RegExp( "^" + identifier + "$" ), + + matchExpr = { + "ID": new RegExp( "^#(" + identifier + ")" ), + "CLASS": new RegExp( "^\\.(" + identifier + ")" ), + "TAG": new RegExp( "^(" + identifier + "|[*])" ), + "ATTR": new RegExp( "^" + attributes ), + "PSEUDO": new RegExp( "^" + pseudos ), + "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + + whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + + whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), + "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), + + // For use in libraries implementing .is() + // We use this for POS matching in `select` + "needsContext": new RegExp( "^" + whitespace + + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) + }, + + rhtml = /HTML$/i, + rinputs = /^(?:input|select|textarea|button)$/i, + rheader = /^h\d$/i, + + rnative = /^[^{]+\{\s*\[native \w/, + + // Easily-parseable/retrievable ID or TAG or CLASS selectors + rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, + + rsibling = /[+~]/, + + // CSS escapes + // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters + runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), + funescape = function( escape, nonHex ) { + var high = "0x" + escape.slice( 1 ) - 0x10000; + + return nonHex ? + + // Strip the backslash prefix from a non-hex escape sequence + nonHex : + + // Replace a hexadecimal escape sequence with the encoded Unicode code point + // Support: IE <=11+ + // For values outside the Basic Multilingual Plane (BMP), manually construct a + // surrogate pair + high < 0 ? + String.fromCharCode( high + 0x10000 ) : + String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); + }, + + // CSS string/identifier serialization + // https://drafts.csswg.org/cssom/#common-serializing-idioms + rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, + fcssescape = function( ch, asCodePoint ) { + if ( asCodePoint ) { + + // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER + if ( ch === "\0" ) { + return "\uFFFD"; + } + + // Control characters and (dependent upon position) numbers get escaped as code points + return ch.slice( 0, -1 ) + "\\" + + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; + } + + // Other potentially-special ASCII characters get backslash-escaped + return "\\" + ch; + }, + + // Used for iframes + // See setDocument() + // Removing the function wrapper causes a "Permission Denied" + // error in IE + unloadHandler = function() { + setDocument(); + }, + + inDisabledFieldset = addCombinator( + function( elem ) { + return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; + }, + { dir: "parentNode", next: "legend" } + ); + +// Optimize for push.apply( _, NodeList ) +try { + push.apply( + ( arr = slice.call( preferredDoc.childNodes ) ), + preferredDoc.childNodes + ); + + // Support: Android<4.0 + // Detect silently failing push.apply + // eslint-disable-next-line no-unused-expressions + arr[ preferredDoc.childNodes.length ].nodeType; +} catch ( e ) { + push = { apply: arr.length ? + + // Leverage slice if possible + function( target, els ) { + pushNative.apply( target, slice.call( els ) ); + } : + + // Support: IE<9 + // Otherwise append directly + function( target, els ) { + var j = target.length, + i = 0; + + // Can't trust NodeList.length + while ( ( target[ j++ ] = els[ i++ ] ) ) {} + target.length = j - 1; + } + }; +} -"use strict"; +function Sizzle( selector, context, results, seed ) { + var m, i, elem, nid, match, groups, newSelector, + newContext = context && context.ownerDocument, + + // nodeType defaults to 9, since context defaults to document + nodeType = context ? context.nodeType : 9; + + results = results || []; + + // Return early from calls with invalid selector or context + if ( typeof selector !== "string" || !selector || + nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { + + return results; + } + + // Try to shortcut find operations (as opposed to filters) in HTML documents + if ( !seed ) { + setDocument( context ); + context = context || document; + + if ( documentIsHTML ) { + + // If the selector is sufficiently simple, try using a "get*By*" DOM method + // (excepting DocumentFragment context, where the methods don't exist) + if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { + + // ID selector + if ( ( m = match[ 1 ] ) ) { + + // Document context + if ( nodeType === 9 ) { + if ( ( elem = context.getElementById( m ) ) ) { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( elem.id === m ) { + results.push( elem ); + return results; + } + } else { + return results; + } + + // Element context + } else { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( newContext && ( elem = newContext.getElementById( m ) ) && + contains( context, elem ) && + elem.id === m ) { + + results.push( elem ); + return results; + } + } + + // Type selector + } else if ( match[ 2 ] ) { + push.apply( results, context.getElementsByTagName( selector ) ); + return results; + + // Class selector + } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && + context.getElementsByClassName ) { + + push.apply( results, context.getElementsByClassName( m ) ); + return results; + } + } + + // Take advantage of querySelectorAll + if ( support.qsa && + !nonnativeSelectorCache[ selector + " " ] && + ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && + + // Support: IE 8 only + // Exclude object elements + ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { + + newSelector = selector; + newContext = context; + + // qSA considers elements outside a scoping root when evaluating child or + // descendant combinators, which is not what we want. + // In such cases, we work around the behavior by prefixing every selector in the + // list with an ID selector referencing the scope context. + // The technique has to be used as well when a leading combinator is used + // as such selectors are not recognized by querySelectorAll. + // Thanks to Andrew Dupont for this technique. + if ( nodeType === 1 && + ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { + + // Expand context for sibling selectors + newContext = rsibling.test( selector ) && testContext( context.parentNode ) || + context; + + // We can use :scope instead of the ID hack if the browser + // supports it & if we're not changing the context. + if ( newContext !== context || !support.scope ) { + + // Capture the context ID, setting it first if necessary + if ( ( nid = context.getAttribute( "id" ) ) ) { + nid = nid.replace( rcssescape, fcssescape ); + } else { + context.setAttribute( "id", ( nid = expando ) ); + } + } + + // Prefix every selector in the list + groups = tokenize( selector ); + i = groups.length; + while ( i-- ) { + groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + + toSelector( groups[ i ] ); + } + newSelector = groups.join( "," ); + } + + try { + push.apply( results, + newContext.querySelectorAll( newSelector ) + ); + return results; + } catch ( qsaError ) { + nonnativeSelectorCache( selector, true ); + } finally { + if ( nid === expando ) { + context.removeAttribute( "id" ); + } + } + } + } + } + + // All others + return select( selector.replace( rtrim, "$1" ), context, results, seed ); +} +/** + * Create key-value caches of limited size + * @returns {function(string, object)} Returns the Object data after storing it on itself with + * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) + * deleting the oldest entry + */ +function createCache() { + var keys = []; -Object.defineProperty(exports, "__esModule", { - value: true -}); + function cache( key, value ) { -var _baseControl = __webpack_require__(9); + // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) + if ( keys.push( key + " " ) > Expr.cacheLength ) { -var _baseControl2 = _interopRequireDefault(_baseControl); + // Only keep the most recent entries + delete cache[ keys.shift() ]; + } + return ( cache[ key + " " ] = value ); + } + return cache; +} -var _widgetFactory = __webpack_require__(3); +/** + * Mark a function for special use by Sizzle + * @param {Function} fn The function to mark + */ +function markFunction( fn ) { + fn[ expando ] = true; + return fn; +} -var WidgetFactory = _interopRequireWildcard(_widgetFactory); +/** + * Support testing using an element + * @param {Function} fn Passed the created element and returns a boolean result + */ +function assert( fn ) { + var el = document.createElement( "fieldset" ); + + try { + return !!fn( el ); + } catch ( e ) { + return false; + } finally { + + // Remove from its parent by default + if ( el.parentNode ) { + el.parentNode.removeChild( el ); + } + + // release memory in IE + el = null; + } +} -function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } } +/** + * Adds the same handler for all of the specified attrs + * @param {String} attrs Pipe-separated list of attributes + * @param {Function} handler The method that will be applied + */ +function addHandle( attrs, handler ) { + var arr = attrs.split( "|" ), + i = arr.length; -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + while ( i-- ) { + Expr.attrHandle[ arr[ i ] ] = handler; + } +} /** - * Created by mk90.kim on 2017-05-10. + * Checks document order of two siblings + * @param {Element} a + * @param {Element} b + * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b */ - -function FramePieControl(parentId, options, headerKey) { - _baseControl2.default.call(this, parentId, options, headerKey); +function siblingCheck( a, b ) { + var cur = b && a, + diff = cur && a.nodeType === 1 && b.nodeType === 1 && + a.sourceIndex - b.sourceIndex; + + // Use IE sourceIndex if available on both nodes + if ( diff ) { + return diff; + } + + // Check if b follows a + if ( cur ) { + while ( ( cur = cur.nextSibling ) ) { + if ( cur === b ) { + return -1; + } + } + } + + return a ? 1 : -1; } -FramePieControl.prototype = Object.create(_baseControl2.default.prototype); -FramePieControl.prototype.constructor = FramePieControl; +/** + * Returns a function to use in pseudos for input types + * @param {String} type + */ +function createInputPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === type; + }; +} -FramePieControl.prototype._init = function () { - _baseControl2.default.prototype._init.call(this); - this.styleOption = this.options.chartOption.chart; - this.gridOption = this.options.chartOption.grid; - this.display = this.options.setting.display; - if (!this.display.center) this.display.center = []; -}; +/** + * Returns a function to use in pseudos for buttons + * @param {String} type + */ +function createButtonPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return ( name === "input" || name === "button" ) && elem.type === type; + }; +} -FramePieControl.prototype._createContents = function ($parent) { - _baseControl2.default.prototype._createContents.call(this, $parent); +/** + * Returns a function to use in pseudos for :enabled/:disabled + * @param {Boolean} disabled true for :disabled; false for :enabled + */ +function createDisabledPseudo( disabled ) { + + // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable + return function( elem ) { + + // Only certain elements can match :enabled or :disabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled + if ( "form" in elem ) { + + // Check for inherited disabledness on relevant non-disabled elements: + // * listed form-associated elements in a disabled fieldset + // https://html.spec.whatwg.org/multipage/forms.html#category-listed + // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled + // * option elements in a disabled optgroup + // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled + // All such elements have a "form" property. + if ( elem.parentNode && elem.disabled === false ) { + + // Option elements defer to a parent optgroup if present + if ( "label" in elem ) { + if ( "label" in elem.parentNode ) { + return elem.parentNode.disabled === disabled; + } else { + return elem.disabled === disabled; + } + } + + // Support: IE 6 - 11 + // Use the isDisabled shortcut property to check for disabled fieldset ancestors + return elem.isDisabled === disabled || + + // Where there is no isDisabled, check manually + /* jshint -W018 */ + elem.isDisabled !== !disabled && + inDisabledFieldset( elem ) === disabled; + } + + return elem.disabled === disabled; + + // Try to winnow out elements that can't be disabled before trusting the disabled property. + // Some victims get caught in our net (label, legend, menu, track), but it shouldn't + // even exist on them, let alone have a boolean value. + } else if ( "label" in elem ) { + return elem.disabled === disabled; + } + + // Remaining elements are neither :enabled nor :disabled + return false; + }; +} - this.createComponentContents(); -}; +/** + * Returns a function to use in pseudos for positionals + * @param {Function} fn + */ +function createPositionalPseudo( fn ) { + return markFunction( function( argument ) { + argument = +argument; + return markFunction( function( seed, matches ) { + var j, + matchIndexes = fn( [], seed.length, argument ), + i = matchIndexes.length; + + // Match elements found at the specified indexes + while ( i-- ) { + if ( seed[ ( j = matchIndexes[ i ] ) ] ) { + seed[ j ] = !( matches[ j ] = seed[ j ] ); + } + } + } ); + } ); +} -FramePieControl.prototype.createComponentContents = function (contentsOption) { - _baseControl2.default.prototype.createComponentContents.call(this, contentsOption); - this._createChartPositionComponent(); - this._createChartSizeComponent(); - this._createBackgroundComponent(); - this._createBorderComponent(); -}; +/** + * Checks a node for validity as a Sizzle context + * @param {Element|Object=} context + * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value + */ +function testContext( context ) { + return context && typeof context.getElementsByTagName !== "undefined" && context; +} -FramePieControl.prototype._createChartPositionComponent = function (contentsOption) { - var $chartPositionComponent = $('' + '
' + '
Center Position
' + '
' + '
' + '
'); +// Expose support vars for convenience +support = Sizzle.support = {}; - this.$controlContents.append($chartPositionComponent); - var $chartPositionComponentContents = $chartPositionComponent.find('.bo-component-chart-position-contents'); +/** + * Detects XML nodes + * @param {Element|Object} elem An element or a document + * @returns {Boolean} True iff elem is a non-HTML XML node + */ +isXML = Sizzle.isXML = function( elem ) { + var namespace = elem.namespaceURI, + docElem = ( elem.ownerDocument || elem ).documentElement; - var _this = this; + // Support: IE <=8 + // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes + // https://bugs.jquery.com/ticket/4833 + return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); +}; - var positionOptions = { - width: '50px', - height: '30px', - value: { - left: this.display.center[0], - top: this.display.center[1] - }, - position: ['left', 'top'], - problemKeyList: ['value-003'], - onChanged: { - left: function left(value) { - _this.display.center[0] = value ? value : '50%'; - _this.options.onChanged('onChartOptionChanged', { plotOptions: _this.options.chartOption.plotOptions }); - }, - top: function top(value) { - _this.display.center[1] = value ? value : '50%'; - _this.options.onChanged('onChartOptionChanged', { plotOptions: _this.options.chartOption.plotOptions }); - } - } - }; +/** + * Sets document-related variables once based on the current document + * @param {Element|Object} [doc] An element or document object to use to set the document + * @returns {Object} Returns the current document + */ +setDocument = Sizzle.setDocument = function( node ) { + var hasCompare, subWindow, + doc = node ? node.ownerDocument || node : preferredDoc; + + // Return early if doc is invalid or already selected + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { + return document; + } + + // Update global variables + document = doc; + docElem = document.documentElement; + documentIsHTML = !isXML( document ); + + // Support: IE 9 - 11+, Edge 12 - 18+ + // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( preferredDoc != document && + ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { + + // Support: IE 11, Edge + if ( subWindow.addEventListener ) { + subWindow.addEventListener( "unload", unloadHandler, false ); + + // Support: IE 9 - 10 only + } else if ( subWindow.attachEvent ) { + subWindow.attachEvent( "onunload", unloadHandler ); + } + } + + // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, + // Safari 4 - 5 only, Opera <=11.6 - 12.x only + // IE/Edge & older browsers don't support the :scope pseudo-class. + // Support: Safari 6.0 only + // Safari 6.0 supports :scope but it's an alias of :root there. + support.scope = assert( function( el ) { + docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); + return typeof el.querySelectorAll !== "undefined" && + !el.querySelectorAll( ":scope fieldset div" ).length; + } ); + + /* Attributes + ---------------------------------------------------------------------- */ + + // Support: IE<8 + // Verify that getAttribute really returns attributes and not properties + // (excepting IE8 booleans) + support.attributes = assert( function( el ) { + el.className = "i"; + return !el.getAttribute( "className" ); + } ); + + /* getElement(s)By* + ---------------------------------------------------------------------- */ + + // Check if getElementsByTagName("*") returns only elements + support.getElementsByTagName = assert( function( el ) { + el.appendChild( document.createComment( "" ) ); + return !el.getElementsByTagName( "*" ).length; + } ); + + // Support: IE<9 + support.getElementsByClassName = rnative.test( document.getElementsByClassName ); + + // Support: IE<10 + // Check if getElementById returns elements by name + // The broken getElementById methods don't pick up programmatically-set names, + // so use a roundabout getElementsByName test + support.getById = assert( function( el ) { + docElem.appendChild( el ).id = expando; + return !document.getElementsByName || !document.getElementsByName( expando ).length; + } ); + + // ID filter and find + if ( support.getById ) { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + return elem.getAttribute( "id" ) === attrId; + }; + }; + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var elem = context.getElementById( id ); + return elem ? [ elem ] : []; + } + }; + } else { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + var node = typeof elem.getAttributeNode !== "undefined" && + elem.getAttributeNode( "id" ); + return node && node.value === attrId; + }; + }; + + // Support: IE 6 - 7 only + // getElementById is not reliable as a find shortcut + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var node, i, elems, + elem = context.getElementById( id ); + + if ( elem ) { + + // Verify the id attribute + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + + // Fall back on getElementsByName + elems = context.getElementsByName( id ); + i = 0; + while ( ( elem = elems[ i++ ] ) ) { + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + } + } + + return []; + } + }; + } + + // Tag + Expr.find[ "TAG" ] = support.getElementsByTagName ? + function( tag, context ) { + if ( typeof context.getElementsByTagName !== "undefined" ) { + return context.getElementsByTagName( tag ); + + // DocumentFragment nodes don't have gEBTN + } else if ( support.qsa ) { + return context.querySelectorAll( tag ); + } + } : + + function( tag, context ) { + var elem, + tmp = [], + i = 0, + + // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too + results = context.getElementsByTagName( tag ); + + // Filter out possible comments + if ( tag === "*" ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem.nodeType === 1 ) { + tmp.push( elem ); + } + } + + return tmp; + } + return results; + }; + + // Class + Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { + if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { + return context.getElementsByClassName( className ); + } + }; + + /* QSA/matchesSelector + ---------------------------------------------------------------------- */ + + // QSA and matchesSelector support + + // matchesSelector(:active) reports false when true (IE9/Opera 11.5) + rbuggyMatches = []; + + // qSa(:focus) reports false when true (Chrome 21) + // We allow this because of a bug in IE8/9 that throws an error + // whenever `document.activeElement` is accessed on an iframe + // So, we allow :focus to pass through QSA all the time to avoid the IE error + // See https://bugs.jquery.com/ticket/13378 + rbuggyQSA = []; + + if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { + + // Build QSA regex + // Regex strategy adopted from Diego Perini + assert( function( el ) { + + var input; + + // Select is set to empty string on purpose + // This is to test IE's treatment of not explicitly + // setting a boolean content attribute, + // since its presence should be enough + // https://bugs.jquery.com/ticket/12359 + docElem.appendChild( el ).innerHTML = "" + + ""; + + // Support: IE8, Opera 11-12.16 + // Nothing should be selected when empty strings follow ^= or $= or *= + // The test attribute must be unknown in Opera but "safe" for WinRT + // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section + if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { + rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); + } + + // Support: IE8 + // Boolean attributes and "value" are not treated correctly + if ( !el.querySelectorAll( "[selected]" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); + } + + // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ + if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { + rbuggyQSA.push( "~=" ); + } + + // Support: IE 11+, Edge 15 - 18+ + // IE 11/Edge don't find elements on a `[name='']` query in some cases. + // Adding a temporary attribute to the document before the selection works + // around the issue. + // Interestingly, IE 10 & older don't seem to have the issue. + input = document.createElement( "input" ); + input.setAttribute( "name", "" ); + el.appendChild( input ); + if ( !el.querySelectorAll( "[name='']" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + + whitespace + "*(?:''|\"\")" ); + } + + // Webkit/Opera - :checked should return selected option elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + // IE8 throws error here and will not see later tests + if ( !el.querySelectorAll( ":checked" ).length ) { + rbuggyQSA.push( ":checked" ); + } + + // Support: Safari 8+, iOS 8+ + // https://bugs.webkit.org/show_bug.cgi?id=136851 + // In-page `selector#id sibling-combinator selector` fails + if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { + rbuggyQSA.push( ".#.+[+~]" ); + } + + // Support: Firefox <=3.6 - 5 only + // Old Firefox doesn't throw on a badly-escaped identifier. + el.querySelectorAll( "\\\f" ); + rbuggyQSA.push( "[\\r\\n\\f]" ); + } ); + + assert( function( el ) { + el.innerHTML = "" + + ""; + + // Support: Windows 8 Native Apps + // The type and name attributes are restricted during .innerHTML assignment + var input = document.createElement( "input" ); + input.setAttribute( "type", "hidden" ); + el.appendChild( input ).setAttribute( "name", "D" ); + + // Support: IE8 + // Enforce case-sensitivity of name attribute + if ( el.querySelectorAll( "[name=d]" ).length ) { + rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); + } + + // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) + // IE8 throws error here and will not see later tests + if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: IE9-11+ + // IE's :disabled selector does not pick up the children of disabled fieldsets + docElem.appendChild( el ).disabled = true; + if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: Opera 10 - 11 only + // Opera 10-11 does not throw on post-comma invalid pseudos + el.querySelectorAll( "*,:x" ); + rbuggyQSA.push( ",.*:" ); + } ); + } + + if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || + docElem.webkitMatchesSelector || + docElem.mozMatchesSelector || + docElem.oMatchesSelector || + docElem.msMatchesSelector ) ) ) ) { + + assert( function( el ) { + + // Check to see if it's possible to do matchesSelector + // on a disconnected node (IE 9) + support.disconnectedMatch = matches.call( el, "*" ); + + // This should fail with an exception + // Gecko does not error, returns false instead + matches.call( el, "[s!='']:x" ); + rbuggyMatches.push( "!=", pseudos ); + } ); + } + + rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); + rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); + + /* Contains + ---------------------------------------------------------------------- */ + hasCompare = rnative.test( docElem.compareDocumentPosition ); + + // Element contains another + // Purposefully self-exclusive + // As in, an element does not contain itself + contains = hasCompare || rnative.test( docElem.contains ) ? + function( a, b ) { + var adown = a.nodeType === 9 ? a.documentElement : a, + bup = b && b.parentNode; + return a === bup || !!( bup && bup.nodeType === 1 && ( + adown.contains ? + adown.contains( bup ) : + a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 + ) ); + } : + function( a, b ) { + if ( b ) { + while ( ( b = b.parentNode ) ) { + if ( b === a ) { + return true; + } + } + } + return false; + }; + + /* Sorting + ---------------------------------------------------------------------- */ + + // Document order sorting + sortOrder = hasCompare ? + function( a, b ) { + + // Flag for duplicate removal + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + // Sort on method existence if only one input has compareDocumentPosition + var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; + if ( compare ) { + return compare; + } + + // Calculate position if both inputs belong to the same document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? + a.compareDocumentPosition( b ) : + + // Otherwise we know they are disconnected + 1; + + // Disconnected nodes + if ( compare & 1 || + ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { + + // Choose the first element that is related to our preferred document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( a == document || a.ownerDocument == preferredDoc && + contains( preferredDoc, a ) ) { + return -1; + } + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( b == document || b.ownerDocument == preferredDoc && + contains( preferredDoc, b ) ) { + return 1; + } + + // Maintain original order + return sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + } + + return compare & 4 ? -1 : 1; + } : + function( a, b ) { + + // Exit early if the nodes are identical + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + var cur, + i = 0, + aup = a.parentNode, + bup = b.parentNode, + ap = [ a ], + bp = [ b ]; + + // Parentless nodes are either documents or disconnected + if ( !aup || !bup ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + return a == document ? -1 : + b == document ? 1 : + /* eslint-enable eqeqeq */ + aup ? -1 : + bup ? 1 : + sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + + // If the nodes are siblings, we can do a quick check + } else if ( aup === bup ) { + return siblingCheck( a, b ); + } + + // Otherwise we need full lists of their ancestors for comparison + cur = a; + while ( ( cur = cur.parentNode ) ) { + ap.unshift( cur ); + } + cur = b; + while ( ( cur = cur.parentNode ) ) { + bp.unshift( cur ); + } + + // Walk down the tree looking for a discrepancy + while ( ap[ i ] === bp[ i ] ) { + i++; + } + + return i ? + + // Do a sibling check if the nodes have a common ancestor + siblingCheck( ap[ i ], bp[ i ] ) : + + // Otherwise nodes in our document sort first + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + ap[ i ] == preferredDoc ? -1 : + bp[ i ] == preferredDoc ? 1 : + /* eslint-enable eqeqeq */ + 0; + }; + + return document; +}; + +Sizzle.matches = function( expr, elements ) { + return Sizzle( expr, null, null, elements ); +}; + +Sizzle.matchesSelector = function( elem, expr ) { + setDocument( elem ); + + if ( support.matchesSelector && documentIsHTML && + !nonnativeSelectorCache[ expr + " " ] && + ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && + ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { + + try { + var ret = matches.call( elem, expr ); + + // IE 9's matchesSelector returns false on disconnected nodes + if ( ret || support.disconnectedMatch || + + // As well, disconnected nodes are said to be in a document + // fragment in IE 9 + elem.document && elem.document.nodeType !== 11 ) { + return ret; + } + } catch ( e ) { + nonnativeSelectorCache( expr, true ); + } + } + + return Sizzle( expr, document, null, [ elem ] ).length > 0; +}; + +Sizzle.contains = function( context, elem ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( context.ownerDocument || context ) != document ) { + setDocument( context ); + } + return contains( context, elem ); +}; + +Sizzle.attr = function( elem, name ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( elem.ownerDocument || elem ) != document ) { + setDocument( elem ); + } - if (this.options.setting.display) { - $.extend(true, positionOptions, this.options.setting.display); - } + var fn = Expr.attrHandle[ name.toLowerCase() ], - var positionWidget = WidgetFactory.createCenterPositionWidget($chartPositionComponentContents, positionOptions); + // Don't get fooled by Object.prototype properties (jQuery #13807) + val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? + fn( elem, name, !documentIsHTML ) : + undefined; - this._widgetList.push(positionWidget); + return val !== undefined ? + val : + support.attributes || !documentIsHTML ? + elem.getAttribute( name ) : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; }; -FramePieControl.prototype._createChartSizeComponent = function (contentsOption) { - var $chartSizeComponent = $('' + '
' + '
Chart Size
' + '
' + '
' + '
'); +Sizzle.escape = function( sel ) { + return ( sel + "" ).replace( rcssescape, fcssescape ); +}; - this.$controlContents.append($chartSizeComponent); - var $chartSizeComponentContents = $chartSizeComponent.find('.bo-component-chart-size-contents'); +Sizzle.error = function( msg ) { + throw new Error( "Syntax error, unrecognized expression: " + msg ); +}; - var _this = this; - - var sizeOptions = { - width: '50px', - height: '30px', - value: { - size: this.display.radius - }, - position: ['size'], - problemKeyList: ['value-003'], - onChanged: { - size: function size(value) { - _this.display.radius = value ? value : '75%'; - _this.options.onChanged('onChartOptionChanged', { plotOptions: _this.options.chartOption.plotOptions }); - } - } - }; +/** + * Document sorting and removing duplicates + * @param {ArrayLike} results + */ +Sizzle.uniqueSort = function( results ) { + var elem, + duplicates = [], + j = 0, + i = 0; + + // Unless we *know* we can detect duplicates, assume their presence + hasDuplicate = !support.detectDuplicates; + sortInput = !support.sortStable && results.slice( 0 ); + results.sort( sortOrder ); + + if ( hasDuplicate ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem === results[ i ] ) { + j = duplicates.push( i ); + } + } + while ( j-- ) { + results.splice( duplicates[ j ], 1 ); + } + } + + // Clear input after sorting to release objects + // See https://github.com/jquery/sizzle/pull/225 + sortInput = null; + + return results; +}; - if (this.options.setting.display) { - $.extend(true, sizeOptions, this.options.setting.display); - } +/** + * Utility function for retrieving the text value of an array of DOM nodes + * @param {Array|Element} elem + */ +getText = Sizzle.getText = function( elem ) { + var node, + ret = "", + i = 0, + nodeType = elem.nodeType; - var sizeWidget = WidgetFactory.createCenterPositionWidget($chartSizeComponentContents, sizeOptions); + if ( !nodeType ) { - this._widgetList.push(sizeWidget); -}; + // If no nodeType, this is expected to be an array + while ( ( node = elem[ i++ ] ) ) { -FramePieControl.prototype._createBackgroundComponent = function (contentsOption) { - var $backgroundComponent = $('' + '
' + '
Background
' + '
' + '
' + '
' + '
' + '
'); + // Do not traverse comment nodes + ret += getText( node ); + } + } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { - this.$controlContents.append($backgroundComponent); - var $backgroundComponentContents = $backgroundComponent.find('.bo-component-frame-contents'); + // Use textContent for elements + // innerText usage removed for consistency of new lines (jQuery #11153) + if ( typeof elem.textContent === "string" ) { + return elem.textContent; + } else { - var _this = this; - this.opacity = function () { - if (_this.styleOption.background == 'transparent') { - return 0; - } else { - return _this.styleOption.background.replace(/^(.*,)(.+)\)/, '$2'); - } - }(); + // Traverse its children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + ret += getText( elem ); + } + } + } else if ( nodeType === 3 || nodeType === 4 ) { + return elem.nodeValue; + } - var colorPickerWidget = WidgetFactory.createColorPickerWidget($backgroundComponentContents.find('.bo-component-frame-background-color'), { - width: '50px', - height: '30px', - hasOpacity: true, - value: this.styleOption.background, - onChanged: function onChanged(color) { - _this.styleOption.background = color.replace(/^(.*,).+\)/, '$1') + _this.opacity + ')'; - _this.setExpanderPreview.call(_this); - _this.options.onChanged('onChartOptionChanged', { chart: _this.styleOption }); - } - }); - this._widgetList.push(colorPickerWidget); + // Do not include comment or processing instruction nodes - var opacitySelectorWidget = WidgetFactory.createOpacitySelectorWidget($backgroundComponentContents.find('.bo-component-frame-background-opacity'), { - value: this.opacity, - onChanged: function onChanged(opacity) { - _this.opacity = opacity; - var resultRgba = _this.styleOption.background.replace(/^(.*,).+\)/, '$1'); - resultRgba = resultRgba + opacity + ')'; - _this.styleOption.background = resultRgba; - _this.setExpanderPreview.call(_this); - _this.options.onChanged('onChartOptionChanged', { chart: _this.styleOption }); - } - }); - this._widgetList.push(opacitySelectorWidget); + return ret; }; -FramePieControl.prototype._createBorderComponent = function (contentsOption) { - - var _this = this; +Expr = Sizzle.selectors = { - var _getBorderStr2Obj = function _getBorderStr2Obj(inptOption) { - var emptyDiv = $('
