APIs

Top-level Classes

OpenMLDataset(name, description[, format, …])

Dataset object.

OpenMLRun(task_id, flow_id, dataset_id[, …])

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

OpenMLTask(task_id, str, None], …)

OpenMLSplit(name, description, split)

OpenMLFlow(name, description, model, …[, …])

OpenML Flow.

OpenMLEvaluation(run_id, task_id, setup_id, …)

Contains all meta-information about a run / evaluation combination, according to the evaluation/list function

Extensions

Extension

Defines the interface to connect machine learning libraries to OpenML-Python.

sklearn.SklearnExtension

Connect scikit-learn to OpenML-Python.

register_extension(extension)

Register an extension.

get_extension_by_model(model, …)

Get an extension which can handle the given flow.

get_extension_by_flow(flow, …)

Get an extension which can handle the given flow.

Modules

openml.datasets: Dataset Functions

attributes_arff_from_df(df)

Describe attributes of the dataframe according to ARFF specification.

check_datasets_active(dataset_ids)

Check if the dataset ids provided are active.

create_dataset(name, description, creator, …)

Create a dataset.

get_dataset(\*args, \*\*kwargs)

get_datasets(dataset_ids, int]], download_data)

Download datasets.

list_datasets([offset, size, status, tag])

Return a list of all dataset which are on OpenML.

openml.evaluations: Evaluation Functions

list_evaluations(function[, offset, size, …])

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

openml.flows: Flow Functions

flow_exists(name, external_version)

Retrieves the flow id.

get_flow(\*args, \*\*kwargs)

list_flows(offset, size, tag, \*\*kwargs)

Return a list of all flows which are on OpenML.

openml.runs: Run Functions

get_run(\*args, \*\*kwargs)

get_runs(run_ids)

Gets all runs in run_ids list.

get_run_trace(run_id)

Get the optimization trace object for a given run id.

initialize_model_from_run(run_id)

Initialized a model based on a run_id (i.e., using the exact same parameter settings)

initialize_model_from_trace(run_id, repeat, …)

Initialize a model based on the parameters that were set by an optimization procedure (i.e., using the exact same parameter settings)

list_runs([offset, size, id, task, setup, …])

List all runs matching all of the given filters.

run_model_on_task(model, task, …)

Run the model on the dataset defined by the task.

run_flow_on_task(flow, task, …)

Run the model provided by the flow on the dataset defined by task.

openml.setups: Setup Functions

get_setup(setup_id)

Downloads the setup (configuration) description from OpenML

initialize_model(setup_id)

Initialized a model based on a setup_id (i.e., using the exact same parameter settings)

list_setups([offset, size, flow, tag, setup])

List all setups matching all of the given filters.

setup_exists(flow)

Checks whether a hyperparameter configuration already exists on the server.

openml.study: Study Functions

get_study(study_id, str], …)

Retrieves all relevant information of an OpenML study from the server.

openml.tasks: Task Functions

get_task(\*args, \*\*kwargs)

get_tasks(task_ids[, download_data])

Download tasks.

list_tasks([task_type_id, offset, size, tag])

Return a number of tasks having the given tag and task_type_id