-
Notifications
You must be signed in to change notification settings - Fork 2
/
hacerPrediccion.py
243 lines (211 loc) · 8.53 KB
/
hacerPrediccion.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
import weka.core.jvm as jvm
import sys
import traceback
from weka.core.converters import Loader
import weka.core.serialization as serialization
from weka.classifiers import Classifier
from weka.filters import Filter, MultiFilter
from extractor_de_features import Tweet,TweetFeatureExtractor
# Fuentes:
# https://github.com/fracpete/python-weka-wrapper-examples/blob/master/src/wekaexamples/classifiers/output_class_distribution.py
# http://pythonhosted.org/python-weka-wrapper/examples.html
topics = {0:'video chavista periodista sentada dtb'.split(" "),\
1:'venezuela vivo cnn soyfdelrincon senal'.split(" "),\
2:'trump donald mes opinion mexico'.split(" "),\
3:'muerte dolares angeles prostitutas venden'.split(" "),\
4:'ecuador vuelta cne lomasvisto resultados'.split(" "),\
5:'uu ee indocumentados inmigrantes aissami'.split(" "),\
6:'sports illustrated 2017 rubia portada'.split(" "),\
7:'fotos lomasvisto anos accesorios orgullo'.split(" "),\
8:'muere brutal embarazada companeras recibir'.split(" "),\
9:'jong kim nam muerte corea'.split(" ")}
header = "tweet_id,hashtags,mentions,uppercase,nonalpha,urls,len,numbers,topic_id,favorite_count,retweet_count,spam\n"
def asignarTopico(tweetText):
"""
#
# @tweetText : texto del tuit
#
# @return maxIndx : indice del topico mas cercano al tuit
#
"""
palabras = tweetText.split(" ")
puntuacion = [0 for i in topics]
maxIndx = 0
maxim = 0
for indxT in topics :
for palabra in palabras :
for topWord in topics[indxT]:
if topWord == palabra :
puntuacion[indxT] += 1
if puntuacion[indxT] > maxim :
maxim = puntuacion[indxT]
maxIndx = indxT
return maxIndx
def guardarVectores(testCSVFilename,vectores) :
with open(testCSVFilename,"w") as ofile:
ofile.write(header)
for vector in vectores :
ofile.write(str(vector["tweet_id"]))
ofile.write(",")
ofile.write(str(vector["hashtags"]))
ofile.write(",")
ofile.write(str(vector["mentions"]))
ofile.write(",")
ofile.write(str(vector["uppercase"]))
ofile.write(",")
ofile.write(str(vector["nonalpha"]))
ofile.write(",")
ofile.write(str(vector["urls"]))
ofile.write(",")
ofile.write(str(vector["len"]))
ofile.write(",")
ofile.write(str(vector["numbers"]))
ofile.write(",")
ofile.write(str(vector["topic_id"]))
ofile.write(",")
ofile.write(str(vector["favorite_count"]))
ofile.write(",")
ofile.write(str(vector["retweet_count"]))
ofile.write(",")
ofile.write(str(vector["spam"]))
ofile.write("\n")
ofile.close()
def detectarSpam_(tuitsConDatos,modeloFilename) :
vectores = []
for status in tuitsConDatos :
vector = construirFeature(status["tweetText"], \
status["tweet_id"],\
status["favorite_count"],\
status["retweet_count"])
vectores.append(vector)
ifileName = "predictMe.csv"
#modelFilename = "tweets/modelos/naivebayes.model"
#modelFilename = "tweets/modelos/usado_en_interfaz_knn.model"
guardarVectores(ifileName,vectores)
predicciones = predictWithWeka(ifileName,modeloFilename)
return predicciones
def detectarSpam(tuitsConDatos,modeloFilename):
"""
