.. 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_0_20_0.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_0_20_0.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_release_highlights_plot_release_highlights_0_20_0.py:


========================================
Release Highlights for tttrlib 0.23
========================================

.. currentmodule:: tttrlib

We are pleased to announce the release of tttrlib 0.20, which comes
with many bug fixes and new 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_0_20>`.

To install the latest version with conda::

    conda install -c tpeulen tttrlib

.. GENERATED FROM PYTHON SOURCE LINES 20-25

Test for plotting
------------------------------------

The :func:`inspection.permutation_importance` can be used to get an
estimate of the importance of each feature, for any fitted estimator:

.. GENERATED FROM PYTHON SOURCE LINES 25-37

.. code-block:: Python


    import numpy as np
    import matplotlib.pyplot as plt

    fig, ax = plt.subplots()
    x = np.linspace(0, 5)
    ax.plot(x, np.sin(x))
    ax.set_title("Permutation Importance of each feature")
    ax.set_ylabel("Features")
    fig.tight_layout()
    plt.show()




.. image-sg:: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_0_20_0_001.png
   :alt: Permutation Importance of each feature
   :srcset: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_0_20_0_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 38-45

Improved PTU header support
-------------------------------------------------------

The :class:`ensemble.HistGradientBoostingClassifier`
and :class:`ensemble.HistGradientBoostingRegressor` now have native
support for missing values (NaNs). This means that there is no need for
imputing data when training or predicting.

.. GENERATED FROM PYTHON SOURCE LINES 45-48

.. code-block:: Python


    print("Better Header support")





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

 .. code-block:: none

    Better Header support




.. GENERATED FROM PYTHON SOURCE LINES 49-60

Writing of TTTR data
------------------------------------------
Most estimators based on nearest neighbors graphs now accept precomputed
sparse graphs as input, to reuse the same graph for multiple estimator fits.
To use this feature in a pipeline, one can use the `memory` parameter, along
with one of the two new transformers,
:class:`neighbors.KNeighborsTransformer` and
:class:`neighbors.RadiusNeighborsTransformer`. The precomputation
can also be performed by custom estimators to use alternative
implementations, such as approximate nearest neighbors methods.
See more details in the :ref:`User Guide <neighbors_transformer>`.

.. GENERATED FROM PYTHON SOURCE LINES 60-77

.. code-block:: Python


    from tempfile import TemporaryDirectory
    import tttrlib


    # with TemporaryDirectory(prefix="tttrlib_temp_") as tmpdir:
    #     estimator = make_pipeline(
    #         KNeighborsTransformer(n_neighbors=10, mode='distance'),
    #         Isomap(n_neighbors=10, metric='precomputed'),
    #         memory=tmpdir)
    #     estimator.fit(X)
    #
    #     # We can decrease the number of neighbors and the graph will not be
    #     # recomputed.
    #     estimator.set_params(isomap__n_neighbors=5)
    #     estimator.fit(X)









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

   **Total running time of the script:** (0 minutes 0.231 seconds)


.. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_0_20_0.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_release_highlights_0_20_0.ipynb <plot_release_highlights_0_20_0.ipynb>`

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

      :download:`Download Python source code: plot_release_highlights_0_20_0.py <plot_release_highlights_0_20_0.py>`


.. only:: html

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

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