.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/model_selection/plot_successive_halving_iterations.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_model_selection_plot_successive_halving_iterations.py: Successive Halving Iterations ============================= This example illustrates how a successive halving search (:class:`~sklearn.model_selection.HalvingGridSearchCV` and :class:`~sklearn.model_selection.HalvingRandomSearchCV`) iteratively chooses the best parameter combination out of multiple candidates. .. GENERATED FROM PYTHON SOURCE LINES 12-24 .. code-block:: default import pandas as pd from sklearn import datasets import matplotlib.pyplot as plt from scipy.stats import randint import numpy as np from sklearn.experimental import enable_halving_search_cv # noqa from sklearn.model_selection import HalvingRandomSearchCV from sklearn.ensemble import RandomForestClassifier .. GENERATED FROM PYTHON SOURCE LINES 25-27 We first define the parameter space and train a :class:`~sklearn.model_selection.HalvingRandomSearchCV` instance. .. GENERATED FROM PYTHON SOURCE LINES 27-47 .. code-block:: default rng = np.random.RandomState(0) X, y = datasets.make_classification(n_samples=400, n_features=12, random_state=rng) clf = RandomForestClassifier(n_estimators=20, random_state=rng) param_dist = { "max_depth": [3, None], "max_features": randint(1, 6), "min_samples_split": randint(2, 11), "bootstrap": [True, False], "criterion": ["gini", "entropy"], } rsh = HalvingRandomSearchCV( estimator=clf, param_distributions=param_dist, factor=2, random_state=rng ) rsh.fit(X, y) .. raw:: html

HalvingRandomSearchCV(estimator=RandomForestClassifier(n_estimators=20,
                                                           random_state=RandomState(MT19937) at 0x7EFFF8AAE340),
                          factor=2,
                          param_distributions={'bootstrap': [True, False],
                                               'criterion': ['gini', 'entropy'],
                                               'max_depth': [3, None],
                                               'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7effee6d1eb0>,
                                               'min_samples_split': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7effed3b4cd0>},
                          random_state=RandomState(MT19937) at 0x7EFFF8AAE340)

Please rerun this cell to show the HTML repr or trust the notebook.

.. GENERATED FROM PYTHON SOURCE LINES 48-50 We can now use the `cv_results_` attribute of the search estimator to inspect and plot the evolution of the search. .. GENERATED FROM PYTHON SOURCE LINES 50-72 .. code-block:: default results = pd.DataFrame(rsh.cv_results_) results["params_str"] = results.params.apply(str) results.drop_duplicates(subset=("params_str", "iter"), inplace=True) mean_scores = results.pivot( index="iter", columns="params_str", values="mean_test_score" ) ax = mean_scores.plot(legend=False, alpha=0.6) labels = [ f"iter={i}\nn_samples={rsh.n_resources_[i]}\nn_candidates={rsh.n_candidates_[i]}" for i in range(rsh.n_iterations_) ] ax.set_xticks(range(rsh.n_iterations_)) ax.set_xticklabels(labels, rotation=45, multialignment="left") ax.set_title("Scores of candidates over iterations") ax.set_ylabel("mean test score", fontsize=15) ax.set_xlabel("iterations", fontsize=15) plt.tight_layout() plt.show() .. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_successive_halving_iterations_001.png :alt: Scores of candidates over iterations :srcset: /auto_examples/model_selection/images/sphx_glr_plot_successive_halving_iterations_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 73-87 Number of candidates and amount of resource at each iteration ------------------------------------------------------------- At the first iteration, a small amount of resources is used. The resource here is the number of samples that the estimators are trained on. All candidates are evaluated. At the second iteration, only the best half of the candidates is evaluated. The number of allocated resources is doubled: candidates are evaluated on twice as many samples. This process is repeated until the last iteration, where only 2 candidates are left. The best candidate is the candidate that has the best score at the last iteration. .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 3.952 seconds) .. _sphx_glr_download_auto_examples_model_selection_plot_successive_halving_iterations.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.0.X?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_successive_halving_iterations.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_successive_halving_iterations.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_successive_halving_iterations.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_