').css({ border: inptOption }); - return { - borderColor: emptyDiv.css('border-color'), - // 5px -> 5 - borderWidth: emptyDiv.css('border-width').replace('px', ''), - borderType: emptyDiv.css('border-style') - }; - }; - var _getBorderObj2Str = function _getBorderObj2Str(borderObj) { - var borderStr = ''; - borderStr += borderObj.borderColor ? borderObj.borderColor + ' ' : ''; - borderStr += borderObj.borderWidth ? borderObj.borderWidth + 'px ' : ''; - borderStr += borderObj.borderType ? borderObj.borderType : ''; - return borderStr; - }; + // Can be adjusted by the user + cacheLength: 50, - this.borderAttrObj = _getBorderStr2Obj(this.styleOption.border); - var lineStyleWidget = WidgetFactory.createLineStyleWidget(this.$controlContents, { - label: 'Border', - value: [this.borderAttrObj.borderWidth, this.borderAttrObj.borderColor, this.borderAttrObj.borderType], - onChanged: [function (inputVal) { - _this.borderAttrObj.borderWidth = inputVal; - _this.styleOption.border = _getBorderObj2Str(_this.borderAttrObj); - _this.setExpanderPreview.call(_this); - _this.options.onChanged('onChartOptionChanged', { chart: _this.styleOption }); - }, function (changedColor) { - _this.borderAttrObj.borderColor = changedColor; - _this.styleOption.border = _getBorderObj2Str(_this.borderAttrObj); - _this.setExpanderPreview.call(_this); - _this.options.onChanged('onChartOptionChanged', { chart: _this.styleOption }); - }, function (inputVal) { - _this.borderAttrObj.borderType = inputVal; - _this.styleOption.border = _getBorderObj2Str(_this.borderAttrObj); - _this.setExpanderPreview.call(_this); - _this.options.onChanged('onChartOptionChanged', { chart: _this.styleOption }); - }] - }); + createPseudo: markFunction, - this._widgetList.push(lineStyleWidget); -}; + match: matchExpr, -FramePieControl.prototype.renderProblem = function () { - var _this = this; - this._widgetList.forEach(function (widget) { - widget.renderProblem(_this.options.problemList); - }); -}; + attrHandle: {}, -exports.default = FramePieControl; + find: {}, + + relative: { + ">": { dir: "parentNode", first: true }, + " ": { dir: "parentNode" }, + "+": { dir: "previousSibling", first: true }, + "~": { dir: "previousSibling" } + }, + + preFilter: { + "ATTR": function( match ) { + match[ 1 ] = match[ 1 ].replace( runescape, funescape ); + + // Move the given value to match[3] whether quoted or unquoted + match[ 3 ] = ( match[ 3 ] || match[ 4 ] || + match[ 5 ] || "" ).replace( runescape, funescape ); + + if ( match[ 2 ] === "~=" ) { + match[ 3 ] = " " + match[ 3 ] + " "; + } + + return match.slice( 0, 4 ); + }, + + "CHILD": function( match ) { + + /* matches from matchExpr["CHILD"] + 1 type (only|nth|...) + 2 what (child|of-type) + 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) + 4 xn-component of xn+y argument ([+-]?\d*n|) + 5 sign of xn-component + 6 x of xn-component + 7 sign of y-component + 8 y of y-component + */ + match[ 1 ] = match[ 1 ].toLowerCase(); + + if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { + + // nth-* requires argument + if ( !match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + // numeric x and y parameters for Expr.filter.CHILD + // remember that false/true cast respectively to 0/1 + match[ 4 ] = +( match[ 4 ] ? + match[ 5 ] + ( match[ 6 ] || 1 ) : + 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); + match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); + + // other types prohibit arguments + } else if ( match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + return match; + }, + + "PSEUDO": function( match ) { + var excess, + unquoted = !match[ 6 ] && match[ 2 ]; + + if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { + return null; + } + + // Accept quoted arguments as-is + if ( match[ 3 ] ) { + match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; + + // Strip excess characters from unquoted arguments + } else if ( unquoted && rpseudo.test( unquoted ) && + + // Get excess from tokenize (recursively) + ( excess = tokenize( unquoted, true ) ) && + + // advance to the next closing parenthesis + ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { + + // excess is a negative index + match[ 0 ] = match[ 0 ].slice( 0, excess ); + match[ 2 ] = unquoted.slice( 0, excess ); + } + + // Return only captures needed by the pseudo filter method (type and argument) + return match.slice( 0, 3 ); + } + }, + + filter: { + + "TAG": function( nodeNameSelector ) { + var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); + return nodeNameSelector === "*" ? + function() { + return true; + } : + function( elem ) { + return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; + }; + }, + + "CLASS": function( className ) { + var pattern = classCache[ className + " " ]; + + return pattern || + ( pattern = new RegExp( "(^|" + whitespace + + ")" + className + "(" + whitespace + "|$)" ) ) && classCache( + className, function( elem ) { + return pattern.test( + typeof elem.className === "string" && elem.className || + typeof elem.getAttribute !== "undefined" && + elem.getAttribute( "class" ) || + "" + ); + } ); + }, + + "ATTR": function( name, operator, check ) { + return function( elem ) { + var result = Sizzle.attr( elem, name ); + + if ( result == null ) { + return operator === "!="; + } + if ( !operator ) { + return true; + } + + result += ""; + + /* eslint-disable max-len */ + + return operator === "=" ? result === check : + operator === "!=" ? result !== check : + operator === "^=" ? check && result.indexOf( check ) === 0 : + operator === "*=" ? check && result.indexOf( check ) > -1 : + operator === "$=" ? check && result.slice( -check.length ) === check : + operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : + operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : + false; + /* eslint-enable max-len */ + + }; + }, + + "CHILD": function( type, what, _argument, first, last ) { + var simple = type.slice( 0, 3 ) !== "nth", + forward = type.slice( -4 ) !== "last", + ofType = what === "of-type"; + + return first === 1 && last === 0 ? + + // Shortcut for :nth-*(n) + function( elem ) { + return !!elem.parentNode; + } : + + function( elem, _context, xml ) { + var cache, uniqueCache, outerCache, node, nodeIndex, start, + dir = simple !== forward ? "nextSibling" : "previousSibling", + parent = elem.parentNode, + name = ofType && elem.nodeName.toLowerCase(), + useCache = !xml && !ofType, + diff = false; + + if ( parent ) { + + // :(first|last|only)-(child|of-type) + if ( simple ) { + while ( dir ) { + node = elem; + while ( ( node = node[ dir ] ) ) { + if ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) { + + return false; + } + } + + // Reverse direction for :only-* (if we haven't yet done so) + start = dir = type === "only" && !start && "nextSibling"; + } + return true; + } + + start = [ forward ? parent.firstChild : parent.lastChild ]; + + // non-xml :nth-child(...) stores cache data on `parent` + if ( forward && useCache ) { + + // Seek `elem` from a previously-cached index + + // ...in a gzip-friendly way + node = parent; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex && cache[ 2 ]; + node = nodeIndex && parent.childNodes[ nodeIndex ]; + + while ( ( node = ++nodeIndex && node && node[ dir ] || + + // Fallback to seeking `elem` from the start + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + // When found, cache indexes on `parent` and break + if ( node.nodeType === 1 && ++diff && node === elem ) { + uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; + break; + } + } + + } else { + + // Use previously-cached element index if available + if ( useCache ) { + + // ...in a gzip-friendly way + node = elem; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex; + } + + // xml :nth-child(...) + // or :nth-last-child(...) or :nth(-last)?-of-type(...) + if ( diff === false ) { + + // Use the same loop as above to seek `elem` from the start + while ( ( node = ++nodeIndex && node && node[ dir ] || + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + if ( ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) && + ++diff ) { + + // Cache the index of each encountered element + if ( useCache ) { + outerCache = node[ expando ] || + ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + uniqueCache[ type ] = [ dirruns, diff ]; + } + + if ( node === elem ) { + break; + } + } + } + } + } + + // Incorporate the offset, then check against cycle size + diff -= last; + return diff === first || ( diff % first === 0 && diff / first >= 0 ); + } + }; + }, + + "PSEUDO": function( pseudo, argument ) { + + // pseudo-class names are case-insensitive + // http://www.w3.org/TR/selectors/#pseudo-classes + // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters + // Remember that setFilters inherits from pseudos + var args, + fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || + Sizzle.error( "unsupported pseudo: " + pseudo ); + + // The user may use createPseudo to indicate that + // arguments are needed to create the filter function + // just as Sizzle does + if ( fn[ expando ] ) { + return fn( argument ); + } + + // But maintain support for old signatures + if ( fn.length > 1 ) { + args = [ pseudo, pseudo, "", argument ]; + return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? + markFunction( function( seed, matches ) { + var idx, + matched = fn( seed, argument ), + i = matched.length; + while ( i-- ) { + idx = indexOf( seed, matched[ i ] ); + seed[ idx ] = !( matches[ idx ] = matched[ i ] ); + } + } ) : + function( elem ) { + return fn( elem, 0, args ); + }; + } + + return fn; + } + }, + + pseudos: { + + // Potentially complex pseudos + "not": markFunction( function( selector ) { + + // Trim the selector passed to compile + // to avoid treating leading and trailing + // spaces as combinators + var input = [], + results = [], + matcher = compile( selector.replace( rtrim, "$1" ) ); + + return matcher[ expando ] ? + markFunction( function( seed, matches, _context, xml ) { + var elem, + unmatched = matcher( seed, null, xml, [] ), + i = seed.length; + + // Match elements unmatched by `matcher` + while ( i-- ) { + if ( ( elem = unmatched[ i ] ) ) { + seed[ i ] = !( matches[ i ] = elem ); + } + } + } ) : + function( elem, _context, xml ) { + input[ 0 ] = elem; + matcher( input, null, xml, results ); + + // Don't keep the element (issue #299) + input[ 0 ] = null; + return !results.pop(); + }; + } ), + + "has": markFunction( function( selector ) { + return function( elem ) { + return Sizzle( selector, elem ).length > 0; + }; + } ), + + "contains": markFunction( function( text ) { + text = text.replace( runescape, funescape ); + return function( elem ) { + return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; + }; + } ), + + // "Whether an element is represented by a :lang() selector + // is based solely on the element's language value + // being equal to the identifier C, + // or beginning with the identifier C immediately followed by "-". + // The matching of C against the element's language value is performed case-insensitively. + // The identifier C does not have to be a valid language name." + // http://www.w3.org/TR/selectors/#lang-pseudo + "lang": markFunction( function( lang ) { + + // lang value must be a valid identifier + if ( !ridentifier.test( lang || "" ) ) { + Sizzle.error( "unsupported lang: " + lang ); + } + lang = lang.replace( runescape, funescape ).toLowerCase(); + return function( elem ) { + var elemLang; + do { + if ( ( elemLang = documentIsHTML ? + elem.lang : + elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { + + elemLang = elemLang.toLowerCase(); + return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; + } + } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); + return false; + }; + } ), + + // Miscellaneous + "target": function( elem ) { + var hash = window.location && window.location.hash; + return hash && hash.slice( 1 ) === elem.id; + }, + + "root": function( elem ) { + return elem === docElem; + }, + + "focus": function( elem ) { + return elem === document.activeElement && + ( !document.hasFocus || document.hasFocus() ) && + !!( elem.type || elem.href || ~elem.tabIndex ); + }, + + // Boolean properties + "enabled": createDisabledPseudo( false ), + "disabled": createDisabledPseudo( true ), + + "checked": function( elem ) { + + // In CSS3, :checked should return both checked and selected elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + var nodeName = elem.nodeName.toLowerCase(); + return ( nodeName === "input" && !!elem.checked ) || + ( nodeName === "option" && !!elem.selected ); + }, + + "selected": function( elem ) { + + // Accessing this property makes selected-by-default + // options in Safari work properly + if ( elem.parentNode ) { + // eslint-disable-next-line no-unused-expressions + elem.parentNode.selectedIndex; + } + + return elem.selected === true; + }, + + // Contents + "empty": function( elem ) { + + // http://www.w3.org/TR/selectors/#empty-pseudo + // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), + // but not by others (comment: 8; processing instruction: 7; etc.) + // nodeType < 6 works because attributes (2) do not appear as children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + if ( elem.nodeType < 6 ) { + return false; + } + } + return true; + }, + + "parent": function( elem ) { + return !Expr.pseudos[ "empty" ]( elem ); + }, + + // Element/input types + "header": function( elem ) { + return rheader.test( elem.nodeName ); + }, + + "input": function( elem ) { + return rinputs.test( elem.nodeName ); + }, + + "button": function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === "button" || name === "button"; + }, + + "text": function( elem ) { + var attr; + return elem.nodeName.toLowerCase() === "input" && + elem.type === "text" && + + // Support: IE<8 + // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" + ( ( attr = elem.getAttribute( "type" ) ) == null || + attr.toLowerCase() === "text" ); + }, + + // Position-in-collection + "first": createPositionalPseudo( function() { + return [ 0 ]; + } ), + + "last": createPositionalPseudo( function( _matchIndexes, length ) { + return [ length - 1 ]; + } ), + + "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { + return [ argument < 0 ? argument + length : argument ]; + } ), + + "even": createPositionalPseudo( function( matchIndexes, length ) { + var i = 0; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "odd": createPositionalPseudo( function( matchIndexes, length ) { + var i = 1; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? + argument + length : + argument > length ? + length : + argument; + for ( ; --i >= 0; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? argument + length : argument; + for ( ; ++i < length; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ) + } +}; + +Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; + +// Add button/input type pseudos +for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { + Expr.pseudos[ i ] = createInputPseudo( i ); +} +for ( i in { submit: true, reset: true } ) { + Expr.pseudos[ i ] = createButtonPseudo( i ); +} -/***/ }), -/* 291 */ -/***/ (function(module, exports, __webpack_require__) { +// Easy API for creating new setFilters +function setFilters() {} +setFilters.prototype = Expr.filters = Expr.pseudos; +Expr.setFilters = new setFilters(); + +tokenize = Sizzle.tokenize = function( selector, parseOnly ) { + var matched, match, tokens, type, + soFar, groups, preFilters, + cached = tokenCache[ selector + " " ]; + + if ( cached ) { + return parseOnly ? 0 : cached.slice( 0 ); + } + + soFar = selector; + groups = []; + preFilters = Expr.preFilter; + + while ( soFar ) { + + // Comma and first run + if ( !matched || ( match = rcomma.exec( soFar ) ) ) { + if ( match ) { + + // Don't consume trailing commas as valid + soFar = soFar.slice( match[ 0 ].length ) || soFar; + } + groups.push( ( tokens = [] ) ); + } + + matched = false; + + // Combinators + if ( ( match = rcombinators.exec( soFar ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + + // Cast descendant combinators to space + type: match[ 0 ].replace( rtrim, " " ) + } ); + soFar = soFar.slice( matched.length ); + } + + // Filters + for ( type in Expr.filter ) { + if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || + ( match = preFilters[ type ]( match ) ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + type: type, + matches: match + } ); + soFar = soFar.slice( matched.length ); + } + } + + if ( !matched ) { + break; + } + } + + // Return the length of the invalid excess + // if we're just parsing + // Otherwise, throw an error or return tokens + return parseOnly ? + soFar.length : + soFar ? + Sizzle.error( selector ) : + + // Cache the tokens + tokenCache( selector, groups ).slice( 0 ); +}; + +function toSelector( tokens ) { + var i = 0, + len = tokens.length, + selector = ""; + for ( ; i < len; i++ ) { + selector += tokens[ i ].value; + } + return selector; +} -"use strict"; +function addCombinator( matcher, combinator, base ) { + var dir = combinator.dir, + skip = combinator.next, + key = skip || dir, + checkNonElements = base && key === "parentNode", + doneName = done++; + + return combinator.first ? + + // Check against closest ancestor/preceding element + function( elem, context, xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + return matcher( elem, context, xml ); + } + } + return false; + } : + + // Check against all ancestor/preceding elements + function( elem, context, xml ) { + var oldCache, uniqueCache, outerCache, + newCache = [ dirruns, doneName ]; + + // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching + if ( xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + if ( matcher( elem, context, xml ) ) { + return true; + } + } + } + } else { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + outerCache = elem[ expando ] || ( elem[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ elem.uniqueID ] || + ( outerCache[ elem.uniqueID ] = {} ); + + if ( skip && skip === elem.nodeName.toLowerCase() ) { + elem = elem[ dir ] || elem; + } else if ( ( oldCache = uniqueCache[ key ] ) && + oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { + + // Assign to newCache so results back-propagate to previous elements + return ( newCache[ 2 ] = oldCache[ 2 ] ); + } else { + + // Reuse newcache so results back-propagate to previous elements + uniqueCache[ key ] = newCache; + + // A match means we're done; a fail means we have to keep checking + if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { + return true; + } + } + } + } + } + return false; + }; +} +function elementMatcher( matchers ) { + return matchers.length > 1 ? + function( elem, context, xml ) { + var i = matchers.length; + while ( i-- ) { + if ( !matchers[ i ]( elem, context, xml ) ) { + return false; + } + } + return true; + } : + matchers[ 0 ]; +} -Object.defineProperty(exports, "__esModule", { - value: true -}); +function multipleContexts( selector, contexts, results ) { + var i = 0, + len = contexts.length; + for ( ; i < len; i++ ) { + Sizzle( selector, contexts[ i ], results ); + } + return results; +} -var _chartOptionBase = __webpack_require__(6); +function condense( unmatched, map, filter, context, xml ) { + var elem, + newUnmatched = [], + i = 0, + len = unmatched.length, + mapped = map != null; + + for ( ; i < len; i++ ) { + if ( ( elem = unmatched[ i ] ) ) { + if ( !filter || filter( elem, context, xml ) ) { + newUnmatched.push( elem ); + if ( mapped ) { + map.push( i ); + } + } + } + } + + return newUnmatched; +} -var _chartOptionBase2 = _interopRequireDefault(_chartOptionBase); +function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { + if ( postFilter && !postFilter[ expando ] ) { + postFilter = setMatcher( postFilter ); + } + if ( postFinder && !postFinder[ expando ] ) { + postFinder = setMatcher( postFinder, postSelector ); + } + return markFunction( function( seed, results, context, xml ) { + var temp, i, elem, + preMap = [], + postMap = [], + preexisting = results.length, + + // Get initial elements from seed or context + elems = seed || multipleContexts( + selector || "*", + context.nodeType ? [ context ] : context, + [] + ), + + // Prefilter to get matcher input, preserving a map for seed-results synchronization + matcherIn = preFilter && ( seed || !selector ) ? + condense( elems, preMap, preFilter, context, xml ) : + elems, + + matcherOut = matcher ? + + // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, + postFinder || ( seed ? preFilter : preexisting || postFilter ) ? + + // ...intermediate processing is necessary + [] : + + // ...otherwise use results directly + results : + matcherIn; + + // Find primary matches + if ( matcher ) { + matcher( matcherIn, matcherOut, context, xml ); + } + + // Apply postFilter + if ( postFilter ) { + temp = condense( matcherOut, postMap ); + postFilter( temp, [], context, xml ); + + // Un-match failing elements by moving them back to matcherIn + i = temp.length; + while ( i-- ) { + if ( ( elem = temp[ i ] ) ) { + matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); + } + } + } + + if ( seed ) { + if ( postFinder || preFilter ) { + if ( postFinder ) { + + // Get the final matcherOut by condensing this intermediate into postFinder contexts + temp = []; + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) ) { + + // Restore matcherIn since elem is not yet a final match + temp.push( ( matcherIn[ i ] = elem ) ); + } + } + postFinder( null, ( matcherOut = [] ), temp, xml ); + } + + // Move matched elements from seed to results to keep them synchronized + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) && + ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { + + seed[ temp ] = !( results[ temp ] = elem ); + } + } + } + + // Add elements to results, through postFinder if defined + } else { + matcherOut = condense( + matcherOut === results ? + matcherOut.splice( preexisting, matcherOut.length ) : + matcherOut + ); + if ( postFinder ) { + postFinder( null, results, matcherOut, xml ); + } else { + push.apply( results, matcherOut ); + } + } + } ); +} -var _chartOptionAreaStacked = __webpack_require__(292); +function matcherFromTokens( tokens ) { + var checkContext, matcher, j, + len = tokens.length, + leadingRelative = Expr.relative[ tokens[ 0 ].type ], + implicitRelative = leadingRelative || Expr.relative[ " " ], + i = leadingRelative ? 1 : 0, + + // The foundational matcher ensures that elements are reachable from top-level context(s) + matchContext = addCombinator( function( elem ) { + return elem === checkContext; + }, implicitRelative, true ), + matchAnyContext = addCombinator( function( elem ) { + return indexOf( checkContext, elem ) > -1; + }, implicitRelative, true ), + matchers = [ function( elem, context, xml ) { + var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( + ( checkContext = context ).nodeType ? + matchContext( elem, context, xml ) : + matchAnyContext( elem, context, xml ) ); + + // Avoid hanging onto element (issue #299) + checkContext = null; + return ret; + } ]; + + for ( ; i < len; i++ ) { + if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { + matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; + } else { + matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); + + // Return special upon seeing a positional matcher + if ( matcher[ expando ] ) { + + // Find the next relative operator (if any) for proper handling + j = ++i; + for ( ; j < len; j++ ) { + if ( Expr.relative[ tokens[ j ].type ] ) { + break; + } + } + return setMatcher( + i > 1 && elementMatcher( matchers ), + i > 1 && toSelector( + + // If the preceding token was a descendant combinator, insert an implicit any-element `*` + tokens + .slice( 0, i - 1 ) + .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) + ).replace( rtrim, "$1" ), + matcher, + i < j && matcherFromTokens( tokens.slice( i, j ) ), + j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), + j < len && toSelector( tokens ) + ); + } + matchers.push( matcher ); + } + } + + return elementMatcher( matchers ); +} -var _chartOptionAreaStacked2 = _interopRequireDefault(_chartOptionAreaStacked); +function matcherFromGroupMatchers( elementMatchers, setMatchers ) { + var bySet = setMatchers.length > 0, + byElement = elementMatchers.length > 0, + superMatcher = function( seed, context, xml, results, outermost ) { + var elem, j, matcher, + matchedCount = 0, + i = "0", + unmatched = seed && [], + setMatched = [], + contextBackup = outermostContext, + + // We must always have either seed elements or outermost context + elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), + + // Use integer dirruns iff this is the outermost matcher + dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), + len = elems.length; + + if ( outermost ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + outermostContext = context == document || context || outermost; + } + + // Add elements passing elementMatchers directly to results + // Support: IE<9, Safari + // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id + for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { + if ( byElement && elem ) { + j = 0; + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( !context && elem.ownerDocument != document ) { + setDocument( elem ); + xml = !documentIsHTML; + } + while ( ( matcher = elementMatchers[ j++ ] ) ) { + if ( matcher( elem, context || document, xml ) ) { + results.push( elem ); + break; + } + } + if ( outermost ) { + dirruns = dirrunsUnique; + } + } + + // Track unmatched elements for set filters + if ( bySet ) { + + // They will have gone through all possible matchers + if ( ( elem = !matcher && elem ) ) { + matchedCount--; + } + + // Lengthen the array for every element, matched or not + if ( seed ) { + unmatched.push( elem ); + } + } + } + + // `i` is now the count of elements visited above, and adding it to `matchedCount` + // makes the latter nonnegative. + matchedCount += i; + + // Apply set filters to unmatched elements + // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` + // equals `i`), unless we didn't visit _any_ elements in the above loop because we have + // no element matchers and no seed. + // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that + // case, which will result in a "00" `matchedCount` that differs from `i` but is also + // numerically zero. + if ( bySet && i !== matchedCount ) { + j = 0; + while ( ( matcher = setMatchers[ j++ ] ) ) { + matcher( unmatched, setMatched, context, xml ); + } + + if ( seed ) { + + // Reintegrate element matches to eliminate the need for sorting + if ( matchedCount > 0 ) { + while ( i-- ) { + if ( !( unmatched[ i ] || setMatched[ i ] ) ) { + setMatched[ i ] = pop.call( results ); + } + } + } + + // Discard index placeholder values to get only actual matches + setMatched = condense( setMatched ); + } + + // Add matches to results + push.apply( results, setMatched ); + + // Seedless set matches succeeding multiple successful matchers stipulate sorting + if ( outermost && !seed && setMatched.length > 0 && + ( matchedCount + setMatchers.length ) > 1 ) { + + Sizzle.uniqueSort( results ); + } + } + + // Override manipulation of globals by nested matchers + if ( outermost ) { + dirruns = dirrunsUnique; + outermostContext = contextBackup; + } + + return unmatched; + }; + + return bySet ? + markFunction( superMatcher ) : + superMatcher; +} -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } +compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { + var i, + setMatchers = [], + elementMatchers = [], + cached = compilerCache[ selector + " " ]; + + if ( !cached ) { + + // Generate a function of recursive functions that can be used to check each element + if ( !match ) { + match = tokenize( selector ); + } + i = match.length; + while ( i-- ) { + cached = matcherFromTokens( match[ i ] ); + if ( cached[ expando ] ) { + setMatchers.push( cached ); + } else { + elementMatchers.push( cached ); + } + } + + // Cache the compiled function + cached = compilerCache( + selector, + matcherFromGroupMatchers( elementMatchers, setMatchers ) + ); + + // Save selector and tokenization + cached.selector = selector; + } + return cached; +}; /** - * Created by SDS on 2017-05-10. + * A low-level selection function that works with Sizzle's compiled + * selector functions + * @param {String|Function} selector A selector or a pre-compiled + * selector function built with Sizzle.compile + * @param {Element} context + * @param {Array} [results] + * @param {Array} [seed] A set of elements to match against */ +select = Sizzle.select = function( selector, context, results, seed ) { + var i, tokens, token, type, find, + compiled = typeof selector === "function" && selector, + match = !seed && tokenize( ( selector = compiled.selector || selector ) ); + + results = results || []; + + // Try to minimize operations if there is only one selector in the list and no seed + // (the latter of which guarantees us context) + if ( match.length === 1 ) { + + // Reduce context if the leading compound selector is an ID + tokens = match[ 0 ] = match[ 0 ].slice( 0 ); + if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && + context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { + + context = ( Expr.find[ "ID" ]( token.matches[ 0 ] + .replace( runescape, funescape ), context ) || [] )[ 0 ]; + if ( !context ) { + return results; + + // Precompiled matchers will still verify ancestry, so step up a level + } else if ( compiled ) { + context = context.parentNode; + } + + selector = selector.slice( tokens.shift().value.length ); + } + + // Fetch a seed set for right-to-left matching + i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; + while ( i-- ) { + token = tokens[ i ]; + + // Abort if we hit a combinator + if ( Expr.relative[ ( type = token.type ) ] ) { + break; + } + if ( ( find = Expr.find[ type ] ) ) { + + // Search, expanding context for leading sibling combinators + if ( ( seed = find( + token.matches[ 0 ].replace( runescape, funescape ), + rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || + context + ) ) ) { + + // If seed is empty or no tokens remain, we can return early + tokens.splice( i, 1 ); + selector = seed.length && toSelector( tokens ); + if ( !selector ) { + push.apply( results, seed ); + return results; + } + + break; + } + } + } + } + + // Compile and execute a filtering function if one is not provided + // Provide `match` to avoid retokenization if we modified the selector above + ( compiled || compile( selector, match ) )( + seed, + context, + !documentIsHTML, + results, + !context || rsibling.test( selector ) && testContext( context.parentNode ) || context + ); + return results; +}; + +// One-time assignments + +// Sort stability +support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; + +// Support: Chrome 14-35+ +// Always assume duplicates if they aren't passed to the comparison function +support.detectDuplicates = !!hasDuplicate; + +// Initialize against the default document +setDocument(); + +// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) +// Detached nodes confoundingly follow *each other* +support.sortDetached = assert( function( el ) { + + // Should return 1, but returns 4 (following) + return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; +} ); + +// Support: IE<8 +// Prevent attribute/property "interpolation" +// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx +if ( !assert( function( el ) { + el.innerHTML = ""; + return el.firstChild.getAttribute( "href" ) === "#"; +} ) ) { + addHandle( "type|href|height|width", function( elem, name, isXML ) { + if ( !isXML ) { + return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); + } + } ); +} -function AreaStacked100ChartOption(parentId, options) { - _chartOptionBase2.default.call(this, parentId, options); +// Support: IE<9 +// Use defaultValue in place of getAttribute("value") +if ( !support.attributes || !assert( function( el ) { + el.innerHTML = ""; + el.firstChild.setAttribute( "value", "" ); + return el.firstChild.getAttribute( "value" ) === ""; +} ) ) { + addHandle( "value", function( elem, _name, isXML ) { + if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { + return elem.defaultValue; + } + } ); } -AreaStacked100ChartOption.prototype = Object.create(_chartOptionBase2.default.prototype); -AreaStacked100ChartOption.prototype.constructor = AreaStacked100ChartOption; +// Support: IE<9 +// Use getAttributeNode to fetch booleans when getAttribute lies +if ( !assert( function( el ) { + return el.getAttribute( "disabled" ) == null; +} ) ) { + addHandle( booleans, function( elem, name, isXML ) { + var val; + if ( !isXML ) { + return elem[ name ] === true ? name.toLowerCase() : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; + } + } ); +} -AreaStacked100ChartOption.prototype._init = function () { - _chartOptionBase2.default.prototype._init.call(this); - var _this = this; - var columnSelectorSetting = [[{ - key: 'xAxis', - ref: _this.options.chartOption.xAxis[0] - }, { - key: 'yAxis', - ref: _this.options.chartOption.yAxis[0] - }, { - key: 'stackBy', - ref: _this.options.chartOption.plotOptions.area.stackBy[0], - getColumnChangedOption: function getColumnChangedOption(value) { - return { - plotOptions: { - area: $.extend(true, {}, _this.options.chartOption.plotOptions.area, { - stackBy: [{ selected: value }] - }) - } - }; - } - }]]; - columnSelectorSetting = this._configureColumnConf(columnSelectorSetting); - this.options.setting.columnSelector = columnSelectorSetting; - this.options.setting.marker = this.options.chartOption.plotOptions.area.marker; - this.options.setting.stripLine = this.options.chartOption.plotOptions.area.stripLine; - this.options.setting.tooltip = this.options.chartOption.plotOptions.area.tooltip; - this.options.setting.showSymbol = this.options.chartOption.plotOptions.area.showSymbol; -}; +return Sizzle; -AreaStacked100ChartOption.prototype.getDefaultControlContainerList = function () { - return ['Data', 'ToolTipTrigger', 'Title', 'Axis', 'Legend', 'Marker', 'Lines', 'Frame']; -}; +} )( window ); -exports.default = AreaStacked100ChartOption; -/***/ }), -/* 292 */ -/***/ (function(module, exports, __webpack_require__) { -"use strict"; +jQuery.find = Sizzle; +jQuery.expr = Sizzle.selectors; +// Deprecated +jQuery.expr[ ":" ] = jQuery.expr.pseudos; +jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; +jQuery.text = Sizzle.getText; +jQuery.isXMLDoc = Sizzle.isXML; +jQuery.contains = Sizzle.contains; +jQuery.escapeSelector = Sizzle.escape; -Object.defineProperty(exports, "__esModule", { - value: true -}); -var _chartOptionBase = __webpack_require__(6); -var _chartOptionBase2 = _interopRequireDefault(_chartOptionBase); -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } +var dir = function( elem, dir, until ) { + var matched = [], + truncate = until !