.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/over-sampling/plot_illustration_generation_sample.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_over-sampling_plot_illustration_generation_sample.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_over-sampling_plot_illustration_generation_sample.py:


============================================
Sample generator used in SMOTE-like samplers
============================================

This example illustrates how a new sample is generated taking into account the
neighbourhood of this sample. A new sample is generated by selecting the
randomly 2 samples of the same class and interpolating a point between these
samples.

.. GENERATED FROM PYTHON SOURCE LINES 11-14

.. code-block:: Python


    # Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
    # License: MIT







.. GENERATED FROM PYTHON SOURCE LINES 15-73

.. code-block:: Python

    print(__doc__)

    import matplotlib.pyplot as plt
    import numpy as np
    import seaborn as sns

    sns.set_context("poster")

    rng = np.random.RandomState(18)

    f, ax = plt.subplots(figsize=(8, 8))

    # generate some data points
    y = np.array([3.65284, 3.52623, 3.51468, 3.22199, 3.21])
    z = np.array([0.43, 0.45, 0.6, 0.4, 0.211])
    y_2 = np.array([3.3, 3.6])
    z_2 = np.array([0.58, 0.34])

    # plot the majority and minority samples
    ax.scatter(z, y, label="Minority class", s=100)
    ax.scatter(z_2, y_2, label="Majority class", s=100)

    idx = rng.randint(len(y), size=2)
    annotation = [r"$x_i$", r"$x_{zi}$"]

    for a, i in zip(annotation, idx):
        ax.annotate(a, (z[i], y[i]), xytext=tuple([z[i] + 0.01, y[i] + 0.005]), fontsize=15)

    # draw the circle in which the new sample will generated
    radius = np.sqrt((z[idx[0]] - z[idx[1]]) ** 2 + (y[idx[0]] - y[idx[1]]) ** 2)
    circle = plt.Circle((z[idx[0]], y[idx[0]]), radius=radius, alpha=0.2)
    ax.add_artist(circle)

    # plot the line on which the sample will be generated
    ax.plot(z[idx], y[idx], "--", alpha=0.5)

    # create and plot the new sample
    step = rng.uniform()
    y_gen = y[idx[0]] + step * (y[idx[1]] - y[idx[0]])
    z_gen = z[idx[0]] + step * (z[idx[1]] - z[idx[0]])

    ax.scatter(z_gen, y_gen, s=100)
    ax.annotate(
        r"$x_{new}$",
        (z_gen, y_gen),
        xytext=tuple([z_gen + 0.01, y_gen + 0.005]),
        fontsize=15,
    )

    # make the plot nicer with legend and label
    sns.despine(ax=ax, offset=10)
    ax.set_xlim([0.2, 0.7])
    ax.set_ylim([3.2, 3.7])
    plt.xlabel(r"$X_1$")
    plt.ylabel(r"$X_2$")
    plt.legend()
    plt.tight_layout()
    plt.show()



.. image-sg:: /auto_examples/over-sampling/images/sphx_glr_plot_illustration_generation_sample_001.png
   :alt: plot illustration generation sample
   :srcset: /auto_examples/over-sampling/images/sphx_glr_plot_illustration_generation_sample_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.624 seconds)

**Estimated memory usage:**  10 MB


.. _sphx_glr_download_auto_examples_over-sampling_plot_illustration_generation_sample.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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