
.. 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_1_0.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_1_1_0.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_release_highlights_plot_release_highlights_1_1_0.py:


=======================================
Release Highlights for scikit-learn 1.1
=======================================

.. currentmodule:: sklearn

We are pleased to announce the release of scikit-learn 1.1! 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_1>`.

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 25-29

Quantile loss in :class:`ensemble.HistGradientBoostingRegressor`
----------------------------------------------------------------
:class:`ensemble.HistGradientBoostingRegressor` can model quantiles with
`loss="quantile"` and the new parameter `quantile`.

.. GENERATED FROM PYTHON SOURCE LINES 29-55

.. code-block:: default

    from sklearn.ensemble import HistGradientBoostingRegressor
    import numpy as np
    import matplotlib.pyplot as plt

    # Simple regression function for X * cos(X)
    rng = np.random.RandomState(42)
    X_1d = np.linspace(0, 10, num=2000)
    X = X_1d.reshape(-1, 1)
    y = X_1d * np.cos(X_1d) + rng.normal(scale=X_1d / 3)

    quantiles = [0.95, 0.5, 0.05]
    parameters = dict(loss="quantile", max_bins=32, max_iter=50)
    hist_quantiles = {
        f"quantile={quantile:.2f}": HistGradientBoostingRegressor(
            **parameters, quantile=quantile
        ).fit(X, y)
        for quantile in quantiles
    }

    fig, ax = plt.subplots()
    ax.plot(X_1d, y, "o", alpha=0.5, markersize=1)
    for quantile, hist in hist_quantiles.items():
        ax.plot(X_1d, hist.predict(X), label=quantile)
    _ = ax.legend(loc="lower left")





.. image-sg:: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_1_1_0_001.png
   :alt: plot release highlights 1 1 0
   :srcset: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_1_1_0_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 56-61

`get_feature_names_out` Available in all Transformers
-----------------------------------------------------
:term:`get_feature_names_out` is now available in all Transformers. This enables
:class:`pipeline.Pipeline` to construct the output feature names for more complex
pipelines:

.. GENERATED FROM PYTHON SOURCE LINES 61-91

.. code-block:: default

    from sklearn.compose import ColumnTransformer
    from sklearn.preprocessing import OneHotEncoder, StandardScaler
    from sklearn.pipeline import make_pipeline
    from sklearn.impute import SimpleImputer
    from sklearn.feature_selection import SelectKBest
    from sklearn.datasets import fetch_openml
    from sklearn.linear_model import LogisticRegression

    X, y = fetch_openml(
        "titanic", version=1, as_frame=True, return_X_y=True, parser="pandas"
    )
    numeric_features = ["age", "fare"]
    numeric_transformer = make_pipeline(SimpleImputer(strategy="median"), StandardScaler())
    categorical_features = ["embarked", "pclass"]

    preprocessor = ColumnTransformer(
        [
            ("num", numeric_transformer, numeric_features),
            (
                "cat",
                OneHotEncoder(handle_unknown="ignore", sparse_output=False),
                categorical_features,
            ),
        ],
        verbose_feature_names_out=False,
    )
    log_reg = make_pipeline(preprocessor, SelectKBest(k=7), LogisticRegression())
    log_reg.fit(X, y)




.. rst-class:: sphx-glr-script-out

.. code-block:: pytb

    Traceback (most recent call last):
      File "/build/scikit-learn-0WW6ur/scikit-learn-1.2.1+dfsg/examples/release_highlights/plot_release_highlights_1_1_0.py", line 69, in <module>
        X, y = fetch_openml(
               ^^^^^^^^^^^^^
      File "/build/scikit-learn-0WW6ur/scikit-learn-1.2.1+dfsg/.pybuild/cpython3_3.11/build/sklearn/datasets/_openml.py", line 864, in fetch_openml
        raise TimeoutError('Debian Policy Section 4.9 prohibits network access during build')
    TimeoutError: Debian Policy Section 4.9 prohibits network access during build




.. GENERATED FROM PYTHON SOURCE LINES 92-96

Here we slice the pipeline to include all the steps but the last one. The output
feature names of this pipeline slice are the features put into logistic
regression. These names correspond directly to the coefficients in the logistic
regression:

.. GENERATED FROM PYTHON SOURCE LINES 96-103

.. code-block:: default

    import pandas as pd

    log_reg_input_features = log_reg[:-1].get_feature_names_out()
    pd.Series(log_reg[-1].coef_.ravel(), index=log_reg_input_features).plot.bar()
    plt.tight_layout()



.. GENERATED FROM PYTHON SOURCE LINES 104-110

Grouping infrequent categories in :class:`OneHotEncoder`
--------------------------------------------------------
:class:`OneHotEncoder` supports aggregating infrequent categories into a single
output for each feature. The parameters to enable the gathering of infrequent
categories are `min_frequency` and `max_categories`. See the
:ref:`User Guide <one_hot_encoder_infrequent_categories>` for more details.

