Pyplot图未正确显示

Question

我正在尝试使用matplotlib显示混淆矩阵。 混淆矩阵应该显示我的分类器结果的预测标签与真实标签。

不幸的是，该图显示不正确。混淆矩阵的第一行和最后一行被压缩，难以阅读。

import matplotlib.pyplot as plt
from sklearn.utils.multiclass import unique_labels
from sklearn import preprocessing
from sklearn.metrics import confusion_matrix

def plot_confusion_matrix(y_true, y_pred, classes,
                          normalize=False,
                          title=None,
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    if not title:
        if normalize:
            title = 'Normalized confusion matrix'
        else:
            title = 'Confusion matrix, without normalization'


    cm = confusion_matrix(y_true, y_pred)

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

    fig, ax = plt.subplots()

    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
    ax.figure.colorbar(im, ax=ax)
    # We want to show all ticks...
    ax.set(xticks=np.arange(cm.shape[1]),
           yticks=np.arange(cm.shape[0]),
           # ... and label them with the respective list entries
           xticklabels=classes, yticklabels=classes,
           title=title,
           ylabel='True label',
           xlabel='Predicted label')

    # Rotate the tick labels and set their alignment.
    plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
             rotation_mode="anchor")

    # Loop over data dimensions and create text annotations.
    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.
    for i in range(cm.shape[0]):
        for j in range(cm.shape[1]):
            ax.text(j, i, format(cm[i, j], fmt),
                    ha="center", va="center",
                    color="white" if cm[i, j] > thresh else "black")
    fig.tight_layout()
    return ax


np.set_printoptions(precision=2)

# Plot normalized confusion matrix
plot_confusion_matrix(y_test, y_pred, classes=['joy', 'guilt', 'sadness', 'anger', 'fear', 'disgust', 'shame'], normalize=True,
                      title='Normalized confusion matrix')

plt.show()

结果如下：

我试图调整数字的大小，但没有成功。