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26 files changed, 3279 insertions, 292 deletions
@@ -1,6 +1,7 @@ PYTHON=python3 +branch := $(shell git symbolic-ref --short -q HEAD) help : @echo "The following make targets are available:" @@ -57,6 +58,16 @@ rdoc : notebook : ipython notebook --matplotlib=inline --notebook-dir=notebooks/ +bench : + @git stash >/dev/null 2>&1 + @echo 'Branch master' + @git checkout master >/dev/null 2>&1 + python3 $(script) + @echo 'Branch $(branch)' + @git checkout $(branch) >/dev/null 2>&1 + python3 $(script) + @git stash apply >/dev/null 2>&1 + autopep8 : autopep8 -ir test ot examples --jobs -1 diff --git a/docs/source/auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_001.png b/docs/source/auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_001.png Binary files differnew file mode 100644 index 0000000..3500812 --- /dev/null +++ b/docs/source/auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_001.png diff --git a/docs/source/auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_002.png b/docs/source/auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_002.png Binary files differnew file mode 100644 index 0000000..37fef68 --- /dev/null +++ b/docs/source/auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_002.png diff --git a/docs/source/auto_examples/images/sphx_glr_plot_otda_linear_mapping_001.png b/docs/source/auto_examples/images/sphx_glr_plot_otda_linear_mapping_001.png Binary files differnew file mode 100644 index 0000000..88796df --- /dev/null +++ b/docs/source/auto_examples/images/sphx_glr_plot_otda_linear_mapping_001.png diff --git a/docs/source/auto_examples/images/sphx_glr_plot_otda_linear_mapping_002.png b/docs/source/auto_examples/images/sphx_glr_plot_otda_linear_mapping_002.png Binary files differnew file mode 100644 index 0000000..22b5d0c --- /dev/null +++ b/docs/source/auto_examples/images/sphx_glr_plot_otda_linear_mapping_002.png diff --git a/docs/source/auto_examples/images/sphx_glr_plot_otda_linear_mapping_004.png b/docs/source/auto_examples/images/sphx_glr_plot_otda_linear_mapping_004.png Binary files differnew file mode 100644 index 0000000..ff10b72 --- /dev/null +++ b/docs/source/auto_examples/images/sphx_glr_plot_otda_linear_mapping_004.png diff --git a/docs/source/auto_examples/images/thumb/sphx_glr_plot_barycenter_lp_vs_entropic_thumb.png b/docs/source/auto_examples/images/thumb/sphx_glr_plot_barycenter_lp_vs_entropic_thumb.png Binary files differnew file mode 100644 index 0000000..c68e95f --- /dev/null +++ b/docs/source/auto_examples/images/thumb/sphx_glr_plot_barycenter_lp_vs_entropic_thumb.png diff --git a/docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_linear_mapping_thumb.png b/docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_linear_mapping_thumb.png Binary files differnew file mode 100644 index 0000000..277950e --- /dev/null +++ b/docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_linear_mapping_thumb.png diff --git a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.ipynb b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.ipynb new file mode 100644 index 0000000..2199162 --- /dev/null +++ b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.ipynb @@ -0,0 +1,108 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# 1D Wasserstein barycenter comparison between exact LP and entropic regularization\n\n\nThis example illustrates the computation of regularized Wasserstein Barycenter\nas proposed in [3] and exact LP barycenters using standard LP solver.\n\nIt reproduces approximately Figure 3.1 and 3.2 from the following paper:\nCuturi, M., & Peyr\u00e9, G. (2016). A smoothed dual approach for variational\nWasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343.\n\n[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyr\u00e9, G. (2015).\nIterative Bregman projections for regularized transportation problems\nSIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Remi Flamary <remi.flamary@unice.fr>\n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection # noqa\n\n#import ot.lp.cvx as cvx" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Gaussian Data\n-------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% parameters\n\nproblems = []\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\n# Gaussian distributions\na1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n#%% barycenter computation\n\nalpha = 0.5 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\not.tic()\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\not.toc()\n\n\not.tic()\nbary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\not.toc()\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\npl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\nproblems.append([A, [bary_l2, bary_wass, bary_wass2]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dirac Data\n----------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% parameters\n\na1 = 1.0 * (x > 10) * (x < 50)\na2 = 1.0 * (x > 60) * (x < 80)\n\na1 /= a1.sum()\na2 /= a2.sum()\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n\n#%% barycenter computation\n\nalpha = 0.5 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\not.tic()\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\not.toc()\n\n\not.tic()\nbary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\not.toc()\n\n\nproblems.append([A, [bary_l2, bary_wass, bary_wass2]])\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\npl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\n#%% parameters\n\na1 = np.zeros(n)\na2 = np.zeros(n)\n\na1[10] = .25\na1[20] = .5\na1[30] = .25\na2[80] = 1\n\n\na1 /= a1.sum()\na2 /= a2.sum()\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n\n#%% barycenter computation\n\nalpha = 0.5 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\not.tic()\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\not.toc()\n\n\not.tic()\nbary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\not.toc()\n\n\nproblems.append([A, [bary_l2, bary_wass, bary_wass2]])\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\npl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Final figure\n------------\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% plot\n\nnbm = len(problems)\nnbm2 = (nbm // 2)\n\n\npl.figure(2, (20, 6))\npl.clf()\n\nfor i in range(nbm):\n\n A = problems[i][0]\n bary_l2 = problems[i][1][0]\n bary_wass = problems[i][1][1]\n bary_wass2 = problems[i][1][2]\n\n pl.subplot(2, nbm, 1 + i)\n for j in range(n_distributions):\n pl.plot(x, A[:, j])\n if i == nbm2:\n pl.title('Distributions')\n pl.xticks(())\n pl.yticks(())\n\n pl.subplot(2, nbm, 1 + i + nbm)\n\n pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)')\n pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\n pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n if i == nbm - 1:\n pl.legend()\n if i == nbm2:\n pl.title('Barycenters')\n\n pl.xticks(())\n pl.yticks(())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}
\ No newline at end of file diff --git a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.py b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.py new file mode 100644 index 0000000..b82765e --- /dev/null +++ b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.py @@ -0,0 +1,281 @@ +# -*- coding: utf-8 -*- +""" +================================================================================= +1D Wasserstein barycenter comparison between exact LP and entropic regularization +================================================================================= + +This example illustrates the computation of regularized Wasserstein Barycenter +as proposed in [3] and exact LP barycenters using standard LP solver. + +It reproduces approximately Figure 3.1 and 3.2 from the following paper: +Cuturi, M., & Peyré, G. (2016). A smoothed dual approach for variational +Wasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343. + +[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). +Iterative Bregman projections for regularized transportation problems +SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + +""" + +# Author: Remi Flamary <remi.flamary@unice.fr> +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot +# necessary for 3d plot even if not used +from mpl_toolkits.mplot3d import Axes3D # noqa +from matplotlib.collections import PolyCollection # noqa + +#import ot.lp.cvx as cvx + +############################################################################## +# Gaussian Data +# ------------- + +#%% parameters + +problems = [] + +n = 100 # nb bins + +# bin positions +x = np.arange(n, dtype=np.float64) + +# Gaussian distributions +# Gaussian distributions +a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std +a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) + +# creating matrix A containing all distributions +A = np.vstack((a1, a2)).T +n_distributions = A.shape[1] + +# loss matrix + normalization +M = ot.utils.dist0(n) +M /= M.max() + + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') +pl.tight_layout() + +#%% barycenter computation + +alpha = 0.5 # 0<=alpha<=1 +weights = np.array([1 - alpha, alpha]) + +# l2bary +bary_l2 = A.dot(weights) + +# wasserstein +reg = 1e-3 +ot.tic() +bary_wass = ot.bregman.barycenter(A, M, reg, weights) +ot.toc() + + +ot.tic() +bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) +ot.toc() + +pl.figure(2) +pl.clf() +pl.subplot(2, 1, 1) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') + +pl.subplot(2, 1, 2) +pl.plot(x, bary_l2, 'r', label='l2') +pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') +pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') +pl.legend() +pl.title('Barycenters') +pl.tight_layout() + +problems.append([A, [bary_l2, bary_wass, bary_wass2]]) + +############################################################################## +# Dirac Data +# ---------- + +#%% parameters + +a1 = 1.0 * (x > 10) * (x < 50) +a2 = 1.0 * (x > 60) * (x < 80) + +a1 /= a1.sum() +a2 /= a2.sum() + +# creating matrix A containing all distributions +A = np.vstack((a1, a2)).T +n_distributions = A.shape[1] + +# loss matrix + normalization +M = ot.utils.dist0(n) +M /= M.max() + + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') +pl.tight_layout() + + +#%% barycenter computation + +alpha = 0.5 # 0<=alpha<=1 +weights = np.array([1 - alpha, alpha]) + +# l2bary +bary_l2 = A.dot(weights) + +# wasserstein +reg = 1e-3 +ot.tic() +bary_wass = ot.bregman.barycenter(A, M, reg, weights) +ot.toc() + + +ot.tic() +bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) +ot.toc() + + +problems.append([A, [bary_l2, bary_wass, bary_wass2]]) + +pl.figure(2) +pl.clf() +pl.subplot(2, 1, 1) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') + +pl.subplot(2, 1, 2) +pl.plot(x, bary_l2, 'r', label='l2') +pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') +pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') +pl.legend() +pl.title('Barycenters') +pl.tight_layout() + +#%% parameters + +a1 = np.zeros(n) +a2 = np.zeros(n) + +a1[10] = .25 +a1[20] = .5 +a1[30] = .25 +a2[80] = 1 + + +a1 /= a1.sum() +a2 /= a2.sum() + +# creating matrix A containing all distributions +A = np.vstack((a1, a2)).T +n_distributions = A.shape[1] + +# loss matrix + normalization +M = ot.utils.dist0(n) +M /= M.max() + + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') +pl.tight_layout() + + +#%% barycenter computation + +alpha = 0.5 # 0<=alpha<=1 +weights = np.array([1 - alpha, alpha]) + +# l2bary +bary_l2 = A.dot(weights) + +# wasserstein +reg = 1e-3 +ot.tic() +bary_wass = ot.bregman.barycenter(A, M, reg, weights) +ot.toc() + + +ot.tic() +bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) +ot.toc() + + +problems.append([A, [bary_l2, bary_wass, bary_wass2]]) + +pl.figure(2) +pl.clf() +pl.subplot(2, 1, 1) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') + +pl.subplot(2, 1, 2) +pl.plot(x, bary_l2, 'r', label='l2') +pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') +pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') +pl.legend() +pl.title('Barycenters') +pl.tight_layout() + + +############################################################################## +# Final figure +# ------------ +# + +#%% plot + +nbm = len(problems) +nbm2 = (nbm // 2) + + +pl.figure(2, (20, 6)) +pl.clf() + +for i in range(nbm): + + A = problems[i][0] + bary_l2 = problems[i][1][0] + bary_wass = problems[i][1][1] + bary_wass2 = problems[i][1][2] + + pl.subplot(2, nbm, 1 + i) + for j in range(n_distributions): + pl.plot(x, A[:, j]) + if i == nbm2: + pl.title('Distributions') + pl.xticks(()) + pl.yticks(()) + + pl.subplot(2, nbm, 1 + i + nbm) + + pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)') + pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') + pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') + if i == nbm - 1: + pl.legend() + if i == nbm2: + pl.title('Barycenters') + + pl.xticks(()) + pl.yticks(()) diff --git a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.rst b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.rst new file mode 100644 index 0000000..bd1c710 --- /dev/null +++ b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.rst @@ -0,0 +1,447 @@ + + +.. _sphx_glr_auto_examples_plot_barycenter_lp_vs_entropic.py: + + +================================================================================= +1D Wasserstein barycenter comparison between exact LP and entropic regularization +================================================================================= + +This example illustrates the computation of regularized Wasserstein Barycenter +as proposed in [3] and exact LP barycenters using standard LP solver. + +It reproduces approximately Figure 3.1 and 3.2 from the following paper: +Cuturi, M., & Peyré, G. (2016). A smoothed dual approach for variational +Wasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343. + +[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). +Iterative Bregman projections for regularized transportation problems +SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + + + + +.. code-block:: python + + + # Author: Remi Flamary <remi.flamary@unice.fr> + # + # License: MIT License + + import numpy as np + import matplotlib.pylab as pl + import ot + # necessary for 3d plot even if not used + from mpl_toolkits.mplot3d import Axes3D # noqa + from matplotlib.collections import PolyCollection # noqa + + #import ot.lp.cvx as cvx + + + + + + + +Gaussian Data +------------- + + + +.. code-block:: python + + + #%% parameters + + problems = [] + + n = 100 # nb bins + + # bin positions + x = np.arange(n, dtype=np.float64) + + # Gaussian distributions + # Gaussian distributions + a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std + a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) + + # creating matrix A containing all distributions + A = np.vstack((a1, a2)).T + n_distributions = A.shape[1] + + # loss matrix + normalization + M = ot.utils.dist0(n) + M /= M.max() + + + #%% plot the distributions + + pl.figure(1, figsize=(6.4, 3)) + for i in range(n_distributions): + pl.plot(x, A[:, i]) + pl.title('Distributions') + pl.tight_layout() + + #%% barycenter computation + + alpha = 0.5 # 0<=alpha<=1 + weights = np.array([1 - alpha, alpha]) + + # l2bary + bary_l2 = A.dot(weights) + + # wasserstein + reg = 1e-3 + ot.tic() + bary_wass = ot.bregman.barycenter(A, M, reg, weights) + ot.toc() + + + ot.tic() + bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) + ot.toc() + + pl.figure(2) + pl.clf() + pl.subplot(2, 1, 1) + for i in range(n_distributions): + pl.plot(x, A[:, i]) + pl.title('Distributions') + + pl.subplot(2, 1, 2) + pl.plot(x, bary_l2, 'r', label='l2') + pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') + pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') + pl.legend() + pl.title('Barycenters') + pl.tight_layout() + + problems.append([A, [bary_l2, bary_wass, bary_wass2]]) + + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_001.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_002.png + :scale: 47 + + +.. rst-class:: sphx-glr-script-out + + Out:: + + Elapsed time : 0.010712385177612305 s + Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective + 1.0 1.0 1.0 - 1.0 1700.336700337 + 0.006776453137632 0.006776453137633 0.006776453137633 0.9932238647293 0.006776453137633 125.6700527543 + 0.004018712867874 0.004018712867874 0.004018712867874 0.4301142633 0.004018712867874 12.26594150093 + 0.001172775061627 0.001172775061627 0.001172775061627 0.7599932455029 0.001172775061627 0.3378536968897 + 0.0004375137005385 0.0004375137005385 0.0004375137005385 0.6422331807989 0.0004375137005385 0.1468420566358 + 0.000232669046734 0.0002326690467341 0.000232669046734 0.5016999460893 0.000232669046734 0.09381703231432 + 7.430121674303e-05 7.430121674303e-05 7.430121674303e-05 0.7035962305812 7.430121674303e-05 0.0577787025717 + 5.321227838876e-05 5.321227838875e-05 5.321227838876e-05 0.308784186441 5.321227838876e-05 0.05266249477203 + 1.990900379199e-05 1.990900379196e-05 1.990900379199e-05 0.6520472013244 1.990900379199e-05 0.04526054405519 + 6.305442046799e-06 6.30544204682e-06 6.3054420468e-06 0.7073953304075 6.305442046798e-06 0.04237597591383 + 2.290148391577e-06 2.290148391582e-06 2.290148391578e-06 0.6941812711492 2.29014839159e-06 0.041522849321 + 1.182864875387e-06 1.182864875406e-06 1.182864875427e-06 0.508455204675 1.182864875445e-06 0.04129461872827 + 3.626786381529e-07 3.626786382468e-07 3.626786382923e-07 0.7101651572101 3.62678638267e-07 0.04113032448923 + 1.539754244902e-07 1.539754249276e-07 1.539754249356e-07 0.6279322066282 1.539754253892e-07 0.04108867636379 + 5.193221323143e-08 5.193221463044e-08 5.193221462729e-08 0.6843453436759 5.193221708199e-08 0.04106859618414 + 1.888205054507e-08 1.888204779723e-08 1.88820477688e-08 0.6673444085651 1.888205650952e-08 0.041062141752 + 5.676855206925e-09 5.676854518888e-09 5.676854517651e-09 0.7281705804232 5.676885442702e-09 0.04105958648713 + 3.501157668218e-09 3.501150243546e-09 3.501150216347e-09 0.414020345194 3.501164437194e-09 0.04105916265261 + 1.110594251499e-09 1.110590786827e-09 1.11059083379e-09 0.6998954759911 1.110636623476e-09 0.04105870073485 + 5.770971626386e-10 5.772456113791e-10 5.772456200156e-10 0.4999769658132 5.77013379477e-10 0.04105859769135 + 1.535218204536e-10 1.536993317032e-10 1.536992771966e-10 0.7516471627141 1.536205005991e-10 0.04105851679958 + 6.724209350756e-11 6.739211232927e-11 6.739210470901e-11 0.5944802416166 6.735465384341e-11 0.04105850033766 + 1.743382199199e-11 1.736445896691e-11 1.736448490761e-11 0.7573407808104 1.734254328931e-11 0.04105849088824 + Optimization terminated successfully. + Elapsed time : 2.883899211883545 s + + +Dirac Data +---------- + + + +.. code-block:: python + + + #%% parameters + + a1 = 1.0 * (x > 10) * (x < 50) + a2 = 1.0 * (x > 60) * (x < 80) + + a1 /= a1.sum() + a2 /= a2.sum() + + # creating matrix A containing all distributions + A = np.vstack((a1, a2)).T + n_distributions = A.shape[1] + + # loss matrix + normalization + M = ot.utils.dist0(n) + M /= M.max() + + + #%% plot the distributions + + pl.figure(1, figsize=(6.4, 3)) + for i in range(n_distributions): + pl.plot(x, A[:, i]) + pl.title('Distributions') + pl.tight_layout() + + + #%% barycenter computation + + alpha = 0.5 # 0<=alpha<=1 + weights = np.array([1 - alpha, alpha]) + + # l2bary + bary_l2 = A.dot(weights) + + # wasserstein + reg = 1e-3 + ot.tic() + bary_wass = ot.bregman.barycenter(A, M, reg, weights) + ot.toc() + + + ot.tic() + bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) + ot.toc() + + + problems.append([A, [bary_l2, bary_wass, bary_wass2]]) + + pl.figure(2) + pl.clf() + pl.subplot(2, 1, 1) + for i in range(n_distributions): + pl.plot(x, A[:, i]) + pl.title('Distributions') + + pl.subplot(2, 1, 2) + pl.plot(x, bary_l2, 'r', label='l2') + pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') + pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') + pl.legend() + pl.title('Barycenters') + pl.tight_layout() + + #%% parameters + + a1 = np.zeros(n) + a2 = np.zeros(n) + + a1[10] = .25 + a1[20] = .5 + a1[30] = .25 + a2[80] = 1 + + + a1 /= a1.sum() + a2 /= a2.sum() + + # creating matrix A containing all distributions + A = np.vstack((a1, a2)).T + n_distributions = A.shape[1] + + # loss matrix + normalization + M = ot.utils.dist0(n) + M /= M.max() + + + #%% plot the distributions + + pl.figure(1, figsize=(6.4, 3)) + for i in range(n_distributions): + pl.plot(x, A[:, i]) + pl.title('Distributions') + pl.tight_layout() + + + #%% barycenter computation + + alpha = 0.5 # 0<=alpha<=1 + weights = np.array([1 - alpha, alpha]) + + # l2bary + bary_l2 = A.dot(weights) + + # wasserstein + reg = 1e-3 + ot.tic() + bary_wass = ot.bregman.barycenter(A, M, reg, weights) + ot.toc() + + + ot.tic() + bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) + ot.toc() + + + problems.append([A, [bary_l2, bary_wass, bary_wass2]]) + + pl.figure(2) + pl.clf() + pl.subplot(2, 1, 1) + for i in range(n_distributions): + pl.plot(x, A[:, i]) + pl.title('Distributions') + + pl.subplot(2, 1, 2) + pl.plot(x, bary_l2, 'r', label='l2') + pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') + pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') + pl.legend() + pl.title('Barycenters') + pl.tight_layout() + + + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_003.