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+.. only:: html
+
+ .. note::
+ :class: sphx-glr-download-link-note
+
+ Click :ref:`here <sphx_glr_download_auto_examples_plot_otda_jcpot.py>` to download the full example code
+ .. rst-class:: sphx-glr-example-title
+
+ .. _sphx_glr_auto_examples_plot_otda_jcpot.py:
+
+
+========================
+OT for multi-source target shift
+========================
+
+This example introduces a target shift problem with two 2D source and 1 target domain.
+
+
+
+.. code-block:: default
+
+
+ # Authors: Remi Flamary <remi.flamary@unice.fr>
+ # Ievgen Redko <ievgen.redko@univ-st-etienne.fr>
+ #
+ # License: MIT License
+
+ import pylab as pl
+ import numpy as np
+ import ot
+ from ot.datasets import make_data_classif
+
+
+
+
+
+
+
+
+Generate data
+-------------
+
+
+.. code-block:: default
+
+ n = 50
+ sigma = 0.3
+ np.random.seed(1985)
+
+ p1 = .2
+ dec1 = [0, 2]
+
+ p2 = .9
+ dec2 = [0, -2]
+
+ pt = .4
+ dect = [4, 0]
+
+ xs1, ys1 = make_data_classif('2gauss_prop', n, nz=sigma, p=p1, bias=dec1)
+ xs2, ys2 = make_data_classif('2gauss_prop', n + 1, nz=sigma, p=p2, bias=dec2)
+ xt, yt = make_data_classif('2gauss_prop', n, nz=sigma, p=pt, bias=dect)
+
+ all_Xr = [xs1, xs2]
+ all_Yr = [ys1, ys2]
+
+
+
+
+
+
+
+
+.. code-block:: default
+
+
+ da = 1.5
+
+
+ def plot_ax(dec, name):
+ pl.plot([dec[0], dec[0]], [dec[1] - da, dec[1] + da], 'k', alpha=0.5)
+ pl.plot([dec[0] - da, dec[0] + da], [dec[1], dec[1]], 'k', alpha=0.5)
+ pl.text(dec[0] - .5, dec[1] + 2, name)
+
+
+
+
+
+
+
+
+
+Fig 1 : plots source and target samples
+---------------------------------------
+
+
+.. code-block:: default
+
+
+ pl.figure(1)
+ pl.clf()
+ plot_ax(dec1, 'Source 1')
+ plot_ax(dec2, 'Source 2')
+ plot_ax(dect, 'Target')
+ pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9,
+ label='Source 1 ({:1.2f}, {:1.2f})'.format(1 - p1, p1))
+ pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9,
+ label='Source 2 ({:1.2f}, {:1.2f})'.format(1 - p2, p2))
+ pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9,
+ label='Target ({:1.2f}, {:1.2f})'.format(1 - pt, pt))
+ pl.title('Data')
+
+ pl.legend()
+ pl.axis('equal')
+ pl.axis('off')
+
+
+
+
+.. image:: /auto_examples/images/sphx_glr_plot_otda_jcpot_001.png
+ :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+
+ (-1.85, 5.85, -4.1171725099266725, 4.197384527473105)
+
+
+
+Instantiate Sinkhorn transport algorithm and fit them for all source domains
+----------------------------------------------------------------------------
+
+
+.. code-block:: default
+
+ ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1, metric='sqeuclidean')
+
+
+ def print_G(G, xs, ys, xt):
+ for i in range(G.shape[0]):
+ for j in range(G.shape[1]):
+ if G[i, j] > 5e-4:
+ if ys[i]:
+ c = 'b'
+ else:
+ c = 'r'
+ pl.plot([xs[i, 0], xt[j, 0]], [xs[i, 1], xt[j, 1]], c, alpha=.2)
+
+
+
+
+
+
+
+
+
+Fig 2 : plot optimal couplings and transported samples
+------------------------------------------------------
+
+
+.. code-block:: default
+
+ pl.figure(2)
+ pl.clf()
+ plot_ax(dec1, 'Source 1')
+ plot_ax(dec2, 'Source 2')
+ plot_ax(dect, 'Target')
+ print_G(ot_sinkhorn.fit(Xs=xs1, Xt=xt).coupling_, xs1, ys1, xt)
+ print_G(ot_sinkhorn.fit(Xs=xs2, Xt=xt).coupling_, 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)
+
+ pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1')
+ pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2')
+
+ pl.title('Independent OT')
+
+ pl.legend()
+ pl.axis('equal')
+ pl.axis('off')
+
+
+
+
+.. image:: /auto_examples/images/sphx_glr_plot_otda_jcpot_002.png
+ :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+
+ (-1.85, 5.85, -4.11901398007908, 4.201462272227509)
+
+
+
+Instantiate JCPOT adaptation algorithm and fit it
+----------------------------------------------------------------------------
+
+
+.. code-block:: default
+
+ 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])
+ ws2 = otda.proportions_.dot(otda.log_['D2'][1])
+
+ pl.figure(3)
+ 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-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)
+
+ pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1')
+ pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2')
+
+ pl.title('OT with prop estimation ({:1.3f},{:1.3f})'.format(otda.proportions_[0], otda.proportions_[1]))
+
+ pl.legend()
+ pl.axis('equal')
+ pl.axis('off')
+
+
+
+
+.. image:: /auto_examples/images/sphx_glr_plot_otda_jcpot_003.png
+ :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+
+ (-1.85, 5.85, -4.11901398007908, 4.201462272227509)
+
+
+
+Run oracle transport algorithm with known proportions
+----------------------------------------------------------------------------
+
+
+.. code-block:: default
+
+ h_res = np.array([1 - pt, pt])
+
+ ws1 = h_res.dot(otda.log_['D2'][0])
+ ws2 = h_res.dot(otda.log_['D2'][1])
+
+ pl.figure(4)
+ 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-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)
+
+ pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1')
+ pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2')
+
+ pl.title('OT with known proportion ({:1.1f},{:1.1f})'.format(h_res[0], h_res[1]))
+
+ pl.legend()
+ pl.axis('equal')
+ pl.axis('off')
+ pl.show()
+
+
+
+.. image:: /auto_examples/images/sphx_glr_plot_otda_jcpot_004.png
+ :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+ /home/rflamary/PYTHON/POT/examples/plot_otda_jcpot.py:171: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
+ pl.show()
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+ **Total running time of the script:** ( 0 minutes 4.725 seconds)
+
+
+.. _sphx_glr_download_auto_examples_plot_otda_jcpot.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+ :class: sphx-glr-footer-example
+
+
+
+ .. container:: sphx-glr-download sphx-glr-download-python
+
+ :download:`Download Python source code: plot_otda_jcpot.py <plot_otda_jcpot.py>`
+
+
+
+ .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+ :download:`Download Jupyter notebook: plot_otda_jcpot.ipynb <plot_otda_jcpot.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+ `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
generated by cgit v1.2.3 (git 2.39.1) at 2024-07-02 08:55:46 +0000