def make_training_xy(self, data):
X = self.vectorizer.fit_transform(data.body)
if self.tfidf:
X = TfidfTransformer().fit_transform(X)
X = X.tocsc()
Y = normalize_scores(data.net, data.subreddit[0])
return X,Y
def make_training_xy(self, data):
X = self.vectorizer.fit_transform(data.body)
if self.tfidf:
X = TfidfTransformer().fit_transform(X)
X = X.tocsc()
Y = normalize_scores(data.net, data.subreddit[0])
return X, Y
class MySearch(object):
'''
该class可以为中/英文语料库（文件夹/Iterable对象）建立基于tf-idf的检索模型，若涉及文件操作（除stop_words.txt，userdict.txt）
外，只支持编码utf-8，其中中文分词可选jieba或者pkuseg，tf-idf依赖sklearn
实现以下功能：
1.检索模型、语料库的建立（训练） -- 保存 -- 使用 -- 添加语料（从列表/目录） -- 删除 -- 部分删除
2.根据搜索词汇，对Iterable元素（元素为字符串）进行相关性排序，返回序列号等
3.根据搜索词汇，对指定目录下的文件进行相关性排序，返回文件名序列号等
:param seg: str 缺省时默认选择jieba进行中文分词，当为'pkuseg'时使用pkuseg进行中文分词
:param 参数同 sklearn.feature_extraction.text.TfidfTransformer(), 主要是为了支持不同的tf-idf模式，
在Train()中点击相应函数查看介绍。
Example：见test(),test2(),go()
函数简介：
关于停用词和用户词汇：
默认停用词存放在stop_words.txt，可自行修改，也可以在使用 add_stopwords() 函数添加
默认用户词存放在userdict.txt，可自行修改，也可以在使用 add_userword() 函数添加
add_stopwords()或add_userword() 使用后，将在下一次self.Train()使用时生效
add_stopwords(self, my_stopword_list) 添加停用词
my_stopword_list: list[str,str,...,str] 待添加的停用词
:return: 成功返回True，失败返回False
add_userword(self, my_word_list) 添加词汇，用来优化中文分词，使jieba、pkuseg语料库中没有的词能正确切分。
当self.Train()使用e='e'时，不使用jieba、pkuseg，用户词汇无效。
my_word_list: list[str,str,...,str] 待添加的分词
:return:成功返回True，失败返回False
Train(self, argc, e=None) 训练函数，用于建立检索模型
argc支持str类型和其它Iterable（list, tuple等，其element应为str类型，表示文本）型变量：
argc为str类型时，argc代表语料文件夹目录，Train()将使用该目录下的文档建立检索模型。
argc为Iterable类型时，argc即待训练语料库，argc中的element应为str类型，表示语料文本，
Train()将使用argc中的element作为语料文本建立检索模型。
e默认为None，启用jieba、pkuseg库，当待使用文本为纯英文或其它 *由空格隔开* 无需分词的语料时，可
以令e='e',将不使用jieba、pkuseg库，可一定程度提高效率，但用户词汇无效。
Query(self, query_str, corpus_name=None) 查询函数
query_str为查询字符串，corpus_name为查询语料库名称，corpus_name为None时优先查询self.Train(),
其次查询默认语料库，若corpus_name不为空，则查询corpus_name指向的语料库，若没有该语料库将出错。
:return: list[dict,dict,...,dict]
其中dict{'index', 'score', 'content'} or dict{'index', 'score', 'filename', 'content'}
注：以'_corpus'结尾的文件夹将被作为保存好的语料库，文件夹名即为语料库名，查询保存好的
语料库时，corpus_name结尾可以带'_corpus'也可以不带。另，'_model'结尾的文件被作为保存好的模型。
SaveModel(self, corpus_name=None, filename=None) 保存模型
self.Train()之后才能self.SaveModel(),否则将出错
corpus_name:str 保存成名为corpus_name的语料库，缺省时将根据时间生成一个语料库名称
filename: list[str,str,...,str] 用于给语料文档document命名，缺省时使用原名称，没有原名称时用index
:return:成功将返回True，失败会报错
AddCorpus(self, corpus_name, corpus_name2=None, filename=None) 向指定语料库添加语料文档，并更新检索模型
向corpus_name指定的语料库中添加语料文档，语料文档来自corpus_name2指定的语料库，当corpus_name2为None时，
默认为self.Train()生成的检索模型和其语料库。filename参数可用于给新添加的文档命名，格式错误时视为没有该参数,
默认为保存原名称或者按其在原语料库中的Index序号命名。另，名称与新语料库中的名称相同时，将增加‘-副本’后缀
