functions

__list_evaluations(api_call)

Helper function to parse API calls which are lists of runs

Source code in openml/evaluations/functions.py

def __list_evaluations(api_call: str) -> list[OpenMLEvaluation]:
    """Helper function to parse API calls which are lists of runs"""
    xml_string = openml._api_calls._perform_api_call(api_call, "get")
    evals_dict = xmltodict.parse(xml_string, force_list=("oml:evaluation",))
    # Minimalistic check if the XML is useful
    if "oml:evaluations" not in evals_dict:
        raise ValueError(
            "Error in return XML, does not contain " f'"oml:evaluations": {evals_dict!s}',
        )

    assert isinstance(evals_dict["oml:evaluations"]["oml:evaluation"], list), type(
        evals_dict["oml:evaluations"],
    )

    uploader_ids = list(
        {eval_["oml:uploader"] for eval_ in evals_dict["oml:evaluations"]["oml:evaluation"]},
    )
    api_users = "user/list/user_id/" + ",".join(uploader_ids)
    xml_string_user = openml._api_calls._perform_api_call(api_users, "get")

    users = xmltodict.parse(xml_string_user, force_list=("oml:user",))
    user_dict = {user["oml:id"]: user["oml:username"] for user in users["oml:users"]["oml:user"]}

    evals = []
    for eval_ in evals_dict["oml:evaluations"]["oml:evaluation"]:
        run_id = int(eval_["oml:run_id"])
        value = float(eval_["oml:value"]) if "oml:value" in eval_ else None
        values = json.loads(eval_["oml:values"]) if eval_.get("oml:values", None) else None
        array_data = eval_.get("oml:array_data")

        evals.append(
            OpenMLEvaluation(
                run_id=run_id,
                task_id=int(eval_["oml:task_id"]),
                setup_id=int(eval_["oml:setup_id"]),
                flow_id=int(eval_["oml:flow_id"]),
                flow_name=eval_["oml:flow_name"],
                data_id=int(eval_["oml:data_id"]),
                data_name=eval_["oml:data_name"],
                function=eval_["oml:function"],
                upload_time=eval_["oml:upload_time"],
                uploader=int(eval_["oml:uploader"]),
                uploader_name=user_dict[eval_["oml:uploader"]],
                value=value,
                values=values,
                array_data=array_data,
            )
        )

    return evals

list_estimation_procedures()

Return list of evaluation procedures available.

The function performs an API call to retrieve the entire list of evaluation procedures' names that are available.

Returns:

Type	Description
list

Source code in openml/evaluations/functions.py

def list_estimation_procedures() -> list[str]:
    """Return list of evaluation procedures available.

    The function performs an API call to retrieve the entire list of
    evaluation procedures' names that are available.

    Returns
    -------
    list
    """
    api_call = "estimationprocedure/list"
    xml_string = openml._api_calls._perform_api_call(api_call, "get")
    api_results = xmltodict.parse(xml_string)

    # Minimalistic check if the XML is useful
    if "oml:estimationprocedures" not in api_results:
        raise ValueError('Error in return XML, does not contain "oml:estimationprocedures"')

    if "oml:estimationprocedure" not in api_results["oml:estimationprocedures"]:
        raise ValueError('Error in return XML, does not contain "oml:estimationprocedure"')

    if not isinstance(api_results["oml:estimationprocedures"]["oml:estimationprocedure"], list):
        raise TypeError('Error in return XML, does not contain "oml:estimationprocedure" as a list')

    return [
        prod["oml:name"]
        for prod in api_results["oml:estimationprocedures"]["oml:estimationprocedure"]
    ]

list_evaluation_measures()

Return list of evaluation measures available.

The function performs an API call to retrieve the entire list of evaluation measures that are available.

Returns:

Type	Description
list

Source code in openml/evaluations/functions.py

def list_evaluation_measures() -> list[str]:
    """Return list of evaluation measures available.

    The function performs an API call to retrieve the entire list of
    evaluation measures that are available.

