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utils.py
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utils.py
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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.
"""
# 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
########################
#### YOUR CODE HERE ####
########################
# remove this when you're done
raise NotImplementedError(
"Open utils.py and fill with your code. In case of Google Colab, download"
"(https://github.com/hse-aml/natural-language-processing/blob/master/project/utils.py), "
"edit locally and upload using '> arrow on the left edge' -> Files -> UPLOAD")
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 ####
########################
# remove this when you're done
raise NotImplementedError(
"Open utils.py and fill with your code. In case of Google Colab, download"
"(https://github.com/hse-aml/natural-language-processing/blob/master/project/utils.py), "
"edit locally and upload using '> arrow on the left edge' -> Files -> UPLOAD")
def unpickle_file(filename):
"""Returns the result of unpickling the file content."""
with open(filename, 'rb') as f:
return pickle.load(f)