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July 15, 2018 17:34
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Сортировка списка предложений через LSA и t-SNE
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# -*- coding: utf-8 -*- | |
''' | |
Сортировка списка предложений через последовательное применение LSA и t-SNE (встраивание | |
векторов LSA в 1d) | |
''' | |
from __future__ import division # for python2 compatability | |
from __future__ import print_function | |
import codecs | |
import numpy as np | |
from sklearn.manifold import TSNE | |
from sklearn.decomposition import TruncatedSVD | |
from sklearn.feature_extraction.text import TfidfVectorizer | |
from sklearn.pipeline import Pipeline | |
input_path = r'e:\polygon\paraphrasing\data\facts5_1s.txt' | |
output_path = '../tmp/facts5_1s.txt' | |
LSA_DIMS = 60 | |
def v_cosine(a, b): | |
return np.dot(a,b)/(np.linalg.norm(a)*np.linalg.norm(b)) | |
print('Buidling tf-idf corpus...') | |
tfidf_corpus = set() | |
with codecs.open(input_path, 'r', 'utf-8') as rdr: | |
for line in rdr: | |
phrase = line.strip() | |
if len(phrase) > 0: | |
tfidf_corpus.add(phrase) | |
tfidf_corpus = list(tfidf_corpus) | |
print('{} phrases in tfidf corpus'.format(len(tfidf_corpus))) | |
print('Fitting LSA...') | |
vectorizer = TfidfVectorizer(max_features=None, ngram_range=(3, 5), min_df=1, analyzer='char') | |
svd_model = TruncatedSVD(n_components=LSA_DIMS, algorithm='randomized', n_iter=20, random_state=42) | |
svd_transformer = Pipeline([('tfidf', vectorizer), ('svd', svd_model)]) | |
svd_transformer.fit(tfidf_corpus) | |
print('Calculating LSA vectors for query phrases...') | |
phrase_ls = svd_transformer.transform(tfidf_corpus) | |
print('Running t-SNE') | |
tsne = TSNE(n_components=1) | |
phrases_1d = tsne.fit_transform(phrase_ls) | |
print('Printing results') | |
with codecs.open(output_path, 'w', 'utf-8') as wrt: | |
phrases = [(tfidf_corpus[i], phrases_1d[i]) for i in range(len(tfidf_corpus))] | |
phrases = sorted(phrases, key=lambda z: z[1]) | |
for phrase, _ in phrases: | |
wrt.write(u'{}\n'.format(phrase)) |
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