.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/ensemble/plot_monotonic_constraints.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_ensemble_plot_monotonic_constraints.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_ensemble_plot_monotonic_constraints.py:


=====================
القيود الرتيبة
=====================

يوضح هذا المثال تأثير القيود الرتيبة على مقدر التعزيز المتدرج.

نقوم ببناء مجموعة بيانات اصطناعية حيث تكون قيمة الهدف بشكل عام مرتبطة بشكل إيجابي
بالميزة الأولى (مع بعض الاختلافات العشوائية وغير العشوائية)، ومرتبطة بشكل سلبي
بالميزة الثانية بشكل عام.

من خلال فرض قيد زيادة رتيبة أو قيد نقصان رتيب، على التوالي،
على الميزات أثناء عملية التعلم، يكون المقدر قادرًا على اتباع الاتجاه
العام بشكل صحيح بدلاً من أن يخضع للاختلافات.

استوحى هذا المثال من `وثائق XGBoost
<https://xgboost.readthedocs.io/en/latest/tutorials/monotonic.html>`_.

.. GENERATED FROM PYTHON SOURCE LINES 20-24

.. code-block:: Python


    # Authors: The scikit-learn developers
    # SPDX-License-Identifier: BSD-3-Clause








.. GENERATED FROM PYTHON SOURCE LINES 25-43

.. code-block:: Python

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.ensemble import HistGradientBoostingRegressor
    from sklearn.inspection import PartialDependenceDisplay

    rng = np.random.RandomState(0)

    n_samples = 1000
    f_0 = rng.rand(n_samples)
    f_1 = rng.rand(n_samples)
    X = np.c_[f_0, f_1]
    noise = rng.normal(loc=0.0, scale=0.01, size=n_samples)

    # y مرتبط بشكل إيجابي بـ f_0، ومرتبط بشكل سلبي بـ f_1
    y = 5 * f_0 + np.sin(10 * np.pi * f_0) - 5 * f_1 - np.cos(10 * np.pi * f_1) + noise









.. GENERATED FROM PYTHON SOURCE LINES 44-45

ملاءمة نموذج أول على مجموعة البيانات هذه بدون أي قيود.

.. GENERATED FROM PYTHON SOURCE LINES 45-48

.. code-block:: Python

    gbdt_no_cst = HistGradientBoostingRegressor()
    gbdt_no_cst.fit(X, y)






.. raw:: html

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      --sklearn-color-unfitted-level-3: chocolate;
      /* Definition of color scheme for fitted estimators */
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         default hidden behavior on the sphinx rendered scikit-learn.org.
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      position: relative;
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    </style><div id="sk-container-id-9" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>HistGradientBoostingRegressor()</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"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-55" type="checkbox" checked><label for="sk-estimator-id-55" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>HistGradientBoostingRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html">?<span>Documentation for HistGradientBoostingRegressor</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>HistGradientBoostingRegressor()</pre></div> </div></div></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 49-51

ملاءمة نموذج ثانٍ على مجموعة البيانات هذه مع قيود زيادة رتيبة (1)
وقيد نقصان رتيب (-1)، على التوالي.

.. GENERATED FROM PYTHON SOURCE LINES 51-55

.. code-block:: Python

    gbdt_with_monotonic_cst = HistGradientBoostingRegressor(monotonic_cst=[1, -1])
    gbdt_with_monotonic_cst.fit(X, y)







.. raw:: html

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    /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
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    - Pipeline and ColumnTransformer use this feature and define the default style
    - Estimators will overwrite some part of the style using the `sk-estimator` class
    */

    /* Pipeline and ColumnTransformer style (default) */

    #sk-container-id-10 div.sk-toggleable {
      /* Default theme specific background. It is overwritten whether we have a
      specific estimator or a Pipeline/ColumnTransformer */
      background-color: var(--sklearn-color-background);
    }

    /* Toggleable label */
    #sk-container-id-10 label.sk-toggleable__label {
      cursor: pointer;
      display: flex;
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      padding: 0.5em;
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      gap: 0.5em;
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      font-size: 0.6rem;
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      color: var(--sklearn-color-text-muted);
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    #sk-container-id-10 label.sk-toggleable__label-arrow:before {
      /* Arrow on the left of the label */
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      float: left;
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      color: var(--sklearn-color-icon);
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    #sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {
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      max-height: 0;
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    #sk-container-id-10 div.sk-toggleable__content.fitted {
      /* fitted */
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    #sk-container-id-10 div.sk-toggleable__content pre {
      margin: 0.2em;
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      /* unfitted */
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    #sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {
      /* Expand drop-down */
      max-height: 200px;
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    }

    #sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
      content: "▾";
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    /* Pipeline/ColumnTransformer-specific style */

    #sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-10 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Estimator-specific style */

    /* Colorize estimator box */
    #sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-10 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-2);
    }

    #sk-container-id-10 div.sk-label label.sk-toggleable__label,
    #sk-container-id-10 div.sk-label label {
      /* The background is the default theme color */
      color: var(--sklearn-color-text-on-default-background);
    }

    /* On hover, darken the color of the background */
    #sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {
      color: var(--sklearn-color-text);
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    }

