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intent_snips_ner_glove.py
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intent_snips_ner_glove.py
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from keras.backend.tensorflow_backend import set_session
import tensorflow as tf
config = tf.ConfigProto()
# config.gpu_options.per_process_gpu_memory_fraction = 0.95
config.gpu_options.allow_growth = True
config.gpu_options.visible_device_list = '0'
set_session(tf.Session(config=config))
import pandas as pd
import numpy as np
import sklearn.model_selection
import keras
from keras.optimizers import Adam, SGD
from keras.callbacks import TensorBoard, EarlyStopping, ModelCheckpoint
import json
from keras import backend as K
from metrics import fmeasure
from fasttext_embeddings import text2embeddings
from intent_models import cnn_word_model
from report_intent import report
SEED = 23
np.random.seed(SEED)
tf.set_random_seed(SEED)
train_data = []
train_data.append(pd.read_csv("./intent_data/snips_ner_2017_ground_truth/snips_train_0.csv"))
train_data.append(pd.read_csv("./intent_data/snips_ner_2017_ground_truth/snips_train_1.csv"))
train_data.append(pd.read_csv("./intent_data/snips_ner_2017_ground_truth/snips_train_2.csv"))
test_data = []
test_data.append(pd.read_csv("./intent_data/snips_ner_2017_ground_truth/snips_test_0.csv"))
test_data.append(pd.read_csv("./intent_data/snips_ner_2017_ground_truth/snips_test_1.csv"))
test_data.append(pd.read_csv("./intent_data/snips_ner_2017_ground_truth/snips_test_2.csv"))
filename = '/home/dilyara.baymurzina/data_preprocessing/glove.6B/glove.6B.50d.txt'
def loadGloVe(filename):
vocab = []
embd = []
file = io.open(filename,'r' , encoding='utf-8')
for line in file.readlines():
row = line.strip().split(' ')
vocab.append(row[0])
embd.append(row[1:])
print('Loaded GloVe!')
file.close()
return vocab,embd
vocabulary, embedding_ = loadGloVe(filename)
vocabulary_size = len(vocabulary)
embedding_size = len(embedding_[0])
embedding = np.asarray(embedding_)
print ('Vocabulary size:', vocabulary_size)
print ('Embedding size: ', embedding_size)
#-------------PARAMETERS----------------
text_size = 25
embedding_size = 100
n_splits = 5
filters_cnn = 256
kernel_sizes = [1,2,3]
coef_reg_cnn = 0.001
coef_reg_den = 0.001
dense_size = 100
dropout_rate = 0.5
lear_rate = 0.1
lear_rate_decay = 0.1
batch_size = 64
epochs = 500
num_of_tags = 40
intents = ['AddToPlaylist', 'BookRestaurant', 'GetWeather',
'PlayMusic', 'RateBook', 'SearchCreativeWork',
'SearchScreeningEvent']
#---------------------------------------
# tag2id = {'O': 0, 'album': 38, 'artist': 25, 'best_rating': 31, 'city': 32, 'condition_description': 22,
# 'condition_temperature': 30, 'country': 27, 'cuisine': 18, 'current_location': 16, 'entity_name': 20,
# 'facility': 9, 'genre': 24, 'geographic_poi': 7, 'location_name': 33, 'movie_name': 21, 'movie_type': 37,
# 'music_item': 29, 'object_location_type': 19, 'object_name': 13, 'object_part_of_series_type': 17,
# 'object_select': 6, 'object_type': 10, 'party_size_description': 26, 'party_size_number': 4, 'playlist': 14,
# 'playlist_owner': 1, 'poi': 36, 'rating_unit': 2, 'rating_value': 35, 'restaurant_name': 28, 'restaurant_type': 8,
# 'served_dish': 15, 'service': 34, 'sort': 3, 'spatial_relation': 11, 'state': 23, 'timeRange': 5, 'track': 39, 'year': 12}
# id2tag = dict()
# for tag_ind