.. GENERATED FROM PYTHON SOURCE LINES 110-119

.. code-block:: default

    from sklearn.preprocessing import OneHotEncoder
    import numpy as np

    X = np.array(
        [["dog"] * 5 + ["cat"] * 20 + ["rabbit"] * 10 + ["snake"] * 3], dtype=object
    ).T
    enc = OneHotEncoder(min_frequency=6, sparse_output=False).fit(X)
    enc.infrequent_categories_


.. GENERATED FROM PYTHON SOURCE LINES 120-122

Since dog and snake are infrequent categories, they are grouped together when
transformed:

.. GENERATED FROM PYTHON SOURCE LINES 122-125

.. code-block:: default

    encoded = enc.transform(np.array([["dog"], ["snake"], ["cat"], ["rabbit"]]))
    pd.DataFrame(encoded, columns=enc.get_feature_names_out())


.. GENERATED FROM PYTHON SOURCE LINES 126-166

Performance improvements
------------------------
Reductions on pairwise distances for dense float64 datasets has been refactored
to better take advantage of non-blocking thread parallelism. For example,
:meth:`neighbors.NearestNeighbors.kneighbors` and
:meth:`neighbors.NearestNeighbors.radius_neighbors` can respectively be up to ×20 and
×5 faster than previously. In summary, the following functions and estimators
now benefit from improved performance:

- :func:`metrics.pairwise_distances_argmin`
- :func:`metrics.pairwise_distances_argmin_min`
- :class:`cluster.AffinityPropagation`
- :class:`cluster.Birch`
- :class:`cluster.MeanShift`
- :class:`cluster.OPTICS`
- :class:`cluster.SpectralClustering`
- :func:`feature_selection.mutual_info_regression`
- :class:`neighbors.KNeighborsClassifier`
- :class:`neighbors.KNeighborsRegressor`
- :class:`neighbors.RadiusNeighborsClassifier`
- :class:`neighbors.RadiusNeighborsRegressor`
- :class:`neighbors.LocalOutlierFactor`
- :class:`neighbors.NearestNeighbors`
- :class:`manifold.Isomap`
- :class:`manifold.LocallyLinearEmbedding`
- :class:`manifold.TSNE`
- :func:`manifold.trustworthiness`
- :class:`semi_supervised.LabelPropagation`
- :class:`semi_supervised.LabelSpreading`

To know more about the technical details of this work, you can read
`this suite of blog posts <https://blog.scikit-learn.org/technical/performances/>`_.

Moreover, the computation of loss functions has been refactored using
Cython resulting in performance improvements for the following estimators:

- :class:`linear_model.LogisticRegression`
- :class:`linear_model.GammaRegressor`
- :class:`linear_model.PoissonRegressor`
- :class:`linear_model.TweedieRegressor`

.. GENERATED FROM PYTHON SOURCE LINES 168-177

MiniBatchNMF: an online version of NMF
--------------------------------------
The new class :class:`decomposition.MiniBatchNMF` implements a faster but less
accurate version of non-negative matrix factorization (:class:`decomposition.NMF`).
:class:`MiniBatchNMF` divides the data into mini-batches and optimizes the NMF model
in an online manner by cycling over the mini-batches, making it better suited for
large datasets. In particular, it implements `partial_fit`, which can be used for
online learning when the data is not readily available from the start, or when the
data does not fit into memory.

.. GENERATED FROM PYTHON SOURCE LINES 177-200

.. code-block:: default

    import numpy as np
    from sklearn.decomposition import MiniBatchNMF

    rng = np.random.RandomState(0)
    n_samples, n_features, n_components = 10, 10, 5
    true_W = rng.uniform(size=(n_samples, n_components))
    true_H = rng.uniform(size=(n_components, n_features))
    X = true_W @ true_H

    nmf = MiniBatchNMF(n_components=n_components, random_state=0)

    for _ in range(10):
        nmf.partial_fit(X)

    W = nmf.transform(X)
    H = nmf.components_
    X_reconstructed = W @ H

    print(
        f"relative reconstruction error: ",
        f"{np.sum((X - X_reconstructed) ** 2) / np.sum(X**2):.5f}",
    )


.. GENERATED FROM PYTHON SOURCE LINES 201-208

BisectingKMeans: divide and cluster
-----------------------------------
The new class :class:`cluster.BisectingKMeans` is a variant of :class:`KMeans`, using
divisive hierarchical clustering. Instead of creating all centroids at once, centroids
are picked progressively based on a previous clustering: a cluster is split into two
new clusters repeatedly until the target number of clusters is reached, giving a
hierarchical structure to the clustering.

.. GENERATED FROM PYTHON SOURCE LINES 208-227

.. code-block:: default

    from sklearn.datasets import make_blobs
    from sklearn.cluster import KMeans, BisectingKMeans
    import matplotlib.pyplot as plt

    X, _ = make_blobs(n_samples=1000, centers=2, random_state=0)

    km = KMeans(n_clusters=5, random_state=0, n_init="auto").fit(X)
    bisect_km = BisectingKMeans(n_clusters=5, random_state=0).fit(X)

    fig, ax = plt.subplots(1, 2, figsize=(10, 5))
    ax[0].scatter(X[:, 0], X[:, 1], s=10, c=km.labels_)
    ax[0].scatter(km.cluster_centers_[:, 0], km.cluster_centers_[:, 1], s=20, c="r")
    ax[0].set_title("KMeans")

    ax[1].scatter(X[:, 0], X[:, 1], s=10, c=bisect_km.labels_)
    ax[1].scatter(
        bisect_km.cluster_centers_[:, 0], bisect_km.cluster_centers_[:, 1], s=20, c="r"
    )
    _ = ax[1].set_title("BisectingKMeans")


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.709 seconds)


.. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_1_1_0.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_release_highlights_1_1_0.py <plot_release_highlights_1_1_0.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: plot_release_highlights_1_1_0.ipynb <plot_release_highlights_1_1_0.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
