2019-04-13 00:37:00 +08:00
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# -*- coding: UTF-8 -*-
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# !/usr/bin/python
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# @time :2019/4/1 10:35
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# @author :Mo
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# @function :calculate Similarity of text and vector, which are tf-idf and pinyin
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2019-04-09 15:26:07 +08:00
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from FeatureProject.distance_text_or_vec import euclidean_distance, cosine_distance, manhattan_distance, euclidean_distance, jaccard_similarity_coefficient_distance
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from FeatureProject.distance_text_or_vec import chebyshev_distance, minkowski_distance, euclidean_distance_standardized
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from FeatureProject.distance_text_or_vec import mahalanobis_distance, bray_curtis_distance, pearson_correlation_distance
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from FeatureProject.distance_text_or_vec import wmd_distance, normalization, z_score
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from FeatureProject.distance_text_or_vec import hamming_distance, edit_levenshtein, ratio_levenshtein, jaro_levenshtein, set_ratio_fuzzywuzzy, sort_ratio_fuzzywuzzy
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from FeatureProject.distance_text_or_vec import clear_sentence, chinese2pinyin, num_of_common_sub_str
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from conf.path_config import word2_vec_path, td_idf_path, td_idf_path_pinyin
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from FeatureProject.distance_vec_TS_SS import TS_SS
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from gensim import corpora, models, matutils
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from conf.path_config import projectdir
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from gensim.models import KeyedVectors
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import pandas as pd
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import numpy as np
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import pickle
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import jieba
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import time
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import os
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class SentenceSimFeature:
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def __init__(self):
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self.sen1 = None
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self.sen2 = None
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self.seg1 = None
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self.seg2 = None
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self.sen_vec1 = None
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self.sen_vec2 = None
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self.tfidf_vec1 = None
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self.tfidf_vec2 = None
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self.dictionary = None
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self.tfidf_model = None
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self.w2c_model = None
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self.tfidf_pinyin_model = None
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self.dictionary_pinyin = None
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self.sen1_pinyin = None
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self.sen2_pinyin = None
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self.seg1_pinyin = None
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self.seg2_pinyin = None
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self.tfidf_vec1_pinyin = None
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self.tfidf_vec2_pinyin = None
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def set_data(self, sen1, sen2):
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sen1 = clear_sentence(sen1)
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sen2 = clear_sentence(sen2)
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self.sen1 = str(sen1).strip()
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self.sen2 = str(sen2).strip()
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self.seg1 = list(jieba.cut(sen1))
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self.seg2 = list(jieba.cut(sen2))
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self.sen1_pinyin = chinese2pinyin(sen1)
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self.sen2_pinyin = chinese2pinyin(sen2)
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self.seg1_pinyin = (self.sen1_pinyin).split(' ')
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self.seg2_pinyin = (self.sen2_pinyin).split(' ')
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self.sen_vec1 = np.zeros(300)
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self.sen_vec2 = np.zeros(300)
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# self.tfidf_vec1 = np.array((self.tfidf_model.transform([' '.join(self.seg1)])).toarray().tolist()[0])
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# self.tfidf_vec2 = np.array((self.tfidf_model.transform([' '.join(self.seg2)])).toarray().tolist()[0])
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# self.tfidf_vec1_pinyin = np.array((self.tfidf_pinyin_model.transform([' '.join(self.seg1_pinyin)])).toarray().tolist()[0])
