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Merge pull request #28 from Slasnista/domain_adaptation_corrections
Domain adaptation corrections, closes #26
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‎examples/da/plot_otda_semi_supervised.py‎

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# -*- coding: utf-8 -*-
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"""
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============================================
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OTDA unsupervised vs semi-supervised setting
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============================================
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This example introduces a semi supervised domain adaptation in a 2D setting.
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It explicits the problem of semi supervised domain adaptation and introduces
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some optimal transport approaches to solve it.
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Quantities such as optimal couplings, greater coupling coefficients and
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transported samples are represented in order to give a visual understanding
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of what the transport methods are doing.
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"""
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# Authors: Remi Flamary <remi.flamary@unice.fr>
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# Stanislas Chambon <stan.chambon@gmail.com>
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#
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# License: MIT License
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import matplotlib.pylab as pl
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import ot
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##############################################################################
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# generate data
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##############################################################################
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n_samples_source = 150
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n_samples_target = 150
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Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source)
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Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target)
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##############################################################################
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# Transport source samples onto target samples
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##############################################################################
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# unsupervised domain adaptation
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ot_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1)
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ot_sinkhorn_un.fit(Xs=Xs, Xt=Xt)
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transp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs)
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# semi-supervised domain adaptation
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ot_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1)
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ot_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt)
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transp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs)
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# semi supervised DA uses available labaled target samples to modify the cost
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# matrix involved in the OT problem. The cost of transporting a source sample
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# of class A onto a target sample of class B != A is set to infinite, or a
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# very large value
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# note that in the present case we consider that all the target samples are
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# labeled. For daily applications, some target sample might not have labels,
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# in this case the element of yt corresponding to these samples should be
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# filled with -1.
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# Warning: we recall that -1 cannot be used as a class label
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##############################################################################
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# Fig 1 : plots source and target samples + matrix of pairwise distance
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##############################################################################
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pl.figure(1, figsize=(10, 10))
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pl.subplot(2, 2, 1)
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pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
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pl.xticks([])
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pl.yticks([])
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pl.legend(loc=0)
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pl.title('Source samples')
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pl.subplot(2, 2, 2)
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pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
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pl.xticks([])
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pl.yticks([])
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pl.legend(loc=0)
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pl.title('Target samples')
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pl.subplot(2, 2, 3)
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pl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest')
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pl.xticks([])
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pl.yticks([])
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pl.title('Cost matrix - unsupervised DA')
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pl.subplot(2, 2, 4)
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pl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest')
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pl.xticks([])
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pl.yticks([])
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pl.title('Cost matrix - semisupervised DA')
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pl.tight_layout()
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# the optimal coupling in the semi-supervised DA case will exhibit " shape
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# similar" to the cost matrix, (block diagonal matrix)
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##############################################################################
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# Fig 2 : plots optimal couplings for the different methods
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##############################################################################
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pl.figure(2, figsize=(8, 4))
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pl.subplot(1, 2, 1)
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pl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest')
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pl.xticks([])
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pl.yticks([])
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pl.title('Optimal coupling\nUnsupervised DA')
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pl.subplot(1, 2, 2)
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pl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest')
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pl.xticks([])
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pl.yticks([])
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pl.title('Optimal coupling\nSemi-supervised DA')
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pl.tight_layout()
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##############################################################################
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# Fig 3 : plot transported samples
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##############################################################################
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# display transported samples
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pl.figure(4, figsize=(8, 4))
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pl.subplot(1, 2, 1)
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pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
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label='Target samples', alpha=0.5)
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pl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys,
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marker='+', label='Transp samples', s=30)
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pl.title('Transported samples\nEmdTransport')
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pl.legend(loc=0)
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pl.xticks([])
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pl.yticks([])
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pl.subplot(1, 2, 2)
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pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
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label='Target samples', alpha=0.5)
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pl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys,
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marker='+', label='Transp samples', s=30)
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pl.title('Transported samples\nSinkhornTransport')
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pl.xticks([])
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pl.yticks([])
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pl.tight_layout()
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pl.show()

