def roc_auc ( y_true , y_score ) : notnull = ~ np . isnan ( y_true ) fpr , tpr , thresholds = sklearn . metrics . roc_curve ( y_true [ notnull ] , y_score [ notnull ] ) return sklearn . metrics . auc ( fpr , tpr )
roc auc curve in python
Returns are under the ROC curve
def roc_auc(y_true, y_score):
    """
    Returns are under the ROC curve
    """
    notnull = ~np.isnan(y_true)
    fpr, tpr, thresholds = sklearn.metrics.roc_curve(y_true[notnull], y_score[notnull])
    return sklearn.metrics.auc(fpr, tpr)