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_004.png + :scale: 47 + + +.. rst-class:: sphx-glr-script-out + + Out:: + + Elapsed time : 0.014938592910766602 s + Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective + 1.0 1.0 1.0 - 1.0 1700.336700337 + 0.006776466288966 0.006776466288966 0.006776466288966 0.9932238515788 0.006776466288966 125.6649255808 + 0.004036918865495 0.004036918865495 0.004036918865495 0.4272973099316 0.004036918865495 12.3471617011 + 0.00121923268707 0.00121923268707 0.00121923268707 0.749698685599 0.00121923268707 0.3243835647408 + 0.0003837422984432 0.0003837422984432 0.0003837422984432 0.6926882608284 0.0003837422984432 0.1361719397493 + 0.0001070128410183 0.0001070128410183 0.0001070128410183 0.7643889137854 0.0001070128410183 0.07581952832518 + 0.0001001275033711 0.0001001275033711 0.0001001275033711 0.07058704837812 0.0001001275033712 0.0734739493635 + 4.550897507844e-05 4.550897507841e-05 4.550897507844e-05 0.5761172484828 4.550897507845e-05 0.05555077655047 + 8.557124125522e-06 8.5571241255e-06 8.557124125522e-06 0.8535925441152 8.557124125522e-06 0.04439814660221 + 3.611995628407e-06 3.61199562841e-06 3.611995628414e-06 0.6002277331554 3.611995628415e-06 0.04283007762152 + 7.590393750365e-07 7.590393750491e-07 7.590393750378e-07 0.8221486533416 7.590393750381e-07 0.04192322976248 + 8.299929287441e-08 8.299929286079e-08 8.299929287532e-08 0.9017467938799 8.29992928758e-08 0.04170825633295 + 3.117560203449e-10 3.117560130137e-10 3.11756019954e-10 0.997039969226 3.11756019952e-10 0.04168179329766 + 1.559749653711e-14 1.558073160926e-14 1.559756940692e-14 0.9999499686183 1.559750643989e-14 0.04168169240444 + Optimization terminated successfully. + Elapsed time : 2.642659902572632 s + Elapsed time : 0.002908945083618164 s + Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective + 1.0 1.0 1.0 - 1.0 1700.336700337 + 0.006774675520727 0.006774675520727 0.006774675520727 0.9932256422636 0.006774675520727 125.6956034743 + 0.002048208707562 0.002048208707562 0.002048208707562 0.7343095368143 0.002048208707562 5.213991622123 + 0.000269736547478 0.0002697365474781 0.0002697365474781 0.8839403501193 0.000269736547478 0.505938390389 + 6.832109993943e-05 6.832109993944e-05 6.832109993944e-05 0.7601171075965 6.832109993943e-05 0.2339657807272 + 2.437682932219e-05 2.43768293222e-05 2.437682932219e-05 0.6663448297475 2.437682932219e-05 0.1471256246325 + 1.13498321631e-05 1.134983216308e-05 1.13498321631e-05 0.5553643816404 1.13498321631e-05 0.1181584941171 + 3.342312725885e-06 3.342312725884e-06 3.342312725885e-06 0.7238133571615 3.342312725885e-06 0.1006387519747 + 7.078561231603e-07 7.078561231509e-07 7.078561231604e-07 0.8033142552512 7.078561231603e-07 0.09474734646269 + 1.966870956916e-07 1.966870954537e-07 1.966870954468e-07 0.752547917788 1.966870954633e-07 0.09354342735766 + 4.19989524849e-10 4.199895164852e-10 4.199895238758e-10 0.9984019849375 4.19989523951e-10 0.09310367785861 + 2.101015938666e-14 2.100625691113e-14 2.101023853438e-14 0.999949974425 2.101023691864e-14 0.09310274466458 + Optimization terminated successfully. + Elapsed time : 2.690450668334961 s + + +Final figure +------------ + + + + +.. code-block:: python + + + #%% plot + + nbm = len(problems) + nbm2 = (nbm // 2) + + + pl.figure(2, (20, 6)) + pl.clf() + + for i in range(nbm): + + A = problems[i][0] + bary_l2 = problems[i][1][0] + bary_wass = problems[i][1][1] + bary_wass2 = problems[i][1][2] + + pl.subplot(2, nbm, 1 + i) + for j in range(n_distributions): + pl.plot(x, A[:, j]) + if i == nbm2: + pl.title('Distributions') + pl.xticks(()) + pl.yticks(()) + + pl.subplot(2, nbm, 1 + i + nbm) + + pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)') + pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') + pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') + if i == nbm - 1: + pl.legend() + if i == nbm2: + pl.title('Barycenters') + + pl.xticks(()) + pl.yticks(()) + + + +.. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_006.png + :align: center + + + + +**Total running time of the script:** ( 0 minutes 8.892 seconds) + + + +.. only :: html + + .. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_barycenter_lp_vs_entropic.py <plot_barycenter_lp_vs_entropic.py>` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_barycenter_lp_vs_entropic.ipynb <plot_barycenter_lp_vs_entropic.ipynb>` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_ diff --git a/docs/source/auto_examples/plot_otda_linear_mapping.ipynb b/docs/source/auto_examples/plot_otda_linear_mapping.ipynb new file mode 100644 index 0000000..027b6cb --- /dev/null +++ b/docs/source/auto_examples/plot_otda_linear_mapping.ipynb @@ -0,0 +1,180 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Linear OT mapping estimation\n\n\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Remi Flamary <remi.flamary@unice.fr>\n#\n# License: MIT License\n\nimport numpy as np\nimport pylab as pl\nimport ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n-------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n = 1000\nd = 2\nsigma = .1\n\n# source samples\nangles = np.random.rand(n, 1) * 2 * np.pi\nxs = np.concatenate((np.sin(angles), np.cos(angles)),\n axis=1) + sigma * np.random.randn(n, 2)\nxs[:n // 2, 1] += 2\n\n\n# target samples\nanglet = np.random.rand(n, 1) * 2 * np.pi\nxt = np.concatenate((np.sin(anglet), np.cos(anglet)),\n axis=1) + sigma * np.random.randn(n, 2)\nxt[:n // 2, 1] += 2\n\n\nA = np.array([[1.5, .7], [.7, 1.5]])\nb = np.array([[4, 2]])\nxt = xt.dot(A) + b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n---------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(1, (5, 5))\npl.plot(xs[:, 0], xs[:, 1], '+')\npl.plot(xt[:, 0], xt[:, 1], 'o')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Estimate linear mapping and transport\n-------------------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "Ae, be = ot.da.OT_mapping_linear(xs, xt)\n\nxst = xs.dot(Ae) + be" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot transported samples\n------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(1, (5, 5))\npl.clf()\npl.plot(xs[:, 0], xs[:, 1], '+')\npl.plot(xt[:, 0], xt[:, 1], 'o')\npl.plot(xst[:, 0], xst[:, 1], '+')\n\npl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load image data\n---------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\ndef mat2im(X, shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\n\ndef minmax(I):\n return np.clip(I, 0, 1)\n\n\n# Loading images\nI1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256\nI2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n\n\nX1 = im2mat(I1)\nX2 = im2mat(I2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Estimate mapping and adapt\n----------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "mapping = ot.da.LinearTransport()\n\nmapping.fit(Xs=X1, Xt=X2)\n\n\nxst = mapping.transform(Xs=X1)\nxts = mapping.inverse_transform(Xt=X2)\n\nI1t = minmax(mat2im(xst, I1.shape))\nI2t = minmax(mat2im(xts, I2.shape))\n\n# %%" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot transformed images\n-----------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(2, figsize=(10, 7))\n\npl.subplot(2, 2, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Im. 1')\n\npl.subplot(2, 2, 2)\npl.imshow(I2)\npl.axis('off')\npl.title('Im. 2')\n\npl.subplot(2, 2, 3)\npl.imshow(I1t)\npl.axis('off')\npl.title('Mapping Im. 1')\n\npl.subplot(2, 2, 4)\npl.imshow(I2t)\npl.axis('off')\npl.title('Inverse mapping Im. 2')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}
\ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_linear_mapping.py b/docs/source/auto_examples/plot_otda_linear_mapping.py new file mode 100644 index 0000000..c65bd4f --- /dev/null +++ b/docs/source/auto_examples/plot_otda_linear_mapping.py @@ -0,0 +1,144 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +============================ +Linear OT mapping estimation +============================ + + +""" + +# Author: Remi Flamary <remi.flamary@unice.fr> +# +# License: MIT License + +import numpy as np +import pylab as pl +import ot + +############################################################################## +# Generate data +# ------------- + +n = 1000 +d = 2 +sigma = .1 + +# source samples +angles = np.random.rand(n, 1) * 2 * np.pi +xs = np.concatenate((np.sin(angles), np.cos(angles)), + axis=1) + sigma * np.random.randn(n, 2) +xs[:n // 2, 1] += 2 + + +# target samples +anglet = np.random.rand(n, 1) * 2 * np.pi +xt = np.concatenate((np.sin(anglet), np.cos(anglet)), + axis=1) + sigma * np.random.randn(n, 2) +xt[:n // 2, 1] += 2 + + +A = np.array([[1.5, .7], [.7, 1.5]]) +b = np.array([[4, 2]]) +xt = xt.dot(A) + b + +############################################################################## +# Plot data +# --------- + +pl.figure(1, (5, 5)) +pl.plot(xs[:, 0], xs[:, 1], '+') +pl.plot(xt[:, 0], xt[:, 1], 'o') + + +############################################################################## +# Estimate linear mapping and transport +# ------------------------------------- + +Ae, be = ot.da.OT_mapping_linear(xs, xt) + +xst = xs.dot(Ae) + be + + +############################################################################## +# Plot transported samples +# ------------------------ + +pl.figure(1, (5, 5)) +pl.clf() +pl.plot(xs[:, 0], xs[:, 1], '+') +pl.plot(xt[:, 0], xt[:, 1], 'o') +pl.plot(xst[:, 0], xst[:, 1], '+') + +pl.show() + +############################################################################## +# Load image data +# --------------- + + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + +def mat2im(X, shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + + +def minmax(I): + return np.clip(I, 0, 1) + + +# Loading images +I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 + + +X1 = im2mat(I1) +X2 = im2mat(I2) + +############################################################################## +# Estimate mapping and adapt +# ---------------------------- + +mapping = ot.da.LinearTransport() + +mapping.fit(Xs=X1, Xt=X2) + + +xst = mapping.transform(Xs=X1) +xts = mapping.inverse_transform(Xt=X2) + +I1t = minmax(mat2im(xst, I1.shape)) +I2t = minmax(mat2im(xts, I2.shape)) + +# %% + + +############################################################################## +# Plot transformed images +# ----------------------- + +pl.figure(2, figsize=(10, 7)) + +pl.subplot(2, 2, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Im. 1') + +pl.subplot(2, 2, 2) +pl.imshow(I2) +pl.axis('off') +pl.title('Im. 2') + +pl.subplot(2, 2, 3) +pl.imshow(I1t) +pl.axis('off') +pl.title('Mapping Im. 1') + +pl.subplot(2, 2, 4) +pl.imshow(I2t) +pl.axis('off') +pl.title('Inverse mapping Im. 2') diff --git a/docs/source/auto_examples/plot_otda_linear_mapping.rst b/docs/source/auto_examples/plot_otda_linear_mapping.rst new file mode 100644 index 0000000..8e2e0cf --- /dev/null +++ b/docs/source/auto_examples/plot_otda_linear_mapping.rst @@ -0,0 +1,260 @@ + + +.. _sphx_glr_auto_examples_plot_otda_linear_mapping.py: + + +============================ +Linear OT mapping estimation +============================ + + + + + +.. code-block:: python + + + # Author: Remi Flamary <remi.flamary@unice.fr> + # + # License: MIT License + + import numpy as np + import pylab as pl + import ot + + + + + + + +Generate data +------------- + + + +.. code-block:: python + + + n = 1000 + d = 2 + sigma = .1 + + # source samples + angles = np.random.rand(n, 1) * 2 * np.pi + xs = np.concatenate((np.sin(angles), np.cos(angles)), + axis=1) + sigma * np.random.randn(n, 2) + xs[:n // 2, 1] += 2 + + + # target samples + anglet = np.random.rand(n, 1) * 2 * np.pi + xt = np.concatenate((np.sin(anglet), np.cos(anglet)), + axis=1) + sigma * np.random.randn(n, 2) + xt[:n // 2, 1] += 2 + + + A = np.array([[1.5, .7], [.7, 1.5]]) + b = np.array([[4, 2]]) + xt = xt.dot(A) + b + + + + + + + +Plot data +--------- + + + +.. code-block:: python + + + pl.figure(1, (5, 5)) + pl.plot(xs[:, 0], xs[:, 1], '+') + pl.plot(xt[:, 0], xt[:, 1], 'o') + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_linear_mapping_001.png + :align: center + + + + +Estimate linear mapping and transport +------------------------------------- + + + +.. code-block:: python + + + Ae, be = ot.da.OT_mapping_linear(xs, xt) + + xst = xs.dot(Ae) + be + + + + + + + + +Plot transported samples +------------------------ + + + +.. code-block:: python + + + pl.figure(1, (5, 5)) + pl.clf() + pl.plot(xs[:, 0], xs[:, 1], '+') + pl.plot(xt[:, 0], xt[:, 1], 'o') + pl.plot(xst[:, 0], xst[:, 1], '+') + + pl.show() + + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_linear_mapping_002.png + :align: center + + + + +Load image data +--------------- + + + +.. code-block:: python + + + + def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + + def mat2im(X, shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + + + def minmax(I): + return np.clip(I, 0, 1) + + + # Loading images + I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 + I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 + + + X1 = im2mat(I1) + X2 = im2mat(I2) + + + + + + + +Estimate mapping and adapt +---------------------------- + + + +.. code-block:: python + + + mapping = ot.da.LinearTransport() + + mapping.fit(Xs=X1, Xt=X2) + + + xst = mapping.transform(Xs=X1) + xts = mapping.inverse_transform(Xt=X2) + + I1t = minmax(mat2im(xst, I1.shape)) + I2t = minmax(mat2im(xts, I2.shape)) + + # %% + + + + + + + + +Plot transformed images +----------------------- + + + +.. code-block:: python + + + pl.figure(2, figsize=(10, 7)) + + pl.subplot(2, 2, 1) + pl.imshow(I1) + pl.axis('off') + pl.title('Im. 1') + + pl.subplot(2, 2, 2) + pl.imshow(I2) + pl.axis('off') + pl.title('Im. 2') + + pl.subplot(2, 2, 3) + pl.imshow(I1t) + pl.axis('off') + pl.title('Mapping Im. 1') + + pl.subplot(2, 2, 4) + pl.imshow(I2t) + pl.axis('off') + pl.title('Inverse mapping Im. 2') + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_linear_mapping_004.png + :align: center + + + + +**Total running time of the script:** ( 0 minutes 0.635 seconds) + + + +.. only :: html + + .. