.. 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 JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_release_highlights_plot_release_highlights_0_23_0.py: ======================================== Release Highlights for scikit-learn 0.23 ======================================== .. currentmodule:: sklearn We are pleased to announce the release of scikit-learn 0.23! 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 25-36 Generalized Linear Models, and Poisson loss for gradient boosting ----------------------------------------------------------------- Long-awaited Generalized Linear Models with non-normal loss functions are now available. In particular, three new regressors were implemented: :class:`~sklearn.linear_model.PoissonRegressor`, :class:`~sklearn.linear_model.GammaRegressor`, and :class:`~sklearn.linear_model.TweedieRegressor`. The Poisson regressor can be used to model positive integer counts, or relative frequencies. Read more in the :ref:`User Guide `. Additionally, :class:`~sklearn.ensemble.HistGradientBoostingRegressor` supports a new 'poisson' loss as well. .. GENERATED FROM PYTHON SOURCE LINES 36-55 .. 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) # positive integer target correlated with X[:, 5] with many zeros: 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 0.35776189065725783 0.42425183539869415 .. GENERATED FROM PYTHON SOURCE LINES 56-64 Rich visual representation of estimators ----------------------------------------- Estimators can now be visualized in notebooks by enabling the `display='diagram'` option. This is particularly useful to summarise the structure of pipelines and other composite estimators, with interactivity to provide detail. Click on the example image below to expand Pipeline elements. See :ref:`visualizing_composite_estimators` for how you can use this feature. .. GENERATED FROM PYTHON SOURCE LINES 64-88 .. 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 89-97 Scalability and stability improvements to KMeans ------------------------------------------------ The :class:`~sklearn.cluster.KMeans` estimator was entirely re-worked, and it is now significantly faster and more stable. In addition, the Elkan algorithm is now compatible with sparse matrices. The estimator uses OpenMP based parallelism instead of relying on joblib, so the `n_jobs` parameter has no effect anymore. For more details on how to control the number of threads, please refer to our :ref:`parallelism` notes. .. GENERATED FROM PYTHON SOURCE LINES 97-111 .. 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 0.5673318426584951 .. GENERATED FROM PYTHON SOURCE LINES 112-126 Improvements to the histogram-based Gradient Boosting estimators ---------------------------------------------------------------- Various improvements were made to :class:`~sklearn.ensemble.HistGradientBoostingClassifier` and :class:`~sklearn.ensemble.HistGradientBoostingRegressor`. On top of the Poisson loss mentioned above, these estimators now support :ref:`sample weights `. Also, an automatic early-stopping criterion was added: early-stopping is enabled by default when the number of samples exceeds 10k. Finally, users can now define :ref:`monotonic constraints ` to constrain the predictions based on the variations of specific features. In the following example, we construct a target that is generally positively correlated with the first feature, with some noise. Applying monotoinc constraints allows the prediction to capture the global effect of the first feature, instead of fitting the noise. .. GENERATED FROM PYTHON SOURCE LINES 126-169 .. 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 has been removed in version 1.2. From 1.2, use # PartialDependenceDisplay instead. # 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 170-174 Sample-weight support for Lasso and ElasticNet ---------------------------------------------- The two linear regressors :class:`~sklearn.linear_model.Lasso` and :class:`~sklearn.linear_model.ElasticNet` now support sample weights. .. GENERATED FROM PYTHON SOURCE LINES 174-190 .. 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 0.690 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:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/release_highlights/plot_release_highlights_0_23_0.ipynb :alt: Launch JupyterLite :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 ` .. include:: plot_release_highlights_0_23_0.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_