improve extract_tags; unify extract_tags and testrank

README.md

@@ -153,17 +153,16 @@ jieba.analyse.textrank(raw_text)
 来自`__main__`的示例结果：
 
 ```
-吉林 100.0
-欧亚 86.4592606421
-置业 55.3262889963
-实现 52.0353476663
-收入 37.9475518129
-增资 35.5042189944
-子公司 34.9286032861
-全资 30.8154823412
-城市 30.6031961172
-商业 30.4779050167
-
+吉林 1.0
+欧亚 0.864834432786
+置业 0.553465925497
+实现 0.520660869531
+收入 0.379699688954
+增资 0.355086023683
+子公司 0.349758490263
+全资 0.308537396283
+城市 0.306103738053
+商业 0.304837414946
 ```
 
 4) : 词性标注

jieba/analyse/__init__.py

@@ -1,6 +1,7 @@
 #encoding=utf-8
 import jieba
 import os
+from operator import itemgetter
 try:
     from analyzer import ChineseAnalyzer
 except ImportError:
@@ -26,13 +27,11 @@ def set_new_path(self, new_idf_path):
         if self.path != new_idf_path:
             content = open(new_idf_path, 'rb').read().decode('utf-8')
             idf_freq = {}
-            lines = content.split('\n')
-            if lines and not lines[-1]:
-                lines.pop(-1)
+            lines = content.rstrip('\n').split('\n')
             for line in lines:
                 word, freq = line.split(' ')
                 idf_freq[word] = float(freq)
-            median_idf = sorted(idf_freq.values())[len(idf_freq)/2]
+            median_idf = sorted(idf_freq.values())[len(idf_freq)//2]
             self.idf_freq = idf_freq
             self.median_idf = median_idf
             self.path = new_idf_path
@@ -60,27 +59,32 @@ def set_stop_words(stop_words_path):
         STOP_WORDS.add(line)
 
 def extract_tags(sentence, topK=20, withWeight=False):
-    global STOP_WORDS
+    """
+    Extract keywords from sentence using TF-IDF algorithm.
+    Parameter:
+        - topK: return how many top keywords. `None` for all possible words.
+        - withWeight: if True, return a list of (word, weight);
+                      if False, return a list of words.
+    """
+    global STOP_WORDS, idf_loader
 
     idf_freq, median_idf = idf_loader.get_idf()
 
     words = jieba.cut(sentence)
     freq = {}
     for w in words:
-        if len(w.strip()) < 2:
-            continue
-        if w.lower() in STOP_WORDS:
+        if len(w.strip()) < 2 or w.lower() in STOP_WORDS:
             continue
         freq[w] = freq.get(w, 0.0) + 1.0
     total = sum(freq.values())
-    freq = [(k,v/total) for k,v in freq.iteritems()]
-
-    tf_idf_list = [(v*idf_freq.get(k,median_idf), k) for k,v in freq]
-    st_list = sorted(tf_idf_list, reverse=True)
+    for k in freq:
+        freq[k] *= idf_freq.get(k, median_idf) / total
 
     if withWeight:
-        tags = st_list[:topK]
+        tags = sorted(freq.items(), key=itemgetter(1), reverse=True)
+    else:
+        tags = sorted(freq, key=freq.__getitem__, reverse=True)
+    if topK:
+        return tags[:topK]
     else:
-        top_tuples = st_list[:topK]
-        tags = [a[1] for a in top_tuples]
-    return tags
+        return tags

jieba/analyse/textrank.py

@@ -1,9 +1,10 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
-import jieba.posseg as pseg
-import collections
 import sys
+import collections
+from operator import itemgetter
+import jieba.posseg as pseg
 
 class UndirectWeightedGraph:
     d = 0.85
@@ -41,17 +42,25 @@ def rank(self):
                 max_rank = w
 
         for n, w in ws.items():
-            ws[n] = (w - min_rank / 10.0) / (max_rank - min_rank / 10.0) * 100
+            # to unify the weights, don't *100.
+            ws[n] = (w - min_rank / 10.0) / (max_rank - min_rank / 10.0)
 
