Python混淆矩阵matplotlib基于类别数的自动图形大小

我使用以下函数生成混淆矩阵:

def plot_confusion_matrix(cm, classes, normalize=False, cmap=cm.Blues, png_output=None, show=True):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        title='Normalized confusion matrix'
    else:
        title='Confusion matrix'

    f = plt.figure()

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

    if png_output is not None:
        os.makedirs(png_output, exist_ok=True)
        f.savefig(os.path.join(png_output,'confusion_matrix.png'), bbox_inches='tight')

    if show:
        plt.show()
        plt.close(f)
    else:
        plt.close(f)

当我有几节课的时候,我会得到一张整洁的图表,像这样:

我尝试使用与此解决方案相同的方法 Python boxplot matplotlib automatic figure size based on the number of categories

我怎样才能让我的混淆矩阵根据类的数量来调整它的大小呢?

更新1

包括记号位置和动态宽度之后

def plot_confusion_matrix(y_true,y_pred, classes, normalize=False, cmap=cm.Blues, png_output=None, show=True):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """

    cm = confusion_matrix(y_true,y_pred)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        title='Normalized confusion matrix'
    else:
        title='Confusion matrix'

    # Calculate chart area size
    leftmargin = 0.5 # inches
    rightmargin = 0.5 # inches
    categorysize = 0.5 # inches
    figwidth = leftmargin + rightmargin + (len(classes) * categorysize)           

    f = plt.figure(figsize=(figwidth, figwidth))

    # Create an axes instance and ajust the subplot size
    ax = f.add_subplot(111)
    ax.set_aspect(1)
    f.subplots_adjust(left=leftmargin/figwidth, right=1-rightmargin/figwidth, top=0.94, bottom=0.1)

    res = ax.imshow(cm, interpolation='nearest', cmap=cmap)

    plt.title(title)
    plt.colorbar(res)
    ax.set_xticks(range(len(classes)))
    ax.set_yticks(range(len(classes)))
    ax.set_xticklabels(classes, rotation=45, ha='right')
    ax.set_yticklabels(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])):
        ax.text(j, i, format(cm[i, j], fmt),
                horizontalalignment="center",
                color="white" if cm[i, j] > thresh else "black")

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

    if png_output is not None:
        os.makedirs(png_output, exist_ok=True)
        f.savefig(os.path.join(png_output,'confusion_matrix.png'), bbox_inches='tight')

    if show:
        plt.show()
        plt.close(f)
    else:
        plt.close(f)

克莱森·里奥斯。