[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, 320 deletions

diff --git a/docs/source/auto_examples/plot_barycenter_fgw.rst b/docs/source/auto_examples/plot_barycenter_fgw.rst
deleted file mode 100644
index ad4c275..0000000
--- a/docs/source/auto_examples/plot_barycenter_fgw.rst
+++ /dev/null
@@ -1,320 +0,0 @@
-.. only:: html
-
-    .. note::
-        :class: sphx-glr-download-link-note
-
-        Click :ref:`here <sphx_glr_download_auto_examples_plot_barycenter_fgw.py>`     to download the full example code
-    .. rst-class:: sphx-glr-example-title
-
-    .. _sphx_glr_auto_examples_plot_barycenter_fgw.py:
-
-
-=================================
-Plot graphs' barycenter using FGW
-=================================
-
-This example illustrates the computation barycenter of labeled graphs using FGW
-
-Requires networkx >=2
-
-.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain
-      and Courty Nicolas
-    "Optimal Transport for structured data with application on graphs"
-    International Conference on Machine Learning (ICML). 2019.
-
-
-
-.. code-block:: default
-
-
-    # Author: Titouan Vayer <titouan.vayer@irisa.fr>
-    #
-    # License: MIT License
-
-
-
-
-
-
-
-
-
-.. code-block:: default
-
-    import numpy as np
-    import matplotlib.pyplot as plt
-    import networkx as nx
-    import math
-    from scipy.sparse.csgraph import shortest_path
-    import matplotlib.colors as mcol
-    from matplotlib import cm
-    from ot.gromov import fgw_barycenters
-
-
-
-
-
-
-
-
-.. code-block:: default
-
-
-
-    def find_thresh(C, inf=0.5, sup=3, step=10):
-        """ Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected
-            Tthe threshold is found by a linesearch between values "inf" and "sup" with "step" thresholds tested.
-            The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix
-            and the original matrix.
-        Parameters
-        ----------
-        C : ndarray, shape (n_nodes,n_nodes)
-                The structure matrix to threshold
-        inf : float
-              The beginning of the linesearch
-        sup : float
-              The end of the linesearch
-        step : integer
-                Number of thresholds tested
-        """
-        dist = []
-        search = np.linspace(inf, sup, step)
-        for thresh in search:
-            Cprime = sp_to_adjency(C, 0, thresh)
-            SC = shortest_path(Cprime, method='D')
-            SC[SC == float('inf')] = 100
-            dist.append(np.linalg.norm(SC - C))
-        return search[np.argmin(dist)], dist
-
-
-    def sp_to_adjency(C, threshinf=0.2, threshsup=1.8):
-        """ Thresholds the structure matrix in order to compute an adjency matrix.
-        All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0
-        Parameters
-        ----------
-        C : ndarray, shape (n_nodes,n_nodes)
-            The structure matrix to threshold
-        threshinf : float
-            The minimum value of distance from which the new value is set to 1
-        threshsup : float
-            The maximum value of distance from which the new value is set to 1
-        Returns
-        -------
-        C : ndarray, shape (n_nodes,n_nodes)
-            The threshold matrix. Each element is in {0,1}
-        """
-        H = np.zeros_like(C)
-        np.fill_diagonal(H, np.diagonal(C))
-        C = C - H
-        C = np.minimum(np.maximum(C, threshinf), threshsup)
-        C[C == threshsup] = 0
-        C[C != 0] = 1
-
-        return C
-
-
-    def build_noisy_circular_graph(N=20, mu=0, sigma=0.3, with_noise=False, structure_noise=False, p=None):
-        """ Create a noisy circular graph
-        """
-        g = nx.Graph()
-        g.add_nodes_from(list(range(N)))
-        for i in range(N):
-            noise = float(np.random.normal(mu, sigma, 1))
-            if with_noise:
-                g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise)
-            else:
-                g.add_node(i, attr_name=math.sin(2 * i * math.pi / N))
