Rename transform_pairs parameter to transform_type_pairs

Author: folmos-at-orange

doc/samples/samples_sklearn.rst

@@ -723,7 +723,7 @@ Samples
         keep_initial_variables=True,
         transform_type_categorical="part_id",
         transform_type_numerical="part_id",
-        transform_pairs="part_id",
+        transform_type_pairs="part_id",
     )
     khe.fit(X, y)
 

khiops/samples/samples_sklearn.ipynb

@@ -865,7 +865,7 @@
     "    keep_initial_variables=True,\n",
     "    transform_type_categorical=\"part_id\",\n",
     "    transform_type_numerical=\"part_id\",\n",
-    "    transform_pairs=\"part_id\",\n",
+    "    transform_type_pairs=\"part_id\",\n",
     ")\n",
     "khe.fit(X, y)\n",
     "\n",

khiops/samples/samples_sklearn.py

@@ -761,7 +761,7 @@ def khiops_encoder_with_hyperparameters():
         keep_initial_variables=True,
         transform_type_categorical="part_id",
         transform_type_numerical="part_id",
-        transform_pairs="part_id",
+        transform_type_pairs="part_id",
     )
     khe.fit(X, y)
 

khiops/sklearn/estimators.py

@@ -2731,7 +2731,7 @@ class KhiopsEncoder(TransformerMixin, KhiopsSupervisedEstimator):
 
         See the documentation for the ``numerical_recoding_method`` parameter of the
         `~.api.train_recoder` function for more details.
-    transform_pairs: str, default "part_id"
+    transform_type_pairs : str, default "part_id"
         Type of transformation for bivariate features. Valid values:
             - "part_id"
             - "part_label"
@@ -2811,7 +2811,7 @@ def __init__(
         keep_initial_variables=False,
         transform_type_categorical="part_id",
         transform_type_numerical="part_id",
-        transform_pairs="part_id",
+        transform_type_pairs="part_id",
         verbose=False,
         output_dir=None,
         auto_sort=True,
@@ -2835,7 +2835,7 @@ def __init__(
         self.group_target_value = group_target_value
         self.transform_type_categorical = transform_type_categorical
         self.transform_type_numerical = transform_type_numerical
-        self.transform_pairs = transform_pairs
+        self.transform_type_pairs = transform_type_pairs
         self.informative_features_only = informative_features_only
         self.keep_initial_variables = keep_initial_variables
         self._khiops_model_prefix = "R_"
@@ -2892,12 +2892,12 @@ def _pairs_transform_method(self):
             "conditional_info": "conditional info",
             None: "none",
         }
-        if self.transform_pairs not in _transform_types:
+        if self.transform_type_pairs not in _transform_types:
             raise ValueError(
-                "'transform_pairs' must be one of the following:"
+                "'transform_type_pairs' must be one of the following:"
                 ",".join(_transform_types.keys)
             )
-        return _transform_types[self.transform_pairs]
+        return _transform_types[self.transform_type_pairs]
 
     def _fit_check_params(self, ds, **kwargs):
         # Call parent method
@@ -2931,10 +2931,12 @@ def _fit_check_params(self, ds, **kwargs):
                 "transform_type_categorical and transform_type_numerical "
                 "cannot be both None with n_trees == 0."
             )
-        # Check 'transform_pairs' parameter
-        if not isinstance(self.transform_pairs, str):
+        # Check 'transform_type_pairs' parameter
+        if not isinstance(self.transform_type_pairs, str):
             raise TypeError(
-                type_error_message("transform_pairs", self.transform_pairs, str)
+                type_error_message(
+                    "transform_type_pairs", self.transform_type_pairs, str
+                )
             )
         self._pairs_transform_method()  # Raises ValueError if invalid
 
@@ -3036,7 +3038,7 @@ def _fit_prepare_training_function_inputs(self, ds, computation_dir):
 
         del kwargs["transform_type_categorical"]
         del kwargs["transform_type_numerical"]
-        del kwargs["transform_pairs"]
+        del kwargs["transform_type_pairs"]
         del kwargs["categorical_target"]
 
         return args, kwargs

tests/test_sklearn.py

@@ -2265,7 +2265,7 @@ def test_parameter_transfer_encoder_fit_from_monotable_dataframe(self):
                 "keep_initial_variables": False,
                 "transform_type_categorical": "part_id",
                 "transform_type_numerical": "part_id",
-                "transform_pairs": "part_id",
+                "transform_type_pairs": "part_id",
             },
         )
 
@@ -2289,7 +2289,7 @@ def test_parameter_transfer_encoder_fit_from_monotable_dataframe_with_df_y(
                 "keep_initial_variables": False,
                 "transform_type_categorical": "part_id",
                 "transform_type_numerical": "part_id",
-                "transform_pairs": "part_id",
+                "transform_type_pairs": "part_id",
             },
         )
 
@@ -2311,7 +2311,7 @@ def test_parameter_transfer_encoder_fit_from_monotable_file_dataset(self):
                 "keep_initial_variables": False,
                 "transform_type_categorical": "part_id",
                 "transform_type_numerical": "part_id",
-                "transform_pairs": "part_id",
+                "transform_type_pairs": "part_id",
             },
         )
 
@@ -2334,7 +2334,7 @@ def test_parameter_transfer_encoder_fit_from_multitable_dataframe(self):
                 "keep_initial_variables": False,
                 "transform_type_categorical": "part_id",
                 "transform_type_numerical": "part_id",
-                "transform_pairs": "part_id",
+                "transform_type_pairs": "part_id",
             },
         )
 
@@ -2357,7 +2357,7 @@ def test_parameter_transfer_encoder_fit_from_multitable_file_dataset(self):
                 "keep_initial_variables": False,
                 "transform_type_categorical": "part_id",
                 "transform_type_numerical": "part_id",
-                "transform_pairs": "part_id",
+                "transform_type_pairs": "part_id",
             },
         )