如何更改混乱矩阵中的刻度线? [英] How to change the ticks in a confusion matrix?

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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