Scikit-learn: sklearn.metrics.classification_report不正确？

描述错误

sklearn.metrics.classification可能会报告翻转后的值以进行精确度和召回率？

复制步骤/代码

from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.neighbors import KNeighborsClassifier
from sklearn import datasets

def calc_precision_recall(conf_matrix, class_labels):

    # for each class
    for i in range(len(class_labels)):

        # calculate true positives
        true_positives =(conf_matrix[i, i])

        # false positives
        false_positives = (conf_matrix[i, :].sum() - true_positives)

        # false negatives
        false_negatives = 0
        for j in range(len(class_labels)):
            false_negatives += conf_matrix[j, i]
        false_negatives -= true_positives

        # and finally true negatives
        true_negatives= (conf_matrix.sum() - false_positives - false_negatives - true_positives)

        # print calculated values
        print(
            "Class label", class_labels[i],
            "T_positive", true_positives,
            "F_positive", false_positives,
            "T_negative", true_negatives,
            "F_negative", false_negatives,
            "\nSensitivity/recall", true_positives / (true_positives + false_negatives),
            "Specificity", true_negatives / (true_negatives + false_positives),
            "Precision", true_positives/(true_positives+false_positives), "\n"
        )

    return

# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, 0:3]  # we only take the first two features.
y = iris.target

# Random_state parameter is just a random seed that can be used to reproduce these specific results.
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=27)

# Instantiate a K-Nearest Neighbors Classifier:
KNN_model = KNeighborsClassifier(n_neighbors=2)

# Fit the classifiers:
KNN_model.fit(X_train, y_train)

# Predict and store the prediction:
KNN_prediction = KNN_model.predict(X_test)

# Generate the confusion matrix
conf_matrix = confusion_matrix(KNN_prediction, y_test)

# Print the classification report
print(classification_report(KNN_prediction, y_test))

# Dummy class labels for the three iris classes
class_labels = [0,1,2]

# Own function to calculate precision and recall from the confusion matrix
calc_precision_recall(conf_matrix, class_labels)

预期成绩

我的函数为每个类返回以下内容：

类标签0 T_正7 F_正0 T_负23 F_负0
灵敏度/召回率1.0特异性1.0精度1.0

类标签1 T_正11 F_正1 T_负18 F_负0
灵敏度/召回率1.0特异性0.9473684210526315精度0.9166666666666666

类标签2 T_positive 11 F_positive 0 T_negative 18 F_negative 1
灵敏度/召回率0.9166666666666666特异性1.0精度1.0

          precision    recall  

       0       1.00      1.00      
       1       0.92      1.00    
       2       1.00      0.92

我的功能假定混淆矩阵由顶部x轴的实际值和左侧y轴的预测值构成。 该结构与Wikipedia中使用的结构相同，并且文档中为混淆矩阵函数引用的结构与之相同。

实际结果

相比之下，这些是sklearn.metrics报告的结果。

           precision    recall  f1-score   support

       0       1.00      1.00      1.00         7
       1       1.00      0.92      0.96        12
       2       0.92      1.00      0.96        11

版本号

系统：
python：3.8.1（默认，2020年1月8日，22:29:32）[GCC 7.3.0]
可执行文件：/ home / will / anaconda3 / envs / ElStatLearn / bin / python
机器：Linux-4.15.0-91-generic-x86_64-with-glibc2.10

Python依赖项：
点：20.0.2
设置工具：38.2.5
sklearn：0.22.1
numpy的：1.18.1
scipy：1.4.1
Cython：无
熊猫：1.0.1
matplotlib：3.1.3
作业库：0.14.1

使用OpenMP构建：True