update to v0.33

Changelog

@@ -1,3 +1,9 @@
+2014-08-31: version 0.33
+1. 支持自定义stop words; by @fukuball
+2. 支持自定义idf词典; by @fukuball
+3. 修复自定义词典的词性不能正常显示的bug; by @ShuraChow
+
+
 2014-02-07: version 0.32
 1. 新增分词选项：可以关闭新词发现功能；详见：https://github.com/fxsjy/jieba/blob/master/test/test_no_hmm.py#L8
 2. 修复posseg子模块的Bug；详见: https://github.com/fxsjy/jieba/issues/111 https://github.com/fxsjy/jieba/issues/132

README.md

@@ -1,6 +1,6 @@
 jieba
 ========
-"结巴"中文分词：做最好的Python中文分词组件
+"结巴"中文分词：做最好的 Python 中文分词组件
 "Jieba" (Chinese for "to stutter") Chinese text segmentation: built to be the best Python Chinese word segmentation module.
 - _Scroll down for English documentation._
 
@@ -8,7 +8,6 @@ jieba
 注意！
 ========
 这个branch `jieba3k`是专门用于Python3.x的版本
-=======
 
 
 Feature
@@ -36,52 +35,54 @@ http://jiebademo.ap01.aws.af.cm/
 Python 2.x 下的安装
 ===================
 * 全自动安装：`easy_install jieba` 或者 `pip install jieba`
-* 半自动安装：先下载http://pypi.python.org/pypi/jieba/ ，解压后运行python setup.py install
+* 半自动安装：先下载 http://pypi.python.org/pypi/jieba/ ，解压后运行 python setup.py install
-* 手动安装：将jieba目录放置于当前目录或者site-packages目录
+* 手动安装：将 jieba 目录放置于当前目录或者 site-packages 目录
-* 通过import jieba 来引用 （第一次import时需要构建Trie树，需要几秒时间）
+* 通过 import jieba 来引用
 
 
 Python 3.x 下的安装
 ====================
-* 目前master分支是只支持Python2.x 的
+* 目前 master 分支是只支持 Python2.x 的
 * Python3.x 版本的分支也已经基本可用： https://github.com/fxsjy/jieba/tree/jieba3k
 
         git clone https://github.com/fxsjy/jieba.git
         git checkout jieba3k
         python setup.py install
+* 或使用pip3安装： pip3 install jieba3k
 
-结巴分词Java版本
+
+结巴分词 Java 版本
 ================
 作者：piaolingxue
 地址：https://github.com/huaban/jieba-analysis
 
-结巴分词C++版本
+结巴分词 C++ 版本
 ================
 作者：Aszxqw
 地址：https://github.com/aszxqw/cppjieba
 
-结巴分词Node.js版本
+结巴分词 Node.js 版本
 ================
 作者：Aszxqw
 地址：https://github.com/aszxqw/nodejieba
 
-结巴分词Erlang版本
+结巴分词 Erlang 版本
 ================
 作者：falood
 https://github.com/falood/exjieba
 
 Algorithm
 ========
-* 基于Trie树结构实现高效的词图扫描，生成句子中汉字所有可能成词情况所构成的有向无环图（DAG)
+* 基于 Trie 树结构实现高效的词图扫描，生成句子中汉字所有可能成词情况所构成的有向无环图（DAG)
 * 采用了动态规划查找最大概率路径, 找出基于词频的最大切分组合
-* 对于未登录词，采用了基于汉字成词能力的HMM模型，使用了Viterbi算法
+* 对于未登录词，采用了基于汉字成词能力的 HMM 模型，使用了 Viterbi 算法
 
 功能 1)：分词
 ==========
-* `jieba.cut`方法接受两个输入参数: 1) 第一个参数为需要分词的字符串 2）cut_all参数用来控制是否采用全模式
+* `jieba.cut` 方法接受两个输入参数: 1) 第一个参数为需要分词的字符串 2）cut_all 参数用来控制是否采用全模式
-* `jieba.cut_for_search`方法接受一个参数：需要分词的字符串,该方法适合用于搜索引擎构建倒排索引的分词，粒度比较细
+* `jieba.cut_for_search` 方法接受一个参数：需要分词的字符串,该方法适合用于搜索引擎构建倒排索引的分词，粒度比较细
-* 注意：待分词的字符串可以是gbk字符串、utf-8字符串或者unicode
+* 注意：待分词的字符串可以是gbk字符串、utf-8 字符串或者 unicode
-* `jieba.cut`以及`jieba.cut_for_search`返回的结构都是一个可迭代的generator，可以使用for循环来获得分词后得到的每一个词语(unicode)，也可以用list(jieba.cut(...))转化为list
+* `jieba.cut` 以及 `jieba.cut_for_search` 返回的结构都是一个可迭代的 generator，可以使用 for 循环来获得分词后得到的每一个词语(unicode)，也可以用 list(jieba.cut(...))转化为 list
 
