本文整理匯總了Python中tgrocery.Grocery類的典型用法代碼示例。如果您正苦於以下問題：Python Grocery類的具體用法？Python Grocery怎麽用？Python Grocery使用的例子？那麽, 這裏精選的類代碼示例或許可以為您提供幫助。
在下文中一共展示了Grocery類的15個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: train
def train():
print 'train start '+'.'*30
#grocery=Grocery('sample')
grocery=Grocery('version1.0')
grocery.train(trainlist)
grocery.save()
print 'train end '+'.'*30示例2: __init__
def __init__(self, keyword):
print '進行新聞分類'
(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 == '新聞背景':
tag = 1
elif tag == '事實陳述':
tag = 2
elif tag == '事件演化':
tag = 3
elif tag == '各方態度':
tag = 4
elif tag == '直接關聯':
tag = 6
elif tag == '暫無關聯':
tag = 7
cursor.execute("update news set tag=%s where id=%s", [tag, item['id']])
closedb(db, cursor)
return示例3: GET
def GET(self,name):
#i = web.input(name=None)
#url = "http://"+name
#html = urllib2.urlopen(url).read()
#soup = BeautifulSoup(html)
#title = soup.html.head.title.contents.pop().encode('utf-8')
title = name.encode('utf-8')
new_grocery = Grocery('sample')
new_grocery.load()
return new_grocery.predict(title)示例4: __train__model__
def __train__model__():
dataframe = pd.read_excel(Classify.__FILE_PATH__)
data = dataframe[[u'類型', u'釋義']]
train_data = [(x[0],x[1]) for x in data.values]
grocery = Grocery('Classify')
grocery.train(train_data)
grocery.save()
Classify.__MODEL__ = grocery示例5: tGrocery
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))示例6: GroceryModel
class GroceryModel(object):
def __init__(self):
self.grocery = Grocery('TextClassify')
def train(self,train_file):
f = open(train_file,'r')
line = f.readline().decode('utf8')
dataset = []
while line:
tmp = line.split('\t')
dataset.append((tmp[0],''.join(tmp[1:])))
line = f.readline().decode('utf8')
f.close()
self.grocery.train(dataset)
self.grocery.save()
def load_model(self):
self.grocery.load()
def test(self,test_src):
self.load_model()
f = open(test_src,'r')
line = f.readline().decode('utf8')
dataset = []
while line:
tmp = line.split('\t')
dataset.append((tmp[0],''.join(tmp[1:])))
line = f.readline().decode('utf8')
f.close()
result = self.grocery.test(dataset)
print result
def predict(self,text):
print self.grocery.predict(text)示例7: test_main
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('考生必讀：新托福寫作考試評分標準') == 'education'
# cleanup
if self.grocery_name and os.path.exists(self.grocery_name):
shutil.rmtree(self.grocery_name)示例8: test_grocery
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()示例9: predict_corpus
def predict_corpus(input_file,output_csv):
import csv
csvfile = file(output_csv, 'wb')
writer = csv.writer(csvfile)
corpus = []
f = xlrd.open_workbook(input_file)
table = f.sheet_by_name('Sheet1')
nrows = table.nrows # 讀取行數
for rownum in range(0, nrows):
row = table.row_values(rownum)
row[2].strip()
corpus.append(row[2])
corpus_grocery = Grocery(project_name)
corpus_grocery.load()
output = []
for sentence in corpus:
predict = corpus_grocery.predict(sentence)
output.append((sentence,predict))
writer.writerows(output)
print('Done!')
