# coding: utf-8

# In[1]:

import pandas as pd

import numpy as np

import warnings

from pandas import DataFrame, read_csv

from sklearn import svm

from sklearn import cross_validation

from sklearn.cross_validation import KFold

from sklearn.cross_validation import train_test_split

from sklearn.naive_bayes import MultinomialNB

from sklearn.neighbors import KNeighborsClassifier

from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score

warnings.filterwarnings('ignore')

df = pd.read_csv('D:/SKRIPSI/percobaan/1332data9klas/data_1332_9kelas.csv')

y = df.klasifikasi.values

X = pd.read_csv('D:/SKRIPSI/percobaan/1332data9klas/tfidf1332.csv')

# In[2]:

kf = KFold(len(X), n_folds=10, shuffle=True, random_state=9999)

model_train_index = []

model_test_index = []

model = 0

for k, (index_train, index_test) in enumerate(kf):

X_train, X_test, y_train, y_test = X.ix[index_train,:], X.ix[index_test,:],y[index_train], y[index_test]

clf = MultinomialNB(alpha=0.1, fit_prior=True, class_prior=None).fit(X_train, y_train)

score = clf.score(X_test, y_test)

f1score = f1_score(y_test, clf.predict(X_test))

precision = precision_score(y_test, clf.predict(X_test))

recall = recall_score(y_test, clf.predict(X_test))

print('Model %d has accuracy %f with | f1score: %f | precision: %f | recall : %f'%(k,score, f1score, precision, recall))

model_train_index.append(index_train)

model_test_index.append(index_test)

model+=1

# In[5]:

temp = df.klasifikasi

# In[ ]:

# In[ ]: