def tGrocery():
outFile = open('testResult.tmp', 'w')
[trainingSet, benchmark] = pickle.load(open('SampleSeg.pk'))
testingSet = []
correctLabel = []
for i in xrange(len(benchmark)):
print '%d out of %d' % (i, len(benchmark))
testingSet.append(benchmark[i][1])
correctLabel.append(benchmark[i][0])
grocery = Grocery('test')
grocery.train(trainingSet)
grocery.save()
# load
new_grocery = Grocery('test')
new_grocery.load()
Prediction = []
for i in xrange(len(testingSet)):
print '%d out of %d' % (i, len(testingSet))
prediction = new_grocery.predict(testingSet[i])
Prediction.append(prediction)
temp = correctLabel[i] + '<-->' + prediction + ' /x01' + testingSet[i] + '\n'
outFile.write(temp)
correct = 0
for i in xrange(len(Prediction)):
print Prediction[i], correctLabel[i],
if Prediction[i] == correctLabel[i]:
correct += 1
print 'Correct'
else:
print 'False'
print 'Correct Count:', correct
print 'Accuracy: %f' % (1.0 * correct / len(Prediction))
def test_grocery():
grocery = Grocery('model_redian')
grocery.train('trdata_4.txt')
grocery.save()
new_grocery = Grocery('model_redian')
new_grocery.load()
test_result = new_grocery.test('tedata_4.txt')
print test_result.accuracy_labels
print test_result.recall_labels
test_result.show_result()
def test_main(self):
grocery = Grocery(self.grocery_name)
grocery.train(self.train_src)
grocery.save()
new_grocery = Grocery('test')
new_grocery.load()
assert grocery.get_load_status()
assert grocery.predict('考生必读：新托福写作考试评分标准') == 'education'
# cleanup
if self.grocery_name and os.path.exists(self.grocery_name):
shutil.rmtree(self.grocery_name)
def predict_test(model_path, data):
# 加载模型
try:
model_path = os.path.join(BASE_DIR, 'learn', model_path)
new_grocery = Grocery(model_path.encode('utf-8'))
new_grocery.load()
except Exception as e:
return {'IsErr': True, 'ErrDesc': u'学习模型加载不成功，请检查路径'}
# 整理输入数据
result = list()
sentences = data.split(';')
if sentences[-1] == '':
sentences.pop()
if len(sentences) == 0:
return {'IsErr': True, 'ErrDesc': u'输入的句子结构有错误或没有数据'}
# 分词，再判断
stop_words = read_lines(os.path.join(BASE_DIR, 'learn', 's_w.txt'))
for s in sentences:
tmp_s = ''
words = jieba.cut(s)
for word in words:
if word in stop_words:
continue
else:
tmp_s += word + ' '
result.append({
'tag':
str(new_grocery.predict(tmp_s.strip().encode('utf-8'))),
'sentence':
s,
})
return {'IsErr': False, 'ErrDesc': u'成功', 'data': result}
def get_data(ids, b_date, end_data, log, stop_word):
b_date = b_date.strftime('%Y-%m-%d')
end_data = end_data.strftime('%Y-%m-%d')
# 选择数据来源
df = load_data(ids, b_date, end_data)
# df = load_data_excel()
# df = pd.read_excel('data_treasure.xls')
df['RateDate'] = pd.to_datetime(df['RateDate'])
# df_group = df['RateDate'].groupby([df.RateDate.values.astype('datetime64[D]')]).size()
res = list()
log.info('Have %d comments need to process' % len(df))
# 分类模型导入
new_grocery = Grocery('sample2')
new_grocery.load()
for record_data in range(0, len(df)):
# 按日期分类摘取内容
# tmp_df = df[df['RateDate'] > df_group.index[record_data]][df['RateDate'] < df_group.index[record_data + 1]]
# 自然语言处理
content_sw, level, tag = nlp_process_with_sw(df.iloc[record_data],
new_grocery, stop_word)
# 记录结果
res.append({
'RateContent': json.dumps(content_sw, ensure_ascii=False),
'RateDate': df.iloc[record_data]['RateDate'],
'TreasureID': df.iloc[record_data]['TreasureID'],
'Level': level,
'Tag': tag,
'Sentence': df.iloc[record_data]['RateContent'],
})
return res
def train():
print 'train start '+'.'*30
#grocery=Grocery('sample')
grocery=Grocery('version1.0')
grocery.train(trainlist)
grocery.save()
print 'train end '+'.'*30
def test(test_path):
new_grocery = Grocery('cv_' + str(fold) +
'_model') #, custom_tokenize=segment)
new_grocery.load()
test_src = []
with open(test_path) as f:
for line in f:
label, text = line.strip().split("|text|")
label = yiji_label[classify_dict[label]]
test_src.append((label, text))
test_result = new_grocery.test(test_src)
#print test_result
#print test_result.accuracy_overall
#accs = test_result.accuracy_labels
recalls = test_result.recall_labels
#print "Recall for each class: ", recalls
predictlabels = test_result.predicted_y
truelabels = test_result.true_y
acc = accuracy_score(truelabels, predictlabels)