 代码示例( 分词 )
 
@@ -89,17 +90,15 @@ Algorithm
 	import jieba
 
 	seg_list = jieba.cut("我来到北京清华大学",cut_all=True)
-	print("Full Mode:", "/ ".join(seg_list)) #全模式
+	print("Full Mode:", "/ ".join(seg_list)) # 全模式
 
 	seg_list = jieba.cut("我来到北京清华大学",cut_all=False)
-	print("Default Mode:", "/ ".join(seg_list)) #精确模式
+	print("Default Mode:", "/ ".join(seg_list)) # 精确模式
 
-
+	seg_list = jieba.cut("他来到了网易杭研大厦")  # 默认是精确模式
-	seg_list = jieba.cut("他来到了网易杭研大厦") #默认是精确模式
 	print(", ".join(seg_list))
 
-
+	seg_list = jieba.cut_for_search("小明硕士毕业于中国科学院计算所，后在日本京都大学深造")  # 搜索引擎模式
-	seg_list = jieba.cut_for_search("小明硕士毕业于中国科学院计算所，后在日本京都大学深造") #搜索引擎模式
 	print(", ".join(seg_list))
 
 Output:
@@ -115,36 +114,48 @@ Output:
 功能 2) ：添加自定义词典
 ================
 
-* 开发者可以指定自己自定义的词典，以便包含jieba词库里没有的词。虽然jieba有新词识别能力，但是自行添加新词可以保证更高的正确率
+* 开发者可以指定自己自定义的词典，以便包含 jieba 词库里没有的词。虽然 jieba 有新词识别能力，但是自行添加新词可以保证更高的正确率
-* 用法： jieba.load_userdict(file_name) # file_name为自定义词典的路径
+* 用法： jieba.load_userdict(file_name) # file_name 为自定义词典的路径
 * 词典格式和`dict.txt`一样，一个词占一行；每一行分三部分，一部分为词语，另一部分为词频，最后为词性（可省略），用空格隔开
 * 范例：
 
 	* 自定义词典：https://github.com/fxsjy/jieba/blob/master/test/userdict.txt
-	
+
 	* 用法示例：https://github.com/fxsjy/jieba/blob/master/test/test_userdict.py
-	
+
 
 		* 之前： 李小福 / 是 / 创新 / 办 / 主任 / 也 / 是 / 云 / 计算 / 方面 / 的 / 专家 /
 
 		* 加载自定义词库后：　李小福 / 是 / 创新办 / 主任 / 也 / 是 / 云计算 / 方面 / 的 / 专家 /
 
-		
+
 * "通过用户自定义词典来增强歧义纠错能力" --- https://github.com/fxsjy/jieba/issues/14
 
 功能 3) ：关键词提取
 ================
-* jieba.analyse.extract_tags(sentence,topK) #需要先import jieba.analyse
+* jieba.analyse.extract_tags(sentence,topK) #需要先 import jieba.analyse
-* setence为待提取的文本
+* setence 为待提取的文本
-* topK为返回几个TF/IDF权重最大的关键词，默认值为20
+* topK 为返回几个 TF/IDF 权重最大的关键词，默认值为 20
 
 代码示例 （关键词提取）
 
 	https://github.com/fxsjy/jieba/blob/master/test/extract_tags.py
 
+关键词提取所使用逆向文件频率（IDF）文本语料库可以切换成自定义语料库的路径
+
+* 用法： jieba.analyse.set_idf_path(file_name) # file_name为自定义语料库的路径
+* 自定义语料库示例：https://github.com/fxsjy/jieba/blob/master/extra_dict/idf.txt.big
+* 用法示例：https://github.com/fxsjy/jieba/blob/master/test/extract_tags_idfpath.py
+
+关键词提取所使用停止词（Stop Words）文本语料库可以切换成自定义语料库的路径
+
+* 用法： jieba.analyse.set_stop_words(file_name) # file_name为自定义语料库的路径
+* 自定义语料库示例：https://github.com/fxsjy/jieba/blob/master/extra_dict/stop_words.txt
+* 用法示例：https://github.com/fxsjy/jieba/blob/master/test/extract_tags_stop_words.py
+
 功能 4) : 词性标注
 ================
-* 标注句子分词后每个词的词性，采用和ictclas兼容的标记法
+* 标注句子分词后每个词的词性，采用和 ictclas 兼容的标记法
 * 用法示例
 
