From a303cc6b483d3cd958c399621e22e40574bcbbc8 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 21 Apr 2020 17:48:37 +0200 Subject: [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 --- docs/source/auto_examples/plot_stochastic.rst | 518 -------------------------- 1 file changed, 518 deletions(-) delete mode 100644 docs/source/auto_examples/plot_stochastic.rst (limited to 'docs/source/auto_examples/plot_stochastic.rst') diff --git a/docs/source/auto_examples/plot_stochastic.rst b/docs/source/auto_examples/plot_stochastic.rst deleted file mode 100644 index 63fc74f..0000000 --- a/docs/source/auto_examples/plot_stochastic.rst +++ /dev/null @@ -1,518 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_stochastic.py: - - -========================== -Stochastic examples -========================== - -This example is designed to show how to use the stochatic optimization -algorithms for descrete and semicontinous measures from the POT library. - - - -.. code-block:: default - - - # Author: Kilian Fatras - # - # License: MIT License - - import matplotlib.pylab as pl - import numpy as np - import ot - import ot.plot - - - - - - - - - -COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM -############################################################################ -############################################################################ - DISCRETE CASE: - - Sample two discrete measures for the discrete case - --------------------------------------------- - - Define 2 discrete measures a and b, the points where are defined the source - and the target measures and finally the cost matrix c. - - -.. code-block:: default - - - n_source = 7 - n_target = 4 - reg = 1 - numItermax = 1000 - - a = ot.utils.unif(n_source) - b = ot.utils.unif(n_target) - - rng = np.random.RandomState(0) - X_source = rng.randn(n_source, 2) - Y_target = rng.randn(n_target, 2) - M = ot.dist(X_source, Y_target) - - - - - - - - -Call the "SAG" method to find the transportation matrix in the discrete case ---------------------------------------------- - -Define the method "SAG", call ot.solve_semi_dual_entropic and plot the -results. - - -.. code-block:: default - - - method = "SAG" - sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, - numItermax) - print(sag_pi) - - - - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - [[2.55553509e-02 9.96395660e-02 1.76579142e-02 4.31178196e-06] - [1.21640234e-01 1.25357448e-02 1.30225078e-03 7.37891338e-03] - [3.56123975e-03 7.61451746e-02 6.31505947e-02 1.33831456e-07] - [2.61515202e-02 3.34246014e-02 8.28734709e-02 4.07550428e-04] - [9.85500870e-03 7.52288517e-04 1.08262628e-02 1.21423583e-01] - [2.16904253e-02 9.03825797e-04 1.87178503e-03 1.18391107e-01] - [4.15462212e-02 2.65987989e-02 7.23177216e-02 2.39440107e-03]] - - - - -SEMICONTINOUS CASE: - -Sample one general measure a, one discrete measures b for the semicontinous -case ---------------------------------------------- - -Define one general measure a, one discrete measures b, the points where -are defined the source and the target measures and finally the cost matrix c. - - -.. code-block:: default - - - n_source = 7 - n_target = 4 - reg = 1 - numItermax = 1000 - log = True - - a = ot.utils.unif(n_source) - b = ot.utils.unif(n_target) - - rng = np.random.RandomState(0) - X_source = rng.randn(n_source, 2) - Y_target = rng.randn(n_target, 2) - M = ot.dist(X_source, Y_target) - - - - - - - - -Call the "ASGD" method to find the transportation matrix in the semicontinous -case ---------------------------------------------- - -Define the method "ASGD", call ot.solve_semi_dual_entropic and plot the -results. - - -.. code-block:: default - - - method = "ASGD" - asgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, - numItermax, log=log) - print(log_asgd['alpha'], log_asgd['beta']) - print(asgd_pi) - - - - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - [3.89943264 7.64823414 3.9284189 2.67501041 1.42825446 3.26039819 - 2.79237712] [-2.50786905 -2.42684838 -0.93647774 5.87119517] - [[2.50229922e-02 1.00367920e-01 1.74615056e-02 4.72486104e-06] - [1.20583329e-01 1.27839737e-02 1.30373565e-03 8.18610462e-03] - [3.49243139e-03 7.68200813e-02 6.25444833e-02 1.46879008e-07] - [2.58205995e-02 3.39501207e-02 8.26360982e-02 4.50324517e-04] - [8.94164918e-03 7.02183713e-04 9.92028326e-03 1.23293027e-01] - [1.97360234e-02 8.46022708e-04 1.72001583e-03 1.20555081e-01] - [4.10386980e-02 2.70289873e-02 7.21425804e-02 2.64687723e-03]] - - - - -Compare the results with the Sinkhorn algorithm ---------------------------------------------- - -Call the Sinkhorn algorithm from POT - - -.. code-block:: default - - - sinkhorn_pi = ot.sinkhorn(a, b, M, reg) - print(sinkhorn_pi) - - - - - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - [[2.55553508e-02 9.96395661e-02 1.76579142e-02 4.31178193e-06] - [1.21640234e-01 1.25357448e-02 1.30225079e-03 7.37891333e-03] - [3.56123974e-03 7.61451746e-02 6.31505947e-02 1.33831455e-07] - [2.61515201e-02 3.34246014e-02 8.28734709e-02 4.07550425e-04] - [9.85500876e-03 7.52288523e-04 1.08262629e-02 1.21423583e-01] - [2.16904255e-02 9.03825804e-04 1.87178504e-03 1.18391107e-01] - [4.15462212e-02 2.65987989e-02 7.23177217e-02 2.39440105e-03]] - - - - -PLOT TRANSPORTATION MATRIX -############################################################################# - -Plot SAG results ----------------- - - -.. code-block:: default - - - pl.figure(4, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, sag_pi, 'semi-dual : OT matrix SAG') - pl.show() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_stochastic_001.