    Returns
    -------
    list

    """
    api_call = "evaluationmeasure/list"
    xml_string = openml._api_calls._perform_api_call(api_call, "get")
    qualities = xmltodict.parse(xml_string, force_list=("oml:measures"))
    # Minimalistic check if the XML is useful
    if "oml:evaluation_measures" not in qualities:
        raise ValueError('Error in return XML, does not contain "oml:evaluation_measures"')

    if not isinstance(qualities["oml:evaluation_measures"]["oml:measures"][0]["oml:measure"], list):
        raise TypeError('Error in return XML, does not contain "oml:measure" as a list')

    return qualities["oml:evaluation_measures"]["oml:measures"][0]["oml:measure"]

list_evaluations(function, offset=None, size=None, tasks=None, setups=None, flows=None, runs=None, uploaders=None, tag=None, study=None, per_fold=None, sort_order=None, output_format='object')

list_evaluations(function: str, offset: int | None = None, size: int | None = None, tasks: list[str | int] | None = None, setups: list[str | int] | None = None, flows: list[str | int] | None = None, runs: list[str | int] | None = None, uploaders: list[str | int] | None = None, tag: str | None = None, study: int | None = None, per_fold: bool | None = None, sort_order: str | None = None, output_format: Literal['dataframe'] = ...) -> pd.DataFrame

list_evaluations(function: str, offset: int | None = None, size: int | None = None, tasks: list[str | int] | None = None, setups: list[str | int] | None = None, flows: list[str | int] | None = None, runs: list[str | int] | None = None, uploaders: list[str | int] | None = None, tag: str | None = None, study: int | None = None, per_fold: bool | None = None, sort_order: str | None = None, output_format: Literal['object'] = 'object') -> dict[int, OpenMLEvaluation]

List all run-evaluation pairs matching all of the given filters.

(Supports large amount of results)

Parameters:

Name	Type	Description	Default
function	str	the evaluation function. e.g., predictive_accuracy	required
offset	int	the number of runs to skip, starting from the first	None
size	int	The maximum number of runs to show. If set to None, it returns all the results.	10000
tasks	list[int, str]	the list of task IDs	None
setups	list[str | int] | None	the list of setup IDs	None
flows	list[int, str]	the list of flow IDs	None
runs	list[str | int] | None	the list of run IDs	None
uploaders	list[int, str]	the list of uploader IDs	None
tag	str	filter evaluation based on given tag	None
study	int		None
per_fold	bool		None
sort_order	str	order of sorting evaluations, ascending ("asc") or descending ("desc")	None
output_format	Literal['object', 'dataframe']	The parameter decides the format of the output. - If 'object' the output is a dict of OpenMLEvaluation objects - If 'dataframe' the output is a pandas DataFrame	'object'

Returns:

Type	Description
dict or dataframe

Source code in openml/evaluations/functions.py

def list_evaluations(
    function: str,
    offset: int | None = None,
    size: int | None = None,
    tasks: list[str | int] | None = None,
    setups: list[str | int] | None = None,
    flows: list[str | int] | None = None,
    runs: list[str | int] | None = None,
    uploaders: list[str | int] | None = None,
    tag: str | None = None,
    study: int | None = None,
    per_fold: bool | None = None,
    sort_order: str | None = None,
    output_format: Literal["object", "dataframe"] = "object",
) -> dict[int, OpenMLEvaluation] | pd.DataFrame:
    """List all run-evaluation pairs matching all of the given filters.

    (Supports large amount of results)

    Parameters
    ----------
    function : str
        the evaluation function. e.g., predictive_accuracy
    offset : int, optional
        the number of runs to skip, starting from the first
    size : int, default 10000
        The maximum number of runs to show.
        If set to ``None``, it returns all the results.

    tasks : list[int,str], optional
        the list of task IDs
    setups: list[int,str], optional
        the list of setup IDs
    flows : list[int,str], optional
        the list of flow IDs
    runs :list[int,str], optional
        the list of run IDs
    uploaders : list[int,str], optional
        the list of uploader IDs
    tag : str, optional
        filter evaluation based on given tag

    study : int, optional

    per_fold : bool, optional

    sort_order : str, optional
       order of sorting evaluations, ascending ("asc") or descending ("desc")

    output_format: str, optional (default='object')
        The parameter decides the format of the output.
        - If 'object' the output is a dict of OpenMLEvaluation objects
        - If 'dataframe' the output is a pandas DataFrame

    Returns
    -------
    dict or dataframe
    """
    if output_format not in ("dataframe", "object"):
        raise ValueError("Invalid output format. Only 'object', 'dataframe'.")

    per_fold_str = None
    if per_fold is not None:
        per_fold_str = str(per_fold).lower()

    listing_call = partial(
        _list_evaluations,
        function=function,
        tasks=tasks,
        setups=setups,
        flows=flows,
        runs=runs,
        uploaders=uploaders,
        tag=tag,
        study=study,
        sort_order=sort_order,
        per_fold=per_fold_str,
    )
    eval_collection = openml.utils._list_all(listing_call, offset=offset, limit=size)

    flattened = list(chain.from_iterable(eval_collection))
    if output_format == "dataframe":
        records = [item._to_dict() for item in flattened]
        return pd.DataFrame.from_records(records)  # No index...

    return {e.run_id: e for e in flattened}

list_evaluations_setups(function, offset=None, size=None, tasks=None, setups=None, flows=None, runs=None, uploaders=None, tag=None, per_fold=None, sort_order=None, parameters_in_separate_columns=False)

List all run-evaluation pairs matching all of the given filters and their hyperparameter settings.