 		>>> import jieba.posseg as pseg
@@ -156,11 +167,11 @@ Output:
 		爱 v
 		北京 ns
 		天安门 ns
-		
+
 功能 5) : 并行分词
 ==================
-* 原理：将目标文本按行分隔后，把各行文本分配到多个python进程并行分词，然后归并结果，从而获得分词速度的可观提升
+* 原理：将目标文本按行分隔后，把各行文本分配到多个 python 进程并行分词，然后归并结果，从而获得分词速度的可观提升
-* 基于python自带的multiprocessing模块，目前暂不支持windows
+* 基于 python 自带的 multiprocessing 模块，目前暂不支持 windows
 * 用法：
 	* `jieba.enable_parallel(4)` # 开启并行分词模式，参数为并行进程数
 	* `jieba.disable_parallel()` # 关闭并行分词模式
@@ -168,12 +179,12 @@ Output:
 * 例子：
 		https://github.com/fxsjy/jieba/blob/master/test/parallel/test_file.py
 
-* 实验结果：在4核3.4GHz Linux机器上，对金庸全集进行精确分词，获得了1MB/s的速度，是单进程版的3.3倍。
+* 实验结果：在 4 核 3.4GHz Linux 机器上，对金庸全集进行精确分词，获得了 1MB/s 的速度，是单进程版的 3.3 倍。
 
 
 功能 6) : Tokenize：返回词语在原文的起始位置
 ============================================
-* 注意，输入参数只接受unicode
+* 注意，输入参数只接受 str
 * 默认模式
 
 ```python
@@ -206,9 +217,9 @@ word 有限                start: 6                end:8
 word 公司                start: 8                end:10
 word 有限公司            start: 6                end:10
 ```
-  
+
-  
+
-功能 7) : ChineseAnalyzer for Whoosh搜索引擎
+功能 7) : ChineseAnalyzer for Whoosh 搜索引擎
 ============================================
 * 引用： `from jieba.analyse import ChineseAnalyzer `
 * 用法示例：https://github.com/fxsjy/jieba/blob/master/test/test_whoosh.py
@@ -222,19 +233,19 @@ https://github.com/fxsjy/jieba/raw/master/extra_dict/dict.txt.small
 2. 支持繁体分词更好的词典文件
 https://github.com/fxsjy/jieba/raw/master/extra_dict/dict.txt.big
 
-下载你所需要的词典，然后覆盖jieba/dict.txt 即可或者用`jieba.set_dictionary('data/dict.txt.big')`
+下载你所需要的词典，然后覆盖jieba/dict.txt 即可或者用 `jieba.set_dictionary('data/dict.txt.big')`
 
 
 模块初始化机制的改变:lazy load （从0.28版本开始）
 ================================================
 
-jieba采用延迟加载，"import jieba"不会立即触发词典的加载，一旦有必要才开始加载词典构建trie。如果你想手工初始jieba，也可以手动初始化。
+jieba 采用延迟加载，"import jieba" 不会立即触发词典的加载，一旦有必要才开始加载词典构建trie。如果你想手工初始 jieba，也可以手动初始化。
 
     import jieba
     jieba.initialize()  # 手动初始化（可选）
 
 
-在0.28之前的版本是不能指定主词典的路径的，有了延迟加载机制后，你可以改变主词典的路径:
+在 0.28 之前的版本是不能指定主词典的路径的，有了延迟加载机制后，你可以改变主词典的路径:
 
 
     jieba.set_dictionary('data/dict.txt.big')
@@ -335,9 +346,9 @@ Function 2): Add a custom dictionary
 		李小福 2
 		创新办 3
 
-		之前： 李小福 / 是 / 创新 / 办 / 主任 / 也 / 是 / 云 / 计算 / 方面 / 的 / 专家 /
+		[Before]： 李小福 / 是 / 创新 / 办 / 主任 / 也 / 是 / 云 / 计算 / 方面 / 的 / 专家 /
 
-		加载自定义词库后：　李小福 / 是 / 创新办 / 主任 / 也 / 是 / 云计算 / 方面 / 的 / 专家 /
+		[After]：　李小福 / 是 / 创新办 / 主任 / 也 / 是 / 云计算 / 方面 / 的 / 专家 /
 
