scikit-learn Receiver Operating Characteristic (ROC) ROC-AUC score with overriding and cross validation

Example

One needs the predicted probabilities in order to calculate the ROC-AUC (area under the curve) score. The cross_val_predict uses the predict methods of classifiers. In order to be able to get the ROC-AUC score, one can simply subclass the classifier, overriding the predict method, so that it would act like predict_proba.

from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import cross_val_predict
from sklearn.metrics import roc_auc_score

class LogisticRegressionWrapper(LogisticRegression):
    def predict(self, X):
        return super(LogisticRegressionWrapper, self).predict_proba(X)

X, y = make_classification(n_samples = 1000, n_features=10, n_classes = 2, flip_y = 0.5)

log_reg_clf = LogisticRegressionWrapper(C=0.1, class_weight=None, dual=False,
             fit_intercept=True)

y_hat = cross_val_predict(log_reg_clf, X, y)[:,1]

print("ROC-AUC score: {}".format(roc_auc_score(y, y_hat)))

output:

ROC-AUC score: 0.724972396025