macro_precision, macro_recall, macro_fscore, _ = precision_recall_fscore_support(
truelabels, predictlabels, average='macro')
print "Accuracy: ", acc, "Macro-average Precision:", macro_precision, "Macro-average Recall:", macro_recall, "Macro-average Fscore:", macro_fscore
labellist = [
'safe_and_stable', 'industrial_information', 'politics',
'culture_health', 'social_livelihood', 'economic_and_financial'
]
precision, recall, fscore, _ = precision_recall_fscore_support(
truelabels, predictlabels, average=None, labels=labellist)
precisions = dict()
recalls = dict()
for idx, p in enumerate(precision):
precisions[labellist[idx]] = p
for idx, c in enumerate(recall):
recalls[labellist[idx]] = c
def __init__(self, keyword):
print '进行新闻分类'
(db, cursor) = connectdb()
cursor.execute("update task set status=1 where keyword=%s", [keyword])
cursor.execute("select id, title from news where keyword=%s",
[keyword])
news = cursor.fetchall()
new_grocery = Grocery('static/paris')
new_grocery.load()
for item in news:
tag = new_grocery.predict(item['title'])
if tag == '新闻背景':
tag = 1
elif tag == '事实陈述':
tag = 2
elif tag == '事件演化':
tag = 3
elif tag == '各方态度':
tag = 4
elif tag == '直接关联':
tag = 6
elif tag == '暂无关联':
tag = 7
cursor.execute("update news set tag=%s where id=%s",
[tag, item['id']])
closedb(db, cursor)
return
def train_compare_result(train_src, test_src):
grocery = Grocery('test')
grocery.train(train_src)
print grocery.get_load_status()
len_test = len(test_src)
print len_test
Predict_num = 0
History = []
for test in test_src:
Predict_result = {
'predict_title': test[1],
'predict_class': None,
'true_class': None
}
predict_title = Predict_result['predict_title']
predict_result = grocery.predict(predict_title)
Predict_result['predict_class'], Predict_result['true_class'] = test[
0], predict_result
if str(predict_result) == str(test[0]):
# print 'prediction is True'
Predict_num += 1
History.append(Predict_result)
# print 'prediction is False'
predict_precision = float(Predict_num) / len_test
return predict_precision, History
def test_main(self):
grocery = Grocery(self.grocery_name)
grocery.train(self.train_src)
grocery.save()
new_grocery = Grocery('test')
new_grocery.load()
assert grocery.get_load_status()
result = grocery.predict('just a testing')
print(result)
result = grocery.predict('考生必读：新托福写作考试评分标准')
print(result)
print("type of result is :",type(result))
assert str(grocery.predict('考生必读：新托福写作考试评分标准')) == 'education'
assert str(grocery.predict('法网')) == 'sports'
# cleanup
if self.grocery_name and os.path.exists(self.grocery_name):
shutil.rmtree(self.grocery_name)
def labelmaker(self):
result=[]
grocery = Grocery('11c_20k_20171226')
grocery.load()
label_confidence=sorted(grocery.predict(self.shorttext).dec_values.items(), lambda x, y: cmp(x[1], y[1]), reverse=True)[0]
result.append(label_confidence[0])#置信度最高的分类结果
result.append(label_confidence[1])# 置信度
return result
def __load_model__():
if not Classify.__MODEL_LOADED__:
Classify.__MODEL_LOADED__ = True
Classify.__train__model__()
else:
if Classify.__MODEL__:
Classify.__MODEL__ = Grocery('Classify')
Classify.__MODEL__.load()
def __init__(self, *args, **kwargs):
self.grocery_name = str(kwargs["grocery_name"])
method = str(kwargs["method"])
train_src = str(kwargs["train_src"])
self.PREFIX = conf.load("predict_label")["prefix"]
self.MODEL_DIR = conf.load("predict_label")["model_dir"]
self.kwargs = kwargs
if method == "normal":
self.key_ext = keyExt()
self.grocery = Grocery(self.grocery_name,
custom_tokenize=self._custom_tokenize)
elif method == "jieba":
self.grocery = Grocery(self.grocery_name)
elif method == "processed":
self.grocery = Grocery(self.grocery_name,
custom_tokenize=self._custom_tokenize)
pass
def phgrocery(text):
# result_text = []
model_grocery = Grocery('model_redian_5')
model_grocery.load()
result = int(model_grocery.predict(text).predicted_y)
# print result
# if result == 1:
# result_text.append(text)
return result
def tgrocery_train(train_data,test_data):
'''model预测'''
print("训练语料总数为: " + str(len(train_data)))
test_corpus, test_label = test_split(test_data)
grocery = Grocery('TextGrocery')
print("start training......")
