Metadata-Version: 2.0
Name: arbok
Version: 0.0.7
Summary: A wrapper toolbox that provides compatibility layers between TPOT and Auto-Sklearn and OpenML
Home-page: https://github.com/Yatoom/arbok
Author: Jeroen van Hoof
Author-email: jeroen@jeroenvanhoof.nl
License: UNKNOWN
Description-Content-Type: UNKNOWN
Platform: UNKNOWN
Requires-Dist: sklearn
Requires-Dist: auto-sklearn
Requires-Dist: tpot
Requires-Dist: numpy

Arbok
=====

Arbok (**A**\ utoml w\ **r**\ apper tool\ **b**\ ox for **o**\ penml
**c**\ ompatibility) provides wrappers for TPOT and Auto-Sklearn, as a
compatibility layer between these tools and OpenML.

The wrapper extends Sklearn’s ``BaseSearchCV`` and provides all the
internal parameters that OpenML needs, such as ``cv_results_``,
``best_index_``, ``best_params_``, ``best_score_`` and ``classes_``.

Installation
------------

::

    pip install arbok

Example usage
-------------

.. code:: python

    import openml
    from arbok import AutoSklearnWrapper, TPOTWrapper

    task = openml.tasks.get_task(31)

    # Get the AutoSklearn wrapper and pass parameters like you would to AutoSklearn
    clf = AutoSklearnWrapper(time_left_for_this_task=25, per_run_time_limit=5)

    # Or get the TPOT wrapper and pass parameters like you would to TPOT
    clf = TPOTWrapper(generations=2, population_size=2, verbosity=2)

    # Execute the task
    run = openml.runs.run_model_on_task(task, clf)
    run.publish()

    print('URL for run: %s/run/%d' % (openml.config.server, run.run_id))


