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author | RĂ©mi Flamary <remi.flamary@gmail.com> | 2020-04-21 17:48:37 +0200 |
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committer | GitHub <noreply@github.com> | 2020-04-21 17:48:37 +0200 |
commit | a303cc6b483d3cd958c399621e22e40574bcbbc8 (patch) | |
tree | dea049cb692020462da8f00d9e117f93b839bb55 /docs/source/auto_examples/plot_otda_classes.py | |
parent | 0b2d808aaebb1cab60a272ea7901d5f77df43a9f (diff) |
[MRG] Actually run sphinx-gallery (#146)
* generate gallery
* remove mock
* add sklearn to requirermnt?txt for example
* remove latex from fgw example
* add networks for graph example
* remove all
* add requirement.txt rtd
* rtd debug
* update readme
* eradthedoc with redirection
* add conf rtd
Diffstat (limited to 'docs/source/auto_examples/plot_otda_classes.py')
-rw-r--r-- | docs/source/auto_examples/plot_otda_classes.py | 149 |
1 files changed, 0 insertions, 149 deletions
diff --git a/docs/source/auto_examples/plot_otda_classes.py b/docs/source/auto_examples/plot_otda_classes.py deleted file mode 100644 index f028022..0000000 --- a/docs/source/auto_examples/plot_otda_classes.py +++ /dev/null @@ -1,149 +0,0 @@ -# -*- coding: utf-8 -*- -""" -======================== -OT for domain adaptation -======================== - -This example introduces a domain adaptation in a 2D setting and the 4 OTDA -approaches currently supported in POT. - -""" - -# Authors: Remi Flamary <remi.flamary@unice.fr> -# Stanislas Chambon <stan.chambon@gmail.com> -# -# 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=1e-1) -ot_sinkhorn.fit(Xs=Xs, Xt=Xt) - -# Sinkhorn Transport with Group lasso regularization -ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0) -ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt) - -# Sinkhorn Transport with Group lasso regularization l1l2 -ot_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20, - verbose=True) -ot_l1l2.fit(Xs=Xs, ys=ys, 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_lpl1 = ot_lpl1.transform(Xs=Xs) -transp_Xs_l1l2 = ot_l1l2.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, 4, 1) -pl.imshow(ot_emd.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nEMDTransport') - -pl.subplot(2, 4, 2) -pl.imshow(ot_sinkhorn.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSinkhornTransport') - -pl.subplot(2, 4, 3) -pl.imshow(ot_lpl1.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSinkhornLpl1Transport') - -pl.subplot(2, 4, 4) -pl.imshow(ot_l1l2.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSinkhornL1l2Transport') - -pl.subplot(2, 4, 5) -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, 4, 6) -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, 4, 7) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nSinkhornLpl1Transport') - -pl.subplot(2, 4, 8) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nSinkhornL1l2Transport') -pl.tight_layout() - -pl.show() |