.. 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 ` 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: ===================== Monotonic Constraints ===================== This example illustrates the effect of monotonic constraints on a gradient boosting estimator. We build an artificial dataset where the target value is in general positively correlated with the first feature (with some random and non-random variations), and in general negatively correlated with the second feature. By imposing a monotonic increase or a monotonic decrease constraint, respectively, on the features during the learning process, the estimator is able to properly follow the general trend instead of being subject to the variations. This example was inspired by the `XGBoost documentation `_. .. GENERATED FROM PYTHON SOURCE LINES 22-26 .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause .. GENERATED FROM PYTHON SOURCE LINES 27-45 .. 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 is positively correlated with f_0, and negatively correlated with 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 46-47 Fit a first model on this dataset without any constraints. .. GENERATED FROM PYTHON SOURCE LINES 47-50 .. code-block:: Python gbdt_no_cst = HistGradientBoostingRegressor() gbdt_no_cst.fit(X, y) .. raw:: html

HistGradientBoostingRegressor()

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.. GENERATED FROM PYTHON SOURCE LINES 51-53 Fit a second model on this dataset with monotonic increase (1) and a monotonic decrease (-1) constraints, respectively. .. GENERATED FROM PYTHON SOURCE LINES 53-57 .. code-block:: Python gbdt_with_monotonic_cst = HistGradientBoostingRegressor(monotonic_cst=[1, -1]) gbdt_with_monotonic_cst.fit(X, y) .. raw:: html

HistGradientBoostingRegressor(monotonic_cst=[1, -1])

.. GENERATED FROM PYTHON SOURCE LINES 58-59 Let's display the partial dependence of the predictions on the two features. .. GENERATED FROM PYTHON SOURCE LINES 59-89 .. code-block:: Python fig, ax = plt.subplots() disp = PartialDependenceDisplay.from_estimator( gbdt_no_cst, X, features=[0, 1], feature_names=( "First feature", "Second feature", ), line_kw={"linewidth": 4, "label": "unconstrained", "color": "tab:blue"}, ax=ax, ) PartialDependenceDisplay.from_estimator( gbdt_with_monotonic_cst, X, features=[0, 1], line_kw={"linewidth": 4, "label": "constrained", "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("Monotonic constraints effect on partial dependences") plt.show() .. image-sg:: /auto_examples/ensemble/images/sphx_glr_plot_monotonic_constraints_001.png :alt: Monotonic constraints effect on partial dependences :srcset: /auto_examples/ensemble/images/sphx_glr_plot_monotonic_constraints_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 90-93 We can see that the predictions of the unconstrained model capture the oscillations of the data while the constrained model follows the general trend and ignores the local variations. .. GENERATED FROM PYTHON SOURCE LINES 95-102 .. _monotonic_cst_features_names: Using feature names to specify monotonic constraints ---------------------------------------------------- Note that if the training data has feature names, it's possible to specify the monotonic constraints by passing a dictionary: .. GENERATED FROM PYTHON SOURCE LINES 102-113 .. 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 0.518 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/1.6.X?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 ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_monotonic_constraints.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_monotonic_constraints.zip ` .. include:: plot_monotonic_constraints.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_