corpus_name1: name of Corpus1 str
corpus_name2: name of Corpus2 str
filename: filename of Corpus2's file list [str,str...，str]
:return: 成功返回True，失败将报错
GetDefaultCorpusName(self)
获取当前默认的搜索库名称
AdjustDefaultCorpus(self, default_corpus=None)
调整默认搜索库，参数为新库名称，缺省则使用当前训练的语料库名称，有默认搜索库时，将调整为新搜索库，没有默认搜索库时将设置
default_corpus: str,
name of the new default corpus
:return: 成功返回True，失败返回False
RemoveCorpus(self, corpus_name=None) 删除保存好的语料库
删除保存好的语料库，参数为None时删除self.corpus_name指定的语料库，语料库不存在将报错
corpus_name:想要删除的语料库名称
:return:成功返回True,失败将报错
DelDocument(self, documents, corpus_name=None) 删除保存好的语料库的部分语料文档document
删除保存好的语料库的部分语料文档document
documents: list[str,str,...,str] 待删除的语料文档document的文件名
corpus_name: str 语料库名
:return: 成功将返回True,失败会报错
'''
def __init__(self, seg=None, norm='l2', use_idf=True, smooth_idf=True,
sublinear_tf=False):
'''
:param seg: str 缺省时默认选择jieba进行中文分词，当为'pkuseg'时使用pkuseg进行中文分词
:param norm,use_idf,use_idf,smooth_idf,sublinear_tf,这几个参数详见sklearn.feature_extraction.text.TfidfTransformer()
的说明，在Train()中点击相应函数查看介绍。
'''
self.seg = seg
self.myseg = None
self.stop_flag = ['x', 'c', 'u', 'd', 'p', 't', 'uj', 'm', 'f', 'r']
self.stop_word_path = "stop_words.txt"
self.stopwords = self.__get_stopwords(self.stop_word_path)
self.user_word_path = "userdict.txt"
self.my_word_list = self.__get_userdict(self.user_word_path)
self.corpus = []
self.corpus_name = ''
self.files = []
self.tfidf = None
self.word_dict = {}
self.norm = norm
self.use_idf = use_idf
self.smooth_idf = smooth_idf
self.sublinear_tf = sublinear_tf
def __get_stopwords(self, stop_words):
stopwords = codecs.open(stop_words, 'r').readlines()
stopwords = [w.strip() for w in stopwords]
return ['', ' '] + stopwords
def __get_userdict(self, user_words):
with open(user_words, 'r', encoding='utf-8') as f:
userwords = f.readlines()
userwords = [w.strip().split(' ')[0] for w in userwords if w.strip()]
return userwords
def add_stopwords(self, my_stopword_list):
'''
添加停用词，add_stopwords()使用后，将在下一次self.Train()使用时生效
:param my_stopword_list: list[str,str,...,str] 待添加的停用词
:return: 成功返回True，失败返回False
'''
for word in my_stopword_list:
if type(word) != str:
return False
self.stopwords += my_stopword_list
return True
def add_userword(self, my_word_list):
'''
添加词汇，用来优化中文分词，使jieba、pkuseg语料库中没有的词能正确切分，
add_userword() 使用后，将在下一次self.Train()使用时生效，当self.Train()使用e='e'时，不使用jieba、pkuseg，用户词汇无效。
:param my_word_list: list[str,str,...,str] 待添加的分词
:return:成功返回True，失败返回False
'''
if self.seg == 'jieba':
for word in self.my_word_list:
if type(word) != str:
return False
jieba.add_word(word)
self.my_word_list += my_word_list
return True
else:
self.my_word_list += my_word_list
def __Path2Corpus(self):