 Function 3): Keyword Extraction
 ================
@@ -349,6 +360,18 @@ Code sample (keyword extraction)
 
 	https://github.com/fxsjy/jieba/blob/master/test/extract_tags.py
 
+Developers can specify their own custom IDF corpus in jieba keyword extraction
+
+* Usage： `jieba.analyse.set_idf_path(file_name) # file_name is a custom corpus path`
+* Custom Corpus Sample：https://github.com/fxsjy/jieba/blob/master/extra_dict/idf.txt.big
+* Sample Code：https://github.com/fxsjy/jieba/blob/master/test/extract_tags_idfpath.py
+
+Developers can specify their own custom stop words corpus in jieba keyword extraction
+
+* Usage： `jieba.analyse.set_stop_words(file_name) # file_name is a custom corpus path`
+* Custom Corpus Sample：https://github.com/fxsjy/jieba/blob/master/extra_dict/stop_words.txt
+* Sample Code：https://github.com/fxsjy/jieba/blob/master/test/extract_tags_stop_words.py
+
 Using Other Dictionaries
 ========
 It is possible to supply Jieba with your own custom dictionary, and there are also two dictionaries readily available for download:

extra_dict/stop_words.txt

@@ -0,0 +1,51 @@
+the
+of
+is
+and
+to
+in
+that
+we
+for
+an
+are
+by
+be
+as
+on
+with
+can
+if
+from
+which
+you
+it
+this
+then
+at
+have
+all
+not
+one
+has
+or
+that
+的
+了
+和
+是
+就
+都
+而
+及
+與
+著
+或
+一個
+沒有
+我們
+你們
+妳們
+他們
+她們
+是否

jieba/__init__.py

@@ -91,8 +91,8 @@ def initialize(*args):
 
         if load_from_cache_fail:
             trie,FREQ,total = gen_trie(abs_path)
-            FREQ = dict([(k,log(float(v)/total)) for k,v in FREQ.items()]) #normalize
+            FREQ = dict([(k,log(float(v)/total)) for k,v in FREQ.iteritems()]) #normalize
-            min_freq = min(FREQ.values())
+            min_freq = min(FREQ.itervalues())
             logger.debug("dumping model to file cache %s" % cache_file)
             try:
                 tmp_suffix = "."+str(random.random())
@@ -131,7 +131,7 @@ def require_initialized(fn):
 def __cut_all(sentence):
     dag = get_DAG(sentence)
     old_j = -1
-    for k,L in dag.items():
+    for k,L in dag.iteritems():
         if len(L)==1 and k>old_j:
             yield sentence[k:L[0]+1]
             old_j = L[0]
@@ -195,7 +195,7 @@ def __cut_DAG_NO_HMM(sentence):
             if len(buf)>0:
                 yield buf
                 buf = ''
-            yield l_word        
+            yield l_word
             x =y
     if len(buf)>0:
         yield buf
@@ -243,8 +243,8 @@ def __cut_DAG(sentence):
                     yield elem
 
 def cut(sentence,cut_all=False,HMM=True):
-    '''The main function that segments an entire sentence that contains 
+    '''The main function that segments an entire sentence that contains
-    Chinese characters into seperated words. 
+    Chinese characters into seperated words.
     Parameter:
         - sentence: The String to be segmented
         - cut_all: Model. True means full pattern, false means accurate pattern.
@@ -257,8 +257,8 @@ def cut(sentence,cut_all=False,HMM=True):
             sentence = sentence.decode('gbk','ignore')
     '''
         \\u4E00-\\u9FA5a-zA-Z0-9+#&\._ : All non-space characters. Will be handled with re_han
-        \r\n|\s : whitespace characters. Will not be Handled. 
+        \r\n|\s : whitespace characters. Will not be Handled.
-    ''' 
+    '''
     re_han, re_skip = re.compile(r"([\u4E00-\u9FA5a-zA-Z0-9+#&\._]+)", re.U), re.compile(r"(\r\n|\s)")
     if cut_all:
         re_han, re_skip = re.compile(r"([\u4E00-\u9FA5]+)", re.U), re.compile(r"[^a-zA-Z0-9+#\n]")
@@ -306,7 +306,7 @@ def load_userdict(f):
     ''' Load personalized dict to improve detect rate.
     Parameter:
         - f : A plain text file contains words and their ocurrences.
-    Structure of dict file: 
+    Structure of dict file:
     word1 freq1 word_type1
     word2 freq2 word_type2
     ...
@@ -372,7 +372,7 @@ def enable_parallel(processnum=None):
     def pcut(sentence,cut_all=False,HMM=True):
         parts = re.compile('([\r\n]+)').split(sentence)
         if cut_all:
-            result = pool.map(__lcut_all,parts) 
+            result = pool.map(__lcut_all,parts)
         else:
             if HMM:
                 result = pool.map(__lcut,parts)

jieba/analyse/__init__.py

@@ -1,3 +1,4 @@
+#encoding=utf-8
 import jieba
 import os
 try:
@@ -5,30 +6,57 @@ try:
 except ImportError:
     pass
 
