/
Main.py
296 lines (229 loc) · 9.4 KB
/
Main.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
#!/usr/bin/python
# -*- coding: utf-8 -*-
import LoadData as Ld
import PplAndLda as Ppl
import MySVM as Ms
import MyNaiveBayes as Mnb
import EvaluateAndShow as Eas
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import time
import json
import TfIdf1
class Run:
def __init__(self):
print('Initiating ...')
# 标签
self.labelList = ['财经', '房产', '股票', '教育', '科技',
'社会', '时政', '体育', '游戏', '娱乐']
self.labelLen = len(self.labelList)
# 算法生成向量维度
self.dimension = 5000
# LDA模型遍历语料库次数
self.ldaPasses = 10
#print('Loading data ...')
# 导入数据
#self.dL = Ld.LoadData()
#self.trainData = self.dL.loadCsvData('tmp/trainDataSet.csv')
#self.testData = self.dL.loadCsvData('tmp/testDataSet.csv')
#self.texts, self.labels = self.dL.loadScData('../THUCNews_final')
#print('datalen:',len(self.texts))
#print('Spliting data ...')
#self.x_train, self.x_test, self.y_train, self.y_test = train_test_split(self.texts, self.labels, test_size=0.5)
# SVM对象
self.svm = Ms.MySVM(self.labelLen)
# 贝叶斯对象
self.valuesNumForBayes = 50
self.naiveBayes = Mnb.MyNaiveBayes(self.labelLen, self.valuesNumForBayes)
# EvaluateAndShow对象
self.eas = Eas.EvaluateAndShow()
pass
def foreProcessOfTrainByLda(self, texts, labels, tarDataPath = 'tmp/trainDataSet.csv'):
# 文本分词+去名词+去停用词
operateFunction = Ppl.PplAndLda(self.labelList, self.dimension, self.ldaPasses)
resultOfPpl = operateFunction.ppl(texts)
# 生成字典和Lda模型
operateFunction.generateLdaModel(resultOfPpl)
# 使用模型生成预处理数据集并存入csv文件
data = operateFunction.generateVectorData(resultOfPpl, labels, tarDataPath = tarDataPath)
return data
def foreProcessOfTestByLda(self, texts, labels, tarDataPath = 'tmp/testDataSet.csv'):
# 文本分词+去名词+去停用词
operateFunction = Ppl.PplAndLda(self.labelList, self.dimension, self.ldaPasses)
# resultOfPpl = operateFunction.ppl(texts)
# 使用模型生成预处理数据集并存入csv文件
data = operateFunction.generateVectorData(texts, labels, tarDataPath = tarDataPath)
return data
def trainByBayes(self, data):
X = data[:, :-1]
Y = data[:, -1]
Y = Y.astype('int')
#
print('prepro...')
X = X / np.max(X, axis = 1).reshape(X.shape[0], 1)
X = X * self.valuesNumForBayes + 1
X = np.minimum(X, self.valuesNumForBayes)
X = np.rint(X)
X = X.astype('int')
print('training...')
t = time.time()
self.naiveBayes.train(X, Y)
t = time.time() - t
print('train Bayes using time:', t)
t = time.time()
predictResult = self.naiveBayes.predict(X)
t = time.time() -t
print('predict Bayes on trainSet using time:', t)
self.eas.Evaluate(predictResult, Y, list(range(self.labelLen)))
pass
def trainBySvm(self, data):
X = data[:, :-1]
Y = data[:, -1]
t = time.time()
self.svm.train(X, Y)
t = time.time() - t
print('train SVM using time:', t)
t = time.time()
predictResult = self.svm.predict(X)
t = time.time() - t
print('predict SVM on trainSet using time:', t)
self.eas.Evaluate(predictResult, Y, list(range(self.labelLen)))
pass
def testByBayes(self, data):
X = data[:, :-1]
Y = data[:, -1]
Y = Y.astype('int')
#
print('prepro...')
X = X / np.max(X, axis = 1).reshape(X.shape[0], 1)
X = X * self.valuesNumForBayes + 1
X = np.minimum(X, self.valuesNumForBayes)
X = np.rint(X)
X.astype('int')
t = time.time()
predictResult = self.naiveBayes.predict(X)
t = time.time() - t
print('predict Bayes on testSet using time:', t)
self.eas.Evaluate(predictResult, Y, list(range(self.labelLen)))
pass
def testBySvm(self, data):
X = data[:, :-1]
Y = data[:, -1]
t = time.time()
predictResult = self.svm.predict(X)
t = time.time() - t
print('predict SVM on testSet using time:', t)
self.eas.Evaluate(predictResult, Y, list(range(self.labelLen)))
pass
def process1(self):
print('Loading data ...')
self.texts, self.labels = self.dL.loadScData('../THUCNews_final')
print('datalen:',len(self.texts))
print('Spliting data ...')
self.x_train, self.x_test, self.y_train, self.y_test = train_test_split(self.texts, self.labels, test_size=0.5)
print('foreProcessOfTrain ...')
trainData = self.foreProcessOfTest(self.x_train, self.y_train,'tmp/trainDataSet.csv')
print('foreProcessOfTest ...')
testData = self.foreProcessOfTest(self.x_test, self.y_test,'tmp/testDataSet.csv')
print('trainByBayes ...')