grocery.train(train_data)
grocery.save()
new_grocery = Grocery('TextGrocery')
new_grocery.load()
predict_label = []
for sample in test_corpus:
label = new_grocery.predict(sample)
predict_label.append(str(label))
# print(predict_label)
return test_corpus,test_label,predict_label
def __train__model__():
dataframe = pd.read_excel(Classify.__FILE_PATH__)
data = dataframe[[u'类型', u'释义']]
train_data = [(x[0], x[1]) for x in data.values]
grocery = Grocery('Classify')
grocery.train(train_data)
grocery.save()
Classify.__MODEL__ = grocery
def predict_phrasing(self, text=u'曾被年轻人嫌弃，如今却媲美Zara'):
'''
:param text:
:param model_name:
:return:
'''
new_grocery = Grocery(self.model_name)
new_grocery.load()
result = new_grocery.predict(text)
return result.dec_values[u'postive']
def __init__(self):
self.degreedic = set( line.strip() for line in codecs.open('./data/degrees.txt','rb','utf-8')) # 载入学历词库
self.majordic =set( line.strip() for line in codecs.open('./data/majordic.txt','rb','utf-8')) # 载入专业词库
self.citydic = set( line.strip() for line in codecs.open("./data/citydic.txt",'rb','utf-8')) # 载入城市词库
self.firmnames =set( line.strip() for line in codecs.open('./data/firm.txt','rb','utf-8')) # 载入公司缩写名库
self.jobdic = set(line.strip() for line in codecs.open('./data/jobposition.txt','rb','utf-8') ) # 载入招聘职位名库
self.skills = set( line.strip() for line in codecs.open('./data/skills.txt','rb','utf-8'))
# self.wordlisttf = pickle.load(open('./data/wordlist.pkl')) # 出现频率最高的2000个单词
# self.w2vdict = json.load(open('./data/word2vec_50.json')) # 2000个词的word2vector
self.clf = Grocery("jdclf") # 句子分类器，分为demand，duty，other
self.clf.load()
self.SEX = re.compile(u"性别不限|性别|男|女")
self.AGE = re.compile(u"\d+周?岁|年龄")
self.DEGREE = re.compile(u"(全日制)?(初中|高中|中专|大专|专科|大学专科|中职|本科|大学本科|硕士|研究生|博士|博士后)(.?以上)?")
self.MAJOR = re.compile(u"\S+(相关专业|专业优先|及其.专业|[类等]专业[优先]?)")
self.EXP = re.compile(u"工作经验：|工作经[历验]|工作年限|年.{0,4}经[历验]|经[历验].{1,6}年")
self.PUB_TIME = re.compile(u"(\d+)(天前发布)")
self.INCNAME = re.compile(u"\S+(有限公司|酒店|银行|集团|研究中心|研究所|学校|旅行社|分?公司|研发中心|技术部|事.部|招聘)")
self.NOT_INC = re.compile(u"职位|描述|收藏|推荐|地址|邮箱|主页|介绍|欢迎|加入|要求|简介|险一金|奖金|包吃住|社区|厂房|人员|职责")
self.INCTAG = re.compile(u"大公司|五百强|全球500强|小公司|成长型公司|创业公司|私有经济|集体经济|集团|外企|已上市|稳定性高|平均年龄\d+岁|妹纸多|学历高|福利待遇好|晋升机会大|民营公司|民营企业|互联网|创业型|国企|央企")
self.JOBNAME = re.compile(u'\S*(研发工程师|工程师|经理|助理|顾问|前台|秘书|主管|研究员|实习生|操作员|专员|教学人员|技术人员|管理员|业务员|公关|程序员|教师|老师|培训生|\
文员|研究员|策划|主任|总监|设计师|分析师|架构师|摄影师|编辑|BD|游戏UI|Android(开发)?|PHP(开发)?|Python(开发)?|.?(急招|急聘|初级|中级|高级|方向).?[\s）】\)])|\
|行政人事|网店设计|客服|会计|电话销售|外贸跟单|web前端|游戏UI|后.开发|产品运营|商业数据分析')
self.START_DEMAND = re.compile(u"(岗位要求|应聘条件|任职要求|岗位资格|任职资格|岗位条件|工作要求|任职条件|人员条件|职位.求|职位条件|职位描述|岗位资格|职位资格|具备条件)[:：\s]\
|如果你.{0,10}[:：\s]|我们希望你.{0,12}[:：\s]|(要求|条件)[:：\s]|你需要?具备什么.+[？\?:：\s]|任职资格[:：\s]")
self.DEMAND = re.compile(u"熟悉|熟练|具有|善于|懂得|掌握|具备|能够|优先|不少于|不超过|至少|团队.作|良好的|工作经验|开发经验|实习经历|能力强|富有|以上学历|经验|喜欢|\
较强的.{2,8}能力|相关专业|相关学历|者优先|精通|了解|及以上|技术全面|.强的责任心|[能有]独立|英文流利")
self.DUTY = re.compile(u"跟进|协助|负责|配合|其他工作|领导交办的|对.+提供|审核|参与|提出|跟踪|报告|为.+提出|日常.+工作|指导|对.+进行|为.+提供|跟进|拓展|运营|用户|客户|协调|拟写|通过|协同|完成|沟通|需求|秘书.{2,5}翻译")
self.START_DUTY = re.compile(u"(岗位职责|岗位描述|职位描述|职责描述|任职描述|职位职责|工作职责|工作职能|职位职能|工作内容|实习内容|职位内容)[:：\s]|做这样的事[:：\s]|职责.{0,5}[:：\s]")
self.PAY = re.compile(u"薪酬|待遇|月薪|薪资|年薪|底薪|\d+k|\d+万|\d+元|工资|报酬|薪水|福利")
self.BENEFIT = re.compile(u"周休|补助|补贴|假日|餐补|提成|交通补助|食宿|加班工资|期权|年假|领导|扁平化|管理|氛围|空间|休假|月假|带薪|全休|晋升|培训|舒适的|旅游|奖励|过节费|五险一金|奖金|\
|弹性工作|氛围|成长空间|实训|培训|高薪|前景|旅游|活动|分红")
self.SPLIT_JD = re.compile(u"岗位[【（]?[一二三四五六七八九][】）][:：\s]|(^招聘岗位\S+|岗位\d|岗位[一二三四五六])[:：\s]")
self.CLEAR_NUM = re.compile(u"^\d[\.: ：。、]|^[\(（【]?\d[\))】\.]")
self.CLEAR_COLO = re.compile(u"^[\s\.。）（【】，,]|[。；，\.;,]$|^\d[\.]")
self.SKILL = re.compile(u"精通|了解|熟练|熟悉|掌握|懂得|优先|具备|具有|者优先|擅长|善于|较强的.{2,6}能力|良好的|有.+经验|能力|极强的")
jieba.load_userdict('./data/majordic.txt')
jieba.load_userdict('./data/skills.txt')
jieba.load_userdict('./data/firm.txt')
jieba.load_userdict('./data/degrees.txt')
jieba.load_userdict('./data/benefits.txt')
self.jdStr = ""
self.linelist = []
self.lineindex = defaultdict(int)
self.result = OrderedDict()
def sentiment_train(gro_name, train_set):
"""
tgGrocery svm train
:param gro_name:
:param train_set:
:return:
"""
gro_ins = Grocery(gro_name)
# gro_ins.load()
gro_ins.train(train_set)
print("Is trained? ", gro_ins.get_load_status())
gro_ins.save()
def train(train_origin_path, fold):
grocery = Grocery('cv_' + str(fold) +
'_model') #, custom_tokenize=segment)
train_src = []
with open(train_origin_path) as f:
for line in f:
label, text = line.strip().split("|text|")
label = yiji_label[classify_dict[label]]
train_src.append((label, text))
grocery.train(train_src)
grocery.save()
def train_phrasing_and_save(self, trainsets=all):
'''
:param trainsets:
:param model_name:
:return:
'''
try:
grocery = Grocery(self.model_name)
grocery.train(trainsets)
grocery.save()
return True
except:
return False
def __init__(self):
self.data = []
self.clf = Grocery("jdclf")
self.clf.load()
self.SEX = re.compile(u"性别不限|性别|男|女")
self.AGE = re.compile(u"\d+周?岁|年龄")
self.DEGREE = re.compile(
u"(全日制)?(初中|高中|中专|大专|专科|大学专科|中职|本科|大学本科|硕士|研究生|博士|博士后)(.?以上)?")
