openml.runs.OpenMLRun

class openml.runs.OpenMLRun(task_id: int, flow_id: int | None, dataset_id: int | None, setup_string: str | None = None, output_files: dict[str, int] | None = None, setup_id: int | None = None, tags: list[str] | None = None, uploader: int | None = None, uploader_name: str | None = None, evaluations: dict | None = None, fold_evaluations: dict | None = None, sample_evaluations: dict | None = None, data_content: list[list] | None = None, trace: OpenMLRunTrace | None = None, model: object | None = None, task_type: str | None = None, task_evaluation_measure: str | None = None, flow_name: str | None = None, parameter_settings: list[dict[str, Any]] | None = None, predictions_url: str | None = None, task: OpenMLTask | None = None, flow: OpenMLFlow | None = None, run_id: int | None = None, description_text: str | None = None, run_details: str | None = None)

OpenML Run: result of running a model on an OpenML dataset.

Parameters:

task_id: int

The ID of the OpenML task associated with the run.

flow_id: int

The ID of the OpenML flow associated with the run.

dataset_id: int

The ID of the OpenML dataset used for the run.

setup_string: str

The setup string of the run.

output_files: Dict[str, int]

Specifies where each related file can be found.

setup_id: int

An integer representing the ID of the setup used for the run.

tags: List[str]

Representing the tags associated with the run.

uploader: int

User ID of the uploader.

uploader_name: str

The name of the person who uploaded the run.

evaluations: Dict

Representing the evaluations of the run.

fold_evaluations: Dict

The evaluations of the run for each fold.

sample_evaluations: Dict

The evaluations of the run for each sample.

data_content: List[List]

The predictions generated from executing this run.

trace: OpenMLRunTrace

The trace containing information on internal model evaluations of this run.

model: object

The untrained model that was evaluated in the run.

task_type: str

The type of the OpenML task associated with the run.

task_evaluation_measure: str

The evaluation measure used for the task.

flow_name: str

The name of the OpenML flow associated with the run.

parameter_settings: list[OrderedDict]

Representing the parameter settings used for the run.

predictions_url: str

The URL of the predictions file.

task: OpenMLTask

An instance of the OpenMLTask class, representing the OpenML task associated with the run.

flow: OpenMLFlow

An instance of the OpenMLFlow class, representing the OpenML flow associated with the run.

run_id: int

The ID of the run.

description_text: str, optional

Description text to add to the predictions file. If left None, is set to the time the arff file is generated.

run_details: str, optional (default=None)

Description of the run stored in the run meta-data.

classmethod from_filesystem(directory: str | Path, expect_model: bool = True) → OpenMLRun

The inverse of the to_filesystem method. Instantiates an OpenMLRun object based on files stored on the file system.

Parameters:

directorystr

a path leading to the folder where the results are stored

expect_modelbool

if True, it requires the model pickle to be present, and an error will be thrown if not. Otherwise, the model might or might not be present.

Returns:

runOpenMLRun

the re-instantiated run object

get_metric_fn(sklearn_fn: Callable, kwargs: dict | None = None) → np.ndarray

Calculates metric scores based on predicted values. Assumes the run has been executed locally (and contains run_data). Furthermore, it assumes that the ‘correct’ or ‘truth’ attribute is specified in the arff (which is an optional field, but always the case for openml-python runs)

Parameters:

sklearn_fnfunction

a function pointer to a sklearn function that accepts y_true, y_pred and **kwargs

kwargsdict

kwargs for the function

Returns:

scoresndarray of scores of length num_folds * num_repeats

metric results

property id: int | None

The ID of the run, None if not uploaded to the server yet.

open_in_browser() → None

Opens the OpenML web page corresponding to this object in your default browser.

property openml_url: str | None

The URL of the object on the server, if it was uploaded, else None.

property predictions: DataFrame

Return a DataFrame with predictions for this run

publish() → OpenMLBase

Publish the object on the OpenML server.

push_tag(tag: str) → None

Annotates this entity with a tag on the server.

Parameters:

tagstr

Tag to attach to the flow.

remove_tag(tag: str) → None

Removes a tag from this entity on the server.

Parameters:

tagstr

Tag to attach to the flow.

to_filesystem(directory: str | Path, store_model: bool = True) → None

The inverse of the from_filesystem method. Serializes a run on the filesystem, to be uploaded later.

Parameters:

directorystr

a path leading to the folder where the results will be stored. Should be empty

store_modelbool, optional (default=True)

if True, a model will be pickled as well. As this is the most storage expensive part, it is often desirable to not store the model.

classmethod url_for_id(id_: int) → str

Return the OpenML URL for the object of the class entity with the given id.