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

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

.. _sphx_glr_auto_examples_plot_sandwich.py:


Sandwich demo
=============

Sandwich demo based on code from http://nbviewer.ipython.org/6576096

.. GENERATED FROM PYTHON SOURCE LINES 10-15

.. note::

    In order to show the charts of the examples you need a graphical
    ``matplotlib`` backend installed. For intance, use ``pip install pyqt5``
    to get Qt graphical interface or use your favorite one.

.. GENERATED FROM PYTHON SOURCE LINES 15-106

.. code-block:: default


    import numpy as np
    from matplotlib import pyplot as plt
    from sklearn.metrics import pairwise_distances
    from sklearn.neighbors import NearestNeighbors

    from metric_learn import (LMNN, ITML_Supervised, LSML_Supervised,
                              SDML_Supervised)


    def sandwich_demo():
      x, y = sandwich_data()
      knn = nearest_neighbors(x, k=2)
      ax = plt.subplot(3, 1, 1)  # take the whole top row
      plot_sandwich_data(x, y, ax)
      plot_neighborhood_graph(x, knn, y, ax)
      ax.set_title('input space')
      ax.set_aspect('equal')
      ax.set_xticks([])
      ax.set_yticks([])

      mls = [
          LMNN(),
          ITML_Supervised(n_constraints=200),
          SDML_Supervised(n_constraints=200, balance_param=0.001),
          LSML_Supervised(n_constraints=200),
      ]

      for ax_num, ml in enumerate(mls, start=3):
        ml.fit(x, y)
        tx = ml.transform(x)
        ml_knn = nearest_neighbors(tx, k=2)
        ax = plt.subplot(3, 2, ax_num)
        plot_sandwich_data(tx, y, axis=ax)
        plot_neighborhood_graph(tx, ml_knn, y, axis=ax)
        ax.set_title(ml.__class__.__name__)
        ax.set_xticks([])
        ax.set_yticks([])
      plt.show()


    # TODO: use this somewhere
    def visualize_class_separation(X, labels):
      _, (ax1, ax2) = plt.subplots(ncols=2)
      label_order = np.argsort(labels)
      ax1.imshow(pairwise_distances(X[label_order]), interpolation='nearest')
      ax2.imshow(pairwise_distances(labels[label_order, None]),
                 interpolation='nearest')


    def nearest_neighbors(X, k=5):
      knn = NearestNeighbors(n_neighbors=k)
      knn.fit(X)
      return knn.kneighbors(X, return_distance=False)


    def sandwich_data():
      # number of distinct classes
      num_classes = 6
      # number of points per class
      num_points = 9
      # distance between layers, the points of each class are in a layer
      dist = 0.7

      data = np.zeros((num_classes, num_points, 2), dtype=float)
      labels = np.zeros((num_classes, num_points), dtype=int)

      x_centers = np.arange(num_points, dtype=float) - num_points / 2
      y_centers = dist * (np.arange(num_classes, dtype=float) - num_classes / 2)
      for i, yc in enumerate(y_centers):
        for k, xc in enumerate(x_centers):
          data[i, k, 0] = np.random.normal(xc, 0.1)
          data[i, k, 1] = np.random.normal(yc, 0.1)
        labels[i, :] = i
      return data.reshape((-1, 2)), labels.ravel()


    def plot_sandwich_data(x, y, axis=plt, colors='rbgmky'):
      for idx, val in enumerate(np.unique(y)):
        xi = x[y == val]
        axis.scatter(*xi.T, s=50, facecolors='none', edgecolors=colors[idx])


    def plot_neighborhood_graph(x, nn, y, axis=plt, colors='rbgmky'):
      for i, a in enumerate(x):
        b = x[nn[i, 1]]
        axis.plot((a[0], b[0]), (a[1], b[1]), colors[y[i]])


    if __name__ == '__main__':
      sandwich_demo()



.. image-sg:: /auto_examples/images/sphx_glr_plot_sandwich_001.png
   :alt: input space, LMNN, ITML_Supervised, SDML_Supervised, LSML_Supervised
   :srcset: /auto_examples/images/sphx_glr_plot_sandwich_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    /Users/william.vezelhes/metric-learn/metric_learn/constraints.py:224: UserWarning: Only generated 199 positive constraints (requested 200)
      warnings.warn("Only generated %d %s constraints (requested %d)" % (
    /Users/william.vezelhes/miniconda3/envs/docenvbis/lib/python3.11/site-packages/sklearn/covariance/_graph_lasso.py:329: FutureWarning: The cov_init parameter is deprecated in 1.3 and will be removed in 1.5. It does not have any effect.
      warnings.warn(





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

   **Total running time of the script:** (0 minutes 3.314 seconds)


.. _sphx_glr_download_auto_examples_plot_sandwich.py:

.. only:: html

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




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

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

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

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


.. only:: html

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

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