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_otda_linear_mapping.py <plot_otda_linear_mapping.py>` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_otda_linear_mapping.ipynb <plot_otda_linear_mapping.ipynb>` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_ diff --git a/examples/plot_OT_1D_smooth.py b/examples/plot_OT_1D_smooth.py new file mode 100644 index 0000000..b690751 --- /dev/null +++ b/examples/plot_OT_1D_smooth.py @@ -0,0 +1,110 @@ +# -*- coding: utf-8 -*- +""" +=========================== +1D smooth optimal transport +=========================== + +This example illustrates the computation of EMD, Sinkhorn and smooth OT plans +and their visualization. + +""" + +# Author: Remi Flamary <remi.flamary@unice.fr> +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot +import ot.plot +from ot.datasets import make_1D_gauss as gauss + +############################################################################## +# Generate data +# ------------- + + +#%% parameters + +n = 100 # nb bins + +# bin positions +x = np.arange(n, dtype=np.float64) + +# Gaussian distributions +a = gauss(n, m=20, s=5) # m= mean, s= std +b = gauss(n, m=60, s=10) + +# loss matrix +M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) +M /= M.max() + + +############################################################################## +# Plot distributions and loss matrix +# ---------------------------------- + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +pl.plot(x, a, 'b', label='Source distribution') +pl.plot(x, b, 'r', label='Target distribution') +pl.legend() + +#%% plot distributions and loss matrix + +pl.figure(2, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') + +############################################################################## +# Solve EMD +# --------- + + +#%% EMD + +G0 = ot.emd(a, b, M) + +pl.figure(3, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') + +############################################################################## +# Solve Sinkhorn +# -------------- + + +#%% Sinkhorn + +lambd = 2e-3 +Gs = ot.sinkhorn(a, b, M, lambd, verbose=True) + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn') + +pl.show() + +############################################################################## +# Solve Smooth OT +# -------------- + + +#%% Smooth OT with KL regularization + +lambd = 2e-3 +Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='kl') + +pl.figure(5, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT KL reg.') + +pl.show() + + +#%% Smooth OT with KL regularization + +lambd = 1e-1 +Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='l2') + +pl.figure(6, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT l2 reg.') + +pl.show() diff --git a/examples/plot_free_support_barycenter.py b/examples/plot_free_support_barycenter.py new file mode 100644 index 0000000..b6efc59 --- /dev/null +++ b/examples/plot_free_support_barycenter.py @@ -0,0 +1,69 @@ +# -*- coding: utf-8 -*- +""" +==================================================== +2D free support Wasserstein barycenters of distributions +==================================================== + +Illustration of 2D Wasserstein barycenters if discributions that are weighted +sum of diracs. + +""" + +# Author: Vivien Seguy <vivien.seguy@iip.ist.i.kyoto-u.ac.jp> +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot + + +############################################################################## +# Generate data +# ------------- +#%% parameters and data generation +N = 3 +d = 2 +measures_locations = [] +measures_weights = [] + +for i in range(N): + + n_i = np.random.randint(low=1, high=20) # nb samples + + mu_i = np.random.normal(0., 4., (d,)) # Gaussian mean + + A_i = np.random.rand(d, d) + cov_i = np.dot(A_i, A_i.transpose()) # Gaussian covariance matrix + + x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i) # Dirac locations + b_i = np.random.uniform(0., 1., (n_i,)) + b_i = b_i / np.sum(b_i) # Dirac weights + + measures_locations.append(x_i) + measures_weights.append(b_i) + + +############################################################################## +# Compute free support barycenter +# ------------- + +k = 10 # number of Diracs of the barycenter +X_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations +b = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized) + +X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b) + + +############################################################################## +# Plot data +# --------- + +pl.figure(1) +for (x_i, b_i) in zip(measures_locations, measures_weights): + color = np.random.randint(low=1, high=10 * N) + pl.scatter(x_i[:, 0], x_i[:, 1], s=b * 1000, label='input measure') +pl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter') +pl.title('Data measures and their barycenter') +pl.legend(loc=0) +pl.show() diff --git a/notebooks/plot_barycenter_lp_vs_entropic.ipynb b/notebooks/plot_barycenter_lp_vs_entropic.ipynb new file mode 100644 index 0000000..9c8e83e --- /dev/null +++ b/notebooks/plot_barycenter_lp_vs_entropic.ipynb @@ -0,0 +1,497 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# 1D Wasserstein barycenter comparison between exact LP and entropic regularization\n", + "\n", + "\n", + "This example illustrates the computation of regularized Wasserstein Barycenter\n", + "as proposed in [3] and exact LP barycenters using standard LP solver.\n", + "\n", + "It reproduces approximately Figure 3.1 and 3.2 from the following paper:\n", + "Cuturi, M., & Peyré, G. (2016). A smoothed dual approach for variational\n", + "Wasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343.\n", + "\n", + "[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015).\n", + "Iterative Bregman projections for regularized transportation problems\n", + "SIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Remi Flamary <remi.flamary@unice.fr>\n", + "#\n", + "# License: MIT License\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot\n", + "# necessary for 3d plot even if not used\n", + "from mpl_toolkits.mplot3d import Axes3D # noqa\n", + "from matplotlib.collections import PolyCollection # noqa\n", + "\n", + "#import ot.lp.cvx as cvx" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Gaussian Data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time : 0.010422945022583008 s\n", + "Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective \n", + "1.0 1.0 1.0 - 1.0 1700.336700337 \n", + "0.006776453137632 0.006776453137633 0.006776453137633 0.9932238647293 0.006776453137633 125.6700527543 \n", + "0.004018712867874 0.004018712867874 0.004018712867874 0.4301142633 0.004018712867874 12.26594150093 \n", + "0.001172775061627 0.001172775061627 0.001172775061627 0.7599932455029 0.001172775061627 0.3378536968897 \n", + "0.0004375137005385 0.0004375137005385 0.0004375137005385 0.6422331807989 0.0004375137005385 0.1468420566358 \n", + "0.000232669046734 0.0002326690467341 0.000232669046734 0.5016999460893 0.000232669046734 0.09381703231432 \n", + "7.430121674303e-05 7.430121674303e-05 7.430121674303e-05 0.7035962305812 7.430121674303e-05 0.0577787025717 \n", + "5.321227838876e-05 5.321227838875e-05 5.321227838876e-05 0.308784186441 5.321227838876e-05 0.05266249477203 \n", + "1.990900379199e-05 1.990900379196e-05 1.990900379199e-05 0.6520472013244 1.990900379199e-05 0.04526054405519 \n", + "6.305442046799e-06 6.30544204682e-06 6.3054420468e-06 0.7073953304075 6.305442046798e-06 0.04237597591383 \n", + "2.290148391577e-06 2.290148391582e-06 2.290148391578e-06 0.6941812711492 2.29014839159e-06 0.041522849321 \n", + "1.182864875387e-06 1.182864875406e-06 1.182864875427e-06 0.508455204675 1.182864875445e-06 0.04129461872827 \n", + "3.626786381529e-07 3.626786382468e-07 3.626786382923e-07 0.7101651572101 3.62678638267e-07 0.04113032448923 \n", + "1.539754244902e-07 1.539754249276e-07 1.539754249356e-07 0.6279322066282 1.539754253892e-07 0.04108867636379 \n", + "5.193221323143e-08 5.193221463044e-08 5.193221462729e-08 0.6843453436759 5.193221708199e-08 0.04106859618414 \n", + "1.888205054507e-08 1.888204779723e-08 1.88820477688e-08 0.6673444085651 1.888205650952e-08 0.041062141752 \n", + "5.676855206925e-09 5.676854518888e-09 5.676854517651e-09 0.7281705804232 5.676885442702e-09 0.04105958648713 \n", + "3.501157668218e-09 3.501150243546e-09 3.501150216347e-09 0.414020345194 3.501164437194e-09 0.04105916265261 \n", + "1.110594251499e-09 1.110590786827e-09 1.11059083379e-09 0.6998954759911 1.110636623476e-09 0.04105870073485 \n", + "5.770971626386e-10 5.772456113791e-10 5.772456200156e-10 0.4999769658132 5.77013379477e-10 0.04105859769135 \n", + "1.535218204536e-10 1.536993317032e-10 1.536992771966e-10 0.7516471627141 1.536205005991e-10 0.04105851679958 \n", + "6.724209350756e-11 6.739211232927e-11 6.739210470901e-11 0.5944802416166 6.735465384341e-11 0.04105850033766 \n", + "1.743382199199e-11 1.736445896691e-11 1.736448490761e-11 0.7573407808104 1.734254328931e-11 0.04105849088824 \n", + "Optimization terminated successfully.