How To Get Roc Auc For Binary Classification In Sklearn

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. Co

Solution 1:

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

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

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.multiclassimport 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)
[0000000000000000000000000000000000000000000000000011111111111111111111111111111111111111111111111111]

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}

and

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