.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/release_highlights/plot_release_highlights_0_23_0.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_release_highlights_plot_release_highlights_0_23_0.py: ======================================== scikit-learn 0.23 版本发布亮点 ======================================== .. currentmodule:: sklearn 我们很高兴地宣布发布 scikit-learn 0.23！此次更新包含了许多错误修复和改进，以及一些新的关键功能。以下是本次发布的一些主要功能。 **有关所有更改的详尽列表** ，请参阅 :ref:`发布说明 ` 。 要安装最新版本（使用 pip）:: pip install --upgrade scikit-learn 或使用 conda:: conda install -c conda-forge scikit-learn .. GENERATED FROM PYTHON SOURCE LINES 22-30 广义线性模型和用于梯度提升的泊松损失 ----------------------------------------------------------------- 期待已久的具有非正态损失函数的广义线性模型现已可用。特别是，三个新的回归器已实现： :class:`~sklearn.linear_model.PoissonRegressor` ， :class:`~sklearn.linear_model.GammaRegressor` ，和 :class:`~sklearn.linear_model.TweedieRegressor` 。泊松回归器可用于建模正整数计数或相对频率。更多内容请参阅 :ref:`用户指南 ` 。此外， :class:`~sklearn.ensemble.HistGradientBoostingRegressor` 也支持新的 'poisson' 损失。 .. GENERATED FROM PYTHON SOURCE LINES 30-49 .. code-block:: Python import numpy as np from sklearn.model_selection import train_test_split from sklearn.linear_model import PoissonRegressor from sklearn.ensemble import HistGradientBoostingRegressor n_samples, n_features = 1000, 20 rng = np.random.RandomState(0) X = rng.randn(n_samples, n_features) # 与X[:, 5]相关的正整数，包含许多零： y = rng.poisson(lam=np.exp(X[:, 5]) / 2) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng) glm = PoissonRegressor() gbdt = HistGradientBoostingRegressor(loss="poisson", learning_rate=0.01) glm.fit(X_train, y_train) gbdt.fit(X_train, y_train) print(glm.score(X_test, y_test)) print(gbdt.score(X_test, y_test)) .. rst-class:: sphx-glr-script-out .. code-block:: none 0.35776189065725805 0.42425183539869415 .. GENERATED FROM PYTHON SOURCE LINES 50-53 丰富的估计器可视化表示 ----------------------------------------- 现在可以通过启用 `display='diagram'` 选项在笔记本中可视化估计器。这对于总结管道和其他复合估计器的结构特别有用，并具有提供详细信息的交互性。点击下面的示例图像以展开管道元素。有关如何使用此功能，请参见 :ref:`visualizing_composite_estimators` 。 .. GENERATED FROM PYTHON SOURCE LINES 53-77 .. code-block:: Python from sklearn import set_config from sklearn.pipeline import make_pipeline from sklearn.preprocessing import OneHotEncoder, StandardScaler from sklearn.impute import SimpleImputer from sklearn.compose import make_column_transformer from sklearn.linear_model import LogisticRegression set_config(display="diagram") num_proc = make_pipeline(SimpleImputer(strategy="median"), StandardScaler()) cat_proc = make_pipeline( SimpleImputer(strategy="constant", fill_value="missing"), OneHotEncoder(handle_unknown="ignore"), ) preprocessor = make_column_transformer( (num_proc, ("feat1", "feat3")), (cat_proc, ("feat0", "feat2")) ) clf = make_pipeline(preprocessor, LogisticRegression()) clf .. raw:: html 
Pipeline(steps=[('columntransformer',
                     ColumnTransformer(transformers=[('pipeline-1',
                                                      Pipeline(steps=[('simpleimputer',
                                                                       SimpleImputer(strategy='median')),
                                                                      ('standardscaler',
                                                                       StandardScaler())]),
                                                      ('feat1', 'feat3')),
                                                     ('pipeline-2',
                                                      Pipeline(steps=[('simpleimputer',
                                                                       SimpleImputer(fill_value='missing',
                                                                                     strategy='constant')),
                                                                      ('onehotencoder',
                                                                       OneHotEncoder(handle_unknown='ignore'))]),
                                                      ('feat0', 'feat2'))])),
                    ('logisticregression', LogisticRegression())])