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# self.tfidf_vec2_pinyin = np.array((self.tfidf_pinyin_model.transform([' '.join(self.seg2_pinyin)])).toarray().tolist()[0])
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self.tfidf_vec1 = self.tfidf_model[self.dictionary.doc2bow(self.seg1)]
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self.tfidf_vec2 = self.tfidf_model[self.dictionary.doc2bow(self.seg2)]
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self.tfidf_vec1_pinyin = self.tfidf_pinyin_model[self.dictionary_pinyin.doc2bow(self.seg1_pinyin)]
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self.tfidf_vec2_pinyin = self.tfidf_pinyin_model[self.dictionary_pinyin.doc2bow(self.seg2_pinyin)]
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def same_word_count(self):
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count_left = 0
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for s in self.seg1:
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if s in self.seg2:
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count_left += 1
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count_right = 0
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for s in self.seg2:
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if s in self.seg1:
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count_right += 1
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return min(count_left, count_right)
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def same_char_count(self):
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seg1 = list(self.sen1)
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seg2 = list(self.sen2)
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count_left = 0
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for s in seg1:
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if s in seg2:
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count_left += 1
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count_right = 0
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for s in seg2:
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if s in seg1:
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count_right += 1
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return min(count_left, count_right)
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def sentence_length(self):
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len_sen1 = len(self.sen1)
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len_sen2 = len(self.sen2)
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len_abs_sub = abs(len_sen1 - len_sen2)
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len_rate = len_sen1 / len_sen2
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len_add_rate = len_sen1 * len_sen2 / (len_sen1 + len_sen2)
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return [len_abs_sub, len_rate, len_add_rate]
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def init_sentence_vector(self):
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# file_path = os.path.dirname(__file__)
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print('load w2v model begin')
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# model_path = os.path.join(file_path, word2_vec_path)
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self.w2c_model = KeyedVectors.load_word2vec_format(word2_vec_path, unicode_errors='ignore', limit=None) # ,binary=True)
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print('load w2v model success')
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def encode_sentence_vector(self):
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for s in self.seg1:
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try:
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self.sen_vec1 += self.w2c_model[s]
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except:
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self.sen_vec1 += np.zeros(300)
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continue
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for s in self.seg2:
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try:
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self.sen_vec2 += self.w2c_model[s]
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except:
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self.sen_vec2 += np.zeros(300)
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continue
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def init_tfidf(self):
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file = open(td_idf_path, 'rb')
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tfidf_dictionary_model = pickle.load(file)
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self.dictionary = tfidf_dictionary_model[0]
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self.tfidf_model = tfidf_dictionary_model[1]
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file = open(td_idf_path_pinyin, 'rb')
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tfidf_dictionary_pinyin_model = pickle.load(file)
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self.dictionary_pinyin = tfidf_dictionary_pinyin_model[0]
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self.tfidf_pinyin_model = tfidf_dictionary_pinyin_model[1]
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print("init_tfidf ok!")
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def w2c_all_vec(self):
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w2c_Cosine = cosine_distance(self.sen_vec1, self.sen_vec2)
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w2c_TS_SS = TS_SS(self.sen_vec1, self.sen_vec2)
