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analyzer.py
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analyzer.py
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import copy
import numpy as np
import os
import pandas as pd
import tensorflow as tf
from data.preparer import *
from datetime import datetime
from google.oauth2 import service_account
from googleapiclient import discovery
from modeler import Modeler
from sklearn.feature_extraction.text import CountVectorizer
from snorkel.analysis import metric_score
from snorkel.labeling import filter_unlabeled_dataframe
from snorkel.utils import preds_to_probs
from tensorflow.keras.layers import Bidirectional
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Embedding
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential
import shutil
stat_history = pd.DataFrame()
modeler = None
def load_dataset(task: str, DELIMITER='#'):
set_seeds()
if task == "Amazon":
df_train, df_dev, df_valid, df_test, df_test_heldout = load_amazon_dataset(delimiter=DELIMITER)
elif task =="Youtube":
df_train, df_dev, df_valid, df_test, df_test_heldout = load_youtube_dataset(delimiter=DELIMITER)
elif task == "Film":
df_train, df_dev, df_valid, df_test, df_test_heldout = load_film_dataset()
elif (task == "News") or (task == "Debug"):
df_train, df_dev, df_valid, df_test, df_test_heldout = load_news_dataset()
global modeler
modeler = Modeler(df_train, df_dev, df_valid, df_test, df_test_heldout)
update_stats({}, "load_data")
return (df_train, df_dev, df_valid, df_test)
def set_seeds():
# set all random seeds
import tensorflow as tf
from numpy.random import seed as np_seed
from random import seed as py_seed
from snorkel.utils import set_seed as snork_seed
snork_seed(123)
tf.random.set_seed(123)
np_seed(123)
py_seed(123)
def get_keras_logreg(input_dim, output_dim=2):
set_seeds()
model = Sequential()
if output_dim == 1:
loss = "binary_crossentropy"
activation = tf.nn.sigmoid
else:
loss = "categorical_crossentropy"
activation = tf.math.softmax
dense = tf.keras.layers.Dense(
units=output_dim,
input_dim=input_dim,
activation=activation,
kernel_regularizer=tf.keras.regularizers.l2(0.001),
)
model.add(dense)
opt = tf.keras.optimizers.Adam(lr=0.01)
model.compile(optimizer=opt, loss=loss, metrics=["accuracy"])
return model
def get_keras_early_stopping(patience=10, monitor="val_accuracy"):
"""Stops training if monitor value doesn't exceed the current max value after patience num of epochs"""
return tf.keras.callbacks.EarlyStopping(
monitor=monitor, patience=patience, verbose=1, restore_best_weights=True
)
def train_model(label_model, L_train):
probs_train = label_model.predict_proba(L=L_train)
df_train_filtered, probs_train_filtered = filter_unlabeled_dataframe(
X=modeler.df_train, y=probs_train, L=L_train
)
print("{} out of {} examples used for training data".format(len(df_train_filtered), len(modeler.df_train)))
return train_model_from_probs(df_train_filtered, probs_train_filtered, modeler.df_valid, modeler.df_test)
def train_model_from_probs(df_train_filtered, probs_train_filtered, df_valid, df_test):
set_seeds()
vectorizer = modeler.vectorizer
X_train = vectorizer.fit_transform(df_train_filtered.text.tolist())
X_valid = vectorizer.transform(df_valid["text"].tolist())
X_test = vectorizer.transform(df_test["text"].tolist())
Y_valid = df_valid["label"].values
Y_test = df_test["label"].values
# Define a vanilla logistic regression model with Keras
keras_model = get_keras_logreg(input_dim=X_train.shape[1])
keras_model.fit(
x=X_train,
y=probs_train_filtered,
validation_data=(X_valid, preds_to_probs(Y_valid, 2)),
