.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/compose/plot_transformed_target.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_compose_plot_transformed_target.py: ====================================================== 转换回归模型中的目标变量的效果 ====================================================== 在这个示例中，我们概述了:class:`~sklearn.compose.TransformedTargetRegressor` 。我们使用两个示例来说明在学习线性回归模型之前转换目标变量的好处。第一个示例使用合成数据，而第二个示例基于Ames房价数据集。 .. GENERATED FROM PYTHON SOURCE LINES 9-15 .. code-block:: Python # 作者：scikit-learn 开发者 # SPDX-License-Identifier：BSD-3-Clause print(__doc__) .. GENERATED FROM PYTHON SOURCE LINES 16-17 合成示例 .. GENERATED FROM PYTHON SOURCE LINES 17-34 .. code-block:: Python ################### # # 生成一个合成的随机回归数据集。目标值 ``y`` 被修改为： # # 1. 将所有目标值平移，使得所有条目都是非负的（通过加上最小的 ``y`` 的绝对值），并且 # 2. 应用指数函数以获得无法使用简单线性模型拟合的非线性目标值。 # # 因此，在训练线性回归模型并使用其进行预测之前，将使用对数函数（ `np.log1p` ）和指数函数（ `np.expm1` ）对目标进行转换。 import numpy as np from sklearn.datasets import make_regression X, y = make_regression(n_samples=10_000, noise=100, random_state=0) y = np.expm1((y + abs(y.min())) / 200) y_trans = np.log1p(y) .. GENERATED FROM PYTHON SOURCE LINES 35-36 下面我们绘制了应用对数函数前后目标的概率密度函数。 .. GENERATED FROM PYTHON SOURCE LINES 36-58 .. code-block:: Python import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split f, (ax0, ax1) = plt.subplots(1, 2) ax0.hist(y, bins=100, density=True) ax0.set_xlim([0, 2000]) ax0.set_ylabel("Probability") ax0.set_xlabel("Target") ax0.set_title("Target distribution") ax1.hist(y_trans, bins=100, density=True) ax1.set_ylabel("Probability") ax1.set_xlabel("Target") ax1.set_title("Transformed target distribution") f.suptitle("Synthetic data", y=1.05) plt.tight_layout() X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) .. image-sg:: /auto_examples/compose/images/sphx_glr_plot_transformed_target_001.png :alt: Synthetic data, Target distribution, Transformed target distribution :srcset: /auto_examples/compose/images/sphx_glr_plot_transformed_target_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 59-60 首先，将在线性模型上应用原始目标。由于非线性，训练的模型在预测时不会精确。随后，使用对数函数对目标进行线性化，即使使用类似的线性模型，也能通过中位绝对误差（MedAE）报告更好的预测结果。 .. GENERATED FROM PYTHON SOURCE LINES 60-71 .. code-block:: Python from sklearn.metrics import median_absolute_error, r2_score def compute_score(y_true, y_pred): return { "R2": f"{r2_score(y_true, y_pred):.3f}", "MedAE": f"{median_absolute_error(y_true, y_pred):.3f}", } .. GENERATED FROM PYTHON SOURCE LINES 72-112 .. code-block:: Python from sklearn.compose import TransformedTargetRegressor from sklearn.linear_model import RidgeCV from sklearn.metrics import PredictionErrorDisplay f, (ax0, ax1) = plt.subplots(1, 2, sharey=True) ridge_cv = RidgeCV().fit(X_train, y_train) y_pred_ridge = ridge_cv.predict(X_test) ridge_cv_with_trans_target = TransformedTargetRegressor( regressor=RidgeCV(), func=np.log1p, inverse_func=np.expm1 ).fit(X_train, y_train) y_pred_ridge_with_trans_target = ridge_cv_with_trans_target.predict(X_test) PredictionErrorDisplay.from_predictions( y_test, y_pred_ridge, kind="actual_vs_predicted", ax=ax0, scatter_kwargs={"alpha": 0.5}, ) PredictionErrorDisplay.from_predictions( y_test, y_pred_ridge_with_trans_target, kind="actual_vs_predicted", ax=ax1, scatter_kwargs={"alpha": 0.5}, ) # 在每个轴的图例中添加分数 for ax, y_pred in zip([ax0, ax1], [y_pred_ridge, y_pred_ridge_with_trans_target]): for name, score in compute_score(y_test, y_pred).items(): ax.plot([], [], " ", label=f"{name}={score}") ax.legend(loc="upper left") ax0.set_title("Ridge regression \n without target transformation") ax1.set_title("Ridge regression \n with target transformation") f.suptitle("Synthetic data", y=1.05) plt.tight_layout() .. image-sg:: /auto_examples/compose/images/sphx_glr_plot_transformed_target_002.png :alt: Synthetic data, Ridge regression without target transformation, Ridge regression with target transformation :srcset: /auto_examples/compose/images/sphx_glr_plot_transformed_target_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 113-114 真实世界数据集 .. GENERATED FROM PYTHON SOURCE LINES 116-117 以类似的方式，Ames 房价数据集被用来展示在学习模型之前转换目标变量的影响。在这个例子中，要预测的目标是每栋房子的售价。 .. GENERATED FROM PYTHON SOURCE LINES 118-132 .. code-block:: Python from sklearn.datasets import fetch_openml from sklearn.preprocessing import quantile_transform ames = fetch_openml(name="house_prices", as_frame=True) # 只保留数值列 X = ames.data.select_dtypes(np.number) # 删除包含NaN或Inf值的列 X = X.drop(columns=["LotFrontage", "GarageYrBlt", "MasVnrArea"]) # 令价格以千美元为单位 y = ames.target / 1000 y_trans = quantile_transform( y.to_frame(), n_quantiles=900, output_distribution="normal", copy=True ).squeeze() .. GENERATED FROM PYTHON SOURCE LINES 133-134 一个 :class:`~sklearn.preprocessing.QuantileTransformer` 被用来在应用 :class:`~sklearn.linear_model.RidgeCV` 模型之前对目标分布进行归一化。 .. GENERATED FROM PYTHON SOURCE LINES 134-150 .. code-block:: Python f, (ax0, ax1) = plt.subplots(1, 2) ax0.hist(y, bins=100, density=True) ax0.set_ylabel("Probability") ax0.set_xlabel("Target") ax0.set_title("Target distribution") ax1.hist(y_trans, bins=100, density=True) ax1.set_ylabel("Probability") ax1.set_xlabel("Target") ax1.set_title("Transformed target distribution") f.suptitle("Ames housing data: selling price", y=1.05) plt.tight_layout() .. image-sg:: /auto_examples/compose/images/sphx_glr_plot_transformed_target_003.png :alt: Ames housing data: selling price, Target distribution, Transformed target distribution :srcset: /auto_examples/compose/images/sphx_glr_plot_transformed_target_003.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 151-153 .. code-block:: Python X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1) .. GENERATED FROM PYTHON SOURCE LINES 154-155 变压器的效果比在合成数据上要弱。然而，转换结果使 :math:`R^2` 增加，且 MedAE 大幅减少。残差图（预测目标 - 真实目标 vs 预测目标）在没有目标转换的情况下，由于残差值随预测目标值的变化而变化，呈现出弯曲的“反向微笑”形状。通过目标转换，形状更加线性，表明模型拟合更好。 .. GENERATED FROM PYTHON SOURCE LINES 155-215 .. code-block:: Python from sklearn.preprocessing import QuantileTransformer f, (ax0, ax1) = plt.subplots(2, 2, sharey="row", figsize=(6.5, 8)) ridge_cv = RidgeCV().fit(X_train, y_train) y_pred_ridge = ridge_cv.predict(X_test) ridge_cv_with_trans_target = TransformedTargetRegressor( regressor=RidgeCV(), transformer=QuantileTransformer(n_quantiles=900, output_distribution="normal"), ).fit(X_train, y_train) y_pred_ridge_with_trans_target = ridge_cv_with_trans_target.predict(X_test) # 绘制实际值与预测值的对比图 PredictionErrorDisplay.from_predictions( y_test, y_pred_ridge, kind="actual_vs_predicted", ax=ax0[0], scatter_kwargs={"alpha": 0.5}, ) PredictionErrorDisplay.from_predictions( y_test, y_pred_ridge_with_trans_target, kind="actual_vs_predicted", ax=ax0[1], scatter_kwargs={"alpha": 0.5}, ) # 在每个轴的图例中添加分数 for ax, y_pred in zip([ax0[0], ax0[1]], [y_pred_ridge, y_pred_ridge_with_trans_target]): for name, score in compute_score(y_test, y_pred).items(): ax.plot([], [], " ", label=f"{name}={score}") ax.legend(loc="upper left") ax0[0].set_title("Ridge regression \n without target transformation") ax0[1].set_title("Ridge regression \n with target transformation") # 绘制残差与预测值的关系图 PredictionErrorDisplay.from_predictions( y_test, y_pred_ridge, kind="residual_vs_predicted", ax=ax1[0], scatter_kwargs={"alpha": 0.5}, ) PredictionErrorDisplay.from_predictions( y_test, y_pred_ridge_with_trans_target, kind="residual_vs_predicted", ax=ax1[1], scatter_kwargs={"alpha": 0.5}, ) ax1[0].set_title("Ridge regression \n without target transformation") ax1[1].set_title("Ridge regression \n with target transformation") f.suptitle("Ames housing data: selling price", y=1.05) plt.tight_layout() plt.show() .. image-sg:: /auto_examples/compose/images/sphx_glr_plot_transformed_target_004.png :alt: Ames housing data: selling price, Ridge regression without target transformation, Ridge regression with target transformation, Ridge regression without target transformation, Ridge regression with target transformation :srcset: /auto_examples/compose/images/sphx_glr_plot_transformed_target_004.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.722 seconds) .. _sphx_glr_download_auto_examples_compose_plot_transformed_target.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/compose/plot_transformed_target.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_transformed_target.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_transformed_target.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_transformed_target.zip ` .. include:: plot_transformed_target.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_