Pushing the docs to dev/ for branch: main, commit e70fb3f91b00f59899c63dc32cd8fc9162284b60

Author: sklearn-ci

dev/.buildinfo

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dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

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+# flake8: noqa
+"""
+=======================================
+Release Highlights for scikit-learn 1.2
+=======================================
+
+.. currentmodule:: sklearn
+
+We are pleased to announce the release of scikit-learn 1.2! 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 <changes_1_2>`.
+
+To install the latest version (with pip)::
+
+    pip install --upgrade scikit-learn
+
+or with conda::
+
+    conda install -c conda-forge scikit-learn
+
+"""
+
+# %%
+# Pandas output with `set_output` API
+# -----------------------------------
+# scikit-learn's transformers now support pandas output with the `set_output` API.
+# To learn more about the `set_output` API see the example:
+# :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py` and
+# # this `video, pandas DataFrame output for scikit-learn transformers
+# (some examples) <https://youtu.be/5bCg8VfX2x8>`__.
+
+import numpy as np
+from sklearn.datasets import load_iris
+from sklearn.preprocessing import StandardScaler, KBinsDiscretizer
+from sklearn.compose import ColumnTransformer
+
+X, y = load_iris(as_frame=True, return_X_y=True)
+sepal_cols = ["sepal length (cm)", "sepal width (cm)"]
+petal_cols = ["petal length (cm)", "petal width (cm)"]
+
+preprocessor = ColumnTransformer(
+    [
+        ("scaler", StandardScaler(), sepal_cols),
+        ("kbin", KBinsDiscretizer(encode="ordinal"), petal_cols),
+    ],
+    verbose_feature_names_out=False,
+).set_output(transform="pandas")
+
+X_out = preprocessor.fit_transform(X)
+X_out.sample(n=5, random_state=0)
+
+# %%
+# Interaction constraints in Histogram-based Gradient Boosting Trees
+# ------------------------------------------------------------------
+# :class:`~ensemble.HistGradientBoostingRegressor` and
+# :class:`~ensemble.HistGradientBoostingClassifier` now supports interaction constraints
+# with the `interaction_cst` parameter. For details, see the
+# :ref:`User Guide <interaction_cst_hgbt>`. In the following example, features are not
+# allowed to interact.
+from sklearn.datasets import load_diabetes
+from sklearn.ensemble import HistGradientBoostingRegressor
+
+X, y = load_diabetes(return_X_y=True, as_frame=True)
+
+hist_no_interact = HistGradientBoostingRegressor(
+    interaction_cst=[[i] for i in range(X.shape[1])], random_state=0
+)
+hist_no_interact.fit(X, y)
+
+# %%
+# Experimental Array API support in :class:`~discriminant_analysis.LinearDiscriminantAnalysis`
+# --------------------------------------------------------------------------------------------
+# Experimental support for the `Array API <https://data-apis.org/array-api/latest/>`_
+# specification was added to :class:`~discriminant_analysis.LinearDiscriminantAnalysis`.
+# The estimator can now run on any Array API compliant libraries such as
+# `CuPy <https://docs.cupy.dev/en/stable/overview.html>`__, a GPU-accelerated array
+# library. For details, see the :ref:`User Guide <array_api>`.