== undefined; -function AreaStackedChartOption(parentId, options) { - _chartOptionBase2.default.call(this, parentId, options); -} /** - * Created by SDS on 2017-05-10. - */ + while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { + if ( elem.nodeType === 1 ) { + if ( truncate && jQuery( elem ).is( until ) ) { + break; + } + matched.push( elem ); + } + } + return matched; +}; -AreaStackedChartOption.prototype = Object.create(_chartOptionBase2.default.prototype); -AreaStackedChartOption.prototype.constructor = AreaStackedChartOption; -AreaStackedChartOption.prototype._init = function () { - _chartOptionBase2.default.prototype._init.call(this); - var _this = this; - var columnSelectorSetting = [[{ - key: 'xAxis', - ref: this.options.chartOption.xAxis[0] - }, { - key: 'yAxis', - ref: this.options.chartOption.yAxis[0] - }, { - key: 'stackBy', - ref: this.options.chartOption.plotOptions.area.stackBy[0], - getColumnChangedOption: function getColumnChangedOption(value) { - return { - plotOptions: { - area: $.extend(true, {}, _this.options.chartOption.plotOptions.area, { - stackBy: [{ selected: value }] - }) - } - }; - } - }]]; +var siblings = function( n, elem ) { + var matched = []; - columnSelectorSetting = this._configureColumnConf(columnSelectorSetting); - this.options.setting.columnSelector = columnSelectorSetting; - this.options.setting.marker = this.options.chartOption.plotOptions.area.marker; - this.options.setting.stripLine = this.options.chartOption.plotOptions.area.stripLine; - this.options.setting.tooltip = this.options.chartOption.plotOptions.area.tooltip; - this.options.setting.showSymbol = this.options.chartOption.plotOptions.area.showSymbol; -}; + for ( ; n; n = n.nextSibling ) { + if ( n.nodeType === 1 && n !== elem ) { + matched.push( n ); + } + } -AreaStackedChartOption.prototype.getDefaultControlContainerList = function () { - return ['Data', 'ToolTipTrigger', 'Title', 'Axis', 'Legend', 'Marker', 'Lines', 'Frame']; + return matched; }; -exports.default = AreaStackedChartOption; -/***/ }), -/* 293 */ -/***/ (function(module, exports, __webpack_require__) { +var rneedsContext = jQuery.expr.match.needsContext; -"use strict"; -Object.defineProperty(exports, "__esModule", { - value: true -}); +function nodeName( elem, name ) { -var _chartOptionBase = __webpack_require__(6); + return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); -var _chartOptionBase2 = _interopRequireDefault(_chartOptionBase); +}; +var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); -var _chartOptionArea = __webpack_require__(118); -var _chartOptionArea2 = _interopRequireDefault(_chartOptionArea); -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } +// Implement the identical functionality for filter and not +function winnow( elements, qualifier, not ) { + if ( isFunction( qualifier ) ) { + return jQuery.grep( elements, function( elem, i ) { + return !!qualifier.call( elem, i, elem ) !== not; + } ); + } -/** - * Created by SDS on 2017-05-10. - */ + // Single element + if ( qualifier.nodeType ) { + return jQuery.grep( elements, function( elem ) { + return ( elem === qualifier ) !== not; + } ); + } -function BarStacked100ChartOption(parentId, options) { - _chartOptionBase2.default.call(this, parentId, options); + // Arraylike of elements (jQuery, arguments, Array) + if ( typeof qualifier !== "string" ) { + return jQuery.grep( elements, function( elem ) { + return ( indexOf.call( qualifier, elem ) > -1 ) !== not; + } ); + } + + // Filtered directly for both simple and complex selectors + return jQuery.filter( qualifier, elements, not ); } -BarStacked100ChartOption.prototype = Object.create(_chartOptionBase2.default.prototype); -BarStacked100ChartOption.prototype.constructor = BarStacked100ChartOption; +jQuery.filter = function( expr, elems, not ) { + var elem = elems[ 0 ]; + + if ( not ) { + expr = ":not(" + expr + ")"; + } + + if ( elems.length === 1 && elem.nodeType === 1 ) { + return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; + } + + return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { + return elem.nodeType === 1; + } ) ); +}; + +jQuery.fn.extend( { + find: function( selector ) { + var i, ret, + len = this.length, + self = this; + + if ( typeof selector !== "string" ) { + return this.pushStack( jQuery( selector ).filter( function() { + for ( i = 0; i < len; i++ ) { + if ( jQuery.contains( self[ i ], this ) ) { + return true; + } + } + } ) ); + } + + ret = this.pushStack( [] ); + + for ( i = 0; i < len; i++ ) { + jQuery.find( selector, self[ i ], ret ); + } + + return len > 1 ? jQuery.uniqueSort( ret ) : ret; + }, + filter: function( selector ) { + return this.pushStack( winnow( this, selector || [], false ) ); + }, + not: function( selector ) { + return this.pushStack( winnow( this, selector || [], true ) ); + }, + is: function( selector ) { + return !!winnow( + this, + + // If this is a positional/relative selector, check membership in the returned set + // so $("p:first").is("p:last") won't return true for a doc with two "p". + typeof selector === "string" && rneedsContext.test( selector ) ? + jQuery( selector ) : + selector || [], + false + ).length; + } +} ); + + +// Initialize a jQuery object + + +// A central reference to the root jQuery(document) +var rootjQuery, + + // A simple way to check for HTML strings + // Prioritize #id over to avoid XSS via location.hash (#9521) + // Strict HTML recognition (#11290: must start with <) + // Shortcut simple #id case for speed + rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, + + init = jQuery.fn.init = function( selector, context, root ) { + var match, elem; + + // HANDLE: $(""), $(null), $(undefined), $(false) + if ( !selector ) { + return this; + } + + // Method init() accepts an alternate rootjQuery + // so migrate can support jQuery.sub (gh-2101) + root = root || rootjQuery; + + // Handle HTML strings + if ( typeof selector === "string" ) { + if ( selector[ 0 ] === "<" && + selector[ selector.length - 1 ] === ">" && + selector.length >= 3 ) { + + // Assume that strings that start and end with <> are HTML and skip the regex check + match = [ null, selector, null ]; + + } else { + match = rquickExpr.exec( selector ); + } + + // Match html or make sure no context is specified for #id + if ( match && ( match[ 1 ] || !context ) ) { + + // HANDLE: $(html) -> $(array) + if ( match[ 1 ] ) { + context = context instanceof jQuery ? context[ 0 ] : context; + + // Option to run scripts is true for back-compat + // Intentionally let the error be thrown if parseHTML is not present + jQuery.merge( this, jQuery.parseHTML( + match[ 1 ], + context && context.nodeType ? context.ownerDocument || context : document, + true + ) ); + + // HANDLE: $(html, props) + if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { + for ( match in context ) { + + // Properties of context are called as methods if possible + if ( isFunction( this[ match ] ) ) { + this[ match ]( context[ match ] ); + + // ...and otherwise set as attributes + } else { + this.attr( match, context[ match ] ); + } + } + } + + return this; + + // HANDLE: $(#id) + } else { + elem = document.getElementById( match[ 2 ] ); + + if ( elem ) { + + // Inject the element directly into the jQuery object + this[ 0 ] = elem; + this.length = 1; + } + return this; + } + + // HANDLE: $(expr, $(...)) + } else if ( !context || context.jquery ) { + return ( context || root ).find( selector ); + + // HANDLE: $(expr, context) + // (which is just equivalent to: $(context).find(expr) + } else { + return this.constructor( context ).find( selector ); + } + + // HANDLE: $(DOMElement) + } else if ( selector.nodeType ) { + this[ 0 ] = selector; + this.length = 1; + return this; + + // HANDLE: $(function) + // Shortcut for document ready + } else if ( isFunction( selector ) ) { + return root.ready !== undefined ? + root.ready( selector ) : + + // Execute immediately if ready is not present + selector( jQuery ); + } + + return jQuery.makeArray( selector, this ); + }; + +// Give the init function the jQuery prototype for later instantiation +init.prototype = jQuery.fn; + +// Initialize central reference +rootjQuery = jQuery( document ); + + +var rparentsprev = /^(?:parents|prev(?:Until|All))/, + + // Methods guaranteed to produce a unique set when starting from a unique set + guaranteedUnique = { + children: true, + contents: true, + next: true, + prev: true + }; + +jQuery.fn.extend( { + has: function( target ) { + var targets = jQuery( target, this ), + l = targets.length; + + return this.filter( function() { + var i = 0; + for ( ; i < l; i++ ) { + if ( jQuery.contains( this, targets[ i ] ) ) { + return true; + } + } + } ); + }, + + closest: function( selectors, context ) { + var cur, + i = 0, + l = this.length, + matched = [], + targets = typeof selectors !== "string" && jQuery( selectors ); + + // Positional selectors never match, since there's no _selection_ context + if ( !rneedsContext.test( selectors ) ) { + for ( ; i < l; i++ ) { + for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { + + // Always skip document fragments + if ( cur.nodeType < 11 && ( targets ? + targets.index( cur ) > -1 : + + // Don't pass non-elements to Sizzle + cur.nodeType === 1 && + jQuery.find.matchesSelector( cur, selectors ) ) ) { + + matched.push( cur ); + break; + } + } + } + } + + return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); + }, + + // Determine the position of an element within the set + index: function( elem ) { + + // No argument, return index in parent + if ( !elem ) { + return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; + } + + // Index in selector + if ( typeof elem === "string" ) { + return indexOf.call( jQuery( elem ), this[ 0 ] ); + } + + // Locate the position of the desired element + return indexOf.call( this, + + // If it receives a jQuery object, the first element is used + elem.jquery ? elem[ 0 ] : elem + ); + }, + + add: function( selector, context ) { + return this.pushStack( + jQuery.uniqueSort( + jQuery.merge( this.get(), jQuery( selector, context ) ) + ) + ); + }, + + addBack: function( selector ) { + return this.add( selector == null ? + this.prevObject : this.prevObject.filter( selector ) + ); + } +} ); + +function sibling( cur, dir ) { + while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} + return cur; +} -BarStacked100ChartOption.prototype._init = function () { - _chartOptionBase2.default.prototype._init.call(this); - var _this = this; - var columnSelectorSetting = [[{ - key: 'xAxis', - ref: _this.options.chartOption.xAxis[0] - }, { - key: 'yAxis', - ref: _this.options.chartOption.yAxis[0] - }, { - key: 'stackBy', - ref: _this.options.chartOption.plotOptions.bar.stackBy[0], - getColumnChangedOption: function getColumnChangedOption(value) { - return { - plotOptions: { - bar: $.extend(true, {}, _this.options.chartOption.plotOptions.bar, { - stackBy: [{ selected: value }] - }) - } - }; - } - }]]; - columnSelectorSetting = this._configureColumnConf(columnSelectorSetting); - this.options.setting.columnSelector = columnSelectorSetting; - this.options.setting.stripLine = this.options.chartOption.plotOptions.bar.stripLine; - this.options.setting.tooltip = this.options.chartOption.plotOptions.bar.tooltip; -}; +jQuery.each( { + parent: function( elem ) { + var parent = elem.parentNode; + return parent && parent.nodeType !== 11 ? parent : null; + }, + parents: function( elem ) { + return dir( elem, "parentNode" ); + }, + parentsUntil: function( elem, _i, until ) { + return dir( elem, "parentNode", until ); + }, + next: function( elem ) { + return sibling( elem, "nextSibling" ); + }, + prev: function( elem ) { + return sibling( elem, "previousSibling" ); + }, + nextAll: function( elem ) { + return dir( elem, "nextSibling" ); + }, + prevAll: function( elem ) { + return dir( elem, "previousSibling" ); + }, + nextUntil: function( elem, _i, until ) { + return dir( elem, "nextSibling", until ); + }, + prevUntil: function( elem, _i, until ) { + return dir( elem, "previousSibling", until ); + }, + siblings: function( elem ) { + return siblings( ( elem.parentNode || {} ).firstChild, elem ); + }, + children: function( elem ) { + return siblings( elem.firstChild ); + }, + contents: function( elem ) { + if ( elem.contentDocument != null && + + // Support: IE 11+ + // elements with no `data` attribute has an object + // `contentDocument` with a `null` prototype. + getProto( elem.contentDocument ) ) { + + return elem.contentDocument; + } + + // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only + // Treat the template element as a regular one in browsers that + // don't support it. + if ( nodeName( elem, "template" ) ) { + elem = elem.content || elem; + } + + return jQuery.merge( [], elem.childNodes ); + } +}, function( name, fn ) { + jQuery.fn[ name ] = function( until, selector ) { + var matched = jQuery.map( this, fn, until ); + + if ( name.slice( -5 ) !== "Until" ) { + selector = until; + } + + if ( selector && typeof selector === "string" ) { + matched = jQuery.filter( selector, matched ); + } + + if ( this.length > 1 ) { + + // Remove duplicates + if ( !guaranteedUnique[ name ] ) { + jQuery.uniqueSort( matched ); + } + + // Reverse order for parents* and prev-derivatives + if ( rparentsprev.test( name ) ) { + matched.reverse(); + } + } + + return this.pushStack( matched ); + }; +} ); +var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); + + + +// Convert String-formatted options into Object-formatted ones +function createOptions( options ) { + var object = {}; + jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { + object[ flag ] = true; + } ); + return object; +} -BarStacked100ChartOption.prototype.getDefaultControlContainerList = function () { - return ['Data', 'ToolTipTrigger', 'Title', 'Axis', 'Legend', 'Marker', 'Lines', 'Frame']; -}; +/* + * Create a callback list using the following parameters: + * + * options: an optional list of space-separated options that will change how + * the callback list behaves or a more traditional option object + * + * By default a callback list will act like an event callback list and can be + * "fired" multiple times. + * + * Possible options: + * + * once: will ensure the callback list can only be fired once (like a Deferred) + * + * memory: will keep track of previous values and will call any callback added + * after the list has been fired right away with the latest "memorized" + * values (like a Deferred) + * + * unique: will ensure a callback can only be added once (no duplicate in the list) + * + * stopOnFalse: interrupt callings when a callback returns false + * + */ +jQuery.Callbacks = function( options ) { + + // Convert options from String-formatted to Object-formatted if needed + // (we check in cache first) + options = typeof options === "string" ? + createOptions( options ) : + jQuery.extend( {}, options ); + + var // Flag to know if list is currently firing + firing, + + // Last fire value for non-forgettable lists + memory, + + // Flag to know if list was already fired + fired, + + // Flag to prevent firing + locked, + + // Actual callback list + list = [], + + // Queue of execution data for repeatable lists + queue = [], + + // Index of currently firing callback (modified by add/remove as needed) + firingIndex = -1, + + // Fire callbacks + fire = function() { + + // Enforce single-firing + locked = locked || options.once; + + // Execute callbacks for all pending executions, + // respecting firingIndex overrides and runtime changes + fired = firing = true; + for ( ; queue.length; firingIndex = -1 ) { + memory = queue.shift(); + while ( ++firingIndex < list.length ) { + + // Run callback and check for early termination + if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && + options.stopOnFalse ) { + + // Jump to end and forget the data so .add doesn't re-fire + firingIndex = list.length; + memory = false; + } + } + } + + // Forget the data if we're done with it + if ( !options.memory ) { + memory = false; + } + + firing = false; + + // Clean up if we're done firing for good + if ( locked ) { + + // Keep an empty list if we have data for future add calls + if ( memory ) { + list = []; + + // Otherwise, this object is spent + } else { + list = ""; + } + } + }, + + // Actual Callbacks object + self = { + + // Add a callback or a collection of callbacks to the list + add: function() { + if ( list ) { + + // If we have memory from a past run, we should fire after adding + if ( memory && !firing ) { + firingIndex = list.length - 1; + queue.push( memory ); + } + + ( function add( args ) { + jQuery.each( args, function( _, arg ) { + if ( isFunction( arg ) ) { + if ( !options.unique || !self.has( arg ) ) { + list.push( arg ); + } + } else if ( arg && arg.length && toType( arg ) !== "string" ) { + + // Inspect recursively + add( arg ); + } + } ); + } )( arguments ); + + if ( memory && !firing ) { + fire(); + } + } + return this; + }, + + // Remove a callback from the list + remove: function() { + jQuery.each( arguments, function( _, arg ) { + var index; + while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { + list.splice( index, 1 ); + + // Handle firing indexes + if ( index <= firingIndex ) { + firingIndex--; + } + } + } ); + return this; + }, + + // Check if a given callback is in the list. + // If no argument is given, return whether or not list has callbacks attached. + has: function( fn ) { + return fn ? + jQuery.inArray( fn, list ) > -1 : + list.length > 0; + }, + + // Remove all callbacks from the list + empty: function() { + if ( list ) { + list = []; + } + return this; + }, + + // Disable .fire and .add + // Abort any current/pending executions + // Clear all callbacks and values + disable: function() { + locked = queue = []; + list = memory = ""; + return this; + }, + disabled: function() { + return !list; + }, + + // Disable .fire + // Also disable .add unless we have memory (since it would have no effect) + // Abort any pending executions + lock: function() { + locked = queue = []; + if ( !memory && !firing ) { + list = memory = ""; + } + return this; + }, + locked: function() { + return !!locked; + }, + + // Call all callbacks with the given context and arguments + fireWith: function( context, args ) { + if ( !locked ) { + args = args || []; + args = [ context, args.slice ? args.slice() : args ]; + queue.push( args ); + if ( !firing ) { + fire(); + } + } + return this; + }, + + // Call all the callbacks with the given arguments + fire: function() { + self.fireWith( this, arguments ); + return this; + }, + + // To know if the callbacks have already been called at least once + fired: function() { + return !!fired; + } + }; + + return self; +}; + + +function Identity( v ) { + return v; +} +function Thrower( ex ) { + throw ex; +} -exports.default = BarStacked100ChartOption; +function adoptValue( value, resolve, reject, noValue ) { + var method; -/***/ }), -/* 294 */ -/***/ (function(module, exports, __webpack_require__) { + try { -"use strict"; + // Check for promise aspect first to privilege synchronous behavior + if ( value && isFunction( ( method = value.promise ) ) ) { + method.call( value ).done( resolve ).fail( reject ); + // Other thenables + } else if ( value && isFunction( ( method = value.then ) ) ) { + method.call( value, resolve, reject ); -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.set = set; -exports.get = get; + // Other non-thenables + } else { -var setupReg = {}; + // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: + // * false: [ value ].slice( 0 ) => resolve( value ) + // * true: [ value ].slice( 1 ) => resolve() + resolve.apply( undefined, [ value ].slice( noValue ) ); + } -function set(key, value) { - setupReg[key] = value; -} + // For Promises/A+, convert exceptions into rejections + // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in + // Deferred#then to conditionally suppress rejection. + } catch ( value ) { -function get(key) { - return setupReg[key]; + // Support: Android 4.0 only + // Strict mode functions invoked without .call/.apply get global-object context + reject.apply( undefined, [ value ] ); + } } -exports.default = setupReg; +jQuery.extend( { + + Deferred: function( func ) { + var tuples = [ + + // action, add listener, callbacks, + // ... .then handlers, argument index, [final state] + [ "notify", "progress", jQuery.Callbacks( "memory" ), + jQuery.Callbacks( "memory" ), 2 ], + [ "resolve", "done", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 0, "resolved" ], + [ "reject", "fail", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 1, "rejected" ] + ], + state = "pending", + promise = { + state: function() { + return state; + }, + always: function() { + deferred.done( arguments ).fail( arguments ); + return this; + }, + "catch": function( fn ) { + return promise.then( null, fn ); + }, + + // Keep pipe for back-compat + pipe: function( /* fnDone, fnFail, fnProgress */ ) { + var fns = arguments; + + return jQuery.Deferred( function( newDefer ) { + jQuery.each( tuples, function( _i, tuple ) { + + // Map tuples (progress, done, fail) to arguments (done, fail, progress) + var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; + + // deferred.progress(function() { bind to newDefer or newDefer.notify }) + // deferred.done(function() { bind to newDefer or newDefer.resolve }) + // deferred.fail(function() { bind to newDefer or newDefer.reject }) + deferred[ tuple[ 1 ] ]( function() { + var returned = fn && fn.apply( this, arguments ); + if ( returned && isFunction( returned.promise ) ) { + returned.promise() + .progress( newDefer.notify ) + .done( newDefer.resolve ) + .fail( newDefer.reject ); + } else { + newDefer[ tuple[ 0 ] + "With" ]( + this, + fn ? [ returned ] : arguments + ); + } + } ); + } ); + fns = null; + } ).promise(); + }, + then: function( onFulfilled, onRejected, onProgress ) { + var maxDepth = 0; + function resolve( depth, deferred, handler, special ) { + return function() { + var that = this, + args = arguments, + mightThrow = function() { + var returned, then; + + // Support: Promises/A+ section 2.3.3.3.3 + // https://promisesaplus.com/#point-59 + // Ignore double-resolution attempts + if ( depth < maxDepth ) { + return; + } + + returned = handler.apply( that, args ); + + // Support: Promises/A+ section 2.3.1 + // https://promisesaplus.com/#point-48 + if ( returned === deferred.promise() ) { + throw new TypeError( "Thenable self-resolution" ); + } + + // Support: Promises/A+ sections 2.3.3.1, 3.5 + // https://promisesaplus.com/#point-54 + // https://promisesaplus.com/#point-75 + // Retrieve `then` only once + then = returned && + + // Support: Promises/A+ section 2.3.4 + // https://promisesaplus.com/#point-64 + // Only check objects and functions for thenability + ( typeof returned === "object" || + typeof returned === "function" ) && + returned.then; + + // Handle a returned thenable + if ( isFunction( then ) ) { + + // Special processors (notify) just wait for resolution + if ( special ) { + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ) + ); + + // Normal processors (resolve) also hook into progress + } else { + + // ...and disregard older resolution values + maxDepth++; + + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ), + resolve( maxDepth, deferred, Identity, + deferred.notifyWith ) + ); + } + + // Handle all other returned values + } else { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Identity ) { + that = undefined; + args = [ returned ]; + } + + // Process the value(s) + // Default process is resolve + ( special || deferred.resolveWith )( that, args ); + } + }, + + // Only normal processors (resolve) catch and reject exceptions + process = special ? + mightThrow : + function() { + try { + mightThrow(); + } catch ( e ) { + + if ( jQuery.Deferred.exceptionHook ) { + jQuery.Deferred.exceptionHook( e, + process.stackTrace ); + } + + // Support: Promises/A+ section 2.3.3.3.4.1 + // https://promisesaplus.com/#point-61 + // Ignore post-resolution exceptions + if ( depth + 1 >= maxDepth ) { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Thrower ) { + that = undefined; + args = [ e ]; + } + + deferred.rejectWith( that, args ); + } + } + }; + + // Support: Promises/A+ section 2.3.3.3.1 + // https://promisesaplus.com/#point-57 + // Re-resolve promises immediately to dodge false rejection from + // subsequent errors + if ( depth ) { + process(); + } else { + + // Call an optional hook to record the stack, in case of exception + // since it's otherwise lost when execution goes async + if ( jQuery.Deferred.getStackHook ) { + process.stackTrace = jQuery.Deferred.getStackHook(); + } + window.setTimeout( process ); + } + }; + } + + return jQuery.Deferred( function( newDefer ) { + + // progress_handlers.add( ... ) + tuples[ 0 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onProgress ) ? + onProgress : + Identity, + newDefer.notifyWith + ) + ); + + // fulfilled_handlers.add( ... ) + tuples[ 1 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onFulfilled ) ? + onFulfilled : + Identity + ) + ); + + // rejected_handlers.add( ... ) + tuples[ 2 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onRejected ) ? + onRejected : + Thrower + ) + ); + } ).promise(); + }, + + // Get a promise for this deferred + // If obj is provided, the promise aspect is added to the object + promise: function( obj ) { + return obj != null ? jQuery.extend( obj, promise ) : promise; + } + }, + deferred = {}; + + // Add list-specific methods + jQuery.each( tuples, function( i, tuple ) { + var list = tuple[ 2 ], + stateString = tuple[ 5 ]; + + // promise.progress = list.add + // promise.done = list.add + // promise.fail = list.add + promise[ tuple[ 1 ] ] = list.add; + + // Handle state + if ( stateString ) { + list.add( + function() { + + // state = "resolved" (i.e., fulfilled) + // state = "rejected" + state = stateString; + }, + + // rejected_callbacks.disable + // fulfilled_callbacks.disable + tuples[ 3 - i ][ 2 ].disable, + + // rejected_handlers.disable + // fulfilled_handlers.disable + tuples[ 3 - i ][ 3 ].disable, + + // progress_callbacks.lock + tuples[ 0 ][ 2 ].lock, + + // progress_handlers.lock + tuples[ 0 ][ 3 ].lock + ); + } + + // progress_handlers.fire + // fulfilled_handlers.fire + // rejected_handlers.fire + list.add( tuple[ 3 ].fire ); + + // deferred.notify = function() { deferred.notifyWith(...) } + // deferred.resolve = function() { deferred.resolveWith(...) } + // deferred.reject = function() { deferred.rejectWith(...) } + deferred[ tuple[ 0 ] ] = function() { + deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); + return this; + }; + + // deferred.notifyWith = list.fireWith + // deferred.resolveWith = list.fireWith + // deferred.rejectWith = list.fireWith + deferred[ tuple[ 0 ] + "With" ] = list.fireWith; + } ); + + // Make the deferred a promise + promise.promise( deferred ); + + // Call given func if any + if ( func ) { + func.call( deferred, deferred ); + } + + // All done! + return deferred; + }, + + // Deferred helper + when: function( singleValue ) { + var + + // count of uncompleted subordinates + remaining = arguments.length, + + // count of unprocessed arguments + i = remaining, -/***/ }), -/* 295 */ -/***/ (function(module, exports, __webpack_require__) { + // subordinate fulfillment data + resolveContexts = Array( i ), + resolveValues = slice.call( arguments ), -"use strict"; + // the master Deferred + master = jQuery.Deferred(), + // subordinate callback factory + updateFunc = function( i ) { + return function( value ) { + resolveContexts[ i ] = this; + resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; + if ( !( --remaining ) ) { + master.resolveWith( resolveContexts, resolveValues ); + } + }; + }; -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.buildAutoOptionSelector = buildAutoOptionSelector; + // Single- and empty arguments are adopted like Promise.resolve + if ( remaining <= 1 ) { + adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject, + !remaining ); -var _resolvers = __webpack_require__(663); + // Use .then() to unwrap secondary thenables (cf. gh-3000) + if ( master.state() === "pending" || + isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { -function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } } + return master.then(); + } + } -function _toArray(arr) { return Array.isArray(arr) ? arr : Array.from(arr); } /* ----------------------------------------------------- - * auto-option-selector.js - * Created by hyunseok.oh@samsung.com on 2018-08-31. - * ---------------------------------------------------- */ + // Multiple arguments are aggregated like Promise.all array elements + while ( i-- ) { + adoptValue( resolveValues[ i ], updateFunc( i ), master.reject ); + } + return master.promise(); + } +} ); -function buildAutoOptionSelector(_config) { - var config = _config || {}; - var map = {}; - // -------------------------------------------------------------------------------------------- - var xyResolver = function xyResolver(a, b) { - return (0, _resolvers.andResolver)([(0, _resolvers.xResolver)({ type: a }), (0, _resolvers.yResolver)({ type: b })]); - }; +// These usually indicate a programmer mistake during development, +// warn about them ASAP rather than swallowing them by default. +var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; - var nnCol = xyResolver(_resolvers.COL_TYPE.NUMERIC, _resolvers.COL_TYPE.NUMERIC); - var ncCol = xyResolver(_resolvers.COL_TYPE.NUMERIC, _resolvers.COL_TYPE.CATEGORICAL); - var cnCol = xyResolver(_resolvers.COL_TYPE.CATEGORICAL, _resolvers.COL_TYPE.NUMERIC); +jQuery.Deferred.exceptionHook = function( error, stack ) { - Object.assign(map, { - 'histogram': [(0, _resolvers.xResolver)({ type: _resolvers.COL_TYPE.NUMERIC })], - 'boxplot': [cnCol], - 'line': [nnCol], - 'line-stacked': [nnCol], - 'line-stacked-100': [nnCol], - 'bar': [ncCol], - 'bar-stacked': [ncCol], - 'bar-stacked-100': [ncCol], - 'column': [cnCol], - 'column-stacked': [cnCol], - 'column-stacked-100': [cnCol], - 'scatter': [nnCol], - 'area': [cnCol], - 'area-stacked': [cnCol], - 'area-stacked-100': [cnCol], - 'biplot': [], - 'heatmap': [], - 'heatmap-matrix': [], - 'roccurve': [], - 'network': [], - 'decisionTree': [], - 'dendrogram': [], - 'treemap': [], - 'bubble': [], - 'qqplot': [], - 'pairwise-scatter': [], - 'complex': [], - 'card': [], - 'image-grid': [(0, _resolvers.imageColumnResolver)()] - }); - // -------------------------------------------------------------------------------------------- + // Support: IE 8 - 9 only + // Console exists when dev tools are open, which can happen at any time + if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { + window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); + } +}; - // validate resolvers - var _iteratorNormalCompletion = true; - var _didIteratorError = false; - var _iteratorError = undefined; - try { - for (var _iterator = Object.values(map)[Symbol.iterator](), _step; !(_iteratorNormalCompletion = (_step = _iterator.next()).done); _iteratorNormalCompletion = true) { - var v = _step.value; - v.forEach(function (e) { - if (!e.pre) { - throw new Error('resolver must implement a "pre". see option-selector.js'); - } - if (!e.resolve) { - throw new Error('resolver must implement a "resolve". see option-selector.js'); - } - }); - } - } catch (err) { - _didIteratorError = true; - _iteratorError = err; - } finally { - try { - if (!_iteratorNormalCompletion && _iterator.return) { - _iterator.return(); - } - } finally { - if (_didIteratorError) { - throw _iteratorError; - } - } - } - return function (type, chartOption, cols) { - if (!map[type] || typeof config[type] !== 'undefined' && !config[type] || typeof config.default !== 'undefined' && !config.default) { - return chartOption; - } - var resolverList = map[type]; - return resolverList.reduce(function (_ref, r) { - var _ref2 = _toArray(_ref), - p = _ref2.slice(0); +jQuery.readyException = function( error ) { + window.setTimeout( function() { + throw error; + } ); +}; - if (r.pre.apply(r, _toConsumableArray(p))) return r.resolve.apply(r, _toConsumableArray(p)); - return p; - }, [chartOption, cols])[0]; - }; -} -/***/ }), -/* 296 */ -/***/ (function(module, exports, __webpack_require__) { -"use strict"; +// The deferred used on DOM ready +var readyList = jQuery.Deferred(); -Object.defineProperty(exports, "__esModule", { - value: true -}); +jQuery.fn.ready = function( fn ) { -var _typeutils = __webpack_require__(664); + readyList + .then( fn ) -var TypeUtils = _interopRequireWildcard(_typeutils); + // Wrap jQuery.readyException in a function so that the lookup + // happens at the time of error handling instead of callback + // registration. + .catch( function( error ) { + jQuery.readyException( error ); + } ); -function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } } + return this; +}; -/** - * Source: datasource.js - * Created by daewon.park on 2017-04-28. - */ +jQuery.extend( { -var ChartWidget = Brightics.Chart.Widget; + // Is the DOM ready to be used? Set to true once it occurs. + isReady: false, + // A counter to track how many items to wait for before + // the ready event fires. See #6781 + readyWait: 1, -/** - options = - { - id: 'asdf', - name: 'Data Source #1', //데이터 소스 Title, - updateTime: '2017-03-30T01:33:50.304Z', - chartTab: { - show: true - }, - columnDraggable: true (default: false), - headerDraggable: true (default: false), - drawer: { - show: true, //false - menu: { - edit: { - show: true, - title: 'Edit', - click: function (event, index, options) { + // Handle when the DOM is ready + ready: function( wait ) { - } - }, - connect: { - show: true, - title: 'Connect Schedule', - click: function (event, index, options) { + // Abort if there are pending holds or we're already ready + if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { + return; + } - } - }, - delete: { - show: true, - title: 'Delete', - click: function (event, index, options) { + // Remember that the DOM is ready + jQuery.isReady = true; - } - } - } - }, - charts: [], - columns: [{ - name: 'sepal_length', - type: 'number', - internalType: 'Double' - }, { - name: 'sepal_width', - type: 'number', - internalType: 'Double' - }, { - name: 'petal_length', - type: 'number', - internalType: 'Double' - }, { - name: 'petal_width', - type: 'number', - internalType: 'Double' - }, { - name: 'species', - type: 'string', - internalType: 'String' - }] - } - **/ + // If a normal DOM Ready event fired, decrement, and wait if need be + if ( wait !== true && --jQuery.readyWait > 0 ) { + return; + } -function DataSource(parentId, options) { - ChartWidget.call(this, parentId, options); + // If there are functions bound, to execute + readyList.resolveWith( document, [ jQuery ] ); + } +} ); + +jQuery.ready.then = readyList.then; + +// The ready event handler and self cleanup method +function completed() { + document.removeEventListener( "DOMContentLoaded", completed ); + window.removeEventListener( "load", completed ); + jQuery.ready(); } -DataSource.prototype = Object.create(ChartWidget.prototype); -DataSource.prototype.constructor = DataSource; +// Catch cases where $(document).ready() is called +// after the browser event has already occurred. +// Support: IE <=9 - 10 only +// Older IE sometimes signals "interactive" too soon +if ( document.readyState === "complete" || + ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { -DataSource.prototype._init = function () { - ChartWidget.prototype._init.call(this); - this.setOptions(this.options); -}; + // Handle it asynchronously to allow scripts the opportunity to delay ready + window.setTimeout( jQuery.ready ); -// Draggable To Other Object -// drag-type: 'dataSource', 'column', 'chartTemplate' -// $helper.data('data', dataSource) -DataSource.prototype.setOptions = function (options) { - this.options = $.extend(true, this.options || {}, options, { - drawer: { - menu: {} - } - }); -}; +} else { -DataSource.prototype.setName = function (name) { - this.options.name = name; -}; + // Use the handy event callback + document.addEventListener( "DOMContentLoaded", completed ); -DataSource.prototype.destroy = function () { - if (this.$drawerMenu) { - this.$drawerMenu.jqxMenu('destroy'); - this.$drawerMenu = null; - } + // A fallback to window.onload, that will always work + window.addEventListener( "load", completed ); +} - var $parent = this.$mainControl.parents('.bcharts-ds-panel'); - this.$mainControl.remove(); - $parent.perfectScrollbar('update'); -}; -DataSource.prototype._createContents = function ($parent) { - this.$mainControl = $('