-_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__) )  )
-f_name = os.path.join(_curpath,"idf.txt")
+abs_path = os.path.join(_curpath, "idf.txt")
-content = open(f_name,'rb').read().decode('utf-8')
 
+IDF_DICTIONARY = abs_path
+STOP_WORDS = set([
+    "the","of","is","and","to","in","that","we","for","an","are","by","be","as","on","with","can","if","from","which","you","it","this","then","at","have","all","not","one","has","or","that"
+])
+
+def set_idf_path(idf_path):
+    global IDF_DICTIONARY
+    abs_path = os.path.normpath( os.path.join( os.getcwd(), idf_path )  )
+    if not os.path.exists(abs_path):
+        raise Exception("jieba: path does not exist:" + abs_path)
+    IDF_DICTIONARY = abs_path
+    return
+
+def get_idf(abs_path):
+    content = open(abs_path,'rb').read().decode('utf-8')
 idf_freq = {}
 lines = content.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]
+    return idf_freq, median_idf
 
-median_idf = sorted(idf_freq.values())[int(len(idf_freq)/2)]
+def set_stop_words(stop_words_path):
-stop_words= set([
+    global STOP_WORDS
-"the","of","is","and","to","in","that","we","for","an","are","by","be","as","on","with","can","if","from","which","you","it","this","then","at","have","all","not","one","has","or","that"
+    abs_path = os.path.normpath( os.path.join( os.getcwd(), stop_words_path )  )
-])
+    if not os.path.exists(abs_path):
+        raise Exception("jieba: path does not exist:" + abs_path)
+    content = open(abs_path,'rb').read().decode('utf-8')
+    lines = content.split('\n')
+    for line in lines:
+        STOP_WORDS.add(line)
+    return
 
 def extract_tags(sentence,topK=20):
+    global IDF_DICTIONARY
+    global STOP_WORDS
+
+    idf_freq, median_idf = get_idf(IDF_DICTIONARY)
+
     words = jieba.cut(sentence)
     freq = {}
     for w in words:
         if len(w.strip())<2: continue
-        if w.lower() in stop_words: continue
+        if 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.items()]
+    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)

setup.py

@@ -1,6 +1,6 @@
 from distutils.core import setup  
 setup(name='jieba3k',  
-      version='0.32',  
+      version='0.33',  
       description='Chinese Words Segementation Utilities',  
       author='Sun, Junyi',  
       author_email='ccnusjy@gmail.com',  

test/demo.py

@@ -4,14 +4,14 @@ sys.path.append("../")
 
 import jieba
 
-seg_list = jieba.cut("我来到北京清华大学",cut_all=True)
+seg_list = jieba.cut("我来到北京清华大学", cut_all=True)
-print("Full Mode:", "/ ".join(seg_list)) #全模式
+print("Full Mode:", "/ ".join(seg_list))  # 全模式
 
-seg_list = jieba.cut("我来到北京清华大学",cut_all=False)
+seg_list = jieba.cut("我来到北京清华大学", cut_all=False)
-print("Default Mode:", "/ ".join(seg_list)) #默认模式
+print("Default Mode:", "/ ".join(seg_list))  # 默认模式
 
 seg_list = jieba.cut("他来到了网易杭研大厦")
 print(", ".join(seg_list))
 
-seg_list = jieba.cut_for_search("小明硕士毕业于中国科学院计算所，后在日本京都大学深造") #搜索引擎模式
+seg_list = jieba.cut_for_search("小明硕士毕业于中国科学院计算所，后在日本京都大学深造")  # 搜索引擎模式
 print(", ".join(seg_list))

test/extract_tags.py

@@ -12,7 +12,7 @@ parser.add_option("-k", dest="topK")
 opt, args = parser.parse_args()
 
