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toxic-comment-pooled-gru-v5.py
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toxic-comment-pooled-gru-v5.py
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#Forked from https://github.com/PavelOstyakov/toxic/blob/master/fit_predict.py
# python fit_predict.py train.csv test.csv crawl-300d-2M.vec
import argparse
import numpy as np
import os
import pandas as pd
import nltk
import tqdm
from keras.layers import Dense, Embedding, Input
from keras.layers import Bidirectional, Dropout, CuDNNGRU
from keras.models import Model
from keras.optimizers import RMSprop
from sklearn.metrics import log_loss
def _train_model(model, batch_size, train_x, train_y, val_x, val_y):
best_loss = -1
best_weights = None
best_epoch = 0
current_epoch = 0
while True:
model.fit(train_x, train_y, batch_size=batch_size, epochs=1)
y_pred = model.predict(val_x, batch_size=batch_size)
total_loss = 0
for j in range(6):
loss = log_loss(val_y[:, j], y_pred[:, j])
total_loss += loss
total_loss /= 6.
print("Epoch {0} loss {1} best_loss {2}".format(current_epoch, total_loss, best_loss))
current_epoch += 1
if total_loss < best_loss or best_loss == -1:
best_loss = total_loss
best_weights = model.get_weights()
best_epoch = current_epoch
else:
if current_epoch - best_epoch == 5:
break
model.set_weights(best_weights)
return model
def train_folds(X, y, fold_count, batch_size, get_model_func):
fold_size = len(X) // fold_count
models = []
for fold_id in range(0, fold_count):
fold_start = fold_size * fold_id
fold_end = fold_start + fold_size
if fold_id == fold_size - 1:
fold_end = len(X)
train_x = np.concatenate([X[:fold_start], X[fold_end:]])
train_y = np.concatenate([y[:fold_start], y[fold_end:]])
val_x = X[fold_start:fold_end]
val_y = y[fold_start:fold_end]
model = _train_model(get_model_func(), batch_size, train_x, train_y, val_x, val_y)
models.append(model)
return models
def get_model(embedding_matrix, sequence_length, dropout_rate, recurrent_units, dense_size):
input_layer = Input(shape=(sequence_length,))
embedding_layer = Embedding(embedding_matrix.shape[0], embedding_matrix.shape[1],
weights=[embedding_matrix], trainable=False)(input_layer)
x = Bidirectional(CuDNNGRU(recurrent_units, return_sequences=True))(embedding_layer)
x = Dropout(dropout_rate)(x)
x = Bidirectional(CuDNNGRU(recurrent_units, return_sequences=False))(x)
x = Dense(dense_size, activation="relu")(x)
output_layer = Dense(6, activation="sigmoid")(x)
model = Model(inputs=input_layer, outputs=output_layer)
model.compile(loss='binary_crossentropy',
optimizer=RMSprop(clipvalue=1, clipnorm=1),
metrics=['accuracy'])
return model
def read_embedding_list(file_path):
embedding_word_dict = {}
embedding_list = []
with open(file_path) as f:
for row in tqdm.tqdm(f.read().split("\n")[1:-1]):
data = row.split(" ")
word = data[0]
embedding = np.array([float(num) for num in data[1:-1]])
embedding_list.append(embedding)
embedding_word_dict[word] = len(embedding_word_dict)
embedding_list = np.array(embedding_list)
return embedding_list, embedding_word_dict
def clear_embedding_list(embedding_list, embedding_word_dict, words_dict):
cleared_embedding_list = []
cleared_embedding_word_dict = {}
for word in words_dict:
if word not in embedding_word_dict:
continue
word_id = embedding_word_dict[word]
row = embedding_list[word_id]
cleared_embedding_list.append(row)
cleared_embedding_word_dict[word] = len(cleared_embedding_word_dict)
return cleared_embedding_list, cleared_embedding_word_dict
def convert_tokens_to_ids(tokenized_sentences, words_list, embedding_word_dict, sentences_length):
words_train = []
for sentence in tokenized_sentences:
current_words = []
for word_index in sentence:
word = words_list[word_index]
word_id = embedding_word_dict.get(word, len(embedding_word_dict) - 2)
current_words.append(word_id)
if len(current_words) >= sentences_length:
current_words = current_words[:sentences_length]
else:
current_words += [len(embedding_word_dict) - 1] * (sentences_length - len(current_words))
words_train.append(current_words)
return words_train
def tokenize_sentences(sentences, words_dict):
tokenized_sentences = []
for sentence in tqdm.tqdm(sentences):
if hasattr(sentence, "decode"):
sentence = sentence.decode("utf-8")
tokens = nltk.tokenize.word_tokenize(sentence)
result = []
for word in tokens:
word = word.lower()
if word not in words_dict:
words_dict[word] = len(words_dict)
word_index = words_dict[word]
result.append(word_index)
tokenized_sentences.append(result)
return tokenized_sentences, words_dict
UNKNOWN_WORD = "_UNK_"
END_WORD = "_END_"
NAN_WORD = "_NAN_"
CLASSES = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]
PROBABILITIES_NORMALIZE_COEFFICIENT = 1.4
SENTENCE_LENGTH = 500
DROPOUT = 0.2
RECURRENT_UNITS = 64
DENSE_SIZE = 32
BATCH_SIZE = 256
RESULT_PATH = "toxic_results"
def main():
# parser = argparse.ArgumentParser(
# description="Recurrent neural network for identifying and classifying toxic online comments")
#
# parser.add_argument("train_file_path")
# parser.add_argument("test_file_path")
# parser.add_argument("embedding_path")
# parser.add_argument("--result-path", default="toxic_results")
# parser.add_argument("--batch-size", type=int, default=256)
# parser.add_argument("--sentences-length", type=int, default=500)
# parser.add_argument("--recurrent-units", type=int, default=64)
# parser.add_argument("--dropout-rate", type=float, default=0.3)
# parser.add_argument("--dense-size", type=int, default=32)
# parser.add_argument("--fold-count", type=int, default=10)
#
# args = parser.parse_args()
#
# if args.fold_count <= 1:
# raise ValueError("fold-count should be more than 1")
print("Loading data...")