'); - $parent.append(this.$mainControl); - this._createDataSourceHeader(this.$mainControl); - this._createDrawerMenu(this.$mainControl); - this._createDataSourceContents(this.$mainControl); - this.render(); -}; -DataSource.prototype._createDataSourceHeader = function ($parent) { - this.$headerControl = $('' + '
' + '
' + '
' + '
' + '
' + '
'); - $parent.append(this.$headerControl); - this._applyExpander(); - this._applyDrawer(); - if (this.options.headerDraggable) { - this._bindDraggableEventToDataSourceHeader(); - } -}; -DataSource.prototype._applyExpander = function () { - // DataSource Content (bcharts-ds-contents) 접고 펴는 기능입니다. - // UX 디자인 나오는거 보고, jqxExpander를 적용할지 결정할 예정. - var _this = this; - this.$headerControl.click(function (event) { - if (!($(event.target).hasClass('bcharts-ds-drawer') || $(event.target).hasClass('bcharts-ds-drawer-button'))) { - var $arrow = $(this).find('.bcharts-expander-arrow'); - if ($arrow.hasClass('bcharts-icon-arrow-up')) { - $arrow.removeClass('bcharts-icon-arrow-up').addClass('bcharts-icon-arrow-down'); - _this.$mainControl.addClass('bcharts-ds-collapsed').removeClass('bcharts-ds-expanded'); - _this.$mainControl.find('.bcharts-ds-contents').hide(); - } else { - $arrow.removeClass('bcharts-icon-arrow-down').addClass('bcharts-icon-arrow-up'); - _this.$mainControl.removeClass('bcharts-ds-collapsed').addClass('bcharts-ds-expanded'); - _this.$mainControl.find('.bcharts-ds-contents').show(); - } +// Multifunctional method to get and set values of a collection +// The value/s can optionally be executed if it's a function +var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { + var i = 0, + len = elems.length, + bulk = key == null; - // 접고 펼 때 상위 div의 perfectScrollbar를 업데이트한다. - _this.$mainControl.parents('.bcharts-ds-panel').perfectScrollbar('update'); - } - }); -}; + // Sets many values + if ( toType( key ) === "object" ) { + chainable = true; + for ( i in key ) { + access( elems, fn, i, key[ i ], true, emptyGet, raw ); + } -DataSource.prototype._applyDrawer = function () { - var _this = this; - if (!this.options.drawer || !this.options.drawer.show) { - this.$headerControl.find('.bcharts-ds-drawer').hide(); - } + // Sets one value + } else if ( value !== undefined ) { + chainable = true; - // Click하면 Context Menu가 popup으로 생성 - this.$headerControl.find('.bcharts-ds-drawer').click(function () { - var anchorWidth = $(this).width(); - var anchorHeight = $(this).height(); - var anchorOffset = $(this).offset(); - var menuWidth = _this.$drawerMenu.width(); - if (_this.$drawerMenu.css('display') === 'none') { - _this.$drawerMenu.jqxMenu('open', anchorOffset.left + anchorWidth - menuWidth - 4, anchorOffset.top + anchorHeight); - } - }); -}; + if ( !isFunction( value ) ) { + raw = true; + } -DataSource.prototype._bindDraggableEventToDataSourceHeader = function () { - var _this = this; - this.$headerControl.draggable({ - appendTo: 'body', - helper: function helper(event) { - var $helper = $(this).clone(); - var width = $(this).width(); - var height = $(this).height(); - $helper.css({ - 'z-index': 5100, - 'width': width, - 'height': height, - 'overflow': 'hidden', - 'white-space': 'nowrap', - 'text-overflow': 'ellipsis' - }); //.ui-draggable-dragging - $helper.attr('drag-type', 'dataSource'); - $helper.data('datasource', $.extend(true, {}, _this.options)); - return $helper; - } - }); -}; + if ( bulk ) { -DataSource.prototype._createDrawerMenu = function ($parent) { - var $ul = $('
    '); - for (var key in this.options.drawer.menu) { - if (this.options.drawer.menu[key].show) { - var $li = $('
  • '); - $li.attr('action', key); - $li.text(this.options.drawer.menu[key].title); - $ul.append($li); - } - } + // Bulk operations run against the entire set + if ( raw ) { + fn.call( elems, value ); + fn = null; - this.$drawerMenu = $('
    ').append($ul); - $parent.append(this.$drawerMenu); - this.$drawerMenu.jqxMenu({ - theme: 'office', - autoOpenPopup: false, - mode: 'popup', - width: 140 //jqxMenu - li tag의 text에 space key가 들어있으면 space 부분까지만 width가 적용됨.. - }); - this._bindItemClickEventToDrawer(); -}; + // ...except when executing function values + } else { + bulk = fn; + fn = function( elem, _key, value ) { + return bulk.call( jQuery( elem ), value ); + }; + } + } -DataSource.prototype._bindItemClickEventToDrawer = function () { - var _this = this; - this.$drawerMenu.on('itemclick', function (event) { - var element = event.args; - var menuKey = $(element).attr('action'); - var dataSourceIndex = _this.$mainControl.prevAll('.bcharts-ds').length; - /* menu 옵션에 사용자가 설정한 click 함수 실행 로직 - * drawer: { menu: { '사용자가 정한 기능': click: function(){}}} - * drawer: { menu: { '사용자가 정한 기능': click: [function(){}, function(){}]}} function Array도 가능 - * drawer: { menu: { '사용자가 정한 기능': action: function(){}}} click이 아니라 action이나 다른 이름으로 함수를 지정해도 실행할 수 있도록 하였음. - * - * 부연설명 - * click: function(){} 만 사용하면 아래 3줄로 표현할 수 있지만 - * 사실 여기서 click() 이렇게 함수를 실행하니까 jquery click 함수와 매우 헷갈렸습니다. - * if (typeof _this.options.drawer.menu[menuKey].click === 'function') { - * _this.options.drawer.menu[menuKey].click(); - * } - */ - // Logic Start - if (_this.options.drawer.menu[menuKey]) { - var targetMenu = _this.options.drawer.menu[menuKey]; - for (var key in targetMenu) { - // click이나 action같은 key가 function인 경우 함수 실행 - if (typeof targetMenu[key] === 'function') { - targetMenu[key](event, dataSourceIndex, _this.options); - } - // function Array인 경우 - else if (targetMenu[key] instanceof Array && targetMenu[key].length) { - for (var i in targetMenu[key]) { - // 함수 실행 - if (typeof targetMenu[key][i] === 'function') { - targetMenu[key][i](event, dataSourceIndex, _this.options); - } - } - } - } - } - // Logic End - }); -}; + if ( fn ) { + for ( ; i < len; i++ ) { + fn( + elems[ i ], key, raw ? + value : + value.call( elems[ i ], i, fn( elems[ i ], key ) ) + ); + } + } + } -DataSource.prototype._createDataSourceContents = function ($parent) { - var _this = this; - this.$contentsControl = $('' + '
    ' + '
      ' + '
      '); - $parent.append(this.$contentsControl); - this._createTabs(this.$contentsControl); + if ( chainable ) { + return elems; + } - if (this.$contentsControl.find('li').length) { - this.$contentsControl.jqxTabs({ - theme: 'office', - scrollable: false - }); + // Gets + if ( bulk ) { + return fn.call( elems ); + } - this.$contentsControl.on('selected', function (event) { - //var clickedItem = event.args.item; - _this._handleJqxTabsSelectedClick(); - }); - } + return len ? fn( elems[ 0 ], key ) : emptyGet; }; -DataSource.prototype._handleJqxTabsSelectedClick = function () { - // tab이 선택될 때 상위 div의 perfectScrollbar를 업데이트한다. - this.$mainControl.parents('.bcharts-ds-panel').perfectScrollbar('update'); -}; -// DataSource 상속 받는 Class의 경우 여기서 Tab 갯수와 기능 등등을 override 할 수 있다. -DataSource.prototype._createTabs = function ($parent) { - this._createColumnsTab($parent); -}; +// Matches dashed string for camelizing +var rmsPrefix = /^-ms-/, + rdashAlpha = /-([a-z])/g; -DataSource.prototype._createColumnsTab = function ($parent) { - var $columnsTab = $('
    • Columns
      0
      • '); - $parent.find('.bcharts-ds-contents-tabs').append($columnsTab); - $parent.append($('
      ')); -}; +// Used by camelCase as callback to replace() +function fcamelCase( _all, letter ) { + return letter.toUpperCase(); +} + +// Convert dashed to camelCase; used by the css and data modules +// Support: IE <=9 - 11, Edge 12 - 15 +// Microsoft forgot to hump their vendor prefix (#9572) +function camelCase( string ) { + return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); +} +var acceptData = function( owner ) { -DataSource.prototype._updateColumnsCount = function (number) { - // Columns Tab에 표시되는 숫자 변경 - this.$contentsControl.find('.bcharts-ds-contents-columns-count').text(number); + // Accepts only: + // - Node + // - Node.ELEMENT_NODE + // - Node.DOCUMENT_NODE + // - Object + // - Any + return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); }; -DataSource.prototype.fillColumnsTab = function (columns) { - var $container = $('
      '); - this.$contentsControl.jqxTabs('setContentAt', this.$contentsControl.jqxTabs('length') - 1, $container.html()); - $container = this.$contentsControl.find('.bcharts-ds-columns-container'); - this._createColumns($container, columns); - this.$contentsControl.find('.bcharts-ds-columns').perfectScrollbar(); - // Columns Tab에 표시되는 숫자 변경 - if (columns && columns instanceof Array) this._updateColumnsCount(columns.length); - this._createUtilToolsArea(); -}; -DataSource.prototype._createUtilToolsArea = function () { - var $container = this.$contentsControl.find('.bcharts-ds-columns-container'); - var $utilToolsArea = $('
      '); - $container.prepend($utilToolsArea); +function Data() { + this.expando = jQuery.expando + Data.uid++; +} - this._createFilterControl($utilToolsArea); -}; +Data.uid = 1; + +Data.prototype = { + + cache: function( owner ) { + + // Check if the owner object already has a cache + var value = owner[ this.expando ]; + + // If not, create one + if ( !value ) { + value = {}; + + // We can accept data for non-element nodes in modern browsers, + // but we should not, see #8335. + // Always return an empty object. + if ( acceptData( owner ) ) { + + // If it is a node unlikely to be stringify-ed or looped over + // use plain assignment + if ( owner.nodeType ) { + owner[ this.expando ] = value; + + // Otherwise secure it in a non-enumerable property + // configurable must be true to allow the property to be + // deleted when data is removed + } else { + Object.defineProperty( owner, this.expando, { + value: value, + configurable: true + } ); + } + } + } + + return value; + }, + set: function( owner, data, value ) { + var prop, + cache = this.cache( owner ); + + // Handle: [ owner, key, value ] args + // Always use camelCase key (gh-2257) + if ( typeof data === "string" ) { + cache[ camelCase( data ) ] = value; + + // Handle: [ owner, { properties } ] args + } else { + + // Copy the properties one-by-one to the cache object + for ( prop in data ) { + cache[ camelCase( prop ) ] = data[ prop ]; + } + } + return cache; + }, + get: function( owner, key ) { + return key === undefined ? + this.cache( owner ) : + + // Always use camelCase key (gh-2257) + owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; + }, + access: function( owner, key, value ) { + + // In cases where either: + // + // 1. No key was specified + // 2. A string key was specified, but no value provided + // + // Take the "read" path and allow the get method to determine + // which value to return, respectively either: + // + // 1. The entire cache object + // 2. The data stored at the key + // + if ( key === undefined || + ( ( key && typeof key === "string" ) && value === undefined ) ) { + + return this.get( owner, key ); + } + + // When the key is not a string, or both a key and value + // are specified, set or extend (existing objects) with either: + // + // 1. An object of properties + // 2. A key and value + // + this.set( owner, key, value ); + + // Since the "set" path can have two possible entry points + // return the expected data based on which path was taken[*] + return value !== undefined ? value : key; + }, + remove: function( owner, key ) { + var i, + cache = owner[ this.expando ]; + + if ( cache === undefined ) { + return; + } + + if ( key !== undefined ) { + + // Support array or space separated string of keys + if ( Array.isArray( key ) ) { + + // If key is an array of keys... + // We always set camelCase keys, so remove that. + key = key.map( camelCase ); + } else { + key = camelCase( key ); + + // If a key with the spaces exists, use it. + // Otherwise, create an array by matching non-whitespace + key = key in cache ? + [ key ] : + ( key.match( rnothtmlwhite ) || [] ); + } + + i = key.length; + + while ( i-- ) { + delete cache[ key[ i ] ]; + } + } + + // Remove the expando if there's no more data + if ( key === undefined || jQuery.isEmptyObject( cache ) ) { + + // Support: Chrome <=35 - 45 + // Webkit & Blink performance suffers when deleting properties + // from DOM nodes, so set to undefined instead + // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) + if ( owner.nodeType ) { + owner[ this.expando ] = undefined; + } else { + delete owner[ this.expando ]; + } + } + }, + hasData: function( owner ) { + var cache = owner[ this.expando ]; + return cache !== undefined && !jQuery.isEmptyObject( cache ); + } +}; +var dataPriv = new Data(); + +var dataUser = new Data(); + + + +// Implementation Summary +// +// 1. Enforce API surface and semantic compatibility with 1.9.x branch +// 2. Improve the module's maintainability by reducing the storage +// paths to a single mechanism. +// 3. Use the same single mechanism to support "private" and "user" data. +// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) +// 5. Avoid exposing implementation details on user objects (eg. expando properties) +// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 + +var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, + rmultiDash = /[A-Z]/g; + +function getData( data ) { + if ( data === "true" ) { + return true; + } + + if ( data === "false" ) { + return false; + } + + if ( data === "null" ) { + return null; + } + + // Only convert to a number if it doesn't change the string + if ( data === +data + "" ) { + return +data; + } + + if ( rbrace.test( data ) ) { + return JSON.parse( data ); + } + + return data; +} -DataSource.prototype._createFilterControl = function ($parent) { - var _this = this; - var $searchArea = $('
      '); - $parent.append($searchArea); +function dataAttr( elem, key, data ) { + var name; + + // If nothing was found internally, try to fetch any + // data from the HTML5 data-* attribute + if ( data === undefined && elem.nodeType === 1 ) { + name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); + data = elem.getAttribute( name ); + + if ( typeof data === "string" ) { + try { + data = getData( data ); + } catch ( e ) {} + + // Make sure we set the data so it isn't changed later + dataUser.set( elem, key, data ); + } else { + data = undefined; + } + } + return data; +} - var $filterInput = $(''); - $searchArea.append($filterInput); +jQuery.extend( { + hasData: function( elem ) { + return dataUser.hasData( elem ) || dataPriv.hasData( elem ); + }, + + data: function( elem, name, data ) { + return dataUser.access( elem, name, data ); + }, + + removeData: function( elem, name ) { + dataUser.remove( elem, name ); + }, + + // TODO: Now that all calls to _data and _removeData have been replaced + // with direct calls to dataPriv methods, these can be deprecated. + _data: function( elem, name, data ) { + return dataPriv.access( elem, name, data ); + }, + + _removeData: function( elem, name ) { + dataPriv.remove( elem, name ); + } +} ); + +jQuery.fn.extend( { + data: function( key, value ) { + var i, name, data, + elem = this[ 0 ], + attrs = elem && elem.attributes; + + // Gets all values + if ( key === undefined ) { + if ( this.length ) { + data = dataUser.get( elem ); + + if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { + i = attrs.length; + while ( i-- ) { + + // Support: IE 11 only + // The attrs elements can be null (#14894) + if ( attrs[ i ] ) { + name = attrs[ i ].name; + if ( name.indexOf( "data-" ) === 0 ) { + name = camelCase( name.slice( 5 ) ); + dataAttr( elem, name, data[ name ] ); + } + } + } + dataPriv.set( elem, "hasDataAttrs", true ); + } + } + + return data; + } + + // Sets multiple values + if ( typeof key === "object" ) { + return this.each( function() { + dataUser.set( this, key ); + } ); + } + + return access( this, function( value ) { + var data; + + // The calling jQuery object (element matches) is not empty + // (and therefore has an element appears at this[ 0 ]) and the + // `value` parameter was not undefined. An empty jQuery object + // will result in `undefined` for elem = this[ 0 ] which will + // throw an exception if an attempt to read a data cache is made. + if ( elem && value === undefined ) { + + // Attempt to get data from the cache + // The key will always be camelCased in Data + data = dataUser.get( elem, key ); + if ( data !== undefined ) { + return data; + } + + // Attempt to "discover" the data in + // HTML5 custom data-* attrs + data = dataAttr( elem, key ); + if ( data !== undefined ) { + return data; + } + + // We tried really hard, but the data doesn't exist. + return; + } + + // Set the data... + this.each( function() { + + // We always store the camelCased key + dataUser.set( this, key, value ); + } ); + }, null, value, arguments.length > 1, null, true ); + }, + + removeData: function( key ) { + return this.each( function() { + dataUser.remove( this, key ); + } ); + } +} ); + + +jQuery.extend( { + queue: function( elem, type, data ) { + var queue; + + if ( elem ) { + type = ( type || "fx" ) + "queue"; + queue = dataPriv.get( elem, type ); + + // Speed up dequeue by getting out quickly if this is just a lookup + if ( data ) { + if ( !queue || Array.isArray( data ) ) { + queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); + } else { + queue.push( data ); + } + } + return queue || []; + } + }, + + dequeue: function( elem, type ) { + type = type || "fx"; + + var queue = jQuery.queue( elem, type ), + startLength = queue.length, + fn = queue.shift(), + hooks = jQuery._queueHooks( elem, type ), + next = function() { + jQuery.dequeue( elem, type ); + }; + + // If the fx queue is dequeued, always remove the progress sentinel + if ( fn === "inprogress" ) { + fn = queue.shift(); + startLength--; + } + + if ( fn ) { + + // Add a progress sentinel to prevent the fx queue from being + // automatically dequeued + if ( type === "fx" ) { + queue.unshift( "inprogress" ); + } + + // Clear up the last queue stop function + delete hooks.stop; + fn.call( elem, next, hooks ); + } + + if ( !startLength && hooks ) { + hooks.empty.fire(); + } + }, + + // Not public - generate a queueHooks object, or return the current one + _queueHooks: function( elem, type ) { + var key = type + "queueHooks"; + return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { + empty: jQuery.Callbacks( "once memory" ).add( function() { + dataPriv.remove( elem, [ type + "queue", key ] ); + } ) + } ); + } +} ); + +jQuery.fn.extend( { + queue: function( type, data ) { + var setter = 2; + + if ( typeof type !== "string" ) { + data = type; + type = "fx"; + setter--; + } + + if ( arguments.length < setter ) { + return jQuery.queue( this[ 0 ], type ); + } + + return data === undefined ? + this : + this.each( function() { + var queue = jQuery.queue( this, type, data ); + + // Ensure a hooks for this queue + jQuery._queueHooks( this, type ); + + if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { + jQuery.dequeue( this, type ); + } + } ); + }, + dequeue: function( type ) { + return this.each( function() { + jQuery.dequeue( this, type ); + } ); + }, + clearQueue: function( type ) { + return this.queue( type || "fx", [] ); + }, + + // Get a promise resolved when queues of a certain type + // are emptied (fx is the type by default) + promise: function( type, obj ) { + var tmp, + count = 1, + defer = jQuery.Deferred(), + elements = this, + i = this.length, + resolve = function() { + if ( !( --count ) ) { + defer.resolveWith( elements, [ elements ] ); + } + }; + + if ( typeof type !== "string" ) { + obj = type; + type = undefined; + } + type = type || "fx"; + + while ( i-- ) { + tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); + if ( tmp && tmp.empty ) { + count++; + tmp.empty.add( resolve ); + } + } + resolve(); + return defer.promise( obj ); + } +} ); +var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; + +var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); + + +var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; + +var documentElement = document.documentElement; + + + + var isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ); + }, + composed = { composed: true }; + + // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only + // Check attachment across shadow DOM boundaries when possible (gh-3504) + // Support: iOS 10.0-10.2 only + // Early iOS 10 versions support `attachShadow` but not `getRootNode`, + // leading to errors. We need to check for `getRootNode`. + if ( documentElement.getRootNode ) { + isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ) || + elem.getRootNode( composed ) === elem.ownerDocument; + }; + } +var isHiddenWithinTree = function( elem, el ) { + + // isHiddenWithinTree might be called from jQuery#filter function; + // in that case, element will be second argument + elem = el || elem; + + // Inline style trumps all + return elem.style.display === "none" || + elem.style.display === "" && + + // Otherwise, check computed style + // Support: Firefox <=43 - 45 + // Disconnected elements can have computed display: none, so first confirm that elem is + // in the document. + isAttached( elem ) && + + jQuery.css( elem, "display" ) === "none"; + }; + + + +function adjustCSS( elem, prop, valueParts, tween ) { + var adjusted, scale, + maxIterations = 20, + currentValue = tween ? + function() { + return tween.cur(); + } : + function() { + return jQuery.css( elem, prop, "" ); + }, + initial = currentValue(), + unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), + + // Starting value computation is required for potential unit mismatches + initialInUnit = elem.nodeType && + ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && + rcssNum.exec( jQuery.css( elem, prop ) ); + + if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { + + // Support: Firefox <=54 + // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) + initial = initial / 2; + + // Trust units reported by jQuery.css + unit = unit || initialInUnit[ 3 ]; + + // Iteratively approximate from a nonzero starting point + initialInUnit = +initial || 1; + + while ( maxIterations-- ) { + + // Evaluate and update our best guess (doubling guesses that zero out). + // Finish if the scale equals or crosses 1 (making the old*new product non-positive). + jQuery.style( elem, prop, initialInUnit + unit ); + if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { + maxIterations = 0; + } + initialInUnit = initialInUnit / scale; + + } + + initialInUnit = initialInUnit * 2; + jQuery.style( elem, prop, initialInUnit + unit ); + + // Make sure we update the tween properties later on + valueParts = valueParts || []; + } + + if ( valueParts ) { + initialInUnit = +initialInUnit || +initial || 0; + + // Apply relative offset (+=/-=) if specified + adjusted = valueParts[ 1 ] ? + initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : + +valueParts[ 2 ]; + if ( tween ) { + tween.unit = unit; + tween.start = initialInUnit; + tween.end = adjusted; + } + } + return adjusted; +} - $filterInput.jqxInput({ - placeHolder: 'Search Column', - theme: 'office' - }); - var applyFilter = function applyFilter(event) { - var filterValue = event.target.value.toLowerCase(); - var $columns = _this.$contentsControl.find('.bcharts-ds-columns-container .bcharts-ds-columns'); - $.each($columns.find('.bcharts-ds-column'), function (index, column) { - var columnName = $(column).find('.bcharts-ds-column-name').attr('title').toLowerCase(); - if (columnName.indexOf(filterValue) != -1) { - $(column).css('display', 'flex'); - } else { - $(column).css('display', 'none'); - } - }); - _this.$contentsControl.find('.bcharts-ds-columns').perfectScrollbar('update'); - _this.$mainControl.parents('.bcharts-ds-panel').perfectScrollbar('update'); - }; - $filterInput.keyup(function (event) { - applyFilter(event); - }); +var defaultDisplayMap = {}; - $filterInput.on('search', function (event) { - applyFilter(event); - }); -}; +function getDefaultDisplay( elem ) { + var temp, + doc = elem.ownerDocument, + nodeName = elem.nodeName, + display = defaultDisplayMap[ nodeName ]; -DataSource.prototype._createColumns = function ($parent, columns) { - var $columns = $('
      '); - $parent.append($columns); + if ( display ) { + return display; + } - for (var i in columns) { - var $col = $('
      '); - var dataTypeTitle = TypeUtils.convertDataTypeForTitle(columns[i].type, columns[i].internalType); - var dataTypeText = TypeUtils.convertDataTypeForText(columns[i].type, columns[i].internalType); - $col.find('.bcharts-ds-column-type').attr('title', dataTypeTitle); - $col.find('.bcharts-ds-column-type').text(dataTypeText); - $col.find('.bcharts-ds-column-name').attr('title', columns[i].name); - $col.find('.bcharts-ds-column-name .bcharts-ds-column-name-ellipsis').text(columns[i].name); - $columns.append($col); - if (this.options.columnDraggable) this._bindDraggableEventToDataSourceColumns($col, i); - } -}; + temp = doc.body.appendChild( doc.createElement( nodeName ) ); + display = jQuery.css( temp, "display" ); -DataSource.prototype._bindDraggableEventToDataSourceColumns = function ($target, index) { - var _this = this; - $target.draggable({ - appendTo: 'body', - scroll: false, - cursor: 'move', - helper: function helper(event) { - var $helper = $(this).clone(); - var width = $(this).width(); - var height = $(this).height(); - $helper.css({ - 'z-index': 5100, - 'width': width, - 'height': height, - 'overflow': 'hidden', - 'white-space': 'nowrap', - 'text-overflow': 'ellipsis' - }); //.ui-draggable-dragging - $helper.attr('drag-type', 'column'); - var helperData = $.extend(true, {}, _this.options); - // if (helperData.columns && helperData.columns.length) { - // //console.log(JSON.stringify(helperData.columns[index])); - // helperData.columns = [helperData.columns[index]]; - // } else { - // helperData.columns = []; - // } + temp.parentNode.removeChild( temp ); - $helper.data('datasource', helperData); - $helper.data('column', helperData.columns[index]); - return $helper; - } - }); -}; + if ( display === "none" ) { + display = "block"; + } + defaultDisplayMap[ nodeName ] = display; -DataSource.prototype.render = function () { - this.fillName(); - this.fillContents(); -}; + return display; +} -DataSource.prototype.fillName = function () { - this.$headerControl.find('.bcharts-ds-title').attr('title', this.options.name); - this.$headerControl.find('.bcharts-ds-title .bcharts-ds-title-ellipsis').text(this.options.name); -}; +function showHide( elements, show ) { + var display, elem, + values = [], + index = 0, + length = elements.length; + + // Determine new display value for elements that need to change + for ( ; index < length; index++ ) { + elem = elements[ index ]; + if ( !elem.style ) { + continue; + } + + display = elem.style.display; + if ( show ) { + + // Since we force visibility upon cascade-hidden elements, an immediate (and slow) + // check is required in this first loop unless we have a nonempty display value (either + // inline or about-to-be-restored) + if ( display === "none" ) { + values[ index ] = dataPriv.get( elem, "display" ) || null; + if ( !values[ index ] ) { + elem.style.display = ""; + } + } + if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { + values[ index ] = getDefaultDisplay( elem ); + } + } else { + if ( display !== "none" ) { + values[ index ] = "none"; + + // Remember what we're overwriting + dataPriv.set( elem, "display", display ); + } + } + } + + // Set the display of the elements in a second loop to avoid constant reflow + for ( index = 0; index < length; index++ ) { + if ( values[ index ] != null ) { + elements[ index ].style.display = values[ index ]; + } + } + + return elements; +} -DataSource.prototype.fillContents = function () { - this.fillColumnsTab(this.options.columns); -}; +jQuery.fn.extend( { + show: function() { + return showHide( this, true ); + }, + hide: function() { + return showHide( this ); + }, + toggle: function( state ) { + if ( typeof state === "boolean" ) { + return state ? this.show() : this.hide(); + } + + return this.each( function() { + if ( isHiddenWithinTree( this ) ) { + jQuery( this ).show(); + } else { + jQuery( this ).hide(); + } + } ); + } +} ); +var rcheckableType = ( /^(?:checkbox|radio)$/i ); + +var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); + +var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); + + + +( function() { + var fragment = document.createDocumentFragment(), + div = fragment.appendChild( document.createElement( "div" ) ), + input = document.createElement( "input" ); + + // Support: Android 4.0 - 4.3 only + // Check state lost if the name is set (#11217) + // Support: Windows Web Apps (WWA) + // `name` and `type` must use .setAttribute for WWA (#14901) + input.setAttribute( "type", "radio" ); + input.setAttribute( "checked", "checked" ); + input.setAttribute( "name", "t" ); + + div.appendChild( input ); + + // Support: Android <=4.1 only + // Older WebKit doesn't clone checked state correctly in fragments + support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; + + // Support: IE <=11 only + // Make sure textarea (and checkbox) defaultValue is properly cloned + div.innerHTML = ""; + support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; + + // Support: IE <=9 only + // IE <=9 replaces "; + support.option = !!div.lastChild; +} )(); + + +// We have to close these tags to support XHTML (#13200) +var wrapMap = { + + // XHTML parsers do not magically insert elements in the + // same way that tag soup parsers do. So we cannot shorten + // this by omitting or other required elements. + thead: [ 1, "", "
      " ], + col: [ 2, "", "
      " ], + tr: [ 2, "", "
      " ], + td: [ 3, "", "
