.. 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_4_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_4_0.py: ======================================= Release Highlights for scikit-learn 1.4 ======================================= .. currentmodule:: sklearn We are pleased to announce the release of scikit-learn 1.4! 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 HistGradientBoosting Natively Supports Categorical DTypes in DataFrames ----------------------------------------------------------------------- :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor` now directly supports dataframes with categorical features. Here we have a dataset with a mixture of categorical and numerical features: .. GENERATED FROM PYTHON SOURCE LINES 31-39 .. code-block:: Python from sklearn.datasets import fetch_openml X_adult, y_adult = fetch_openml("adult", version=2, return_X_y=True) # Remove redundant and non-feature columns X_adult = X_adult.drop(["education-num", "fnlwgt"], axis="columns") X_adult.dtypes .. rst-class:: sphx-glr-script-out .. code-block:: none age int64 workclass category education category marital-status category occupation category relationship category race category sex category capital-gain int64 capital-loss int64 hours-per-week int64 native-country category dtype: object .. GENERATED FROM PYTHON SOURCE LINES 40-43 By setting `categorical_features="from_dtype"`, the gradient boosting classifier treats the columns with categorical dtypes as categorical features in the algorithm: .. GENERATED FROM PYTHON SOURCE LINES 43-54 .. code-block:: Python from sklearn.ensemble import HistGradientBoostingClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import roc_auc_score X_train, X_test, y_train, y_test = train_test_split(X_adult, y_adult, random_state=0) hist = HistGradientBoostingClassifier(categorical_features="from_dtype") hist.fit(X_train, y_train) y_decision = hist.decision_function(X_test) print(f"ROC AUC score is {roc_auc_score(y_test, y_decision)}") .. rst-class:: sphx-glr-script-out .. code-block:: none ROC AUC score is 0.9286646152847772 .. GENERATED FROM PYTHON SOURCE LINES 55-58 Polars output in `set_output` ----------------------------- scikit-learn's transformers now support polars output with the `set_output` API. .. GENERATED FROM PYTHON SOURCE LINES 58-78 .. code-block:: Python import polars as pl from sklearn.preprocessing import StandardScaler from sklearn.preprocessing import OneHotEncoder from sklearn.compose import ColumnTransformer df = pl.DataFrame( {"height": [120, 140, 150, 110, 100], "pet": ["dog", "cat", "dog", "cat", "cat"]} ) preprocessor = ColumnTransformer( [ ("numerical", StandardScaler(), ["height"]), ("categorical", OneHotEncoder(sparse_output=False), ["pet"]), ], verbose_feature_names_out=False, ) preprocessor.set_output(transform="polars") df_out = preprocessor.fit_transform(df) df_out .. raw:: html
shape: (5, 3)
heightpet_catpet_dog
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.. GENERATED FROM PYTHON SOURCE LINES 79-81 .. code-block:: Python print(f"Output type: {type(df_out)}") .. rst-class:: sphx-glr-script-out .. code-block:: none Output type: .. GENERATED FROM PYTHON SOURCE LINES 82-89 Missing value support for Random Forest --------------------------------------- The classes :class:`ensemble.RandomForestClassifier` and :class:`ensemble.RandomForestRegressor` now support missing values. When training every individual tree, the splitter evaluates each potential threshold with the missing values going to the left and right nodes. More details in the :ref:`User Guide `. .. GENERATED FROM PYTHON SOURCE LINES 89-98 .. code-block:: Python import numpy as np from sklearn.ensemble import RandomForestClassifier X = np.array([0, 1, 6, np.nan]).reshape(-1, 1) y = [0, 0, 1, 1] forest = RandomForestClassifier(random_state=0).fit(X, y) forest.predict(X) .. rst-class:: sphx-glr-script-out .. code-block:: none array([0, 0, 1, 1]) .. GENERATED FROM PYTHON SOURCE LINES 99-105 Add support for monotonic constraints in tree-based models ---------------------------------------------------------- While we added support for monotonic constraints in histogram-based gradient boosting in scikit-learn 0.23, we now support this feature for all other tree-based models as trees, random forests, extra-trees, and exact gradient boosting. Here, we show this feature for random forest on a regression problem. .. GENERATED FROM PYTHON SOURCE LINES 105-140 .. code-block:: Python import matplotlib.pyplot as plt from sklearn.inspection import PartialDependenceDisplay from sklearn.ensemble import RandomForestRegressor 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 rf_no_cst = RandomForestRegressor().fit(X, y) rf_cst = RandomForestRegressor(monotonic_cst=[1, 