utils.py

import tqdm
import numpy as np
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Embedding, LSTM, Dropout, Dense
from keras.models import Sequential
import keras_metrics

SEQUENCE_LENGTH = 100 # the length of all sequences (number of words per sample)
EMBEDDING_SIZE = 100  # Using 100-Dimensional GloVe embedding vectors
TEST_SIZE = 0.25 # ratio of testing set

BATCH_SIZE = 64
EPOCHS = 20 # number of epochs

label2int = {"ham": 0, "spam": 1}
int2label = {0: "ham", 1: "spam"}

def get_embedding_vectors(tokenizer, dim=100):
    embedding_index = {}
    with open(f"data/glove.6B.{dim}d.txt", encoding='utf8') as f:
        for line in tqdm.tqdm(f, "Reading GloVe"):
            values = line.split()
            word = values[0]
            vectors = np.asarray(values[1:], dtype='float32')
            embedding_index[word] = vectors

    word_index = tokenizer.word_index
    # we do +1 because Tokenizer() starts from 1
    embedding_matrix = np.zeros((len(word_index)+1, dim))
    for word, i in word_index.items():
        embedding_vector = embedding_index.get(word)
        if embedding_vector is not None:
            # words not found will be 0s
            embedding_matrix[i] = embedding_vector
            
    return embedding_matrix


def get_model(tokenizer, lstm_units):
    """
    Constructs the model,
    Embedding vectors => LSTM => 2 output Fully-Connected neurons with softmax activation
    """
    # get the GloVe embedding vectors
    embedding_matrix = get_embedding_vectors(tokenizer)
    model = Sequential()
    model.add(Embedding(len(tokenizer.word_index)+1,
              EMBEDDING_SIZE,
              weights=[embedding_matrix],
              trainable=False,
              input_length=SEQUENCE_LENGTH))

    model.add(LSTM(lstm_units, recurrent_dropout=0.2))
    model.add(Dropout(0.3))
    model.add(Dense(2, activation="softmax"))
    # compile as rmsprop optimizer
    # aswell as with recall metric
    model.compile(optimizer="rmsprop", loss="categorical_crossentropy",
                  metrics=["accuracy", keras_metrics.precision(), keras_metrics.recall()])
    model.summary()
    return model