utils.py

import re
from collections import Counter

def preprocess(text):

    # Replace punctuation with tokens so we can use them in our model
    text = text.lower()
    text = text.replace('.', ' <PERIOD> ')
    text = text.replace(',', ' <COMMA> ')
    text = text.replace('"', ' <QUOTATION_MARK> ')
    text = text.replace(';', ' <SEMICOLON> ')
    text = text.replace('!', ' <EXCLAMATION_MARK> ')
    text = text.replace('?', ' <QUESTION_MARK> ')
    text = text.replace('(', ' <LEFT_PAREN> ')
    text = text.replace(')', ' <RIGHT_PAREN> ')
    text = text.replace('--', ' <HYPHENS> ')
    text = text.replace('?', ' <QUESTION_MARK> ')
    # text = text.replace('\n', ' <NEW_LINE> ')
    text = text.replace(':', ' <COLON> ')
    words = text.split()
    
    # Remove all words with  5 or fewer occurences
    word_counts = Counter(words)
    trimmed_words = [word for word in words if word_counts[word] > 5]

    return trimmed_words

def get_batches(int_text, batch_size, seq_length):
    """
    Return batches of input and target
    :param int_text: Text with the words replaced by their ids
    :param batch_size: The size of batch
    :param seq_length: The length of sequence
    :return: A list where each item is a tuple of (batch of input, batch of target).
    """
    n_batches = int(len(int_text) / (batch_size * seq_length))

    # Drop the last few characters to make only full batches
    xdata = np.array(int_text[: n_batches * batch_size * seq_length])
    ydata = np.array(int_text[1: n_batches * batch_size * seq_length + 1])

    x_batches = np.split(xdata.reshape(batch_size, -1), n_batches, 1)
    y_batches = np.split(ydata.reshape(batch_size, -1), n_batches, 1)

    return list(zip(x_batches, y_batches))


def create_lookup_tables(words):
    """
    Create lookup tables for vocabulary
    :param words: Input list of words
    :return: A tuple of dicts.  The first dict....
    """
    word_counts = Counter(words)
    sorted_vocab = sorted(word_counts, key=word_counts.get, reverse=True)
    int_to_vocab = {ii: word for ii, word in enumerate(sorted_vocab)}
    vocab_to_int = {word: ii for ii, word in int_to_vocab.items()}

    return vocab_to_int, int_to_vocab