         return ws
 
 
-def textrank(raw, topk=10):
+def textrank(sentence, topK=10, withWeight=False):
+    """
+    Extract keywords from sentence using TextRank algorithm.
+    Parameter:
+        - topK: return how many top keywords. `None` for all possible words.
+        - withWeight: if True, return a list of (word, weight);
+                      if False, return a list of words.
+    """
     pos_filt = frozenset(('ns', 'n', 'vn', 'v'))
     g = UndirectWeightedGraph()
     cm = collections.defaultdict(int)
     span = 5
-    words = [x for x in pseg.cut(raw)]
+    words = list(pseg.cut(sentence))
     for i in xrange(len(words)):
         if words[i].flag in pos_filt:
             for j in xrange(i + 1, i + span):
@@ -65,10 +74,16 @@ def textrank(raw, topk=10):
         g.addEdge(terms[0], terms[1], w)
 
     nodes_rank = g.rank()
-    nrs = sorted(nodes_rank.items(), key=lambda x: x[1], reverse=True)
-    return nrs[:topk]
+    if withWeight:
+        tags = sorted(nodes_rank.items(), key=itemgetter(1), reverse=True)
+    else:
+        tags = sorted(nodes_rank, key=nodes_rank.__getitem__, reverse=True)
+    if topK:
+        return tags[:topK]
+    else:
+        return tags
 
 if __name__ == '__main__':
     s = "此外，公司拟对全资子公司吉林欧亚置业有限公司增资4.3亿元，增资后，吉林欧亚置业注册资本由7000万元增加到5亿元。吉林欧亚置业主要经营范围为房地产开发及百货零售等业务。目前在建吉林欧亚城市商业综合体项目。2013年，实现营业收入0万元，实现净利润-139.13万元。"
-    for x, w in textrank(s):
+    for x, w in textrank(s, withWeight=True):
         print x, w

jieba/finalseg/__init__.py

@@ -19,25 +19,22 @@
 }
 
 def load_model():
-    _curpath=os.path.normpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
+    _curpath = os.path.normpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
 
     start_p = {}
     abs_path = os.path.join(_curpath, PROB_START_P)
-    with open(abs_path, mode='r') as f:
+    with open(abs_path, 'rb') as f:
         start_p = marshal.load(f)
-    f.closed
 
     trans_p = {}
     abs_path = os.path.join(_curpath, PROB_TRANS_P)
-    with open(abs_path, 'r') as f:
+    with open(abs_path, 'rb') as f:
         trans_p = marshal.load(f)
-    f.closed
 
     emit_p = {}
     abs_path = os.path.join(_curpath, PROB_EMIT_P)
-    with open(abs_path, 'r') as f:
+    with open(abs_path, 'rb') as f:
         emit_p = marshal.load(f)
-    f.closed
 
     return start_p, trans_p, emit_p
 

jieba/posseg/__init__.py

@@ -25,27 +25,24 @@ def load_model(f_name, isJython=True):
                 continue
             word, _, tag = line.split(' ')
             result[word.decode('utf-8')] = tag
-    f.closed
+
     if not isJython:
         return result
 
     start_p = {}
     abs_path = os.path.join(_curpath, PROB_START_P)
-    with open(abs_path, mode='r') as f:
+    with open(abs_path, 'rb') as f:
         start_p = marshal.load(f)
-    f.closed
 
     trans_p = {}
     abs_path = os.path.join(_curpath, PROB_TRANS_P)
-    with open(abs_path, 'r') as f:
+    with open(abs_path, 'rb') as f:
         trans_p = marshal.load(f)
-    f.closed
 
     emit_p = {}
     abs_path = os.path.join(_curpath, PROB_EMIT_P)
-    with open(abs_path, 'r') as f:
+    with open(abs_path, 'rb') as f:
         emit_p = marshal.load(f)
-    f.closed
 
     state = {}
     abs_path = os.path.join(_curpath, CHAR_STATE_TAB_P)