# train_data = pd.read_csv(args.train_file_path)
# test_data = pd.read_csv(args.test_file_path)
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
list_sentences_train = train_data["comment_text"].fillna(NAN_WORD).values
list_sentences_test = test_data["comment_text"].fillna(NAN_WORD).values
y_train = train_data[CLASSES].values
print("Tokenizing sentences in train set...")
tokenized_sentences_train, words_dict = tokenize_sentences(list_sentences_train, {})
print("Tokenizing sentences in test set...")
tokenized_sentences_test, words_dict = tokenize_sentences(list_sentences_test, words_dict)
words_dict[UNKNOWN_WORD] = len(words_dict)
print("Loading embeddings...")
# embedding_list, embedding_word_dict = read_embedding_list(args.embedding_path)
embedding_list, embedding_word_dict = read_embedding_list('crawl-300d-2M.vec')
embedding_size = len(embedding_list[0])
print("Preparing data...")
embedding_list, embedding_word_dict = clear_embedding_list(embedding_list, embedding_word_dict, words_dict)
embedding_word_dict[UNKNOWN_WORD] = len(embedding_word_dict)
embedding_list.append([0.] * embedding_size)
embedding_word_dict[END_WORD] = len(embedding_word_dict)
embedding_list.append([-1.] * embedding_size)
embedding_matrix = np.array(embedding_list)
id_to_word = dict((id, word) for word, id in words_dict.items())
# train_list_of_token_ids = convert_tokens_to_ids(
# tokenized_sentences_train,
# id_to_word,
# embedding_word_dict,
# args.sentences_length)
train_list_of_token_ids = convert_tokens_to_ids(
tokenized_sentences_train,
id_to_word,
embedding_word_dict,
SENTENCE_LENGTH)
# test_list_of_token_ids = convert_tokens_to_ids(
# tokenized_sentences_test,
# id_to_word,
# embedding_word_dict,
# args.sentences_length)
test_list_of_token_ids = convert_tokens_to_ids(
tokenized_sentences_test,
id_to_word,
embedding_word_dict,
SENTENCE_LENGTH)
X_train = np.array(train_list_of_token_ids)
X_test = np.array(test_list_of_token_ids)
# get_model_func = lambda: get_model(
# embedding_matrix,
# args.sentences_length,
# args.dropout_rate,
# args.recurrent_units,
# args.dense_size)
get_model_func = lambda: get_model(
embedding_matrix,
SENTENCE_LENGTH,
DROPOUT,
RECURRENT_UNITS,
DENSE_SIZE)
print("Starting to train models...")
# models = train_folds(X_train, y_train, args.fold_count, args.batch_size, get_model_func)
models = train_folds(X_train, y_train, 10, BATCH_SIZE, get_model_func)
# if not os.path.exists(args.result_path):
# os.mkdir(args.result_path)
print("Predicting results...")
test_predicts_list = []
for fold_id, model in enumerate(models):
# model_path = os.path.join(args.result_path, "model{0}_weights.npy".format(fold_id))
model_path = "model{0}_weights.npy".format(fold_id)
np.save(model_path, model.get_weights())
# test_predicts_path = os.path.join(args.result_path, "test_predicts{0}.npy".format(fold_id))
test_predicts_path = "test_predicts{0}.npy".format(fold_id)
test_predicts = model.predict(X_test, batch_size = BATCH_SIZE)
test_predicts_list.append(test_predicts)
np.save(test_predicts_path, test_predicts)
test_predicts = np.ones(test_predicts_list[0].shape)
for fold_predict in test_predicts_list:
test_predicts *= fold_predict
test_predicts **= (1. / len(test_predicts_list))
test_predicts **= PROBABILITIES_NORMALIZE_COEFFICIENT
test_ids = test_data["id"].values
test_ids = test_ids.reshape((len(test_ids), 1))
test_predicts = pd.DataFrame(data=test_predicts, columns=CLASSES)
test_predicts["id"] = test_ids
test_predicts = test_predicts[["id"] + CLASSES]
# submit_path = os.path.join(args.result_path, "submit")
submit_path = "submission-pooled-gru-v5.csv"
test_predicts.to_csv(submit_path, index=False)
if __name__ == "__main__":
main()