splearn/pipeline.py
def transform(self, Z):
        """TODO: rewrite docstring
        Transform X separately by each transformer, concatenate results.
        Parameters
        ----------
        X : array-like or sparse matrix, shape (n_samples, n_features)
            Input data to be transformed.
        Returns
        -------
        X_t : array-like or sparse matrix, shape (n_samples, sum_n_components)
            hstack of results of transformers. sum_n_components is the
            sum of n_components (output dimension) over transformers.
        """
        if isinstance(Z, DictRDD):
            X = Z[:, 'X']
        else:
            X = Z

        Zs = [_transform_one(trans, name, X, self.transformer_weights)
              for name, trans in self.transformer_list]
        X_rdd = reduce(lambda x, y: x.zip(y._rdd), Zs)
        X_rdd = X_rdd.map(flatten)
        mapper = np.hstack
        for item in X_rdd.first():
            if sp.issparse(item):
                mapper = sp.hstack
        X_rdd = X_rdd.map(lambda x: mapper(x))

        if isinstance(Z, DictRDD):
            return DictRDD([X_rdd, Z[:, 'y']],
                           columns=Z.columns,
                           dtype=Z.dtype,
                           bsize=Z.bsize)
        else:
            return X_rdd
def transform(self, Z):
        """TODO: rewrite docstring
        Transform X separately by each transformer, concatenate results.
        Parameters
        ----------
        X : array-like or sparse matrix, shape (n_samples, n_features)
            Input data to be transformed.
        Returns
        -------
        X_t : array-like or sparse matrix, shape (n_samples, sum_n_components)
            hstack of results of transformers. sum_n_components is the
            sum of n_components (output dimension) over transformers.
        """
        if isinstance(Z, DictRDD):
            X = Z[:, 'X']
        else:
            X = Z

        Zs = [_transform_one(trans, name, X, self.transformer_weights)
              for name, trans in self.transformer_list]
        X_rdd = reduce(lambda x, y: x.zip(y._rdd), Zs)
        X_rdd = X_rdd.map(flatten)
        mapper = np.hstack
        for item in X_rdd.first():
            if sp.issparse(item):
                mapper = sp.hstack
        X_rdd = X_rdd.map(lambda x: mapper(x))

        if isinstance(Z, DictRDD):
            return DictRDD([X_rdd, Z[:, 'y']],
                           columns=Z.columns,
                           dtype=Z.dtype,
                           bsize=Z.bsize)
        else:
            return X_rdd
def transform ( self , Z ) : if isinstance ( Z , DictRDD ) : X = Z [ : , 'X' ] else : X = Z Zs = [ _transform_one ( trans , name , X , self . transformer_weights ) for name , trans in self . transformer_list ] X_rdd = reduce ( lambda x , y : x . zip ( y . _rdd ) , Zs ) X_rdd = X_rdd . map ( flatten ) mapper = np . hstack for item in X_rdd . first ( ) : if sp . issparse ( item ) : mapper = sp . hstack X_rdd = X_rdd . map ( lambda x : mapper ( x ) ) if isinstance ( Z , DictRDD ) : return DictRDD ( [ X_rdd , Z [ : , 'y' ] ] , columns = Z . columns , dtype = Z . dtype , bsize = Z . bsize ) else : return X_rdd
TODO: rewrite docstring
        Transform X separately by each transformer, concatenate results.
        Parameters
        ----------
        X : array-like or sparse matrix, shape (n_samples, n_features)
            Input data to be transformed.
        Returns
        -------
        X_t : array-like or sparse matrix, shape (n_samples, sum_n_components)
            hstack of results of transformers. sum_n_components is the
            sum of n_components (output dimension) over transformers.
TODO : rewrite docstring Transform X separately by each transformer concatenate results . Parameters ---------- X : array - like or sparse matrix shape ( n_samples n_features ) Input data to be transformed . Returns ------- X_t : array - like or sparse matrix shape ( n_samples sum_n_components ) hstack of results of transformers . sum_n_components is the sum of n_components ( output dimension ) over transformers .