.. 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_1_1_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_1_1_0.py: ======================================= scikit-learn 1.1 版本发布亮点 ======================================= .. currentmodule:: sklearn 我们很高兴地宣布发布 scikit-learn 1.1 版本！此版本包含了许多错误修复和改进，以及一些新的关键功能。以下是此版本的一些主要功能。**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 22-27 .. _quantile_support_hgbdt: 在 :class:`~ensemble.HistGradientBoostingRegressor` 中的分位数损失 ----------------------------------------------------------------- :class:`~ensemble.HistGradientBoostingRegressor` 可以通过 `loss="quantile"` 和新参数 `quantile` 来建模分位数。 .. GENERATED FROM PYTHON SOURCE LINES 27-52 .. code-block:: Python from sklearn.ensemble import HistGradientBoostingRegressor import numpy as np import matplotlib.pyplot as plt # X * cos(X) 的简单回归函数 rng = np.random.RandomState(42) X_1d = np.linspace(0, 10, num=2000) X = X_1d.reshape(-1, 1) y = X_1d * np.cos(X_1d) + rng.normal(scale=X_1d / 3) quantiles = [0.95, 0.5, 0.05] parameters = dict(loss="quantile", max_bins=32, max_iter=50) hist_quantiles = { f"quantile={quantile:.2f}": HistGradientBoostingRegressor( **parameters, quantile=quantile ).fit(X, y) for quantile in quantiles } fig, ax = plt.subplots() ax.plot(X_1d, y, "o", alpha=0.5, markersize=1) for quantile, hist in hist_quantiles.items(): ax.plot(X_1d, hist.predict(X), label=quantile) _ = ax.legend(loc="lower left") .. image-sg:: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_1_1_0_001.png :alt: plot release highlights 1 1 0 :srcset: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_1_1_0_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 53-55 对于用例示例，请参见 :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py` .. GENERATED FROM PYTHON SOURCE LINES 58-62 `get_feature_names_out` 现已在所有转换器中可用 ------------------------------------------------- :term:`get_feature_names_out` 现已在所有转换器中可用。这使得 :class:`~pipeline.Pipeline` 能够为更复杂的管道构建输出特征名称： .. GENERATED FROM PYTHON SOURCE LINES 62-93 .. code-block:: Python from sklearn.compose import ColumnTransformer from sklearn.preprocessing import OneHotEncoder, StandardScaler from sklearn.pipeline import make_pipeline from sklearn.impute import SimpleImputer from sklearn.feature_selection import SelectKBest from sklearn.datasets import fetch_openml from sklearn.linear_model import LogisticRegression X, y = fetch_openml( "titanic", version=1, as_frame=True, return_X_y=True, parser="pandas" ) numeric_features = ["age", "fare"] numeric_transformer = make_pipeline(SimpleImputer(strategy="median"), StandardScaler()) categorical_features = ["embarked", "pclass"] preprocessor = ColumnTransformer( [ ("num", numeric_transformer, numeric_features), ( "cat", OneHotEncoder(handle_unknown="ignore", sparse_output=False), categorical_features, ), ], verbose_feature_names_out=False, ) log_reg = make_pipeline(preprocessor, SelectKBest(k=7), LogisticRegression()) log_reg.fit(X, y) .. raw:: html 
Pipeline(steps=[('columntransformer',
                     ColumnTransformer(transformers=[('num',
                                                      Pipeline(steps=[('simpleimputer',
                                                                       SimpleImputer(strategy='median')),
                                                                      ('standardscaler',
                                                                       StandardScaler())]),
                                                      ['age', 'fare']),
                                                     ('cat',
                                                      OneHotEncoder(handle_unknown='ignore',
                                                                    sparse_output=False),
                                                      ['embarked', 'pclass'])],
                                       verbose_feature_names_out=False)),
                    ('selectkbest', SelectKBest(k=7)),
                    ('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 94-95 在这里，我们将管道切片以包含除最后一步之外的所有步骤。此管道切片的输出特征名称是输入到逻辑回归中的特征。这些名称直接对应于逻辑回归中的系数： .. GENERATED FROM PYTHON SOURCE LINES 95-103 .. code-block:: Python import pandas as pd log_reg_input_features = log_reg[:-1].get_feature_names_out() pd.Series(log_reg[-1].coef_.ravel(), index=log_reg_input_features).plot.bar() plt.tight_layout() .. image-sg:: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_1_1_0_002.png :alt: plot release highlights 1 1 0 :srcset: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_1_1_0_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 104-107 将不常见类别分组在 :class:`~preprocessing.OneHotEncoder` 中 ----------------------------------------------------------------------- :class:`~preprocessing.OneHotEncoder` 支持将不常见类别聚合为每个特征的单一输出。启用不常见类别聚合的参数是 `min_frequency` 和 `max_categories` 。有关更多详细信息，请参阅 :ref:`用户指南 ` 。 .. GENERATED FROM PYTHON SOURCE LINES 107-117 .. code-block:: Python from sklearn.preprocessing import OneHotEncoder import numpy as np X = np.array( [["dog"] * 5 + ["cat"] * 20 + ["rabbit"] * 10 + ["snake"] * 3], dtype=object ).T enc = OneHotEncoder(min_frequency=6, sparse_output=False).fit(X) enc.infrequent_categories_ .. rst-class:: sphx-glr-script-out .. code-block:: none [array(['dog', 'snake'], dtype=object)] .. GENERATED FROM PYTHON SOURCE LINES 118-119 由于狗和蛇是罕见类别，因此在转换时将它们归为一类： .. GENERATED FROM PYTHON SOURCE LINES 119-123 .. code-block:: Python encoded = enc.transform(np.array([["dog"], ["snake"], ["cat"], ["rabbit"]])) pd.DataFrame(encoded, columns=enc.get_feature_names_out()) .. raw:: html