 
-if len(args) <1:
+if len(args) < 1:
     print(USAGE)
     sys.exit(1)
 

test/extract_tags_idfpath.py

@@ -0,0 +1,32 @@
+import sys
+sys.path.append('../')
+
+import jieba
+import jieba.analyse
+from optparse import OptionParser
+
+USAGE = "usage:    python extract_tags_idfpath.py [file name] -k [top k]"
+
+parser = OptionParser(USAGE)
+parser.add_option("-k", dest="topK")
+opt, args = parser.parse_args()
+
+
+if len(args) < 1:
+    print(USAGE)
+    sys.exit(1)
+
+file_name = args[0]
+
+if opt.topK is None:
+    topK = 10
+else:
+    topK = int(opt.topK)
+
+content = open(file_name, 'rb').read()
+
+jieba.analyse.set_idf_path("../extra_dict/idf.txt.big");
+
+tags = jieba.analyse.extract_tags(content, topK=topK)
+
+print(",".join(tags))

test/extract_tags_stop_words.py

@@ -0,0 +1,33 @@
+import sys
+sys.path.append('../')
+
+import jieba
+import jieba.analyse
+from optparse import OptionParser
+
+USAGE = "usage:    python extract_tags_stop_words.py [file name] -k [top k]"
+
+parser = OptionParser(USAGE)
+parser.add_option("-k", dest="topK")
+opt, args = parser.parse_args()
+
+
+if len(args) < 1:
+    print(USAGE)
+    sys.exit(1)
+
+file_name = args[0]
+
+if opt.topK is None:
+    topK = 10
+else:
+    topK = int(opt.topK)
+
+content = open(file_name, 'rb').read()
+
+jieba.analyse.set_stop_words("../extra_dict/stop_words.txt")
+jieba.analyse.set_idf_path("../extra_dict/idf.txt.big");
+
+tags = jieba.analyse.extract_tags(content, topK=topK)
+
+print(",".join(tags))

test/extract_topic.py

@@ -12,7 +12,7 @@ import os
 import random
 
 if len(sys.argv)<2:
-    print "usage: extract_topic.py directory [n_topic] [n_top_words]"
+    print("usage: extract_topic.py directory [n_topic] [n_top_words]")
     sys.exit(0)
 
 n_topic = 10
@@ -28,27 +28,27 @@ count_vect = CountVectorizer()
 docs = []
 
 pattern = os.path.join(sys.argv[1],"*.txt") 
-print "read "+pattern
+print("read "+pattern)
 
 for f_name in glob.glob(pattern):
     with open(f_name) as f:
-        print "read file:", f_name
+        print("read file:", f_name)
         for line in f: #one line as a document
             words = " ".join(jieba.cut(line))
             docs.append(words)
 
 random.shuffle(docs)
 
-print "read done."
+print("read done.")
 
-print "transform"
+print("transform")
 counts = count_vect.fit_transform(docs)
 tfidf = TfidfTransformer().fit_transform(counts)
-print tfidf.shape
+print(tfidf.shape)
 
 
 t0 = time.time()
-print "training..."
+print("training...")
 
 nmf = decomposition.NMF(n_components=n_topic).fit(tfidf)
 print("done in %0.3fs." % (time.time() - t0))

test/jieba_test.py

@@ -1,7 +1,7 @@
 #-*-coding: utf-8 -*-
 import sys
+import imp
 sys.path.append("../")
-from imp import reload
 import unittest
 import types
 import jieba
@@ -98,7 +98,7 @@ test_contents = [
 
 class JiebaTestCase(unittest.TestCase):
     def setUp(self):
-        reload(jieba)
+        imp.reload(jieba)
 
     def tearDown(self):
         pass

test/jiebacmd.py

@@ -23,6 +23,6 @@ while True:
         break
     line = line.strip()
     for word in jieba.cut(line):
-        print(word.encode(default_encoding))
+        print(word)
 
 

test/parallel/extract_tags.py

@@ -29,6 +29,6 @@ content = open(file_name,'rb').read()
 
 tags = jieba.analyse.extract_tags(content,topK=topK)
 
-print(",".join(tags) )
+print(",".join(tags))
 
 

test/parallel/test.py

@@ -6,7 +6,9 @@ jieba.enable_parallel(4)
 
 def cuttest(test_sent):
     result = jieba.cut(test_sent)
-    print( "/ ".join(result) ) 
+    for word in result:
+        print(word, "/", end=' ') 
+    print("")
 
 
 if __name__ == "__main__":

test/parallel/test2.py

@@ -6,7 +6,9 @@ jieba.enable_parallel(4)
 
 def cuttest(test_sent):
     result = jieba.cut(test_sent,cut_all=True)
-    print("/ ".join(result))
+    for word in result:
+        print(word, "/", end=' ') 
+    print("")
 
 
 if __name__ == "__main__":

test/parallel/test_cut_for_search.py

@@ -6,7 +6,9 @@ jieba.enable_parallel(4)
 
