features.py 文件源码

def _calculate(self, X, y, categorical, metafeatures, helpers):
        import sklearn.lda
        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.
        try:
            for train, test in kf:
                lda = sklearn.lda.LDA()

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

                predictions = lda.predict(X[test])
                accuracy += sklearn.metrics.accuracy_score(predictions, y[test])
            return accuracy / 10
        except LinAlgError as e:
            self.logger.warning("LDA failed: %s Returned 0 instead!" % e)
            return np.NaN
        except ValueError as e:
            self.logger.warning("LDA failed: %s Returned 0 instead!" % e)
            return np.NaN