.. 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

        :ref:`Go to the end <sphx_glr_download_auto_examples_model_selection_plot_successive_halving_iterations.py>`
        to download the full example code. or to run this example in your browser via JupyterLite or Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_model_selection_plot_successive_halving_iterations.py:


=============================
عمليات التقسيم المتتالية
=============================

يوضح هذا المثال كيف تبحث عملية التقسيم المتتالية (:class:`~sklearn.model_selection.HalvingGridSearchCV` و :class:`~sklearn.model_selection.HalvingRandomSearchCV`) بشكل تكراري عن أفضل مجموعة من المعلمات من بين العديد من المرشحين.

# المؤلفون: مطوري سكايلرن
# معرف الترخيص: BSD-3-Clause

.. GENERATED FROM PYTHON SOURCE LINES 11-21

.. code-block:: Python

    import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd
    from scipy.stats import randint

    from sklearn import datasets
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.experimental import enable_halving_search_cv  # noqa
    from sklearn.model_selection import HalvingRandomSearchCV








.. GENERATED FROM PYTHON SOURCE LINES 22-24

نحن نحدد أولاً مساحة المعلمات وندرب مثالاً على:
:class:`~sklearn.model_selection.HalvingRandomSearchCV`.

.. GENERATED FROM PYTHON SOURCE LINES 24-44

.. code-block:: Python


    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)






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    /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
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    /* Common style for "i" and "?" */

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    /* On hover */
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    div.sk-label-container:hover .sk-estimator-doc-link:hover,
    .sk-estimator-doc-link:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-3);
      color: var(--sklearn-color-background);
      text-decoration: none;
    }

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    .sk-estimator-doc-link.fitted:hover,
    div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
    .sk-estimator-doc-link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
      color: var(--sklearn-color-background);
      text-decoration: none;
    }

    /* Span, style for the box shown on hovering the info icon */
    .sk-estimator-doc-link span {
      display: none;
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      color: var(--sklearn-color-text);
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      line-height: 1em;
      font-family: monospace;
      background-color: var(--sklearn-color-background);
      border-radius: 1rem;
      height: 1rem;
      width: 1rem;
      text-decoration: none;
      /* unfitted */
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      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
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    #sk-container-id-20 a.estimator_doc_link.fitted {
      /* fitted */
      border: var(--sklearn-color-fitted-level-1) 1pt solid;
      color: var(--sklearn-color-fitted-level-1);
    }

    /* On hover */
    #sk-container-id-20 a.estimator_doc_link:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-3);
      color: var(--sklearn-color-background);
      text-decoration: none;
    }

    #sk-container-id-20 a.estimator_doc_link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
    }
    </style><div id="sk-container-id-20" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>HalvingRandomSearchCV(estimator=RandomForestClassifier(n_estimators=20,
                                                           random_state=RandomState(MT19937) at 0x7FE4BCB30140),
                          factor=2,
                          param_distributions={&#x27;bootstrap&#x27;: [True, False],
                                               &#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],
                                               &#x27;max_depth&#x27;: [3, None],
                                               &#x27;max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7fe4a4bc4700&gt;,
                                               &#x27;min_samples_split&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7fe49ea09240&gt;},
                          random_state=RandomState(MT19937) at 0x7FE4BCB30140)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-54" type="checkbox" ><label for="sk-estimator-id-54" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>HalvingRandomSearchCV</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.HalvingRandomSearchCV.html">?<span>Documentation for HalvingRandomSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>HalvingRandomSearchCV(estimator=RandomForestClassifier(n_estimators=20,
                                                           random_state=RandomState(MT19937) at 0x7FE4BCB30140),
                          factor=2,
                          param_distributions={&#x27;bootstrap&#x27;: [True, False],
                                               &#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],
                                               &#x27;max_depth&#x27;: [3, None],
                                               &#x27;max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7fe4a4bc4700&gt;,
                                               &#x27;min_samples_split&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x7fe49ea09240&gt;},
                          random_state=RandomState(MT19937) at 0x7FE4BCB30140)</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-55" type="checkbox" ><label for="sk-estimator-id-55" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>best_estimator_: RandomForestClassifier</div></div></label><div class="sk-toggleable__content fitted"><pre>RandomForestClassifier(bootstrap=False, criterion=&#x27;entropy&#x27;, max_features=4,
                           min_samples_split=5, n_estimators=20,
                           random_state=RandomState(MT19937) at 0x7FE48BEAD140)</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-56" type="checkbox" ><label for="sk-estimator-id-56" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>RandomForestClassifier</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.RandomForestClassifier.html">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class="sk-toggleable__content fitted"><pre>RandomForestClassifier(bootstrap=False, criterion=&#x27;entropy&#x27;, max_features=4,
                           min_samples_split=5, n_estimators=20,
                           random_state=RandomState(MT19937) at 0x7FE48BEAD140)</pre></div> </div></div></div></div></div></div></div></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 45-46

يمكننا الآن استخدام خاصية `cv_results_` لمُقدر البحث لفحص وتصوير تطور البحث.

.. GENERATED FROM PYTHON SOURCE LINES 46-68

.. code-block:: Python


    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 69-77

عدد المرشحين وكمية الموارد في كل تكرار
-------------------------------------------------------------

في التكرار الأول، يتم استخدام كمية صغيرة من الموارد. المورد هنا هو عدد العينات التي يتم تدريب المُقدرات عليها. يتم تقييم جميع المرشحين.

في التكرار الثاني، يتم تقييم نصف أفضل المرشحين فقط.
يتم مضاعفة عدد الموارد المخصصة: يتم تقييم المرشحين على ضعف عدد العينات.

تتكرر هذه العملية حتى التكرار الأخير، حيث يتبقى مرشحان فقط. المرشح الأفضل هو المرشح الذي يحصل على أفضل نتيجة في التكرار الأخير.


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

   **Total running time of the script:** (0 minutes 6.385 seconds)


.. _sphx_glr_download_auto_examples_model_selection_plot_successive_halving_iterations.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_successive_halving_iterations.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/index.html?path=auto_examples/model_selection/plot_successive_halving_iterations.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

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

    .. 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-zip

      :download:`Download zipped: plot_successive_halving_iterations.zip <plot_successive_halving_iterations.zip>`


.. include:: plot_successive_halving_iterations.recommendations


.. only:: html

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

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