plot_confusion.py

from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels
import itertools


def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix',
                          cmap=plt.cm.get_cmap('Blues'), figsize=None, savefig=None):
    # Função importada dos exemplos do SKLearn
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]

    if (figsize is not None):
        fig = plt.figure(figsize=figsize)
    else:
        fig = plt.figure()
        
    ax = fig.add_subplot(1, 1, 1)
    
    img = ax.imshow(cm, interpolation='nearest', cmap=cmap)
    ax.set_title(title)
    fig.colorbar(img)
    tick_marks = np.arange(len(classes))
    ax.set_xticks(tick_marks)
    ax.set_xticklabels(classes, rotation=45)
    ax.set_yticks(tick_marks)
    ax.set_yticklabels(classes, rotation=45)

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

    ax.set_ylabel('True label')
    ax.set_xlabel('Predicted label')
    fig.tight_layout()
    if (savefig is not None):
        fig.savefig(savefig, dpi=300)