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Expose keep_selected_variables_only for sklearn predictors (KhiopsClassifier and KhiopsRegressor) #567

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Expose keep_selected_variables_only for sklearn predictors (KhiopsClassifier and KhiopsRegressor) #567

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5 changes: 5 additions & 0 deletions CHANGELOG.md
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,11 @@
- Example: 10.2.1.4 is the 5th version that supports khiops 10.2.1.
- Internals: Changes in *Internals* sections are unlikely to be of interest for data scientists.

## Unreleased

### Added
- (`sklearn`) `keep_selected_variables_only` parameter to the predictors (KhiopsClassifier and KhiopsRegressor)

## 11.0.0.3 - 2026-03-06

### Added
Expand Down
12 changes: 12 additions & 0 deletions khiops/sklearn/estimators.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1196,6 +1196,7 @@ def __init__(
all_possible_pairs=True,
construction_rules=None,
n_feature_parts=0,
keep_selected_variables_only=True,
verbose=False,
output_dir=None,
auto_sort=True,
Expand All @@ -1213,6 +1214,7 @@ def __init__(
self.all_possible_pairs = all_possible_pairs
self.construction_rules = construction_rules
self.n_feature_parts = n_feature_parts
self.keep_selected_variables_only = keep_selected_variables_only
self._original_target_dtype = None
self._predicted_target_meta_data_tag = None
self._khiops_baseline_model_prefix = None
Expand Down Expand Up @@ -1523,6 +1525,7 @@ def __init__(
all_possible_pairs=True,
construction_rules=None,
n_feature_parts=0,
keep_selected_variables_only=True,
verbose=False,
output_dir=None,
auto_sort=True,
Expand All @@ -1536,6 +1539,7 @@ def __init__(
all_possible_pairs=all_possible_pairs,
construction_rules=construction_rules,
n_feature_parts=n_feature_parts,
keep_selected_variables_only=keep_selected_variables_only,
verbose=verbose,
output_dir=output_dir,
auto_sort=auto_sort,
Expand Down Expand Up @@ -1703,6 +1707,8 @@ class KhiopsClassifier(ClassifierMixin, KhiopsPredictor):
group_target_value : bool, default ``False``
Allows grouping of the target values in classification. It can substantially
increase the training time.
keep_selected_variables_only : bool, default ``True``
Keeps only predictor-selected variables in the supervised analysis report.
verbose : bool, default ``False``
If ``True`` it prints debug information and it does not erase temporary files
when fitting, predicting or transforming.
Expand Down Expand Up @@ -1760,6 +1766,7 @@ def __init__(
construction_rules=None,
n_feature_parts=0,
group_target_value=False,
keep_selected_variables_only=True,
verbose=False,
output_dir=None,
auto_sort=True,
Expand All @@ -1773,6 +1780,7 @@ def __init__(
n_evaluated_features=n_evaluated_features,
construction_rules=construction_rules,
n_feature_parts=n_feature_parts,
keep_selected_variables_only=keep_selected_variables_only,
verbose=verbose,
output_dir=output_dir,
auto_sort=auto_sort,
Expand Down Expand Up @@ -2105,6 +2113,8 @@ class KhiopsRegressor(RegressorMixin, KhiopsPredictor):
n_feature_parts : int, default 0
Maximum number of variable parts produced by preprocessing methods. If equal
to 0 it is automatically calculated.
keep_selected_variables_only : bool, default ``True``
Keeps only predictor-selected variables in the supervised analysis report.
verbose : bool, default ``False``
If ``True`` it prints debug information and it does not erase temporary files
when fitting, predicting or transforming.
Expand Down Expand Up @@ -2149,6 +2159,7 @@ def __init__(
n_evaluated_features=0,
construction_rules=None,
n_feature_parts=0,
keep_selected_variables_only=True,
verbose=False,
output_dir=None,
auto_sort=True,
Expand All @@ -2162,6 +2173,7 @@ def __init__(
n_evaluated_features=n_evaluated_features,
construction_rules=construction_rules,
n_feature_parts=n_feature_parts,
keep_selected_variables_only=keep_selected_variables_only,
verbose=verbose,
output_dir=output_dir,
auto_sort=auto_sort,
Expand Down
6 changes: 6 additions & 0 deletions tests/test_sklearn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -766,6 +766,7 @@ def setUpClass(cls):
"max_parts": 3,
"group_target_value": False,
"additional_data_tables": {},
"keep_selected_variables_only": False,
}
},
"predict": {
Expand Down Expand Up @@ -794,6 +795,7 @@ def setUpClass(cls):
"max_evaluated_variables": 3,
"construction_rules": ["TableMode", "TableSelection"],
"max_parts": 5,
"keep_selected_variables_only": False,
"additional_data_tables": {},
}
},
Expand Down Expand Up @@ -1449,6 +1451,7 @@ def test_parameter_transfer_classifier_fit_from_monotable_dataframe(self):
"construction_rules": ["TableMode", "TableSelection"],
"n_feature_parts": 3,
"group_target_value": False,
"keep_selected_variables_only": False,
},
)

Expand All @@ -1474,6 +1477,7 @@ def test_parameter_transfer_classifier_fit_from_monotable_dataframe_with_df_y(
"construction_rules": ["TableMode", "TableSelection"],
"n_feature_parts": 3,
"group_target_value": False,
"keep_selected_variables_only": False,
},
)

Expand Down Expand Up @@ -1631,6 +1635,7 @@ def test_parameter_transfer_regressor_fit_from_monotable_dataframe(self):
"type_text_features": "ngrams",
"construction_rules": ["TableMode", "TableSelection"],
"n_feature_parts": 5,
"keep_selected_variables_only": False,
},
)

Expand All @@ -1651,6 +1656,7 @@ def test_parameter_transfer_regressor_fit_from_monotable_dataframe_with_df_y(
"type_text_features": "ngrams",
"construction_rules": ["TableMode", "TableSelection"],
"n_feature_parts": 5,
"keep_selected_variables_only": False,
},
)

Expand Down
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