在Python中是否已經實現了一些東西，可以為多類混淆矩陣計算TP，TN，FP和FN？

Question

Sklearn.metrics具有用於獲取分類指標的強大功能，盡管我認為缺少的功能是在給定預測標簽序列和實際標簽序列的情況下返回TP，FN，FP和FN計數的功能。 甚至來自混亂矩陣。

我知道可以使用sklearn獲得混淆矩陣，但是我需要實際的TP，FN，FP和FN計數（對於多標簽分類-超過2個標簽），並為每個類別獲取這些計數。

可以這么說，我下面有3類混亂矩陣。 是否有一些軟件包可以從中獲取每個班級的數量？ 我什么都找不到。

Answer 1

Scikit-learn可以計算和繪制多類混淆矩陣，請參閱文檔中的以下示例（ Jupiter筆記本上的演示 ）：

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, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    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')

# Compute confusion matrix
cnf_matrix = confusion_matrix(y_test, y_pred)
np.set_printoptions(precision=2)

# Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=class_names,
                      title='Confusion matrix, without normalization')

# Plot normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,
                      title='Normalized confusion matrix')

plt.show()

結果（txt）：

Confusion matrix, without normalization
[[13  0  0]
 [ 0 10  6]
 [ 0  0  9]]

Normalized confusion matrix
[[ 1.    0.    0.  ]
 [ 0.    0.62  0.38]
 [ 0.    0.    1.  ]]

繪制結果：

---

請參見下面的鏈接上的代碼：
在JUPYTER筆記本上演示

Answer 2

由於找不到任何東西，我最終自己實現了它。 這是代碼，以防將來有人尋找它：

def counts_from_confusion(confusion):
    """
    Obtain TP, FN FP, and TN for each class in the confusion matrix
    """

    counts_list = []

    # Iterate through classes and store the counts
    for i in range(confusion.shape[0]):
        tp = confusion[i, i]

        fn_mask = np.zeros(confusion.shape)
        fn_mask[i, :] = 1
        fn_mask[i, i] = 0
        fn = np.sum(np.multiply(confusion, fn_mask))

        fp_mask = np.zeros(confusion.shape)
        fp_mask[:, i] = 1
        fp_mask[i, i] = 0
        fp = np.sum(np.multiply(confusion, fp_mask))

        tn_mask = 1 - (fn_mask + fp_mask)
        tn_mask[i, i] = 0
        tn = np.sum(np.multiply(confusion, tn_mask))

        counts_list.append({'Class': i,
                            'TP': tp,
                            'FN': fn,
                            'FP': fp,
                            'TN': tn})

    return counts_list