 def cuttest(test_sent):
     result = jieba.cut_for_search(test_sent)
-    print("/ ".join(result))
+    for word in result:
+        print(word, "/", end=' ') 
+    print("")
 
 
 if __name__ == "__main__":

test/parallel/test_file.py

@@ -1,3 +1,4 @@
+import urllib.request, urllib.error, urllib.parse
 import sys,time
 import sys
 sys.path.append("../../")
@@ -6,16 +7,15 @@ import jieba
 jieba.enable_parallel()
 
 url = sys.argv[1]
-with open(url,"rb") as content:
+content = open(url,"rb").read()
-    content = content.read()
+t1 = time.time()
-    t1 = time.time()
+words = "/ ".join(jieba.cut(content))
-    words = "/ ".join(jieba.cut(content))
-    t2 = time.time()
-    tm_cost = t2-t1
-    print('cost',tm_cost)
-    print('speed' , len(content)/tm_cost, " bytes/second")
 
-with open("1.log","wb") as log_f:
+t2 = time.time()
-    log_f.write(words.encode('utf-8'))
+tm_cost = t2-t1
 
+log_f = open("1.log","wb")
+log_f.write(words.encode('utf-8'))
+
+print('speed' , len(content)/tm_cost, " bytes/second")
 

test/parallel/test_pos.py

@@ -8,7 +8,7 @@ import jieba.posseg as pseg
 def cuttest(test_sent):
     result = pseg.cut(test_sent)
     for w in result:
-        sys.stdout.write(w.word+ "/"+ w.flag + ", ") 
+        print(w.word, "/", w.flag, ", ", end=' ')  
     print("")
 
 

test/parallel/test_pos_file.py

@@ -1,4 +1,4 @@
-import urllib2
+import urllib.request, urllib.error, urllib.parse
 import sys,time
 import sys
 sys.path.append("../../")
@@ -16,7 +16,7 @@ tm_cost = t2-t1
 
 log_f = open("1.log","wb")
 for w in words:
-    print >> log_f, w.encode("utf-8"), "/" ,
+    print(w.encode("utf-8"), "/", end=' ', file=log_f)
 
-print 'speed' , len(content)/tm_cost, " bytes/second"
+print('speed' , len(content)/tm_cost, " bytes/second")
 

test/test.py

@@ -3,9 +3,10 @@ import sys
 sys.path.append("../")
 import jieba
 
+
 def cuttest(test_sent):
     result = jieba.cut(test_sent)
-    print("/ ".join(result))
+    print(" / ".join(result))
 
 
 if __name__ == "__main__":

test/test_change_dictpath.py

@@ -5,7 +5,7 @@ import jieba
 
 def cuttest(test_sent):
     result = jieba.cut(test_sent)
-    print("  ".join(result) )
+    print("  ".join(result))
 
 def testcase():
     cuttest("这是一个伸手不见五指的黑夜。我叫孙悟空，我爱北京，我爱Python和C++。")

test/test_cut_for_search.py

@@ -5,8 +5,9 @@ import jieba
 
 def cuttest(test_sent):
     result = jieba.cut_for_search(test_sent)
-    print("/ ".join(result))
+    for word in result:
-
+        print(word, "/", end=' ') 
+    print("")
 
 
 if __name__ == "__main__":
@@ -93,4 +94,4 @@ if __name__ == "__main__":
     cuttest('张晓梅去人民医院做了个B超然后去买了件T恤')
     cuttest('AT&T是一件不错的公司，给你发offer了吗？')
     cuttest('C++和c#是什么关系？11+122=133，是吗？PI=3.14159')
-    cuttest('你认识那个和主席握手的的哥吗？他开一辆黑色的士。')
+    cuttest('你认识那个和主席握手的的哥吗？他开一辆黑色的士。')

test/test_cutall.py

@@ -5,7 +5,9 @@ import jieba
 
 def cuttest(test_sent):
     result = jieba.cut(test_sent,cut_all=True)
-    print("/ ".join(result))
+    for word in result:
+        print(word, "/", end=' ') 
+    print("")
 
 
 if __name__ == "__main__":
@@ -92,4 +94,4 @@ if __name__ == "__main__":
     cuttest('张晓梅去人民医院做了个B超然后去买了件T恤')
     cuttest('AT&T是一件不错的公司，给你发offer了吗？')
     cuttest('C++和c#是什么关系？11+122=133，是吗？PI=3.14159')
-    cuttest('你认识那个和主席握手的的哥吗？他开一辆黑色的士。')
+    cuttest('你认识那个和主席握手的的哥吗？他开一辆黑色的士。')

