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, task_type_id, task_type, …)
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

openml.datasets: Dataset Functions

attributes_arff_from_df
check_datasets_active(dataset_ids) Check if the dataset ids provided are active.
create_dataset(name, description, creator, …) Create a dataset.
get_dataset(dataset_id) Download a dataset.
get_datasets(dataset_ids) 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.
flow_to_sklearn(o[, components, …]) Initializes a sklearn model based on a flow.
get_flow(flow_id[, reinstantiate]) Download the OpenML flow for a given flow ID.
list_flows([offset, size, tag]) Return a list of all flows which are on OpenML.
sklearn_to_flow(o[, parent_model])

openml.runs: Run Functions

get_run(run_id) Gets run corresponding to run_id.
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, fold) 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[, …]) See run_flow_on_task for a documentation.
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[, entity_type]) Retrieves all relevant information of an OpenML study from the server Note that some of the (data, tasks, flows, setups) fields can be empty (depending on information on the server)

openml.tasks: Task Functions

get_task(task_id) Download the OpenML task for a given task ID.
get_tasks(task_ids) Download tasks.
list_tasks([task_type_id, offset, size, tag]) Return a number of tasks having the given tag and task_type_id Parameters ———- Filter task_type_id is separated from the other filters because it is used as task_type_id in the task description, but it is named type when used as a filter in list tasks call.