ada_boost_classification.py

"""
    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
import matplotlib.pyplot as plt
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from brightics.common.repr import BrtcReprBuilder
from brightics.common.repr import strip_margin
from brightics.common.repr import plt2MD
from brightics.common.repr import dict2MD
from brightics.common.groupby import _function_by_group
from brightics.common.utils import check_required_parameters
from brightics.common.validation import validate, greater_than_or_equal_to, greater_than
from brightics.common.utils import get_default_from_parameters_if_required
from brightics.function.utils import _model_dict
from brightics.common.classify_input_type import check_col_type


def ada_boost_classification_train(table, group_by=None, **params):
    check_required_parameters(_ada_boost_classification_train, params, ['table'])
    params = get_default_from_parameters_if_required(params, _ada_boost_classification_train)
    param_validation_check = [greater_than_or_equal_to(params, 1, 'max_depth'),
                              greater_than_or_equal_to(params, 1, 'n_estimators'),
                              greater_than(params, 0, 'learning_rate')]
    validate(*param_validation_check)
    if group_by is not None:
        return _function_by_group(_ada_boost_classification_train, table, group_by=group_by, **params)
    else:
        return _ada_boost_classification_train(table, **params)


def _plot_feature_importance(feature_cols, classifier):
    
    feature_importance = classifier.feature_importances_
    indices = np.argsort(feature_importance)
    sorted_feature_cols = np.array(feature_cols)[indices]
    
    n_features = len(feature_cols)
    plt.barh(range(n_features), 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(np.arange(n_features), sorted_feature_cols)
    plt.xlabel("Feature importance")
    plt.ylabel("Feature")
    plt.tight_layout()
    fig_feature_importance = plt2MD(plt)
    plt.close()
    return fig_feature_importance
    
    
def _ada_boost_classification_train(table, feature_cols, label_col, max_depth=1,
                                    n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', random_state=None):
    
    feature_names, x_train = check_col_type(table, feature_cols)
    y_train = table[label_col]

    base_estimator = DecisionTreeClassifier(max_depth=max_depth)

    classifier = AdaBoostClassifier(base_estimator, n_estimators, learning_rate, algorithm, random_state)

    classifier.fit(x_train, y_train)

    params = {'feature_cols': feature_cols,
              'label_col': label_col,
              'feature_importance': classifier.feature_importances_,
              'n_estimators': n_estimators,
              'learning_rate': learning_rate,
              'algorithm': algorithm,
              'random_state': random_state}
    
    model = _model_dict('ada_boost_classification_model')
    get_param = classifier.get_params()
    model['parameters'] = get_param
    model['classifier'] = classifier
    model['params'] = params

    fig_feature_importance = _plot_feature_importance(feature_names, classifier)
    params = dict2MD(get_param)

    rb = BrtcReprBuilder()
    rb.addMD(strip_margin("""
    | ## AdaBoost Classification Train Result
    |
    | ### Feature Importance
    | {fig_feature_importance}
    |
    | ### Parameters
    | {list_parameters}
    |
    """.format(fig_feature_importance=fig_feature_importance,
               list_parameters=params
               )))

    model['_repr_brtc_'] = rb.get()
    feature_importance = classifier.feature_importances_
    feature_importance_table = pd.DataFrame([[feature_names[i], feature_importance[i]] for i in range(len(feature_names))], columns=['feature_name', 'importance'])
    model['feature_importance_table'] = feature_importance_table
    return {'model': model}


def ada_boost_classification_predict(table, model, **params):
    check_required_parameters(_ada_boost_classification_predict, params, ['table', 'model'])
    if '_grouped_data' in model:
        return _function_by_group(_ada_boost_classification_predict, table, model, **params)
    else:
        return _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'])
    
    out_table[pred_col_name] = classifier.predict(test_data)   
    
    classes = classifier.classes_
    if suffix == 'index':
        suffixes = [i for i, _ in enumerate(classes)]
    else:
        suffixes = classes
        
    prob = classifier.predict_proba(test_data)
    prob_col_name = ['{prob_col_prefix}_{suffix}'.format(prob_col_prefix=prob_col_prefix, suffix=suffix) for suffix in suffixes]    
    out_col_prob = pd.DataFrame(data=prob, columns=prob_col_name)

    out_table = pd.concat([out_table, out_col_prob], axis=1)
    return {'out_table': out_table}