python绘制混淆矩阵

Posted 2023-04-03 梁小憨憨

之前就了解过混淆矩阵，但是一直没有实践，今天刚好有数据实践一下，这里记录一下代码实现过程，方便以后查阅。

python绘制混淆矩阵

• matplotlib实现
• sklearn实现
• • sklearn.metrics.ConfusionMatrixDisplay
  • • from_estimator
    • from_predictions
  • 代码实现
• 颜色参考
• 参考资料

关于混淆矩阵，在这篇博客也提到过：机器学习|模型评估——AUC

matplotlib实现

import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
def plot_confusion_matrix(cm, labels_name, title, colorbar=False, cmap=None):
    plt.imshow(cm, interpolation='nearest', cmap=cmap)    # 在特定的窗口上显示图像
    for i in range(len(cm)):
        for j in range(len(cm)):
            plt.annotate(cm[j, i], xy=(i, j), horizontalalignment='center', verticalalignment='center')
    if colorbar:
        plt.colorbar()
    num_local = np.array(range(len(labels_name)))    
    plt.xticks(num_local, labels_name)    # 将标签印在x轴坐标上
    plt.yticks(num_local, labels_name)    # 将标签印在y轴坐标上
    plt.title(title)    # 图像标题
    plt.ylabel('True label')    
    plt.xlabel('Predicted label')

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

plot_confusion_matrix(cm, ["ant", "bird", "cat"], "Confusion Matrix")

sklearn实现

sklearn.metrics.ConfusionMatrixDisplay

from_estimator

from_predictions

代码实现

from sklearn.metrics import confusion_matrix
from sklearn.metrics import ConfusionMatrixDisplay
y_true = [2, 0, 2, 2, 0, 1]
y_pred = [0, 0, 2, 2, 0, 2]
ConfusionMatrixDisplay.from_predictions(y_true, y_pred, display_labels=["ant", "bird", "cat"], cmap=plt.cm.Reds, colorbar=True)
plt.title("Confusion Matrix")

颜色参考

参考资料

Python中生成并绘制混淆矩阵（confusion matrix）

利用python绘制混淆矩阵

Visualizations with Display Objects

sklearn.metrics.ConfusionMatrixDisplay

python 绘制混淆矩阵

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


def print_confusion_matrix():
    # Get the true classifications for the test-set.
    # Get the confusion matrix using sklearn.
    cls_true = []
    cls_pred = []
    cm = confusion_matrix(y_true=cls_true,
                          y_pred=cls_pred)

    # Print the confusion matrix as text.
    print(cm)

    # Plot the confusion matrix as an image.
    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)

    # Make various adjustments to the plot.
    plt.tight_layout()
    plt.colorbar()
    tick_marks = np.arange(num_classes)
    plt.xticks(tick_marks, range(num_classes))
    plt.yticks(tick_marks, range(num_classes))
    plt.xlabel('Predicted')
    plt.ylabel('True')
    
    # Ensure the plot is shown correctly with multiple plots
    # in a single Notebook cell.
    plt.show()