How to compute accuracy and the confusion matrix u

I tried to do K-fold cross-validation with K=30 folds, with one confusion matrix for each fold. How to compute the accuracy and the confusion matrix to the model with confidence interval? Could someone help me?

My code is:

import numpy as np
from sklearn import model_selection
from sklearn import datasets
from sklearn import svm
import pandas as pd
from sklearn.linear_model import LogisticRegression

UNSW = pd.read_csv('/home/sec/Desktop/CEFET/tudao.csv')

previsores = UNSW.iloc[:,UNSW.columns.isin(('sload','dload',
                                                   'spkts','dpkts','swin','dwin','smean','dmean',
'sjit','djit','sinpkt','dinpkt','tcprtt','synack','ackdat','ct_srv_src','ct_srv_dst','ct_dst_ltm',
 'ct_src_ltm','ct_src_dport_ltm','ct_dst_sport_ltm','ct_dst_src_ltm')) ].values


classe= UNSW.iloc[:, -1].values


X_train, X_test, y_train, y_test = model_selection.train_test_split(
previsores, classe, test_size=0.4, random_state=0)

print(X_train.shape, y_train.shape)
#((90, 4), (90,))
print(X_test.shape, y_test.shape)
#((60, 4), (60,))

logmodel = LogisticRegression()
logmodel.fit(X_train,y_train)
print(previsores.shape)


########K FOLD
print('########K FOLD########K FOLD########K FOLD########K FOLD')
from sklearn.model_selection import KFold
from sklearn.metrics import confusion_matrix

kf = KFold(n_splits=30, random_state=None, shuffle=False)
kf.get_n_splits(previsores)
for train_index, test_index in kf.split(previsores):

    X_train, X_test = previsores[train_index], previsores[test_index]
    y_train, y_test = classe[train_index], classe[test_index]

    logmodel.fit(X_train, y_train)
    print (confusion_matrix(y_test, logmodel.predict(X_test)))
print(10* '#')

For accuracy, I would use the function cross_val_score that does exactly what you are looking for. It outputs a list of 30 validation accuracies and you can then compute their mean, standard deviation, etc and create some kind of a confidence interval (mean +- 2*std) .

Since confusion matrix cannot be seen as a performance metric (not a single number but a matrix) I would recommend creating a list and then iteratively just append it with a corresponding validation confusion matrix (currently you just print it). At the end, you can use this list to extract a lot of interesting information.

UPDATE:

...
...
cm_holder = []
for train_index, test_index in kf.split(previsores):
    X_train, X_test = previsores[train_index], previsores[test_index]
    y_train, y_test = classe[train_index], classe[test_index]

    logmodel.fit(X_train, y_train)
    cm_holder.append(confusion_matrix(y_test, logmodel.predict(X_test))))

Note that the len(cm_holder) = 30 and each of the elements is an array of shape=(n_classes, n_classes).