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TST Add TransformedTargetRegressor to test_meta_estimators_delegate_data_validation #20175

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Merged
merged 7 commits into from
Jun 3, 2021
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TST Add TransformedTargetRegressor to test_meta_estimators_delegate_data_validation #20175

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f214f33
add transformed target regressor in test_meta_estimators_delegate_dat…
jeremiedbb May 31, 2021
90d6d88
enable the compose module for n_features_in_ common test
jeremiedbb May 31, 2021
5e31152
doc n_features_in_
jeremiedbb Jun 1, 2021
b9a73e6
lint
jeremiedbb Jun 1, 2021
6d7e19e
cln
jeremiedbb Jun 1, 2021
b0f3677
Merge branch 'main' into n_features_in_compose
glemaitre Jun 2, 2021
64b5dba
trigger ci
jeremiedbb Jun 3, 2021
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6 changes: 6 additions & 0 deletions sklearn/compose/_column_transformer.py
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Expand Up @@ -141,6 +141,12 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):

.. versionadded:: 1.0

n_features_in_ : int
Number of features seen during :term:`fit`. Only defined if the
underlying transformers expose such an attribute when fit.

.. versionadded:: 0.24
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If this is not covered by common tests, maybe we should add a dedicated test.

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There are many untested estimators. I'd rather try to find a way to include more estimators in the common tests. We've already started to discuss that with @glemaitre.


Notes
-----
The order of the columns in the transformed feature matrix follows the
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6 changes: 6 additions & 0 deletions sklearn/compose/_target.py
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Expand Up @@ -82,6 +82,12 @@ class TransformedTargetRegressor(RegressorMixin, BaseEstimator):
transformer_ : object
Transformer used in ``fit`` and ``predict``.

n_features_in_ : int
Number of features seen during :term:`fit`. Only defined if the
underlying regressor exposes such an attribute when fit.

.. versionadded:: 0.24

Examples
--------
>>> import numpy as np
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2 changes: 1 addition & 1 deletion sklearn/tests/test_common.py
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Expand Up @@ -261,7 +261,7 @@ def test_search_cv(estimator, check, request):
#
# check_classifiers_train would need to be updated with the error message
N_FEATURES_IN_AFTER_FIT_MODULES_TO_IGNORE = {
'compose',
'feature_extraction',
'model_selection',
'multiclass',
'multioutput',
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2 changes: 1 addition & 1 deletion sklearn/tests/test_metaestimators.py
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Expand Up @@ -169,7 +169,7 @@ def _generate_meta_estimator_instances_with_pipeline():
for _, Estimator in sorted(all_estimators()):
sig = set(signature(Estimator).parameters)

if "estimator" in sig or "base_estimator" in sig:
if "estimator" in sig or "base_estimator" in sig or "regressor" in sig:
if is_regressor(Estimator):
estimator = make_pipeline(TfidfVectorizer(), Ridge())
param_grid = {"ridge__alpha": [0.1, 1.0]}
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