\n", + "Elapsed time : 2.927520990371704 s\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 460.8x216 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% parameters\n", + "\n", + "problems = []\n", + "\n", + "n = 100 # nb bins\n", + "\n", + "# bin positions\n", + "x = np.arange(n, dtype=np.float64)\n", + "\n", + "# Gaussian distributions\n", + "# Gaussian distributions\n", + "a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\n", + "a2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n", + "\n", + "# creating matrix A containing all distributions\n", + "A = np.vstack((a1, a2)).T\n", + "n_distributions = A.shape[1]\n", + "\n", + "# loss matrix + normalization\n", + "M = ot.utils.dist0(n)\n", + "M /= M.max()\n", + "\n", + "\n", + "#%% plot the distributions\n", + "\n", + "pl.figure(1, figsize=(6.4, 3))\n", + "for i in range(n_distributions):\n", + " pl.plot(x, A[:, i])\n", + "pl.title('Distributions')\n", + "pl.tight_layout()\n", + "\n", + "#%% barycenter computation\n", + "\n", + "alpha = 0.5 # 0<=alpha<=1\n", + "weights = np.array([1 - alpha, alpha])\n", + "\n", + "# l2bary\n", + "bary_l2 = A.dot(weights)\n", + "\n", + "# wasserstein\n", + "reg = 1e-3\n", + "ot.tic()\n", + "bary_wass = ot.bregman.barycenter(A, M, reg, weights)\n", + "ot.toc()\n", + "\n", + "\n", + "ot.tic()\n", + "bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\n", + "ot.toc()\n", + "\n", + "pl.figure(2)\n", + "pl.clf()\n", + "pl.subplot(2, 1, 1)\n", + "for i in range(n_distributions):\n", + " pl.plot(x, A[:, i])\n", + "pl.title('Distributions')\n", + "\n", + "pl.subplot(2, 1, 2)\n", + "pl.plot(x, bary_l2, 'r', label='l2')\n", + "pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\n", + "pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n", + "pl.legend()\n", + "pl.title('Barycenters')\n", + "pl.tight_layout()\n", + "\n", + "problems.append([A, [bary_l2, bary_wass, bary_wass2]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dirac Data\n", + "----------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time : 0.014856815338134766 s\n", + "Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective \n", + "1.0 1.0 1.0 - 1.0 1700.336700337 \n", + "0.006776466288966 0.006776466288966 0.006776466288966 0.9932238515788 0.006776466288966 125.6649255808 \n", + "0.004036918865495 0.004036918865495 0.004036918865495 0.4272973099316 0.004036918865495 12.3471617011 \n", + "0.00121923268707 0.00121923268707 0.00121923268707 0.749698685599 0.00121923268707 0.3243835647408 \n", + "0.0003837422984432 0.0003837422984432 0.0003837422984432 0.6926882608284 0.0003837422984432 0.1361719397493 \n", + "0.0001070128410183 0.0001070128410183 0.0001070128410183 0.7643889137854 0.0001070128410183 0.07581952832518 \n", + "0.0001001275033711 0.0001001275033711 0.0001001275033711 0.07058704837812 0.0001001275033712 0.0734739493635 \n", + "4.550897507844e-05 4.550897507841e-05 4.550897507844e-05 0.5761172484828 4.550897507845e-05 0.05555077655047 \n", + "8.557124125522e-06 8.5571241255e-06 8.557124125522e-06 0.8535925441152 8.557124125522e-06 0.04439814660221 \n", + "3.611995628407e-06 3.61199562841e-06 3.611995628414e-06 0.6002277331554 3.611995628415e-06 0.04283007762152 \n", + "7.590393750365e-07 7.590393750491e-07 7.590393750378e-07 0.8221486533416 7.590393750381e-07 0.04192322976248 \n", + "8.299929287441e-08 8.299929286079e-08 8.299929287532e-08 0.9017467938799 8.29992928758e-08 0.04170825633295 \n", + "3.117560203449e-10 3.117560130137e-10 3.11756019954e-10 0.997039969226 3.11756019952e-10 0.04168179329766 \n", + "1.559749653711e-14 1.558073160926e-14 1.559756940692e-14 0.9999499686183 1.559750643989e-14 0.04168169240444 \n", + "Optimization terminated successfully.\n", + "Elapsed time : 2.703077793121338 s\n", + "Elapsed time : 0.0029761791229248047 s\n", + "Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective \n", + "1.0 1.0 1.0 - 1.0 1700.336700337 \n", + "0.006774675520727 0.006774675520727 0.006774675520727 0.9932256422636 0.006774675520727 125.6956034743 \n", + "0.002048208707562 0.002048208707562 0.002048208707562 0.7343095368143 0.002048208707562 5.213991622123 \n", + "0.000269736547478 0.0002697365474781 0.0002697365474781 0.8839403501193 0.000269736547478 0.505938390389 \n", + "6.832109993943e-05 6.832109993944e-05 6.832109993944e-05 0.7601171075965 6.832109993943e-05 0.2339657807272 \n", + "2.437682932219e-05 2.43768293222e-05 2.437682932219e-05 0.6663448297475 2.437682932219e-05 0.1471256246325 \n", + "1.13498321631e-05 1.134983216308e-05 1.13498321631e-05 0.5553643816404 1.13498321631e-05 0.1181584941171 \n", + "3.342312725885e-06 3.342312725884e-06 3.342312725885e-06 0.7238133571615 3.342312725885e-06 0.1006387519747 \n", + "7.078561231603e-07 7.078561231509e-07 7.078561231604e-07 0.8033142552512 7.078561231603e-07 0.09474734646269 \n", + "1.966870956916e-07 1.966870954537e-07 1.966870954468e-07 0.752547917788 1.966870954633e-07 0.09354342735766 \n", + "4.19989524849e-10 4.199895164852e-10 4.199895238758e-10 0.9984019849375 4.19989523951e-10 0.09310367785861 \n", + "2.101015938666e-14 2.100625691113e-14 2.101023853438e-14 0.999949974425 2.101023691864e-14 0.09310274466458 \n", + "Optimization terminated successfully.\n", + "Elapsed time : 2.6085386276245117 s\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 460.8x216 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% parameters\n", + "\n", + "a1 = 1.0 * (x > 10) * (x < 50)\n", + "a2 = 1.0 * (x > 60) * (x < 80)\n", + "\n", + "a1 /= a1.sum()\n", + "a2 /= a2.sum()\n", + "\n", + "# creating matrix A containing all distributions\n", + "A = np.vstack((a1, a2)).T\n", + "n_distributions = A.shape[1]\n", + "\n", + "# loss matrix + normalization\n", + "M = ot.utils.dist0(n)\n", + "M /= M.max()\n", + "\n", + "\n", + "#%% plot the distributions\n", + "\n", + "pl.figure(1, figsize=(6.4, 3))\n", + "for i in range(n_distributions):\n", + " pl.plot(x, A[:, i])\n", + "pl.title('Distributions')\n", + "pl.tight_layout()\n", + "\n", + "\n", + "#%% barycenter computation\n", + "\n", + "alpha = 0.5 # 0<=alpha<=1\n", + "weights = np.array([1 - alpha, alpha])\n", + "\n", + "# l2bary\n", + "bary_l2 = A.dot(weights)\n", + "\n", + "# wasserstein\n", + "reg = 1e-3\n", + "ot.tic()\n", + "bary_wass = ot.bregman.barycenter(A, M, reg, weights)\n", + "ot.toc()\n", + "\n", + "\n", + "ot.tic()\n", + "bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\n", + "ot.toc()\n", + "\n", + "\n", + "problems.append([A, [bary_l2, bary_wass, bary_wass2]])\n", + "\n", + "pl.figure(2)\n", + "pl.clf()\n", + "pl.subplot(2, 1, 1)\n", + "for i in range(n_distributions):\n", + " pl.plot(x, A[:, i])\n", + "pl.title('Distributions')\n", + "\n", + "pl.subplot(2, 1, 2)\n", + "pl.plot(x, bary_l2, 'r', label='l2')\n", + "pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\n", + "pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n", + "pl.legend()\n", + "pl.title('Barycenters')\n", + "pl.tight_layout()\n", + "\n", + "#%% parameters\n", + "\n", + "a1 = np.zeros(n)\n", + "a2 = np.zeros(n)\n", + "\n", + "a1[10] = .25\n", + "a1[20] = .5\n", + "a1[30] = .25\n", + "a2[80] = 1\n", + "\n", + "\n", + "a1 /= a1.sum()\n", + "a2 /= a2.sum()\n", + "\n", + "# creating matrix A containing all distributions\n", + "A = np.vstack((a1, a2)).T\n", + "n_distributions = A.shape[1]\n", + "\n", + "# loss matrix + normalization\n", + "M = ot.utils.dist0(n)\n", + "M /= M.max()\n", + "\n", + "\n", + "#%% plot the distributions\n", + "\n", + "pl.figure(1, figsize=(6.4, 3))\n", + "for i in range(n_distributions):\n", + " pl.plot(x, A[:, i])\n", + "pl.title('Distributions')\n", + "pl.tight_layout()\n", + "\n", + "\n", + "#%% barycenter computation\n", + "\n", + "alpha = 0.5 # 0<=alpha<=1\n", + "weights = np.array([1 - alpha, alpha])\n", + "\n", + "# l2bary\n", + "bary_l2 = A.dot(weights)\n", + "\n", + "# wasserstein\n", + "reg = 1e-3\n", + "ot.tic()\n", + "bary_wass = ot.bregman.barycenter(A, M, reg, weights)\n", + "ot.toc()\n", + "\n", + "\n", + "ot.tic()\n", + "bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\n", + "ot.toc()\n", + "\n", + "\n", + "problems.append([A, [bary_l2, bary_wass, bary_wass2]])\n", + "\n", + "pl.figure(2)\n", + "pl.clf()\n", + "pl.subplot(2, 1, 1)\n", + "for i in range(n_distributions):\n", + " pl.plot(x, A[:, i])\n", + "pl.title('Distributions')\n", + "\n", + "pl.subplot(2, 1, 2)\n", + "pl.plot(x, bary_l2, 'r', label='l2')\n", + "pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\n", + "pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n", + "pl.legend()\n", + "pl.title('Barycenters')\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Final figure\n", + "------------\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 1440x432 with 6 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% plot\n", + "\n", + "nbm = len(problems)\n", + "nbm2 = (nbm // 2)\n", + "\n", + "\n", + "pl.figure(2, (20, 6))\n", + "pl.clf()\n", + "\n", + "for i in range(nbm):\n", + "\n", + " A = problems[i][0]\n", + " bary_l2 = problems[i][1][0]\n", + " bary_wass = problems[i][1][1]\n", + " bary_wass2 = problems[i][1][2]\n", + "\n", + " pl.subplot(2, nbm, 1 + i)\n", + " for j in range(n_distributions):\n", + " pl.plot(x, A[:, j])\n", + " if i == nbm2:\n", + " pl.title('Distributions')\n", + " pl.xticks(())\n", + " pl.yticks(())\n", + "\n", + " pl.subplot(2, nbm, 1 + i + nbm)\n", + "\n", + " pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)')\n", + " pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\n", + " pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n", + " if i == nbm - 1:\n", + " pl.legend()\n", + " if i == nbm2:\n", + " pl.title('Barycenters')\n", + "\n", + " pl.xticks(())\n", + " pl.yticks(())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_otda_linear_mapping.ipynb b/notebooks/plot_otda_linear_mapping.ipynb new file mode 100644 index 0000000..4b6713d --- /dev/null +++ b/notebooks/plot_otda_linear_mapping.ipynb @@ -0,0 +1,339 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Linear OT mapping estimation\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Remi Flamary <remi.flamary@unice.fr>\n", + "#\n", + "# License: MIT License\n", + "\n", + "import numpy as np\n", + "import pylab as pl\n", + "import ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n = 1000\n", + "d = 2\n", + "sigma = .1\n", + "\n", + "# source samples\n", + "angles = np.random.rand(n, 1) * 2 * np.pi\n", + "xs = np.concatenate((np.sin(angles), np.cos(angles)),\n", + " axis=1) + sigma * np.random.randn(n, 2)\n", + "xs[:n // 2, 1] += 2\n", + "\n", + "\n", + "# target samples\n", + "anglet = np.random.rand(n, 1) * 2 * np.pi\n", + "xt = np.concatenate((np.sin(anglet), np.cos(anglet)),\n", + " axis=1) + sigma * np.random.randn(n, 2)\n", + "xt[:n // 2, 1] += 2\n", + "\n", + "\n", + "A = np.array([[1.5, .7], [.7, 1.5]])\n", + "b = np.array([[4, 2]])\n", + "xt = xt.dot(A) + b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x7f3ed402e748>]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 360x360 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1, (5, 5))\n", + "pl.plot(xs[:, 0], xs[:, 1], '+')\n", + "pl.plot(xt[:, 0], xt[:, 1], 'o')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Estimate linear mapping and transport\n", + "-------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "Ae, be = ot.da.OT_mapping_linear(xs, xt)\n", + "\n", + "xst = xs.dot(Ae) + be" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot transported samples\n", + "------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 360x360 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1, (5, 5))\n", + "pl.clf()\n", + "pl.plot(xs[:, 0], xs[:, 1], '+')\n", + "pl.plot(xt[:, 0], xt[:, 1], 'o')\n", + "pl.plot(xst[:, 0], xst[:, 1], '+')\n", + "\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load image data\n", + "---------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def im2mat(I):\n", + " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n", + " return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n", + "\n", + "\n", + "def mat2im(X, shape):\n", + " \"\"\"Converts back a matrix to an image\"\"\"\n", + " return X.reshape(shape)\n", + "\n", + "\n", + "def minmax(I):\n", + " return np.clip(I, 0, 1)\n", + "\n", + "\n", + "# Loading images\n", + "I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256\n", + "I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n", + "\n", + "\n", + "X1 = im2mat(I1)\n", + "X2 = im2mat(I2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Estimate mapping and adapt\n", + "----------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "mapping = ot.da.LinearTransport()\n", + "\n", + "mapping.fit(Xs=X1, Xt=X2)\n", + "\n", + "\n", + "xst = mapping.transform(Xs=X1)\n", + "xts = mapping.inverse_transform(Xt=X2)\n", + "\n", + "I1t = minmax(mat2im(xst, I1.shape))\n", + "I2t = minmax(mat2im(xts, I2.shape))\n", + "\n", + "# %%" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot transformed images\n", + "-----------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5,1,'Inverse mapping Im. 2')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 720x504 with 4 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(2, figsize=(10, 7))\n", + "\n", + "pl.subplot(2, 2, 1)\n", + "pl.imshow(I1)\n", + "pl.axis('off')\n", + "pl.title('Im. 1')\n", + "\n", + "pl.subplot(2, 2, 2)\n", + "pl.imshow(I2)\n", + "pl.axis('off')\n", + "pl.title('Im. 2')\n", + "\n", + "pl.subplot(2, 2, 3)\n", + "pl.imshow(I1t)\n", + "pl.axis('off')\n", + "pl.title('Mapping Im. 1')\n", + "\n", + "pl.subplot(2, 2, 4)\n", + "pl.imshow(I2t)\n", + "pl.axis('off')\n", + "pl.title('Inverse mapping Im. 2')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ot/bregman.py b/ot/bregman.py index b017c1a..c755f51 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -344,8 +344,13 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, # print(reg) - K = np.exp(-M / reg) + # Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute + K = np.empty(M.shape, dtype=M.dtype) + np.divide(M, -reg, out=K) + np.exp(K, out=K) + # print(np.min(K)) + tmp2 = np.empty(b.shape, dtype=M.dtype) Kp = (1 / a).reshape(-1, 1) * K cpt = 0 @@ -353,6 +358,7 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, while (err > stopThr and cpt < numItermax): uprev = u vprev = v + KtransposeU = np.dot(K.T, u) v = np.divide(b, KtransposeU) u = 1. / np.dot(Kp, v) @@ -373,8 +379,9 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, err = np.sum((u - uprev)**2) / np.sum((u)**2) + \ np.sum((v - vprev)**2) / np.sum((v)**2) else: - transp = u.reshape(-1, 1) * (K * v) - err = np.linalg.norm((np.sum(transp, axis=0) - b))**2 + # compute right marginal tmp2= (diag(u)Kdiag(v))^T1 + np.einsum('i,ij,j->j', u, K, v, out=tmp2) + err = np.linalg.norm(tmp2 - b)**2 # violation of marginal if log: log['err'].append(err) @@ -389,10 +396,7 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, log['v'] = v if nbb: # return only loss - res = np.zeros((nbb)) - for i in range(nbb): - res[i] = np.sum( - u[:, i].reshape((-1, 1)) * K * v[:, i].reshape((1, -1)) * M) + res = np.einsum('ik,ij,jk,ij->k', u, K, v, M) if log: return res, log else: @@ -15,7 +15,7 @@ import scipy.linalg as linalg from .bregman import sinkhorn from .lp import emd from .utils import unif, dist, kernel, cost_normalization -from .utils import check_params, deprecated, BaseEstimator +from .utils import check_params, BaseEstimator from .optim import cg from .optim import gcg @@ -740,288 +740,6 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, return A, b -@deprecated("The class OTDA is deprecated in 0.3.1 and will be " - "removed in 0.5" - "\n\tfor standard transport use class EMDTransport instead.") -class OTDA(object): - - """Class for domain adaptation with optimal transport as proposed in [5] - - - References - ---------- - - .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, - "Optimal Transport for Domain Adaptation," in IEEE Transactions on - Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 - - """ - - def __init__(self, metric='sqeuclidean', norm=None): - """ Class initialization""" - self.xs = 0 - self.xt = 0 - self.G = 0 - self.metric = metric - self.norm = norm - self.computed = False - - def fit(self, xs, xt, ws=None, wt=None, max_iter=100000): - """Fit domain adaptation between samples is xs and xt - (with optional weights)""" - self.xs = xs - self.xt = xt - - if wt is None: - wt = unif(xt.shape[0]) - if ws is None: - ws = unif(xs.shape[0]) - - self.ws = ws - self.wt = wt - - self.M = dist(xs, xt, metric=self.metric) - self.M = cost_normalization(self.M, self.norm) - self.G = emd(ws, wt, self.M, max_iter) - self.computed = True - - def interp(self, direction=1): - """Barycentric interpolation for the source (1) or target (-1) samples - - This Barycentric interpolation solves for each source (resp target) - sample xs (resp xt) the following optimization problem: - - .. math:: - arg\min_x \sum_i \gamma_{k,i} c(x,x_i^t) - - where k is the index of the sample in xs - - For the moment only squared euclidean distance is provided but more - metric could be used in the future. - - """ - if direction > 0: # >0 then source to target - G = self.G - w = self.ws.reshape((self.xs.shape[0], 1)) - x = self.xt - else: - G = self.G.T - w = self.wt.reshape((self.xt.shape[0], 1)) - x = self.xs - - if self.computed: - if self.metric == 'sqeuclidean': - return np.dot(G / w, x) # weighted mean - else: - print( - "Warning, metric not handled yet, using weighted average") - return np.dot(G / w, x) # weighted mean - return None - else: - print("Warning, model not fitted yet, returning None") - return None - - def predict(self, x, direction=1): - """ Out of sample mapping using the formulation from [6] - - For each sample x to map, it finds the nearest source sample xs and - map the samle x to the position xst+(x-xs) wher xst is the barycentric - interpolation of source sample xs. - - References - ---------- - - .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). - Regularized discrete optimal transport. SIAM Journal on Imaging - Sciences, 7(3), 1853-1882. - - """ - if direction > 0: # >0 then source to target - xf = self.xt - x0 = self.xs - else: - xf = self.xs - x0 = self.xt - - D0 = dist(x, x0) # dist netween new samples an source - idx = np.argmin(D0, 1) # closest one - xf = self.interp(direction) # interp the source samples - # aply the delta to the interpolation - return xf[idx, :] + x - x0[idx, :] - - -@deprecated("The class OTDA_sinkhorn is deprecated in 0.3.1 and will be" - " removed in 0.5 \nUse class SinkhornTransport instead.") -class OTDA_sinkhorn(OTDA): - - """Class for domain adaptation with optimal transport with entropic - regularization - - - """ - - def fit(self, xs, xt, reg=1, ws=None, wt=None, **kwargs): - """Fit regularized domain adaptation between samples is xs and xt - (with optional weights)""" - self.xs = xs - self.xt = xt - - if wt is None: - wt = unif(xt.shape[0]) - if ws is None: - ws = unif(xs.shape[0]) - - self.ws = ws - self.wt = wt - - self.M = dist(xs, xt, metric=self.metric) - self.M = cost_normalization(self.M, self.norm) - self.G = sinkhorn(ws, wt, self.M, reg, **kwargs) - self.computed = True - - -@deprecated("The class OTDA_lpl1 is deprecated in 0.3.1 and will be" - " removed in 0.5 \nUse class SinkhornLpl1Transport instead.") -class OTDA_lpl1(OTDA): - - """Class for domain adaptation with optimal transport with entropic and - group regularization""" - - def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, **kwargs): - """Fit regularized domain adaptation between samples is xs and xt - (with optional weights), See ot.da.sinkhorn_lpl1_mm for fit - parameters""" - self.xs = xs - self.xt = xt - - if wt is None: - wt = unif(xt.shape[0]) - if ws is None: - ws = unif(xs.shape[0]) - - self.ws = ws - self.wt = wt - - self.M = dist(xs, xt, metric=self.metric) - self.M = cost_normalization(self.M, self.norm) - self.G = sinkhorn_lpl1_mm(ws, ys, wt, self.M, reg, eta, **kwargs) - self.computed = True - - -@deprecated("The class OTDA_l1L2 is deprecated in 0.3.1 and will be" - " removed in 0.5 \nUse class SinkhornL1l2Transport instead.") -class OTDA_l1l2(OTDA): - - """Class for domain adaptation with optimal transport with entropic - and group lasso regularization""" - - def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, **kwargs): - """Fit regularized domain adaptation between samples is xs and xt - (with optional weights), See ot.da.sinkhorn_lpl1_gl for fit - parameters""" - self.xs = xs - self.xt = xt - - if wt is None: - wt = unif(xt.shape[0]) - if ws is None: - ws = unif(xs.shape[0]) - - self.ws = ws - self.wt = wt - - self.M = dist(xs, xt, metric=self.metric) - self.M = cost_normalization(self.M, self.norm) - self.G = sinkhorn_l1l2_gl(ws, ys, wt, self.M, reg, eta, **kwargs) - self.computed = True - - -@deprecated("The class OTDA_mapping_linear is deprecated in 0.3.1 and will be" - " removed in 0.5 \nUse class MappingTransport instead.") -class OTDA_mapping_linear(OTDA): - - """Class for optimal transport with joint linear mapping estimation as in - [8] - """ - - def __init__(self): - """ Class initialization""" - - self.xs = 0 - self.xt = 0 - self.G = 0 - self.L = 0 - self.bias = False - self.computed = False - self.metric = 'sqeuclidean' - - def fit(self, xs, xt, mu=1, eta=1, bias=False, **kwargs): - """ Fit domain adaptation between samples is xs and xt (with optional - weights)""" - self.xs = xs - self.xt = xt - self.bias = bias - - self.ws = unif(xs.shape[0]) - self.wt = unif(xt.shape[0]) - - self.G, self.L = joint_OT_mapping_linear( - xs, xt, mu=mu, eta=eta, bias=bias, **kwargs) - self.computed = True - - def mapping(self): - return lambda x: self.predict(x) - - def predict(self, x): - """ Out of sample mapping estimated during the call to fit""" - if self.computed: - if self.bias: - x = np.hstack((x, np.ones((x.shape[0], 1)))) - return x.dot(self.L) # aply the delta to the interpolation - else: - print("Warning, model not fitted yet, returning None") - return None - - -@deprecated("The class OTDA_mapping_kernel is deprecated in 0.3.1 and will be" - " removed in 0.5 \nUse class MappingTransport instead.") -class OTDA_mapping_kernel(OTDA_mapping_linear): - - """Class for optimal transport with joint nonlinear mapping - estimation as in [8]""" - - def fit(self, xs, xt, mu=1, eta=1, bias=False, kerneltype='gaussian', - sigma=1, **kwargs): - """ Fit domain adaptation between samples is xs and xt """ - self.xs = xs - self.xt = xt - self.bias = bias - - self.ws = unif(xs.shape[0]) - self.wt = unif(xt.shape[0]) - self.kernel = kerneltype - self.sigma = sigma - self.kwargs = kwargs - - self.G, self.L = joint_OT_mapping_kernel( - xs, xt, mu=mu, eta=eta, bias=bias, **kwargs) - self.computed = True - - def predict(self, x): - """ Out of sample mapping estimated during the call to fit""" - - if self.computed: - K = kernel( - x, self.xs, method=self.kernel, sigma=self.sigma, - **self.kwargs) - if self.bias: - K = np.hstack((K, np.ones((x.shape[0], 1)))) - return K.dot(self.L) - else: - print("Warning, model not fitted yet, returning None") - return None - - def distribution_estimation_uniform(X): """estimates a uniform distribution from an array of samples X diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 5dda82a..02cbd8c 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -17,6 +17,9 @@ from .import cvx from .emd_wrap import emd_c, check_result from ..utils import parmap from .cvx import barycenter +from ..utils import dist + +__all__=['emd', 'emd2', 'barycenter', 'free_support_barycenter', 'cvx'] def emd(a, b, M, numItermax=100000, log=False): @@ -214,3 +217,95 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), res = parmap(f, [b[:, i] for i in range(nb)], processes) return res + + + +def free_support_barycenter(measures_locations, measures_weights, X_init, b=None, weights=None, numItermax=100, stopThr=1e-7, verbose=False, log=None): + """ + Solves the free support (locations of the barycenters are optimized, not the weights) Wasserstein barycenter problem (i.e. the weighted Frechet mean for the 2-Wasserstein distance) + + The function solves the Wasserstein barycenter problem when the barycenter measure is constrained to be supported on k atoms. + This problem is considered in [1] (Algorithm 2). There are two differences with the following codes: + - we do not optimize over the weights + - we do not do line search for the locations updates, we use i.e. theta = 1 in [1] (Algorithm 2). This can be seen as a discrete implementation of the fixed-point algorithm of [2] proposed in the continuous setting. + + Parameters + ---------- + measures_locations : list of (k_i,d) np.ndarray + The discrete support of a measure supported on k_i locations of a d-dimensional space (k_i can be different for each element of the list) + measures_weights : list of (k_i,) np.ndarray + Numpy arrays where each numpy array has k_i non-negatives values summing to one representing the weights of each discrete input measure + + X_init : (k,d) np.ndarray + Initialization of the support locations (on k atoms) of the barycenter + b : (k,) np.ndarray + Initialization of the weights of the barycenter (non-negatives, sum to 1) + weights : (k,) np.ndarray + Initialization of the coefficients of the barycenter (non-negatives, sum to 1) + + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + Returns + ------- + X : (k,d) np.ndarray + Support locations (on k atoms) of the barycenter + + References + ---------- + + .. [1] Cuturi, Marco, and Arnaud Doucet. "Fast computation of Wasserstein barycenters." International Conference on Machine Learning. 