.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/domain-adaptation/plot_otda_jcpot.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_domain-adaptation_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. .. GENERATED FROM PYTHON SOURCE LINES 10-21 .. code-block:: Python # Authors: Remi Flamary # Ievgen Redko # # License: MIT License import pylab as pl import numpy as np import ot from ot.datasets import make_data_classif .. GENERATED FROM PYTHON SOURCE LINES 22-24 Generate data ------------- .. GENERATED FROM PYTHON SOURCE LINES 24-43 .. code-block:: Python 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] .. GENERATED FROM PYTHON SOURCE LINES 44-54 .. code-block:: Python 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) .. GENERATED FROM PYTHON SOURCE LINES 55-57 Fig 1 : plots source and target samples --------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 57-75 .. code-block:: Python 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-sg:: /auto_examples/domain-adaptation/images/sphx_glr_plot_otda_jcpot_001.png :alt: Data :srcset: /auto_examples/domain-adaptation/images/sphx_glr_plot_otda_jcpot_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none (-1.85, 5.85, -4.046431138906241, 4.129455496299416) .. GENERATED FROM PYTHON SOURCE LINES 76-78 Instantiate Sinkhorn transport algorithm and fit them for all source domains ---------------------------------------------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 78-92 .. code-block:: Python 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) .. GENERATED FROM PYTHON SOURCE LINES 93-95 Fig 2 : plot optimal couplings and transported samples ------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 95-115 .. code-block:: Python 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-sg:: /auto_examples/domain-adaptation/images/sphx_glr_plot_otda_jcpot_002.png :alt: Independent OT :srcset: /auto_examples/domain-adaptation/images/sphx_glr_plot_otda_jcpot_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none (-1.85, 5.85, -4.046431138906241, 4.129455496299416) .. GENERATED FROM PYTHON SOURCE LINES 116-118 Instantiate JCPOT adaptation algorithm and fit it ---------------------------------------------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 118-144 .. code-block:: Python 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-sg:: /auto_examples/domain-adaptation/images/sphx_glr_plot_otda_jcpot_003.png :alt: OT with prop estimation (0.615,0.385) :srcset: /auto_examples/domain-adaptation/images/sphx_glr_plot_otda_jcpot_003.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none It. |Err ------------------- 0|1.069551e+00| (-1.85, 5.85, -4.046431138906241, 4.129455496299416) .. GENERATED FROM PYTHON SOURCE LINES 145-147 Run oracle transport algorithm with known proportions ---------------------------------------------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 147-172 .. code-block:: Python 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-sg:: /auto_examples/domain-adaptation/images/sphx_glr_plot_otda_jcpot_004.png :alt: OT with known proportion (0.6,0.4) :srcset: /auto_examples/domain-adaptation/images/sphx_glr_plot_otda_jcpot_004.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none /home/circleci/project/ot/bregman/_sinkhorn.py:531: UserWarning: Sinkhorn did not converge. You might want to increase the number of iterations `numItermax` or the regularization parameter `reg`. warnings.warn("Sinkhorn did not converge. You might want to " .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 2.598 seconds) .. _sphx_glr_download_auto_examples_domain-adaptation_plot_otda_jcpot.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_otda_jcpot.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_otda_jcpot.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_