Fix usage of optuna distributions

docs/changes/newsfragments/268.bugfix

@@ -0,0 +1 @@
+Fix usage of optuna distributions as hyperparameters in older versions of scikit-learn by `Fede Raimondo`_.

julearn/model_selection/_optuna_searcher.py

@@ -38,6 +38,29 @@ def register_optuna_searcher():
     _recreate_reset_copy()
 
 
+def is_optuna_valid_distribution(obj: Any) -> bool:
+    """Check if an object is a valid Optuna distribution.
+
+    Parameters
+    ----------
+    obj : any
+        The object to check.
+
+    Returns
+    -------
+    bool
+        Whether the object is a valid Optuna distribution.
+
+    """
+    _valid_classes = [
+        "IntDistribution",
+        "FloatDistribution",
+        "CategoricalDistribution",
+    ]
+
+    return obj.__class__.__name__ in _valid_classes
+
+
 def _prepare_optuna_hyperparameters_distributions(
     params_to_tune: Dict[str, Any],
 ) -> Dict[str, Any]:

julearn/model_selection/tests/test_optuna_searcher.py

@@ -9,6 +9,7 @@
 
 from julearn.model_selection._optuna_searcher import (
     _prepare_optuna_hyperparameters_distributions,
+    is_optuna_valid_distribution,
 )
 
 
@@ -164,3 +165,35 @@ def test__prepare_optuna_hyperparameters_distributions(
                 )
         else:
             pytest.fail("Invalid distribution type")
+
+
+@pytest.mark.parametrize(
+    "obj,expected",
+    [
+        (optd.IntDistribution(1, 20, log=False), True),
+        (optd.FloatDistribution(0.2, 0.7, log=False), True),
+        (optd.CategoricalDistribution([1, 2, 3]), True),
+        (optd.CategoricalDistribution(["a", "b", "c"]), True),
+        (optd.CategoricalDistribution(["a", "b", "c", "d"]), True),
+        ("uniform", False),
+        ("log-uniform", False),
+        ("categorical", False),
+        (1, False),
+        (1.0, False),
+        ([1, 2, 3], False),
+        (["a", "b", "c"], False),
+    ],
+)
+def test_optuna_valid_distributions(obj: Any, expected: bool) -> None:
+    """Test the optuna_valid_distributions function.
+
+    Parameters
+    ----------
+    obj : Any
+        The object to check.
+    expected : bool
+        The expected result.
+
+    """
+    out = is_optuna_valid_distribution(obj)
+    assert out == expected

julearn/pipeline/pipeline_creator.py

@@ -16,6 +16,7 @@
 from ..base import ColumnTypes, ColumnTypesLike, JuTransformer, WrapModel
 from ..model_selection._optuna_searcher import (
     _prepare_optuna_hyperparameters_distributions,
+    is_optuna_valid_distribution,
 )
 from ..model_selection._skopt_searcher import (
     _prepare_skopt_hyperparameters_distributions,
@@ -263,6 +264,9 @@ def add(
                 # If it is a distribution, we will tune it.
                 logger.info(f"Tuning hyperparameter {param} = {vals}")
                 params_to_tune[param] = vals
+            elif is_optuna_valid_distribution(vals):
+                logger.info(f"Tuning hyperparameter {param} = {vals}")
+                params_to_tune[param] = vals
             else:
                 logger.info(f"Setting hyperparameter {param} = {vals}")
                 params_to_set[param] = vals

julearn/pipeline/tests/test_merger.py

@@ -51,7 +51,7 @@ def test_merger_pipelines() -> None:
     assert "scaler" == named_steps[1]
     assert "rf" == named_steps[2]
     assert len(merged.param_distributions) == 3  # type: ignore
-    assert merged.param_distributions[-1][
+    assert merged.param_distributions[-1][  # type: ignore
         "rf__max_features"
     ] == [  # type: ignore
         2,

julearn/transformers/tests/test_jucolumntransformers.py

@@ -157,7 +157,7 @@ def test_JuColumnTransformer_row_select():
 
     mean_both = (
         transformer_both.fit(X)
-        .column_transformer_.transformers_[0][1]
+        .column_transformer_.transformers_[0][1]  # type: ignore
         .mean_  # type: ignore
     )