Flows and Runs

A simple tutorial on how to train/run a model and how to upload the results.

# License: BSD 3-Clause

import openml
from sklearn import ensemble, neighbors

Warning

This example uploads data. For that reason, this example connects to the test server at test.openml.org. This prevents the main server from crowding with example datasets, tasks, runs, and so on. The use of this test server can affect behaviour and performance of the OpenML-Python API.

openml.config.start_using_configuration_for_example()

/home/runner/work/openml-python/openml-python/examples/20_basic/simple_flows_and_runs_tutorial.py:17: UserWarning: Switching to the test server https://test.openml.org/api/v1/xml to not upload results to the live server. Using the test server may result in reduced performance of the API!
  openml.config.start_using_configuration_for_example()

Train a machine learning model

# NOTE: We are using dataset 20 from the test server: https://test.openml.org/d/20
dataset = openml.datasets.get_dataset(20)
X, y, categorical_indicator, attribute_names = dataset.get_data(
    target=dataset.default_target_attribute
)
clf = neighbors.KNeighborsClassifier(n_neighbors=3)
clf.fit(X, y)

/home/runner/work/openml-python/openml-python/examples/20_basic/simple_flows_and_runs_tutorial.py:24: FutureWarning: Starting from Version 0.15 `download_data`, `download_qualities`, and `download_features_meta_data` will all be ``False`` instead of ``True`` by default to enable lazy loading. To disable this message until version 0.15 explicitly set `download_data`, `download_qualities`, and `download_features_meta_data` to a bool while calling `get_dataset`.
  dataset = openml.datasets.get_dataset(20)

KNeighborsClassifier(n_neighbors=3)

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Running a model on a task

task = openml.tasks.get_task(119)
clf = ensemble.RandomForestClassifier()
run = openml.runs.run_model_on_task(clf, task)
print(run)

/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/sphinx_gallery/gen_rst.py:722: FutureWarning: Starting from Version 0.15.0 `download_splits` will default to ``False`` instead of ``True`` and be independent from `download_data`. To disable this message until version 0.15 explicitly set `download_splits` to a bool.
  exec(self.code, self.fake_main.__dict__)
/home/runner/work/openml-python/openml-python/openml/tasks/functions.py:442: FutureWarning: Starting from Version 0.15 `download_data`, `download_qualities`, and `download_features_meta_data` will all be ``False`` instead of ``True`` by default to enable lazy loading. To disable this message until version 0.15 explicitly set `download_data`, `download_qualities`, and `download_features_meta_data` to a bool while calling `get_dataset`.
  dataset = get_dataset(task.dataset_id, *dataset_args, **get_dataset_kwargs)
/home/runner/work/openml-python/openml-python/openml/tasks/task.py:150: FutureWarning: Starting from Version 0.15 `download_data`, `download_qualities`, and `download_features_meta_data` will all be ``False`` instead of ``True`` by default to enable lazy loading. To disable this message until version 0.15 explicitly set `download_data`, `download_qualities`, and `download_features_meta_data` to a bool while calling `get_dataset`.
  return datasets.get_dataset(self.dataset_id)
/home/runner/work/openml-python/openml-python/openml/runs/functions.py:789: FutureWarning: Starting from Version 0.15 `download_data`, `download_qualities`, and `download_features_meta_data` will all be ``False`` instead of ``True`` by default to enable lazy loading. To disable this message until version 0.15 explicitly set `download_data`, `download_qualities`, and `download_features_meta_data` to a bool while calling `get_dataset`.
  openml.datasets.get_dataset(task.dataset_id).name,
OpenML Run
==========
Uploader Name...................: None
Metric..........................: None
Local Result - Accuracy (+- STD): 0.7549 +- 0.0000
Local Runtime - ms (+- STD).....: 142.0182 +- 0.0000
Run ID..........................: None
Task ID.........................: 119
Task Type.......................: None
Task URL........................: https://test.openml.org/t/119
Flow ID.........................: 31001
Flow Name.......................: sklearn.ensemble._forest.RandomForestClassifier
Flow URL........................: https://test.openml.org/f/31001
Setup ID........................: None
Setup String....................: Python_3.8.18. Sklearn_1.3.2. NumPy_1.23.5. SciPy_1.10.1.
Dataset ID......................: 20
Dataset URL.....................: https://test.openml.org/d/20

Publishing the run

myrun = run.publish()
print(f"Run was uploaded to {myrun.openml_url}")
print(f"The flow can be found at {myrun.flow.openml_url}")

/home/runner/work/openml-python/openml-python/openml/runs/run.py:650: FutureWarning: Starting from Version 0.15.0 `download_splits` will default to ``False`` instead of ``True`` and be independent from `download_data`. To disable this message until version 0.15 explicitly set `download_splits` to a bool.
  predictions = arff.dumps(self._generate_arff_dict())
/home/runner/work/openml-python/openml-python/openml/tasks/functions.py:442: FutureWarning: Starting from Version 0.15 `download_data`, `download_qualities`, and `download_features_meta_data` will all be ``False`` instead of ``True`` by default to enable lazy loading. To disable this message until version 0.15 explicitly set `download_data`, `download_qualities`, and `download_features_meta_data` to a bool while calling `get_dataset`.
  dataset = get_dataset(task.dataset_id, *dataset_args, **get_dataset_kwargs)
Run was uploaded to https://test.openml.org/r/8463
The flow can be found at https://test.openml.org/f/31001

openml.config.stop_using_configuration_for_example()