-            g.add_edge(i, i + 1)
-            if structure_noise:
-                randomint = np.random.randint(0, p)
-                if randomint == 0:
-                    if i <= N - 3:
-                        g.add_edge(i, i + 2)
-                    if i == N - 2:
-                        g.add_edge(i, 0)
-                    if i == N - 1:
-                        g.add_edge(i, 1)
-        g.add_edge(N, 0)
-        noise = float(np.random.normal(mu, sigma, 1))
-        if with_noise:
-            g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise)
-        else:
-            g.add_node(N, attr_name=math.sin(2 * N * math.pi / N))
-        return g
-
-
-    def graph_colors(nx_graph, vmin=0, vmax=7):
-        cnorm = mcol.Normalize(vmin=vmin, vmax=vmax)
-        cpick = cm.ScalarMappable(norm=cnorm, cmap='viridis')
-        cpick.set_array([])
-        val_map = {}
-        for k, v in nx.get_node_attributes(nx_graph, 'attr_name').items():
-            val_map[k] = cpick.to_rgba(v)
-        colors = []
-        for node in nx_graph.nodes():
-            colors.append(val_map[node])
-        return colors
-
-
-
-
-
-
-
-
-Generate data
--------------
-
-We build a dataset of noisy circular graphs.
-Noise is added on the structures by random connections and on the features by gaussian noise.
-
-
-.. code-block:: default
-
-
-
-    np.random.seed(30)
-    X0 = []
-    for k in range(9):
-        X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3))
-
-
-
-
-
-
-
-
-Plot data
----------
-
-
-.. code-block:: default
-
-
-    plt.figure(figsize=(8, 10))
-    for i in range(len(X0)):
-        plt.subplot(3, 3, i + 1)
-        g = X0[i]
-        pos = nx.kamada_kawai_layout(g)
-        nx.draw(g, pos=pos, node_color=graph_colors(g, vmin=-1, vmax=1), with_labels=False, node_size=100)
-    plt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20)
-    plt.show()
-
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_barycenter_fgw_001.png
-    :class: sphx-glr-single-img
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-    /home/rflamary/PYTHON/POT/examples/plot_barycenter_fgw.py:155: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-      plt.show()
-
-
-
-
-Barycenter computation
-----------------------
-
-Features distances are the euclidean distances
-
-
-.. code-block:: default
-
-    Cs = [shortest_path(nx.adjacency_matrix(x)) for x in X0]
-    ps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0]
-    Ys = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0]
-    lambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel()
-    sizebary = 15  # we choose a barycenter with 15 nodes
-
-    A, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True)
-
-
-
-
-
-
-
-
-Plot Barycenter
--------------------------
-
-
-.. code-block:: default
-
-    bary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0]))
-    for i, v in enumerate(A.ravel()):
-        bary.add_node(i, attr_name=v)
-
-
-
-
-
-
-
-
-
-.. code-block:: default
-
-    pos = nx.kamada_kawai_layout(bary)
-    nx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False)
-    plt.suptitle('Barycenter', fontsize=20)
-    plt.show()
-
-
-
-.. image:: /auto_examples/images/sphx_glr_plot_barycenter_fgw_002.png
-    :class: sphx-glr-single-img
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-    /home/rflamary/PYTHON/POT/examples/plot_barycenter_fgw.py:184: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-      plt.show()
-
-
-
-
-
-.. rst-class:: sphx-glr-timing
-
-   **Total running time of the script:** ( 0 minutes  1.949 seconds)
-
-
-.. _sphx_glr_download_auto_examples_plot_barycenter_fgw.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_barycenter_fgw.py <plot_barycenter_fgw.py>`
-
-
-
-  .. container:: sphx-glr-download sphx-glr-download-jupyter
-
-     :download:`Download Jupyter notebook: plot_barycenter_fgw.ipynb <plot_barycenter_fgw.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
-    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_