.. 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 Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_ensemble_plot_monotonic_constraints.py: ===================== 单调约束 ===================== 这个例子说明了单调约束对梯度提升估计器的影响。我们构建了一个人工数据集，其中目标值通常与第一个特征正相关（带有一些随机和非随机的变化），并且通常与第二个特征负相关。通过在学习过程中分别对特征施加单调增加或单调减少的约束，估计器能够正确地遵循总体趋势，而不是受到变化的影响。这个例子灵感来自于 `XGBoost 文档 `_ 。 .. GENERATED FROM PYTHON SOURCE LINES 20-38 .. 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 与 f_0 正相关，与 f_1 负相关 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 39-40 在此数据集上拟合第一个没有任何约束的模型。 .. GENERATED FROM PYTHON SOURCE LINES 40-44 .. code-block:: Python gbdt_no_cst = HistGradientBoostingRegressor() gbdt_no_cst.fit(X, y) .. raw:: html

HistGradientBoostingRegressor()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

.. GENERATED FROM PYTHON SOURCE LINES 45-46 在此数据集上分别拟合一个具有单调增加（1）和单调减少（-1）约束的第二模型。 .. GENERATED FROM PYTHON SOURCE LINES 46-51 .. 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 52-53 让我们展示预测结果对这两个特征的部分依赖性。 .. GENERATED FROM PYTHON SOURCE LINES 53-84 .. 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 85-86 我们可以看到，无约束模型的预测捕捉到了数据的波动，而约束模型则遵循总体趋势，忽略了局部变化。 .. GENERATED FROM PYTHON SOURCE LINES 89-94 .. _monotonic_cst_features_names: 使用特征名称指定单调约束请注意，如果训练数据具有特征名称，可以通过传递字典来指定单调约束： .. GENERATED FROM PYTHON SOURCE LINES 94-105 .. 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 0.562 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:: 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 `_