def _hstack(self, Xs):
"""Stacks Xs horizontally.
This allows subclasses to control the stacking behavior, while reusing
everything else from ColumnTransformer.
Parameters
----------
Xs : list of {array-like, sparse matrix, dataframe}
"""
if self.sparse_output_:
try:
# since all columns should be numeric before stacking them
# in a sparse matrix, `check_array` is used for the
# dtype conversion if necessary.
converted_Xs = [
check_array(X, accept_sparse=True, force_all_finite=False)
for X in Xs
]
except ValueError as e:
raise ValueError(
"For a sparse output, all columns should "
"be a numeric or convertible to a numeric.") from e
return cu_sparse.hstack(converted_Xs).tocsr()
else:
Xs = [f.toarray() if issparse(f) else f for f in Xs]
return np.hstack(Xs)
def _concat_cupy_sparse(L, axis=0):
if axis == 0:
return vstack(L)
elif axis == 1:
return hstack(L)
else:
msg = ("Can only concatenate cupy sparse matrices for axis in "
"{0, 1}. Got %s" % axis)
raise ValueError(msg)