如何在 python 中使我的混淆矩阵图只有 1 个小数？

Question

我在 scikit learn 中使用混淆矩阵。 但我只想要图中的 1 个小数（图 A）。不在数组中（图B），可以用我标记为!!!!!!!!!!!!!!!的代码更改

图A

图B

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=1) #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
print('Confusion matrix, without normalization')
print(cm)
fig, ax = plt.subplots()
plot_confusion_matrix(cm)

plt.show()

Answer 1

改变

    plt.text(j, i, cm[i, j], 

到

    plt.text(j, i, format(cm[i, j], '.1f'),

.1f 告诉format 将浮点数cm[i, j] 转换为精度为小数点后一位的字符串。

---

import itertools
import numpy as np
import matplotlib.pyplot as plt

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(cm.shape[1])
    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, format(cm[i, j], '.1f'),
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')

cm = np.array([(1,0,0), (0,0.625,0.375), (0,0,1)])
np.set_printoptions(precision=1) 
print('Confusion matrix, without normalization')
print(cm)
fig, ax = plt.subplots()
plot_confusion_matrix(cm)

plt.show()