import numpy as np
import matplotlib.pyplot as plt

from sklearn import datasets, svm
from sklearn.model_selection import train_test_split
from sklearn.metrics import plot_confusion_matrix

np.random.seed(3851)

# import some data to play with
bc = datasets.load_breast_cancer()
X = bc.data
y = bc.target
class_names = bc.target_names

# Split the data into a training set and a test set
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
np.random.shuffle(y_test)

# Run classifier, using a model that is too regularized (C too low) to see
# the impact on the results
classifier = svm.SVC(kernel='linear', C=0.0001).fit(X_train, y_train)

np.set_printoptions(precision=2)

# Plot non-normalized confusion matrix
titles_options = [("Confusion matrix, without normalization", None),
                  ("Normalized confusion matrix", 'true')]
for title, normalize in titles_options:
    disp = plot_confusion_matrix(classifier, X_test, y_test,
                                 cmap=plt.cm.Blues,
                                 normalize=normalize)
    disp.ax_.set_title(title)

    print(title)
    print(disp.confusion_matrix)

plt.show()