csvfile.close()開發者ID:frederic89，項目名稱:Event_Classification_and_Domain_Recognition，代碼行數:21，代碼來源:domain_predict_py2.py
示例10: jdParser
class jdParser(object):
def __init__(self):
self.clf = Grocery("./jdclf")
self.clf.load()
self.LINE_SPLIT = re.compile(u"[；。;\n]")
def get_demand_and_duty(self,jdstr):
linelist = [ line.strip() for line in self.LINE_SPLIT.split(jdstr) if len(line.strip()>4) ]
result = {}
demand = []
duty = []
for line in linelist:
pred = str(self.clf.predict(line))
if pred =="demand":
demand.append(line)
elif pred == "duty":
duty.append(line)
result['demand'] = '\n'.join(demand)
result['duty'] = '\n'.join(duty)示例11: sentiment_train
def sentiment_train(gro_name, train_set):
"""
: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()示例12: MyGrocery
class MyGrocery(object):
def __init__(self, name):
super(MyGrocery, self).__init__()
self.grocery = Grocery(name)
self.loaded = False
self.correct = 1.0
def train(self, src):
lines = []
for line in csv.reader(open(src)):
label, s = line[0],line[1]
text = s.decode('utf8')
lines.append((label, text))
self.grocery.train(lines)
def save_model(self):
self.grocery.save()
def train_and_save(self, src):
self.train(src)
self.save_model()
def load_model(self):
if not self.loaded:
self.grocery.load()
self.loaded = True
def predict(self, text):
self.load_model()
return self.grocery.predict(text)
def test(self, src):
self.load_model()
total, wrong_num = 0.0, 0.0
for line in csv.reader(open(src)):
total += 1
if line[0] != self.predict(line[1]):
wrong_num += 1
print "load test file from " + src
correct = (total - wrong_num ) / total
self.correct = correct
print "total: %d , wrong_num: %d, success percentage: %f" %(total, wrong_num, correct)
result = dict(type="test", total=total, wrong_num=wrong_num, correct=correct)
return json.dumps(result)示例13: __init__
def __init__(self):
self.CLEAN_TEXT = re.compile(u"[^\u4e00-\u9fa5\w\d；:：;,。、\.，。！[email protected]（）\r\n\(\)\-\+ － ]")
self.clf = Grocery(base_dir+"/jdclf")
self.clf.load()
self.SPLIT_LINE = re.compile(u"[\r\n；:：。！？;]|[ \s \xa0\u724b]{4,}")
self.CLEAN_LINE = re.compile(u"^[\u2022（【\[\s\t\r\n\(\- ]?[\da-z１２３４５７８９]{1,2}[\.,。、，：:）】\]\)\s]|^[！＠＃￥％……＆×（）\(\)｛｝：“｜、－\-，。：:\.]|^[一二三四五六七八九１２３４５６７８９\d]{0,2}[\.、\s:： ]|[，；。、\s \.]$|^[\s \u2022 \uff0d \u25cf]")
self.CLEAN_JOBNAME = re.compile(u"急聘|誠聘|高薪|包[食住宿餐]|.險一金|待遇|^急?招|職位編號\s?[\s\d:：]")
self.PAY = re.compile("(\d{3,}\-)?\d{3,}元")
self.SEX = re.compile(u"性別|男|女")
self.AGE = re.compile(u"\d+周?歲|年齡")
self.JOB_TAG = re.compile(u"全職|實習")
self.DEGREE = re.compile(u"小學|初中|高中|職技|本科|研究生|碩士|博士|教授|專科|大專|中專|無要求|不限|無限")
self.START_DEMAND = re.compile(u"(任職資格|崗位要求|工作要求|任職條件|任職要求|職位要求)[：:\s】\n ]?")
self.START_DUTY = re.compile(u"(工作內容|崗位職責|工作職責|職位描述|工作描述|職位介紹|職位職責|崗位描述)[:：\s 】\n ]")
self.START_BENEFIT = re.compile(u"(福利待遇|待遇|福利)[:：\s\n】]")
self.INC_URL = re.compile(u"(主頁|網站|網址|官網).{0,3}[\w\d_/\.:\-]+")
self.DEMAND = re.compile(u"精通|熟悉|熟練|有.+經驗")
self.DUTY = re.compile(u"負責|促成|為客戶|安排的其.工作")
self.BENEFIT = re.compile(u".險一金|福利|晉身|休假|帶薪|補助|補貼")
self.CERT = re.compile(u"(\S{2,8}證書|CET-\d|普通話|英語|口語|.語|日文|雅思|托福|托業)(至少)?(通過)?[\d一二三四五六七八九]級[及或]?(以上)?|(英語)?CET-\d級?(以上)?|\
醫學.{0,3}證|會計.{0,3}證|律師.{0,3}證|有.{1,8}證書")
self.degreedic = set([line.strip() for line in codecs.open(base_dir+'/data/degrees.txt','rb','utf-8')])
self.majordic = set([line.strip() for line in codecs.open(base_dir+'/data/majordic.txt','rb','utf-8')])
self.skilldic = set([line.strip() for line in codecs.open(base_dir+'/data/skills.txt','rb','utf-8')])
self.jobdic = set([line.strip() for line in codecs.open(base_dir+'/data/jobnames.txt','rb','utf-8')])
jieba.load_userdict(base_dir+'/data/majordic.txt')
jieba.load_userdict(base_dir+'/data/skills.txt')
jieba.load_userdict(base_dir+'/data/firm.txt')
jieba.load_userdict(base_dir+'/data/degrees.txt')
jieba.load_userdict(base_dir+'/data/benefits.txt')示例14: __init__
def __init__(self):
self.data = []
self.clf = Grocery("jdclf")
self.clf.load()
self.SEX = re.compile(u"性別不限|性別|男|女")
self.AGE = re.compile(u"\d+周?歲|年齡")
self.DEGREE = re.compile(u"(全日製)?(初中|高中|中專|大專|專科|大學專科|中職|本科|大學本科|碩士|研究生|博士|博士後)(.?以上)?")