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w2c_Manhattan = manhattan_distance(self.sen_vec1, self.sen_vec2)
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w2c_Euclidean = euclidean_distance(self.sen_vec1, self.sen_vec2)
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w2c_Jaccard = jaccard_similarity_coefficient_distance(self.sen_vec1, self.sen_vec2)
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w2c_Chebyshev = chebyshev_distance(self.sen_vec1, self.sen_vec2)
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w2c_Minkowski = minkowski_distance(self.sen_vec1, self.sen_vec2)
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w2c_Euclidean_Standard = euclidean_distance_standardized(self.sen_vec1, self.sen_vec2)
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w2c_Mahalanobis = mahalanobis_distance(self.sen_vec1, self.sen_vec2)
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w2c_Bray = bray_curtis_distance(self.sen_vec1, self.sen_vec2)
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w2c_Pearson = pearson_correlation_distance(self.sen_vec1, self.sen_vec2)
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# w2c_Wmd = Wmd_Distance(self.w2c_model, self.sen_vec1, self.sen_vec2)
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return [w2c_Cosine, w2c_TS_SS, w2c_Manhattan, w2c_Euclidean, w2c_Jaccard, w2c_Chebyshev,
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w2c_Minkowski, w2c_Euclidean_Standard, w2c_Mahalanobis, w2c_Bray, w2c_Pearson]
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def tdidf_all_vec(self):
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return matutils.cossim(self.tfidf_vec1, self.tfidf_vec2)
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def edit_all_str(self):
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str_hamming = hamming_distance(self.sen1, self.sen2)
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str_edit = edit_levenshtein(self.sen1, self.sen2)
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str_ratio = ratio_levenshtein(self.sen1, self.sen2)
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str_jaro = jaro_levenshtein(self.sen1, self.sen2)
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str_set_ratio_fuzz = set_ratio_fuzzywuzzy(self.sen1, self.sen2)
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str_sort_ratio_fuzz = sort_ratio_fuzzywuzzy(self.sen1, self.sen2)
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str_commonsubstr = num_of_common_sub_str(self.sen1, self.sen2)
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str_list_Wmd = wmd_distance(self.w2c_model, self.seg1, self.seg2)
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return [str_hamming, str_edit, str_ratio, str_jaro,
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str_set_ratio_fuzz, str_sort_ratio_fuzz, str_commonsubstr, str_list_Wmd]
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def word_jaccard(self):
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a = list(set(self.seg1).intersection(set(self.seg2)))
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b = list(set(self.seg1).union(set(self.seg2)))
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return float(len(a) / len(b))
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def char_jaccard(self):
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a = list(set(list(self.sen1)).intersection(set(list(self.sen2))))
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b = list(set(list(self.sen1)).union(set(list(self.sen2))))
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return float(len(a) / len(b))
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def tdidf_all_vec_pinyin(self):
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return matutils.cossim(self.tfidf_vec1_pinyin, self.tfidf_vec2_pinyin)
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def edit_all_pinyin(self):
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pinyin_hamming = hamming_distance(self.sen1_pinyin, self.sen2_pinyin)
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pinyin_edit = edit_levenshtein(self.sen1_pinyin, self.sen2_pinyin)
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pinyin_ratio = ratio_levenshtein(self.sen1_pinyin, self.sen2_pinyin)
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pinyin_jaro = jaro_levenshtein(self.sen1_pinyin, self.sen2_pinyin)
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pinyin_set_ratio_fuzz = set_ratio_fuzzywuzzy(self.sen1_pinyin, self.sen2_pinyin)
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pinyin_sort_ratio_fuzz = sort_ratio_fuzzywuzzy(self.sen1_pinyin, self.sen2_pinyin)
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pinyin_commonsubstr = num_of_common_sub_str(self.sen1_pinyin, self.sen2_pinyin)
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pinyin_list_Wmd = wmd_distance(self.w2c_model, self.seg1_pinyin, self.seg2_pinyin)
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return [pinyin_hamming, pinyin_edit, pinyin_ratio, pinyin_jaro,
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pinyin_set_ratio_fuzz, pinyin_sort_ratio_fuzz, pinyin_commonsubstr, pinyin_list_Wmd]
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def word_jaccard_pinyin(self):
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a = list(set(self.seg1_pinyin).intersection(set(self.seg2_pinyin)))
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b = list(set(self.seg1_pinyin).union(set(self.seg2_pinyin)))
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return float(len(a) / len(b))
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def char_jaccard_pinyin(self):
