print(__doc__)

import itertools
import numpy as np
import matplotlib.pyplot as plt

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

# Input data:
# dilMask_0 (perf seg):
cnf_matrix = np.matrix([[34, 41], [21, 54]])
title = 'Perfect Segmentation'

# dilMask_50:
#cnf_matrix = np.matrix([[42, 33], [26, 49]])
#title = 'Partial Segmentation'

# unsegmented:
#cnf_matrix =  np.matrix([[18, 57], [16, 59]])
#title = 'Unsegmented'

class_names = ['benign', 'malignant']

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title=title,
                          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')


# Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=class_names,
                      title=title)

# Plot normalized confusion matrix
#plt.figure()
#plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,
#                      title='Normalized confusion matrix')

plt.show()