self.MAJOR = re.compile(u"\S+(相關專業|專業優先|及其.專業|[類等]專業[優先]?)")
self.EXP = re.compile(u"工作經驗：|工作經[曆驗]|工作年限|年.{0,4}經[曆驗]|經[曆驗].{1,6}年")
self.PUB_TIME = re.compile(u"(\d+)(天前發布)")
self.INCNAME = re.compile(u"\S+(有限公司|酒店|銀行|集團|研究中心|研究所|學校|旅行社|分?公司|研發中心|技術部|事.部|招聘)")
self.NOT_INC = re.compile(u"職位|描述|收藏|推薦|地址|郵箱|主頁|介紹|歡迎|加入|要求|簡介|險一金|獎金|包吃住|社區|廠房|人員|職責")
self.INCTAG = re.compile(u"大公司|五百強|全球500強|小公司|成長型公司|創業公司|私有經濟|集體經濟|集團|外企|已上市|穩定性高|平均年齡\d歲|妹紙多|學曆高|福利待遇好|晉升機會大|民營公司|民營企業\
|互聯網|創業型|國企|央企")
self.JOBNAME = re.compile(u'\S*(研發工程師|工程師|經理|助理|顧問|前台|秘書|主管|研究員|實習生|操作員|專員|教學人員|技術人員|管理員|業務員|公關|程序員|教師|老師|培訓生|\
文員|研究員|策劃|主任|總監|設計師|分析師|架構師|攝影師|編輯|BD|遊戲UI|Android(開發)?|PHP(開發)?|Python(開發)?|.?(急招|急聘|初級|中級|高級|方向).?[\s）】\)])|\
|行政人事|網店設計|客服|會計|電話銷售|外貿跟單|web前端|遊戲UI|後.開發|產品運營|商業數據分析')
self.START_DEMAND = re.compile(u"(崗位要求|應聘條件|任職要求|崗位資格|任職資格|崗位條件|工作要求|任職條件|人員條件|職位.求|職位條件|職位描述|崗位資格|職位資格|具備條件)[:：\s]\
|如果你.{0,10}[:：\s]|我們希望你.{0,12}[:：\s]|(要求|條件)[:：\s]|你需要?具備什麽.+[？\?:：\s]|任職資格[:：\s]")
self.DEMAND = re.compile(u"熟悉|熟練|具有|善於|懂得|掌握|具備|能夠|優先|不少於|不超過|至少|團隊.作|良好的|工作經驗|開發經驗|實習經曆|能力強|富有|以上學曆|經驗|喜歡|\
較強的.{2,8}能力|相關專業|相關學曆|者優先|精通|了解|及以上|技術全麵|.強的責任心|[能有]獨立|英文流利")
self.DUTY = re.compile(u"跟進|協助|負責|配合|其他工作|領導交辦的|對.+提供|審核|參與|提出|跟蹤|報告|為.+提出|日常.+工作|指導|跟進|拓展|運營|用戶|客戶|協調|擬寫|通過|協同\
|完成|溝通|需求|秘書.{2,5}翻譯")
self.START_DUTY = re.compile(u"(崗位職責|崗位描述|職位描述|職責描述|任職描述|職位職責|工作職責|工作職能|職位職能|工作內容|實習內容|職位內容)[:：\s]|做這樣的事[:：\s]|職責.{0,5}[:：\s]")
self.PAY = re.compile(u"薪酬|待遇|月薪|薪資|年薪|底薪|\d+k|\d+萬|\d+元|工資|報酬|薪水|福利")
self.BENEFIT = re.compile(u"周休|補助|補貼|假日|餐補|提成|交通補助|食宿|加班工資|期權|年假|領導|扁平化|管理|氛圍|空間|休假|月假|帶薪|全休|晉升|培訓|舒適的|旅遊|獎勵|過節費|五險一金|獎金|\
|彈性工作|氛圍|成長空間|實訓|培訓|高薪|前景|旅遊|活動|分紅")示例15: reload
# -*- 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