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word_embeddings.py
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word_embeddings.py
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import json
import fasttext
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
import keras.layers as layers
from keras.models import Model
from keras.datasets import imdb
from gensim.models import Word2Vec
from gensim.models import FastText
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Input,Embedding,Dense,Flatten
from sklearn.metrics import accuracy_score,classification_report
def json_to_dict(json_set):
for k,v in json_set.items():
if v == "True":
json_set[k]= True
elif v == "False":
json_set[k]=False
else:
json_set[k]=v
return json_set
with open("model_params.json", "r") as f:
model_params = json.load(f)
model_params = json_to_dict(model_params)
def load_data(vocab_size,max_len):
"""
Loads the keras imdb dataset
Args:
vocab_size = {int} the size of the vocabulary
max_len = {int} the maximum length of input considered for padding
Returns:
X_train = tokenized train data
X_test = tokenized test data
"""
INDEX_FROM = 3
(X_train,y_train),(X_test,y_test) = imdb.load_data(num_words = vocab_size,index_from = INDEX_FROM)
return X_train,X_test,y_train,y_test
def prepare_data_for_word_vectors_imdb(X_train):
"""
Prepares the input
Args:
X_train = tokenized train data
Returns:
sentences = {list} sentences containing words as tokens
word_index = {dict} word and its indexes in whole of imdb corpus
"""
INDEX_FROM = 3
word_to_index = imdb.get_word_index()
word_to_index = {k:(v+INDEX_FROM) for k,v in word_to_index.items()}
word_to_index["<START>"] =1
word_to_index["<UNK>"]=2
index_to_word = {v:k for k,v in word_to_index.items()}
sentences = []
for i in range(len(X_train)):
temp = [index_to_word[ids] for ids in X_train[i]]
sentences.append(temp)
"""
tokenizer = Tokenizer()
tokenizer.fit_on_texts(sentences)
word_indexes = tokenizer.word_index
"""
return sentences,word_to_index
def prepare_data_for_word_vectors(X):
sentences_as_words=[]
word_to_index={}
count=1
for sent in X:
temp = sent.split()
sentences_as_words.append(temp)
for sent in sentences_as_words:
for word in sent:
if word_to_index.get(word,None) is None:
word_to_index[word] = count
count +=1
index_to_word = {v:k for k,v in word_to_index.items()}
sentences=[]
for i in range(len(sentences_as_words)):
temp = [word_to_index[w] for w in sentences_as_words[i]]
sentences.append(temp)
return sentences_as_words,sentences,word_to_index
def data_prep_ELMo(train_x,train_y,test_x,test_y,max_len):
INDEX_FROM = 3
word_to_index = imdb.get_word_index()
word_to_index = {k:(v+INDEX_FROM) for k,v in word_to_index.items()}
word_to_index["<START>"] =1
word_to_index["<UNK>"]=2
index_to_word = {v:k for k,v in word_to_index.items()}
sentences=[]
for i in range(len(train_x)):
temp = [index_to_word[ids] for ids in train_x[i]]
sentences.append(temp)
test_sentences=[]
for i in range(len(test_x)):
temp = [index_to_word[ids] for ids in test_x[i]]
test_sentences.append(temp)
train_text = [' '.join(sentences[i][:max_len]) for i in range(len(sentences))]
train_text = np.array(train_text, dtype=object)[:, np.newaxis]
train_label = train_y.tolist()
test_text = [' '.join(test_sentences[i][:500]) for i in range(len(test_sentences))]
test_text = np.array(test_text , dtype=object)[:, np.newaxis]
test_label = test_y.tolist()
return train_text,train_label,test_text,test_label
def building_word_vector_model(option,sentences,embed_dim,workers,window,y_train):
"""
Builds the word vector
Args:
type = {bool} 0 for Word2vec. 1 for gensim Fastext. 2 for Fasttext 2018.
