openml.runs.run_flow_on_task¶
- openml.runs.run_flow_on_task(flow: OpenMLFlow, task: OpenMLTask, avoid_duplicate_runs: bool = True, flow_tags: List[str] | None = None, seed: int | None = None, add_local_measures: bool = True, upload_flow: bool = False, dataset_format: str = 'dataframe', n_jobs: int | None = None) OpenMLRun¶
Run the model provided by the flow on the dataset defined by task.
Takes the flow and repeat information into account. The Flow may optionally be published.
- Parameters:
- flowOpenMLFlow
A flow wraps a machine learning model together with relevant information. The model has a function fit(X,Y) and predict(X), all supervised estimators of scikit learn follow this definition of a model (https://scikit-learn.org/stable/tutorial/statistical_inference/supervised_learning.html)
- taskOpenMLTask
Task to perform. This may be an OpenMLFlow instead if the first argument is an OpenMLTask.
- avoid_duplicate_runsbool, optional (default=True)
If True, the run will throw an error if the setup/task combination is already present on the server. This feature requires an internet connection.
- avoid_duplicate_runsbool, optional (default=True)
If True, the run will throw an error if the setup/task combination is already present on the server. This feature requires an internet connection.
- flow_tagsList[str], optional (default=None)
A list of tags that the flow should have at creation.
- seed: int, optional (default=None)
Models that are not seeded will get this seed.
- add_local_measuresbool, optional (default=True)
Determines whether to calculate a set of evaluation measures locally, to later verify server behaviour.
- upload_flowbool (default=False)
If True, upload the flow to OpenML if it does not exist yet. If False, do not upload the flow to OpenML.
- dataset_formatstr (default=’dataframe’)
If ‘array’, the dataset is passed to the model as a numpy array. If ‘dataframe’, the dataset is passed to the model as a pandas dataframe.
- n_jobsint (default=None)
The number of processes/threads to distribute the evaluation asynchronously. If None or 1, then the evaluation is treated as synchronous and processed sequentially. If -1, then the job uses as many cores available.
- Returns:
- runOpenMLRun
Result of the run.