Python实现混淆矩阵
废话少说,直接上干货:
from sklearn.utils.multiclass import unique_labels
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
# y_test为真实label,y_pred为预测label,classes为类别名称,是个ndarray数组,内容为string类型的标签
class_names = np.array(["0","1"]) #按你的实际需要修改名称
plot_confusion_matrix(y_test, y_pred, classes=class_names, normalize=False)
plot_confusion_matrix函数的具体实现如下,直接复制粘贴到你的代码中即可!
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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)]
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
#print("Normalized confusion matrix")
else:
pass
#print('Confusion matrix, without normalization')
#print(cm)
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')
ax.set_ylim(len(classes)-0.5, -0.5)
# 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
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