      " ], + + _default: [ 0, "", "" ] +}; + +wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; +wrapMap.th = wrapMap.td; + +// Support: IE <=9 only +if ( !support.option ) { + wrapMap.optgroup = wrapMap.option = [ 1, "" ]; +} -exports.default = DataSource; -/***/ }), -/* 297 */ -/***/ (function(module, exports, __webpack_require__) { +function getAll( context, tag ) { -"use strict"; + // Support: IE <=9 - 11 only + // Use typeof to avoid zero-argument method invocation on host objects (#15151) + var ret; + if ( typeof context.getElementsByTagName !== "undefined" ) { + ret = context.getElementsByTagName( tag || "*" ); -Object.defineProperty(exports, "__esModule", { - value: true -}); + } else if ( typeof context.querySelectorAll !== "undefined" ) { + ret = context.querySelectorAll( tag || "*" ); -var _widgetFactory = __webpack_require__(3); + } else { + ret = []; + } -var WidgetFactory = _interopRequireWildcard(_widgetFactory); + if ( tag === undefined || tag && nodeName( context, tag ) ) { + return jQuery.merge( [ context ], ret ); + } -function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } } + return ret; +} -/** - * Source: chart-panel.js - * Created by daewon.park on 2017-05-10. - */ -var ChartWidget = Brightics.Chart.Widget; -var ChartUtil = Brightics.Chart.Helper.ChartUtils; +// Mark scripts as having already been evaluated +function setGlobalEval( elems, refElements ) { + var i = 0, + l = elems.length; -function ChartPanel(parentId, options) { - ChartWidget.call(this, parentId, options); + for ( ; i < l; i++ ) { + dataPriv.set( + elems[ i ], + "globalEval", + !refElements || dataPriv.get( refElements[ i ], "globalEval" ) + ); + } } -ChartPanel.prototype = Object.create(ChartWidget.prototype); -ChartPanel.prototype.constructor = ChartPanel; -ChartPanel.prototype._init = function () { - ChartWidget.prototype._init.call(this); -}; +var rhtml = /<|&#?\w+;/; -ChartPanel.prototype.destroy = function () { - if (this.$contextMenu) { - this.$contextMenu.jqxMenu('destroy'); - } - if (this.$contentsControl) { - this.$contentsControl.bcharts('destroy'); - } - $(window).off('resize', this.__resizeHandler); -}; +function buildFragment( elems, context, scripts, selection, ignored ) { + var elem, tmp, tag, wrap, attached, j, + fragment = context.createDocumentFragment(), + nodes = [], + i = 0, + l = elems.length; -ChartPanel.prototype.getOptions = function () { - return this.options; -}; + for ( ; i < l; i++ ) { + elem = elems[ i ]; -ChartPanel.prototype._createContents = function ($parent) { - this.$mainControl = $('
      '); - this.$mainControl.css(this.options.style); - this.$mainControl.attr('id', this.options.id); - this.$mainControl.attr('chart', this.options.chartOption.chart.type); - $parent.append(this.$mainControl); + if ( elem || elem === 0 ) { - this._createPanelHeader(this.$mainControl); - this._createPanelContents(this.$mainControl); - this._createDroppable(this.$mainControl); + // Add nodes directly + if ( toType( elem ) === "object" ) { - this._configureChartOptions(); - this._createChart(); - // this._connectGroup(); -}; + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); -ChartPanel.prototype._configureChartOptions = function () {}; + // Convert non-html into a text node + } else if ( !rhtml.test( elem ) ) { + nodes.push( context.createTextNode( elem ) ); -ChartPanel.prototype._createPanelHeader = function ($parent) { - var _this2 = this; + // Convert html into DOM nodes + } else { + tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); - var _this = this; + // Deserialize a standard representation + tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); + wrap = wrapMap[ tag ] || wrapMap._default; + tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; - this.$headerControl = $('' + '
      ' + '
      ' + '
      ' + '
      ' + '
      '); - $parent.append(this.$headerControl); - this.$headerControl.perfectScrollbar(); - this.$headerControl.perfectScrollbar('update'); - this.__resizeHandler = function (e) { - return _this2.$headerControl.perfectScrollbar('update'); - }; - $(window).on('resize', this.__resizeHandler); - this._createChartSelector(); - this._createDraggable(); -}; + // Descend through wrappers to the right content + j = wrap[ 0 ]; + while ( j-- ) { + tmp = tmp.lastChild; + } -ChartPanel.prototype._createDraggable = function () { - var _this = this; + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, tmp.childNodes ); - this.$headerControl.find('.bcharts-ws-panel-draggable').draggable({ - appendTo: 'body', - scroll: false, - cursor: 'move', - cursorAt: { left: 90, top: 20 }, - helper: function helper(event) { - var $helper = $('
      '); - _this.$headerControl.find('.bcharts-ws-panel-chart-selector').clone().addClass('bcharts-non-editable').appendTo($helper); - $helper.data('bcharts-ws-panel', $(this).closest('.bcharts-ws-panel')); - return $helper; - }, - start: function start(event, ui) { - _this.$mainControl.closest('.bcharts-worksheet').find('.bcharts-ws-panel-droppable').show(); - _this.$mainControl.find('.bcharts-ws-panel-droppable').hide(); - }, - stop: function stop(event, ui) { - _this.$mainControl.closest('.bcharts-worksheet').find('.bcharts-ws-panel-droppable').hide(); - } - }); -}; + // Remember the top-level container + tmp = fragment.firstChild; -ChartPanel.prototype._createChartSelector = function () { - var _this = this; - this.chartTypeSelector = WidgetFactory.createChartTypeSelectorWidget(this.$headerControl.find('.bcharts-ws-panel-chart-selector'), { - chartTypeList: this.options.chartTypeList, - chartTypeSelectable: this.options.chartTypeSelectable, - onChanged: function onChanged(type) { - if (typeof _this.options.chartTypeChanged === 'function') { - _this.options.chartTypeChanged(_this.options.id, type); - } - } - }); -}; + // Ensure the created nodes are orphaned (#12392) + tmp.textContent = ""; + } + } + } -ChartPanel.prototype._createPanelContents = function ($parent) { - this.$contentsControl = $('' + '
      ' + '
      '); - $parent.append(this.$contentsControl); -}; + // Remove wrapper from fragment + fragment.textContent = ""; -ChartPanel.prototype._createDroppable = function ($parent) { - var _this = this; + i = 0; + while ( ( elem = nodes[ i++ ] ) ) { - $parent.append('' + '
      ' + '
      ' + '
      ' + '
      ' + ''); + // Skip elements already in the context collection (trac-4087) + if ( selection && jQuery.inArray( elem, selection ) > -1 ) { + if ( ignored ) { + ignored.push( elem ); + } + continue; + } - $parent.find('.bcharts-ws-panel-droppable').droppable({ - accept: '.bcharts-ws-panel-draggable', - activeClass: 'bcharts-droppable-state-active', - hoverClass: 'bcharts-droppable-state-hover', - over: function over(event, ui) { - var $dropLocation = $('
      '); - if ($(this).attr('anchor') === 'left') $dropLocation.css({ - width: '50%', - height: '100%', - top: 0 - });else if ($(this).attr('anchor') === 'right') $dropLocation.css({ - width: '50%', - height: '100%', - right: 0, - top: 0 - });else if ($(this).attr('anchor') === 'top') $dropLocation.css({ - width: '100%', - height: '50%', - top: 0 - });else if ($(this).attr('anchor') === 'bottom') $dropLocation.css({ - width: '100%', - height: '50%', - bottom: 0 - }); + attached = isAttached( elem ); - _this.$mainControl.find('.bcharts-ws-panel-droppable-location').remove(); - _this.$mainControl.append($dropLocation); - }, - out: function out(event, ui) { - _this.$mainControl.find('.bcharts-ws-panel-droppable-location').remove(); - }, - drop: function drop(event, ui) { - _this.$mainControl.find('.bcharts-ws-panel-droppable-location').remove(); - var movePanel = ui.helper.data('bcharts-ws-panel'); - var targetPanel = $(this).closest('.bcharts-ws-panel'); - var anchor = $(this).attr('anchor'); + // Append to fragment + tmp = getAll( fragment.appendChild( elem ), "script" ); - _this._fireEvent('movePanel', [movePanel, targetPanel, anchor]); - } - }); -}; + // Preserve script evaluation history + if ( attached ) { + setGlobalEval( tmp ); + } -ChartPanel.prototype._fireEvent = function (eventName, eventParam) { - this.$mainControl.closest('.bcharts-worksheet').trigger(eventName, eventParam); -}; + // Capture executables + if ( scripts ) { + j = 0; + while ( ( elem = tmp[ j++ ] ) ) { + if ( rscriptType.test( elem.type || "" ) ) { + scripts.push( elem ); + } + } + } + } -ChartPanel.prototype.createToolbar = function (toolbar) { - var _this = this; - var $toolbar = this.$headerControl.find('.bcharts-ws-panel-toolbar'); - for (var key in toolbar.menu) { - var $item = $('
      '); - $item.attr('action', key); - $item.attr('title', toolbar.menu[key].title); - $toolbar.append($item); + return fragment; +} - if (typeof toolbar.menu[key].init === 'function') { - toolbar.menu[key].init($item); - } - if (typeof toolbar.menu[key].click === 'function') { - $item.click(toolbar.menu[key].click.bind(_this)); - } - } -}; -ChartPanel.prototype._createChart = function () { - this.$contentsControl.bcharts(this.options.chartOption); - this._renderChartSelector(); -}; +var + rkeyEvent = /^key/, + rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/, + rtypenamespace = /^([^.]*)(?:\.(.+)|)/; -ChartPanel.prototype.getGroup = function () { - return this.$contentsControl.bcharts('getGroup'); -}; +function returnTrue() { + return true; +} -ChartPanel.prototype._connectGroup = function () { - if (this.options.group) { - this.connect(this.options.group); - } -}; +function returnFalse() { + return false; +} -ChartPanel.prototype.connect = function (group) { - this.$contentsControl.bcharts('connect', group); -}; +// Support: IE <=9 - 11+ +// focus() and blur() are asynchronous, except when they are no-op. +// So expect focus to be synchronous when the element is already active, +// and blur to be synchronous when the element is not already active. +// (focus and blur are always synchronous in other supported browsers, +// this just defines when we can count on it). +function expectSync( elem, type ) { + return ( elem === safeActiveElement() ) === ( type === "focus" ); +} -ChartPanel.prototype.disconnect = function () { - this.$contentsControl.bcharts('disconnect'); -}; +// Support: IE <=9 only +// Accessing document.activeElement can throw unexpectedly +// https://bugs.jquery.com/ticket/13393 +function safeActiveElement() { + try { + return document.activeElement; + } catch ( err ) { } +} + +function on( elem, types, selector, data, fn, one ) { + var origFn, type; + + // Types can be a map of types/handlers + if ( typeof types === "object" ) { + + // ( types-Object, selector, data ) + if ( typeof selector !== "string" ) { + + // ( types-Object, data ) + data = data || selector; + selector = undefined; + } + for ( type in types ) { + on( elem, type, selector, data, types[ type ], one ); + } + return elem; + } + + if ( data == null && fn == null ) { + + // ( types, fn ) + fn = selector; + data = selector = undefined; + } else if ( fn == null ) { + if ( typeof selector === "string" ) { + + // ( types, selector, fn ) + fn = data; + data = undefined; + } else { + + // ( types, data, fn ) + fn = data; + data = selector; + selector = undefined; + } + } + if ( fn === false ) { + fn = returnFalse; + } else if ( !fn ) { + return elem; + } + + if ( one === 1 ) { + origFn = fn; + fn = function( event ) { + + // Can use an empty set, since event contains the info + jQuery().off( event ); + return origFn.apply( this, arguments ); + }; + + // Use same guid so caller can remove using origFn + fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); + } + return elem.each( function() { + jQuery.event.add( this, types, fn, data, selector ); + } ); +} /* - * @param options = { - chart: { - type: 'area', - ... - }, - } + * Helper functions for managing events -- not part of the public interface. + * Props to Dean Edwards' addEvent library for many of the ideas. */ -ChartPanel.prototype.setChartOptions = function (chartOption) { - this._renderChart(chartOption); - if (chartOption.chart && chartOption.chart.type) { - this.$mainControl.attr('chart', chartOption.chart.type); - this._renderChartSelector(chartOption.chart.type); - } - this._fireEvent('changeChart', [this, chartOption]); +jQuery.event = { + + global: {}, + + add: function( elem, types, handler, data, selector ) { + + var handleObjIn, eventHandle, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.get( elem ); + + // Only attach events to objects that accept data + if ( !acceptData( elem ) ) { + return; + } + + // Caller can pass in an object of custom data in lieu of the handler + if ( handler.handler ) { + handleObjIn = handler; + handler = handleObjIn.handler; + selector = handleObjIn.selector; + } + + // Ensure that invalid selectors throw exceptions at attach time + // Evaluate against documentElement in case elem is a non-element node (e.g., document) + if ( selector ) { + jQuery.find.matchesSelector( documentElement, selector ); + } + + // Make sure that the handler has a unique ID, used to find/remove it later + if ( !handler.guid ) { + handler.guid = jQuery.guid++; + } + + // Init the element's event structure and main handler, if this is the first + if ( !( events = elemData.events ) ) { + events = elemData.events = Object.create( null ); + } + if ( !( eventHandle = elemData.handle ) ) { + eventHandle = elemData.handle = function( e ) { + + // Discard the second event of a jQuery.event.trigger() and + // when an event is called after a page has unloaded + return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? + jQuery.event.dispatch.apply( elem, arguments ) : undefined; + }; + } + + // Handle multiple events separated by a space + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // There *must* be a type, no attaching namespace-only handlers + if ( !type ) { + continue; + } + + // If event changes its type, use the special event handlers for the changed type + special = jQuery.event.special[ type ] || {}; + + // If selector defined, determine special event api type, otherwise given type + type = ( selector ? special.delegateType : special.bindType ) || type; + + // Update special based on newly reset type + special = jQuery.event.special[ type ] || {}; + + // handleObj is passed to all event handlers + handleObj = jQuery.extend( { + type: type, + origType: origType, + data: data, + handler: handler, + guid: handler.guid, + selector: selector, + needsContext: selector && jQuery.expr.match.needsContext.test( selector ), + namespace: namespaces.join( "." ) + }, handleObjIn ); + + // Init the event handler queue if we're the first + if ( !( handlers = events[ type ] ) ) { + handlers = events[ type ] = []; + handlers.delegateCount = 0; + + // Only use addEventListener if the special events handler returns false + if ( !special.setup || + special.setup.call( elem, data, namespaces, eventHandle ) === false ) { + + if ( elem.addEventListener ) { + elem.addEventListener( type, eventHandle ); + } + } + } + + if ( special.add ) { + special.add.call( elem, handleObj ); + + if ( !handleObj.handler.guid ) { + handleObj.handler.guid = handler.guid; + } + } + + // Add to the element's handler list, delegates in front + if ( selector ) { + handlers.splice( handlers.delegateCount++, 0, handleObj ); + } else { + handlers.push( handleObj ); + } + + // Keep track of which events have ever been used, for event optimization + jQuery.event.global[ type ] = true; + } + + }, + + // Detach an event or set of events from an element + remove: function( elem, types, handler, selector, mappedTypes ) { + + var j, origCount, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); + + if ( !elemData || !( events = elemData.events ) ) { + return; + } + + // Once for each type.namespace in types; type may be omitted + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // Unbind all events (on this namespace, if provided) for the element + if ( !type ) { + for ( type in events ) { + jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); + } + continue; + } + + special = jQuery.event.special[ type ] || {}; + type = ( selector ? special.delegateType : special.bindType ) || type; + handlers = events[ type ] || []; + tmp = tmp[ 2 ] && + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); + + // Remove matching events + origCount = j = handlers.length; + while ( j-- ) { + handleObj = handlers[ j ]; + + if ( ( mappedTypes || origType === handleObj.origType ) && + ( !handler || handler.guid === handleObj.guid ) && + ( !tmp || tmp.test( handleObj.namespace ) ) && + ( !selector || selector === handleObj.selector || + selector === "**" && handleObj.selector ) ) { + handlers.splice( j, 1 ); + + if ( handleObj.selector ) { + handlers.delegateCount--; + } + if ( special.remove ) { + special.remove.call( elem, handleObj ); + } + } + } + + // Remove generic event handler if we removed something and no more handlers exist + // (avoids potential for endless recursion during removal of special event handlers) + if ( origCount && !handlers.length ) { + if ( !special.teardown || + special.teardown.call( elem, namespaces, elemData.handle ) === false ) { + + jQuery.removeEvent( elem, type, elemData.handle ); + } + + delete events[ type ]; + } + } + + // Remove data and the expando if it's no longer used + if ( jQuery.isEmptyObject( events ) ) { + dataPriv.remove( elem, "handle events" ); + } + }, + + dispatch: function( nativeEvent ) { + + var i, j, ret, matched, handleObj, handlerQueue, + args = new Array( arguments.length ), + + // Make a writable jQuery.Event from the native event object + event = jQuery.event.fix( nativeEvent ), + + handlers = ( + dataPriv.get( this, "events" ) || Object.create( null ) + )[ event.type ] || [], + special = jQuery.event.special[ event.type ] || {}; + + // Use the fix-ed jQuery.Event rather than the (read-only) native event + args[ 0 ] = event; + + for ( i = 1; i < arguments.length; i++ ) { + args[ i ] = arguments[ i ]; + } + + event.delegateTarget = this; + + // Call the preDispatch hook for the mapped type, and let it bail if desired + if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { + return; + } + + // Determine handlers + handlerQueue = jQuery.event.handlers.call( this, event, handlers ); + + // Run delegates first; they may want to stop propagation beneath us + i = 0; + while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { + event.currentTarget = matched.elem; + + j = 0; + while ( ( handleObj = matched.handlers[ j++ ] ) && + !event.isImmediatePropagationStopped() ) { + + // If the event is namespaced, then each handler is only invoked if it is + // specially universal or its namespaces are a superset of the event's. + if ( !event.rnamespace || handleObj.namespace === false || + event.rnamespace.test( handleObj.namespace ) ) { + + event.handleObj = handleObj; + event.data = handleObj.data; + + ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || + handleObj.handler ).apply( matched.elem, args ); + + if ( ret !== undefined ) { + if ( ( event.result = ret ) === false ) { + event.preventDefault(); + event.stopPropagation(); + } + } + } + } + } + + // Call the postDispatch hook for the mapped type + if ( special.postDispatch ) { + special.postDispatch.call( this, event ); + } + + return event.result; + }, + + handlers: function( event, handlers ) { + var i, handleObj, sel, matchedHandlers, matchedSelectors, + handlerQueue = [], + delegateCount = handlers.delegateCount, + cur = event.target; + + // Find delegate handlers + if ( delegateCount && + + // Support: IE <=9 + // Black-hole SVG instance trees (trac-13180) + cur.nodeType && + + // Support: Firefox <=42 + // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) + // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click + // Support: IE 11 only + // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) + !( event.type === "click" && event.button >= 1 ) ) { + + for ( ; cur !== this; cur = cur.parentNode || this ) { + + // Don't check non-elements (#13208) + // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) + if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { + matchedHandlers = []; + matchedSelectors = {}; + for ( i = 0; i < delegateCount; i++ ) { + handleObj = handlers[ i ]; + + // Don't conflict with Object.prototype properties (#13203) + sel = handleObj.selector + " "; + + if ( matchedSelectors[ sel ] === undefined ) { + matchedSelectors[ sel ] = handleObj.needsContext ? + jQuery( sel, this ).index( cur ) > -1 : + jQuery.find( sel, this, null, [ cur ] ).length; + } + if ( matchedSelectors[ sel ] ) { + matchedHandlers.push( handleObj ); + } + } + if ( matchedHandlers.length ) { + handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); + } + } + } + } + + // Add the remaining (directly-bound) handlers + cur = this; + if ( delegateCount < handlers.length ) { + handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); + } + + return handlerQueue; + }, + + addProp: function( name, hook ) { + Object.defineProperty( jQuery.Event.prototype, name, { + enumerable: true, + configurable: true, + + get: isFunction( hook ) ? + function() { + if ( this.originalEvent ) { + return hook( this.originalEvent ); + } + } : + function() { + if ( this.originalEvent ) { + return this.originalEvent[ name ]; + } + }, + + set: function( value ) { + Object.defineProperty( this, name, { + enumerable: true, + configurable: true, + writable: true, + value: value + } ); + } + } ); + }, + + fix: function( originalEvent ) { + return originalEvent[ jQuery.expando ] ? + originalEvent : + new jQuery.Event( originalEvent ); + }, + + special: { + load: { + + // Prevent triggered image.load events from bubbling to window.load + noBubble: true + }, + click: { + + // Utilize native event to ensure correct state for checkable inputs + setup: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Claim the first handler + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + // dataPriv.set( el, "click", ... ) + leverageNative( el, "click", returnTrue ); + } + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Force setup before triggering a click + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + leverageNative( el, "click" ); + } + + // Return non-false to allow normal event-path propagation + return true; + }, + + // For cross-browser consistency, suppress native .click() on links + // Also prevent it if we're currently inside a leveraged native-event stack + _default: function( event ) { + var target = event.target; + return rcheckableType.test( target.type ) && + target.click && nodeName( target, "input" ) && + dataPriv.get( target, "click" ) || + nodeName( target, "a" ); + } + }, + + beforeunload: { + postDispatch: function( event ) { + + // Support: Firefox 20+ + // Firefox doesn't alert if the returnValue field is not set. + if ( event.result !== undefined && event.originalEvent ) { + event.originalEvent.returnValue = event.result; + } + } + } + } +}; + +// Ensure the presence of an event listener that handles manually-triggered +// synthetic events by interrupting progress until reinvoked in response to +// *native* events that it fires directly, ensuring that state changes have +// already occurred before other listeners are invoked. +function leverageNative( el, type, expectSync ) { + + // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add + if ( !expectSync ) { + if ( dataPriv.get( el, type ) === undefined ) { + jQuery.event.add( el, type, returnTrue ); + } + return; + } + + // Register the controller as a special universal handler for all event namespaces + dataPriv.set( el, type, false ); + jQuery.event.add( el, type, { + namespace: false, + handler: function( event ) { + var notAsync, result, + saved = dataPriv.get( this, type ); + + if ( ( event.isTrigger & 1 ) && this[ type ] ) { + + // Interrupt processing of the outer synthetic .trigger()ed event + // Saved data should be false in such cases, but might be a leftover capture object + // from an async native handler (gh-4350) + if ( !saved.length ) { + + // Store arguments for use when handling the inner native event + // There will always be at least one argument (an event object), so this array + // will not be confused with a leftover capture object. + saved = slice.call( arguments ); + dataPriv.set( this, type, saved ); + + // Trigger the native event and capture its result + // Support: IE <=9 - 11+ + // focus() and blur() are asynchronous + notAsync = expectSync( this, type ); + this[ type ](); + result = dataPriv.get( this, type ); + if ( saved !== result || notAsync ) { + dataPriv.set( this, type, false ); + } else { + result = {}; + } + if ( saved !== result ) { + + // Cancel the outer synthetic event + event.stopImmediatePropagation(); + event.preventDefault(); + return result.value; + } + + // If this is an inner synthetic event for an event with a bubbling surrogate + // (focus or blur), assume that the surrogate already propagated from triggering the + // native event and prevent that from happening again here. + // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the + // bubbling surrogate propagates *after* the non-bubbling base), but that seems + // less bad than duplication. + } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { + event.stopPropagation(); + } + + // If this is a native event triggered above, everything is now in order + // Fire an inner synthetic event with the original arguments + } else if ( saved.length ) { + + // ...and capture the result + dataPriv.set( this, type, { + value: jQuery.event.trigger( + + // Support: IE <=9 - 11+ + // Extend with the prototype to reset the above stopImmediatePropagation() + jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), + saved.slice( 1 ), + this + ) + } ); + + // Abort handling of the native event + event.stopImmediatePropagation(); + } + } + } ); +} - $.extend(true, this.options.chartOption, chartOption); -}; +jQuery.removeEvent = function( elem, type, handle ) { -ChartPanel.prototype.setDataSource = function (dataSource) { - $.extend(true, this.options.chartOption.source, dataSource); - ChartUtil.assignArray(this.options.chartOption.source, dataSource); - this._renderChart(this.options.chartOption); -}; + // This "if" is needed for plain objects + if ( elem.removeEventListener ) { + elem.removeEventListener( type, handle ); + } +}; + +jQuery.Event = function( src, props ) { + + // Allow instantiation without the 'new' keyword + if ( !( this instanceof jQuery.Event ) ) { + return new jQuery.Event( src, props ); + } + + // Event object + if ( src && src.type ) { + this.originalEvent = src; + this.type = src.type; + + // Events bubbling up the document may have been marked as prevented + // by a handler lower down the tree; reflect the correct value. + this.isDefaultPrevented = src.defaultPrevented || + src.defaultPrevented === undefined && + + // Support: Android <=2.3 only + src.returnValue === false ? + returnTrue : + returnFalse; + + // Create target properties + // Support: Safari <=6 - 7 only + // Target should not be a text node (#504, #13143) + this.target = ( src.target && src.target.nodeType === 3 ) ? + src.target.parentNode : + src.target; + + this.currentTarget = src.currentTarget; + this.relatedTarget = src.relatedTarget; + + // Event type + } else { + this.type = src; + } + + // Put explicitly provided properties onto the event object + if ( props ) { + jQuery.extend( this, props ); + } + + // Create a timestamp if incoming event doesn't have one + this.timeStamp = src && src.timeStamp || Date.now(); + + // Mark it as fixed + this[ jQuery.expando ] = true; +}; + +// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding +// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html +jQuery.Event.prototype = { + constructor: jQuery.Event, + isDefaultPrevented: returnFalse, + isPropagationStopped: returnFalse, + isImmediatePropagationStopped: returnFalse, + isSimulated: false, + + preventDefault: function() { + var e = this.originalEvent; + + this.isDefaultPrevented = returnTrue; + + if ( e && !this.isSimulated ) { + e.preventDefault(); + } + }, + stopPropagation: function() { + var e = this.originalEvent; + + this.isPropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopPropagation(); + } + }, + stopImmediatePropagation: function() { + var e = this.originalEvent; + + this.isImmediatePropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopImmediatePropagation(); + } + + this.stopPropagation(); + } +}; + +// Includes all common event props including KeyEvent and MouseEvent specific props +jQuery.each( { + altKey: true, + bubbles: true, + cancelable: true, + changedTouches: true, + ctrlKey: true, + detail: true, + eventPhase: true, + metaKey: true, + pageX: true, + pageY: true, + shiftKey: true, + view: true, + "char": true, + code: true, + charCode: true, + key: true, + keyCode: true, + button: true, + buttons: true, + clientX: true, + clientY: true, + offsetX: true, + offsetY: true, + pointerId: true, + pointerType: true, + screenX: true, + screenY: true, + targetTouches: true, + toElement: true, + touches: true, + + which: function( event ) { + var button = event.button; + + // Add which for key events + if ( event.which == null && rkeyEvent.test( event.type ) ) { + return event.charCode != null ? event.charCode : event.keyCode; + } + + // Add which for click: 1 === left; 2 === middle; 3 === right + if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) { + if ( button & 1 ) { + return 1; + } + + if ( button & 2 ) { + return 3; + } + + if ( button & 4 ) { + return 2; + } + + return 0; + } + + return event.which; + } +}, jQuery.event.addProp ); + +jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { + jQuery.event.special[ type ] = { + + // Utilize native event if possible so blur/focus sequence is correct + setup: function() { + + // Claim the first handler + // dataPriv.set( this, "focus", ... ) + // dataPriv.set( this, "blur", ... ) + leverageNative( this, type, expectSync ); + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function() { + + // Force setup before trigger + leverageNative( this, type ); + + // Return non-false to allow normal event-path propagation + return true; + }, + + delegateType: delegateType + }; +} ); + +// Create mouseenter/leave events using mouseover/out and event-time checks +// so that event delegation works in jQuery. +// Do the same for pointerenter/pointerleave and pointerover/pointerout +// +// Support: Safari 7 only +// Safari sends mouseenter too often; see: +// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 +// for the description of the bug (it existed in older Chrome versions as well). +jQuery.each( { + mouseenter: "mouseover", + mouseleave: "mouseout", + pointerenter: "pointerover", + pointerleave: "pointerout" +}, function( orig, fix ) { + jQuery.event.special[ orig ] = { + delegateType: fix, + bindType: fix, + + handle: function( event ) { + var ret, + target = this, + related = event.relatedTarget, + handleObj = event.handleObj; + + // For mouseenter/leave call the handler if related is outside the target. + // NB: No relatedTarget if the mouse left/entered the browser window + if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { + event.type = handleObj.origType; + ret = handleObj.handler.apply( this, arguments ); + event.type = fix; + } + return ret; + } + }; +} ); + +jQuery.fn.extend( { + + on: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn ); + }, + one: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn, 1 ); + }, + off: function( types, selector, fn ) { + var handleObj, type; + if ( types && types.preventDefault && types.handleObj ) { + + // ( event ) dispatched jQuery.Event + handleObj = types.handleObj; + jQuery( types.delegateTarget ).off( + handleObj.namespace ? + handleObj.origType + "." + handleObj.namespace : + handleObj.origType, + handleObj.selector, + handleObj.handler + ); + return this; + } + if ( typeof types === "object" ) { + + // ( types-object [, selector] ) + for ( type in types ) { + this.off( type, selector, types[ type ] ); + } + return this; + } + if ( selector === false || typeof selector === "function" ) { + + // ( types [, fn] ) + fn = selector; + selector = undefined; + } + if ( fn === false ) { + fn = returnFalse; + } + return this.each( function() { + jQuery.event.remove( this, types, fn, selector ); + } ); + } +} ); + + +var + + // Support: IE <=10 - 11, Edge 12 - 13 only + // In IE/Edge using regex groups here causes severe slowdowns. + // See https://connect.microsoft.com/IE/feedback/details/1736512/ + rnoInnerhtml = /\s*$/g; + +// Prefer a tbody over its parent table for containing new rows +function manipulationTarget( elem, content ) { + if ( nodeName( elem, "table" ) && + nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { + + return jQuery( elem ).children( "tbody" )[ 0 ] || elem; + } + + return elem; +} -ChartPanel.prototype.refresh = function () { - this.$contentsControl.bcharts('render', true); - this._renderChartSelector(); -}; +// Replace/restore the type attribute of script elements for safe DOM manipulation +function disableScript( elem ) { + elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; + return elem; +} +function restoreScript( elem ) { + if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { + elem.type = elem.type.slice( 5 ); + } else { + elem.removeAttribute( "type" ); + } + + return elem; +} -ChartPanel.prototype.getFilter = function () { - return this.$contentsControl.bcharts('getFilter'); -}; +function cloneCopyEvent( src, dest ) { + var i, l, type, pdataOld, udataOld, udataCur, events; -ChartPanel.prototype.setFilter = function (filter) { - return this.$contentsControl.bcharts('setFilter', filter); -}; + if ( dest.nodeType !== 1 ) { + return; + } -ChartPanel.prototype.getSelectedRange = function () { - return this.$contentsControl.bcharts('getSelectedRange'); -}; + // 1. Copy private data: events, handlers, etc. + if ( dataPriv.hasData( src ) ) { + pdataOld = dataPriv.get( src ); + events = pdataOld.events; -ChartPanel.prototype._renderChart = function (chartOption) { - this.