Plot a confusion, with the option to normalise the values

plot_confusion_matrix.py

def plot_confusion_matrix(cm,
                          classes,
                          normalize,
                          title):
    '''
    PARAMETERS:
    - cm: SKL.METRICS confustion matrix
    - Classes: the labels used in the classification
    - normalize=False : (True will normalise the values, can be useful for large sample with few outliers)
    - title : should be set to: 'Confusion matrix'
    
    RETURN:
    - Plot of confusion matrix for multi-class classification
    '''

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

    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
    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')