如何用python将混淆矩阵的子图设置为矩形

要将混淆矩阵的子图设置为矩形，可以使用Python中的Matplotlib库。具体步骤如下：

1.导入Matplotlib库和混淆矩阵库

import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix

2.生成混淆矩阵数据

y_true = [0, 1, 2, 0, 1, 2, 0, 1, 2]
y_pred = [0, 0, 2, 0, 1, 1, 1, 2, 2]
cm = confusion_matrix(y_true, y_pred)

3.绘制混淆矩阵热力图

fig, ax = plt.subplots(figsize=(6, 6))
im = ax.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)

4.添加坐标轴标签和标题

ax.set(xticks=np.arange(cm.shape[1]),
           yticks=np.arange(cm.shape[0]),
           xticklabels=class_names, yticklabels=class_names,
           title=title,
           ylabel='True label',
           xlabel='Predicted label')

5.将矩形设置为子图的形状

plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
             rotation_mode="anchor")
    for i in range(cm.shape[0]):
        for j in range(cm.shape[1]):
            ax.text(j, i, format(cm[i, j], 'd'),
                    ha="center", va="center",
                    color="white" if cm[i, j] > thresh else "black")
    fig.tight_layout()
    return ax

这样就可以将混淆矩阵的子图设置为矩形了。完整的代码如下：

import numpy as np
import matplotlib.pyplot as plt
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'

    # Compute confusion matrix
    cm = confusion_matrix(y_true, y_pred)

    # Only use the labels that appear in the data
    # classes = classes[unique_labels(y_true, y_pred)]
    classes = classes

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]

    fig, ax = plt.subplots(figsize=(6, 6))
    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

# Example usage
class_names = ['class 0', 'class 1', 'class 2']
y_true = [0, 1, 2, 0, 1, 2, 0, 1, 2]
y_pred = [0, 0, 2, 0, 1, 1, 1, 2, 2]
plot_confusion_matrix(y_true, y_pred, classes=class_names, title='Confusion matrix, without normalization')
plt.show()