diff options
author | ievred <ievgen.redko@univ-st-etienne.fr> | 2020-04-03 17:29:13 +0200 |
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committer | ievred <ievgen.redko@univ-st-etienne.fr> | 2020-04-03 17:29:13 +0200 |
commit | 98b68f1edc916d3802eeb24a19d0e10d855e01c6 (patch) | |
tree | 8830ef44936292de0d048d3c25170f180e843e7f /examples | |
parent | fa99199c02e497354e34c6ce76e7b4ba15b44d05 (diff) |
autopep+remove sinkhorn+add simtype
Diffstat (limited to 'examples')
-rw-r--r-- | examples/plot_otda_laplacian.py | 38 |
1 files changed, 8 insertions, 30 deletions
diff --git a/examples/plot_otda_laplacian.py b/examples/plot_otda_laplacian.py index d9ae280..965380c 100644 --- a/examples/plot_otda_laplacian.py +++ b/examples/plot_otda_laplacian.py @@ -5,7 +5,7 @@ OT for domain adaptation ======================== This example introduces a domain adaptation in a 2D setting and OTDA -approaches with Laplacian regularization. +approache with Laplacian regularization. """ @@ -36,22 +36,17 @@ ot_emd = ot.da.EMDTransport() ot_emd.fit(Xs=Xs, Xt=Xt) # Sinkhorn Transport -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=.5) +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) -# Sinkhorn Transport with Laplacian regularization -ot_sinkhorn_laplace = ot.da.SinkhornLaplaceTransport(reg_e=.5, reg_lap=100, reg_src=1) -ot_sinkhorn_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) -transp_Xs_sinkhorn_laplace = ot_sinkhorn_laplace.transform(Xs=Xs) ############################################################################## # Fig 1 : plots source and target samples @@ -80,35 +75,27 @@ pl.tight_layout() param_img = {'interpolation': 'nearest'} -n_plots = 2 - pl.figure(2, figsize=(15, 8)) -pl.subplot(2, 2*n_plots, 1) +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, 2*n_plots, 2) +pl.subplot(2, 3, 2) pl.imshow(ot_sinkhorn.coupling_, **param_img) pl.xticks([]) pl.yticks([]) pl.title('Optimal coupling\nSinkhornTransport') -pl.subplot(2, 2*n_plots, 3) +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, 2*n_plots, 4) -pl.imshow(ot_emd_laplace.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSinkhornLaplaceTransport') - -pl.subplot(2, 2*n_plots, 5) +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, @@ -118,7 +105,7 @@ pl.yticks([]) pl.title('Transported samples\nEmdTransport') pl.legend(loc="lower left") -pl.subplot(2, 2*n_plots, 6) +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, @@ -127,7 +114,7 @@ pl.xticks([]) pl.yticks([]) pl.title('Transported samples\nSinkhornTransport') -pl.subplot(2, 2*n_plots, 7) +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, @@ -135,15 +122,6 @@ pl.scatter(transp_Xs_emd_laplace[:, 0], transp_Xs_emd_laplace[:, 1], c=ys, pl.xticks([]) pl.yticks([]) pl.title('Transported samples\nEMDLaplaceTransport') - -pl.subplot(2, 2*n_plots, 8) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_sinkhorn_laplace[:, 0], transp_Xs_sinkhorn_laplace[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nSinkhornLaplaceTransport') pl.tight_layout() pl.show() |