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': 'word_embeddings.tsv',
}


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

    text = text.lower()
    text = replace_by_space_re.sub(' ', text)
    text = bad_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.
    """

    
    embeddings = dict()
    for line in open(embeddings_path, encoding='utf-8'):
        row = line.strip().split('\t')
        embeddings[row[0]] = np.array(row[1:], dtype=np.float32)
    embeddings_dim = embeddings[list(embeddings)[0]].shape[0]
    
    return embeddings, embeddings_dim
    


def question_to_vec(question, embeddings, dim):
    """Transforms a string to an embedding by averaging word embeddings."""
    
    result = np.zeros(dim)
    cnt = 0
    words = question.split()
    for word in words:
        if word in embeddings:
            result += np.array(embeddings[word])
            cnt += 1
    if cnt != 0:
        result /= cnt
    return result 


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