in tag2id:
# id2tag[tag2id[tag_ind]] = tag_ind
# for intent in intents:
# print('\n---------------Intent: %s-----------------\n' % intent)
# data_tags = data.loc[data[intent] == 1,'ner_tag'].values
# tags_for_intent = []
# for request_tags in data_tags:
# request_int_tags = [int(tag) for tag in request_tags.split(' ')]
# tags_for_intent.extend(request_int_tags)
# tags_for_intent = list(set(tags_for_intent))
# print('Tags for that intent:', tags_for_intent)
#__________________________________________________________________________________
train_preds = []
train_true = []
test_preds = []
test_true = []
for ind in range(3):
print("-----TRAIN-----", train_data[ind].head(), "\n-----TEST-----", test_data[ind].head())
print("-----TRAIN-----", train_data[ind].shape[0], "\n-----TEST-----", test_data[ind].shape[0])
X_train, X_test = train_data[ind].loc[:,'request'].values, test_data[ind].loc[:,'request'].values
y_train, y_test = train_data[ind].loc[:,intents].values, test_data[ind].loc[:,intents].values
ner_train, ner_test = train_data[ind].loc[:,'ner_tag'].values, test_data[ind].loc[:,'ner_tag'].values
# X_train_embed = text2embeddings(X_train, fasttext_model, text_size, embedding_size)
# X_test_embed = text2embeddings(X_test, fasttext_model, text_size, embedding_size)
X_train_embed = X_train
X_test_embed = X_test
train_tags_table = []
for k in range(train_data[ind].shape[0]):
tags = [int(tag) for tag in ner_train[k].split(' ')]
request_tags = []
for i_word, tag in enumerate(tags):
request_tags.append([(1 * (tag == m)) for m in range(num_of_tags)])
train_tags_table.append(request_tags)
train_tags_table = keras.preprocessing.sequence.pad_sequences(train_tags_table, maxlen=text_size,
padding='pre')
X_train_embed = np.dstack((X_train_embed, train_tags_table))
test_tags_table = []
for k in range(test_data[ind].shape[0]):
tags = [int(tag) for tag in ner_test[k].split(' ')]
request_tags = []
for i_word, tag in enumerate(tags):
request_tags.append([(1 * (tag == m)) for m in range(num_of_tags)])
test_tags_table.append(request_tags)
test_tags_table = keras.preprocessing.sequence.pad_sequences(test_tags_table, maxlen=text_size,
padding='pre')
X_test_embed = np.dstack((X_test_embed, test_tags_table))
model = cnn_word_model(text_size, embedding_size=embedding_size+num_of_tags, filters_cnn=filters_cnn,
kernel_sizes=kernel_sizes, coef_reg_cnn=coef_reg_cnn, coef_reg_den=coef_reg_den,
dropout_rate=dropout_rate, dense_size=dense_size)
optimizer = Adam(lr=lear_rate, decay=lear_rate_decay)
model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['categorical_accuracy',
fmeasure])
history = model.fit(X_train_embed, y_train.reshape(-1, 7),
batch_size=batch_size,
epochs=epochs,
validation_split=0.1,
verbose=1,
callbacks=[EarlyStopping(monitor='val_loss', min_delta=0.0),
#ModelCheckpoint(filepath="./keras_checkpoints/snips_" + str(n_splits)),
#TensorBoard(log_dir='./keras_logs/keras_log_files_' + str(ind))
])
y_train_pred = model.predict(X_train_embed).reshape(-1, 7)
train_preds.extend(y_train_pred)
train_true.extend(y_train)
y_test_pred = model.predict(X_test_embed).reshape(-1, 7)
test_preds.extend(y_test_pred)
test_true.extend(y_test)
train_preds = np.asarray(train_preds)
train_true = np.asarray(train_true)
test_preds = np.asarray(test_preds)
test_true = np.asarray(test_true)
report(train_true, train_preds, test_true, test_preds, intents)