self.files = os.listdir(self.corpus_name)
for file in self.files:
try:
f = open(self.corpus_name + '/' + file, 'r', -1, encoding='utf-8')
s = f.read()
except:
f = open(self.corpus_name + '/' + file, 'r', -1, encoding='gbk')
s = f.read()
self.corpus.append(s)
f.close()
def __cut_str(self, text):
res = ''
if self.seg == 'jieba' or self.seg == None:
words = pseg.cut(text)
for word, flag in words:
if flag not in self.stop_flag and word not in self.stopwords:
word_for_search = jieba.cut_for_search(word)
for n in word_for_search:
res += n + ' '
elif self.seg == 'pkuseg':
if not self.myseg:
self.myseg = pkuseg.pkuseg()
words = self.myseg.cut(text)
for word in words:
if word not in self.stopwords:
res += word + ' '
elif self.seg == 'e':
words = text.split(' ')
for word in words:
if word not in self.stopwords:
res += word.lower() + ' '
return res
def __cut_corpus(self):
if self.seg == 'jieba' or self.seg == None:
jieba.load_userdict(self.user_word_path)
elif self.seg == 'pkuseg':
self.myseg = pkuseg.pkuseg(user_dict=self.my_word_list)
corpus_cut = []
for s in self.corpus:
corpus_cut.append(self.__cut_str(s))
return corpus_cut
def __cut_for_e(self):
corpus_cut = []
for text in self.corpus:
content = ''
for word in text.split(' '):
if word not in self.stopwords:
content += word.lower() + ' '
corpus_cut.append(content)
return corpus_cut
def __sort_seed(self, document):
return document['score']
def __show(self, document_scores):
if not len(document_scores):
print('Sorry,the words you queried are not in the corpus')
return []
res = []
for document in document_scores:
res.append({'index': document, 'score': document_scores[document]})
res = sorted(res, key=self.__sort_seed, reverse=True)
if self.corpus_name:
try:
self.files = os.listdir(self.corpus_name)
except:
self.files = []
if self.corpus:
for document in res:
document['content'] = self.corpus[document['index']]
elif self.files:
for document in res:
try:
f = open(self.corpus_name + '/' + self.files[document['index']], 'r', encoding='utf-8')
document['content'] = f.read()
f.close()
except:
try:
f = open(self.corpus_name + '/' + self.files[document['index']], 'r', encoding='gbk')
document['content'] = f.read()
f.close()
except:
continue
if self.files:
try:
for document in res:
document['filename'] = self.files[document['index']]
except:
pass
return res
def __save_select(self, corpus_path):
print("%s has existed!"
"Choosing to continue will delete the old one!\n"
"Maybe you can use 'AddCorpus()' or change another corpus_name.\n"
"Do you want to continue?(y/n) :" % corpus_path, end='')
choose = input()
if choose == 'y' or choose == 'Y':
return True
else:
raise ValueError('User termination.')
