pretrained.py

# https://deeplearningcourses.com/c/data-science-natural-language-processing-in-python
# https://www.udemy.com/data-science-natural-language-processing-in-python

# Author: http://lazyprogrammer.me
from __future__ import print_function, division
from future.utils import iteritems
from builtins import range
# Note: you may need to update your version of future
# sudo pip install -U future


# WHERE TO GET THE VECTORS:
# GloVe: https://nlp.stanford.edu/projects/glove/

import numpy as np
from sklearn.metrics.pairwise import pairwise_distances


def dist1(a, b):
    return np.linalg.norm(a - b)
def dist2(a, b):
    return 1 - a.dot(b) / (np.linalg.norm(a) * np.linalg.norm(b))

# pick a distance type
dist, metric = dist2, 'cosine'
# dist, metric = dist1, 'euclidean'


## more intuitive
# def find_analogies(w1, w2, w3):
#   for w in (w1, w2, w3):
#     if w not in word2vec:
#       print("%s not in dictionary" % w)

#   king = word2vec[w1]
#   man = word2vec[w2]
#   woman = word2vec[w3]
#   v0 = king - man + woman

#   min_dist = float('inf')
#   best_word = ''
#   for word, v1 in iteritems(word2vec):
#     if word not in (w1, w2, w3):
#       d = dist(v0, v1)
#       if d < min_dist:
#         min_dist = d
#         best_word = word
#   print(w1, "-", w2, "=", best_word, "-", w3)


## faster
def find_analogies(w1, w2, w3):
  for w in (w1, w2, w3):
    if w not in word2vec:
      print("%s not in dictionary" % w)

  king = word2vec[w1]
  man = word2vec[w2]
  woman = word2vec[w3]
  v0 = king - man + woman

  distances = pairwise_distances(v0.reshape(1, D), embedding, metric=metric).reshape(V)
  idx = distances.argmin()
  best_word = idx2word[idx]

  print(w1, "-", w2, "=", best_word, "-", w3)


# load in pre-trained word vectors
print('Loading word vectors...')
word2vec = {}
embedding = []
idx2word = []
with open('../large_files/glove.6B/glove.6B.50d.txt') as f:
  # is just a space-separated text file in the format:
  # word vec[0] vec[1] vec[2] ...
  for line in f:
    values = line.split()
    word = values[0]
    vec = np.asarray(values[1:], dtype='float32')
    word2vec[word] = vec
    embedding.append(vec)
    idx2word.append(word)
print('Found %s word vectors.' % len(word2vec))
embedding = np.array(embedding)
V, D = embedding.shape


find_analogies('king', 'man', 'woman')
find_analogies('france', 'paris', 'london')
find_analogies('france', 'paris', 'rome')
find_analogies('paris', 'france', 'italy')
find_analogies('france', 'french', 'english')
find_analogies('japan', 'japanese', 'chinese')
find_analogies('japan', 'japanese', 'italian')
find_analogies('japan', 'japanese', 'australian')
find_analogies('december', 'november', 'june')
find_analogies('miami', 'florida', 'texas')
find_analogies('einstein', 'scientist', 'painter')
find_analogies('china', 'rice', 'bread')
find_analogies('man', 'woman', 'she')
find_analogies('man', 'woman', 'aunt')
find_analogies('man', 'woman', 'sister')
find_analogies('man', 'woman', 'wife')
find_analogies('man', 'woman', 'actress')
find_analogies('man', 'woman', 'mother')
find_analogies('heir', 'heiress', 'princess')
find_analogies('nephew', 'niece', 'aunt')
find_analogies('france', 'paris', 'tokyo')
find_analogies('france', 'paris', 'beijing')
find_analogies('february', 'january', 'november')
find_analogies('france', 'paris', 'rome')
find_analogies('paris', 'france', 'italy')