def feature_union_concat(Xs, nsamples, weights):
"""Apply weights and concatenate outputs from a FeatureUnion"""
if any(x is FIT_FAILURE for x in Xs):
return FIT_FAILURE
Xs = [X if w is None else X * w for X, w in zip(Xs, weights)
if X is not None]
if not Xs:
return np.zeros((nsamples, 0))
if any(sparse.issparse(f) for f in Xs):
return sparse.hstack(Xs).tocsr()
return np.hstack(Xs)
# Current set_params isn't threadsafe
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