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tramora merged 2 commits into from
Feb 13, 2026
+54 −8
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Add the n_feature_parts parameter to the supervised estimators #544

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8230bd4
Add the `n_feature_parts` parameter to the supervised estimators
Jan 22, 2026
1275356
Sklearn : rephrase error messages to be more precise
Feb 12, 2026
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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`) `n_feature_parts` parameter to the supervised estimators

## 11.0.0.2 - 2026-01-26

## Fixed
Expand Down
42 changes: 34 additions & 8 deletions khiops/sklearn/estimators.py
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Original file line number Diff line number Diff line change
Expand Up @@ -180,7 +180,7 @@ def _check_pair_parameters(estimator):
if not isinstance(estimator.n_pairs, int):
raise TypeError(type_error_message("n_pairs", estimator.n_pairs, int))
if estimator.n_pairs < 0:
raise ValueError("'n_pairs' must be positive")
raise ValueError("'n_pairs' must be non-negative")
if estimator.specific_pairs is not None:
if not is_list_like(estimator.specific_pairs):
raise TypeError(
Expand Down Expand Up @@ -955,7 +955,7 @@ def _simplify(
type_error_message("'max_part_numbers' values", value, int)
)
elif value < 0:
raise ValueError("'max_part_numbers' values must be positive")
raise ValueError("'max_part_numbers' values must be non-negative")
# Create temporary directory and tables
computation_dir = self._create_computation_dir("simplify")
output_dir = self._get_output_dir(computation_dir)
Expand Down Expand Up @@ -1195,6 +1195,7 @@ def __init__(
specific_pairs=None,
all_possible_pairs=True,
construction_rules=None,
n_feature_parts=0,
verbose=False,
output_dir=None,
auto_sort=True,
Expand All @@ -1211,6 +1212,7 @@ def __init__(
self.specific_pairs = specific_pairs
self.all_possible_pairs = all_possible_pairs
self.construction_rules = construction_rules
self.n_feature_parts = n_feature_parts
self._original_target_dtype = None
self._predicted_target_meta_data_tag = None
self._khiops_baseline_model_prefix = None
Expand Down Expand Up @@ -1270,17 +1272,17 @@ def _fit_check_params(self, ds, **kwargs):
if not isinstance(self.n_features, int):
raise TypeError(type_error_message("n_features", self.n_features, int))
if self.n_features < 0:
raise ValueError("'n_features' must be positive")
raise ValueError("'n_features' must be non-negative")
if not isinstance(self.n_trees, int):
raise TypeError(type_error_message("n_trees", self.n_trees, int))
if self.n_trees < 0:
raise ValueError("'n_trees' must be positive")
raise ValueError("'n_trees' must be non-negative")
if not isinstance(self.n_text_features, int):
raise TypeError(
type_error_message("n_text_features", self.n_text_features, int)
)
if self.n_text_features < 0:
raise ValueError("'n_text_features' must be positive")
raise ValueError("'n_text_features' must be non-negative")
if not isinstance(self.type_text_features, str):
raise TypeError(
type_error_message("type_text_features", self.type_text_features, str)
Expand All @@ -1300,6 +1302,12 @@ def _fit_check_params(self, ds, **kwargs):
for rule in self.construction_rules:
if not isinstance(rule, str):
raise TypeError(type_error_message(rule, rule, str))
if not isinstance(self.n_feature_parts, int):
raise TypeError(
type_error_message("n_feature_parts", self.n_feature_parts, int)
)
if self.n_feature_parts < 0:
raise ValueError("'n_feature_parts' must be non-negative")

def _fit_train_model(self, ds, computation_dir, **kwargs):
# Train the model with Khiops
Expand Down Expand Up @@ -1384,6 +1392,7 @@ def _fit_prepare_training_function_inputs(self, ds, computation_dir):
kwargs["max_trees"] = kwargs.pop("n_trees")
kwargs["max_text_features"] = kwargs.pop("n_text_features")
kwargs["text_features"] = kwargs.pop("type_text_features")
kwargs["max_parts"] = kwargs.pop("n_feature_parts")

# Add the additional_data_tables parameter
kwargs["additional_data_tables"] = additional_data_tables
Expand Down Expand Up @@ -1513,6 +1522,7 @@ def __init__(
specific_pairs=None,
all_possible_pairs=True,
construction_rules=None,
n_feature_parts=0,
verbose=False,
output_dir=None,
auto_sort=True,
Expand All @@ -1525,6 +1535,7 @@ def __init__(
specific_pairs=specific_pairs,
all_possible_pairs=all_possible_pairs,
construction_rules=construction_rules,
n_feature_parts=n_feature_parts,
verbose=verbose,
output_dir=output_dir,
auto_sort=auto_sort,
Expand Down Expand Up @@ -1624,9 +1635,9 @@ def _fit_check_params(self, ds, **kwargs):

# Check estimator parameters
if self.n_evaluated_features < 0:
raise ValueError("'n_evaluated_features' must be positive")
raise ValueError("'n_evaluated_features' must be non-negative")
if self.n_selected_features < 0:
raise ValueError("'n_selected_features' must be positive")
raise ValueError("'n_selected_features' must be non-negative")


# Note: scikit-learn **requires** inherit first the mixins and then other classes
Expand Down Expand Up @@ -1685,7 +1696,10 @@ class KhiopsClassifier(ClassifierMixin, KhiopsPredictor):
construction_rules : list of str, optional
Allowed rules for the automatic feature construction. If not set, Khiops
uses the multi-table construction rules listed in
`kh.DEFAULT_CONSTRUCTION_RULES <khiops.core.api.DEFAULT_CONSTRUCTION_RULES>`
`kh.DEFAULT_CONSTRUCTION_RULES <khiops.core.api.DEFAULT_CONSTRUCTION_RULES>`.
n_feature_parts : int, default 0
Maximum number of variable parts produced by preprocessing methods. If equal
to 0 it is automatically calculated.
group_target_value : bool, default ``False``
Allows grouping of the target values in classification. It can substantially
increase the training time.
Expand Down Expand Up @@ -1744,6 +1758,7 @@ def __init__(
specific_pairs=None,
all_possible_pairs=True,
construction_rules=None,
n_feature_parts=0,
group_target_value=False,
verbose=False,
output_dir=None,
Expand All @@ -1757,6 +1772,7 @@ def __init__(
n_selected_features=n_selected_features,
n_evaluated_features=n_evaluated_features,
construction_rules=construction_rules,
n_feature_parts=n_feature_parts,
verbose=verbose,
output_dir=output_dir,
auto_sort=auto_sort,
Expand Down Expand Up @@ -2086,6 +2102,9 @@ class KhiopsRegressor(RegressorMixin, KhiopsPredictor):
Allowed rules for the automatic feature construction. If not set, Khiops
uses the multi-table construction rules listed in
`kh.DEFAULT_CONSTRUCTION_RULES <khiops.core.api.DEFAULT_CONSTRUCTION_RULES>`.
n_feature_parts : int, default 0
Maximum number of variable parts produced by preprocessing methods. If equal
to 0 it is automatically calculated.
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 @@ -2129,6 +2148,7 @@ def __init__(
n_selected_features=0,
n_evaluated_features=0,
construction_rules=None,
n_feature_parts=0,
verbose=False,
output_dir=None,
auto_sort=True,
Expand All @@ -2141,6 +2161,7 @@ def __init__(
n_selected_features=n_selected_features,
n_evaluated_features=n_evaluated_features,
construction_rules=construction_rules,
n_feature_parts=n_feature_parts,
verbose=verbose,
output_dir=output_dir,
auto_sort=auto_sort,
Expand Down Expand Up @@ -2296,6 +2317,9 @@ class KhiopsEncoder(TransformerMixin, KhiopsSupervisedEstimator):
Allowed rules for the automatic feature construction. If not set, Khiops
uses the multi-table construction rules listed in
`kh.DEFAULT_CONSTRUCTION_RULES <khiops.core.api.DEFAULT_CONSTRUCTION_RULES>`.
n_feature_parts : int, default 0
Maximum number of variable parts produced by preprocessing methods. If equal
to 0 it is automatically calculated.
informative_features_only : bool, default ``True``
If ``True`` keeps only informative features.
group_target_value : bool, default ``False``
Expand Down Expand Up @@ -2374,6 +2398,7 @@ def __init__(
specific_pairs=None,
all_possible_pairs=True,
construction_rules=None,
n_feature_parts=0,
informative_features_only=True,
group_target_value=False,
keep_initial_variables=False,
Expand All @@ -2390,6 +2415,7 @@ def __init__(
n_text_features=n_text_features,
type_text_features=type_text_features,
construction_rules=construction_rules,
n_feature_parts=n_feature_parts,
verbose=verbose,
output_dir=output_dir,
auto_sort=auto_sort,
Expand Down
15 changes: 15 additions & 0 deletions tests/test_sklearn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -762,6 +762,7 @@ def setUpClass(cls):
"specific_pairs": [("age", "race")],
"all_possible_pairs": False,
"construction_rules": ["TableMode", "TableSelection"],
"max_parts": 3,
"group_target_value": False,
"additional_data_tables": {},
}
Expand Down Expand Up @@ -790,6 +791,7 @@ def setUpClass(cls):
"max_selected_variables": 1,
"max_evaluated_variables": 3,
"construction_rules": ["TableMode", "TableSelection"],
"max_parts": 5,
"additional_data_tables": {},
}
},
Expand Down Expand Up @@ -818,6 +820,7 @@ def setUpClass(cls):
"specific_pairs": [("age", "race")],
"all_possible_pairs": False,
"construction_rules": ["TableMode", "TableSelection"],
"max_parts": 7,
"informative_variables_only": True,
"group_target_value": False,
"keep_initial_categorical_variables": False,
Expand Down Expand Up @@ -860,6 +863,7 @@ def setUpClass(cls):
"specific_pairs": [],
"all_possible_pairs": False,
"construction_rules": ["TableMode", "TableSelection"],
"max_parts": 4,
"group_target_value": False,
"additional_data_tables": {"SpliceJunctionDNA"},
}
Expand Down Expand Up @@ -889,6 +893,7 @@ def setUpClass(cls):
"max_selected_variables": 1,
"max_evaluated_variables": 3,
"construction_rules": ["TableMode", "TableSelection"],
"max_parts": 6,
"additional_data_tables": {"SpliceJunctionDNA"},
}
},
Expand Down Expand Up @@ -918,6 +923,7 @@ def setUpClass(cls):
"specific_pairs": [],
"all_possible_pairs": False,
"construction_rules": ["TableMode", "TableSelection"],
"max_parts": 8,
"informative_variables_only": True,
"group_target_value": False,
"keep_initial_categorical_variables": False,
Expand Down Expand Up @@ -1435,6 +1441,7 @@ def test_parameter_transfer_classifier_fit_from_monotable_dataframe(self):
"specific_pairs": [("age", "race")],
"all_possible_pairs": False,
"construction_rules": ["TableMode", "TableSelection"],
"n_feature_parts": 3,
"group_target_value": False,
},
)
Expand All @@ -1458,6 +1465,7 @@ def test_parameter_transfer_classifier_fit_from_monotable_dataframe_with_df_y(
"specific_pairs": [("age", "race")],
"all_possible_pairs": False,
"construction_rules": ["TableMode", "TableSelection"],
"n_feature_parts": 3,
"group_target_value": False,
},
)
Expand All @@ -1480,6 +1488,7 @@ def test_parameter_transfer_classifier_fit_from_multitable_dataframe(self):
"specific_pairs": [],
"all_possible_pairs": False,
"construction_rules": ["TableMode", "TableSelection"],
"n_feature_parts": 4,
"group_target_value": False,
},
)
Expand Down Expand Up @@ -1517,6 +1526,7 @@ def test_parameter_transfer_encoder_fit_from_monotable_dataframe(self):
"specific_pairs": [("age", "race")],
"all_possible_pairs": False,
"construction_rules": ["TableMode", "TableSelection"],
"n_feature_parts": 7,
"informative_features_only": True,
"group_target_value": False,
"keep_initial_variables": False,
Expand All @@ -1543,6 +1553,7 @@ def test_parameter_transfer_encoder_fit_from_monotable_dataframe_with_df_y(
"specific_pairs": [("age", "race")],
"all_possible_pairs": False,
"construction_rules": ["TableMode", "TableSelection"],
"n_feature_parts": 7,
"informative_features_only": True,
"group_target_value": False,
"keep_initial_variables": False,
Expand All @@ -1568,6 +1579,7 @@ def test_parameter_transfer_encoder_fit_from_multitable_dataframe(self):
"specific_pairs": [],
"all_possible_pairs": False,
"construction_rules": ["TableMode", "TableSelection"],
"n_feature_parts": 8,
"informative_features_only": True,
"group_target_value": False,
"keep_initial_variables": False,
Expand Down Expand Up @@ -1608,6 +1620,7 @@ def test_parameter_transfer_regressor_fit_from_monotable_dataframe(self):
"n_text_features": 300000,
"type_text_features": "ngrams",
"construction_rules": ["TableMode", "TableSelection"],
"n_feature_parts": 5,
},
)

Expand All @@ -1626,6 +1639,7 @@ def test_parameter_transfer_regressor_fit_from_monotable_dataframe_with_df_y(
"n_text_features": 300000,
"type_text_features": "ngrams",
"construction_rules": ["TableMode", "TableSelection"],
"n_feature_parts": 5,
},
)

Expand All @@ -1644,6 +1658,7 @@ def test_parameter_transfer_regressor_fit_from_multitable_dataframe(self):
"n_selected_features": 1,
"n_evaluated_features": 3,
"construction_rules": ["TableMode", "TableSelection"],
"n_feature_parts": 6,
},
)

Expand Down
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