[MRG] Actually run sphinx-gallery (#146)

* generate gallery

* remove mock

* add sklearn to requirermnt?txt for example

* remove latex from fgw example

* add networks for graph example

* remove all

* add requirement.txt rtd

* rtd debug

* update readme

* eradthedoc with redirection

* add conf rtd

1 files changed, 0 insertions, 267 deletions

diff --git a/docs/source/auto_examples/plot_otda_semi_supervised.rst b/docs/source/auto_examples/plot_otda_semi_supervised.rst
deleted file mode 100644
index 4a355e7..0000000
--- a/docs/source/auto_examples/plot_otda_semi_supervised.rst
+++ /dev/null
@@ -1,267 +0,0 @@
-.. only:: html
-
-    .. note::
-        :class: sphx-glr-download-link-note
-
-        Click :ref:`here <sphx_glr_download_auto_examples_plot_otda_semi_supervised.py>`     to download the full example code
-    .. rst-class:: sphx-glr-example-title
-
-    .. _sphx_glr_auto_examples_plot_otda_semi_supervised.py:
-
-
-============================================
-OTDA unsupervised vs semi-supervised setting
-============================================
-
-This example introduces a semi supervised domain adaptation in a 2D setting.
-It explicits the problem of semi supervised domain adaptation and introduces
-some optimal transport approaches to solve it.
-
-Quantities such as optimal couplings, greater coupling coefficients and
-transported samples are represented in order to give a visual understanding
-of what the transport methods are doing.
-
-
-.. code-block:: default
-
-
-    # Authors: Remi Flamary <remi.flamary@unice.fr>
-    #          Stanislas Chambon <stan.chambon@gmail.com>
-    #
-    # License: MIT License
-
-    import matplotlib.pylab as pl
-    import ot
-
-
-
-
-
-
-
-
-
-Generate data
--------------
-
-
-.. code-block:: default
-
-
-    n_samples_source = 150
-    n_samples_target = 150
-
-    Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source)
-    Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target)
-
-
-
-
-
-
-
-
-
-Transport source samples onto target samples
---------------------------------------------
-
-
-.. code-block:: default
-
-
-
-    # unsupervised domain adaptation
-    ot_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1)
-    ot_sinkhorn_un.fit(Xs=Xs, Xt=Xt)
-    transp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs)
-
-    # semi-supervised domain adaptation
-    ot_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1)
-    ot_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt)
-    transp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs)
-
-    # semi supervised DA uses available labaled target samples to modify the cost
-    # matrix involved in the OT problem. The cost of transporting a source sample
-    # of class A onto a target sample of class B != A is set to infinite, or a
-    # very large value
-
-    # note that in the present case we consider that all the target samples are
-    # labeled. For daily applications, some target sample might not have labels,
-    # in this case the element of yt corresponding to these samples should be
-    # filled with -1.
-
-    # Warning: we recall that -1 cannot be used as a class label
-
-
-
-
-
-
-
-
-
-Fig 1 : plots source and target samples + matrix of pairwise distance
----------------------------------------------------------------------
-
-
-.. code-block:: default
-
-
-    pl.figure(1, figsize=(10, 10))
-    pl.subplot(2, 2, 1)
-    pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
-    pl.xticks([])
-    pl.yticks([])
-    pl.legend(loc=0)
-    pl.title('Source  samples')
-
-    pl.subplot(2, 2, 2)
-    pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
-    pl.xticks([])
-    pl.yticks([])
-    pl.legend(loc=0)
-    pl.title('Target samples')
-
-    pl.subplot(2, 2, 3)
-    pl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest')
-    pl.xticks([])
-    pl.yticks([])
-    pl.title('Cost matrix - unsupervised DA')
-
-    pl.subplot(2, 2, 4)
-    pl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest')
-    pl.xticks([])
-    pl.yticks([])
-    pl.title('Cost matrix - semisupervised DA')
-
-    pl.tight_layout()
-
-    # the optimal coupling in the semi-supervised DA case will exhibit " shape
-    # similar" to the cost matrix, (block diagonal matrix)
-
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_semi_supervised_001.png
-    :class: sphx-glr-single-img
-
-
-
-
-
-Fig 2 : plots optimal couplings for the different methods
----------------------------------------------------------
-
-
-.. code-block:: default
-
-
-    pl.figure(2, figsize=(8, 4))
-
-    pl.subplot(1, 2, 1)
-    pl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest')
-    pl.xticks([])
-    pl.yticks([])
-    pl.title('Optimal coupling\nUnsupervised DA')
-
-    pl.subplot(1, 2, 2)
-    pl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest')
-    pl.xticks([])
-    pl.yticks([])
-    pl.title('Optimal coupling\nSemi-supervised DA')
-
-    pl.tight_layout()
-
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_semi_supervised_002.png
-    :class: sphx-glr-single-img
-
-
-
-
-
-Fig 3 : plot transported samples
---------------------------------
-
-
-.. code-block:: default
-
-
-    # display transported samples
-    pl.figure(4, figsize=(8, 4))
-    pl.subplot(1, 2, 1)
-    pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
-               label='Target samples', alpha=0.5)
-    pl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys,
-               marker='+', label='Transp samples', s=30)
-    pl.title('Transported samples\nEmdTransport')
-    pl.legend(loc=0)
-    pl.xticks([])
-    pl.yticks([])
-
-    pl.subplot(1, 2, 2)
-    pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
-               label='Target samples', alpha=0.5)
-    pl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys,
-               marker='+', label='Transp samples', s=30)
-    pl.title('Transported samples\nSinkhornTransport')
-    pl.xticks([])
-    pl.yticks([])
-
-    pl.tight_layout()
-    pl.show()
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_otda_semi_supervised_003.png
-    :class: sphx-glr-single-img
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-    /home/rflamary/PYTHON/POT/examples/plot_otda_semi_supervised.py:148: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-      pl.show()
-
-
-
-
-
-.. rst-class:: sphx-glr-timing
-
-   **Total running time of the script:** ( 0 minutes  0.660 seconds)
-
-
-.. _sphx_glr_download_auto_examples_plot_otda_semi_supervised.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_otda_semi_supervised.py <plot_otda_semi_supervised.py>`
-
-
-
-  .. container:: sphx-glr-download sphx-glr-download-jupyter
-
-     :download:`Download Jupyter notebook: plot_otda_semi_supervised.ipynb <plot_otda_semi_supervised.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
-    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_