Flows and Runs¶

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

In [ ]:

import openml
from sklearn import ensemble, neighbors

import openml from sklearn import ensemble, neighbors

.. warning:: .. include:: ../../test_server_usage_warning.txt

In [ ]:

openml.config.start_using_configuration_for_example()

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

In [ ]:

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)

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)

Running a model on a task¶

In [ ]:

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

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

Publishing the run¶

In [ ]:

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

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

In [ ]:

openml.config.stop_using_configuration_for_example()
# License: BSD 3-Clause

openml.config.stop_using_configuration_for_example() # License: BSD 3-Clause