test/test_file.py

@@ -1,3 +1,4 @@
+import urllib.request, urllib.error, urllib.parse
 import sys,time
 import sys
 sys.path.append("../")
@@ -5,15 +6,17 @@ import jieba
 jieba.initialize()
 
 url = sys.argv[1]
-with open(url,"rb") as content:
+content = open(url,"rb").read()
-    content = content.read()
+t1 = time.time()
-    t1 = time.time()
+words = "/ ".join(jieba.cut(content))
-    words = "/ ".join(jieba.cut(content))
+
-    t2 = time.time()
+t2 = time.time()
-    tm_cost = t2-t1
+tm_cost = t2-t1
-    print('cost',tm_cost)
+
-    print('speed' , len(content)/tm_cost, " bytes/second")
+log_f = open("1.log","wb")
+log_f.write(words.encode('utf-8'))
+log_f.close()
+
+print('cost',tm_cost)
+print('speed' , len(content)/tm_cost, " bytes/second")
 
-with open("1.log","wb") as log_f:
-    log_f.write(words.encode('utf-8'))
-    log_f.write(bytes("/ ".join(words),'utf-8'))

test/test_pos.py

@@ -6,7 +6,7 @@ import jieba.posseg as pseg
 def cuttest(test_sent):
     result = pseg.cut(test_sent)
     for w in result:
-        sys.stdout.write(w.word+ "/"+ w.flag + ", ") 
+        print(w.word, "/", w.flag, ", ", end=' ')  
     print("")
 
 

test/test_pos_file.py

@@ -1,3 +1,4 @@
+import urllib.request, urllib.error, urllib.parse
 import sys,time
 import sys
 sys.path.append("../")
@@ -15,7 +16,7 @@ tm_cost = t2-t1
 
 log_f = open("1.log","wb")
 for w in words:
-    log_f.write(bytes(w.word+"/"+w.flag+" ",'utf-8'))
+    print(w.encode("utf-8"), "/", end=' ', file=log_f)
 
 print('speed' , len(content)/tm_cost, " bytes/second")
 

test/test_userdict.py

@@ -14,7 +14,7 @@ for w in words:
 result = pseg.cut(test_sent)
 
 for w in result:
-    print(w.word, "/", w.flag, ", ")
+    print(w.word, "/", w.flag, ", ", end=' ')
 
 print("\n========")
 

test/test_whoosh.py

@@ -59,5 +59,5 @@ for keyword in ("水果世博园","你","first","中文","交换机","交换"):
         print(hit.highlights("content"))
     print("="*10)
 
-for t in analyzer("我的好朋友是李明;我爱北京天安门;IBM和Microsoft; I have a dream."):
+for t in analyzer("我的好朋友是李明;我爱北京天安门;IBM和Microsoft; I have a dream. this is intetesting and interested me a lot"):
     print(t.text)

test/test_whoosh_flie.py

@@ -23,8 +23,8 @@ with open(file_name,"rb") as inf:
     for line in inf:
         i+=1
         writer.add_document(
-            title=u"line"+str(i), 
+            title="line"+str(i), 
-            path=u"/a",
+            path="/a",
             content=line.decode('gbk','ignore')
         )
 writer.commit()
@@ -32,10 +32,10 @@ writer.commit()
 searcher = ix.searcher()
 parser = QueryParser("content", schema=ix.schema)
 
-for keyword in (u"水果小姐",u"你",u"first",u"中文",u"交换机",u"交换"):
+for keyword in ("水果小姐","你","first","中文","交换机","交换"):
-    print "result of ",keyword
+    print("result of ",keyword)
     q = parser.parse(keyword)
     results = searcher.search(q)
     for hit in results:  
-        print hit.highlights("content")
+        print(hit.highlights("content"))
-    print "="*10
+    print("="*10)

test/test_whoosh_flie_read.py

@@ -18,10 +18,10 @@ ix = open_dir("tmp")
 searcher = ix.searcher()
 parser = QueryParser("content", schema=ix.schema)
 
-for keyword in (u"水果小姐",u"你",u"first",u"中文",u"交换机",u"交换",u"少林",u"乔峰"):
+for keyword in ("水果小姐","你","first","中文","交换机","交换","少林","乔峰"):
-    print "result of ",keyword
+    print("result of ",keyword)
     q = parser.parse(keyword)
     results = searcher.search(q)
     for hit in results:  
-        print hit.highlights("content")
+        print(hit.highlights("content"))
-    print "="*10
+    print("="*10)