0]).fit(X, y) disp = PartialDependenceDisplay.from_estimator( rf_no_cst, X, features=[0], feature_names=["feature 0"], line_kw={"linewidth": 4, "label": "unconstrained", "color": "tab:blue"}, ) PartialDependenceDisplay.from_estimator( rf_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) disp.axes_[0, 0].legend() plt.show() .. image-sg:: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_1_4_0_001.png :alt: plot release highlights 1 4 0 :srcset: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_1_4_0_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 141-144 Enriched estimator displays --------------------------- Estimators displays have been enriched: if we look at `forest`, defined above: .. GENERATED FROM PYTHON SOURCE LINES 144-146 .. code-block:: Python forest .. raw:: html 
RandomForestClassifier(random_state=0)
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 147-152 One can access the documentation of the estimator by clicking on the icon "?" on the top right corner of the diagram. In addition, the display changes color, from orange to blue, when the estimator is fitted. You can also get this information by hovering on the icon "i". .. GENERATED FROM PYTHON SOURCE LINES 152-156 .. code-block:: Python from sklearn.base import clone clone(forest) # the clone is not fitted .. raw:: html 
RandomForestClassifier(random_state=0)
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 157-163 Metadata Routing Support ------------------------ Many meta-estimators and cross-validation routines now support metadata routing, which are listed in the :ref:`user guide `. For instance, this is how you can do a nested cross-validation with sample weights and :class:`~model_selection.GroupKFold`: .. GENERATED FROM PYTHON SOURCE LINES 163-211 .. code-block:: Python import sklearn from sklearn.metrics import get_scorer from sklearn.datasets import make_regression from sklearn.linear_model import Lasso from sklearn.model_selection import GridSearchCV, cross_validate, GroupKFold # For now by default metadata routing is disabled, and need to be explicitly # enabled. sklearn.set_config(enable_metadata_routing=True) n_samples = 100 X, y = make_regression(n_samples=n_samples, n_features=5, noise=0.5) rng = np.random.RandomState(7) groups = rng.randint(0, 10, size=n_samples) sample_weights = rng.rand(n_samples) estimator = Lasso().set_fit_request(sample_weight=True) hyperparameter_grid = {"alpha": [0.1, 0.5, 1.0, 2.0]} scoring_inner_cv = get_scorer("neg_mean_squared_error").set_score_request( sample_weight=True ) inner_cv = GroupKFold(n_splits=5) grid_search = GridSearchCV( estimator=estimator, param_grid=hyperparameter_grid, cv=inner_cv, scoring=scoring_inner_cv, ) outer_cv = GroupKFold(n_splits=5) scorers = { "mse": get_scorer("neg_mean_squared_error").set_score_request(sample_weight=True) } results = cross_validate( grid_search, X, y, cv=outer_cv, scoring=scorers, return_estimator=True, params={"sample_weight": sample_weights, "groups": groups}, ) print("cv error on test sets:", results["test_mse"]) # Setting the flag to the default `False` to avoid interference with other # scripts. sklearn.set_config(enable_metadata_routing=False) .. rst-class:: sphx-glr-script-out .. code-block:: none cv error on test sets: [-0.55640021 -0.13849253 -0.37720402 -0.42818511 -0.3886727 ] .. GENERATED FROM PYTHON SOURCE LINES 212-219 Improved memory and runtime efficiency for PCA on sparse data ------------------------------------------------------------- PCA is now able to handle sparse matrices natively for the `arpack` solver by levaraging `scipy.sparse.linalg.LinearOperator` to avoid materializing large sparse matrices when performing the eigenvalue decomposition of the data set covariance matrix. .. GENERATED FROM PYTHON SOURCE LINES 219-235 .. code-block:: Python from sklearn.decomposition import PCA import scipy.sparse as sp from time import time X_sparse = sp.random(m=1000, n=1000, random_state=0) X_dense = X_sparse.toarray() t0 = time() PCA(n_components=10, svd_solver="arpack").fit(X_sparse) time_sparse = time() - t0 t0 = time() PCA(n_components=10, svd_solver="arpack").fit(X_dense) time_dense = time() - t0 print(f"Speedup: {time_dense / time_sparse:.1f}x") .. rst-class:: sphx-glr-script-out .. code-block:: none Speedup: 2.6x .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 4.949 seconds) .. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_1_4_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_4_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_1_4_0.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_release_highlights_1_4_0.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_release_highlights_1_4_0.py ` .. include:: plot_release_highlights_1_4_0.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_