mirror of
https://github.com/fxsjy/jieba.git
synced 2025-07-10 00:01:33 +08:00
83 lines
2.5 KiB
Python
Executable File
83 lines
2.5 KiB
Python
Executable File
#encoding=utf-8
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import jieba
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import os
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try:
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from analyzer import ChineseAnalyzer
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except ImportError:
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pass
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_curpath = os.path.normpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
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abs_path = os.path.join(_curpath, "idf.txt")
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STOP_WORDS = set((
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"the","of","is","and","to","in","that","we","for","an","are",
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"by","be","as","on","with","can","if","from","which","you","it",
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"this","then","at","have","all","not","one","has","or","that"
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))
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class IDFLoader:
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def __init__(self):
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self.path = ""
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self.idf_freq = {}
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self.median_idf = 0.0
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def set_new_path(self, new_idf_path):
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if self.path != new_idf_path:
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content = open(new_idf_path, 'rb').read().decode('utf-8')
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idf_freq = {}
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lines = content.split('\n')
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if lines and not lines[-1]:
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lines.pop(-1)
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for line in lines:
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word, freq = line.split(' ')
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idf_freq[word] = float(freq)
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median_idf = sorted(idf_freq.values())[len(idf_freq)/2]
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self.idf_freq = idf_freq
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self.median_idf = median_idf
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self.path = new_idf_path
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def get_idf(self):
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return self.idf_freq, self.median_idf
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idf_loader = IDFLoader()
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idf_loader.set_new_path(abs_path)
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def set_idf_path(idf_path):
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new_abs_path = os.path.normpath(os.path.join(os.getcwd(), idf_path))
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if not os.path.exists(new_abs_path):
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raise Exception("jieba: path does not exist: " + new_abs_path)
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idf_loader.set_new_path(new_abs_path)
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def set_stop_words(stop_words_path):
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global STOP_WORDS
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abs_path = os.path.normpath(os.path.join(os.getcwd(), stop_words_path))
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if not os.path.exists(abs_path):
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raise Exception("jieba: path does not exist: " + abs_path)
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content = open(abs_path,'rb').read().decode('utf-8')
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lines = content.split('\n')
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for line in lines:
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STOP_WORDS.add(line)
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def extract_tags(sentence, topK=20):
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global STOP_WORDS
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idf_freq, median_idf = idf_loader.get_idf()
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words = jieba.cut(sentence)
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freq = {}
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for w in words:
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if len(w.strip()) < 2:
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continue
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if w.lower() in STOP_WORDS:
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continue
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freq[w] = freq.get(w, 0.0) + 1.0
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total = sum(freq.values())
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freq = [(k,v/total) for k,v in freq.iteritems()]
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tf_idf_list = [(v*idf_freq.get(k,median_idf), k) for k,v in freq]
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st_list = sorted(tf_idf_list, reverse=True)
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top_tuples = st_list[:topK]
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tags = [a[1] for a in top_tuples]
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return tags
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