$contentsControl.bcharts('setOptions', chartOption); -}; + if ( events ) { + dataPriv.remove( dest, "handle events" ); -ChartPanel.prototype._renderChartSelector = function (type) { - var chartType = type || (this.options.chartOption.chart ? this.options.chartOption.chart.type : this.options.chartOption.chart); - if (chartType) { - this.chartTypeSelector.render(chartType); - } -}; + for ( type in events ) { + for ( i = 0, l = events[ type ].length; i < l; i++ ) { + jQuery.event.add( dest, type, events[ type ][ i ] ); + } + } + } + } -exports.default = ChartPanel; + // 2. Copy user data + if ( dataUser.hasData( src ) ) { + udataOld = dataUser.access( src ); + udataCur = jQuery.extend( {}, udataOld ); -/***/ }), -/* 298 */ -/***/ (function(module, exports, __webpack_require__) { + dataUser.set( dest, udataCur ); + } +} -"use strict"; +// Fix IE bugs, see support tests +function fixInput( src, dest ) { + var nodeName = dest.nodeName.toLowerCase(); + // Fails to persist the checked state of a cloned checkbox or radio button. + if ( nodeName === "input" && rcheckableType.test( src.type ) ) { + dest.checked = src.checked; -Object.defineProperty(exports, "__esModule", { - value: true -}); -exports.RangeSlider = exports.ListBox = exports.CheckBox = undefined; + // Fails to return the selected option to the default selected state when cloning options + } else if ( nodeName === "input" || nodeName === "textarea" ) { + dest.defaultValue = src.defaultValue; + } +} -var _filterControlCheckbox = __webpack_require__(674); +function domManip( collection, args, callback, ignored ) { + + // Flatten any nested arrays + args = flat( args ); + + var fragment, first, scripts, hasScripts, node, doc, + i = 0, + l = collection.length, + iNoClone = l - 1, + value = args[ 0 ], + valueIsFunction = isFunction( value ); + + // We can't cloneNode fragments that contain checked, in WebKit + if ( valueIsFunction || + ( l > 1 && typeof value === "string" && + !support.checkClone && rchecked.test( value ) ) ) { + return collection.each( function( index ) { + var self = collection.eq( index ); + if ( valueIsFunction ) { + args[ 0 ] = value.call( this, index, self.html() ); + } + domManip( self, args, callback, ignored ); + } ); + } + + if ( l ) { + fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); + first = fragment.firstChild; + + if ( fragment.childNodes.length === 1 ) { + fragment = first; + } + + // Require either new content or an interest in ignored elements to invoke the callback + if ( first || ignored ) { + scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); + hasScripts = scripts.length; + + // Use the original fragment for the last item + // instead of the first because it can end up + // being emptied incorrectly in certain situations (#8070). + for ( ; i < l; i++ ) { + node = fragment; + + if ( i !== iNoClone ) { + node = jQuery.clone( node, true, true ); + + // Keep references to cloned scripts for later restoration + if ( hasScripts ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( scripts, getAll( node, "script" ) ); + } + } + + callback.call( collection[ i ], node, i ); + } + + if ( hasScripts ) { + doc = scripts[ scripts.length - 1 ].ownerDocument; + + // Reenable scripts + jQuery.map( scripts, restoreScript ); + + // Evaluate executable scripts on first document insertion + for ( i = 0; i < hasScripts; i++ ) { + node = scripts[ i ]; + if ( rscriptType.test( node.type || "" ) && + !dataPriv.access( node, "globalEval" ) && + jQuery.contains( doc, node ) ) { + + if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { + + // Optional AJAX dependency, but won't run scripts if not present + if ( jQuery._evalUrl && !node.noModule ) { + jQuery._evalUrl( node.src, { + nonce: node.nonce || node.getAttribute( "nonce" ) + }, doc ); + } + } else { + DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); + } + } + } + } + } + } + + return collection; +} -var _filterControlCheckbox2 = _interopRequireDefault(_filterControlCheckbox); +function remove( elem, selector, keepData ) { + var node, + nodes = selector ? jQuery.filter( selector, elem ) : elem, + i = 0; + + for ( ; ( node = nodes[ i ] ) != null; i++ ) { + if ( !keepData && node.nodeType === 1 ) { + jQuery.cleanData( getAll( node ) ); + } + + if ( node.parentNode ) { + if ( keepData && isAttached( node ) ) { + setGlobalEval( getAll( node, "script" ) ); + } + node.parentNode.removeChild( node ); + } + } + + return elem; +} -var _filterControlListbox = __webpack_require__(675); +jQuery.extend( { + htmlPrefilter: function( html ) { + return html; + }, + + clone: function( elem, dataAndEvents, deepDataAndEvents ) { + var i, l, srcElements, destElements, + clone = elem.cloneNode( true ), + inPage = isAttached( elem ); + + // Fix IE cloning issues + if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && + !jQuery.isXMLDoc( elem ) ) { + + // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 + destElements = getAll( clone ); + srcElements = getAll( elem ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + fixInput( srcElements[ i ], destElements[ i ] ); + } + } + + // Copy the events from the original to the clone + if ( dataAndEvents ) { + if ( deepDataAndEvents ) { + srcElements = srcElements || getAll( elem ); + destElements = destElements || getAll( clone ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + cloneCopyEvent( srcElements[ i ], destElements[ i ] ); + } + } else { + cloneCopyEvent( elem, clone ); + } + } + + // Preserve script evaluation history + destElements = getAll( clone, "script" ); + if ( destElements.length > 0 ) { + setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); + } + + // Return the cloned set + return clone; + }, + + cleanData: function( elems ) { + var data, elem, type, + special = jQuery.event.special, + i = 0; + + for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { + if ( acceptData( elem ) ) { + if ( ( data = elem[ dataPriv.expando ] ) ) { + if ( data.events ) { + for ( type in data.events ) { + if ( special[ type ] ) { + jQuery.event.remove( elem, type ); + + // This is a shortcut to avoid jQuery.event.remove's overhead + } else { + jQuery.removeEvent( elem, type, data.handle ); + } + } + } + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataPriv.expando ] = undefined; + } + if ( elem[ dataUser.expando ] ) { + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataUser.expando ] = undefined; + } + } + } + } +} ); + +jQuery.fn.extend( { + detach: function( selector ) { + return remove( this, selector, true ); + }, + + remove: function( selector ) { + return remove( this, selector ); + }, + + text: function( value ) { + return access( this, function( value ) { + return value === undefined ? + jQuery.text( this ) : + this.empty().each( function() { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + this.textContent = value; + } + } ); + }, null, value, arguments.length ); + }, + + append: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.appendChild( elem ); + } + } ); + }, + + prepend: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.insertBefore( elem, target.firstChild ); + } + } ); + }, + + before: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this ); + } + } ); + }, + + after: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this.nextSibling ); + } + } ); + }, + + empty: function() { + var elem, + i = 0; + + for ( ; ( elem = this[ i ] ) != null; i++ ) { + if ( elem.nodeType === 1 ) { + + // Prevent memory leaks + jQuery.cleanData( getAll( elem, false ) ); + + // Remove any remaining nodes + elem.textContent = ""; + } + } + + return this; + }, + + clone: function( dataAndEvents, deepDataAndEvents ) { + dataAndEvents = dataAndEvents == null ? false : dataAndEvents; + deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; + + return this.map( function() { + return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); + } ); + }, + + html: function( value ) { + return access( this, function( value ) { + var elem = this[ 0 ] || {}, + i = 0, + l = this.length; + + if ( value === undefined && elem.nodeType === 1 ) { + return elem.innerHTML; + } + + // See if we can take a shortcut and just use innerHTML + if ( typeof value === "string" && !rnoInnerhtml.test( value ) && + !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { + + value = jQuery.htmlPrefilter( value ); + + try { + for ( ; i < l; i++ ) { + elem = this[ i ] || {}; + + // Remove element nodes and prevent memory leaks + if ( elem.nodeType === 1 ) { + jQuery.cleanData( getAll( elem, false ) ); + elem.innerHTML = value; + } + } + + elem = 0; + + // If using innerHTML throws an exception, use the fallback method + } catch ( e ) {} + } + + if ( elem ) { + this.empty().append( value ); + } + }, null, value, arguments.length ); + }, + + replaceWith: function() { + var ignored = []; + + // Make the changes, replacing each non-ignored context element with the new content + return domManip( this, arguments, function( elem ) { + var parent = this.parentNode; + + if ( jQuery.inArray( this, ignored ) < 0 ) { + jQuery.cleanData( getAll( this ) ); + if ( parent ) { + parent.replaceChild( elem, this ); + } + } + + // Force callback invocation + }, ignored ); + } +} ); + +jQuery.each( { + appendTo: "append", + prependTo: "prepend", + insertBefore: "before", + insertAfter: "after", + replaceAll: "replaceWith" +}, function( name, original ) { + jQuery.fn[ name ] = function( selector ) { + var elems, + ret = [], + insert = jQuery( selector ), + last = insert.length - 1, + i = 0; + + for ( ; i <= last; i++ ) { + elems = i === last ? this : this.clone( true ); + jQuery( insert[ i ] )[ original ]( elems ); + + // Support: Android <=4.0 only, PhantomJS 1 only + // .get() because push.apply(_, arraylike) throws on ancient WebKit + push.apply( ret, elems.get() ); + } + + return this.pushStack( ret ); + }; +} ); +var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); + +var getStyles = function( elem ) { + + // Support: IE <=11 only, Firefox <=30 (#15098, #14150) + // IE throws on elements created in popups + // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" + var view = elem.ownerDocument.defaultView; + + if ( !view || !view.opener ) { + view = window; + } + + return view.getComputedStyle( elem ); + }; + +var swap = function( elem, options, callback ) { + var ret, name, + old = {}; + + // Remember the old values, and insert the new ones + for ( name in options ) { + old[ name ] = elem.style[ name ]; + elem.style[ name ] = options[ name ]; + } + + ret = callback.call( elem ); + + // Revert the old values + for ( name in options ) { + elem.style[ name ] = old[ name ]; + } + + return ret; +}; + + +var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); + + + +( function() { + + // Executing both pixelPosition & boxSizingReliable tests require only one layout + // so they're executed at the same time to save the second computation. + function computeStyleTests() { + + // This is a singleton, we need to execute it only once + if ( !div ) { + return; + } + + container.style.cssText = "position:absolute;left:-11111px;width:60px;" + + "margin-top:1px;padding:0;border:0"; + div.style.cssText = + "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + + "margin:auto;border:1px;padding:1px;" + + "width:60%;top:1%"; + documentElement.appendChild( container ).appendChild( div ); + + var divStyle = window.getComputedStyle( div ); + pixelPositionVal = divStyle.top !== "1%"; + + // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 + reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; + + // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 + // Some styles come back with percentage values, even though they shouldn't + div.style.right = "60%"; + pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; + + // Support: IE 9 - 11 only + // Detect misreporting of content dimensions for box-sizing:border-box elements + boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; + + // Support: IE 9 only + // Detect overflow:scroll screwiness (gh-3699) + // Support: Chrome <=64 + // Don't get tricked when zoom affects offsetWidth (gh-4029) + div.style.position = "absolute"; + scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; + + documentElement.removeChild( container ); + + // Nullify the div so it wouldn't be stored in the memory and + // it will also be a sign that checks already performed + div = null; + } + + function roundPixelMeasures( measure ) { + return Math.round( parseFloat( measure ) ); + } + + var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, + reliableTrDimensionsVal, reliableMarginLeftVal, + container = document.createElement( "div" ), + div = document.createElement( "div" ); + + // Finish early in limited (non-browser) environments + if ( !div.style ) { + return; + } + + // Support: IE <=9 - 11 only + // Style of cloned element affects source element cloned (#8908) + div.style.backgroundClip = "content-box"; + div.cloneNode( true ).style.backgroundClip = ""; + support.clearCloneStyle = div.style.backgroundClip === "content-box"; + + jQuery.extend( support, { + boxSizingReliable: function() { + computeStyleTests(); + return boxSizingReliableVal; + }, + pixelBoxStyles: function() { + computeStyleTests(); + return pixelBoxStylesVal; + }, + pixelPosition: function() { + computeStyleTests(); + return pixelPositionVal; + }, + reliableMarginLeft: function() { + computeStyleTests(); + return reliableMarginLeftVal; + }, + scrollboxSize: function() { + computeStyleTests(); + return scrollboxSizeVal; + }, + + // Support: IE 9 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Behavior in IE 9 is more subtle than in newer versions & it passes + // some versions of this test; make sure not to make it pass there! + reliableTrDimensions: function() { + var table, tr, trChild, trStyle; + if ( reliableTrDimensionsVal == null ) { + table = document.createElement( "table" ); + tr = document.createElement( "tr" ); + trChild = document.createElement( "div" ); + + table.style.cssText = "position:absolute;left:-11111px"; + tr.style.height = "1px"; + trChild.style.height = "9px"; + + documentElement + .appendChild( table ) + .appendChild( tr ) + .appendChild( trChild ); + + trStyle = window.getComputedStyle( tr ); + reliableTrDimensionsVal = parseInt( trStyle.height ) > 3; + + documentElement.removeChild( table ); + } + return reliableTrDimensionsVal; + } + } ); +} )(); + + +function curCSS( elem, name, computed ) { + var width, minWidth, maxWidth, ret, + + // Support: Firefox 51+ + // Retrieving style before computed somehow + // fixes an issue with getting wrong values + // on detached elements + style = elem.style; + + computed = computed || getStyles( elem ); + + // getPropertyValue is needed for: + // .css('filter') (IE 9 only, #12537) + // .css('--customProperty) (#3144) + if ( computed ) { + ret = computed.getPropertyValue( name ) || computed[ name ]; + + if ( ret === "" && !isAttached( elem ) ) { + ret = jQuery.style( elem, name ); + } + + // A tribute to the "awesome hack by Dean Edwards" + // Android Browser returns percentage for some values, + // but width seems to be reliably pixels. + // This is against the CSSOM draft spec: + // https://drafts.csswg.org/cssom/#resolved-values + if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { + + // Remember the original values + width = style.width; + minWidth = style.minWidth; + maxWidth = style.maxWidth; + + // Put in the new values to get a computed value out + style.minWidth = style.maxWidth = style.width = ret; + ret = computed.width; + + // Revert the changed values + style.width = width; + style.minWidth = minWidth; + style.maxWidth = maxWidth; + } + } + + return ret !== undefined ? + + // Support: IE <=9 - 11 only + // IE returns zIndex value as an integer. + ret + "" : + ret; +} -var _filterControlListbox2 = _interopRequireDefault(_filterControlListbox); -var _filterControlRangeslider = __webpack_require__(676); +function addGetHookIf( conditionFn, hookFn ) { -var _filterControlRangeslider2 = _interopRequireDefault(_filterControlRangeslider); + // Define the hook, we'll check on the first run if it's really needed. + return { + get: function() { + if ( conditionFn() ) { -function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + // Hook not needed (or it's not possible to use it due + // to missing dependency), remove it. + delete this.get; + return; + } -exports.CheckBox = _filterControlCheckbox2.default; -exports.ListBox = _filterControlListbox2.default; -exports.RangeSlider = _filterControlRangeslider2.default; /** - * Source: filter-control-index - * Created by ji_sung.park on 2018-05-29 - */ + // Hook needed; redefine it so that the support test is not executed again. + return ( this.get = hookFn ).apply( this, arguments ); + } + }; +} -/***/ }), -/* 299 */, -/* 300 */, -/* 301 */, -/* 302 */, -/* 303 */, -/* 304 */, -/* 305 */, -/* 306 */, -/* 307 */, -/* 308 */, -/* 309 */, -/* 310 */, -/* 311 */, -/* 312 */, -/* 313 */, -/* 314 */, -/* 315 */, -/* 316 */, -/* 317 */, -/* 318 */, -/* 319 */, -/* 320 */, -/* 321 */, -/* 322 */, -/* 323 */, -/* 324 */, -/* 325 */, -/* 326 */, -/* 327 */, -/* 328 */, -/* 329 */, -/* 330 */, -/* 331 */, -/* 332 */, -/* 333 */, -/* 334 */, -/* 335 */, -/* 336 */, -/* 337 */, -/* 338 */, -/* 339 */, -/* 340 */, -/* 341 */, -/* 342 */, -/* 343 */, -/* 344 */, -/* 345 */, -/* 346 */, -/* 347 */, -/* 348 */, -/* 349 */, -/* 350 */, -/* 351 */, -/* 352 */, -/* 353 */, -/* 354 */, -/* 355 */, -/* 356 */, -/* 357 */, -/* 358 */, -/* 359 */, -/* 360 */, -/* 361 */, -/* 362 */, -/* 363 */, -/* 364 */, -/* 365 */, -/* 366 */, -/* 367 */, -/* 368 */, -/* 369 */, -/* 370 */, -/* 371 */, -/* 372 */, + +var cssPrefixes = [ "Webkit", "Moz", "ms" ], + emptyStyle = document.createElement( "div" ).style, + vendorProps = {}; + +// Return a vendor-prefixed property or undefined +function vendorPropName( name ) { + + // Check for vendor prefixed names + var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), + i = cssPrefixes.length; + + while ( i-- ) { + name = cssPrefixes[ i ] + capName; + if ( name in emptyStyle ) { + return name; + } + } +} + +// Return a potentially-mapped jQuery.cssProps or vendor prefixed property +function finalPropName( name ) { + var final = jQuery.cssProps[ name ] || vendorProps[ name ]; + + if ( final ) { + return final; + } + if ( name in emptyStyle ) { + return name; + } + return vendorProps[ name ] = vendorPropName( name ) || name; +} + + +var + + // Swappable if display is none or starts with table + // except "table", "table-cell", or "table-caption" + // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display + rdisplayswap = /^(none|table(?!-c[ea]).+)/, + rcustomProp = /^--/, + cssShow = { position: "absolute", visibility: "hidden", display: "block" }, + cssNormalTransform = { + letterSpacing: "0", + fontWeight: "400" + }; + +function setPositiveNumber( _elem, value, subtract ) { + + // Any relative (+/-) values have already been + // normalized at this point + var matches = rcssNum.exec( value ); + return matches ? + + // Guard against undefined "subtract", e.g., when used as in cssHooks + Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : + value; +} + +function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { + var i = dimension === "width" ? 1 : 0, + extra = 0, + delta = 0; + + // Adjustment may not be necessary + if ( box === ( isBorderBox ? "border" : "content" ) ) { + return 0; + } + + for ( ; i < 4; i += 2 ) { + + // Both box models exclude margin + if ( box === "margin" ) { + delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); + } + + // If we get here with a content-box, we're seeking "padding" or "border" or "margin" + if ( !isBorderBox ) { + + // Add padding + delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + + // For "border" or "margin", add border + if ( box !== "padding" ) { + delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + + // But still keep track of it otherwise + } else { + extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + + // If we get here with a border-box (content + padding + border), we're seeking "content" or + // "padding" or "margin" + } else { + + // For "content", subtract padding + if ( box === "content" ) { + delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + } + + // For "content" or "padding", subtract border + if ( box !== "margin" ) { + delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + } + } + + // Account for positive content-box scroll gutter when requested by providing computedVal + if ( !isBorderBox && computedVal >= 0 ) { + + // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border + // Assuming integer scroll gutter, subtract the rest and round down + delta += Math.max( 0, Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + computedVal - + delta - + extra - + 0.5 + + // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter + // Use an explicit zero to avoid NaN (gh-3964) + ) ) || 0; + } + + return delta; +} + +function getWidthOrHeight( elem, dimension, extra ) { + + // Start with computed style + var styles = getStyles( elem ), + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). + // Fake content-box until we know it's needed to know the true value. + boxSizingNeeded = !support.boxSizingReliable() || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + valueIsBorderBox = isBorderBox, + + val = curCSS( elem, dimension, styles ), + offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); + + // Support: Firefox <=54 + // Return a confounding non-pixel value or feign ignorance, as appropriate. + if ( rnumnonpx.test( val ) ) { + if ( !extra ) { + return val; + } + val = "auto"; + } + + + // Support: IE 9 - 11 only + // Use offsetWidth/offsetHeight for when box sizing is unreliable. + // In those cases, the computed value can be trusted to be border-box. + if ( ( !support.boxSizingReliable() && isBorderBox || + + // Support: IE 10 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Interestingly, in some cases IE 9 doesn't suffer from this issue. + !support.reliableTrDimensions() && nodeName( elem, "tr" ) || + + // Fall back to offsetWidth/offsetHeight when value is "auto" + // This happens for inline elements with no explicit setting (gh-3571) + val === "auto" || + + // Support: Android <=4.1 - 4.3 only + // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) + !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && + + // Make sure the element is visible & connected + elem.getClientRects().length ) { + + isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; + + // Where available, offsetWidth/offsetHeight approximate border box dimensions. + // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the + // retrieved value as a content box dimension. + valueIsBorderBox = offsetProp in elem; + if ( valueIsBorderBox ) { + val = elem[ offsetProp ]; + } + } + + // Normalize "" and auto + val = parseFloat( val ) || 0; + + // Adjust for the element's box model + return ( val + + boxModelAdjustment( + elem, + dimension, + extra || ( isBorderBox ? "border" : "content" ), + valueIsBorderBox, + styles, + + // Provide the current computed size to request scroll gutter calculation (gh-3589) + val + ) + ) + "px"; +} + +jQuery.extend( { + + // Add in style property hooks for overriding the default + // behavior of getting and setting a style property + cssHooks: { + opacity: { + get: function( elem, computed ) { + if ( computed ) { + + // We should always get a number back from opacity + var ret = curCSS( elem, "opacity" ); + return ret === "" ? "1" : ret; + } + } + } + }, + + // Don't automatically add "px" to these possibly-unitless properties + cssNumber: { + "animationIterationCount": true, + "columnCount": true, + "fillOpacity": true, + "flexGrow": true, + "flexShrink": true, + "fontWeight": true, + "gridArea": true, + "gridColumn": true, + "gridColumnEnd": true, + "gridColumnStart": true, + "gridRow": true, + "gridRowEnd": true, + "gridRowStart": true, + "lineHeight": true, + "opacity": true, + "order": true, + "orphans": true, + "widows": true, + "zIndex": true, + "zoom": true + }, + + // Add in properties whose names you wish to fix before + // setting or getting the value + cssProps: {}, + + // Get and set the style property on a DOM Node + style: function( elem, name, value, extra ) { + + // Don't set styles on text and comment nodes + if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { + return; + } + + // Make sure that we're working with the right name + var ret, type, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ), + style = elem.style; + + // Make sure that we're working with the right name. We don't + // want to query the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Gets hook for the prefixed version, then unprefixed version + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // Check if we're setting a value + if ( value !== undefined ) { + type = typeof value; + + // Convert "+=" or "-=" to relative numbers (#7345) + if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { + value = adjustCSS( elem, name, ret ); + + // Fixes bug #9237 + type = "number"; + } + + // Make sure that null and NaN values aren't set (#7116) + if ( value == null || value !== value ) { + return; + } + + // If a number was passed in, add the unit (except for certain CSS properties) + // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append + // "px" to a few hardcoded values. + if ( type === "number" && !isCustomProp ) { + value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); + } + + // background-* props affect original clone's values + if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { + style[ name ] = "inherit"; + } + + // If a hook was provided, use that value, otherwise just set the specified value + if ( !hooks || !( "set" in hooks ) || + ( value = hooks.set( elem, value, extra ) ) !== undefined ) { + + if ( isCustomProp ) { + style.setProperty( name, value ); + } else { + style[ name ] = value; + } + } + + } else { + + // If a hook was provided get the non-computed value from there + if ( hooks && "get" in hooks && + ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { + + return ret; + } + + // Otherwise just get the value from the style object + return style[ name ]; + } + }, + + css: function( elem, name, extra, styles ) { + var val, num, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ); + + // Make sure that we're working with the right name. We don't + // want to modify the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Try prefixed name followed by the unprefixed name + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // If a hook was provided get the computed value from there + if ( hooks && "get" in hooks ) { + val = hooks.get( elem, true, extra ); + } + + // Otherwise, if a way to get the computed value exists, use that + if ( val === undefined ) { + val = curCSS( elem, name, styles ); + } + + // Convert "normal" to computed value + if ( val === "normal" && name in cssNormalTransform ) { + val = cssNormalTransform[ name ]; + } + + // Make numeric if forced or a qualifier was provided and val looks numeric + if ( extra === "" || extra ) { + num = parseFloat( val ); + return extra === true || isFinite( num ) ? num || 0 : val; + } + + return val; + } +} ); + +jQuery.each( [ "height", "width" ], function( _i, dimension ) { + jQuery.cssHooks[ dimension ] = { + get: function( elem, computed, extra ) { + if ( computed ) { + + // Certain elements can have dimension info if we invisibly show them + // but it must have a current display style that would benefit + return rdisplayswap.test( jQuery.css( elem, "display" ) ) && + + // Support: Safari 8+ + // Table columns in Safari have non-zero offsetWidth & zero + // getBoundingClientRect().width unless display is changed. + // Support: IE <=11 only + // Running getBoundingClientRect on a disconnected node + // in IE throws an error. + ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? + swap( elem, cssShow, function() { + return getWidthOrHeight( elem, dimension, extra ); + } ) : + getWidthOrHeight( elem, dimension, extra ); + } + }, + + set: function( elem, value, extra ) { + var matches, + styles = getStyles( elem ), + + // Only read styles.position if the test has a chance to fail + // to avoid forcing a reflow. + scrollboxSizeBuggy = !support.scrollboxSize() && + styles.position === "absolute", + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) + boxSizingNeeded = scrollboxSizeBuggy || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + subtract = extra ? + boxModelAdjustment( + elem, + dimension, + extra, + isBorderBox, + styles + ) : + 0; + + // Account for unreliable border-box dimensions by comparing offset* to computed and + // faking a content-box to get border and padding (gh-3699) + if ( isBorderBox && scrollboxSizeBuggy ) { + subtract -= Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + parseFloat( styles[ dimension ] ) - + boxModelAdjustment( elem, dimension, "border", false, styles ) - + 0.5 + ); + } + + // Convert to pixels if value adjustment is needed + if ( subtract && ( matches = rcssNum.exec( value ) ) && + ( matches[ 3 ] || "px" ) !== "px" ) { + + elem.style[ dimension ] = value; + value = jQuery.css( elem, dimension ); + } + + return setPositiveNumber( elem, value, subtract ); + } + }; +} ); + +jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, + function( elem, computed ) { + if ( computed ) { + return ( parseFloat( curCSS( elem, "marginLeft" ) ) || + elem.getBoundingClientRect().left - + swap( elem, { marginLeft: 0 }, function() { + return elem.getBoundingClientRect().left; + } ) + ) + "px"; + } + } +); + +// These hooks are used by animate to expand properties +jQuery.each( { + margin: "", + padding: "", + border: "Width" +}, function( prefix, suffix ) { + jQuery.cssHooks[ prefix + suffix ] = { + expand: function( value ) { + var i = 0, + expanded = {}, + + // Assumes a single number if not a string + parts = typeof value === "string" ? value.split( " " ) : [ value ]; + + for ( ; i < 4; i++ ) { + expanded[ prefix + cssExpand[ i ] + suffix ] = + parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; + } + + return expanded; + } + }; + + if ( prefix !== "margin" ) { + jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; + } +} ); + +jQuery.fn.extend( { + css: function( name, value ) { + return access( this, function( elem, name, value ) { + var styles, len, + map = {}, + i = 0; + + if ( Array.isArray( name ) ) { + styles = getStyles( elem ); + len = name.length; + + for ( ; i < len; i++ ) { + map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); + } + + return map; + } + + return value !== undefined ? + jQuery.style( elem, name, value ) : + jQuery.css( elem, name ); + }, name, value, arguments.length > 1 ); + } +} ); + + +function Tween( elem, options, prop, end, easing ) { + return new Tween.prototype.init( elem, options, prop, end, easing ); +} +jQuery.Tween = Tween; + +Tween.prototype = { + constructor: Tween, + init: function( elem, options, prop, end, easing, unit ) { + this.elem = elem; + this.prop = prop; + this.easing = easing || jQuery.easing._default; + this.options = options; + this.start = this.now = this.cur(); + this.end = end; + this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); + }, + cur: function() { + var hooks = Tween.propHooks[ this.prop ]; + + return hooks && hooks.get ? + hooks.get( this ) : + Tween.propHooks._default.get( this ); + }, + run: function( percent ) { + var eased, + hooks = Tween.propHooks[ this.prop ]; + + if ( this.options.duration ) { + this.pos = eased = jQuery.easing[ this.easing ]( + percent, this.options.duration * percent, 0, 1, this.options.duration + ); + } else { + this.pos = eased = percent; + } + this.now = ( this.end - this.start ) * eased + this.start; + + if ( this.options.step ) { + this.options.step.call( this.elem, this.now, this ); + } + + if ( hooks && hooks.set ) { + hooks.set( this ); + } else { + Tween.propHooks._default.set( this ); + } + return this; + } +}; + +Tween.prototype.init.prototype = Tween.prototype; + +Tween.propHooks = { + _default: { + get: function( tween ) { + var result; + + // Use a property on the element directly when it is not a DOM element, + // or when there is no matching style property that exists. + if ( tween.elem.nodeType !== 1 || + tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { + return tween.elem[ tween.prop ]; + } + + // Passing an empty string as a 3rd parameter to .css will automatically + // attempt a parseFloat and fallback to a string if the parse fails. + // Simple values such as "10px" are parsed to Float; + // complex values such as "rotate(1rad)" are returned as-is. + result = jQuery.css( tween.elem, tween.prop, "" ); + + // Empty strings, null, undefined and "auto" are converted to 0. + return !result || result === "auto" ? 0 : result; + }, + set: function( tween ) { + + // Use step hook for back compat. + // Use cssHook if its there. + // Use .style if available and use plain properties where available. + if ( jQuery.fx.step[ tween.prop ] ) { + jQuery.fx.step[ tween.prop ]( tween ); + } else if ( tween.elem.nodeType === 1 && ( + jQuery.cssHooks[ tween.prop ] || + tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { + jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); + } else { + tween.elem[ tween.prop ] = tween.now; + } + } + } +}; + +// Support: IE <=9 only +// Panic based approach to setting things on disconnected nodes +Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { + set: function( tween ) { + if ( tween.elem.nodeType && tween.elem.parentNode ) { + tween.elem[ tween.prop ] = tween.now; + } + } +}; + +jQuery.easing = { + linear: function( p ) { + return p; + }, + swing: function( p ) { + return 0.5 - Math.cos( p * Math.PI ) / 2; + }, + _default: "swing" +}; + +jQuery.fx = Tween.prototype.init; + +// Back compat <1.8 extension point +jQuery.fx.step = {}; + + + + +var + fxNow, inProgress, + rfxtypes = /^(?:toggle|show|hide)$/, + rrun = /queueHooks$/; + +function schedule() { + if ( inProgress ) { + if ( document.hidden === false && window.requestAnimationFrame ) { + window.requestAnimationFrame( schedule ); + } else { + window.setTimeout( schedule, jQuery.fx.interval ); + } + + jQuery.fx.tick(); + } +} + +// Animations created synchronously will run synchronously +function createFxNow() { + window.setTimeout( function() { + fxNow = undefined; + } ); + return ( fxNow = Date.now() ); +} + +// Generate parameters to create a standard animation +function genFx( type, includeWidth ) { + var which, + i = 0, + attrs = { height: type }; + + // If we include width, step value is 1 to do all cssExpand values, + // otherwise step value is 2 to skip over Left and Right + includeWidth = includeWidth ? 