Parameters:

Name	Type	Description	Default
function	str	the evaluation function. e.g., predictive_accuracy	required
offset	int	the number of runs to skip, starting from the first	None
size	int	the maximum number of runs to show	None
tasks	list[int]	the list of task IDs	None
setups	list | None	the list of setup IDs	None
flows	list[int]	the list of flow IDs	None
runs	list[int]	the list of run IDs	None
uploaders	list[int]	the list of uploader IDs	None
tag	str	filter evaluation based on given tag	None
per_fold	bool		None
sort_order	str	order of sorting evaluations, ascending ("asc") or descending ("desc")	None
parameters_in_separate_columns	bool	Returns hyperparameters in separate columns if set to True. Valid only for a single flow	False

Returns:

Type	Description
dataframe with hyperparameter settings as a list of tuples.

Source code in openml/evaluations/functions.py

def list_evaluations_setups(
    function: str,
    offset: int | None = None,
    size: int | None = None,
    tasks: list | None = None,
    setups: list | None = None,
    flows: list | None = None,
    runs: list | None = None,
    uploaders: list | None = None,
    tag: str | None = None,
    per_fold: bool | None = None,
    sort_order: str | None = None,
    parameters_in_separate_columns: bool = False,  # noqa: FBT001, FBT002
) -> pd.DataFrame:
    """List all run-evaluation pairs matching all of the given filters
    and their hyperparameter settings.

    Parameters
    ----------
    function : str
        the evaluation function. e.g., predictive_accuracy
    offset : int, optional
        the number of runs to skip, starting from the first
    size : int, optional
        the maximum number of runs to show
    tasks : list[int], optional
        the list of task IDs
    setups: list[int], optional
        the list of setup IDs
    flows : list[int], optional
        the list of flow IDs
    runs : list[int], optional
        the list of run IDs
    uploaders : list[int], optional
        the list of uploader IDs
    tag : str, optional
        filter evaluation based on given tag
    per_fold : bool, optional
    sort_order : str, optional
       order of sorting evaluations, ascending ("asc") or descending ("desc")
    parameters_in_separate_columns: bool, optional (default= False)
        Returns hyperparameters in separate columns if set to True.
        Valid only for a single flow

    Returns
    -------
    dataframe with hyperparameter settings as a list of tuples.
    """
    if parameters_in_separate_columns and (flows is None or len(flows) != 1):
        raise ValueError("Can set parameters_in_separate_columns to true only for single flow_id")

    # List evaluations
    evals = list_evaluations(
        function=function,
        offset=offset,
        size=size,
        runs=runs,
        tasks=tasks,
        setups=setups,
        flows=flows,
        uploaders=uploaders,
        tag=tag,
        per_fold=per_fold,
        sort_order=sort_order,
        output_format="dataframe",
    )
    # List setups
    # list_setups by setup id does not support large sizes (exceeds URL length limit)
    # Hence we split the list of unique setup ids returned by list_evaluations into chunks of size N
    _df = pd.DataFrame()
    if len(evals) != 0:
        N = 100  # size of section
        uniq = np.asarray(evals["setup_id"].unique())
        length = len(uniq)

        # array_split - allows indices_or_sections to not equally divide the array
        # array_split -length % N sub-arrays of size length//N + 1 and the rest of size length//N.
        split_size = ((length - 1) // N) + 1
        setup_chunks = np.array_split(uniq, split_size)

        setup_data = pd.DataFrame()
        for _setups in setup_chunks:
            result = openml.setups.list_setups(setup=_setups, output_format="dataframe")
            assert isinstance(result, pd.DataFrame)
            result = result.drop("flow_id", axis=1)
            # concat resulting setup chunks into single datframe
            setup_data = pd.concat([setup_data, result])

        parameters = []
        # Convert parameters of setup into dict of (hyperparameter, value)
        for parameter_dict in setup_data["parameters"]:
            if parameter_dict is not None:
                parameters.append(
                    {param["full_name"]: param["value"] for param in parameter_dict.values()},
                )
            else:
                parameters.append({})
        setup_data["parameters"] = parameters
        # Merge setups with evaluations
        _df = evals.merge(setup_data, on="setup_id", how="left")

    if parameters_in_separate_columns:
        _df = pd.concat(
            [_df.drop("parameters", axis=1), _df["parameters"].apply(pd.Series)],
            axis=1,
        )

    return _df