evaluate.py 文件源码

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=None,
                          zmin=1):
    """Print and plot the confusion matrix for the intent classification.

    Normalization can be applied by setting `normalize=True`."""
    import numpy as np

    zmax = cm.max()
    plt.clf()
    plt.imshow(cm, interpolation='nearest', cmap=cmap if cmap else plt.cm.Blues,
               aspect='auto', norm=LogNorm(vmin=zmin, vmax=zmax))
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=90)
    plt.yticks(tick_marks, classes)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        logger.info("Normalized confusion matrix: \n{}".format(cm))
    else:
        logger.info("Confusion matrix, without normalization: \n{}".format(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.ylabel('True label')
    plt.xlabel('Predicted label')