[ENH] Simplified Unified Get/List API

examples/Advanced/tasks_tutorial.py

@@ -24,13 +24,15 @@
 #
 # We will start by simply listing only *supervised classification* tasks.
 #
-# **openml.tasks.list_tasks()** returns a dictionary of dictionaries by default, but we
-# request a
+# **openml.list_all("task")** (or **openml.tasks.list_tasks()**) returns a dictionary of
+# dictionaries by default, but we request a
 # [pandas dataframe](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html)
 # instead to have better visualization capabilities and easier access:
 
 # %%
-tasks = openml.tasks.list_tasks(task_type=TaskType.SUPERVISED_CLASSIFICATION)
+tasks = openml.list_all("task", task_type=TaskType.SUPERVISED_CLASSIFICATION)
+# Legacy path still works:
+# tasks = openml.tasks.list_tasks(task_type=TaskType.SUPERVISED_CLASSIFICATION)
 print(tasks.columns)
 print(f"First 5 of {len(tasks)} tasks:")
 print(tasks.head())
@@ -66,23 +68,29 @@
 # Similar to listing tasks by task type, we can list tasks by tags:
 
 # %%
-tasks = openml.tasks.list_tasks(tag="OpenML100")
+tasks = openml.list_all("task", tag="OpenML100")
+# Legacy path still works:
+# tasks = openml.tasks.list_tasks(tag="OpenML100")
 print(f"First 5 of {len(tasks)} tasks:")
 print(tasks.head())
 
 # %% [markdown]
 # Furthermore, we can list tasks based on the dataset id:
 
 # %%
-tasks = openml.tasks.list_tasks(data_id=1471)
+tasks = openml.list_all("task", data_id=1471)
+# Legacy path still works:
+# tasks = openml.tasks.list_tasks(data_id=1471)
 print(f"First 5 of {len(tasks)} tasks:")
 print(tasks.head())
 
 # %% [markdown]
 # In addition, a size limit and an offset can be applied both separately and simultaneously:
 
 # %%
-tasks = openml.tasks.list_tasks(size=10, offset=50)
+tasks = openml.list_all("task", size=10, offset=50)
+# Legacy path still works:
+# tasks = openml.tasks.list_tasks(size=10, offset=50)
 print(tasks)
 
 # %% [markdown]
@@ -98,7 +106,9 @@
 # Finally, it is also possible to list all tasks on OpenML with:
 
 # %%
-tasks = openml.tasks.list_tasks()
+tasks = openml.list_all("task")
+# Legacy path still works:
+# tasks = openml.tasks.list_tasks()
 print(len(tasks))
 
 # %% [markdown]
@@ -118,7 +128,9 @@
 
 # %%
 task_id = 31
-task = openml.tasks.get_task(task_id)
+task = openml.get(task_id, object_type="task")
+# Legacy path still works:
+# task = openml.tasks.get_task(task_id)
 
 # %%
 # Properties of the task are stored as member variables:

examples/Basics/simple_datasets_tutorial.py

@@ -14,15 +14,23 @@
 # ## List datasets stored on OpenML
 
 # %%
-datasets_df = openml.datasets.list_datasets()
+datasets_df = openml.list_all("dataset")
 print(datasets_df.head(n=10))
 
+# Legacy path still works:
+# datasets_df = openml.datasets.list_datasets()
+
 # %% [markdown]
 # ## Download a dataset
 
 # %%
 # Iris dataset https://www.openml.org/d/61
-dataset = openml.datasets.get_dataset(dataset_id=61)
+dataset = openml.get(61)
+# You can also fetch by name:
+# dataset = openml.get("Fashion-MNIST")
+
+# Legacy path still works:
+# dataset = openml.datasets.get_dataset(dataset_id=61)
 
 # Print a summary
 print(

examples/Basics/simple_flows_and_runs_tutorial.py

@@ -24,12 +24,25 @@
 # %%
 openml.config.start_using_configuration_for_example()
 
+# %% [markdown]
+# ## Quick: list flows and runs via unified entrypoints
+
+# %%
+flows_df = openml.list_all("flow", size=3)
+print(flows_df.head())
+
+runs_df = openml.list_all("run", size=3)
+print(runs_df.head())
+
 # %% [markdown]
 # ## Train a machine learning model and evaluate it
 # NOTE: We are using task 119 from the test server: https://test.openml.org/d/20
 
 # %%
-task = openml.tasks.get_task(119)
+task = openml.get(119, object_type="task")
+
+# Legacy path still works:
+# task = openml.tasks.get_task(119)
 
 # Get the data
 dataset = task.get_dataset()

examples/Basics/simple_tasks_tutorial.py

@@ -10,7 +10,10 @@
 # [supervised classification on credit-g](https://www.openml.org/search?type=task&id=31&source_data.data_id=31):
 
 # %%
-task = openml.tasks.get_task(31)
+task = openml.get(31, object_type="task")
+
+# Legacy path still works:
+# task = openml.tasks.get_task(31)
 