1 : 0; + for ( ; i < 4; i += 2 - includeWidth ) { + which = cssExpand[ i ]; + attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; + } + + if ( includeWidth ) { + attrs.opacity = attrs.width = type; + } + + return attrs; +} + +function createTween( value, prop, animation ) { + var tween, + collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), + index = 0, + length = collection.length; + for ( ; index < length; index++ ) { + if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { + + // We're done with this property + return tween; + } + } +} + +function defaultPrefilter( elem, props, opts ) { + var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, + isBox = "width" in props || "height" in props, + anim = this, + orig = {}, + style = elem.style, + hidden = elem.nodeType && isHiddenWithinTree( elem ), + dataShow = dataPriv.get( elem, "fxshow" ); + + // Queue-skipping animations hijack the fx hooks + if ( !opts.queue ) { + hooks = jQuery._queueHooks( elem, "fx" ); + if ( hooks.unqueued == null ) { + hooks.unqueued = 0; + oldfire = hooks.empty.fire; + hooks.empty.fire = function() { + if ( !hooks.unqueued ) { + oldfire(); + } + }; + } + hooks.unqueued++; + + anim.always( function() { + + // Ensure the complete handler is called before this completes + anim.always( function() { + hooks.unqueued--; + if ( !jQuery.queue( elem, "fx" ).length ) { + hooks.empty.fire(); + } + } ); + } ); + } + + // Detect show/hide animations + for ( prop in props ) { + value = props[ prop ]; + if ( rfxtypes.test( value ) ) { + delete props[ prop ]; + toggle = toggle || value === "toggle"; + if ( value === ( hidden ? "hide" : "show" ) ) { + + // Pretend to be hidden if this is a "show" and + // there is still data from a stopped show/hide + if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { + hidden = true; + + // Ignore all other no-op show/hide data + } else { + continue; + } + } + orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); + } + } + + // Bail out if this is a no-op like .hide().hide() + propTween = !jQuery.isEmptyObject( props ); + if ( !propTween && jQuery.isEmptyObject( orig ) ) { + return; + } + + // Restrict "overflow" and "display" styles during box animations + if ( isBox && elem.nodeType === 1 ) { + + // Support: IE <=9 - 11, Edge 12 - 15 + // Record all 3 overflow attributes because IE does not infer the shorthand + // from identically-valued overflowX and overflowY and Edge just mirrors + // the overflowX value there. + opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; + + // Identify a display type, preferring old show/hide data over the CSS cascade + restoreDisplay = dataShow && dataShow.display; + if ( restoreDisplay == null ) { + restoreDisplay = dataPriv.get( elem, "display" ); + } + display = jQuery.css( elem, "display" ); + if ( display === "none" ) { + if ( restoreDisplay ) { + display = restoreDisplay; + } else { + + // Get nonempty value(s) by temporarily forcing visibility + showHide( [ elem ], true ); + restoreDisplay = elem.style.display || restoreDisplay; + display = jQuery.css( elem, "display" ); + showHide( [ elem ] ); + } + } + + // Animate inline elements as inline-block + if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { + if ( jQuery.css( elem, "float" ) === "none" ) { + + // Restore the original display value at the end of pure show/hide animations + if ( !propTween ) { + anim.done( function() { + style.display = restoreDisplay; + } ); + if ( restoreDisplay == null ) { + display = style.display; + restoreDisplay = display === "none" ? "" : display; + } + } + style.display = "inline-block"; + } + } + } + + if ( opts.overflow ) { + style.overflow = "hidden"; + anim.always( function() { + style.overflow = opts.overflow[ 0 ]; + style.overflowX = opts.overflow[ 1 ]; + style.overflowY = opts.overflow[ 2 ]; + } ); + } + + // Implement show/hide animations + propTween = false; + for ( prop in orig ) { + + // General show/hide setup for this element animation + if ( !propTween ) { + if ( dataShow ) { + if ( "hidden" in dataShow ) { + hidden = dataShow.hidden; + } + } else { + dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); + } + + // Store hidden/visible for toggle so `.stop().toggle()` "reverses" + if ( toggle ) { + dataShow.hidden = !hidden; + } + + // Show elements before animating them + if ( hidden ) { + showHide( [ elem ], true ); + } + + /* eslint-disable no-loop-func */ + + anim.done( function() { + + /* eslint-enable no-loop-func */ + + // The final step of a "hide" animation is actually hiding the element + if ( !hidden ) { + showHide( [ elem ] ); + } + dataPriv.remove( elem, "fxshow" ); + for ( prop in orig ) { + jQuery.style( elem, prop, orig[ prop ] ); + } + } ); + } + + // Per-property setup + propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); + if ( !( prop in dataShow ) ) { + dataShow[ prop ] = propTween.start; + if ( hidden ) { + propTween.end = propTween.start; + propTween.start = 0; + } + } + } +} + +function propFilter( props, specialEasing ) { + var index, name, easing, value, hooks; + + // camelCase, specialEasing and expand cssHook pass + for ( index in props ) { + name = camelCase( index ); + easing = specialEasing[ name ]; + value = props[ index ]; + if ( Array.isArray( value ) ) { + easing = value[ 1 ]; + value = props[ index ] = value[ 0 ]; + } + + if ( index !== name ) { + props[ name ] = value; + delete props[ index ]; + } + + hooks = jQuery.cssHooks[ name ]; + if ( hooks && "expand" in hooks ) { + value = hooks.expand( value ); + delete props[ name ]; + + // Not quite $.extend, this won't overwrite existing keys. + // Reusing 'index' because we have the correct "name" + for ( index in value ) { + if ( !( index in props ) ) { + props[ index ] = value[ index ]; + specialEasing[ index ] = easing; + } + } + } else { + specialEasing[ name ] = easing; + } + } +} + +function Animation( elem, properties, options ) { + var result, + stopped, + index = 0, + length = Animation.prefilters.length, + deferred = jQuery.Deferred().always( function() { + + // Don't match elem in the :animated selector + delete tick.elem; + } ), + tick = function() { + if ( stopped ) { + return false; + } + var currentTime = fxNow || createFxNow(), + remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), + + // Support: Android 2.3 only + // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) + temp = remaining / animation.duration || 0, + percent = 1 - temp, + index = 0, + length = animation.tweens.length; + + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( percent ); + } + + deferred.notifyWith( elem, [ animation, percent, remaining ] ); + + // If there's more to do, yield + if ( percent < 1 && length ) { + return remaining; + } + + // If this was an empty animation, synthesize a final progress notification + if ( !length ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + } + + // Resolve the animation and report its conclusion + deferred.resolveWith( elem, [ animation ] ); + return false; + }, + animation = deferred.promise( { + elem: elem, + props: jQuery.extend( {}, properties ), + opts: jQuery.extend( true, { + specialEasing: {}, + easing: jQuery.easing._default + }, options ), + originalProperties: properties, + originalOptions: options, + startTime: fxNow || createFxNow(), + duration: options.duration, + tweens: [], + createTween: function( prop, end ) { + var tween = jQuery.Tween( elem, animation.opts, prop, end, + animation.opts.specialEasing[ prop ] || animation.opts.easing ); + animation.tweens.push( tween ); + return tween; + }, + stop: function( gotoEnd ) { + var index = 0, + + // If we are going to the end, we want to run all the tweens + // otherwise we skip this part + length = gotoEnd ? animation.tweens.length : 0; + if ( stopped ) { + return this; + } + stopped = true; + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( 1 ); + } + + // Resolve when we played the last frame; otherwise, reject + if ( gotoEnd ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + deferred.resolveWith( elem, [ animation, gotoEnd ] ); + } else { + deferred.rejectWith( elem, [ animation, gotoEnd ] ); + } + return this; + } + } ), + props = animation.props; + + propFilter( props, animation.opts.specialEasing ); + + for ( ; index < length; index++ ) { + result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); + if ( result ) { + if ( isFunction( result.stop ) ) { + jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = + result.stop.bind( result ); + } + return result; + } + } + + jQuery.map( props, createTween, animation ); + + if ( isFunction( animation.opts.start ) ) { + animation.opts.start.call( elem, animation ); + } + + // Attach callbacks from options + animation + .progress( animation.opts.progress ) + .done( animation.opts.done, animation.opts.complete ) + .fail( animation.opts.fail ) + .always( animation.opts.always ); + + jQuery.fx.timer( + jQuery.extend( tick, { + elem: elem, + anim: animation, + queue: animation.opts.queue + } ) + ); + + return animation; +} + +jQuery.Animation = jQuery.extend( Animation, { + + tweeners: { + "*": [ function( prop, value ) { + var tween = this.createTween( prop, value ); + adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); + return tween; + } ] + }, + + tweener: function( props, callback ) { + if ( isFunction( props ) ) { + callback = props; + props = [ "*" ]; + } else { + props = props.match( rnothtmlwhite ); + } + + var prop, + index = 0, + length = props.length; + + for ( ; index < length; index++ ) { + prop = props[ index ]; + Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; + Animation.tweeners[ prop ].unshift( callback ); + } + }, + + prefilters: [ defaultPrefilter ], + + prefilter: function( callback, prepend ) { + if ( prepend ) { + Animation.prefilters.unshift( callback ); + } else { + Animation.prefilters.push( callback ); + } + } +} ); + +jQuery.speed = function( speed, easing, fn ) { + var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { + complete: fn || !fn && easing || + isFunction( speed ) && speed, + duration: speed, + easing: fn && easing || easing && !isFunction( easing ) && easing + }; + + // Go to the end state if fx are off + if ( jQuery.fx.off ) { + opt.duration = 0; + + } else { + if ( typeof opt.duration !== "number" ) { + if ( opt.duration in jQuery.fx.speeds ) { + opt.duration = jQuery.fx.speeds[ opt.duration ]; + + } else { + opt.duration = jQuery.fx.speeds._default; + } + } + } + + // Normalize opt.queue - true/undefined/null -> "fx" + if ( opt.queue == null || opt.queue === true ) { + opt.queue = "fx"; + } + + // Queueing + opt.old = opt.complete; + + opt.complete = function() { + if ( isFunction( opt.old ) ) { + opt.old.call( this ); + } + + if ( opt.queue ) { + jQuery.dequeue( this, opt.queue ); + } + }; + + return opt; +}; + +jQuery.fn.extend( { + fadeTo: function( speed, to, easing, callback ) { + + // Show any hidden elements after setting opacity to 0 + return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() + + // Animate to the value specified + .end().animate( { opacity: to }, speed, easing, callback ); + }, + animate: function( prop, speed, easing, callback ) { + var empty = jQuery.isEmptyObject( prop ), + optall = jQuery.speed( speed, easing, callback ), + doAnimation = function() { + + // Operate on a copy of prop so per-property easing won't be lost + var anim = Animation( this, jQuery.extend( {}, prop ), optall ); + + // Empty animations, or finishing resolves immediately + if ( empty || dataPriv.get( this, "finish" ) ) { + anim.stop( true ); + } + }; + doAnimation.finish = doAnimation; + + return empty || optall.queue === false ? + this.each( doAnimation ) : + this.queue( optall.queue, doAnimation ); + }, + stop: function( type, clearQueue, gotoEnd ) { + var stopQueue = function( hooks ) { + var stop = hooks.stop; + delete hooks.stop; + stop( gotoEnd ); + }; + + if ( typeof type !== "string" ) { + gotoEnd = clearQueue; + clearQueue = type; + type = undefined; + } + if ( clearQueue ) { + this.queue( type || "fx", [] ); + } + + return this.each( function() { + var dequeue = true, + index = type != null && type + "queueHooks", + timers = jQuery.timers, + data = dataPriv.get( this ); + + if ( index ) { + if ( data[ index ] && data[ index ].stop ) { + stopQueue( data[ index ] ); + } + } else { + for ( index in data ) { + if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { + stopQueue( data[ index ] ); + } + } + } + + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && + ( type == null || timers[ index ].queue === type ) ) { + + timers[ index ].anim.stop( gotoEnd ); + dequeue = false; + timers.splice( index, 1 ); + } + } + + // Start the next in the queue if the last step wasn't forced. + // Timers currently will call their complete callbacks, which + // will dequeue but only if they were gotoEnd. + if ( dequeue || !gotoEnd ) { + jQuery.dequeue( this, type ); + } + } ); + }, + finish: function( type ) { + if ( type !== false ) { + type = type || "fx"; + } + return this.each( function() { + var index, + data = dataPriv.get( this ), + queue = data[ type + "queue" ], + hooks = data[ type + "queueHooks" ], + timers = jQuery.timers, + length = queue ? queue.length : 0; + + // Enable finishing flag on private data + data.finish = true; + + // Empty the queue first + jQuery.queue( this, type, [] ); + + if ( hooks && hooks.stop ) { + hooks.stop.call( this, true ); + } + + // Look for any active animations, and finish them + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && timers[ index ].queue === type ) { + timers[ index ].anim.stop( true ); + timers.splice( index, 1 ); + } + } + + // Look for any animations in the old queue and finish them + for ( index = 0; index < length; index++ ) { + if ( queue[ index ] && queue[ index ].finish ) { + queue[ index ].finish.call( this ); + } + } + + // Turn off finishing flag + delete data.finish; + } ); + } +} ); + +jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { + var cssFn = jQuery.fn[ name ]; + jQuery.fn[ name ] = function( speed, easing, callback ) { + return speed == null || typeof speed === "boolean" ? + cssFn.apply( this, arguments ) : + this.animate( genFx( name, true ), speed, easing, callback ); + }; +} ); + +// Generate shortcuts for custom animations +jQuery.each( { + slideDown: genFx( "show" ), + slideUp: genFx( "hide" ), + slideToggle: genFx( "toggle" ), + fadeIn: { opacity: "show" }, + fadeOut: { opacity: "hide" }, + fadeToggle: { opacity: "toggle" } +}, function( name, props ) { + jQuery.fn[ name ] = function( speed, easing, callback ) { + return this.animate( props, speed, easing, callback ); + }; +} ); + +jQuery.timers = []; +jQuery.fx.tick = function() { + var timer, + i = 0, + timers = jQuery.timers; + + fxNow = Date.now(); + + for ( ; i < timers.length; i++ ) { + timer = timers[ i ]; + + // Run the timer and safely remove it when done (allowing for external removal) + if ( !timer() && timers[ i ] === timer ) { + timers.splice( i--, 1 ); + } + } + + if ( !timers.length ) { + jQuery.fx.stop(); + } + fxNow = undefined; +}; + +jQuery.fx.timer = function( timer ) { + jQuery.timers.push( timer ); + jQuery.fx.start(); +}; + +jQuery.fx.interval = 13; +jQuery.fx.start = function() { + if ( inProgress ) { + return; + } + + inProgress = true; + schedule(); +}; + +jQuery.fx.stop = function() { + inProgress = null; +}; + +jQuery.fx.speeds = { + slow: 600, + fast: 200, + + // Default speed + _default: 400 +}; + + +// Based off of the plugin by Clint Helfers, with permission. +// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ +jQuery.fn.delay = function( time, type ) { + time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; + type = type || "fx"; + + return this.queue( type, function( next, hooks ) { + var timeout = window.setTimeout( next, time ); + hooks.stop = function() { + window.clearTimeout( timeout ); + }; + } ); +}; + + +( function() { + var input = document.createElement( "input" ), + select = document.createElement( "select" ), + opt = select.appendChild( document.createElement( "option" ) ); + + input.type = "checkbox"; + + // Support: Android <=4.3 only + // Default value for a checkbox should be "on" + support.checkOn = input.value !== ""; + + // Support: IE <=11 only + // Must access selectedIndex to make default options select + support.optSelected = opt.selected; + + // Support: IE <=11 only + // An input loses its value after becoming a radio + input = document.createElement( "input" ); + input.value = "t"; + input.type = "radio"; + support.radioValue = input.value === "t"; +} )(); + + +var boolHook, + attrHandle = jQuery.expr.attrHandle; + +jQuery.fn.extend( { + attr: function( name, value ) { + return access( this, jQuery.attr, name, value, arguments.length > 1 ); + }, + + removeAttr: function( name ) { + return this.each( function() { + jQuery.removeAttr( this, name ); + } ); + } +} ); + +jQuery.extend( { + attr: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set attributes on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + // Fallback to prop when attributes are not supported + if ( typeof elem.getAttribute === "undefined" ) { + return jQuery.prop( elem, name, value ); + } + + // Attribute hooks are determined by the lowercase version + // Grab necessary hook if one is defined + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + hooks = jQuery.attrHooks[ name.toLowerCase() ] || + ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); + } + + if ( value !== undefined ) { + if ( value === null ) { + jQuery.removeAttr( elem, name ); + return; + } + + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + elem.setAttribute( name, value + "" ); + return value; + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + ret = jQuery.find.attr( elem, name ); + + // Non-existent attributes return null, we normalize to undefined + return ret == null ? undefined : ret; + }, + + attrHooks: { + type: { + set: function( elem, value ) { + if ( !support.radioValue && value === "radio" && + nodeName( elem, "input" ) ) { + var val = elem.value; + elem.setAttribute( "type", value ); + if ( val ) { + elem.value = val; + } + return value; + } + } + } + }, + + removeAttr: function( elem, value ) { + var name, + i = 0, + + // Attribute names can contain non-HTML whitespace characters + // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 + attrNames = value && value.match( rnothtmlwhite ); + + if ( attrNames && elem.nodeType === 1 ) { + while ( ( name = attrNames[ i++ ] ) ) { + elem.removeAttribute( name ); + } + } + } +} ); + +// Hooks for boolean attributes +boolHook = { + set: function( elem, value, name ) { + if ( value === false ) { + + // Remove boolean attributes when set to false + jQuery.removeAttr( elem, name ); + } else { + elem.setAttribute( name, name ); + } + return name; + } +}; + +jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { + var getter = attrHandle[ name ] || jQuery.find.attr; + + attrHandle[ name ] = function( elem, name, isXML ) { + var ret, handle, + lowercaseName = name.toLowerCase(); + + if ( !isXML ) { + + // Avoid an infinite loop by temporarily removing this function from the getter + handle = attrHandle[ lowercaseName ]; + attrHandle[ lowercaseName ] = ret; + ret = getter( elem, name, isXML ) != null ? + lowercaseName : + null; + attrHandle[ lowercaseName ] = handle; + } + return ret; + }; +} ); + + + + +var rfocusable = /^(?:input|select|textarea|button)$/i, + rclickable = /^(?:a|area)$/i; + +jQuery.fn.extend( { + prop: function( name, value ) { + return access( this, jQuery.prop, name, value, arguments.length > 1 ); + }, + + removeProp: function( name ) { + return this.each( function() { + delete this[ jQuery.propFix[ name ] || name ]; + } ); + } +} ); + +jQuery.extend( { + prop: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set properties on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + + // Fix name and attach hooks + name = jQuery.propFix[ name ] || name; + hooks = jQuery.propHooks[ name ]; + } + + if ( value !== undefined ) { + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + return ( elem[ name ] = value ); + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + return elem[ name ]; + }, + + propHooks: { + tabIndex: { + get: function( elem ) { + + // Support: IE <=9 - 11 only + // elem.tabIndex doesn't always return the + // correct value when it hasn't been explicitly set + // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ + // Use proper attribute retrieval(#12072) + var tabindex = jQuery.find.attr( elem, "tabindex" ); + + if ( tabindex ) { + return parseInt( tabindex, 10 ); + } + + if ( + rfocusable.test( elem.nodeName ) || + rclickable.test( elem.nodeName ) && + elem.href + ) { + return 0; + } + + return -1; + } + } + }, + + propFix: { + "for": "htmlFor", + "class": "className" + } +} ); + +// Support: IE <=11 only +// Accessing the selectedIndex property +// forces the browser to respect setting selected +// on the option +// The getter ensures a default option is selected +// when in an optgroup +// eslint rule "no-unused-expressions" is disabled for this code +// since it considers such accessions noop +if ( !support.optSelected ) { + jQuery.propHooks.selected = { + get: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent && parent.parentNode ) { + parent.parentNode.selectedIndex; + } + return null; + }, + set: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent ) { + parent.selectedIndex; + + if ( parent.parentNode ) { + parent.parentNode.selectedIndex; + } + } + } + }; +} + +jQuery.each( [ + "tabIndex", + "readOnly", + "maxLength", + "cellSpacing", + "cellPadding", + "rowSpan", + "colSpan", + "useMap", + "frameBorder", + "contentEditable" +], function() { + jQuery.propFix[ this.toLowerCase() ] = this; +} ); + + + + + // Strip and collapse whitespace according to HTML spec + // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace + function stripAndCollapse( value ) { + var tokens = value.match( rnothtmlwhite ) || []; + return tokens.join( " " ); + } + + +function getClass( elem ) { + return elem.getAttribute && elem.getAttribute( "class" ) || ""; +} + +function classesToArray( value ) { + if ( Array.isArray( value ) ) { + return value; + } + if ( typeof value === "string" ) { + return value.match( rnothtmlwhite ) || []; + } + return []; +} + +jQuery.fn.extend( { + addClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + if ( cur.indexOf( " " + clazz + " " ) < 0 ) { + cur += clazz + " "; + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + removeClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + if ( !arguments.length ) { + return this.attr( "class", "" ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + + // This expression is here for better compressibility (see addClass) + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + + // Remove *all* instances + while ( cur.indexOf( " " + clazz + " " ) > -1 ) { + cur = cur.replace( " " + clazz + " ", " " ); + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + toggleClass: function( value, stateVal ) { + var type = typeof value, + isValidValue = type === "string" || Array.isArray( value ); + + if ( typeof stateVal === "boolean" && isValidValue ) { + return stateVal ? this.addClass( value ) : this.removeClass( value ); + } + + if ( isFunction( value ) ) { + return this.each( function( i ) { + jQuery( this ).toggleClass( + value.call( this, i, getClass( this ), stateVal ), + stateVal + ); + } ); + } + + return this.each( function() { + var className, i, self, classNames; + + if ( isValidValue ) { + + // Toggle individual class names + i = 0; + self = jQuery( this ); + classNames = classesToArray( value ); + + while ( ( className = classNames[ i++ ] ) ) { + + // Check each className given, space separated list + if ( self.hasClass( className ) ) { + self.removeClass( className ); + } else { + self.addClass( className ); + } + } + + // Toggle whole class name + } else if ( value === undefined || type === "boolean" ) { + className = getClass( this ); + if ( className ) { + + // Store className if set + dataPriv.set( this, "__className__", className ); + } + + // If the element has a class name or if we're passed `false`, + // then remove the whole classname (if there was one, the above saved it). + // Otherwise bring back whatever was previously saved (if anything), + // falling back to the empty string if nothing was stored. + if ( this.setAttribute ) { + this.setAttribute( "class", + className || value === false ? + "" : + dataPriv.get( this, "__className__" ) || "" + ); + } + } + } ); + }, + + hasClass: function( selector ) { + var className, elem, + i = 0; + + className = " " + selector + " "; + while ( ( elem = this[ i++ ] ) ) { + if ( elem.nodeType === 1 && + ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { + return true; + } + } + + return false; + } +} ); + + + + +var rreturn = /\r/g; + +jQuery.fn.extend( { + val: function( value ) { + var hooks, ret, valueIsFunction, + elem = this[ 0 ]; + + if ( !arguments.length ) { + if ( elem ) { + hooks = jQuery.valHooks[ elem.type ] || + jQuery.valHooks[ elem.nodeName.toLowerCase() ]; + + if ( hooks && + "get" in hooks && + ( ret = hooks.get( elem, "value" ) ) !== undefined + ) { + return ret; + } + + ret = elem.value; + + // Handle most common string cases + if ( typeof ret === "string" ) { + return ret.replace( rreturn, "" ); + } + + // Handle cases where value is null/undef or number + return ret == null ? "" : ret; + } + + return; + } + + valueIsFunction = isFunction( value ); + + return this.each( function( i ) { + var val; + + if ( this.nodeType !== 1 ) { + return; + } + + if ( valueIsFunction ) { + val = value.call( this, i, jQuery( this ).val() ); + } else { + val = value; + } + + // Treat null/undefined as ""; convert numbers to string + if ( val == null ) { + val = ""; + + } else if ( typeof val === "number" ) { + val += ""; + + } else if ( Array.isArray( val ) ) { + val = jQuery.map( val, function( value ) { + return value == null ? "" : value + ""; + } ); + } + + hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; + + // If set returns undefined, fall back to normal setting + if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { + this.value = val; + } + } ); + } +} ); + +jQuery.extend( { + valHooks: { + option: { + get: function( elem ) { + + var val = jQuery.find.attr( elem, "value" ); + return val != null ? + val : + + // Support: IE <=10 - 11 only + // option.text throws exceptions (#14686, #14858) + // Strip and collapse whitespace + // https://html.spec.whatwg.org/#strip-and-collapse-whitespace + stripAndCollapse( jQuery.text( elem ) ); + } + }, + select: { + get: function( elem ) { + var value, option, i, + options = elem.options, + index = elem.selectedIndex, + one = elem.type === "select-one", + values = one ? null : [], + max = one ? index + 1 : options.length; + + if ( index < 0 ) { + i = max; + + } else { + i = one ? index : 0; + } + + // Loop through all the selected options + for ( ; i < max; i++ ) { + option = options[ i ]; + + // Support: IE <=9 only + // IE8-9 doesn't update selected after form reset (#2551) + if ( ( option.selected || i === index ) && + + // Don't return options that are disabled or in a disabled optgroup + !option.disabled && + ( !option.parentNode.disabled || + !nodeName( option.parentNode, "optgroup" ) ) ) { + + // Get the specific value for the option + value = jQuery( option ).val(); + + // We don't need an array for one selects + if ( one ) { + return value; + } + + // Multi-Selects return an array + values.push( value ); + } + } + + return values; + }, + + set: function( elem, value ) { + var optionSet, option, + options = elem.options, + values = jQuery.makeArray( value ), + i = options.length; + + while ( i-- ) { + option = options[ i ]; + + /* eslint-disable no-cond-assign */ + + if ( option.selected = + jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 + ) { + optionSet = true; + } + + /* eslint-enable no-cond-assign */ + } + + // Force browsers to behave consistently when non-matching value is set + if ( !optionSet ) { + elem.selectedIndex = -1; + } + return values; + } + } + } +} ); + +// Radios and checkboxes getter/setter +jQuery.each( [ "radio", "checkbox" ], function() { + jQuery.valHooks[ this ] = { + set: function( elem, value ) { + if ( Array.isArray( value ) ) { + return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); + } + } + }; + if ( !support.checkOn ) { + jQuery.valHooks[ this ].get = function( elem ) { + return elem.getAttribute( "value" ) === null ? "on" : elem.value; + }; + } +} ); + + + + +// Return jQuery for attributes-only inclusion + + +support.focusin = "onfocusin" in window; + + +var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, + stopPropagationCallback = function( e ) { + e.stopPropagation(); + }; + +jQuery.extend( jQuery.event, { + + trigger: function( event, data, elem, onlyHandlers ) { + + var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, + eventPath = [ elem || document ], + type = hasOwn.call( event, "type" ) ? event.type : event, + namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; + + cur = lastElement = tmp = elem = elem || document; + + // Don't do events on text and comment nodes + if ( elem.nodeType === 3 || elem.nodeType === 8 ) { + return; + } + + // focus/blur morphs to focusin/out; ensure we're not firing them right now + if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { + return; + } + + if ( type.indexOf( "." ) > -1 ) { + + // Namespaced trigger; create a regexp to match event type in handle() + namespaces = type.split( "." ); + type = namespaces.shift(); + namespaces.sort(); + } + ontype = type.indexOf( ":" ) < 0 && "on" + type; + + // Caller can pass in a jQuery.Event object, Object, or just an event type string + event = event[ jQuery.expando ] ? + event : + new jQuery.Event( type, typeof event === "object" && event ); + + // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) + event.isTrigger = onlyHandlers ? 2 : 3; + event.namespace = namespaces.join( "." ); + event.rnamespace = event.namespace ? + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : + null; + + // Clean up the event in case it is being reused + event.result = undefined; + if ( !event.target ) { + event.target = elem; + } + + // Clone any incoming data and prepend the event, creating the handler arg list + data = data == null ? + [ event ] : + jQuery.makeArray( data, [ event ] ); + + // Allow special events to draw outside the lines + special = jQuery.event.special[ type ] || {}; + if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { + return; + } + + // Determine event propagation path in advance, per W3C events spec (#9951) + // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) + if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { + + bubbleType = special.delegateType || type; + if ( !rfocusMorph.test( bubbleType + type ) ) { + cur = cur.parentNode; + } + for ( ; cur; cur = cur.parentNode ) { + eventPath.push( cur ); + tmp = cur; + } + + // Only add window if we got to document (e.g., not plain obj or detached DOM) + if ( tmp === ( elem.ownerDocument || document ) ) { + eventPath.push( tmp.defaultView || tmp.parentWindow || window ); + } + } + + // Fire handlers on the event path + i = 0; + while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { + lastElement = cur; + event.type = i > 1 ? + bubbleType : + special.bindType || type; + + // jQuery handler + handle = ( + dataPriv.get( cur, "events" ) || Object.create( null ) + )[ event.type ] && + dataPriv.get( cur, "handle" ); + if ( handle ) { + handle.apply( cur, data ); + } + + // Native handler + handle = ontype && cur[ ontype ]; + if ( handle && handle.apply && acceptData( cur ) ) { + event.result = handle.apply( cur, data ); + if ( event.result === false ) { + event.preventDefault(); + } + } + } + event.type = type; + + // If nobody prevented the default action, do it now + if ( !onlyHandlers && !event.isDefaultPrevented() ) { + + if ( ( !special._default || + special._default.apply( eventPath.pop(), data ) === false ) && + acceptData( elem ) ) { + + // Call a native DOM method on the target with the same name as the event. + // Don't do default actions on window, that's where global variables be (#6170) + if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { + + // Don't re-trigger an onFOO event when we call its FOO() method + tmp = elem[ ontype ]; + + if ( tmp ) { + elem[ ontype ] = null; + } + + // Prevent re-triggering of the same event, since we already bubbled it above + jQuery.event.triggered = type; + + if ( event.isPropagationStopped() ) { + lastElement.addEventListener( type, stopPropagationCallback ); + } + + elem[ type ](); + + if ( event.isPropagationStopped() ) { + lastElement.removeEventListener( type, stopPropagationCallback ); + } + + jQuery.event.triggered = undefined; + + if ( tmp ) { + elem[ ontype ] = tmp; + } + } + } + } + + return event.result; + }, + + // Piggyback on a donor event to simulate a different one + // Used only for `focus(in | out)` events + simulate: function( type, elem, event ) { + var e = jQuery.extend( + new jQuery.Event(), + event, + { + type: type, + isSimulated: true + } + ); + + jQuery.event.trigger( e, null, elem ); + } + +} ); + +jQuery.fn.extend( { + + trigger: function( type, data ) { + return this.each( function() { + jQuery.event.trigger( type, data, this ); + } ); + }, + triggerHandler: function( type, data ) { + var elem = this[ 0 ]; + if ( elem ) { + return jQuery.event.trigger( type, data, elem, true ); + } + } +} ); + + +// Support: Firefox <=44 +// Firefox doesn't have focus(in | out) events +// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 +// +// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 +// focus(in | out) events fire after focus & blur events, +// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order +// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 +if ( !support.focusin ) { + jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { + + // Attach a single capturing handler on the document while someone wants focusin/focusout + var handler = function( event ) { + jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); + }; + + jQuery.event.special[ fix ] = { + setup: function() { + + // Handle: regular nodes (via `this.ownerDocument`), window + // (via `this.document`) & document (via `this`). + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ); + + if ( !attaches ) { + doc.addEventListener( orig, handler, true ); + } + dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); + }, + teardown: function() { + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ) - 1; + + if ( !attaches ) { + doc.removeEventListener( orig, handler, true ); + dataPriv.remove( doc, fix ); + + } else { + dataPriv.access( doc, fix, attaches ); + } + } + }; + } ); +} +var location = window.location; + +var nonce = { guid: Date.now() }; + +var rquery = ( /\?