DOC add release highlights for 1.0

examples/release_highlights/plot_release_highlights_1_0.py

@@ -0,0 +1,180 @@
+# flake8: noqa
+"""
+=======================================
+Release Highlights for scikit-learn 1.0
+=======================================
+
+.. currentmodule:: sklearn
+
+We are pleased to announce the release of scikit-learn 1.0! The library has
+been stable for quite some time, releasing version 1.0 is recognizing that and
+signalling it to our users. The release does not include any braking changes,
+and as usual, and we do our best to follow a two release deprecation cycle for
+any breaking changes.
+
+This release includes many bug fixes and improvements, 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_0>`.
+
+To install the latest version (with pip)::
+
+    pip install --upgrade scikit-learn
+
+or with conda::
+
+    conda install -c conda-forge scikit-learn
+"""
+
+##############################################################################
+# keyword and positional arguments
+# ---------------------------------------------------------
+# The scikit-learn API exposes many functions and methods which have many input
+# parameters. For example, before this release, one could instantiate a
+# `ensemble.HistGradientBoostingRegressor` as::
+#
+#         HistGradientBoostingRegressor("squared_error", 0.1, 100, 31, None,
+#             20, 0.0, 255, None, None, False, "auto", "loss", 0.1, 10, 1e-7,
+#             0, None)
+#
+# Understanding the above code requires the reader to go to the API
+# documentation to check each parameter, and what they mean, with the position
+# they have. To improve the readability of code written based on scikit-learn,
+# now users have to provide most parameters with their names, as keyword
+# arguments, instead of positional arguments. For example, the above code would
+# be::
+#
+#     HistGradientBoostingRegressor(
+#         loss="squared_error",
+#         learning_rate=0.1,
+#         max_iter=100,
+#         max_leaf_nodes=31,
+#         max_depth=None,
+#         min_samples_leaf=20,
+#         l2_regularization=0.0,
+#         max_bins=255,
+#         categorical_features=None,
+#         monotonic_cst=None,
+#         warm_start=False,
+#         early_stopping="auto",
+#         scoring="loss",
+#         validation_fraction=0.1,
+#         n_iter_no_change=10,
+#         tol=1e-7,
+#         verbose=0,
+#         random_state=None,
+#     )
+#
+# which is much more readable.
+
+##############################################################################
+# Spline Transformers
+# ---------------------------------------------------------
+# One way to add nonlinear terms the dataset's feature set is to generate
+# spline basis functions for each feature with the
+# :class:`preprocessing.SplineTransformer`. Splines are piecewise polynomials,
+# parametrized by their polynomial degree and the positions of the knots. The
+# :class:`preprocessingSplineTransformer` implements a B-spline basis
+
+# .. figure:: ../linear_model/images/sphx_glr_plot_polynomial_interpolation_001.png
+#   :target: ../linear_model/plot_successive_halving_iterations.html
+#   :align: center
+
+# The following code shows splines in action, for more information, please
+# refer to :ref:`User Guide <spline_transformer>`
+
+import numpy as np
+from sklearn.preprocessing import SplineTransformer
+
+X = np.arange(5).reshape(5, 1)
+X
+spline = SplineTransformer(degree=2, n_knots=3)
+spline.fit_transform(X)
+
+
+##############################################################################
+# Quantile Regressor
+# --------------------------------------------------------------------------
+# Quantile regression estimates the median or other quantiles of :math:`y`
+# conditional on :math:`X`, while ordinary least squares (OLS) estimates the
+# conditional mean.
+#
+# As a linear model, the :class:`QuantileRegressor` gives linear predictions
+# :math:`\hat{y}(w, X) = Xw` for the :math:`q`-th quantile, :math:`q \in (0,
+# 1)`. The weights or coefficients :math:`w` are then found by the following
+# minimization problem:
+
+# .. math::
+#     \min_{w} {\frac{1}{n_{\text{samples}}}
+#     \sum_i PB_q(y_i - X_i w) + \alpha ||w||_1}.
+
+# This consists of the pinball loss (also known as linear loss),
+# see also :class:`~sklearn.metrics.mean_pinball_loss`,
+
+# .. math::
+#     PB_q(t) = q \max(t, 0) + (1 - q) \max(-t, 0) =
+#     \begin{cases}
+#         q t, & t > 0, \\
+#         0,    & t = 0, \\
+#         (1-q) t, & t < 0
+#     \end{cases}
+#
+# and the L1 penalty controlled by parameter ``alpha``, similar to
+# :class:`linear_model.Lasso`.
+#
+# Please check the following example to see how it works, and the :ref:`User
+# Guide <quantile_regression>` for more details.
+
+# .. figure:: /auto_examples/linear_model/images/sphx_glr_plot_quantile_regression_002.png
+#    :target: ../auto_examples/linear_model/plot_quantile_regression.html
+#    :align: center
+#    :scale: 50%
+
+##############################################################################
+# `from_estimator` and `from_predictions` on `*Display` classes
+# --------------------------------------------------------------------------
+# :class:`metrics.ConfusionMatrixDisplay`,
+# :class:`metrics.PrecisionRecallDisplay`, and :class:`metrics.DetCurveDisplay`
+# now expose two class methods: `from_estimator` and `from_predictions` which
+# allow users to create a plot given the predictions or an estimator. This
+# means the corresponsing `plot_*` functions are deprecated. Please check
+# :ref:`example one
+# <sphx_glr_auto_examples_model_selection_plot_confusion_matrix.py>` and
+# :ref:`example two
+# <sphx_glr_auto_examples_classification_plot_digits_classification.py>` for
+# examples of how to use the new plotting functionalities.
+
+##############################################################################
+# Online One-Class SVM
+# --------------------------------------------------------------------------
+# The class :class:`sklearn.linear_model.SGDOneClassSVM` implements an online
+# linear version of the One-Class SVM using a stochastic gradient descent.
+# Combined with kernel approximation techniques,
+# :class:`sklearn.linear_model.SGDOneClassSVM` can be used to approximate the
+# solution of a kernelized One-Class SVM, implemented in
+# :class:`sklearn.svm.OneClassSVM`, with a linear complexity in the number of
+# samples. Note that the complexity of a kernelized One-Class SVM is at best
+# quadratic in the number of samples.
+# :class:`sklearn.linear_model.SGDOneClassSVM` is thus well suited for datasets
+# with a large number of training samples (> 10,000) for which the SGD variant
+# can be several orders of magnitude faster. Please check this :ref:`example
+# <sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py` to see how
+# it's used, and :ref:`User Guide <sgd_online_one_class_svm>` for more details.
+
+# .. figure:: /auto_examples/miscellaneous/images/sphx_glr_plot_anomaly_comparison_001.png
+#    :target: ../auto_examples/miscellaneous/plot_anomaly_comparison.html
+#    :align: center
+
+##############################################################################
+# Histogram Based Gradient Boosting Models
+# --------------------------------------------------------------------------
+# `ensemble.HistGradientBoostingRegressor` and
+# `ensemble.HistGradientBoostingClassifier` are no longer experimental and can
+# simply be imported and used as::
+#
+#     from sklearn.ensemble import HistGradientBoostingClassifier
+
+##############################################################################
+# New documentation improvements
+# ------------------------------
+# TODO