 # %% [markdown]
 # Get the dataset and its data from the task.

openml/__init__.py

@@ -18,6 +18,8 @@
 # License: BSD 3-Clause
 from __future__ import annotations
 
+from typing import Any, Callable, Dict
+
 from . import (
     _api_calls,
     config,
@@ -49,6 +51,102 @@
     OpenMLTask,
 )
 
+ListDispatcher = Dict[str, Callable[..., Any]]
+GetDispatcher = Dict[str, Callable[..., Any]]
+
+_LIST_DISPATCH: ListDispatcher = {
+    "dataset": datasets.functions.list_datasets,
+    "task": tasks.functions.list_tasks,
+    "flow": flows.functions.list_flows,
+    "run": runs.functions.list_runs,
+}
+
+_GET_DISPATCH: GetDispatcher = {
+    "dataset": datasets.functions.get_dataset,
+    "task": tasks.functions.get_task,
+    "flow": flows.functions.get_flow,
+    "run": runs.functions.get_run,
+}
+
+
+def list_all(object_type: str, /, **kwargs: Any) -> Any:
+    """List OpenML objects by type (e.g., datasets, tasks, flows, runs).
+
+    This is a convenience dispatcher that forwards to the existing type-specific
+    ``list_*`` functions. Existing imports remain available for backward compatibility.
+
+    Parameters
+    ----------
+    object_type : str
+        The type of object to list. Must be one of 'dataset', 'task', 'flow', 'run'.
+    **kwargs : Any
+        Additional arguments passed to the underlying list function.
+
+    Returns
+    -------
+    Any
+        The result from the type-specific list function (typically a DataFrame).
+
+    Raises
+    ------
+    ValueError
+        If object_type is not one of the supported types.
+    """
+    if not isinstance(object_type, str):
+        raise TypeError(f"object_type must be a string, got {type(object_type).__name__}")
+
+    func = _LIST_DISPATCH.get(object_type.lower())
+    if func is None:
+        valid_types = ", ".join(repr(k) for k in _LIST_DISPATCH)
+        raise ValueError(
+            f"Unsupported object_type {object_type!r}; expected one of {valid_types}.",
+        )
+
+    return func(**kwargs)
+
+
+def get(identifier: int | str, *, object_type: str = "dataset", **kwargs: Any) -> Any:
+    """Get an OpenML object by identifier.
+
+    Parameters
+    ----------
+    identifier : int | str
+        The ID or name of the object to retrieve.
+    object_type : str, default="dataset"
+        The type of object to get. Must be one of 'dataset', 'task', 'flow', 'run'.
+    **kwargs : Any
+        Additional arguments passed to the underlying get function.
+
+    Returns
+    -------
+    Any
+        The requested OpenML object.
+
+    Raises
+    ------
+    ValueError
+        If object_type is not one of the supported types.
+
+    Examples
+    --------
+    >>> openml.get(61)  # Get dataset 61 (default object_type="dataset")
+    >>> openml.get("Fashion-MNIST")  # Get dataset by name
+    >>> openml.get(31, object_type="task")  # Get task 31
+    >>> openml.get(10, object_type="flow")  # Get flow 10
+    >>> openml.get(20, object_type="run")  # Get run 20
+    """
+    if not isinstance(object_type, str):
+        raise TypeError(f"object_type must be a string, got {type(object_type).__name__}")
+
+    func = _GET_DISPATCH.get(object_type.lower())
+    if func is None:
+        valid_types = ", ".join(repr(k) for k in _GET_DISPATCH)
+        raise ValueError(
+            f"Unsupported object_type {object_type!r}; expected one of {valid_types}.",
+        )
+
+    return func(identifier, **kwargs)
+
 
 def populate_cache(
     task_ids: list[int] | None = None,
@@ -120,4 +218,6 @@ def populate_cache(
     "utils",
     "_api_calls",
     "__version__",
+    "get",
+    "list_all",
 ]

tests/test_openml/test_openml.py

@@ -41,3 +41,27 @@ def test_populate_cache(
         assert task_mock.call_count == 2
         for argument, fixture in zip(task_mock.call_args_list, [(1,), (2,)]):
             assert argument[0] == fixture
+
+    @mock.patch("openml.tasks.functions.list_tasks")
+    @mock.patch("openml.datasets.functions.list_datasets")
+    def test_list_dispatch(self, list_datasets_mock, list_tasks_mock):
+        openml.list("dataset", output_format="dataframe")
+        list_datasets_mock.assert_called_once_with(output_format="dataframe")
+
+        openml.list("task", size=5)
+        list_tasks_mock.assert_called_once_with(size=5)
+
+    @mock.patch("openml.tasks.functions.get_task")
+    @mock.patch("openml.datasets.functions.get_dataset")
+    def test_get_dispatch(self, get_dataset_mock, get_task_mock):
+        openml.get("dataset", 61)
+        get_dataset_mock.assert_called_with(61)
+
+        openml.get("dataset", "Fashion-MNIST", version=2)
+        get_dataset_mock.assert_called_with("Fashion-MNIST", version=2)
+
+        openml.get("Fashion-MNIST")
+        get_dataset_mock.assert_called_with("Fashion-MNIST")
+
+        openml.get("task", 31)
+        get_task_mock.assert_called_with(31)