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)