def trainClassifiers(self, xml_file, type):
self.prepareModels(xml_file, type)
comentarios = self.procesar(xml_file, type)
if type == 1:
data = load_data_from_disk(tfidfModel)
data_expanded = []
for i in data:
vec = expand(i)
data_expanded.append(vec)
labels = []
for i in comentarios:
labels.append(i[1])
fileClassifiers = [SVM, NB, ME, DT]
for i in range(4):
classifier = SC(data_expanded, labels, i + 1)
fClass = classifier.train()
write_data_to_disk(fileClassifiers[i], fClass)
else:
data = load_data_from_disk(tfidfModelp)
data_expanded = []
for i in data:
vec = expand(i)
data_expanded.append(vec)
labels = []
for i in comentarios:
labels.append(i[1])
fileClassifiers = [SVMp, NBp, MEp, DTp]
for i in range(4):
classifier = SC(data_expanded, labels, i + 1)
fClass = classifier.train()
write_data_to_disk(fileClassifiers[i], fClass)
def __testClassifier(self, segmentos, entities, model, fileClass):
results = []
for j in segmentos:
proc = TextCleaner(j)
procesado = proc.get_processed_comment()
vector = model.get_comment_tf_idf_vector([procesado])
supClass = load_data_from_disk(fileClass)
classifier = SC()
classifier.set_classifier(supClass)
result = classifier.classify(vector)
polaridadSup = result[0][0]
for i in entities:
if j.find(i[0]) != -1:
value = (i[0], polaridadSup)
results.append(value)
return results