lzh
34 lines
1.1 KiB
Python
34 lines
1.1 KiB
Python
# =============混淆矩阵绘制=============
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def plot_confusion_matrix(cm, classes, normalize=False,title='Confusion matrix', cmap=plt.cm.Blues):
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import itertools
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plt.figure()
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plt.imshow(cm, interpolation='nearest', cmap=cmap)
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plt.title(title)
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plt.colorbar()
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tick_marks = np.arange(len(classes))
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plt.xticks(tick_marks, classes, rotation=45)
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plt.yticks(tick_marks, classes)
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fmt = '.2f' if normalize else 'd'
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thresh = cm.max() / 2.
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for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
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plt.text(j, i, format(cm[i, j], fmt),
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horizontalalignment="center",
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color="white" if cm[i, j] > thresh else "black")
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plt.tight_layout()
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plt.ylabel('True label')
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plt.xlabel('Predicted label')
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plt.show()
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return plt
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#分类评估报告
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test_report = classification_report(y_test, y_pred)
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print(test_report)
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# 绘制混淆矩阵
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cnf_matrix = confusion_matrix(y_test, y_pred)
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np.set_printoptions(precision=len(y.unique())) # 设置打印数量的阈值
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class_names = y.unique()
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plt_cm = plot_confusion_matrix(cnf_matrix, classes=class_names, title='Confusion matrix')
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