def __creat_corpus(self, corpus_name=None, filename=None, select=None):
if not self.corpus_name:
if corpus_name:
self.corpus_name = corpus_name
if self.corpus_name[-7:] == '_corpus':
self.corpus_name = self.corpus_name[:-7]
else:
self.corpus_name = time.strftime('%Y%m%d%H%M%S', time.localtime(time.time()))
corpus_path = self.corpus_name + '_corpus'
if os.path.exists(corpus_path):
if select != 'Y':
self.__save_select(corpus_path)
shutil.rmtree(corpus_path)
os.mkdir(corpus_path)
self.files = filename
if not self.files or len(self.files) != len(self.corpus):
if self.files:
print('No filename or filename is not complete')
self.files = []
for i in range(len(self.corpus)):
self.files.append(str(i)+'.txt')
for index in range(len(self.corpus)):
try:
f = open(corpus_path + '/' + self.files[index], 'w', encoding='utf-8')
f.write(self.corpus[index])
f.close()
except:
continue
else:
old_path = self.corpus_name
if corpus_name:
self.corpus_name = corpus_name
self.corpus_name = self.corpus_name.replace('\\', '/')
if '/' in self.corpus_name:
self.corpus_name = self.corpus_name.split('/')[-1]
if self.corpus_name[-7:] == '_corpus':
self.corpus_name = self.corpus_name[:-7]
corpus_path = self.corpus_name + '_corpus'
if os.path.exists(corpus_path) and select != 'Y':
self.__save_select(corpus_path)
if old_path != corpus_path:
old_path1 = old_path if old_path[-7:] != '_corpus' else old_path[:-7]
exist_list = self.__is_corpus_exit(old_path1)
if exist_list[1]:
os.unlink(exist_list[1])
shutil.move(old_path, corpus_path)
else:
files = os.listdir(old_path)
for file in files:
os.unlink(old_path + '/' + file)
for index in range(len(self.corpus)):
try:
f = open(corpus_path + '/' + files[index], 'w', encoding='utf-8')
f.write(self.corpus[index])
f.close()
except:
try:
f = open(corpus_path + '/' + files[index], 'w', encoding='gbk')
f.write(self.corpus[index])
f.close()
except:
continue
if not filename or len(filename) != len(self.corpus):
if filename:
print('No filename or filename is not complete')
else:
self.files = filename
files = os.listdir(corpus_path)
for index in range(len(files)):
os.rename(corpus_path + '/' + files[index], corpus_path + '/' + filename[index])
self.corpus_name = corpus_path
return self.corpus_name[:-7]
def SaveModel(self, corpus_name=None, filename=None, select=None):
'''
self.Train()之后才能self.SaveModel(),否则将出错
:param corpus_name:str 保存成名为corpus_name的语料库，缺省时将根据时间生成一个语料库名称
:param filename: list[str,str,...,str] 用于给语料文档document命名，缺省时使用原名称，没有原名称时用index
:return:成功将返回True，失败会报错
'''
if self.tfidf == None:
ValueError("SaveModel() must be used after Train()")
path = self.__creat_corpus(corpus_name, filename, select)
sparse_matrix_2_save = []
sparse_matrix_2_save.append((list(self.tfidf.data), list(self.tfidf.indices), list(self.tfidf.indptr)))
sparse_matrix_2_save.append(self.tfidf.shape)
sparse_matrix_2_save.append(self.word_dict)
try:
f = open(path + '_model', 'w', encoding='utf-8')
f.write(str(sparse_matrix_2_save))
return True
except:
self.__del_corpus(path)
raise ValueError('save errors!')
def AddCorpus(self, corpus_name, corpus_name2=None, filename=None):
'''
向corpus_name指定的语料库中添加语料文档，语料文档来自corpus_name2指定的语料库，当corpus_name2为None时，
默认为self.Train()生成的检索模型和其语料库。filename参数可用于给新添加的文档命名，格式错误时视为没有该参数,
默认为保存原名称或者按其在原语料库中的Index序号命名。另，名称与新语料库中的名称相同时，将增加‘-副本’后缀
:param corpus_name1: name of Corpus1 str
:param corpus_name2: name of Corpus2 str
:param filename: filename of Corpus2's file list [str,str...]
:return: 成功返回True，失败将报错
'''
exist_list = self.__is_corpus_exit(corpus_name)
if not exist_list[0]:
raise ValueError('no corpus found, %s' % (corpus_name + '_corpus'))
if not exist_list[1]:
raise ValueError('no model found, %s' % (corpus_name + '_model'))
corpus_name = exist_list[0]
if corpus_name2:
exist_list = self.__is_corpus_exit(corpus_name2)
if not exist_list[0]:
raise ValueError('no corpus found, %s' % (corpus_name2 + '_corpus'))
if not exist_list[1]:
raise ValueError('no model found, %s' % (corpus_name2 + '_model'))
temp_name = exist_list[0]
else:
TempSearch = copy.deepcopy(self)
temp_name = str(time.time()) + '_corpus'
TempSearch.SaveModel(temp_name, filename)
try:
files = os.listdir(temp_name)
files2 = os.listdir(corpus_name)
if filename:
for i in filename:
if type(i) != str:
ValueError('Element of filename should be str!')
