Confusion Matrix for 10-fold cross validation in scikit learn

Question

I am new in Machine Learning and scikit. I want to know how can I calculate confusin matrix in 10-fold croos validstion with scikit. How can I find y_test and y_pred?

Answer 1

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[i, j],
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')



from sklearn import datasets
from sklearn.cross_validation import cross_val_score
from sklearn import svm
from sklearn.metrics import confusion_matrix
import itertools
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cross_validation
iris = datasets.load_iris()
class_names = iris.target_names
# shape of data is 150
cv = cross_validation.KFold(150, n_folds=10,shuffle=False,random_state=None)
for train_index, test_index in cv:

    X_tr, X_tes = iris.data[train_index], iris.data[test_index]
    y_tr, y_tes = iris.target[train_index],iris.target[test_index]
    clf = svm.SVC(kernel='linear', C=1).fit(X_tr, y_tr) 

    y_pred=clf.predict(X_tes)
    cnf_matrix = confusion_matrix(y_tes, y_pred)
    np.set_printoptions(precision=2)

    # Plot non-normalized confusion matrix
    plt.figure()
    plot_confusion_matrix(cnf_matrix, classes=class_names,
                      title='Confusion matrix, without normalization')
    # Plot normalized confusion matrix
    plt.figure()
    plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,
                      title='Normalized confusion matrix')

    plt.show()