299 lines
8.4 KiB
Python
Executable File
299 lines
8.4 KiB
Python
Executable File
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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#=========================================================================
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#
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# Copyright (c) 2017 <> All Rights Reserved
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#
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#
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# File: /Users/hain/ai/Synonyms/synonyms/__init__.py
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# Author: Hai Liang Wang
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# Date: 2017-09-27
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#
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#=========================================================================
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"""
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Chinese Synonyms for Natural Language Processing and Understanding.
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"""
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from __future__ import print_function
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from __future__ import division
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__copyright__ = "Copyright (c) 2017 . All Rights Reserved"
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__author__ = "Hu Ying Xi<>, Hai Liang Wang<hailiang.hl.wang@gmail.com>"
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__date__ = "2017-09-27"
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import os
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import sys
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import numpy as np
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curdir = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(curdir)
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PLT = 2
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if sys.version_info[0] < 3:
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default_stdout = sys.stdout
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default_stderr = sys.stderr
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reload(sys)
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sys.stdout = default_stdout
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sys.stderr = default_stderr
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sys.setdefaultencoding("utf-8")
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# raise "Must be using Python 3"
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else:
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PLT = 3
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# Get Environment variables
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ENVIRON = os.environ.copy()
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import json
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import gzip
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import shutil
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from synonyms.word2vec import KeyedVectors
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from synonyms.utils import any2utf8
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from synonyms.utils import any2unicode
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from synonyms.utils import sigmoid
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from synonyms.utils import cosine
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import jieba
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import jieba.posseg as _tokenizer
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'''
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globals
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'''
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_vocab = dict()
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_size = 0
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_vectors = None
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_stopwords = set()
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_cache_nearby = dict()
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'''
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lambda fns
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'''
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# combine similarity scores
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_similarity_smooth = lambda x, y, z: (x * y) + z
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_flat_sum_array = lambda x: np.sum(x, axis=0) # 分子
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'''
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tokenizer settings
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'''
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tokenizer_dict = os.path.join(curdir, 'data', 'vocab.txt')
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if "SYNONYMS_WORDSEG_DICT" in ENVIRON:
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if os.exist(ENVIRON["SYNONYMS_WORDSEG_DICT"]):
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print("info: set wordseg dict with %s" % tokenizer_dict)
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tokenizer_dict = ENVIRON["SYNONYMS_WORDSEG_DICT"]
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else: print("warning: can not find dict at [%s]" % tokenizer_dict)
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print(">> Synonyms load wordseg dict [%s] ... " % tokenizer_dict)
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jieba.set_dictionary(tokenizer_dict)
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# stopwords
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_fin_stopwords_path = os.path.join(curdir, 'data', 'stopwords.txt')
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def _load_stopwords(file_path):
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'''
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load stop words
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'''
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global _stopwords
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if sys.version_info[0] < 3:
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words = open(file_path, 'r')
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else:
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words = open(file_path, 'r', encoding='utf-8')
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stopwords = words.readlines()
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for w in stopwords:
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_stopwords.add(any2unicode(w).strip())
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print(">> Synonyms on loading stopwords [%s] ..." % _fin_stopwords_path)
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_load_stopwords(_fin_stopwords_path)
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def _segment_words(sen):
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'''
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segment words with jieba
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'''
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words, tags = [], []
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m = _tokenizer.cut(sen, HMM=True) # HMM更好的识别新词
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for x in m:
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words.append(x.word)
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tags.append(x.flag)
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return words, tags
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'''
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word embedding
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'''
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# vectors
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_f_model = os.path.join(curdir, 'data', 'words.vector')
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if "SYNONYMS_WORD2VEC_BIN_MODEL_ZH_CN" in ENVIRON:
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_f_model = ENVIRON["SYNONYMS_WORD2VEC_BIN_MODEL_ZH_CN"]
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def _load_w2v(model_file=_f_model, binary=True):
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'''
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load word2vec model
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'''
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if not os.path.exists(model_file):
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print("os.path : ", os.path)
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raise Exception("Model file [%s] does not exist." % model_file)
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return KeyedVectors.load_word2vec_format(
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model_file, binary=binary, unicode_errors='ignore')
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print(">> Synonyms on loading vectors [%s] ..." % _f_model)
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_vectors = _load_w2v(model_file=_f_model)
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def _get_wv(sentence):
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'''
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get word2vec data by sentence
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sentence is segmented string.
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'''
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global _vectors
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vectors = []
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for y in sentence:
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y_ = any2unicode(y).strip()
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if y_ not in _stopwords:
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syns = nearby(y_)[0]
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# print("sentence %s word: %s" %(sentence, y_))
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# print("sentence %s word nearby: %s" %(sentence, " ".join(syns)))
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c = []
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try:
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c.append(_vectors.word_vec(y_))
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except KeyError as error:
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print("not exist in w2v model: %s" % y_)
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# c.append(np.zeros((100,), dtype=float))
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random_state = np.random.RandomState(seed=(hash(y_) % (2**32 - 1)))
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c.append(random_state.uniform(low=-10.0, high=10.0, size=(100,)))
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for n in syns:
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if n is None: continue
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try:
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v = _vectors.word_vec(any2unicode(n))
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except KeyError as error:
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# v = np.zeros((100,), dtype=float)
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random_state = np.random.RandomState(seed=(hash(n) % (2 ** 32 - 1)))
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v = random_state.uniform(low=10.0, high=10.0, size=(100,))
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c.append(v)
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r = np.average(c, axis=0)
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vectors.append(r)
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return vectors
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'''
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Distance
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'''
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# Levenshtein Distance
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def _levenshtein_distance(sentence1, sentence2):
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'''
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Return the Levenshtein distance between two strings.
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Based on:
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http://rosettacode.org/wiki/Levenshtein_distance#Python
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'''
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first = any2utf8(sentence1).decode('utf-8', 'ignore')
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second = any2utf8(sentence2).decode('utf-8', 'ignore')
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sentence1_len, sentence2_len = len(first), len(second)
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maxlen = max(sentence1_len, sentence2_len)
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if sentence1_len > sentence2_len:
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first, second = second, first
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distances = range(len(first) + 1)
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for index2, char2 in enumerate(second):
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new_distances = [index2 + 1]
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for index1, char1 in enumerate(first):
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if char1 == char2:
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new_distances.append(distances[index1])
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else:
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new_distances.append(1 + min((distances[index1],
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distances[index1 + 1],
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new_distances[-1])))
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distances = new_distances
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levenshtein = distances[-1]
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d = float((maxlen - levenshtein)/maxlen)
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# smoothing
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s = (sigmoid(d * 6) - 0.5) * 2
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# print("smoothing[%s| %s]: %s -> %s" % (sentence1, sentence2, d, s))
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return s
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def _nearby_levenshtein_distance(s1, s2):
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'''
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使用空间距离近的词汇优化编辑距离计算
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'''
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s1_len, s2_len = len(s1), len(s2)
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maxlen = max(s1_len, s2_len)
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first, second = (s2, s1) if s1_len == maxlen else (s1, s2)
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ft = set() # all related words with first sentence
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for x in first:
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ft.add(x)
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n, _ = nearby(x)
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for o in n:
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ft.add(o)
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scores = []
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if len(ft) == 0: return 0.0 # invalid length for first string
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for x in second:
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scores.append(max([_levenshtein_distance(x, y) for y in ft]))
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s = np.sum(scores) / maxlen
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return s
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def _similarity_distance(s1, s2):
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'''
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compute similarity with distance measurement
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'''
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g = cosine(_flat_sum_array(_get_wv(s1)), _flat_sum_array(_get_wv(s2)))
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u = _nearby_levenshtein_distance(s1, s2)
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# print("g: %s, u: %s" % (g, u))
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if u > 0.8:
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r = _similarity_smooth(g, 0.1, u)
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elif u > 0.6:
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r = _similarity_smooth(g, 0.25, u)
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elif u > 0.4:
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r = _similarity_smooth(g, 0.5, u)
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else:
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r = _similarity_smooth(g, 1, u)
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if r < 0: r = abs(r)
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r = min(r, 1.0)
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return float("%.3f" % r)
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'''
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Public Methods
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'''
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seg = _segment_words # word segmenter
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def nearby(word):
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'''
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Nearby word
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'''
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w = any2unicode(word)
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# read from cache
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if w in _cache_nearby: return _cache_nearby[w]
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words, scores = [], []
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try:
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for x in _vectors.neighbours(w):
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words.append(x[0])
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scores.append(x[1])
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except: pass # ignore key error, OOV
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# put into cache
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_cache_nearby[w] = (words, scores)
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return words, scores
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def compare(s1, s2, seg=True):
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'''
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compare similarity
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s1 : sentence1
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s2 : sentence2
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seg : True : The original sentences need jieba.cut
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Flase : The original sentences have been cut.
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'''
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if seg:
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s1 = [x for x in jieba.cut(s1)]
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s2 = [x for x in jieba.cut(s2)]
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else:
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s1 = s1.split()
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s2 = s2.split()
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assert len(s1) > 0 and len(s2) > 0, "The length of s1 and s2 should > 0."
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return _similarity_distance(s1, s2)
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def display(word):
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print("'%s'近义词：" % word)
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o = nearby(word)
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assert len(o) == 2, "should contain 2 list"
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if len(o[0]) == 0:
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print(" out of vocabulary")
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for k, v in enumerate(o[0]):
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print(" %d. %s:%s" % (k + 1, v, o[1][k]))
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def main():
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display("人脸")
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display("NOT_EXIST")
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if __name__ == '__main__':
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main()
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