remove jcpot from laplace

1 files changed, 0 insertions, 171 deletions

diff --git a/examples/plot_otda_jcpot.py b/examples/plot_otda_jcpot.py
deleted file mode 100644
index 316fa8b..0000000
--- a/examples/plot_otda_jcpot.py
+++ /dev/null
@@ -1,171 +0,0 @@
-# -*- 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
-from ot.datasets import make_data_classif
-
-##############################################################################
-# Generate data
-# -------------
-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]
-# %%
-
-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 ({: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')
-
-##############################################################################
-# Instantiate Sinkhorn transport algorithm and fit them for all source domains
-# ----------------------------------------------------------------------------
-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
-# ------------------------------------------------------
-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, 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-2), xs1, ys1, xt)
-print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], 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
-# ----------------------------------------------------------------------------
-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-2), xs1, ys1, xt)
-print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], 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()