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a = list(set(list(self.seg1_pinyin)).intersection(set(list(self.seg2_pinyin))))
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b = list(set(list(self.seg1_pinyin)).union(set(list(self.seg2_pinyin))))
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return float(len(a) / len(b))
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def sentence_input_t():
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while True:
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s1 = input('s1: ')
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s2 = input('s2: ')
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start_time = time.time()
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ssf.set_data(s1, s2)
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ssf.encode_sentence_vector()
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time1 = time.time()
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print('set_data time：' + str(time1 - start_time))
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# 相同词、长度
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same_word_count = ssf.same_word_count()
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time2 = time.time()
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print('same_word_count time：' + str(time2 - time1))
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same_char_count = ssf.same_char_count()
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time3 = time.time()
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print('same_char_count time：' + str(time3 - time2))
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[len_abs_sub, len_rate, len_add_rate] = ssf.sentence_length()
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time4 = time.time()
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print('sentence_length time：' + str(time4 - time3))
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# w2c_all_vec
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[w2c_Cosine, w2c_TS_SS, w2c_Manhattan, w2c_Euclidean,
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w2c_Jaccard, w2c_Chebyshev, w2c_Minkowski, w2c_Euclidean_Standard, w2c_Mahalanobis,
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w2c_Bray, w2c_Pearson] = ssf.w2c_all_vec()
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time5 = time.time()
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print('w2c_all_vec time：' + str(time5 - time4))
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# tdidf_all_vec
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# [tdidf_Cosine, tdidf_TS_SS, tdidf_Manhattan, tdidf_Euclidean,
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# tdidf_Jaccard, tdidf_Chebyshev,tdidf_Minkowski, tdidf_Euclidean_Standard, tdidf_Mahalanobis,
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# tdidf_Bray, tdidf_Pearson] = ssf.tdidf_all_vec()
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tdidf_cossim = ssf.tdidf_all_vec()
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time6 = time.time()
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print('tdidf_all_vec time：' + str(time6 - time5))
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# edit_all_str
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[str_hamming, str_edit, str_ratio, str_jaro,
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str_set_ratio_fuzz, str_sort_ratio_fuzz, str_commonsubstr, str_list_Wmd] = ssf.edit_all_str()
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time7 = time.time()
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print('edit_all_str time：' + str(time7 - time6))
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# jaccard系数
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word_jaccard = ssf.word_jaccard()
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char_jaccard = ssf.char_jaccard()
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time8 = time.time()
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print('jaccard系数 time：' + str(time8 - time7))
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# tdidf_all_vec_pinyin
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# [tdidf_piyin_Cosine, tdidf_piyin_TS_SS, tdidf_piyin_Manhattan, tdidf_piyin_Euclidean, tdidf_piyin_Jaccard,
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# tdidf_piyin_Chebyshev, tdidf_piyin_Minkowski, tdidf_piyin_Euclidean_Standard, tdidf_piyin_Mahalanobis,
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# tdidf_piyin_Bray, tdidf_piyin_Pearson] = ssf.tdidf_all_vec_pinyin()
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tdidf_pinyin_cossim = ssf.tdidf_all_vec_pinyin()
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time9 = time.time()
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print('tdidf_all_vec_pinyin time：' + str(time9 - time8))
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# edit_all_pinyin
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[pinyin_hamming, pinyin_edit, pinyin_ratio, pinyin_jaro,
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pinyin_set_ratio_fuzz, pinyin_sort_ratio_fuzz, pinyin_commonsubstr, pinyin_list_Wmd] = ssf.edit_all_pinyin()
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time10 = time.time()
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print('edit_all_pinyin time：' + str(time10 - time9))
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# jaccard系数
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word_jaccard_pinyin = ssf.word_jaccard_pinyin()
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char_jaccard_pinyin = ssf.char_jaccard_pinyin()