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_stochastic.py:119: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - -Plot ASGD results ------------------ - - -.. code-block:: default - - - pl.figure(4, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD') - pl.show() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_stochastic_002.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_stochastic.py:128: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - -Plot Sinkhorn results ---------------------- - - -.. code-block:: default - - - pl.figure(4, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn') - pl.show() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_stochastic_003.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_stochastic.py:137: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - -COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM -############################################################################ -############################################################################ - SEMICONTINOUS CASE: - - Sample one general measure a, one discrete measures b for the semicontinous - case - --------------------------------------------- - - Define one general measure a, one discrete measures b, the points where - are defined the source and the target measures and finally the cost matrix c. - - -.. code-block:: default - - - n_source = 7 - n_target = 4 - reg = 1 - numItermax = 100000 - lr = 0.1 - batch_size = 3 - log = True - - a = ot.utils.unif(n_source) - b = ot.utils.unif(n_target) - - rng = np.random.RandomState(0) - X_source = rng.randn(n_source, 2) - Y_target = rng.randn(n_target, 2) - M = ot.dist(X_source, Y_target) - - - - - - - - -Call the "SGD" dual method to find the transportation matrix in the -semicontinous case ---------------------------------------------- - -Call ot.solve_dual_entropic and plot the results. - - -.. code-block:: default - - - sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg, - batch_size, numItermax, - lr, log=log) - print(log_sgd['alpha'], log_sgd['beta']) - print(sgd_dual_pi) - - - - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - [0.91421006 2.78075506 1.06828701 0.01979397 0.60914807 1.81887037 - 0.1152939 ] [0.33964624 0.47604281 1.57223631 4.93843308] - [[2.18038772e-02 9.24355133e-02 1.08426805e-02 9.39355366e-08] - [1.59966167e-02 1.79248770e-03 1.23251128e-04 2.47779034e-05] - [3.44864558e-03 8.01760930e-02 4.40119061e-02 3.30922887e-09] - [3.12954103e-02 4.34915712e-02 7.13747533e-02 1.24533534e-05] - [6.79742497e-02 5.64192090e-03 5.37416946e-02 2.13851205e-02] - [8.05141568e-02 3.64790957e-03 5.00040902e-03 1.12213345e-02] - [4.86643900e-02 3.38763749e-02 6.09634969e-02 7.16139950e-05]] - - - - -Compare the results with the Sinkhorn algorithm ---------------------------------------------- - -Call the Sinkhorn algorithm from POT - - -.. code-block:: default - - - sinkhorn_pi = ot.sinkhorn(a, b, M, reg) - print(sinkhorn_pi) - - - - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - [[2.55553508e-02 9.96395661e-02 1.76579142e-02 4.31178193e-06] - [1.21640234e-01 1.25357448e-02 1.30225079e-03 7.37891333e-03] - [3.56123974e-03 7.61451746e-02 6.31505947e-02 1.33831455e-07] - [2.61515201e-02 3.34246014e-02 8.28734709e-02 4.07550425e-04] - [9.85500876e-03 7.52288523e-04 1.08262629e-02 1.21423583e-01] - [2.16904255e-02 9.03825804e-04 1.87178504e-03 1.18391107e-01] - [4.15462212e-02 2.65987989e-02 7.23177217e-02 2.39440105e-03]] - - - - -Plot SGD results ------------------ - - -.. code-block:: default - - - pl.figure(4, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD') - pl.show() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_stochastic_004.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_stochastic.py:199: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - -Plot Sinkhorn results ---------------------- - - -.. code-block:: default - - - pl.figure(4, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn') - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_stochastic_005.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_stochastic.py:208: 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 8.885 seconds) - - -.. _sphx_glr_download_auto_examples_plot_stochastic.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_stochastic.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_stochastic.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ -- cgit v1.2.3