.. 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:
=====================
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
<https://xgboost.readthedocs.io/en/latest/tutorials/monotonic.html>`_.
.. 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
<div class="output_subarea output_html rendered_html output_result">
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</style><div id="sk-container-id-24" 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-101" type="checkbox" checked><label for="sk-estimator-id-101" 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/1.6/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 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
<div class="output_subarea output_html rendered_html output_result">
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/* Expand drop-down */
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max-width: 100%;
overflow: auto;
}
#sk-container-id-25 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
content: "▾";
}
/* Pipeline/ColumnTransformer-specific style */
#sk-container-id-25 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-25 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-25 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
/* unfitted */
background-color: var(--sklearn-color-unfitted-level-2);
}
#sk-container-id-25 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-25 div.sk-label label.sk-toggleable__label,
#sk-container-id-25 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 */
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color: var(--sklearn-color-text);
background-color: var(--sklearn-color-unfitted-level-2);
}
/* Label box, darken color on hover, fitted */
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color: var(--sklearn-color-text);
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}
/* Estimator label */
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font-weight: bold;
display: inline-block;
line-height: 1.2em;
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text-align: center;
}
/* Estimator-specific */
#sk-container-id-25 div.sk-estimator {
font-family: monospace;
border: 1px dotted var(--sklearn-color-border-box);
border-radius: 0.25em;
box-sizing: border-box;
margin-bottom: 0.5em;
/* unfitted */
background-color: var(--sklearn-color-unfitted-level-0);
}
#sk-container-id-25 div.sk-estimator.fitted {
/* fitted */
background-color: var(--sklearn-color-fitted-level-0);
}
/* on hover */
#sk-container-id-25 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);
}
/* 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;
height: 1em;
width: 1em;
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a:visited.sk-estimator-doc-link.fitted {
/* fitted */
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color: var(--sklearn-color-fitted-level-1);
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.sk-estimator-doc-link:hover,
div.sk-label-container:hover .sk-estimator-doc-link:hover,
.sk-estimator-doc-link:hover {
/* unfitted */
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color: var(--sklearn-color-background);
text-decoration: none;
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.sk-estimator-doc-link.fitted:hover {
/* fitted */
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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;
z-index: 9999;
position: relative;
font-weight: normal;
right: .2ex;
padding: .5ex;
margin: .5ex;
width: min-content;
min-width: 20ex;
max-width: 50ex;
color: var(--sklearn-color-text);
box-shadow: 2pt 2pt 4pt #999;
/* unfitted */
background: var(--sklearn-color-unfitted-level-0);
border: .5pt solid var(--sklearn-color-unfitted-level-3);
}
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/* fitted */
background: var(--sklearn-color-fitted-level-0);
border: var(--sklearn-color-fitted-level-3);
}
.sk-estimator-doc-link:hover span {
display: block;
}
/* "?"-specific style due to the `<a>` HTML tag */
#sk-container-id-25 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-25 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-25 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-25 a.estimator_doc_link.fitted:hover {
/* fitted */
background-color: var(--sklearn-color-fitted-level-3);
}
</style><div id="sk-container-id-25" 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-102" type="checkbox" checked><label for="sk-estimator-id-102" 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/1.6/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 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 0.548 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 <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>`_