是否已经在 Python 中实现了一些东西来计算多类混淆矩阵的 TP、TN、FP 和 FN? [英] Is there something already implemented in Python to calculate TP, TN, FP, and FN for multiclass confusion matrix?
问题描述
Sklearn.metrics
具有用于获取分类指标的强大功能,尽管我认为缺少的是在给定预测和实际标签序列的情况下返回 TP、FN、FP 和 FN 计数的函数.或者甚至来自混淆矩阵.
我知道可以使用 sklearn
获得混淆矩阵,但我需要实际的 TP、FN、FP 和 FN 计数(用于多标签分类 - 超过 2 个标签),并获得这些对每个类都计数.
所以说,我有下面包含 3 个类的混淆矩阵.是否有一些包可用于从中获取每个类的计数?我找不到任何东西.
Scikit-learn 可以计算和绘制多类混淆矩阵,请参阅文档中的示例 (
<小时>在下面的链接上查看此代码:
JUPYTER 笔记本上的演示
Sklearn.metrics
has great functions for obtaining classification metrics, although something that I think is missing is a function to return the TP, FN, FP and FN counts given the predicted and actual label sequences. Or even from the confusion matrix.
I know it's possible to obtain the confusion matrix using sklearn
, but I need the actual TP, FN, FP and FN counts (for multilabel classification - more than 2 labels), and to obtain those counts for each of the classes.
So say, I have the confusion matrix below with 3 classes. Is there some package available to get the counts for each class from this? I was unable to find anything.
Scikit-learn can calculate and plot a multiclass confusion matrix, see this example from the documentation (Demo on a Jupiter notebook):
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()
Result (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. ]]
Plot results:
See this code working on the link bellow:
DEMO ON A JUPYTER NOTEBOOK
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