callbacks=[get_keras_early_stopping()],
epochs=50,
verbose=0,
)
modeler.keras_model = keras_model
preds_test = keras_model.predict(x=X_test).argmax(axis=1)
stats = modeler.get_stats(modeler.Y_test, preds_test)
update_stats({**stats, "data": "test"}, "train_model")
return stats
def update_stats(new_stats_dict: dict, action: str, label_model=None, applier=None):
if applier is not None:
modeler.L_heldout = applier.apply(df=modeler.df_heldout)
if label_model is not None:
modeler.label_model = label_model
global stat_history
new_stats_dict = copy.deepcopy(new_stats_dict)
new_stats_dict.update({
"time": datetime.now(),
"action": action
})
stat_history = stat_history.append(new_stats_dict, ignore_index=True)
if action=="train_model":
heldout_stats = heldout_stats = modeler.get_heldout_lr_stats()
if len(heldout_stats) > 0:
stat_history = stat_history.append({
"action": "heldout_test_LR_stats",
"time": datetime.now(),
"data": "heldout",
**heldout_stats
}, ignore_index=True)
elif (action=="stats"):
heldout_stats = modeler.get_heldout_stats()
if len(heldout_stats) > 0:
stat_history = stat_history.append({
"action": "heldout_test_stats",
"time": datetime.now(),
"data": "heldout",
**heldout_stats
}, ignore_index=True)
def save_model(USER_ID, TOOL, TASK):
dirname = str.lower(TOOL + "_" + TASK)
experiment_id = "_".join([TOOL, TASK, USER_ID, str(datetime.now())])
update_stats({"dirname": dirname}, "save_model")
try:
os.mkdir(dirname)
except FileExistsError:
pass
try:
os.mkdir(dirname)
except FileExistsError:
pass
modeler.save(dirname)
global stat_history
stat_history["time_delta"] = stat_history["time"] - stat_history["time"].iloc[0]
stat_history["user"] = USER_ID
stat_history["tool"] = TOOL
stat_history["task"] = TASK
stat_history.to_csv(os.path.join(dirname, "statistics_history.csv"))
upload_data(dirname, experiment_id)
upload_stats(dirname, experiment_id)
def upload_stats(dirname, experiment_id):
cur_path = os.path.dirname(os.path.realpath(__file__))
GOOGLE_APPLICATION_CREDENTIALS=os.path.join(cur_path, "data/credentials.json")
creds = credentials = service_account.Credentials.from_service_account_file(GOOGLE_APPLICATION_CREDENTIALS, scopes=['https://www.googleapis.com/auth/drive'])
drive_api = discovery.build('drive', 'v3', credentials=creds)
drive_client = drive_api.files()
stats_file = dirname + "/statistics_history.csv"
file_metadata = {'name': experiment_id + '.csv', 'parents':["1bYXU5TwT_jvmuygkBbBy2r-BN7JUBHX5"]}
from googleapiclient.http import MediaFileUpload
media = MediaFileUpload(stats_file,
mimetype='text/csv')
create_kwargs = {
'body': file_metadata,
'media_body': media,
'fields': 'id'
}
file = drive_client.create(**create_kwargs).execute()
def upload_data(dirname, experiment_id):
zipfile = experiment_id + '.zip'
shutil.make_archive(experiment_id, "zip", dirname)
cur_path = os.path.dirname(os.path.realpath(__file__))
GOOGLE_APPLICATION_CREDENTIALS=os.path.join(cur_path, "data/credentials.json")
creds = credentials = service_account.Credentials.from_service_account_file(GOOGLE_APPLICATION_CREDENTIALS, scopes=['https://www.googleapis.com/auth/drive'])
drive_api = discovery.build('drive', 'v3', credentials=creds)
drive_client = drive_api.files()
file_metadata = {'name': zipfile, 'parents':["1bYXU5TwT_jvmuygkBbBy2r-BN7JUBHX5"]}
from googleapiclient.http import MediaFileUpload
media = MediaFileUpload(zipfile,
mimetype='application/zip')
create_kwargs = {
'body': file_metadata,
'media_body': media,
'fields': 'id'
}
file = drive_client.create(**create_kwargs).execute()
print( 'File ID: ' + file.get('id'))