    /* Label box, darken color on hover, fitted */
    #sk-container-id-10 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
      color: var(--sklearn-color-text);
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    /* Estimator label */

    #sk-container-id-10 div.sk-label label {
      font-family: monospace;
      font-weight: bold;
      display: inline-block;
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    #sk-container-id-10 div.sk-label-container {
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    /* Estimator-specific */
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      font-family: monospace;
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      box-sizing: border-box;
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      /* unfitted */
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    }

    #sk-container-id-10 div.sk-estimator.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    /* on hover */
    #sk-container-id-10 div.sk-estimator:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

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      /* fitted */
      background-color: var(--sklearn-color-fitted-level-2);
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    /* Specification for estimator info (e.g. "i" and "?") */

    /* Common style for "i" and "?" */

    .sk-estimator-doc-link,
    a:link.sk-estimator-doc-link,
    a:visited.sk-estimator-doc-link {
      float: right;
      font-size: smaller;
      line-height: 1em;
      font-family: monospace;
      background-color: var(--sklearn-color-background);
      border-radius: 1em;
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      width: 1em;
      text-decoration: none !important;
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      /* unfitted */
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    a:link.sk-estimator-doc-link.fitted,
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      /* fitted */
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      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;
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      display: none;
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      background: var(--sklearn-color-fitted-level-0);
      border: var(--sklearn-color-fitted-level-3);
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      display: block;
    }

    /* "?"-specific style due to the `<a>` HTML tag */

    #sk-container-id-10 a.estimator_doc_link {
      float: right;
      font-size: 1rem;
      line-height: 1em;
      font-family: monospace;
      background-color: var(--sklearn-color-background);
      border-radius: 1rem;
      height: 1rem;
      width: 1rem;
      text-decoration: none;
      /* unfitted */
      color: var(--sklearn-color-unfitted-level-1);
      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
    }

    #sk-container-id-10 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-10 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-10 a.estimator_doc_link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
    }
    </style><div id="sk-container-id-10" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>HistGradientBoostingRegressor(monotonic_cst=[1, -1])</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"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-56" type="checkbox" checked><label for="sk-estimator-id-56" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>HistGradientBoostingRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html">?<span>Documentation for HistGradientBoostingRegressor</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>HistGradientBoostingRegressor(monotonic_cst=[1, -1])</pre></div> </div></div></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 56-57

دعونا نعرض الاعتماد الجزئي للتنبؤات على الميزتين.

.. GENERATED FROM PYTHON SOURCE LINES 57-87

.. code-block:: Python

    fig, ax = plt.subplots()
    disp = PartialDependenceDisplay.from_estimator(
        gbdt_no_cst,
        X,
        features=[0, 1],
        feature_names=(
            "الميزة الأولى",
            "الميزة الثانية",
        ),
        line_kw={"linewidth": 4, "label": "بدون قيود", "color": "tab:blue"},
        ax=ax,
    )
    PartialDependenceDisplay.from_estimator(
        gbdt_with_monotonic_cst,
        X,
        features=[0, 1],
        line_kw={"linewidth": 4, "label": "مقيد", "color": "tab:orange"},
        ax=disp.axes_,
    )

    for f_idx in (0, 1):
        disp.axes_[0, f_idx].plot(
            X[:, f_idx], y, "o", alpha=0.3, zorder=-1, color="tab:green"
        )
        disp.axes_[0, f_idx].set_ylim(-6, 6)

    plt.legend()
    fig.suptitle("تأثير القيود الرتيبة على التبعيات الجزئية")
    plt.show()




.. image-sg:: /auto_examples/ensemble/images/sphx_glr_plot_monotonic_constraints_001.png
   :alt: تأثير القيود الرتيبة على التبعيات الجزئية
   :srcset: /auto_examples/ensemble/images/sphx_glr_plot_monotonic_constraints_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 88-91

يمكننا أن نرى أن تنبؤات النموذج غير المقيد تلتقط
تذبذبات البيانات بينما يتبع النموذج المقيد الاتجاه
العام ويتجاهل الاختلافات المحلية.

.. GENERATED FROM PYTHON SOURCE LINES 93-100

.. _monotonic_cst_features_names:

استخدام أسماء الميزات لتحديد القيود الرتيبة
----------------------------------------------------

لاحظ أنه إذا كانت بيانات التدريب تحتوي على أسماء ميزات، فمن الممكن تحديد
القيود الرتيبة عن طريق تمرير قاموس:

.. GENERATED FROM PYTHON SOURCE LINES 100-113

.. code-block:: Python

    import pandas as pd

    X_df = pd.DataFrame(X, columns=["f_0", "f_1"])

    gbdt_with_monotonic_cst_df = HistGradientBoostingRegressor(
        monotonic_cst={"f_0": 1, "f_1": -1}
    ).fit(X_df, y)

    np.allclose(
        gbdt_with_monotonic_cst_df.predict(X_df), gbdt_with_monotonic_cst.predict(X)
    )






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

 .. code-block:: none


    True




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

   **Total running time of the script:** (0 minutes 0.832 seconds)


.. _sphx_glr_download_auto_examples_ensemble_plot_monotonic_constraints.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/ensemble/plot_monotonic_constraints.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

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


.. include:: plot_monotonic_constraints.recommendations


.. only:: html

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

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