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Visualize word embeddings, using tsne.
"""
Visualize word embeddings, using tsne.
First computes cosine distance of the 100 closests words, and then shows a clustering graph
of the first 11 closest words (the first one is always the word)
IT REQUIRES GLOVE MODEL.txt
line 31: glove_file = '../TBIR/glove.840B.300d.txt' MODIFY with the appropiate path
To Use it, you can just type: python word_embedding_vis.py <list of words space separated>
e.g: python word_embedding_vis.py cake word embedding music
"""
"""
check some glove words
"""
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.manifold import TSNE
from sys import stdout
import numpy as np
from matplotlib import pyplot
import sys
def build_glove_dictionary():
"""
builds a dictionary based on the glove model.
http://nlp.stanford.edu/projects/glove/
dictionary will have the form of key = token, value = numpy array with the pretrained values
REALLY IMPORTANT the glove dataset. with the big one finds nearly everything....
smallest one...quite baaaaaad...
"""
print ('building glove dictionary...')
glove_file = '../TBIR/glove.840B.300d.txt'
glove_dict = {}
with open(glove_file) as fd_glove:
j=0
for i, input in enumerate(fd_glove):
input_split = input.split(" ")
#print input_split
key = input_split[0] #get key
del input_split[0] # remove key
j+=1
stdout.write("\rloading glove dictionary: %d" % j)
stdout.flush()
values = []
for value in input_split:
values.append(float(value))
np_values = np.asarray(values)
glove_dict[key] = np_values
#else:
#print key
print ""
print 'dictionary build with length', len(glove_dict)
return glove_dict
def build_glove_matrix(glove_dictionary):
"""
return word2idx and matrix
"""
idx2word = {}
glove_matrix = []
i=0
for key, value in glove_dictionary.iteritems():
idx2word[i] = key
glove_matrix.append(value)
i+=1
return np.asarray(glove_matrix), idx2word
def check_similarity(glove_matrix, word):
return cosine_similarity(word.reshape(1, -1), glove_matrix)
def build_matrix_to_tsne(glove_dict, tokens):
matrix = []
for token in tokens:
if token in glove_dict:
matrix.append(glove_dict[token])
return matrix
words = []
if len(sys.argv)<2:
print 'Words not specified'
words = ["plant", "factory", "machine", "houseplant", "cake"]
else:
for i in range(1, len(sys.argv)):
words.append(sys.argv[i])
print 'Words that will be used', words
glove_dict = build_glove_dictionary()
glove_matrix, idx2word = build_glove_matrix(glove_dict)
model = TSNE(n_components=2, random_state=0)
to_plot = []
labels = []
not_found = 0
len_words = len(words)
for word in words:
try:
cosine_matrix = check_similarity(glove_matrix, glove_dict[word])
ind = cosine_matrix[0].argsort()[-100:][::-1]
closest = ind.tolist()
tokens = [idx2word[idx] for idx in closest]
to_reduce = build_matrix_to_tsne(glove_dict, tokens)
#print to_reduce.shape
labels += [token for token in tokens]
to_plot += [x_y for x_y in to_reduce]
except:
len_words-=1
print 'Word not found', word
print len_words
#print to_plot.shape
#print to_plot
X_hdim = np.array(to_plot)
#print X_hdim
print X_hdim.shape
X = model.fit_transform(X_hdim)
X_x = np.zeros((len_words*10, 2))
labels_x = []
print X.shape
k=0
ranges = [x*100 for x in range (0, len_words)]
print ranges
for i in ranges:
for j in range(1, 11):
print i+j-1, k
X_x[k] = X[i+j-1]
k+=1
labels[i+j-1]
labels_x.append(labels[i+j-1])
print labels_x
print X_x.shape
pyplot.scatter(X_x[:,0],X_x[:,1])
for i, label in enumerate(labels_x):
pyplot.annotate(label, (X_x[i,0],X_x[i,1]))
pyplot.show()
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