2014. + + .. [2] Álvarez-Esteban, Pedro C., et al. "A fixed-point approach to barycenters in Wasserstein space." Journal of Mathematical Analysis and Applications 441.2 (2016): 744-762. + + """ + + iter_count = 0 + + N = len(measures_locations) + k = X_init.shape[0] + d = X_init.shape[1] + if b is None: + b = np.ones((k,))/k + if weights is None: + weights = np.ones((N,)) / N + + X = X_init + + log_dict = {} + displacement_square_norms = [] + + displacement_square_norm = stopThr + 1. + + while ( displacement_square_norm > stopThr and iter_count < numItermax ): + + T_sum = np.zeros((k, d)) + + for (measure_locations_i, measure_weights_i, weight_i) in zip(measures_locations, measures_weights, weights.tolist()): + + M_i = dist(X, measure_locations_i) + T_i = emd(b, measure_weights_i, M_i) + T_sum = T_sum + weight_i * np.reshape(1. / b, (-1, 1)) * np.matmul(T_i, measure_locations_i) + + displacement_square_norm = np.sum(np.square(T_sum-X)) + if log: + displacement_square_norms.append(displacement_square_norm) + + X = T_sum + + if verbose: + print('iteration %d, displacement_square_norm=%f\n', iter_count, displacement_square_norm) + + iter_count += 1 + + if log: + log_dict['displacement_square_norms'] = displacement_square_norms + return X, log_dict + else: + return X
\ No newline at end of file diff --git a/ot/lp/cvx.py b/ot/lp/cvx.py index c8c75bc..8e763be 100644 --- a/ot/lp/cvx.py +++ b/ot/lp/cvx.py @@ -11,6 +11,7 @@ import numpy as np import scipy as sp import scipy.sparse as sps + try: import cvxopt from cvxopt import solvers, matrix, spmatrix @@ -26,7 +27,7 @@ def scipy_sparse_to_spmatrix(A): def barycenter(A, M, weights=None, verbose=False, log=False, solver='interior-point'): - """Compute the entropic regularized wasserstein barycenter of distributions A + """Compute the Wasserstein barycenter of distributions A The function solves the following optimization problem [16]: diff --git a/ot/smooth.py b/ot/smooth.py new file mode 100644 index 0000000..5a8e4b5 --- /dev/null +++ b/ot/smooth.py @@ -0,0 +1,600 @@ +#Copyright (c) 2018, Mathieu Blondel +#All rights reserved. +# +#Redistribution and use in source and binary forms, with or without +#modification, are permitted provided that the following conditions are met: +# +#1. Redistributions of source code must retain the above copyright notice, this +#list of conditions and the following disclaimer. +# +#2. Redistributions in binary form must reproduce the above copyright notice, +#this list of conditions and the following disclaimer in the documentation and/or +#other materials provided with the distribution. +# +#THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +#ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +#WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +#IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, +#INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT +#NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, +#OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +#LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR +#OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF +#THE POSSIBILITY OF SUCH DAMAGE. + +# Author: Mathieu Blondel +# Remi Flamary <remi.flamary@unice.fr> + +""" +Implementation of +Smooth and Sparse Optimal Transport. +Mathieu Blondel, Vivien Seguy, Antoine Rolet. +In Proc. of AISTATS 2018. +https://arxiv.org/abs/1710.06276 + +[17] Blondel, M., Seguy, V., & Rolet, A. (2018). Smooth and Sparse Optimal +Transport. Proceedings of the Twenty-First International Conference on +Artificial Intelligence and Statistics (AISTATS). + +Original code from https://github.com/mblondel/smooth-ot/ + +""" + +import numpy as np +from scipy.optimize import minimize + + +def projection_simplex(V, z=1, axis=None): + """ Projection of x onto the simplex, scaled by z + + P(x; z) = argmin_{y >= 0, sum(y) = z} ||y - x||^2 + z: float or array + If array, len(z) must be compatible with V + axis: None or int + - axis=None: project V by P(V.ravel(); z) + - axis=1: project each V[i] by P(V[i]; z[i]) + - axis=0: project each V[:, j] by P(V[:, j]; z[j]) + """ + if axis == 1: + n_features = V.shape[1] + U = np.sort(V, axis=1)[:, ::-1] + z = np.ones(len(V)) * z + cssv = np.cumsum(U, axis=1) - z[:, np.newaxis] + ind = np.arange(n_features) + 1 + cond = U - cssv / ind > 0 + rho = np.count_nonzero(cond, axis=1) + theta = cssv[np.arange(len(V)), rho - 1] / rho + return np.maximum(V - theta[:, np.newaxis], 0) + + elif axis == 0: + return projection_simplex(V.T, z, axis=1).T + + else: + V = V.ravel().reshape(1, -1) + return projection_simplex(V, z, axis=1).ravel() + + +class Regularization(object): + """Base class for Regularization objects + + Notes + ----- + This class is not intended for direct use but as aparent for true + regularizatiojn implementation. + """ + + def __init__(self, gamma=1.0): + """ + + Parameters + ---------- + gamma: float + Regularization parameter. + We recover unregularized OT when gamma -> 0. + + """ + self.gamma = gamma + + def delta_Omega(X): + """ + Compute delta_Omega(X[:, j]) for each X[:, j]. + delta_Omega(x) = sup_{y >= 0} y^T x - Omega(y). + + Parameters + ---------- + X: array, shape = len(a) x len(b) + Input array. + + Returns + ------- + v: array, len(b) + Values: v[j] = delta_Omega(X[:, j]) + G: array, len(a) x len(b) + Gradients: G[:, j] = nabla delta_Omega(X[:, j]) + """ + raise NotImplementedError + + def max_Omega(X, b): + """ + Compute max_Omega_j(X[:, j]) for each X[:, j]. + max_Omega_j(x) = sup_{y >= 0, sum(y) = 1} y^T x - Omega(b[j] y) / b[j]. + + Parameters + ---------- + X: array, shape = len(a) x len(b) + Input array. + + Returns + ------- + v: array, len(b) + Values: v[j] = max_Omega_j(X[:, j]) + G: array, len(a) x len(b) + Gradients: G[:, j] = nabla max_Omega_j(X[:, j]) + """ + raise NotImplementedError + + def Omega(T): + """ + Compute regularization term. + + Parameters + ---------- + T: array, shape = len(a) x len(b) + Input array. + + Returns + ------- + value: float + Regularization term. + """ + raise NotImplementedError + + +class NegEntropy(Regularization): + """ NegEntropy regularization """ + + def delta_Omega(self, X): + G = np.exp(X / self.gamma - 1) + val = self.gamma * np.sum(G, axis=0) + return val, G + + def max_Omega(self, X, b): + max_X = np.max(X, axis=0) / self.gamma + exp_X = np.exp(X / self.gamma - max_X) + val = self.gamma * (np.log(np.sum(exp_X, axis=0)) + max_X) + val -= self.gamma * np.log(b) + G = exp_X / np.sum(exp_X, axis=0) + return val, G + + def Omega(self, T): + return self.gamma * np.sum(T * np.log(T)) + + +class SquaredL2(Regularization): + """ Squared L2 regularization """ + + def delta_Omega(self, X): + max_X = np.maximum(X, 0) + val = np.sum(max_X ** 2, axis=0) / (2 * self.gamma) + G = max_X / self.gamma + return val, G + + def max_Omega(self, X, b): + G = projection_simplex(X / (b * self.gamma), axis=0) + val = np.sum(X * G, axis=0) + val -= 0.5 * self.gamma * b * np.sum(G * G, axis=0) + return val, G + + def Omega(self, T): + return 0.5 * self.gamma * np.sum(T ** 2) + + +def dual_obj_grad(alpha, beta, a, b, C, regul): + """ + Compute objective value and gradients of dual objective. + + Parameters + ---------- + alpha: array, shape = len(a) + beta: array, shape = len(b) + Current iterate of dual potentials. + a: array, shape = len(a) + b: array, shape = len(b) + Input histograms (should be non-negative and sum to 1). + C: array, shape = len(a) x len(b) + Ground cost matrix. + regul: Regularization object + Should implement a delta_Omega(X) method. + + Returns + ------- + obj: float + Objective value (higher is better). + grad_alpha: array, shape = len(a) + Gradient w.r.t. alpha. + grad_beta: array, shape = len(b) + Gradient w.r.t. beta. + """ + obj = np.dot(alpha, a) + np.dot(beta, b) + grad_alpha = a.copy() + grad_beta = b.copy() + + # X[:, j] = alpha + beta[j] - C[:, j] + X = alpha[:, np.newaxis] + beta - C + + # val.shape = len(b) + # G.shape = len(a) x len(b) + val, G = regul.delta_Omega(X) + + obj -= np.sum(val) + grad_alpha -= G.sum(axis=1) + grad_beta -= G.sum(axis=0) + + return obj, grad_alpha, grad_beta + + +def solve_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500, + verbose=False): + """ + Solve the "smoothed" dual objective. + + Parameters + ---------- + a: array, shape = len(a) + b: array, shape = len(b) + Input histograms (should be non-negative and sum to 1). + C: array, shape = len(a) x len(b) + Ground cost matrix. + regul: Regularization object + Should implement a delta_Omega(X) method. + method: str + Solver to be used (passed to `scipy.optimize.minimize`). + tol: float + Tolerance parameter. + max_iter: int + Maximum number of iterations. + + Returns + ------- + alpha: array, shape = len(a) + beta: array, shape = len(b) + Dual potentials. + """ + + def _func(params): + # Unpack alpha and beta. + alpha = params[:len(a)] + beta = params[len(a):] + + obj, grad_alpha, grad_beta = dual_obj_grad(alpha, beta, a, b, C, regul) + + # Pack grad_alpha and grad_beta. + grad = np.concatenate((grad_alpha, grad_beta)) + + # We need to maximize the dual. + return -obj, -grad + + # Unfortunately, `minimize` only supports functions whose argument is a + # vector. So, we need to concatenate alpha and beta. + alpha_init = np.zeros(len(a)) + beta_init = np.zeros(len(b)) + params_init = np.concatenate((alpha_init, beta_init)) + + res = minimize(_func, params_init, method=method, jac=True, + tol=tol, options=dict(maxiter=max_iter, disp=verbose)) + + alpha = res.x[:len(a)] + beta = res.x[len(a):] + + return alpha, beta, res + + +def semi_dual_obj_grad(alpha, a, b, C, regul): + """ + Compute objective value and gradient of semi-dual objective. + + Parameters + ---------- + alpha: array, shape = len(a) + Current iterate of semi-dual potentials. + a: array, shape = len(a) + b: array, shape = len(b) + Input histograms (should be non-negative and sum to 1). + C: array, shape = len(a) x len(b) + Ground cost