#
# @tuitsConDatos : lista de diccionarios status con los indices
# tweetText, tweet_id, favorite_count y retweet_count
#
# @return predicciones : lista de predicciones por cada tuit de input.
# Cada prediccion es un diccionario con los indices
# index, actual, predicted, error y distribution
#
"""
predicciones = []
try:
jvm.start()
jvm.start(system_cp=True, packages=True)
predicciones = detectarSpam_(tuitsConDatos,modeloFilename)
except Exception, e:
print(traceback.format_exc())
finally:
jvm.stop()
return predicciones
def construirFeature(tweetText, tweet_id,favorite_count,retweet_count) :
"""
#
#
# @tweetText : texto del tuit
# @tweet_id : id del tuit
# @favorite_count : numero de favoritos del tuit
# @retweet_count : numero de veces retuiteado
#
# @return featureVector : diccionario con cada atributo del tuit
# sin ser preprocesado
#
"""
idTopico = asignarTopico(tweetText)
tweet = Tweet(tweetText, tweet_id,favorite_count,retweet_count)
extractor = TweetFeatureExtractor(tweet_id)
extractor.extractFeatures(tweet)
featureVector = extractor.getFetureVector()
featureVector["tweet_id"] = tweet_id
featureVector["topic_id"]=idTopico
featureVector["spam"] = 'n' if int(tweet_id) % 2 else 'y'
return featureVector
def predictWithWeka(csvFilenameWithInputToPredict,modelFilename):
"""
# Nota: para usar sin conocer la clase, se puede colocar una clase dummy
# e ignorar los valores actual y error de @return results.
#
# Nota: es necesario que el archivo de nombre @csvFilenameWithInputToPredict
# contenga instancias de ambas clases (spam y sanas)
#
# @csvFilenameWithInputToPredict : nombre del archivo csv con las instancias
# a predecir.
#
# @modelFilename : nombre del archivo de modelo generado por weka y
# compatible con el archivo csv de entrada
#
# @return results : lista de diccionarios con los siguientes indices
# index, actual, predicted, error y distribution
"""
loader = Loader(classname="weka.core.converters.CSVLoader")
cls = Classifier(jobject=serialization.read(modelFilename))
#print(cls)
data = loader.load_file(csvFilenameWithInputToPredict)
data.class_is_last()
multi = MultiFilter()
remove = Filter(classname="weka.filters.unsupervised.attribute.Remove", options=["-R", "first"])
numericToNom = Filter(classname="weka.filters.unsupervised.attribute.NumericToNominal", options=["-R","8,11"])
normalize = Filter(classname="weka.filters.unsupervised.attribute.Normalize",options=["-S","1.0","-T","0.0"])
multi.filters = [remove, numericToNom, normalize]
multi.inputformat(data)
test = multi.filter(data)
results = []
for index, inst in enumerate(test):
result = dict()
pred = cls.classify_instance(inst)
dist = cls.distribution_for_instance(inst)
result["index"] = index+1
result["actual"] = inst.get_string_value(inst.class_index)
result["predicted"] = inst.class_attribute.value(int(pred))
result["error"] = "yes" if pred != inst.get_value(inst.class_index) else "no"
result["distribution"] = str(dist.tolist())
results.append(result)
#print result
return results
def main() :
input_file = csv.DictReader(open("datasets/dumpCNNEE_APLICACION.csv", "r"))
tuits = []
ids = []
for row in input_file :
tuit = dict()
tuit["tweetText"] = row["text"]
tuit["tweet_id"] = row["id"]
tuit["favorite_count"] = row["favorite_count"]
tuit["retweet_count"] = row["retweet_count"]
tuits.append(tuit)
ids.append(row["id"])
ifileName = "predictMe.csv"
modeloFilename = "naivebayes.model"
predicciones = detectarSpam(tuits,modeloFilename)
out = open("resultadosCNNEE_APLICACION.csv","w")
out.write("tweet_id,distribution1,distribution2,predicted\n")
for indx,tweet_id in enumerate(ids) :
out.write(str(tweet_id))
out.write(",")
distribution = ast.literal_eval(predicciones[indx]["distribution"])
out.write(str(distribution[0]))
out.write(",")
out.write(str(distribution[1]))
out.write(",")
out.write(str(predicciones[indx]["predicted"]))
out.write("\n")
out.close()
if __name__ == "__main__":
import ast
import csv
try:
#jvm.start()
#jvm.start(system_cp=True, packages=True)
main()
except Exception, e:
print(traceback.format_exc())
finally:
#jvm.stop()
pass