sentences = {list} list of tokenized words
embed_dim = {int} embedding dimension of the word vectors
workers = {int} no. of worker threads to train the model (faster training with multicore machines)
window = {int} max distance between current and predicted word
y_train = y_train
Returns:
model = Word2vec/Gensim fastText/ Fastext_2018 model trained on the training corpus
"""
if option == 0:
print("Training a word2vec model")
model = Word2Vec(sentences=sentences, size = embed_dim, workers = workers, window = window)
print("Training complete")
elif option == 1:
print("Training a Gensim FastText model")
model = FastText(sentences=sentences, size = embed_dim, workers = workers, window = window)
print("Training complete")
elif option == 2:
print("Training a Fasttext model from Facebook Research")
y_train = ["__label__positive" if i==1 else "__label__negative" for i in y_train]
with open("imdb_train.txt","w") as text_file:
for i in range(len(sentences)):
print(sentences[i],y_train[i],file = text_file)
model = fasttext.skipgram("imdb_train.txt","model_ft_2018_imdb",dim = embed_dim)
print("Training complete")
return model
def padding_input(X_train,X_test,maxlen):
"""
Pads the input upto considered max length
Args:
X_train = tokenized train data
X_test = tokenized test data
Returns:
X_train_pad = padded tokenized train data
X_test_pad = padded tokenized test data
"""
X_train_pad = pad_sequences(X_train,maxlen=maxlen,padding="post")
X_test_pad = pad_sequences(X_test,maxlen=maxlen,padding="post")
return X_train_pad,X_test_pad
def ELMoEmbedding(x):
elmo_model = hub.Module("https://tfhub.dev/google/elmo/1", trainable=True)
return elmo_model(tf.squeeze(tf.cast(x, tf.string)), signature="default", as_dict=True)["default"]
def classification_model(embed_dim,X_train_pad,X_test_pad,y_train,y_test,vocab_size,word_index,w2vmodel,
trainable_param,option):
"""
Builds the classification model for sentiment analysis
Args:
embded_dim = {int} dimension of the word vectors
X_train_pad = padded tokenized train data
X_test_pad = padded tokenized test data
vocab_size = {int} size of the vocabulary
word_index = {dict} word and its indexes in whole of imdb corpus
w2vmodel = Word2Vec model
trainable_param = {bool} whether to train the word embeddings in the Embedding layer
option = {int} choice of word embedding
"""
embedding_matrix = np.zeros((vocab_size,embed_dim))
for word, i in word_index.items():
try:
embedding_vector = w2vmodel[word]
except:
pass
try:
if embedding_vector is not None:
embedding_matrix[i]=embedding_vector
except:
pass
embed_layer = Embedding(vocab_size,embed_dim,weights =[embedding_matrix],trainable=trainable_param)
input_seq = Input(shape=(X_train_pad.shape[1],))
embed_seq = embed_layer(input_seq)
x = Dense(256,activation ="relu")(embed_seq)
x = Flatten()(x)
preds = Dense(1,activation="sigmoid")(x)
model = Model(input_seq,preds)
model.compile(loss=model_params["loss"],optimizer=model_params["optimizer"],metrics= model_params["metrics"])
model.fit(X_train_pad,y_train,epochs=model_params["epochs"],batch_size=model_params["batch_size"],validation_data=(X_test_pad,y_test))
predictions = model.predict(X_test_pad)
predictions = [0 if i<0.5 else 1 for i in predictions]
print("Accuracy: ",accuracy_score(y_test,predictions))
print("Classification Report: ",classification_report(y_test,predictions))
return model
def Classification_model_with_ELMo(X_train_pad,y_train,X_test_pad,y_test):
input_text = layers.Input(shape=(1,), dtype=tf.string)
embed_seq = layers.Lambda(ELMoEmbedding, output_shape=(1024,))(input_text)
x = Dense(256,activation ="relu")(embed_seq)
preds = Dense(1,activation="sigmoid")(x)
model = Model(input_text,preds)
model.compile(loss="binary_crossentropy",optimizer="adam",metrics=["accuracy"])
model.fit(X_train_pad,y_train,epochs=10,batch_size=512,validation_data=(X_test_pad,y_test))
predictions = model.predict(X_test_pad)
predictions = [0 if i<0.5 else 1 for i in predictions]
print("Accuracy: ",accuracy_score(y_test,predictions))
print("Classification Report: ",classification_report(y_test,predictions))
return model