/ ); + + + +// Cross-browser xml parsing +jQuery.parseXML = function( data ) { + var xml; + if ( !data || typeof data !== "string" ) { + return null; + } + + // Support: IE 9 - 11 only + // IE throws on parseFromString with invalid input. + try { + xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); + } catch ( e ) { + xml = undefined; + } + + if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) { + jQuery.error( "Invalid XML: " + data ); + } + return xml; +}; + + +var + rbracket = /\[\]$/, + rCRLF = /\r?\n/g, + rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, + rsubmittable = /^(?:input|select|textarea|keygen)/i; + +function buildParams( prefix, obj, traditional, add ) { + var name; + + if ( Array.isArray( obj ) ) { + + // Serialize array item. + jQuery.each( obj, function( i, v ) { + if ( traditional || rbracket.test( prefix ) ) { + + // Treat each array item as a scalar. + add( prefix, v ); + + } else { + + // Item is non-scalar (array or object), encode its numeric index. + buildParams( + prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", + v, + traditional, + add + ); + } + } ); + + } else if ( !traditional && toType( obj ) === "object" ) { + + // Serialize object item. + for ( name in obj ) { + buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); + } + + } else { + + // Serialize scalar item. + add( prefix, obj ); + } +} + +// Serialize an array of form elements or a set of +// key/values into a query string +jQuery.param = function( a, traditional ) { + var prefix, + s = [], + add = function( key, valueOrFunction ) { + + // If value is a function, invoke it and use its return value + var value = isFunction( valueOrFunction ) ? + valueOrFunction() : + valueOrFunction; + + s[ s.length ] = encodeURIComponent( key ) + "=" + + encodeURIComponent( value == null ? "" : value ); + }; + + if ( a == null ) { + return ""; + } + + // If an array was passed in, assume that it is an array of form elements. + if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { + + // Serialize the form elements + jQuery.each( a, function() { + add( this.name, this.value ); + } ); + + } else { + + // If traditional, encode the "old" way (the way 1.3.2 or older + // did it), otherwise encode params recursively. + for ( prefix in a ) { + buildParams( prefix, a[ prefix ], traditional, add ); + } + } + + // Return the resulting serialization + return s.join( "&" ); +}; + +jQuery.fn.extend( { + serialize: function() { + return jQuery.param( this.serializeArray() ); + }, + serializeArray: function() { + return this.map( function() { + + // Can add propHook for "elements" to filter or add form elements + var elements = jQuery.prop( this, "elements" ); + return elements ? jQuery.makeArray( elements ) : this; + } ) + .filter( function() { + var type = this.type; + + // Use .is( ":disabled" ) so that fieldset[disabled] works + return this.name && !jQuery( this ).is( ":disabled" ) && + rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && + ( this.checked || !rcheckableType.test( type ) ); + } ) + .map( function( _i, elem ) { + var val = jQuery( this ).val(); + + if ( val == null ) { + return null; + } + + if ( Array.isArray( val ) ) { + return jQuery.map( val, function( val ) { + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ); + } + + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ).get(); + } +} ); + + +var + r20 = /%20/g, + rhash = /#.*$/, + rantiCache = /([?&])_=[^&]*/, + rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, + + // #7653, #8125, #8152: local protocol detection + rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, + rnoContent = /^(?:GET|HEAD)$/, + rprotocol = /^\/\//, + + /* Prefilters + * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) + * 2) These are called: + * - BEFORE asking for a transport + * - AFTER param serialization (s.data is a string if s.processData is true) + * 3) key is the dataType + * 4) the catchall symbol "*" can be used + * 5) execution will start with transport dataType and THEN continue down to "*" if needed + */ + prefilters = {}, + + /* Transports bindings + * 1) key is the dataType + * 2) the catchall symbol "*" can be used + * 3) selection will start with transport dataType and THEN go to "*" if needed + */ + transports = {}, + + // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression + allTypes = "*/".concat( "*" ), + + // Anchor tag for parsing the document origin + originAnchor = document.createElement( "a" ); + originAnchor.href = location.href; + +// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport +function addToPrefiltersOrTransports( structure ) { + + // dataTypeExpression is optional and defaults to "*" + return function( dataTypeExpression, func ) { + + if ( typeof dataTypeExpression !== "string" ) { + func = dataTypeExpression; + dataTypeExpression = "*"; + } + + var dataType, + i = 0, + dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; + + if ( isFunction( func ) ) { + + // For each dataType in the dataTypeExpression + while ( ( dataType = dataTypes[ i++ ] ) ) { + + // Prepend if requested + if ( dataType[ 0 ] === "+" ) { + dataType = dataType.slice( 1 ) || "*"; + ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); + + // Otherwise append + } else { + ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); + } + } + } + }; +} + +// Base inspection function for prefilters and transports +function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { + + var inspected = {}, + seekingTransport = ( structure === transports ); + + function inspect( dataType ) { + var selected; + inspected[ dataType ] = true; + jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { + var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); + if ( typeof dataTypeOrTransport === "string" && + !seekingTransport && !inspected[ dataTypeOrTransport ] ) { + + options.dataTypes.unshift( dataTypeOrTransport ); + inspect( dataTypeOrTransport ); + return false; + } else if ( seekingTransport ) { + return !( selected = dataTypeOrTransport ); + } + } ); + return selected; + } + + return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); +} + +// A special extend for ajax options +// that takes "flat" options (not to be deep extended) +// Fixes #9887 +function ajaxExtend( target, src ) { + var key, deep, + flatOptions = jQuery.ajaxSettings.flatOptions || {}; + + for ( key in src ) { + if ( src[ key ] !== undefined ) { + ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; + } + } + if ( deep ) { + jQuery.extend( true, target, deep ); + } + + return target; +} + +/* Handles responses to an ajax request: + * - finds the right dataType (mediates between content-type and expected dataType) + * - returns the corresponding response + */ +function ajaxHandleResponses( s, jqXHR, responses ) { + + var ct, type, finalDataType, firstDataType, + contents = s.contents, + dataTypes = s.dataTypes; + + // Remove auto dataType and get content-type in the process + while ( dataTypes[ 0 ] === "*" ) { + dataTypes.shift(); + if ( ct === undefined ) { + ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); + } + } + + // Check if we're dealing with a known content-type + if ( ct ) { + for ( type in contents ) { + if ( contents[ type ] && contents[ type ].test( ct ) ) { + dataTypes.unshift( type ); + break; + } + } + } + + // Check to see if we have a response for the expected dataType + if ( dataTypes[ 0 ] in responses ) { + finalDataType = dataTypes[ 0 ]; + } else { + + // Try convertible dataTypes + for ( type in responses ) { + if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { + finalDataType = type; + break; + } + if ( !firstDataType ) { + firstDataType = type; + } + } + + // Or just use first one + finalDataType = finalDataType || firstDataType; + } + + // If we found a dataType + // We add the dataType to the list if needed + // and return the corresponding response + if ( finalDataType ) { + if ( finalDataType !== dataTypes[ 0 ] ) { + dataTypes.unshift( finalDataType ); + } + return responses[ finalDataType ]; + } +} + +/* Chain conversions given the request and the original response + * Also sets the responseXXX fields on the jqXHR instance + */ +function ajaxConvert( s, response, jqXHR, isSuccess ) { + var conv2, current, conv, tmp, prev, + converters = {}, + + // Work with a copy of dataTypes in case we need to modify it for conversion + dataTypes = s.dataTypes.slice(); + + // Create converters map with lowercased keys + if ( dataTypes[ 1 ] ) { + for ( conv in s.converters ) { + converters[ conv.toLowerCase() ] = s.converters[ conv ]; + } + } + + current = dataTypes.shift(); + + // Convert to each sequential dataType + while ( current ) { + + if ( s.responseFields[ current ] ) { + jqXHR[ s.responseFields[ current ] ] = response; + } + + // Apply the dataFilter if provided + if ( !prev && isSuccess && s.dataFilter ) { + response = s.dataFilter( response, s.dataType ); + } + + prev = current; + current = dataTypes.shift(); + + if ( current ) { + + // There's only work to do if current dataType is non-auto + if ( current === "*" ) { + + current = prev; + + // Convert response if prev dataType is non-auto and differs from current + } else if ( prev !== "*" && prev !== current ) { + + // Seek a direct converter + conv = converters[ prev + " " + current ] || converters[ "* " + current ]; + + // If none found, seek a pair + if ( !conv ) { + for ( conv2 in converters ) { + + // If conv2 outputs current + tmp = conv2.split( " " ); + if ( tmp[ 1 ] === current ) { + + // If prev can be converted to accepted input + conv = converters[ prev + " " + tmp[ 0 ] ] || + converters[ "* " + tmp[ 0 ] ]; + if ( conv ) { + + // Condense equivalence converters + if ( conv === true ) { + conv = converters[ conv2 ]; + + // Otherwise, insert the intermediate dataType + } else if ( converters[ conv2 ] !== true ) { + current = tmp[ 0 ]; + dataTypes.unshift( tmp[ 1 ] ); + } + break; + } + } + } + } + + // Apply converter (if not an equivalence) + if ( conv !== true ) { + + // Unless errors are allowed to bubble, catch and return them + if ( conv && s.throws ) { + response = conv( response ); + } else { + try { + response = conv( response ); + } catch ( e ) { + return { + state: "parsererror", + error: conv ? e : "No conversion from " + prev + " to " + current + }; + } + } + } + } + } + } + + return { state: "success", data: response }; +} + +jQuery.extend( { + + // Counter for holding the number of active queries + active: 0, + + // Last-Modified header cache for next request + lastModified: {}, + etag: {}, + + ajaxSettings: { + url: location.href, + type: "GET", + isLocal: rlocalProtocol.test( location.protocol ), + global: true, + processData: true, + async: true, + contentType: "application/x-www-form-urlencoded; charset=UTF-8", + + /* + timeout: 0, + data: null, + dataType: null, + username: null, + password: null, + cache: null, + throws: false, + traditional: false, + headers: {}, + */ + + accepts: { + "*": allTypes, + text: "text/plain", + html: "text/html", + xml: "application/xml, text/xml", + json: "application/json, text/javascript" + }, + + contents: { + xml: /\bxml\b/, + html: /\bhtml/, + json: /\bjson\b/ + }, + + responseFields: { + xml: "responseXML", + text: "responseText", + json: "responseJSON" + }, + + // Data converters + // Keys separate source (or catchall "*") and destination types with a single space + converters: { + + // Convert anything to text + "* text": String, + + // Text to html (true = no transformation) + "text html": true, + + // Evaluate text as a json expression + "text json": JSON.parse, + + // Parse text as xml + "text xml": jQuery.parseXML + }, + + // For options that shouldn't be deep extended: + // you can add your own custom options here if + // and when you create one that shouldn't be + // deep extended (see ajaxExtend) + flatOptions: { + url: true, + context: true + } + }, + + // Creates a full fledged settings object into target + // with both ajaxSettings and settings fields. + // If target is omitted, writes into ajaxSettings. + ajaxSetup: function( target, settings ) { + return settings ? + + // Building a settings object + ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : + + // Extending ajaxSettings + ajaxExtend( jQuery.ajaxSettings, target ); + }, + + ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), + ajaxTransport: addToPrefiltersOrTransports( transports ), + + // Main method + ajax: function( url, options ) { + + // If url is an object, simulate pre-1.5 signature + if ( typeof url === "object" ) { + options = url; + url = undefined; + } + + // Force options to be an object + options = options || {}; + + var transport, + + // URL without anti-cache param + cacheURL, + + // Response headers + responseHeadersString, + responseHeaders, + + // timeout handle + timeoutTimer, + + // Url cleanup var + urlAnchor, + + // Request state (becomes false upon send and true upon completion) + completed, + + // To know if global events are to be dispatched + fireGlobals, + + // Loop variable + i, + + // uncached part of the url + uncached, + + // Create the final options object + s = jQuery.ajaxSetup( {}, options ), + + // Callbacks context + callbackContext = s.context || s, + + // Context for global events is callbackContext if it is a DOM node or jQuery collection + globalEventContext = s.context && + ( callbackContext.nodeType || callbackContext.jquery ) ? + jQuery( callbackContext ) : + jQuery.event, + + // Deferreds + deferred = jQuery.Deferred(), + completeDeferred = jQuery.Callbacks( "once memory" ), + + // Status-dependent callbacks + statusCode = s.statusCode || {}, + + // Headers (they are sent all at once) + requestHeaders = {}, + requestHeadersNames = {}, + + // Default abort message + strAbort = "canceled", + + // Fake xhr + jqXHR = { + readyState: 0, + + // Builds headers hashtable if needed + getResponseHeader: function( key ) { + var match; + if ( completed ) { + if ( !responseHeaders ) { + responseHeaders = {}; + while ( ( match = rheaders.exec( responseHeadersString ) ) ) { + responseHeaders[ match[ 1 ].toLowerCase() + " " ] = + ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) + .concat( match[ 2 ] ); + } + } + match = responseHeaders[ key.toLowerCase() + " " ]; + } + return match == null ? null : match.join( ", " ); + }, + + // Raw string + getAllResponseHeaders: function() { + return completed ? responseHeadersString : null; + }, + + // Caches the header + setRequestHeader: function( name, value ) { + if ( completed == null ) { + name = requestHeadersNames[ name.toLowerCase() ] = + requestHeadersNames[ name.toLowerCase() ] || name; + requestHeaders[ name ] = value; + } + return this; + }, + + // Overrides response content-type header + overrideMimeType: function( type ) { + if ( completed == null ) { + s.mimeType = type; + } + return this; + }, + + // Status-dependent callbacks + statusCode: function( map ) { + var code; + if ( map ) { + if ( completed ) { + + // Execute the appropriate callbacks + jqXHR.always( map[ jqXHR.status ] ); + } else { + + // Lazy-add the new callbacks in a way that preserves old ones + for ( code in map ) { + statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; + } + } + } + return this; + }, + + // Cancel the request + abort: function( statusText ) { + var finalText = statusText || strAbort; + if ( transport ) { + transport.abort( finalText ); + } + done( 0, finalText ); + return this; + } + }; + + // Attach deferreds + deferred.promise( jqXHR ); + + // Add protocol if not provided (prefilters might expect it) + // Handle falsy url in the settings object (#10093: consistency with old signature) + // We also use the url parameter if available + s.url = ( ( url || s.url || location.href ) + "" ) + .replace( rprotocol, location.protocol + "//" ); + + // Alias method option to type as per ticket #12004 + s.type = options.method || options.type || s.method || s.type; + + // Extract dataTypes list + s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; + + // A cross-domain request is in order when the origin doesn't match the current origin. + if ( s.crossDomain == null ) { + urlAnchor = document.createElement( "a" ); + + // Support: IE <=8 - 11, Edge 12 - 15 + // IE throws exception on accessing the href property if url is malformed, + // e.g. http://example.com:80x/ + try { + urlAnchor.href = s.url; + + // Support: IE <=8 - 11 only + // Anchor's host property isn't correctly set when s.url is relative + urlAnchor.href = urlAnchor.href; + s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== + urlAnchor.protocol + "//" + urlAnchor.host; + } catch ( e ) { + + // If there is an error parsing the URL, assume it is crossDomain, + // it can be rejected by the transport if it is invalid + s.crossDomain = true; + } + } + + // Convert data if not already a string + if ( s.data && s.processData && typeof s.data !== "string" ) { + s.data = jQuery.param( s.data, s.traditional ); + } + + // Apply prefilters + inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); + + // If request was aborted inside a prefilter, stop there + if ( completed ) { + return jqXHR; + } + + // We can fire global events as of now if asked to + // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) + fireGlobals = jQuery.event && s.global; + + // Watch for a new set of requests + if ( fireGlobals && jQuery.active++ === 0 ) { + jQuery.event.trigger( "ajaxStart" ); + } + + // Uppercase the type + s.type = s.type.toUpperCase(); + + // Determine if request has content + s.hasContent = !rnoContent.test( s.type ); + + // Save the URL in case we're toying with the If-Modified-Since + // and/or If-None-Match header later on + // Remove hash to simplify url manipulation + cacheURL = s.url.replace( rhash, "" ); + + // More options handling for requests with no content + if ( !s.hasContent ) { + + // Remember the hash so we can put it back + uncached = s.url.slice( cacheURL.length ); + + // If data is available and should be processed, append data to url + if ( s.data && ( s.processData || typeof s.data === "string" ) ) { + cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; + + // #9682: remove data so that it's not used in an eventual retry + delete s.data; + } + + // Add or update anti-cache param if needed + if ( s.cache === false ) { + cacheURL = cacheURL.replace( rantiCache, "$1" ); + uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + + uncached; + } + + // Put hash and anti-cache on the URL that will be requested (gh-1732) + s.url = cacheURL + uncached; + + // Change '%20' to '+' if this is encoded form body content (gh-2658) + } else if ( s.data && s.processData && + ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { + s.data = s.data.replace( r20, "+" ); + } + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + if ( jQuery.lastModified[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); + } + if ( jQuery.etag[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); + } + } + + // Set the correct header, if data is being sent + if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { + jqXHR.setRequestHeader( "Content-Type", s.contentType ); + } + + // Set the Accepts header for the server, depending on the dataType + jqXHR.setRequestHeader( + "Accept", + s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? + s.accepts[ s.dataTypes[ 0 ] ] + + ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : + s.accepts[ "*" ] + ); + + // Check for headers option + for ( i in s.headers ) { + jqXHR.setRequestHeader( i, s.headers[ i ] ); + } + + // Allow custom headers/mimetypes and early abort + if ( s.beforeSend && + ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { + + // Abort if not done already and return + return jqXHR.abort(); + } + + // Aborting is no longer a cancellation + strAbort = "abort"; + + // Install callbacks on deferreds + completeDeferred.add( s.complete ); + jqXHR.done( s.success ); + jqXHR.fail( s.error ); + + // Get transport + transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); + + // If no transport, we auto-abort + if ( !transport ) { + done( -1, "No Transport" ); + } else { + jqXHR.readyState = 1; + + // Send global event + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); + } + + // If request was aborted inside ajaxSend, stop there + if ( completed ) { + return jqXHR; + } + + // Timeout + if ( s.async && s.timeout > 0 ) { + timeoutTimer = window.setTimeout( function() { + jqXHR.abort( "timeout" ); + }, s.timeout ); + } + + try { + completed = false; + transport.send( requestHeaders, done ); + } catch ( e ) { + + // Rethrow post-completion exceptions + if ( completed ) { + throw e; + } + + // Propagate others as results + done( -1, e ); + } + } + + // Callback for when everything is done + function done( status, nativeStatusText, responses, headers ) { + var isSuccess, success, error, response, modified, + statusText = nativeStatusText; + + // Ignore repeat invocations + if ( completed ) { + return; + } + + completed = true; + + // Clear timeout if it exists + if ( timeoutTimer ) { + window.clearTimeout( timeoutTimer ); + } + + // Dereference transport for early garbage collection + // (no matter how long the jqXHR object will be used) + transport = undefined; + + // Cache response headers + responseHeadersString = headers || ""; + + // Set readyState + jqXHR.readyState = status > 0 ? 4 : 0; + + // Determine if successful + isSuccess = status >= 200 && status < 300 || status === 304; + + // Get response data + if ( responses ) { + response = ajaxHandleResponses( s, jqXHR, responses ); + } + + // Use a noop converter for missing script + if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) { + s.converters[ "text script" ] = function() {}; + } + + // Convert no matter what (that way responseXXX fields are always set) + response = ajaxConvert( s, response, jqXHR, isSuccess ); + + // If successful, handle type chaining + if ( isSuccess ) { + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + modified = jqXHR.getResponseHeader( "Last-Modified" ); + if ( modified ) { + jQuery.lastModified[ cacheURL ] = modified; + } + modified = jqXHR.getResponseHeader( "etag" ); + if ( modified ) { + jQuery.etag[ cacheURL ] = modified; + } + } + + // if no content + if ( status === 204 || s.type === "HEAD" ) { + statusText = "nocontent"; + + // if not modified + } else if ( status === 304 ) { + statusText = "notmodified"; + + // If we have data, let's convert it + } else { + statusText = response.state; + success = response.data; + error = response.error; + isSuccess = !error; + } + } else { + + // Extract error from statusText and normalize for non-aborts + error = statusText; + if ( status || !statusText ) { + statusText = "error"; + if ( status < 0 ) { + status = 0; + } + } + } + + // Set data for the fake xhr object + jqXHR.status = status; + jqXHR.statusText = ( nativeStatusText || statusText ) + ""; + + // Success/Error + if ( isSuccess ) { + deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); + } else { + deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); + } + + // Status-dependent callbacks + jqXHR.statusCode( statusCode ); + statusCode = undefined; + + if ( fireGlobals ) { + globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", + [ jqXHR, s, isSuccess ? success : error ] ); + } + + // Complete + completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); + + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); + + // Handle the global AJAX counter + if ( !( --jQuery.active ) ) { + jQuery.event.trigger( "ajaxStop" ); + } + } + } + + return jqXHR; + }, + + getJSON: function( url, data, callback ) { + return jQuery.get( url, data, callback, "json" ); + }, + + getScript: function( url, callback ) { + return jQuery.get( url, undefined, callback, "script" ); + } +} ); + +jQuery.each( [ "get", "post" ], function( _i, method ) { + jQuery[ method ] = function( url, data, callback, type ) { + + // Shift arguments if data argument was omitted + if ( isFunction( data ) ) { + type = type || callback; + callback = data; + data = undefined; + } + + // The url can be an options object (which then must have .url) + return jQuery.ajax( jQuery.extend( { + url: url, + type: method, + dataType: type, + data: data, + success: callback + }, jQuery.isPlainObject( url ) && url ) ); + }; +} ); + +jQuery.ajaxPrefilter( function( s ) { + var i; + for ( i in s.headers ) { + if ( i.toLowerCase() === "content-type" ) { + s.contentType = s.headers[ i ] || ""; + } + } +} ); + + +jQuery._evalUrl = function( url, options, doc ) { + return jQuery.ajax( { + url: url, + + // Make this explicit, since user can override this through ajaxSetup (#11264) + type: "GET", + dataType: "script", + cache: true, + async: false, + global: false, + + // Only evaluate the response if it is successful (gh-4126) + // dataFilter is not invoked for failure responses, so using it instead + // of the default converter is kludgy but it works. + converters: { + "text script": function() {} + }, + dataFilter: function( response ) { + jQuery.globalEval( response, options, doc ); + } + } ); +}; + + +jQuery.fn.extend( { + wrapAll: function( html ) { + var wrap; + + if ( this[ 0 ] ) { + if ( isFunction( html ) ) { + html = html.call( this[ 0 ] ); + } + + // The elements to wrap the target around + wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); + + if ( this[ 0 ].parentNode ) { + wrap.insertBefore( this[ 0 ] ); + } + + wrap.map( function() { + var elem = this; + + while ( elem.firstElementChild ) { + elem = elem.firstElementChild; + } + + return elem; + } ).append( this ); + } + + return this; + }, + + wrapInner: function( html ) { + if ( isFunction( html ) ) { + return this.each( function( i ) { + jQuery( this ).wrapInner( html.call( this, i ) ); + } ); + } + + return this.each( function() { + var self = jQuery( this ), + contents = self.contents(); + + if ( contents.length ) { + contents.wrapAll( html ); + + } else { + self.append( html ); + } + } ); + }, + + wrap: function( html ) { + var htmlIsFunction = isFunction( html ); + + return this.each( function( i ) { + jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html ); + } ); + }, + + unwrap: function( selector ) { + this.parent( selector ).not( "body" ).each( function() { + jQuery( this ).replaceWith( this.childNodes ); + } ); + return this; + } +} ); + + +jQuery.expr.pseudos.hidden = function( elem ) { + return !jQuery.expr.pseudos.visible( elem ); +}; +jQuery.expr.pseudos.visible = function( elem ) { + return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); +}; + + + + +jQuery.ajaxSettings.xhr = function() { + try { + return new window.XMLHttpRequest(); + } catch ( e ) {} +}; + +var xhrSuccessStatus = { + + // File protocol always yields status code 0, assume 200 + 0: 200, + + // Support: IE <=9 only + // #1450: sometimes IE returns 1223 when it should be 204 + 1223: 204 + }, + xhrSupported = jQuery.ajaxSettings.xhr(); + +support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); +support.ajax = xhrSupported = !!xhrSupported; + +jQuery.ajaxTransport( function( options ) { + var callback, errorCallback; + + // Cross domain only allowed if supported through XMLHttpRequest + if ( support.cors || xhrSupported && !options.crossDomain ) { + return { + send: function( headers, complete ) { + var i, + xhr = options.xhr(); + + xhr.open( + options.type, + options.url, + options.async, + options.username, + options.password + ); + + // Apply custom fields if provided + if ( options.xhrFields ) { + for ( i in options.xhrFields ) { + xhr[ i ] = options.xhrFields[ i ]; + } + } + + // Override mime type if needed + if ( options.mimeType && xhr.overrideMimeType ) { + xhr.overrideMimeType( options.mimeType ); + } + + // X-Requested-With header + // For cross-domain requests, seeing as conditions for a preflight are + // akin to a jigsaw puzzle, we simply never set it to be sure. + // (it can always be set on a per-request basis or even using ajaxSetup) + // For same-domain requests, won't change header if already provided. + if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { + headers[ "X-Requested-With" ] = "XMLHttpRequest"; + } + + // Set headers + for ( i in headers ) { + xhr.setRequestHeader( i, headers[ i ] ); + } + + // Callback + callback = function( type ) { + return function() { + if ( callback ) { + callback = errorCallback = xhr.onload = + xhr.onerror = xhr.onabort = xhr.ontimeout = + xhr.onreadystatechange = null; + + if ( type === "abort" ) { + xhr.abort(); + } else if ( type === "error" ) { + + // Support: IE <=9 only + // On a manual native abort, IE9 throws + // errors on any property access that is not readyState + if ( typeof xhr.status !== "number" ) { + complete( 0, "error" ); + } else { + complete( + + // File: protocol always yields status 0; see #8605, #14207 + xhr.status, + xhr.statusText + ); + } + } else { + complete( + xhrSuccessStatus[ xhr.status ] || xhr.status, + xhr.statusText, + + // Support: IE <=9 only + // IE9 has no XHR2 but throws on binary (trac-11426) + // For XHR2 non-text, let the caller handle it (gh-2498) + ( xhr.responseType || "text" ) !== "text" || + typeof xhr.responseText !== "string" ? + { binary: xhr.response } : + { text: xhr.responseText }, + xhr.getAllResponseHeaders() + ); + } + } + }; + }; + + // Listen to events + xhr.onload = callback(); + errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" ); + + // Support: IE 9 only + // Use onreadystatechange to replace onabort + // to handle uncaught aborts + if ( xhr.onabort !== undefined ) { + xhr.onabort = errorCallback; + } else { + xhr.onreadystatechange = function() { + + // Check readyState before timeout as it changes + if ( xhr.readyState === 4 ) { + + // Allow onerror to be called first, + // but that will not handle a native abort + // Also, save errorCallback to a variable + // as xhr.onerror cannot be accessed + window.setTimeout( function() { + if ( callback ) { + errorCallback(); + } + } ); + } + }; + } + + // Create the abort callback + callback = callback( "abort" ); + + try { + + // Do send the request (this may raise an exception) + xhr.send( options.hasContent && options.data || null ); + } catch ( e ) { + + // #14683: Only rethrow if this hasn't been notified as an error yet + if ( callback ) { + throw e; + } + } + }, + + abort: function() { + if ( callback ) { + callback(); + } + } + }; + } +} ); + + + + +// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432) +jQuery.ajaxPrefilter( function( s ) { + if ( s.crossDomain ) { + s.contents.script = false; + } +} ); + +// Install script dataType +jQuery.ajaxSetup( { + accepts: { + script: "text/javascript, application/javascript, " + + "application/ecmascript, application/x-ecmascript" + }, + contents: { + script: /\b(?:java|ecma)script\b/ + }, + converters: { + "text script": function( text ) { + jQuery.globalEval( text ); + return text; + } + } +} ); + +// Handle cache's special case and crossDomain +jQuery.ajaxPrefilter( "script", function( s ) { + if ( s.cache === undefined ) { + s.cache = false; + } + if ( s.crossDomain ) { + s.type = "GET"; + } +} ); + +// Bind script tag hack transport +jQuery.ajaxTransport( "script", function( s ) { + + // This transport only deals with cross domain or forced-by-attrs requests + if ( s.crossDomain || s.scriptAttrs ) { + var script, callback; + return { + send: function( _, complete ) { + script = jQuery( " + diff --git a/va-server/visual-analytics/views/multichart.ejs b/va-server/visual-analytics/views/multichart.ejs index 2a990f372..d1084b5dd 100644 --- a/va-server/visual-analytics/views/multichart.ejs +++ b/va-server/visual-analytics/views/multichart.ejs @@ -29,6 +29,7 @@ + @@ -66,6 +67,7 @@ + diff --git a/va-server/visual-analytics/views/popupchart.ejs b/va-server/visual-analytics/views/popupchart.ejs index f097e5386..ac9edd5da 100644 --- a/va-server/visual-analytics/views/popupchart.ejs +++ b/va-server/visual-analytics/views/popupchart.ejs @@ -27,6 +27,7 @@ + @@ -61,6 +62,7 @@ + diff --git a/va-server/visual-analytics/views/popupimage.ejs b/va-server/visual-analytics/views/popupimage.ejs index 5534b1010..352d53e4c 100644 --- a/va-server/visual-analytics/views/popupimage.ejs +++ b/va-server/visual-analytics/views/popupimage.ejs @@ -22,6 +22,7 @@ + @@ -56,6 +57,7 @@ + diff --git a/va-server/visual-analytics/views/popupmodel.ejs b/va-server/visual-analytics/views/popupmodel.ejs index fef33378a..309568c68 100644 --- a/va-server/visual-analytics/views/popupmodel.ejs +++ b/va-server/visual-analytics/views/popupmodel.ejs @@ -24,6 +24,7 @@ + @@ -58,6 +59,7 @@ +