if filename and len(filename) != len(files):
print('No filename or filename is not complete')
filename = files
for i in range(len(files)):
while filename[i] in files2:
filename[i] = filename[i].split('.')[0] + '-副本.' + filename[i].split('.')[1] if '.' in filename[i] else filename[i] + '-副本'
os.rename(temp_name + '/' + files[i], corpus_name + '/' + filename[i])
self.__del_corpus(temp_name)
NewSearch = MySearch(self.seg)
NewSearch.Train(corpus_name)
NewSearch.SaveModel(select='Y')
except:
ValueError("AddCorpus() error!")
else:
print('Added!')
return True
def GetDefaultCorpusName(self):
'''
:return: str
当前默认的搜索库名称，如果失败将返回False
'''
try:
f = open('venv/default_corpus', 'r', encoding='utf-8')
default_corpus = f.read()
f.close()
except:
print('Get default_corpus error! '
'Make sure venv/default_corpus exist')
return False
return default_corpus
def AdjustDefaultCorpus(self, default_corpus=None):
'''
调整默认搜索库，参数为新库名称,缺省时使用当前class的corpus_name。
有默认搜索库时，将调整为新搜索库，没有默认搜索库时将设置。
:param default_corpus: str,
name of the new default corpus
:return: 成功返回True，失败返回False
'''
if default_corpus == None and self.corpus_name:
default_corpus = self.corpus_name
elif default_corpus == None:
ValueError("Missing name of default corpus!")
exist_list = self.__is_corpus_exit(default_corpus)
if not exist_list[0]:
print('Adjust default corpus failed!'
'Make sure "%s_corpus" exist.\n'
'Maybe you can use "SaveModel()" first.' % default_corpus)
return False
elif not exist_list[1]:
print('Adjust default corpus failed!'
'Make sure "%s_model" exist.\n'
'Maybe you can use "SaveModel()" first.' % default_corpus)
return False
else:
f = open('venv/default_corpus', 'w', encoding='utf-8')
f.write(default_corpus)
f.close()
return True
def __is_corpus_exit(self, corpus_name=None):
if corpus_name == None:
corpus_name = self.GetDefaultCorpusName()
corpus_path = corpus_name + '_corpus'
model_path = corpus_name + '_model'
fs = os.listdir('.')
exist_list = [corpus_path in fs, model_path in fs]
if exist_list[0]:
exist_list[0] = corpus_path
if exist_list[1]:
exist_list[1] = model_path
return exist_list
def __generate_csc_matrix(self, model_path):
f = open(model_path, 'r', encoding='utf-8')
sparse_matrix_saved = eval(f.read())
self.tfidf = csc_matrix(sparse_matrix_saved[0], sparse_matrix_saved[1])
self.word_dict = sparse_matrix_saved[2]
def use_model(self, corpus_name=None):
if corpus_name == None:
corpus_name = self.GetDefaultCorpusName()
exist_list = self.__is_corpus_exit(corpus_name)
if not exist_list[0]:
raise ValueError('no corpus found, %s' % (corpus_name + '_corpus'))
if not exist_list[1]:
raise ValueError('no model found, %s' % (corpus_name + '_model'))
self.corpus_name = exist_list[0]
files = os.listdir(self.corpus_name)
self.__generate_csc_matrix(exist_list[1])
if self.tfidf.shape[1] != len(files) or self.tfidf.shape[1] != len(self.word_dict) or self.tfidf.shape[1] == 0:
ValueError("Error! Corpus was destroyed!\n"
"Suggest you .Train() the corpus.")
def __del_corpus(self, corpus_name=None):
if corpus_name == None:
corpus_name = self.corpus_name
if corpus_name[-7:] == '_corpus':
corpus_name = corpus_name[:-7]
if corpus_name[-6:] == '_model':
corpus_name = corpus_name[:-6]
if corpus_name[-10:] == '_model':
corpus_name = corpus_name[:-10]
files = os.listdir('.')
