.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_plot_successive_halving_iterations.py>`     to download the full example code
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_examples_plot_successive_halving_iterations.py:


Successive Halving
==================



.. image:: /auto_examples/images/sphx_glr_plot_successive_halving_iterations_001.png
    :alt: Candidate scores over iterations
    :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    /home/circleci/miniconda/envs/testenv/lib/python3.8/site-packages/sklearn/utils/validation.py:67: FutureWarning: Pass random_state=RandomState(MT19937) as keyword args. From version 0.25 passing these as positional arguments will result in an error
      warnings.warn("Pass {} as keyword args. From version 0.25 "
    /home/circleci/project/examples/plot_successive_halving_iterations.py:47: UserWarning: FixedFormatter should only be used together with FixedLocator
      ax.set_xticklabels(labels)






|


.. code-block:: default

    import pandas as pd
    from sklearn import datasets
    import matplotlib.pyplot as plt
    from sklearn.ensemble import RandomForestClassifier
    from scipy.stats import randint
    import numpy as np

    from dabl.search import RandomSuccessiveHalving


    rng = np.random.RandomState(0)

    X, y = datasets.make_classification(n_samples=700, random_state=rng)

    clf = RandomForestClassifier(n_estimators=20, random_state=rng)

    param_dist = {"max_depth": [3, None],
                  "max_features": randint(1, 11),
                  "min_samples_split": randint(2, 11),
                  "bootstrap": [True, False],
                  "criterion": ["gini", "entropy"]}

    rsh = RandomSuccessiveHalving(
        estimator=clf,
        param_distributions=param_dist,
        budget_on='n_samples',  # budget is the number of samples
        max_budget='auto',  # max_budget=n_samples
        n_candidates='auto',  # choose n_cdts so that last iter exhausts budget
        cv=5,
        ratio=2,
        random_state=rng)
    rsh.fit(X, y)

    results = pd.DataFrame(rsh.cv_results_)
    results['params_str'] = results.params.apply(str)
    mean_scores = results.pivot(index='iter', columns='params_str',
                                values='mean_test_score')
    ax = mean_scores.plot(legend=False, alpha=.6)

    r_i_list = results.groupby('iter').r_i.unique()
    labels = ['{}\nn_samples={}'.format(i, r_i_list[i])
              for i in range(rsh.n_iterations_)]
    ax.set_xticklabels(labels)
    ax.set_title('Candidate scores over iterations')
    ax.set_ylabel('score')
    plt.show()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  8.893 seconds)


.. _sphx_glr_download_auto_examples_plot_successive_halving_iterations.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_successive_halving_iterations.py <plot_successive_halving_iterations.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: plot_successive_halving_iterations.ipynb <plot_successive_halving_iterations.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_