如何在Sklearn中获得Roc Auc进行二进制分类

问题描述

我有二进制分类问题，我想计算结果的roc_auc.为此，我使用sklearn以两种不同的方式进行了处理.我的代码如下.

I have binary classification problem where I want to calculate the roc_auc of the results. For this purpose, I did it in two different ways using sklearn. My code is as follows.

代码1:

from sklearn.metrics import make_scorer
from sklearn.metrics import roc_auc_score

myscore = make_scorer(roc_auc_score, needs_proba=True)

from sklearn.model_selection import cross_validate
my_value = cross_validate(clf, X, y, cv=10, scoring = myscore)
print(np.mean(my_value['test_score'].tolist()))

我得到的输出为0.60.

代码2:

y_score = cross_val_predict(clf, X, y, cv=k_fold, method="predict_proba")

from sklearn.metrics import roc_curve, auc
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(2):
    fpr[i], tpr[i], _ = roc_curve(y, y_score[:,i])
    roc_auc[i] = auc(fpr[i], tpr[i])
print(roc_auc)

我得到的输出为{0: 0.41, 1: 0.59}.

我很困惑，因为我在两个代码中得到了两个不同的分数.请让我知道为什么会发生这种差异，以及正确的做法是什么.

I am confused since I get two different scores in the two codes. Please let me know why this difference happens and what is the correct way of doing this.

如果需要，我很乐意提供更多详细信息.

I am happy to provide more details if needed.

推荐答案

似乎您从另一个答案中使用了我的代码的一部分，所以我虽然也要回答这个问题.

It seems that you used a part of my code from another answer, so I though to also answer this question.

对于二元分类的情况，您有2个类别，一个是肯定类别.

For a binary classification case, you have 2 classes and one is the positive class.

例如，请参见此处. pos_label是肯定类的标签. pos_label=None时，如果y_true位于{-1, 1}或{0, 1}中，则pos_label设置为1，否则将引发错误.

For example see here. pos_label is the label of the positive class. When pos_label=None, if y_true is in {-1, 1} or {0, 1}, pos_label is set to 1, otherwise an error will be raised..

import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc
from sklearn.multiclass import OneVsRestClassifier
from sklearn.model_selection import cross_val_predict
from sklearn.linear_model import LogisticRegression
import numpy as np

iris = datasets.load_iris()
X = iris.data
y = iris.target
mask = (y!=2)
y = y[mask]
X = X[mask,:]
print(y)
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]

positive_class = 1

clf = OneVsRestClassifier(LogisticRegression())
y_score = cross_val_predict(clf, X, y, cv=10 , method='predict_proba')

fpr = dict()
tpr = dict()
roc_auc = dict()
fpr[positive_class], tpr[positive_class], _ = roc_curve(y, y_score[:, positive_class])
roc_auc[positive_class] = auc(fpr[positive_class], tpr[positive_class])
print(roc_auc)

{1: 1.0}

和

from sklearn.metrics import make_scorer
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import cross_validate

myscore = make_scorer(roc_auc_score, needs_proba=True)

clf = OneVsRestClassifier(LogisticRegression())
my_value = cross_validate(clf, X, y, cv=10, scoring = myscore)
print(np.mean(my_value['test_score'].tolist()))
1.0

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