Pushing the docs to dev/ for branch: main, commit 90209c8cdda28462b0c199f3704721b5cc9923c9

Author: sklearn-ci

dev/.buildinfo

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 # Sphinx build info version 1
 # This file records the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: de3bed91b405c599f604c5b2fb0cf79c
+config: e007e4d79dc485567e1670cec5430da4
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/02192f99342d6d1323161978b6c80bfc/plot_ols_ridge.zip

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+# ruff: noqa: CPY001
+"""
+=======================================
+Release Highlights for scikit-learn 1.7
+=======================================
+
+.. currentmodule:: sklearn
+
+We are pleased to announce the release of scikit-learn 1.7! Many bug fixes
+and improvements were added, as well as some key new features. Below we
+detail the highlights of this release. **For an exhaustive list of
+all the changes**, please refer to the :ref:`release notes <release_notes_1_7>`.
+
+To install the latest version (with pip)::
+
+    pip install --upgrade scikit-learn
+
+or with conda::
+
+    conda install -c conda-forge scikit-learn
+
+"""
+
+# %%
+# Improved estimator's HTML representation
+# ----------------------------------------
+# The HTML representation of estimators now includes a section containing the list of
+# parameters and their values. Non-default parameters are highlighted in orange. A copy
+# button is also available to copy the "fully-qualified" parameter name without the
+# need to call the `get_params` method. It is particularly useful when defining a
+# parameter grid for a grid-search or a randomized-search with a complex pipeline.
+#
+# See the example below and click on the different estimator's blocks to see the
+# improved HTML representation.
+
+from sklearn.linear_model import LogisticRegression
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
+
+model = make_pipeline(StandardScaler(with_std=False), LogisticRegression(C=2.0))
+model
+
+# %%
+# Custom validation set for histogram-based Gradient Boosting estimators
+# ----------------------------------------------------------------------
+# The :class:`ensemble.HistGradientBoostingClassifier` and
+# :class:`ensemble.HistGradientBoostingRegressor` now support directly passing a custom
+# validation set for early stopping to the `fit` method, using the `X_val`, `y_val`, and
+# `sample_weight_val` parameters.
+# In a :class:`pipeline.Pipeline`, the validation set `X_val` can be transformed along
+# with `X` using the `transform_input` parameter.
+
+import sklearn
+from sklearn.datasets import make_classification
+from sklearn.ensemble import HistGradientBoostingClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
+
+sklearn.set_config(enable_metadata_routing=True)
+
+X, y = make_classification(random_state=0)
+X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=0)
+
+clf = HistGradientBoostingClassifier()
+clf.set_fit_request(X_val=True, y_val=True)
+
+model = Pipeline([("sc", StandardScaler()), ("clf", clf)], transform_input=["X_val"])
+model.fit(X, y, X_val=X_val, y_val=y_val)
+
+# %%
+# Plotting ROC curves from cross-validation results
+# -------------------------------------------------
+# The class :class:`metrics.RocCurveDisplay` has a new class method `from_cv_results`
+# that allows to easily plot multiple ROC curves from the results of
+# :func:`model_selection.cross_validate`.
+
+from sklearn.datasets import make_classification
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import RocCurveDisplay
+from sklearn.model_selection import cross_validate
+
+X, y = make_classification(n_samples=150, random_state=0)
+clf = LogisticRegression(random_state=0)
+cv_results = cross_validate(clf, X, y, cv=5, return_estimator=True, return_indices=True)
+_ = RocCurveDisplay.from_cv_results(cv_results, X, y)
+
+# %%
+# Array API support
+# -----------------
+# Several functions have been updated to support array API compatible inputs since
+# version 1.6, especially metrics from the :mod:`sklearn.metrics` module.
+#
+# In addition, it is no longer required to install the `array-api-compat` package to use
+# the experimental array API support in scikit-learn.
+#
+# Please refer to the :ref:`array API support<array_api>` page for instructions to use
+# scikit-learn with array API compatible libraries such as PyTorch or CuPy.
+
+# %%
+# Improved API consistency of Multi-layer Perceptron
+# --------------------------------------------------
+# The :class:`neural_network.MLPRegressor` has a new parameter `loss` and now supports
+# the "poisson" loss in addition to the default "squared_error" loss.
+# Moreover, the :class:`neural_network.MLPClassifier` and
+# :class:`neural_network.MLPRegressor` estimators now support sample weights.
+# These improvements have been made to improve the consistency of these estimators
+# with regard to the other estimators in scikit-learn.
+
+# %%
+# Migration toward sparse arrays
+# ------------------------------
+# In order to prepare `SciPy migration from sparse matrices to sparse arrays <https://docs.scipy.org/doc/scipy/reference/sparse.migration_to_sparray.html>`_,
+# all scikit-learn estimators that accept sparse matrices as input now also accept
+# sparse arrays.