x0_cat x0_rabbit x0_infrequent_sklearn
0 0.0 0.0 1.0
1 0.0 0.0 1.0
2 1.0 0.0 0.0
3 0.0 1.0 0.0


.. GENERATED FROM PYTHON SOURCE LINES 124-157 性能改进 ------------------------ 针对密集 float64 数据集的成对距离计算进行了重构，以更好地利用非阻塞线程并行性。例如，:meth:`neighbors.NearestNeighbors.kneighbors` 和 :meth:`neighbors.NearestNeighbors.radius_neighbors` 的速度分别可以比以前快 20 倍和 5 倍。总之，以下函数和估计器现在受益于性能改进： - :func:`metrics.pairwise_distances_argmin` - :func:`metrics.pairwise_distances_argmin_min` - :class:`cluster.AffinityPropagation` - :class:`cluster.Birch` - :class:`cluster.MeanShift` - :class:`cluster.OPTICS` - :class:`cluster.SpectralClustering` - :func:`feature_selection.mutual_info_regression` - :class:`neighbors.KNeighborsClassifier` - :class:`neighbors.KNeighborsRegressor` - :class:`neighbors.RadiusNeighborsClassifier` - :class:`neighbors.RadiusNeighborsRegressor` - :class:`neighbors.LocalOutlierFactor` - :class:`neighbors.NearestNeighbors` - :class:`manifold.Isomap` - :class:`manifold.LocallyLinearEmbedding` - :class:`manifold.TSNE` - :func:`manifold.trustworthiness` - :class:`semi_supervised.LabelPropagation` - :class:`semi_supervised.LabelSpreading` 要了解有关这项工作的更多技术细节，您可以阅读 `这组博客文章 `_ 。 此外，损失函数的计算已使用 Cython 进行了重构，从而提高了以下估计器的性能： - :class:`linear_model.逻辑回归` - :class:`linear_model.伽马回归` - :class:`linear_model.泊松回归` - :class:`linear_model.特威迪回归` .. GENERATED FROM PYTHON SOURCE LINES 159-163 :class:`~decomposition.MiniBatchNMF` ：NMF 的在线版本 -------------------------------------------------------------- 新的类 :class:`~decomposition.MiniBatchNMF` 实现了非负矩阵分解 (:class:`~decomposition.NMF` ) 的一个更快但不太精确的版本。:class:`~decomposition.MiniBatchNMF` 将数据分成小批量，并通过循环小批量数据以在线方式优化 NMF 模型，使其更适合处理大型数据集。特别是，它实现了 `partial_fit` ，当数据无法从一开始就完全获取，或者数据无法全部加载到内存中时，可以用于在线学习。 .. GENERATED FROM PYTHON SOURCE LINES 163-187 .. code-block:: Python import numpy as np from sklearn.decomposition import MiniBatchNMF rng = np.random.RandomState(0) n_samples, n_features, n_components = 10, 10, 5 true_W = rng.uniform(size=(n_samples, n_components)) true_H = rng.uniform(size=(n_components, n_features)) X = true_W @ true_H nmf = MiniBatchNMF(n_components=n_components, random_state=0) for _ in range(10): nmf.partial_fit(X) W = nmf.transform(X) H = nmf.components_ X_reconstructed = W @ H print( f"relative reconstruction error: ", f"{np.sum((X - X_reconstructed) ** 2) / np.sum(X**2):.5f}", ) .. rst-class:: sphx-glr-script-out .. code-block:: none relative reconstruction error: 0.00364 .. GENERATED FROM PYTHON SOURCE LINES 188-191 :class:`~cluster.BisectingKMeans` ：分割和聚类 ----------------------------------------------------- 新类 :class:`~cluster.BisectingKMeans` 是 :class:`~cluster.KMeans` 的一种变体，使用分裂层次聚类。它不是一次性创建所有质心，而是基于先前的聚类逐步选择质心：一个簇被反复分割成两个新簇，直到达到目标簇数，从而为聚类提供层次结构。 .. GENERATED FROM PYTHON SOURCE LINES 191-211 .. code-block:: Python from sklearn.datasets import make_blobs from sklearn.cluster import KMeans, BisectingKMeans import matplotlib.pyplot as plt X, _ = make_blobs(n_samples=1000, centers=2, random_state=0) km = KMeans(n_clusters=5, random_state=0, n_init="auto").fit(X) bisect_km = BisectingKMeans(n_clusters=5, random_state=0).fit(X) fig, ax = plt.subplots(1, 2, figsize=(10, 5)) ax[0].scatter(X[:, 0], X[:, 1], s=10, c=km.labels_) ax[0].scatter(km.cluster_centers_[:, 0], km.cluster_centers_[:, 1], s=20, c="r") ax[0].set_title("KMeans") ax[1].scatter(X[:, 0], X[:, 1], s=10, c=bisect_km.labels_) ax[1].scatter( bisect_km.cluster_centers_[:, 0], bisect_km.cluster_centers_[:, 1], s=20, c="r" ) _ = ax[1].set_title("BisectingKMeans") .. image-sg:: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_1_1_0_003.png :alt: KMeans, BisectingKMeans :srcset: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_1_1_0_003.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.677 seconds) .. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_1_1_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_1_1_0.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_release_highlights_1_1_0.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_release_highlights_1_1_0.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_release_highlights_1_1_0.zip ` .. include:: plot_release_highlights_1_1_0.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_