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[MRG] add sparse_threshold to make_column_transformer #12152

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Merged
merged 1 commit into from
Sep 25, 2018
Merged

[MRG] add sparse_threshold to make_column_transformer #12152

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12 changes: 11 additions & 1 deletion sklearn/compose/_column_transformer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -669,6 +669,14 @@ def make_column_transformer(*transformers, **kwargs):
non-specified columns will use the ``remainder`` estimator. The
estimator must support `fit` and `transform`.

sparse_threshold : float, default = 0.3
If the transformed output consists of a mix of sparse and dense data,
it will be stacked as a sparse matrix if the density is lower than this
value. Use ``sparse_threshold=0`` to always return dense.
When the transformed output consists of all sparse or all dense data,
the stacked result will be sparse or dense, respectively, and this
keyword will be ignored.

n_jobs : int or None, optional (default=None)
Number of jobs to run in parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
Expand Down Expand Up @@ -705,9 +713,11 @@ def make_column_transformer(*transformers, **kwargs):
"""
n_jobs = kwargs.pop('n_jobs', None)
remainder = kwargs.pop('remainder', 'drop')
sparse_threshold = kwargs.pop('sparse_threshold', 0.3)
if kwargs:
raise TypeError('Unknown keyword arguments: "{}"'
.format(list(kwargs.keys())[0]))
transformer_list = _get_transformer_list(transformers)
return ColumnTransformer(transformer_list, n_jobs=n_jobs,
remainder=remainder)
remainder=remainder,
sparse_threshold=sparse_threshold)
4 changes: 3 additions & 1 deletion sklearn/compose/tests/test_column_transformer.py
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Expand Up @@ -431,11 +431,13 @@ def test_make_column_transformer_kwargs():
scaler = StandardScaler()
norm = Normalizer()
ct = make_column_transformer(('first', scaler), (['second'], norm),
n_jobs=3, remainder='drop')
n_jobs=3, remainder='drop',
sparse_threshold=0.3)
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Can you use a different default here? Otherwise the test below is not checking that the correct value is actually passed through (as 0.3 is the default)

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I just pushed it -again - too late?

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yeah, don't worry (we are in a hurry a bit to get a release done).
Already opened a follow-up PR: #12156

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But thanks for the PR!

assert_equal(ct.transformers, make_column_transformer(
('first', scaler), (['second'], norm)).transformers)
assert_equal(ct.n_jobs, 3)
assert_equal(ct.remainder, 'drop')
assert_equal(ct.sparse_threshold, 0.3)
# invalid keyword parameters should raise an error message
assert_raise_message(
TypeError,
Expand Down