utils.py

import nltk
import pickle
import re
import numpy as np

nltk.download('stopwords')
from nltk.corpus import stopwords

# Paths for all resources for the bot.
RESOURCE_PATH = {
    'INTENT_RECOGNIZER': 'intent_recognizer.pkl',
    'TAG_CLASSIFIER': 'tag_classifier.pkl',
    'TFIDF_VECTORIZER': 'tfidf_vectorizer.pkl',
    'THREAD_EMBEDDINGS_FOLDER': 'thread_embeddings_by_tags',
    'WORD_EMBEDDINGS': 'from_week3/starspace_embeddings.tsv',
}


def text_prepare(text):
    """Performs tokenization and simple preprocessing."""
    replace_by_space_re = re.compile('[/(){}\[\]\|@,;]')
    good_symbols_re = re.compile('[^0-9a-z #+_]')
    stopwords_set = set(stopwords.words('english'))

    text = text.lower()
    text = replace_by_space_re.sub(' ', text)
    text = good_symbols_re.sub('', text)
    text = ' '.join([x for x in text.split() if x and x not in stopwords_set])

    return text.strip()


def load_embeddings(embeddings_path):
    """Loads pre-trained word embeddings from tsv file.
    Args:
      embeddings_path - path to the embeddings file.
    Returns:
      embeddings - dict mapping words to vectors;
      embeddings_dim - dimension of the vectors.
    """

    # Hint: you have already implemented a similar routine in the 3rd assignment.
    # Note that here you also need to know the dimension of the loaded embeddings.
    # When you load the embeddings, use numpy.float32 type as dtype
    starspace_embeddings = {}
    
    for line in open(embeddings_path, encoding='utf-8'):
        word, *vect = line.strip().split('\t')
        starspace_embeddings[word] = np.array(vect, dtype = np.float32)
        
    dim = starspace_embeddings[list(starspace_embeddings)[0]].shape[0]
    return starspace_embeddings, dim        
      


def question_to_vec(question, embeddings, dim):
    """Transforms a string to an embedding by averaging word embeddings."""

    # Hint: you have already implemented exactly this function in the 3rd assignment.

    ########################
    #### YOUR CODE HERE ####
    ########################

    result = np.zeros(dim)
    
    count = 0
    words = question.split()
    for word in question.split():
        if word in embeddings:
            count = count + 1
            result = result + np.array(embeddings[word])
            
    if count > 0:
        result = result / count

    return result


def unpickle_file(filename):
    """Returns the result of unpickling the file content."""
    with open(filename, 'rb') as f:
        return pickle.load(f)