sklearn auc ValueError: Only one class present in y_true

Question

I searched Google, and saw a couple of StackOverflow posts about this error. They are not my cases.

I use keras to train a simple neural network and make some predi

Answer 1

I think your hunch is correct. The AUC (area under ROC curve) needs a sufficient number of either classes in order to make sense.

By default, cross_val_score calculates the performance metric one each fold separately. Another option could be to do cross_val_predict and compute the AUC over all folds combined.

You could do something like:

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


class ProbaEstimator(LogisticRegression):
    """
    This little hack needed, because `cross_val_predict`
    uses `estimator.predict(X)` internally.

    Replace `LogisticRegression` with whatever classifier you like.

    """
    def predict(self, X):
        return super(self.__class__, self).predict_proba(X)[:, 1]


# some example data
X, y = make_classification()

# define your estimator
estimator = ProbaEstimator()

# get predictions
pred = cross_val_predict(estimator, X, y, cv=5)

# compute AUC score
roc_auc_score(y, pred)