features.py 文件源码

def _calculate(self, X, y, categorical, metafeatures, helpers):
        import sklearn.decomposition
        pca = sklearn.decomposition.PCA(copy=True)
        rs = np.random.RandomState(42)
        indices = np.arange(X.shape[0])
        for i in range(10):
            try:
                rs.shuffle(indices)
                pca.fit(X[indices])
                return pca
            except LinAlgError as e:
                pass
        self.logger.warning("Failed to compute a Principle Component Analysis")
        return None