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time11 = time.time()
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print('jaccard系数pinyin time：' + str(time11 - time10))
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sim_all_last = [same_word_count, same_char_count, len_abs_sub, len_rate, len_add_rate,
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|
|
|
w2c_Cosine, w2c_TS_SS, w2c_Manhattan, w2c_Euclidean, w2c_Jaccard, w2c_Chebyshev, w2c_Minkowski,
|
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|
w2c_Euclidean_Standard, w2c_Mahalanobis, w2c_Bray, w2c_Pearson,
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tdidf_cossim, str_hamming, str_edit, str_ratio, str_jaro, str_set_ratio_fuzz,
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str_sort_ratio_fuzz,
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str_commonsubstr, str_list_Wmd,
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word_jaccard, char_jaccard, tdidf_pinyin_cossim,
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pinyin_hamming, pinyin_edit, pinyin_ratio, pinyin_jaro, pinyin_set_ratio_fuzz,
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pinyin_sort_ratio_fuzz,
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pinyin_commonsubstr, pinyin_list_Wmd,
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word_jaccard_pinyin, char_jaccard_pinyin]
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print("sim: ")
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print(sim_all_last)
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|
if __name__ == '__main__':
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|
|
ssf = SentenceSimFeature()
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|
ssf.init_sentence_vector()
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|
|
ssf.init_tfidf()
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|
|
s1 = "你知道Mo的能力上限吗"
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|
|
s2 = "你好呀，Mo水平很差"
|
|
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|
|
start_time = time.time()
|
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|
|
ssf.set_data(s1, s2)
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|
|
ssf.encode_sentence_vector()
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|
|
time1 = time.time()
|
|
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|
|
print('set_data time：' + str(time1 - start_time))
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|
|
# 相同词、长度
|
|
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|
|
|
same_word_count = ssf.same_word_count()
|
|
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|
|
time2 = time.time()
|
|
|
|
|
|
print('same_word_count time：' + str(time2 - time1))
|
|
|
|
|
|
|
|
|
|
|
|
same_char_count = ssf.same_char_count()
|
|
|
|
|
|
time3 = time.time()
|
|
|
|
|
|
print('same_char_count time：' + str(time3 - time2))
|
|
|
|
|
|
|
|
|
|
|
|
[len_abs_sub, len_rate, len_add_rate] = ssf.sentence_length()
|
|
|
|
|
|
time4 = time.time()
|
|
|
|
|
|
print('sentence_length time：' + str(time4 - time3))
|
|
|
|
|
|
|
|
|
|
|
|
# w2c_all_vec
|
|
|
|
|
|
[w2c_Cosine, w2c_TS_SS, w2c_Manhattan, w2c_Euclidean,
|
|
|
|
|
|
w2c_Jaccard, w2c_Chebyshev, w2c_Minkowski, w2c_Euclidean_Standard, w2c_Mahalanobis,
|
|
|
|
|
|
w2c_Bray, w2c_Pearson] = ssf.w2c_all_vec()
|
|
|
|
|
|
time5 = time.time()
|
|
|
|
|
|
print('w2c_all_vec time：' + str(time5 - time4))
|
|
|
|
|
|
|
|
|
|
|
|
# tdidf_all_vec
|
|
|
|
|
|
tdidf_cossim = ssf.tdidf_all_vec()
|
|
|
|
|
|
time6 = time.time()
|
|
|
|
|
|
print('tdidf_all_vec time：' + str(time6 - time5))
|
|
|
|
|
|
|
|
|
|
|
|
# edit_all_str
|
|
|
|
|
|
[str_hamming, str_edit, str_ratio, str_jaro,
|
|
|
|
|
|
str_set_ratio_fuzz, str_sort_ratio_fuzz, str_commonsubstr, str_list_Wmd] = ssf.edit_all_str()
|
|
|
|
|
|
time7 = time.time()
|
|
|
|
|
|
print('edit_all_str time：' + str(time7 - time6))
|
|
|
|
|
|
|
|
|
|
|
|
# jaccard系数
|
|
|
|
|
|
word_jaccard = ssf.word_jaccard()
|
|
|
|
|
|
char_jaccard = ssf.char_jaccard()
|
|
|
|
|
|
time8 = time.time()
|
|
|
|
|
|
print('jaccard系数 time：' + str(time8 - time7))
|
|
|
|
|
|
|
|
|
|
|
|
# pinyin
|
|
|
|
|
|
tdidf_pinyin_cossim = ssf.tdidf_all_vec_pinyin()
|
|
|
|
|
|
time9 = time.time()
|
|
|
|
|
|
print('tdidf_all_vec_pinyin time：' + str(time9 - time8))
|
|
|
|
|
|
|
|
|
|
|
|
# edit_all_pinyin
|
|
|
|
|
|
[pinyin_hamming, pinyin_edit, pinyin_ratio, pinyin_jaro,
|
|
|
|
|
|
pinyin_set_ratio_fuzz, pinyin_sort_ratio_fuzz, pinyin_commonsubstr, pinyin_list_Wmd] = ssf.edit_all_pinyin()
|
|
|
|
|
|
time10 = time.time()
|
|
|
|
|
|
print('edit_all_pinyin time：' + str(time10 - time9))
|
|
|
|
|
|
|
|
|
|
|
|
# jaccard系数
|
|
|
|
|
|
word_jaccard_pinyin = ssf.word_jaccard_pinyin()
|
|
|
|
|
|
char_jaccard_pinyin = ssf.char_jaccard_pinyin()
|
|
|
|
|
|
time11 = time.time()
|
|
|
|
|
|
print('jaccard系数pinyin time：' + str(time11 - time10))
|
|
|
|
|
|
|
|
|
|
|
|
sim_all_last = [same_word_count, same_char_count, len_abs_sub, len_rate, len_add_rate,
|
|
|
|
|
|
w2c_Cosine, w2c_TS_SS, w2c_Manhattan, w2c_Euclidean, w2c_Jaccard, w2c_Chebyshev, w2c_Minkowski,
|
|
|
|
|
|
w2c_Euclidean_Standard, w2c_Mahalanobis, w2c_Bray, w2c_Pearson,
|
|
|
|
|
|
tdidf_cossim, str_hamming, str_edit, str_ratio, str_jaro, str_set_ratio_fuzz, str_sort_ratio_fuzz,
|
|
|
|
|
|
str_commonsubstr, str_list_Wmd,
|
|
|
|
|
|
word_jaccard, char_jaccard, tdidf_pinyin_cossim,
|
|
|
|
|
|
pinyin_hamming, pinyin_edit, pinyin_ratio, pinyin_jaro, pinyin_set_ratio_fuzz,
|
|
|
|
|
|
pinyin_sort_ratio_fuzz,
|
|
|
|
|
|
pinyin_commonsubstr, pinyin_list_Wmd,
|
|
|
|
|
|
word_jaccard_pinyin, char_jaccard_pinyin]
|
|
|
|
|
|
print("小姜机器人计算sim: ")
|
|
|
|
|
|
print(sim_all_last)
|
|
|
|
|
|
|
|
|
|
|
|
sentence_input_t()
|
