def transform(self, X):
        """Select continuous features and transform them using PCA.

        Parameters
        ----------
        X: numpy ndarray, {n_samples, n_components}
            New data, where n_samples is the number of samples and n_components is the number of components.

        Returns
        -------
        array-like, {n_samples, n_components}
        """
        selected = auto_select_categorical_features(X, threshold=self.threshold)
        _, X_sel, n_selected, _ = _X_selected(X, selected)

        if n_selected == 0:
            # No features selected.
            raise ValueError('No continuous feature was found!')
        else:
            pca = PCA(svd_solver=self.svd_solver, iterated_power=self.iterated_power, random_state=self.random_state)
            return pca.fit_transform(X_sel)
def transform ( self , X ) : selected = auto_select_categorical_features ( X , threshold = self . threshold ) _ , X_sel , n_selected , _ = _X_selected ( X , selected ) if n_selected == 0 : raise ValueError ( 'No continuous feature was found!' ) else : pca = PCA ( svd_solver = self . svd_solver , iterated_power = self . iterated_power , random_state = self . random_state ) return pca . fit_transform ( X_sel )
Select continuous features and transform them using PCA.

        Parameters
        ----------
        X: numpy ndarray, {n_samples, n_components}
            New data, where n_samples is the number of samples and n_components is the number of components.

        Returns
        -------
        array-like, {n_samples, n_components}