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 78-81 可扩展性和稳定性改进的KMeans ------------------------------------------------ :class:`~sklearn.cluster.KMeans` 估计器进行了全面的重新设计，现在显著更快且更稳定。此外，Elkan算法现在兼容稀疏矩阵。该估计器使用基于OpenMP的并行处理，而不再依赖于joblib，因此 `n_jobs` 参数不再有效。有关如何控制线程数量的更多详细信息，请参阅我们的 :ref:`parallelism` 说明。 .. GENERATED FROM PYTHON SOURCE LINES 81-95 .. code-block:: Python import scipy import numpy as np from sklearn.model_selection import train_test_split from sklearn.cluster import KMeans from sklearn.datasets import make_blobs from sklearn.metrics import completeness_score rng = np.random.RandomState(0) X, y = make_blobs(random_state=rng) X = scipy.sparse.csr_matrix(X) X_train, X_test, _, y_test = train_test_split(X, y, random_state=rng) kmeans = KMeans(n_init="auto").fit(X_train) print(completeness_score(kmeans.predict(X_test), y_test)) .. rst-class:: sphx-glr-script-out .. code-block:: none 0.7462070982205808 .. GENERATED FROM PYTHON SOURCE LINES 96-99 基于直方图的梯度提升估计器的改进 ---------------------------------------------------------------- 对 :class:`~sklearn.ensemble.HistGradientBoostingClassifier` 和 :class:`~sklearn.ensemble.HistGradientBoostingRegressor` 进行了多项改进。除了上面提到的泊松损失，这些估计器现在还支持 :ref:`样本权重 ` 。此外，还添加了一个自动提前停止标准：当样本数量超过 10k 时，默认启用提前停止。最后，用户现在可以定义 :ref:`单调约束 ` ，以根据特定特征的变化约束预测。在以下示例中，我们构建了一个通常与第一个特征正相关的目标，并带有一些噪声。应用单调约束可以使预测捕捉到第一个特征的全局效应，而不是拟合噪声。有关用例示例，请参见 :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py` 。 .. GENERATED FROM PYTHON SOURCE LINES 99-143 .. code-block:: Python import numpy as np from matplotlib import pyplot as plt from sklearn.model_selection import train_test_split # # from sklearn.inspection import plot_partial_dependence # from sklearn.inspection import PartialDependenceDisplay from sklearn.ensemble import HistGradientBoostingRegressor n_samples = 500 rng = np.random.RandomState(0) X = rng.randn(n_samples, 2) noise = rng.normal(loc=0.0, scale=0.01, size=n_samples) y = 5 * X[:, 0] + np.sin(10 * np.pi * X[:, 0]) - noise gbdt_no_cst = HistGradientBoostingRegressor().fit(X, y) gbdt_cst = HistGradientBoostingRegressor(monotonic_cst=[1, 0]).fit(X, y) # plot_partial_dependence 在版本 1.2 中已被移除。从 1.2 开始，请使用 PartialDependenceDisplay 代替。 # disp = plot_partial_dependence( disp = PartialDependenceDisplay.from_estimator( gbdt_no_cst, X, features=[0], feature_names=["feature 0"], line_kw={"linewidth": 4, "label": "unconstrained", "color": "tab:blue"}, ) # plot_partial_dependence( PartialDependenceDisplay.from_estimator( gbdt_cst, X, features=[0], line_kw={"linewidth": 4, "label": "constrained", "color": "tab:orange"}, ax=disp.axes_, ) disp.axes_[0, 0].plot( X[:, 0], y, "o", alpha=0.5, zorder=-1, label="samples", color="tab:green" ) disp.axes_[0, 0].set_ylim(-3, 3) disp.axes_[0, 0].set_xlim(-1, 1) plt.legend() plt.show() .. image-sg:: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_0_23_0_001.png :alt: plot release highlights 0 23 0 :srcset: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_0_23_0_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 144-148 对Lasso和ElasticNet的样本权重支持 ---------------------------------------------- 现在，两个线性回归器：:class:`~sklearn.linear_model.Lasso` 和 :class:`~sklearn.linear_model.ElasticNet` 支持样本权重。 .. GENERATED FROM PYTHON SOURCE LINES 148-164 .. code-block:: Python from sklearn.model_selection import train_test_split from sklearn.datasets import make_regression from sklearn.linear_model import Lasso import numpy as np n_samples, n_features = 1000, 20 rng = np.random.RandomState(0) X, y = make_regression(n_samples, n_features, random_state=rng) sample_weight = rng.rand(n_samples) X_train, X_test, y_train, y_test, sw_train, sw_test = train_test_split( X, y, sample_weight, random_state=rng ) reg = Lasso() reg.fit(X_train, y_train, sample_weight=sw_train) print(reg.score(X_test, y_test, sw_test)) .. rst-class:: sphx-glr-script-out .. code-block:: none 0.999791942438998 .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.812 seconds) .. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_0_23_0.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/release_highlights/plot_release_highlights_0_23_0.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_release_highlights_0_23_0.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_release_highlights_0_23_0.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_release_highlights_0_23_0.zip ` .. include:: plot_release_highlights_0_23_0.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_