features.py 文件源码

def _calculate(self, X, y, categorical, metafeatures, helpers):
        import sklearn.naive_bayes

        if len(y.shape) == 1 or y.shape[1] == 1:
            kf = cross_validation.StratifiedKFold(y, n_folds=10)
        else:
            kf = cross_validation.KFold(y.shape[0], n_folds=10)

        accuracy = 0.
        for train, test in kf:
            nb = sklearn.naive_bayes.GaussianNB()

            if len(y.shape) == 1 or y.shape[1] == 1:
                nb.fit(X[train], y[train])
            else:
                nb = OneVsRestClassifier(nb)
                nb.fit(X[train], y[train])

            predictions = nb.predict(X[test])
            accuracy += sklearn.metrics.accuracy_score(predictions, y[test])
        return accuracy / 10