.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_examples/plot_comparing_aggregation_schemes.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

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

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

.. _sphx_glr__examples_plot_comparing_aggregation_schemes.py:


Comparing Buitd-in Aggregation Schemes
==========================================
This example demonstrates the comparison of eight built-in aggregation schemes.
We draw 100 samples from two normal distributions with different mean and co-variance.
The samples are then aggregated using the built-in aggregation rules.

.. GENERATED FROM PYTHON SOURCE LINES 8-97

.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np
    import torch

    from fedlib.aggregators import (
        Clippedclustering,
        DnC,
        GeoMed,
        Mean,
        Median,
        Multikrum,
        Trimmedmean,
    )

    plt.rcParams["axes.linewidth"] = 1.5  # set the value globally
    plt.rcParams["font.weight"] = "bold"
    plt.rcParams["font.size"] = 16
    plt.rcParams["axes.labelweight"] = "bold"

    np.random.seed(1)
    sz = 40
    sample_sz = 80

    mean = np.array((0, 0))
    cov = [[20, 0], [0, 20]]
    benign = np.random.multivariate_normal(mean, cov, 60)
    mean = np.array((30, 30))
    cov = [[60, 0], [0, 60]]
    outliers = np.concatenate([np.random.multivariate_normal(mean, cov, 40)])
    all_data = np.concatenate([benign, outliers])

    all_data_tensor = torch.Tensor(np.concatenate([benign, outliers]))

    all_data_tensor = [tensor for tensor in all_data_tensor]

    aggs = [
        Mean(),
        Multikrum(len(outliers), 10),
        GeoMed(),
        Median(),
        DnC(num_byzantine=len(outliers)),
        Trimmedmean(num_byzantine=len(outliers)),
        Clippedclustering(),
    ]

    # sphinx_gallery_thumbnail_number = 1
    fig, axs = plt.subplots(figsize=(8, 8))

    ax = axs
    ax.scatter(
        benign[:, 0],
        benign[:, 1],
        s=sample_sz,
        alpha=0.6,
        color="r",
        linewidths=0.2,
        edgecolors="black",
    )
    ax.scatter(
        outliers[:, 0],
        outliers[:, 1],
        s=sample_sz,
        color=[0.0, 0.7, 0.0, 1.0],
        linewidths=0.2,
        edgecolors="black",
    )


    def plot_agg(ax, agg):
        target = agg(all_data_tensor).cpu().detach().numpy()
        ax.scatter(
            target[0],
            target[1],
            s=sz * 3,
            label=type(agg).__name__,
            linewidths=0.3,
            edgecolors="black",
        )


    list(map(lambda agg: plot_agg(ax, agg), aggs))
    ax.set_xticks([])
    ax.set_yticks([])
    ax.legend()

    fig.tight_layout(pad=0.0, w_pad=0.6, h_pad=0.5)
    plt.show()




.. image-sg:: /_examples/images/sphx_glr_plot_comparing_aggregation_schemes_001.png
   :alt: plot comparing aggregation schemes
   :srcset: /_examples/images/sphx_glr_plot_comparing_aggregation_schemes_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 98-100

In this example, the results of Mean deviated away by the
outliers. All the other are inside the range of benign data.


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

   **Total running time of the script:** (0 minutes 0.722 seconds)


.. _sphx_glr_download__examples_plot_comparing_aggregation_schemes.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_comparing_aggregation_schemes.zip <plot_comparing_aggregation_schemes.zip>`


.. only:: html

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

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