# self.trainByBayes(trainData)
print('testByBayes ...')
# self.testByBayes(testData)
print('trainBySvm ...')
# self.trainBySvm(trainData)
print('testBySvm ...')
# self.testBySvm(testData)
pass
def dataProcessByPpl(self):
print('Loading data ...')
self.texts, self.labels = self.dL.loadScData('../THUCNews_final')
print('datalen:',len(self.texts))
print('Spliting data ...')
self.x_train, self.x_test, self.y_train, self.y_test = train_test_split(self.texts, self.labels, test_size=0.5)
operateFunction = Ppl.PplAndLda(self.labelList, self.dimension, self.ldaPasses)
print('Ppling trainData....')
resultOfPpl = operateFunction.ppl(self.x_train)
resultOfPpl.append(self.y_train)
print('save trainPplData...')
with open('tmp/trainWordsList', 'w') as f:
json.dump(resultOfPpl,f)
print('Ppling testData....')
resultOfPpl = operateFunction.ppl(self.x_test)
resultOfPpl.append(self.y_test)
print('save testPplData...')
with open('tmp/testWordsList', 'w') as f:
json.dump(resultOfPpl,f)
pass
def dataProcessByTfIdf(self):
print('loading train data ...')
#data = []
#with open('tmp/trainWordsList') as f:
# data = json.load(f)
#self.x_train = data[:-1]
#self.y_train = data[-1]
operateFunction = TfIdf1.TFIDF(self.labelList, self.dimension)
#print('TF-IDF run ...')
#operateFunction.generateTfIdf(self.x_train, self.y_train)
print('loading test data ...')
with open('tmp/testWordsList') as f:
data = json.load(f)
self.x_test = data[:-1]
self.y_test = data[-1]
print('using TF-IDF...')
data = operateFunction.useTfidf(self.x_test, self.y_test)
pass
def dataProcessByLda(self):
data = []
with open('tmp/trainWordsList') as f:
data = json.load(f)
self.x_train = data[:-1]
self.y_train = data[-1]
self.foreProcessOfTest(self.x_train, self.y_train,'tmp/trainDataSet.csv')
with open('tmp/testWordsList') as f:
data = json.load(f)
self.x_test = data[:-1]
self.y_test = data[-1]
self.foreProcessOfTest(self.x_test, self.y_test,'tmp/testDataSet.csv')
pass
def processTrainBayes(self):
print('Loading data ...')
# 导入数据
self.dL = Ld.LoadData()
self.trainData = self.dL.loadCsvData('tmp/trainDataSetByT.csv')
#self.testData = self.dL.loadCsvData('tmp/testDataSetByT.csv')
print('training ...')
self.trainByBayes(self.trainData)
def processTestBayes(self):
print('Loading data ...')
# 导入数据
self.dL = Ld.LoadData()
#self.trainData = self.dL.loadCsvData('tmp/trainDataSetByT.csv')
self.testData = self.dL.loadCsvData('tmp/testDataSetByT.csv')
print('testing ...')
self.testByBayes(self.testData)
def processTrainSVM(self):
print('Loading data ...')
# 导入数据
self.dL = Ld.LoadData()
self.trainData = self.dL.loadCsvData('tmp/trainDataSetByT.csv')
#self.testData = self.dL.loadCsvData('tmp/testDataSetByT.csv')
print('training ...')
self.trainBySvm(self.trainData)
def processTestSVM(self):
print('Loading data ...')
# 导入数据
self.dL = Ld.LoadData()
#self.trainData = self.dL.loadCsvData('tmp/trainDataSetByT.csv')
self.testData = self.dL.loadCsvData('tmp/testDataSetByT.csv')
print('testing ...')
self.testBySvm(self.testData)
if __name__ == '__main__':
#Run().dataProcessByPpl()
#Run().dataProcessByLda()
Run().dataProcessByTfIdf()
#Run().processTrainBayes()
#Run().processTestBayes()
#Run().processTrainSVM()
#Run().processTestSVM()
pass