[ENH] Include estimation procedure metadata

openml/evaluations/__init__.py

@@ -1,10 +1,16 @@
 # License: BSD 3-Clause
 
 from .evaluation import OpenMLEvaluation
-from .functions import list_evaluation_measures, list_evaluations, list_evaluations_setups
+from .functions import (
+    list_estimation_procedures,
+    list_evaluation_measures,
+    list_evaluations,
+    list_evaluations_setups,
+)
 
 __all__ = [
     "OpenMLEvaluation",
+    "list_estimation_procedures",
     "list_evaluation_measures",
     "list_evaluations",
     "list_evaluations_setups",

openml/evaluations/functions.py

@@ -156,18 +156,46 @@ def list_evaluation_measures() -> list[str]:
     return openml._backend.evaluation_measure.list()
 
 
-def list_estimation_procedures() -> list[str]:
-    """Return list of evaluation procedures available.
+@overload
+def list_estimation_procedures(
+    output_format: Literal["dataframe"],
+) -> pd.DataFrame: ...
+
+
+@overload
+def list_estimation_procedures(
+    output_format: Literal["dict"] = ...,
+) -> dict[int, dict[str, object]]: ...
+
+
+def list_estimation_procedures(
+    output_format: Literal["dict", "dataframe"] = "dict",
+) -> dict[int, dict[str, object]] | pd.DataFrame:
+    """Return the estimation procedures available on OpenML.
 
     The function performs an API call to retrieve the entire list of
-    evaluation procedures' names that are available.
+    evaluation procedures. Each procedure includes its ID, task type,
+    name, and type.
+
+    Parameters
+    ----------
+    output_format : {"dict", "dataframe"}, default="dict"
+        The format of the returned procedures. The dictionary format maps
+        procedure IDs to their remaining metadata. The DataFrame format has
+        one row per procedure, including an ``id`` column.
 
     Returns
     -------
-    list
+    dict or pandas.DataFrame
+        The available estimation procedures in the requested format.
     """
     result = openml._backend.estimation_procedure.list()
-    return [i.name for i in result]
+    records = [procedure._to_dict() for procedure in result]
+
+    if output_format == "dataframe":
+        return pd.DataFrame.from_records(records)
+
+    return {record.pop("id"): record for record in records}
 
 
 def list_evaluations_setups(

tests/test_evaluations/test_evaluation_functions.py

@@ -1,10 +1,14 @@
 # License: BSD 3-Clause
 from __future__ import annotations
 
+from unittest.mock import patch
+
 import pytest
 
 import openml
 import openml.evaluations
+from openml.estimation_procedures import OpenMLEstimationProcedure
+from openml.tasks import TaskType
 from openml.testing import TestBase
 
 
@@ -239,6 +243,48 @@ def test_list_evaluation_measures(self):
         assert isinstance(measures, list) is True
         assert all(isinstance(s, str) for s in measures) is True
 
+    def test_list_estimation_procedures_dict(self):
+        procedures = [
+            OpenMLEstimationProcedure(
+                id=5,
+                task_type_id=TaskType.SUPERVISED_CLASSIFICATION,
+                name="10-fold Crossvalidation",
+                type="crossvalidation",
+            )
+        ]
+        with patch.object(openml._backend.estimation_procedure, "list", return_value=procedures):
+            result = openml.evaluations.list_estimation_procedures()
+
+        assert result == {
+            5: {
+                "task_type_id": TaskType.SUPERVISED_CLASSIFICATION,
+                "name": "10-fold Crossvalidation",
+                "type": "crossvalidation",
+            }
+        }
+
+    def test_list_estimation_procedures_dataframe(self):
+        procedures = [
+            OpenMLEstimationProcedure(
+                id=5,
+                task_type_id=TaskType.SUPERVISED_CLASSIFICATION,
+                name="10-fold Crossvalidation",
+                type="crossvalidation",
+            )
+        ]
+        with patch.object(openml._backend.estimation_procedure, "list", return_value=procedures):
+            result = openml.evaluations.list_estimation_procedures(output_format="dataframe")
+
+        assert list(result.columns) == ["id", "task_type_id", "name", "type"]
+        assert result.to_dict("records") == [
+            {
+                "id": 5,
+                "task_type_id": TaskType.SUPERVISED_CLASSIFICATION,
+                "name": "10-fold Crossvalidation",
+                "type": "crossvalidation",
+            }
+        ]
+
     @pytest.mark.production_server()
     def test_list_evaluations_setups_filter_flow(self):
         self.use_production_server()