matrix. + regul: Regularization object + Should implement a max_Omega(X) method. + + Returns + ------- + obj: float + Objective value (higher is better). + grad: array, shape = len(a) + Gradient w.r.t. alpha. + """ + obj = np.dot(alpha, a) + grad = a.copy() + + # X[:, j] = alpha - C[:, j] + X = alpha[:, np.newaxis] - C + + # val.shape = len(b) + # G.shape = len(a) x len(b) + val, G = regul.max_Omega(X, b) + + obj -= np.dot(b, val) + grad -= np.dot(G, b) + + return obj, grad + + +def solve_semi_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500, + verbose=False): + """ + Solve the "smoothed" semi-dual objective. + + Parameters + ---------- + a: array, shape = len(a) + b: array, shape = len(b) + Input histograms (should be non-negative and sum to 1). + C: array, shape = len(a) x len(b) + Ground cost matrix. + regul: Regularization object + Should implement a max_Omega(X) method. + method: str + Solver to be used (passed to `scipy.optimize.minimize`). + tol: float + Tolerance parameter. + max_iter: int + Maximum number of iterations. + + Returns + ------- + alpha: array, shape = len(a) + Semi-dual potentials. + """ + + def _func(alpha): + obj, grad = semi_dual_obj_grad(alpha, a, b, C, regul) + # We need to maximize the semi-dual. + return -obj, -grad + + alpha_init = np.zeros(len(a)) + + res = minimize(_func, alpha_init, method=method, jac=True, + tol=tol, options=dict(maxiter=max_iter, disp=verbose)) + + return res.x, res + + +def get_plan_from_dual(alpha, beta, C, regul): + """ + Retrieve optimal transportation plan from optimal dual potentials. + + Parameters + ---------- + alpha: array, shape = len(a) + beta: array, shape = len(b) + Optimal dual potentials. + C: array, shape = len(a) x len(b) + Ground cost matrix. + regul: Regularization object + Should implement a delta_Omega(X) method. + + Returns + ------- + T: array, shape = len(a) x len(b) + Optimal transportation plan. + """ + X = alpha[:, np.newaxis] + beta - C + return regul.delta_Omega(X)[1] + + +def get_plan_from_semi_dual(alpha, b, C, regul): + """ + Retrieve optimal transportation plan from optimal semi-dual potentials. + + Parameters + ---------- + alpha: array, shape = len(a) + Optimal semi-dual potentials. + b: array, shape = len(b) + Second input histogram (should be non-negative and sum to 1). + C: array, shape = len(a) x len(b) + Ground cost matrix. + regul: Regularization object + Should implement a delta_Omega(X) method. + + Returns + ------- + T: array, shape = len(a) x len(b) + Optimal transportation plan. + """ + X = alpha[:, np.newaxis] - C + return regul.max_Omega(X, b)[1] * b + + +def smooth_ot_dual(a, b, M, reg, reg_type='l2', method="L-BFGS-B", stopThr=1e-9, + numItermax=500, verbose=False, log=False): + r""" + Solve the regularized OT problem in the dual and return the OT matrix + + The function solves the smooth relaxed dual formulation (7) in [17]_ : + + .. math:: + \max_{\alpha,\beta}\quad a^T\alpha+b^T\beta-\sum_j\delta_\Omega(\alpha+\beta_j-\mathbf{m}_j) + + where : + + - :math:`\mathbf{m}_j` is the jth column of the cost matrix + - :math:`\delta_\Omega` is the convex conjugate of the regularization term :math:`\Omega` + - a and b are source and target weights (sum to 1) + + The OT matrix can is reconstructed from the gradient of :math:`\delta_\Omega` + (See [17]_ Proposition 1). + The optimization algorithm is using gradient decent (L-BFGS by default). + + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) or np.ndarray (nt,nbb) + samples in the target domain, compute sinkhorn with multiple targets + and fixed M if b is a matrix (return OT loss + dual variables in log) + M : np.ndarray (ns,nt) + loss matrix + reg : float + Regularization term >0 + reg_type : str + Regularization type, can be the following (default ='l2'): + - 'kl' : Kullback Leibler (~ Neg-entropy used in sinkhorn [2]_) + - 'l2' : Squared Euclidean regularization + method : str + Solver to use for scipy.optimize.minimize + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 + + .. [17] Blondel, M., Seguy, V., & Rolet, A. (2018). Smooth and Sparse Optimal Transport. Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS). + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.sinhorn : Entropic regularized OT + ot.optim.cg : General regularized OT + + """ + + if reg_type.lower() in ['l2', 'squaredl2']: + regul = SquaredL2(gamma=reg) + elif reg_type.lower() in ['entropic', 'negentropy', 'kl']: + regul = NegEntropy(gamma=reg) + else: + raise NotImplementedError('Unknown regularization') + + # solve dual + alpha, beta, res = solve_dual(a, b, M, regul, max_iter=numItermax, + tol=stopThr, verbose=verbose) + + # reconstruct transport matrix + G = get_plan_from_dual(alpha, beta, M, regul) + + if log: + log = {'alpha': alpha, 'beta': beta, 'res': res} + return G, log + else: + return G + + +def smooth_ot_semi_dual(a, b, M, reg, reg_type='l2', method="L-BFGS-B", stopThr=1e-9, + numItermax=500, verbose=False, log=False): + r""" + Solve the regularized OT problem in the semi-dual and return the OT matrix + + The function solves the smooth relaxed dual formulation (10) in [17]_ : + + .. math:: + \max_{\alpha}\quad a^T\alpha-OT_\Omega^*(\alpha,b) + + where : + + .. math:: + OT_\Omega^*(\alpha,b)=\sum_j b_j + + - :math:`\mathbf{m}_j` is the jth column of the cost matrix + - :math:`OT_\Omega^*(\alpha,b)` is defined in Eq. (9) in [17] + - a and b are source and target weights (sum to 1) + + The OT matrix can is reconstructed using [17]_ Proposition 2. + The optimization algorithm is using gradient decent (L-BFGS by default). + + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) or np.ndarray (nt,nbb) + samples in the target domain, compute sinkhorn with multiple targets + and fixed M if b is a matrix (return OT loss + dual variables in log) + M : np.ndarray (ns,nt) + loss matrix + reg : float + Regularization term >0 + reg_type : str + Regularization type, can be the following (default ='l2'): + - 'kl' : Kullback Leibler (~ Neg-entropy used in sinkhorn [2]_) + - 'l2' : Squared Euclidean regularization + method : str + Solver to use for scipy.optimize.minimize + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 + + .. [17] Blondel, M., Seguy, V., & Rolet, A. (2018). Smooth and Sparse Optimal Transport. Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS). + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.sinhorn : Entropic regularized OT + ot.optim.cg : General regularized OT + + """ + if reg_type.lower() in ['l2', 'squaredl2']: + regul = SquaredL2(gamma=reg) + elif reg_type.lower() in ['entropic', 'negentropy', 'kl']: + regul = NegEntropy(gamma=reg) + else: + raise NotImplementedError('Unknown regularization') + + # solve dual + alpha, res = solve_semi_dual(a, b, M, regul, max_iter=numItermax, + tol=stopThr, verbose=verbose) + + # reconstruct transport matrix + G = get_plan_from_semi_dual(alpha, b, M, regul) + + if log: + log = {'alpha': alpha, 'res': res} + return G, log + else: + return G diff --git a/ot/utils.py b/ot/utils.py index 7dac283..bb21b38 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -77,6 +77,34 @@ def clean_zeros(a, b, M): return a2, b2, M2 +def euclidean_distances(X, Y, squared=False): + """ + Considering the rows of X (and Y=X) as vectors, compute the + distance matrix between each pair of vectors. + Parameters + ---------- + X : {array-like}, shape (n_samples_1, n_features) + Y : {array-like}, shape (n_samples_2, n_features) + squared : boolean, optional + Return squared Euclidean distances. + Returns + ------- + distances : {array}, shape (n_samples_1, n_samples_2) + """ + XX = np.einsum('ij,ij->i', X, X)[:, np.newaxis] + YY = np.einsum('ij,ij->i', Y, Y)[np.newaxis, :] + distances = np.dot(X, Y.T) + distances *= -2 + distances += XX + distances += YY + np.maximum(distances, 0, out=distances) + if X is Y: + # Ensure that distances between vectors and themselves are set to 0.0. + # This may not be the case due to floating point rounding errors. + distances.flat[::distances.shape[0] + 1] = 0.0 + return distances if squared else np.sqrt(distances, out=distances) + + def dist(x1, x2=None, metric='sqeuclidean'): """Compute distance between samples in x1 and x2 using function scipy.spatial.distance.cdist @@ -104,7 +132,8 @@ def dist(x1, x2=None, metric='sqeuclidean'): """ if x2 is None: x2 = x1 - + if metric == "sqeuclidean": + return euclidean_distances(x1, x2, squared=True) return cdist(x1, x2, metric=metric) diff --git a/test/test_ot.py b/test/test_ot.py index 399e549..45e777a 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -135,6 +135,21 @@ def test_lp_barycenter(): np.testing.assert_allclose(bary.sum(), 1) +def test_free_support_barycenter(): + + measures_locations = [np.array([-1.]).reshape((1, 1)), np.array([1.]).reshape((1, 1))] + measures_weights = [np.array([1.]), np.array([1.])] + + X_init = np.array([-12.]).reshape((1, 1)) + + # obvious barycenter location between two diracs + bar_locations = np.array([0.]).reshape((1, 1)) + + X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init) + + np.testing.assert_allclose(X, bar_locations, rtol=1e-5, atol=1e-7) + + @pytest.mark.skipif(not ot.lp.cvx.cvxopt, reason="No cvxopt available") def test_lp_barycenter_cvxopt(): diff --git a/test/test_smooth.py b/test/test_smooth.py new file mode 100644 index 0000000..2afa4f8 --- /dev/null +++ b/test/test_smooth.py @@ -0,0 +1,79 @@ +"""Tests for ot.smooth model """ + +# Author: Remi Flamary <remi.flamary@unice.fr> +# +# License: MIT License + +import numpy as np +import ot +import pytest + + +def test_smooth_ot_dual(): + + # get data + n = 100 + rng = np.random.RandomState(0) + + x = rng.randn(n, 2) + u = ot.utils.unif(n) + + M = ot.dist(x, x) + + with pytest.raises(NotImplementedError): + Gl2, log = ot.smooth.smooth_ot_dual(u, u, M, 1, reg_type='none') + + Gl2, log = ot.smooth.smooth_ot_dual(u, u, M, 1, reg_type='l2', log=True, stopThr=1e-10) + + # check constratints + np.testing.assert_allclose( + u, Gl2.sum(1), atol=1e-05) # cf convergence sinkhorn + np.testing.assert_allclose( + u, Gl2.sum(0), atol=1e-05) # cf convergence sinkhorn + + # kl regyularisation + G = ot.smooth.smooth_ot_dual(u, u, M, 1, reg_type='kl', stopThr=1e-10) + + # check constratints + np.testing.assert_allclose( + u, G.sum(1), atol=1e-05) # cf convergence sinkhorn + np.testing.assert_allclose( + u, G.sum(0), atol=1e-05) # cf convergence sinkhorn + + G2 = ot.sinkhorn(u, u, M, 1, stopThr=1e-10) + np.testing.assert_allclose(G, G2, atol=1e-05) + + +def test_smooth_ot_semi_dual(): + + # get data + n = 100 + rng = np.random.RandomState(0) + + x = rng.randn(n, 2) + u = ot.utils.unif(n) + + M = ot.dist(x, x) + + with pytest.raises(NotImplementedError): + Gl2, log = ot.smooth.smooth_ot_semi_dual(u, u, M, 1, reg_type='none') + + Gl2, log = ot.smooth.smooth_ot_semi_dual(u, u, M, 1, reg_type='l2', log=True, stopThr=1e-10) + + # check constratints + np.testing.assert_allclose( + u, Gl2.sum(1), atol=1e-05) # cf convergence sinkhorn + np.testing.assert_allclose( + u, Gl2.sum(0), atol=1e-05) # cf convergence sinkhorn + + # kl regyularisation + G = ot.smooth.smooth_ot_semi_dual(u, u, M, 1, reg_type='kl', stopThr=1e-10) + + # check constratints + np.testing.assert_allclose( + u, G.sum(1), atol=1e-05) # cf convergence sinkhorn + np.testing.assert_allclose( + u, G.sum(0), atol=1e-05) # cf convergence sinkhorn + + G2 = ot.sinkhorn(u, u, M, 1, stopThr=1e-10) + np.testing.assert_allclose(G, G2, atol=1e-05) |