self.MAJOR = re.compile(u"\S+(相关专业|专业优先|及其.专业|[类等]专业[优先]?)")
self.EXP = re.compile(u"工作经验：|工作经[历验]|工作年限|年.{0,4}经[历验]|经[历验].{1,6}年")
self.PUB_TIME = re.compile(u"(\d+)(天前发布)")
self.INCNAME = re.compile(
u"\S+(有限公司|酒店|银行|集团|研究中心|研究所|学校|旅行社|分?公司|研发中心|技术部|事.部|招聘)")
self.NOT_INC = re.compile(
u"职位|描述|收藏|推荐|地址|邮箱|主页|介绍|欢迎|加入|要求|简介|险一金|奖金|包吃住|社区|厂房|人员|职责")
self.INCTAG = re.compile(
u"大公司|五百强|全球500强|小公司|成长型公司|创业公司|私有经济|集体经济|集团|外企|已上市|稳定性高|平均年龄\d岁|妹纸多|学历高|福利待遇好|晋升机会大|民营公司|民营企业\
|互联网|创业型|国企|央企")
self.JOBNAME = re.compile(
u'\S*(研发工程师|工程师|经理|助理|顾问|前台|秘书|主管|研究员|实习生|操作员|专员|教学人员|技术人员|管理员|业务员|公关|程序员|教师|老师|培训生|\
文员|研究员|策划|主任|总监|设计师|分析师|架构师|摄影师|编辑|BD|游戏UI|Android(开发)?|PHP(开发)?|Python(开发)?|.?(急招|急聘|初级|中级|高级|方向).?[\s）】\)])|\
|行政人事|网店设计|客服|会计|电话销售|外贸跟单|web前端|游戏UI|后.开发|产品运营|商业数据分析'
)
self.START_DEMAND = re.compile(
u"(岗位要求|应聘条件|任职要求|岗位资格|任职资格|岗位条件|工作要求|任职条件|人员条件|职位.求|职位条件|职位描述|岗位资格|职位资格|具备条件)[:：\s]\
|如果你.{0,10}[:：\s]|我们希望你.{0,12}[:：\s]|(要求|条件)[:：\s]|你需要?具备什么.+[？\?:：\s]|任职资格[:：\s]"
)
self.DEMAND = re.compile(
u"熟悉|熟练|具有|善于|懂得|掌握|具备|能够|优先|不少于|不超过|至少|团队.作|良好的|工作经验|开发经验|实习经历|能力强|富有|以上学历|经验|喜欢|\
较强的.{2,8}能力|相关专业|相关学历|者优先|精通|了解|及以上|技术全面|.强的责任心|[能有]独立|英文流利"
)
self.DUTY = re.compile(
u"跟进|协助|负责|配合|其他工作|领导交办的|对.+提供|审核|参与|提出|跟踪|报告|为.+提出|日常.+工作|指导|跟进|拓展|运营|用户|客户|协调|拟写|通过|协同\
|完成|沟通|需求|秘书.{2,5}翻译")
self.START_DUTY = re.compile(
u"(岗位职责|岗位描述|职位描述|职责描述|任职描述|职位职责|工作职责|工作职能|职位职能|工作内容|实习内容|职位内容)[:：\s]|做这样的事[:：\s]|职责.{0,5}[:：\s]"
)
self.PAY = re.compile(u"薪酬|待遇|月薪|薪资|年薪|底薪|\d+k|\d+万|\d+元|工资|报酬|薪水|福利")
self.BENEFIT = re.compile(
u"周休|补助|补贴|假日|餐补|提成|交通补助|食宿|加班工资|期权|年假|领导|扁平化|管理|氛围|空间|休假|月假|带薪|全休|晋升|培训|舒适的|旅游|奖励|过节费|五险一金|奖金|\
|弹性工作|氛围|成长空间|实训|培训|高薪|前景|旅游|活动|分红")
def demo_flask(image_file):
grocery = Grocery('NameIdAdd_NLP')
model_name = grocery.name
text_converter = None
tgm = GroceryTextModel(text_converter, model_name)
tgm.load(model_name)
grocery.model = tgm
t = time.time()
result_dir = './result'
image = np.array(Image.open(image_file).convert('RGB'))
result, image_framed = ocr_whole.model(image)
output_file = os.path.join(result_dir, image_file.split('/')[-1])
Image.fromarray(image_framed).save(output_file)
name_total = ''
id_total = ''
for key in result:
string1 = result[key][1]
if len(string1) <= 8:
continue
string2 = re.sub("[\s+\.\!\/_,$%^*(+\"\']+|[+——！，。？、~@#￥%……&*{}[]+", "", string1)
no_digit = len(list(filter(str.isdigit, string2.encode('gbk'))))
no_alpha = len(list(filter(is_alphabet, string2)))
if len(set('法定代表人') & set(string2)) >= 2 or len(set('经营范围') & set(string2)) >= 2 or '资本' in string2 or '类型' in string2 or len(set('年月日') & set(string2)) >= 2 or len(set('登记机关') & set(string2)) >= 2 or '电话' in string2:
predict_result = 'others'
elif len(set('经营场所') & set(string2)) >= 3 or '住所' in string2 or len(set('营业场所') & set(string2)) >= 3:
predict_result = 'company-address'
elif len(set('统一社会信用代码') & set(string2)) >= 2 or ((no_digit+no_alpha) / len(string2) > 0.5 and no_digit > 8):
predict_result = 'company-id'
elif '名称' in string2:
predict_result = 'company-name'
else:
predict_result = grocery.predict(string2)
if str(predict_result) == 'company-name':
name_total += string1
break
elif str(predict_result) == 'company-id':
id_total += string1
else:
continue
id_total = re.sub(r'\W', '', id_total)
name_total = stupid_revise(name_total)
print("Mission complete, it took {:.3f}s".format(time.time() - t))
print('\nRecongition Result:\n')
print(id_total)
print(name_total)
return output_file, id_total, name_total
def learn_model(file_name):
path = os.path.join(BASE_DIR, 'learn', file_name)
try:
df = pd.read_excel(path)
except Exception as e:
return {'IsErr': True, 'ErrDesc': u'找不到文档或者读取文档出错'}
try:
# 删去缺失值的行
df = df.dropna(axis=0)
df = df.apply(split_comment, axis=1)
except Exception as e:
return {'IsErr': True, 'ErrDesc': u'文档格式有误，应包含Tag（标签名字），Comment（评价内容）'}
try:
# 拆分学习组和测试组 3 ：2
len_learn = len(df) / 5 * 3
# 生成学习文档和测试文档
learn_file_name, test_file_name = output_file(file_name, df, len_learn)
tmp_learn_name = os.path.join(BASE_DIR, 'learn',
'model_' + learn_file_name.split('.')[0])
grocery = Grocery(tmp_learn_name.encode('utf-8'))
path = os.path.join(BASE_DIR, 'learn', learn_file_name)
grocery.train(path.encode('utf-8'))
grocery.save()
except Exception as e:
return {'IsErr': True, 'ErrDesc': u'学习不成功，没有生产新的模型，请再次尝试。'}
# 测试
res = test_sample(tmp_learn_name, test_file_name)
return {
'IsErr':
False,
'ErrDesc':
u'成功生产新的模型，测试验证的正确率为%s, 模型保存为:%s' %
(res, os.path.split(tmp_learn_name)[1])
}
# coding=utf-8
from tgrocery import Grocery
grocery = Grocery('sample')
train_src = [('education', '名师指导托福语法技巧：名词的复数形式'),
('education', '中国高考成绩海外认可 是“狼来了”吗？'),
('sports', '图文：法网孟菲尔斯苦战进16强 孟菲尔斯怒吼'),
('sports', '四川丹棱举行全国长距登山挑战赛 近万人参与')]
grocery.train(train_src)
#grocery.train('/home/wangjianfei/git/data/train_ch.txt')
# grocery.train('train_ch.txt')
grocery.save()
new_grocery = Grocery('sample')
new_grocery.load()
print(
new_grocery.predict(
'Abbott government spends $8 million on higher education media blitz'))
test_src = [
('education', '福建春季公务员考试报名18日截止 2月6日考试'),
('sports', '意甲首轮补赛交战记录:米兰客场8战不败国米10年连胜'),
]
print("start test..................")
