8000 [MRG+1] Changing default model for IterativeImputer to BayesianRidge by sergeyf · Pull Request #13038 · scikit-learn/scikit-learn · GitHub
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[MRG+1] Changing default model for IterativeImputer to BayesianRidge #13038

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Merged
merged 8 commits into from
Jan 24, 2019
Merged

[MRG+1] Changing default model for IterativeImputer to BayesianRidge #13038

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69c880b
changing default for iterativeimputer
sergeyf Jan 23, 2019
d417047
also changing impute.rst to pass tests
sergeyf Jan 23, 2019
55ff0d8
updating documentation
sergeyf Jan 23, 2019
eabfa97
Merge branch 'iterativeimputer' into iterativeimputer_bayesianridge
sergeyf Jan 23, 2019
51ee29b
Merge branch 'iterativeimputer' into iterativeimputer_bayesianridge
sergeyf Jan 24, 2019
9ee731c
adding regression test
sergeyf Jan 24, 2019
a73e648
simplifying test
sergeyf Jan 24, 2019
5926025
cosmetic change
glemaitre Jan 24, 2019
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16 changes: 4 additions & 12 deletions sklearn/impute.py
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Original file line number Diff line number Diff line change
Expand Up @@ -455,13 +455,10 @@ class IterativeImputer(BaseEstimator, TransformerMixin):
computed during the final round. A round is a single imputation of each
feature with missing values.

predictor : estimator object, default=RidgeCV() or BayesianRidge()
predictor : estimator object, default=BayesianRidge()
The predictor to use at each step of the round-robin imputation.
If ``sample_posterior`` is True, the predictor must support
``return_std`` in its ``predict`` method. Also, if
``sample_posterior=True`` the default predictor will be
:class:`sklearn.linear_model.BayesianRidge` and
:class:`sklearn.linear_model.RidgeCV` otherwise.
``return_std`` in its ``predict`` method.

sample_posterior : boolean, default=False
Whether to sample from the (Gaussian) predictive posterior of the
Expand Down Expand Up @@ -868,13 +865,8 @@ def fit_transform(self, X, y=None):
.format(self.n_iter))

if self.predictor is None:
if self.sample_posterior:
from .linear_model import BayesianRidge
self._predictor = BayesianRidge()
else:
from .linear_model import RidgeCV
# including a very small alpha to approximate OLS
self._predictor = RidgeCV(alphas=np.array([1e-5, 0.1, 1, 10]))
from .linear_model import BayesianRidge
self._predictor = BayesianRidge()
else:
self._predictor = clone(self.predictor)

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6 changes: 4 additions & 2 deletions sklearn/tests/test_impute.py
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Expand Up @@ -547,7 +547,7 @@ def test_iterative_imputer_imputation_order(imputation_order):

@pytest.mark.parametrize(
"predictor",
[DummyRegressor(), BayesianRidge(), ARDRegression(), RidgeCV()]
[None, DummyRegressor(), BayesianRidge(), ARDRegression(), RidgeCV()]
)
def test_iterative_imputer_predictors(predictor):
rng = np.random.RandomState(0)
Expand All @@ -565,7 +565,9 @@ def test_iterative_imputer_predictors(predictor):
# check that types are correct for predictors
hashes = []
for triplet in imputer.imputation_sequence_:
assert isinstance(triplet.predictor, type(predictor))
expected_type = (type(predictor) if predictor is not None
else type(BayesianRidge()))
assert isinstance(triplet.predictor, expected_type)
hashes.append(id(triplet.predictor))

# check that each predictor is unique
Expand Down
0