train_set = [] for i in range(len(data_train)): data1 = data_train.tweet[i] data2 = data_train.label[i] train_tup = (data1, data2) train_set.append(train_tup) tweet_train_set = [tweet for tweet,label in train_set] label_train_set = [label for tweet,label in train_set] ## inisialisasi LSF data train ## train_lsf = [] lsf_train = LSF.LSF(data_train) for i in range(len(lsf_train)): lsf1 = lsf_train[i] lsf2 = lsf_train[i] lsf_train_tup = (lsf1,lsf2) train_lsf.append(lsf_train_tup) train_lsf = np.array(train_lsf) #%% ## inisialisasi data test ## test_set = [] for i in range(len(data_testing)): data3 = data_testing.tweet[i]
# -*- coding: utf-8 -*- """ Created on Tue Apr 24 23:33:53 2018 @author: Latifah """ import pandas as pd import LSF dataset = pd.read_csv("testing_prepros(01).csv", encoding='utf-8') #%% score = pd.Series(LSF.LSF(dataset)) #%% label = [] for i in range(score.shape[0]): if (score.iloc[i] >= 1): label.append(int(1)) else: label.append(int(0)) #%% from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix accuracy = accuracy_score(dataset.label, label) * 100 precision = precision_score(dataset.label, label) * 100 recall = recall_score(dataset.label, label) * 100 confusion_matrix = confusion_matrix(dataset.label, label)