是否可以向混淆矩阵添加额外的列？

问题描述

我创建了一个多类分类器，现在我想以一种干净的方式显示每个类的混淆矩阵和准确度。

我已经在 sklearn 中找到了一个函数，它使我可以显示混淆矩阵：sklearn.metrics.plot_confusion_matrix，但我没有看到添加额外列的方法，我可以在其中放置每个类/行的准确性。

这是一个关于如何绘制混淆矩阵的示例：

import matplotlib.pyplot as plt  
from sklearn.datasets import make_classification
from sklearn.metrics import plot_confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
X,y = make_classification(random_state=0)
X_train,X_test,y_train,y_test = train_test_split(X,y,random_state=0)
clf = SVC(random_state=0)
clf.fit(X_train,y_train)
plot_confusion_matrix(clf,y_test)  
plt.show() 

在下图中，我用颜料画了一些东西来说明“添加额外列”的含义：

有没有办法改变这个例子并添加额外的列？ 或者是否有其他库支持我想做的事情？

解决方法

这看起来不像是开箱即用的，所以我写了一个：

def plot_class_accuracies(plotted_cm,axis,display_labels=None,cmap="viridis"):
    """
    plotted_cm : instance of `ConfusionMatrixDisplay`
        Result of `sklearn.metrics.plot_confusion_matrix`
    axis : matplotlib `AxesSubplot`
        Result of `fig,(ax1,ax2) = plt.subplots(1,2)`
    display_labels : list of labels or None
        Human-readable class names
    cmap : colormap,optional
        Optional colormap
    """
    cmatrix = plotted_cm.confusion_matrix
    normalized_cmatrix = np.diag(cmatrix) / np.sum(cmatrix,axis=1)
    n_classes = len(normalized_cmatrix)

    cmap_min,cmap_max = plotted_cm.im_.cmap(0),plotted_cm.im_.cmap(256)
    thresh = (normalized_cmatrix.max() + normalized_cmatrix.min()) / 2.0

    if display_labels is None:
        labels = np.arange(n_classes)
    else:
        labels = display_labels

    axis.imshow(
        normalized_cmatrix.reshape(n_classes,1),interpolation="nearest",cmap=cmap,)

    for i,value in enumerate(normalized_cmatrix):
        color = cmap_min if value > thresh else cmap_max
        axis.text(0,i,format(value,".2g"),ha="center",va="center",color=color)

    axis.set(
        yticks=np.arange(len(normalized_cmatrix)),ylabel="True label",xlabel="Class accuracy",yticklabels=labels,)
    axis.tick_params(
        axis="x",bottom=False,labelbottom=False,)
    axis.set_ylim((len(normalized_cmatrix) - 0.5,-0.5))

假设这是在文件 cmatrix.py 中：

from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import plot_confusion_matrix

# Import `plot_class_accuracies` from `cmatrix.py`
from cmatrix import plot_class_accuracies

if __name__ == "__main__":

    class ExampleClassifier(LogisticRegression):
        def __init__(self):
            self.classes_ = None
        def predict(self,X_test):
            self.classes_ = np.unique(X_test)
            return X_test

    X_test = np.array([0,1,2,3,2])
    y_test = np.array([0,3])

    fig,2)
    clf = ExampleClassifier()

    disp = plot_confusion_matrix(
        clf,X_test,y_test,ax=ax1,cmap=plt.cm.Blues,normalize="true"
    )

    plot_class_accuracies(disp,ax2,cmap=plt.cm.Blues)
    plt.show()

结果：

这是一个基于 Confusion Matrix example from the sklearn documentation 示例的示例：

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 plot_confusion_matrix

from cmatrix import plot_class_accuracies

iris = datasets.load_iris()
X = iris.data
y = iris.target
class_names = iris.target_names

X_train,y_train,y_test = train_test_split(X,y,random_state=0)
classifier = svm.SVC(kernel='linear',C=0.01).fit(X_train,y_train)

fig,2)

disp = plot_confusion_matrix(classifier,display_labels=class_names,cmap=plt.cm.Blues)

plot_class_accuracies(disp,cmap=plt.cm.Blues)

plt.show()

结果：