如何更改混乱矩阵中的刻度线? [英] How to change the ticks in a confusion matrix?
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问题描述
我正在使用混淆矩阵(图A)
I am working with a confusion matrix (Figure A)
如何使我的ticks
从1到3而不是0到2?
How can I make my ticks
to start from 1 to 3 instead of 0 to 2?
我尝试在tick_marks
中添加+1.但这不起作用(图B)
I tried adding a +1 in tick_marks
. But it does not work (Figure B)
检查我的代码:
import itertools
cm = confusion_matrix(y_test, y_pred)
np.set_printoptions(precision=2)
print('Confusion matrix, without normalization')
print(cm)
plt.figure()
plot_confusion_matrix(cm)
def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Oranges):
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(iris.target_names)) + 1
plt.xticks(tick_marks, rotation=45)
plt.yticks(tick_marks)
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j],
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
图A:
图B
推荐答案
您应该获取plt
的axis
并更改xtick_labels
(如果您要这样做):
You should get the axis
of the plt
and change the xtick_labels
(if that's what you intend to do):
import itertools
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
# import some data to play with
iris = datasets.load_iris()
X = iris.data
y = iris.target
class_names = iris.target_names
# Split the data into a training set and a test set
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
# Run classifier, using a model that is too regularized (C too low) to see
# the impact on the results
classifier = svm.SVC(kernel='linear', C=0.01)
y_pred = classifier.fit(X_train, y_train).predict(X_test)
def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Oranges):
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(iris.target_names))
plt.xticks(tick_marks, rotation=45)
ax = plt.gca()
ax.set_xticklabels((ax.get_xticks() +1).astype(str))
plt.yticks(tick_marks)
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j],
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
cm = confusion_matrix(y_test, y_pred)
np.set_printoptions(precision=2)
print('Confusion matrix, without normalization')
print(cm)
fig, ax = plt.subplots()
plot_confusion_matrix(cm)
plt.show()
结果:
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