.. 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 JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_release_highlights_plot_release_highlights_1_1_0.py: ======================================= Release Highlights for scikit-learn 1.1 ======================================= .. currentmodule:: sklearn We are pleased to announce the release of scikit-learn 1.1! Many bug fixes and improvements were added, as well as some new key features. We detail below a few of the major features of this release. **For an exhaustive list of all the changes**, please refer to the :ref:`release notes `. To install the latest version (with pip):: pip install --upgrade scikit-learn or with conda:: conda install -c conda-forge scikit-learn .. GENERATED FROM PYTHON SOURCE LINES 25-31 .. _quantile_support_hgbdt: Quantile loss in :class:`~ensemble.HistGradientBoostingRegressor` ----------------------------------------------------------------- :class:`~ensemble.HistGradientBoostingRegressor` can model quantiles with `loss="quantile"` and the new parameter `quantile`. .. GENERATED FROM PYTHON SOURCE LINES 31-56 .. code-block:: Python from sklearn.ensemble import HistGradientBoostingRegressor import numpy as np import matplotlib.pyplot as plt # Simple regression function for X * cos(X) 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 57-59 For a usecase example, see :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py` .. GENERATED FROM PYTHON SOURCE LINES 61-66 `get_feature_names_out` Available in all Transformers ----------------------------------------------------- :term:`get_feature_names_out` is now available in all Transformers. This enables :class:`~pipeline.Pipeline` to construct the output feature names for more complex pipelines: .. GENERATED FROM PYTHON SOURCE LINES 66-96 .. 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 97-101 Here we slice the pipeline to include all the steps but the last one. The output feature names of this pipeline slice are the features put into logistic regression. These names correspond directly to the coefficients in the logistic regression: .. GENERATED FROM PYTHON SOURCE LINES 101-108 .. 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 109-116 Grouping infrequent categories in :class:`~preprocessing.OneHotEncoder` ----------------------------------------------------------------------- :class:`~preprocessing.OneHotEncoder` supports aggregating infrequent categories into a single output for each feature. The parameters to enable the gathering of infrequent categories are `min_frequency` and `max_categories`. See the :ref:`User Guide ` for more details. .. GENERATED FROM PYTHON SOURCE LINES 116-125 .. 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 126-128 Since dog and snake are infrequent categories, they are grouped together when transformed: .. GENERATED FROM PYTHON SOURCE LINES 128-131 .. 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 132-172 Performance improvements ------------------------ Reductions on pairwise distances for dense float64 datasets has been refactored to better take advantage of non-blocking thread parallelism. For example, :meth:`neighbors.NearestNeighbors.kneighbors` and :meth:`neighbors.NearestNeighbors.radius_neighbors` can respectively be up to ×20 and ×5 faster than previously. In summary, the following functions and estimators now benefit from improved performance: - :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` To know more about the technical details of this work, you can read `this suite of blog posts `_. Moreover, the computation of loss functions has been refactored using Cython resulting in performance improvements for the following estimators: - :class:`linear_model.LogisticRegression` - :class:`linear_model.GammaRegressor` - :class:`linear_model.PoissonRegressor` - :class:`linear_model.TweedieRegressor` .. GENERATED FROM PYTHON SOURCE LINES 174-184 :class:`~decomposition.MiniBatchNMF`: an online version of NMF -------------------------------------------------------------- The new class :class:`~decomposition.MiniBatchNMF` implements a faster but less accurate version of non-negative matrix factorization (:class:`~decomposition.NMF`). :class:`~decomposition.MiniBatchNMF` divides the data into mini-batches and optimizes the NMF model in an online manner by cycling over the mini-batches, making it better suited for large datasets. In particular, it implements `partial_fit`, which can be used for online learning when the data is not readily available from the start, or when the data does not fit into memory. .. GENERATED FROM PYTHON SOURCE LINES 184-207 .. 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 208-216 :class:`~cluster.BisectingKMeans`: divide and cluster ----------------------------------------------------- The new class :class:`~cluster.BisectingKMeans` is a variant of :class:`~cluster.KMeans`, using divisive hierarchical clustering. Instead of creating all centroids at once, centroids are picked progressively based on a previous clustering: a cluster is split into two new clusters repeatedly until the target number of clusters is reached, giving a hierarchical structure to the clustering. .. GENERATED FROM PYTHON SOURCE LINES 216-235 .. 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 0.957 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/1.6.X?urlpath=lab/tree/notebooks/auto_examples/release_highlights/plot_release_highlights_1_1_0.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/release_highlights/plot_release_highlights_1_1_0.ipynb :alt: Launch JupyterLite :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 `_