utils.py

import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import classification_report, confusion_matrix
import itertools

def read_data():
    with open('./breast-cancer-wisconsin.data', 'r') as data_fid:
        lines = data_fid.readlines()
        records = []
        for line in lines:
            if '?' in line:
                line = line.replace('?', '11')
            line = line.split(',')
            line = [int(item) for item in line][1:]
            records.append(line)
    records = np.array(records)
    X = records[:, :-1]
    y = records[:, -1]
    return X, y

def plot_confusion_matrix(y_true, y_pred, classes = ['benign', 'cancer'],
                          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`.
    """
    print('Confusion Matrix')
    cm = confusion_matrix(y_true, y_pred)
    print('Classification Report')
    target_names = ['benigh', 'cancer']
    print(classification_report(y_true, y_pred, target_names=target_names))

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

    print(cm)

    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)

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

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


def save_cm_figs(y_true, y_pred, arc, target_names = ['begign', 'cancer']):
    # Compute confusion matrix

    np.set_printoptions(precision=2)

    # Plot non-normalized confusion matrix
    plt.figure()
    plot_confusion_matrix(y_true, y_pred, classes=target_names,
                          title='Confusion matrix, without normalization')
    plt.savefig('./result/conf_no_norm_{}.png'.format(arc), bbox_inches='tight')
    # Plot normalized confusion matrix
    plt.figure()
    plot_confusion_matrix(y_true, y_pred, classes=target_names, normalize=True,
                          title='Normalized confusion matrix')
    plt.savefig('./result/conf_norm_{}.png'.format(arc), bbox_inches='tight')
    plt.show()