v1 jcpot example test

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diff --git a/examples/plot_otda_jcpot.py b/examples/plot_otda_jcpot.py
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+# -*- coding: utf-8 -*-
+"""
+========================
+OT for multi-source target shift
+========================
+
+This example introduces a target shift problem with two 2D source and 1 target domain.
+
+"""
+
+# 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
+
+##############################################################################
+# Generate data
+# -------------
+n = 50
+sigma = 0.3
+np.random.seed(1985)
+
+
+def get_data(n, p, dec):
+    y = np.concatenate((np.ones(int(p * n)), np.zeros(int((1 - p) * n))))
+    x = np.hstack((0 * y[:, None] - 0, 1 - 2 * y[:, None])) + sigma * np.random.randn(len(y), 2)
+
+    x[:, 0] += dec[0]
+    x[:, 1] += dec[1]
+
+    return x, y
+
+
+p1 = .2
+dec1 = [0, 2]
+
+p2 = .9
+dec2 = [0, -2]
+
+pt = .4
+dect = [4, 0]
+
+xs1, ys1 = get_data(n, p1, dec1)
+xs2, ys2 = get_data(n + 1, p2, dec2)
+xt, yt = get_data(n, pt, dect)
+all_Xr = [xs1, xs2]
+all_Yr = [ys1, ys2]
+# %%
+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
+# ---------------------------------------
+
+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 (0.8,0.2)')
+pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9, label='Source 2 (0.1,0.9)')
+pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9, label='Target (0.6,0.4)')
+pl.title('Data')
+
+pl.legend()
+pl.axis('equal')
+pl.axis('off')
+
+
+##############################################################################
+# Instantiate Sinkhorn transport algorithm and fit them for all source domains
+# ----------------------------------------------------------------------------
+ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-2, metric='euclidean')
+
+M1 = ot.dist(xs1, xt, 'euclidean')
+M2 = ot.dist(xs2, xt, 'euclidean')
+
+
+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
+# ------------------------------------------------------
+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')
+
+
+##############################################################################
+# Instantiate JCPOT adaptation algorithm and fit it
+# ----------------------------------------------------------------------------
+otda = ot.da.JCPOTTransport(reg_e=1e-2, max_iter=1000, tol=1e-9, verbose=True, log=True)
+otda.fit(all_Xr, all_Yr, xt)
+
+ws1 = otda.proportions_.dot(otda.log_['all_domains'][0]['D2'])
+ws2 = otda.proportions_.dot(otda.log_['all_domains'][1]['D2'])
+
+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, [], M1, reg=1e-2), xs1, ys1, xt)
+print_G(ot.bregman.sinkhorn(ws2, [], M2, reg=1e-2), 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')
+
+##############################################################################
+# Run oracle transport algorithm with known proportions
+# ----------------------------------------------------------------------------
+
+otda = ot.da.JCPOTTransport(reg_e=0.01, max_iter=1000, tol=1e-9, verbose=True, log=True)
+otda.fit(all_Xr, all_Yr, xt)
+
+h_res = np.array([1 - pt, pt])
+
+ws1 = h_res.dot(otda.log_['all_domains'][0]['D2'])
+ws2 = h_res.dot(otda.log_['all_domains'][1]['D2'])
+
+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, [], M1, reg=1e-2), xs1, ys1, xt)
+print_G(ot.bregman.sinkhorn(ws2, [], M2, reg=1e-2), 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()