From c68b52d1623683e86555484bf9a4875a66957bb6 Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 8 Apr 2020 10:08:47 +0200 Subject: remove laplace from jcpot --- examples/plot_otda_jcpot.py | 10 ++-- examples/plot_otda_laplacian.py | 127 ---------------------------------------- 2 files changed, 5 insertions(+), 132 deletions(-) delete mode 100644 examples/plot_otda_laplacian.py (limited to 'examples') diff --git a/examples/plot_otda_jcpot.py b/examples/plot_otda_jcpot.py index 316fa8b..c495690 100644 --- a/examples/plot_otda_jcpot.py +++ b/examples/plot_otda_jcpot.py @@ -115,7 +115,7 @@ pl.axis('off') ############################################################################## # Instantiate JCPOT adaptation algorithm and fit it # ---------------------------------------------------------------------------- -otda = ot.da.JCPOTTransport(reg_e=1e-2, max_iter=1000, metric='sqeuclidean', tol=1e-9, verbose=True, log=True) +otda = ot.da.JCPOTTransport(reg_e=1, max_iter=1000, metric='sqeuclidean', tol=1e-9, verbose=True, log=True) otda.fit(all_Xr, all_Yr, xt) ws1 = otda.proportions_.dot(otda.log_['D2'][0]) @@ -126,8 +126,8 @@ pl.clf() plot_ax(dec1, 'Source 1') plot_ax(dec2, 'Source 2') plot_ax(dect, 'Target') -print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-2), xs1, ys1, xt) -print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-2), xs2, ys2, xt) +print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt) +print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt) pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) @@ -154,8 +154,8 @@ pl.clf() plot_ax(dec1, 'Source 1') plot_ax(dec2, 'Source 2') plot_ax(dect, 'Target') -print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-2), xs1, ys1, xt) -print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-2), xs2, ys2, xt) +print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt) +print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt) pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) diff --git a/examples/plot_otda_laplacian.py b/examples/plot_otda_laplacian.py deleted file mode 100644 index 965380c..0000000 --- a/examples/plot_otda_laplacian.py +++ /dev/null @@ -1,127 +0,0 @@ -# -*- coding: utf-8 -*- -""" -======================== -OT for domain adaptation -======================== - -This example introduces a domain adaptation in a 2D setting and OTDA -approache with Laplacian regularization. - -""" - -# Authors: Ievgen Redko - -# License: MIT License - -import matplotlib.pylab as pl -import ot - -############################################################################## -# Generate data -# ------------- - -n_source_samples = 150 -n_target_samples = 150 - -Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples) -Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples) - - -############################################################################## -# Instantiate the different transport algorithms and fit them -# ----------------------------------------------------------- - -# EMD Transport -ot_emd = ot.da.EMDTransport() -ot_emd.fit(Xs=Xs, Xt=Xt) - -# Sinkhorn Transport -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=.01) -ot_sinkhorn.fit(Xs=Xs, Xt=Xt) - -# EMD Transport with Laplacian regularization -ot_emd_laplace = ot.da.EMDLaplaceTransport(reg_lap=100, reg_src=1) -ot_emd_laplace.fit(Xs=Xs, Xt=Xt) - -# transport source samples onto target samples -transp_Xs_emd = ot_emd.transform(Xs=Xs) -transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) -transp_Xs_emd_laplace = ot_emd_laplace.transform(Xs=Xs) - -############################################################################## -# Fig 1 : plots source and target samples -# --------------------------------------- - -pl.figure(1, figsize=(10, 5)) -pl.subplot(1, 2, 1) -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Source samples') - -pl.subplot(1, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Target samples') -pl.tight_layout() - - -############################################################################## -# Fig 2 : plot optimal couplings and transported samples -# ------------------------------------------------------ - -param_img = {'interpolation': 'nearest'} - -pl.figure(2, figsize=(15, 8)) -pl.subplot(2, 3, 1) -pl.imshow(ot_emd.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nEMDTransport') - -pl.figure(2, figsize=(15, 8)) -pl.subplot(2, 3, 2) -pl.imshow(ot_sinkhorn.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSinkhornTransport') - -pl.subplot(2, 3, 3) -pl.imshow(ot_emd_laplace.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nEMDLaplaceTransport') - -pl.subplot(2, 3, 4) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nEmdTransport') -pl.legend(loc="lower left") - -pl.subplot(2, 3, 5) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nSinkhornTransport') - -pl.subplot(2, 3, 6) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_emd_laplace[:, 0], transp_Xs_emd_laplace[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nEMDLaplaceTransport') -pl.tight_layout() - -pl.show() -- cgit v1.2.3