#grocery.test('/home/wangjianfei/git/data/test.txt')
# grocery.train('train_ch.txt'))
# custom_grocery = Grocery('custom', custom_tokenize=list)
print(new_grocery.test(test_src))
# coding: utf-8
from tgrocery import Grocery
grocery = Grocery('test')
train_src = [('education', '名师指导托福语法技巧：名词的复数形式'),
('education', '中国高考成绩海外认可 是“狼来了”吗？'),
('sports', '图文：法网孟菲尔斯苦战进16强 孟菲尔斯怒吼'),
('sports', '四川丹棱举行全国长距登山挑战赛 近万人参与')]
grocery.train(train_src)
print(grocery.get_load_status())
predict_result = grocery.predict('考生必读：新托福写作考试评分标准')
print(predict_result)
print(predict_result.dec_values)
grocery = Grocery('read_text')
train_src = '../text_src/train_ch.txt'
grocery.train(train_src)
print(grocery.get_load_status())
predict_result = grocery.predict('考生必读：新托福写作考试评分标准')
print(predict_result)
print(predict_result.dec_values)
# -*- coding: utf-8 -*-
import sys
reload(sys)
sys.path.append('../../')
from config import *
from tgrocery import Grocery
STOP_WORDS_FILE = 'stopwords.txt'
USER_DICT_FILE = 'user_dict.txt'
model_fintext = Grocery('model_fintext')
model_fintext.load()
sys.path.append('../')
from get_es import *
es = Elasticsearch([{'host':ES_HOST,'port':ES_PORT}])
def search(index_name):
es_search_options = set_search_optional()
es_result = get_search_result(es_search_options,index=index_name)
# final_result = get_result_list(es_result)
# return final_result
return es_result
def get_result_list(es_result):
final_result = []
for item in es_result:
final_result.append(item['_source'])
return final_result
#!/usr/bin/env python
# coding=utf-8
from tgrocery import Grocery
#grocery = Grocery('age56')
#grocery.train('train4_age_56', ' ')
#grocery.save()
new_grocery = Grocery("age")
new_grocery.load()
predict_result = new_grocery.test('test4_age', ' ')
#print len(predict_result.true_y)
#for i in range(len(predict_result.predicted_y)):
#print predict_result.predicted_y[i]
print predict_result
predict_result.show_result()
# -*- coding:utf-8 -*-
import sys
reload(sys)
sys.path.append('../../')
from config import *
from es import es214 as es
from elasticsearch import Elasticsearch
from elasticsearch.exceptions import TransportError
from elasticsearch.helpers import bulk
from tgrocery import Grocery
model_fintext = Grocery('../fintext_classify/model_fintext')
model_fintext.load()
def match_topic_kw(news_id, keywords_list, source, doc_type, size=10000):
result = []
keyword_str = ''.join(keywords_list)
# 通过一组关键词查找相关文本
query_body = {
"query": {
"match": {
"content": keyword_str #这个可能还得改，争取用一个list
}
},
"size": size
}
# print keyword_str
es_result = es.search(index=source,
doc_type=doc_type,
body=query_body,
request_timeout=400)
# coding: utf-8
from tgrocery import Grocery
# save
grocery = Grocery('test')
train_src = [('education', '名师指导托福语法技巧：名词的复数形式'),
('education', '中国高考成绩海外认可 是“狼来了”吗？'),
('sports', '图文：法网孟菲尔斯苦战进16强 孟菲尔斯怒吼'),
('sports', '四川丹棱举行全国长距登山挑战赛 近万人参与')]
grocery.train(train_src)
grocery.save()
# load
# grocery name must be the same as the previous one
new_grocery = Grocery('test')
new_grocery.load()
print new_grocery.predict('考生必读：新托福写作考试评分标准')