word_embeddings.py

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):
    """
        Prepares the input

        Args:
            X = tokenized data

        Returns:
            sentences = {list} sentences containing words as tokens
            word_index = {dict} word and its indexes in whole of imdb corpus
            X = {list} = tokenized data (indexes)

    """
    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_as_words = []
    for i in range(len(X)):
        temp = [index_to_word[ids] for ids in X[i]]
        sentences_as_words.append(temp)
    """
    tokenizer = Tokenizer()
    tokenizer.fit_on_texts(sentences)
    word_indexes = tokenizer.word_index
    """
    return sentences_as_words,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