‎ot/da.py‎

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@@ -966,8 +966,12 @@ def fit(self, Xs=None, ys=None, Xt=None, yt=None):
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The class labels
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Xt : array-like, shape (n_target_samples, n_features)
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The training input samples.
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yt : array-like, shape (n_labeled_target_samples,)
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The class labels
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yt : array-like, shape (n_target_samples,)
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The class labels. If some target samples are unlabeled, fill the
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yt's elements with -1.
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Warning: Note that, due to this convention -1 cannot be used as a
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class label
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-------
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# assumes labeled source samples occupy the first rows
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# and labeled target samples occupy the first columns
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classes = np.unique(ys)
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classes = [c for c in np.unique(ys) if c != -1]
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for c in classes:
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idx_s = np.where((ys != c) & (ys != -1))
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idx_t = np.where(yt == c)
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The class labels
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Xt : array-like, shape (n_target_samples, n_features)
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The training input samples.
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yt : array-like, shape (n_labeled_target_samples,)
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The class labels
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yt : array-like, shape (n_target_samples,)
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The class labels. If some target samples are unlabeled, fill the
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yt's elements with -1.
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Warning: Note that, due to this convention -1 cannot be used as a
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class label
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The class labels
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Xt : array-like, shape (n_target_samples, n_features)
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The training input samples.
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yt : array-like, shape (n_labeled_target_samples,)
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The class labels
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yt : array-like, shape (n_target_samples,)
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The class labels. If some target samples are unlabeled, fill the
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yt's elements with -1.
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Warning: Note that, due to this convention -1 cannot be used as a
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class label
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batch_size : int, optional (default=128)
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The batch size for out of sample inverse transform
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The class labels
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Xt : array-like, shape (n_target_samples, n_features)
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The training input samples.
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yt : array-like, shape (n_labeled_target_samples,)
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The class labels
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yt : array-like, shape (n_target_samples,)
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The class labels. If some target samples are unlabeled, fill the
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yt's elements with -1.
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Warning: Note that, due to this convention -1 cannot be used as a
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class label
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batch_size : int, optional (default=128)
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The batch size for out of sample inverse transform
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The class labels
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Xt : array-like, shape (n_target_samples, n_features)
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The training input samples.
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yt : array-like, shape (n_labeled_target_samples,)
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The class labels
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yt : array-like, shape (n_target_samples,)
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The class labels. If some target samples are unlabeled, fill the
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yt's elements with -1.
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Warning: Note that, due to this convention -1 cannot be used as a
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class label
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The class labels
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Xt : array-like, shape (n_target_samples, n_features)
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The training input samples.
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yt : array-like, shape (n_labeled_target_samples,)
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The class labels
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yt : array-like, shape (n_target_samples,)
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The class labels. If some target samples are unlabeled, fill the
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yt's elements with -1.
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Warning: Note that, due to this convention -1 cannot be used as a
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class label
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The class labels
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Xt : array-like, shape (n_target_samples, n_features)
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The training input samples.
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yt : array-like, shape (n_labeled_target_samples,)
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The class labels
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yt : array-like, shape (n_target_samples,)
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The class labels. If some target samples are unlabeled, fill the
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yt's elements with -1.
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Warning: Note that, due to this convention -1 cannot be used as a
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class label
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The class labels
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The training input samples.
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yt : array-like, shape (n_labeled_target_samples,)
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The class labels
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yt : array-like, shape (n_target_samples,)
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The class labels. If some target samples are unlabeled, fill the
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yt's elements with -1.
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Warning: Note that, due to this convention -1 cannot be used as a
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class label
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The class labels
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The training input samples.
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yt : array-like, shape (n_labeled_target_samples,)
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The class labels
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The class labels. If some target samples are unlabeled, fill the
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yt's elements with -1.
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Warning: Note that, due to this convention -1 cannot be used as a
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class label
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