def makeSentenceVector(self, sentence): ''' Convert a single sentence to vector ''' sentence = sentence.replace('.', '') senWords = sentence.split(' ') if self.model.currentModel == ModelType.Word2Vec: wordEmbedding = self.model.embedding ps = p.Preprocessing() senWords = ps.removeStopword(senWords) mat = [] for i in senWords: if i in wordEmbedding: mat.append(wordEmbedding[i]) mat = np.array(mat) return np.mean(mat, axis=0) elif self.model.currentModel == ModelType.SelfTrainedDoc2Vec: embedding = self.model.embedding mat = np.array(embedding.infer_vector(senWords)) return mat
manual_testing.to_csv("manual_testing.csv") merge_dataset = pd.concat([fake_news, real_news], axis=0) dataset = merge_dataset.drop(["title", "subject", "date"], axis=1) dataset.isnull().sum() dataset = dataset.sample(frac=1) return dataset dataset = dataImporting() # make object and send with the constructor DataPreprocessingObject = DataPreprocessing.Preprocessing(dataset) X_train, X_test, Y_train, Y_test = DataPreprocessingObject.preprocess() def models(): '''from sklearn.linear_model import LogisticRegression LR_model = LogisticRegression() LR_model.fit(XV_train,Y_train) pred_LR_model=LR_model.predict(XV_test) print("Accuracy : ", LR_model.score(XV_test, Y_test)) return LR_model''' model = Models.models() # print(model.model, model.LR_model, model.DT_model, model.GB_model, model.RF_model)