def test(self, comment, type, corpus): vectorizer = [] transformer = [] if corpus == 1: vectorizer = load_data_from_disk(simpleVectorizer) transformer = load_data_from_disk(tfidfVectorizer) else: vectorizer = load_data_from_disk(simpleVectorizerp) transformer = load_data_from_disk(tfidfVectorizerp) model = VM() model.set_models(vectorizer, transformer) comentario = comment[0] # seg = Segmentation(comentario) seg = Segmentation2() # segmentos = seg.find_sentences() segmentos = seg.segment_text(comentario) entities = comment[1].items() classSVM = "" classNB = "" classME = "" classDT = "" if corpus == 1: classSVM = SVM classNB = NB classME = ME classDT = DT else: classSVM = SVMp classNB = NBp classME = MEp classDT = DTp if type == 1: return self.__testClassifier(segmentos, entities, model, classSVM) elif type == 2: return self.__testClassifier(segmentos, entities, model, classNB) elif type == 3: return self.__testClassifier(segmentos, entities, model, classME) elif type == 4: return self.__testClassifier(segmentos, entities, model, classDT) elif type == 5: return self.__testUnsup(segmentos, entities)
def prepareModels(self, xml_file, type): comentarios = self.procesar(xml_file, type) train = [] for i in comentarios: train.append(i[0]) model = VM(train) vectorModelData = model.prepare_models() modelVectorizer = vectorModelData[0] modelVectorizerTFIDF = vectorModelData[1] modelTFIDF = vectorModelData[2] if type == 1: write_data_to_disk(simpleVectorizer, modelVectorizer) write_data_to_disk(tfidfVectorizer, modelVectorizerTFIDF) write_data_to_disk(tfidfModel, modelTFIDF) else: write_data_to_disk(simpleVectorizerp, modelVectorizer) write_data_to_disk(tfidfVectorizerp, modelVectorizerTFIDF) write_data_to_disk(tfidfModelp, modelTFIDF)