dev/_downloads/02fe21a1f5d14bd1ebbe34aa905d9bf6/plot_multilabel.zip

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Release Highlights for scikit-learn 1.7\n\n.. currentmodule:: sklearn\n\nWe are pleased to announce the release of scikit-learn 1.7! Many bug fixes\nand improvements were added, as well as some key new features. Below we\ndetail the highlights of this release. **For an exhaustive list of\nall the changes**, please refer to the `release notes <release_notes_1_7>`.\n\nTo install the latest version (with pip)::\n\n    pip install --upgrade scikit-learn\n\nor with conda::\n\n    conda install -c conda-forge scikit-learn\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Improved estimator's HTML representation\nThe HTML representation of estimators now includes a section containing the list of\nparameters and their values. Non-default parameters are highlighted in orange. A copy\nbutton is also available to copy the \"fully-qualified\" parameter name without the\nneed to call the `get_params` method. It is particularly useful when defining a\nparameter grid for a grid-search or a randomized-search with a complex pipeline.\n\nSee the example below and click on the different estimator's blocks to see the\nimproved HTML representation.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.linear_model import LogisticRegression\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import StandardScaler\n\nmodel = make_pipeline(StandardScaler(with_std=False), LogisticRegression(C=2.0))\nmodel"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Custom validation set for histogram-based Gradient Boosting estimators\nThe :class:`ensemble.HistGradientBoostingClassifier` and\n:class:`ensemble.HistGradientBoostingRegressor` now support directly passing a custom\nvalidation set for early stopping to the `fit` method, using the `X_val`, `y_val`, and\n`sample_weight_val` parameters.\nIn a :class:`pipeline.Pipeline`, the validation set `X_val` can be transformed along\nwith `X` using the `transform_input` parameter.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import sklearn\nfrom sklearn.datasets import make_classification\nfrom sklearn.ensemble import HistGradientBoostingClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\n\nsklearn.set_config(enable_metadata_routing=True)\n\nX, y = make_classification(random_state=0)\nX_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=0)\n\nclf = HistGradientBoostingClassifier()\nclf.set_fit_request(X_val=True, y_val=True)\n\nmodel = Pipeline([(\"sc\", StandardScaler()), (\"clf\", clf)], transform_input=[\"X_val\"])\nmodel.fit(X, y, X_val=X_val, y_val=y_val)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plotting ROC curves from cross-validation results\nThe class :class:`metrics.RocCurveDisplay` has a new class method `from_cv_results`\nthat allows to easily plot multiple ROC curves from the results of\n:func:`model_selection.cross_validate`.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.datasets import make_classification\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import RocCurveDisplay\nfrom sklearn.model_selection import cross_validate\n\nX, y = make_classification(n_samples=150, random_state=0)\nclf = LogisticRegression(random_state=0)\ncv_results = cross_validate(clf, X, y, cv=5, return_estimator=True, return_indices=True)\n_ = RocCurveDisplay.from_cv_results(cv_results, X, y)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Array API support\nSeveral functions have been updated to support array API compatible inputs since\nversion 1.6, especially metrics from the :mod:`sklearn.metrics` module.\n\nIn addition, it is no longer required to install the `array-api-compat` package to use\nthe experimental array API support in scikit-learn.\n\nPlease refer to the `array API support<array_api>` page for instructions to use\nscikit-learn with array API compatible libraries such as PyTorch or CuPy.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Improved API consistency of Multi-layer Perceptron\nThe :class:`neural_network.MLPRegressor` has a new parameter `loss` and now supports\nthe \"poisson\" loss in addition to the default \"squared_error\" loss.\nMoreover, the :class:`neural_network.MLPClassifier` and\n:class:`neural_network.MLPRegressor` estimators now support sample weights.\nThese improvements have been made to improve the consistency of these estimators\nwith regard to the other estimators in scikit-learn.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Migration toward sparse arrays\nIn order to prepare [SciPy migration from sparse matrices to sparse arrays](https://docs.scipy.org/doc/scipy/reference/sparse.migration_to_sparray.html),\nall scikit-learn estimators that accept sparse matrices as input now also accept\nsparse arrays.\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.17"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}