try:
if corpus_name + '_corpus' in files:
shutil.rmtree(corpus_name + '_corpus')
if corpus_name + '_model' in files:
os.unlink(corpus_name + '_model')
except:
pass
else:
return corpus_name + '_corpus and ' + corpus_name + '_model'
def RemoveCorpus(self, corpus_name=None):
'''
删除保存好的语料库，参数为None时删除self.corpus_name指定的语料库，语料库不存在将报错
:param corpus_name:想要删除的语料库名称
:return:成功返回True,失败将报错
'''
res = self.__del_corpus(corpus_name)
if res:
print("%s have Removed!" % res)
return True
else:
ValueError('Remove Error!')
def DelDocument(self, documents, corpus_name=None):
'''
删除保存好的语料库的部分语料文档document，未保存（SaveModel()）不能删除
:param documents: list[str,str,...,str] 待删除的语料文档document的文件名
:param corpus_name: str 语料库名
:return: 成功将返回True,失败会报错
'''
if not corpus_name:
corpus_name = self.corpus_name
exist_list = self.__is_corpus_exit(corpus_name)
if not exist_list[0]:
raise ValueError('no corpus found, %s' % (corpus_name + '_corpus'))
if not exist_list[1]:
raise ValueError('no model found, %s' % (corpus_name + '_model'))
corpus_name = exist_list[0]
files = os.listdir('.')
if corpus_name in files:
files2 = os.listdir(corpus_name)
try:
for file in documents:
if type(file) != str:
ValueError("The element of parameter 1 must be 'str' type,representing the name of the file to be deleted.")
if file in files2:
os.unlink(corpus_name + '/' + file)
else:
print(file + 'not in ' + (corpus_name + '/'))
except:
ValueError('unlink error.')
else:
NewSearch = MySearch(self.seg)
NewSearch.Train(corpus_name)
NewSearch.SaveModel(select='Y')
print('Deleted!')
return True
else:
ValueError('DelDocument() error, for wrong corpus_name.\n'
'Please indicate the correct corpus_name.')
def Train(self, argc, e=None):
if type(argc) == str:
self.corpus_name = argc
self.__Path2Corpus()
elif isinstance(argc, Iterable):
self.corpus = argc
if e == 'e':
corpus_cut = self.__cut_for_e()
else:
corpus_cut = self.__cut_corpus()
vectorizer = CountVectorizer()
words = vectorizer.fit_transform(corpus_cut)
self.word_dict = vectorizer.vocabulary_
self.tfidf = TfidfTransformer(self.norm, self.use_idf, self.smooth_idf,
self.sublinear_tf).fit_transform(words)
self.tfidf = self.tfidf.tocsc()
return words
def __get_scores(self, query_list):
document_scores = {}
for word in query_list:
index = self.word_dict.get(word)
if index == None:
continue
documents = self.tfidf.indices[self.tfidf.indptr[index]:self.tfidf.indptr[index + 1]]
data = self.tfidf.data[self.tfidf.indptr[index]:self.tfidf.indptr[index + 1]]
for i in range(len(documents)):
document_scores[documents[i]] = document_scores.get(documents[i], 0) + data[i]
return document_scores
def Query(self, query_str, corpus_name=None):
'''
查询函数
:param query_str: str query_str为查询字符串
:param corpus_name: str corpus_name为查询语料库名称，corpus_name为None时优先查询self.Train(),
其次查询默认语料库，若corpus_name不为空，则查询corpus_name指向的语料库，若没有该语料库将出错。
:return: list[dict,dict,...,dict]
其中dict{'index', 'score', 'content'} or dict{'index', 'score', 'filename', 'content'}
注：以'_corpus'结尾的文件夹将被作为保存好的语料库，文件夹名即为语料库名，查询保存好的
语料库时，corpus_name结尾可以带'_corpus'也可以不带。另，'_model'结尾的文件被作为保存好的模型。
'''
if not query_str:
print('Query nothing.')
return []
if self.corpus_name == corpus_name:
corpus_name = None
if self.tfidf != None and corpus_name == None:
temp = self.__cut_str(query_str).split(' ')
query_list = [x.lower() for x in temp if x not in self.stopwords]
document_scores = self.__get_scores(query_list)
res = self.__show(document_scores)
else:
newSerch = MySearch(self.seg)
newSerch.use_model(corpus_name)
res = newSerch.Query(query_str)
return res