diff options
author | Rémi Flamary <remi.flamary@gmail.com> | 2017-09-14 16:54:57 +0200 |
---|---|---|
committer | Rémi Flamary <remi.flamary@gmail.com> | 2017-09-14 16:54:57 +0200 |
commit | aaf7ec83141045f0897d7fbc563ff7e5e7346fd9 (patch) | |
tree | 091d98cdc875e63fa634b943fb1b1f6547308d34 /examples | |
parent | a9427a1dd3fd9e99b0fee349eb714c93ac6faf83 (diff) |
move example
Diffstat (limited to 'examples')
-rw-r--r-- | examples/da/plot_otda_semi_supervised.py | 147 |
1 files changed, 0 insertions, 147 deletions
diff --git a/examples/da/plot_otda_semi_supervised.py b/examples/da/plot_otda_semi_supervised.py deleted file mode 100644 index 8095c4d..0000000 --- a/examples/da/plot_otda_semi_supervised.py +++ /dev/null @@ -1,147 +0,0 @@ -# -*- coding: utf-8 -*- -""" -============================================ -OTDA unsupervised vs semi-supervised setting -============================================ - -This example introduces a semi supervised domain adaptation in a 2D setting. -It explicits the problem of semi supervised domain adaptation and introduces -some optimal transport approaches to solve it. - -Quantities such as optimal couplings, greater coupling coefficients and -transported samples are represented in order to give a visual understanding -of what the transport methods are doing. -""" - -# 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_samples_source = 150 -n_samples_target = 150 - -Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) -Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) - - -############################################################################## -# Transport source samples onto target samples -############################################################################## - -# unsupervised domain adaptation -ot_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1) -ot_sinkhorn_un.fit(Xs=Xs, Xt=Xt) -transp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs) - -# semi-supervised domain adaptation -ot_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1) -ot_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt) -transp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs) - -# semi supervised DA uses available labaled target samples to modify the cost -# matrix involved in the OT problem. The cost of transporting a source sample -# of class A onto a target sample of class B != A is set to infinite, or a -# very large value - -# note that in the present case we consider that all the target samples are -# labeled. For daily applications, some target sample might not have labels, -# in this case the element of yt corresponding to these samples should be -# filled with -1. - -# Warning: we recall that -1 cannot be used as a class label - - -############################################################################## -# Fig 1 : plots source and target samples + matrix of pairwise distance -############################################################################## - -pl.figure(1, figsize=(10, 10)) -pl.subplot(2, 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(2, 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.subplot(2, 2, 3) -pl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Cost matrix - unsupervised DA') - -pl.subplot(2, 2, 4) -pl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Cost matrix - semisupervised DA') - -pl.tight_layout() - -# the optimal coupling in the semi-supervised DA case will exhibit " shape -# similar" to the cost matrix, (block diagonal matrix) - - -############################################################################## -# Fig 2 : plots optimal couplings for the different methods -############################################################################## - -pl.figure(2, figsize=(8, 4)) - -pl.subplot(1, 2, 1) -pl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nUnsupervised DA') - -pl.subplot(1, 2, 2) -pl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSemi-supervised DA') - -pl.tight_layout() - - -############################################################################## -# Fig 3 : plot transported samples -############################################################################## - -# display transported samples -pl.figure(4, figsize=(8, 4)) -pl.subplot(1, 2, 1) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) -pl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Transported samples\nEmdTransport') -pl.legend(loc=0) -pl.xticks([]) -pl.yticks([]) - -pl.subplot(1, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) -pl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Transported samples\nSinkhornTransport') -pl.xticks([]) -pl.yticks([]) - -pl.tight_layout() -pl.show() |