Merge pull request #137 from ievred/jcpot

[MRG] Jcpot : Multi source DA with target shift

Author: rflamary

.travis.yml

@@ -35,6 +35,7 @@ install:
   - pip install -r requirements.txt
   - pip install -U "numpy>=1.14" "scipy<1.3"  # for numpy array formatting in doctests + scipy version: otherwise, pymanopt fails, cf <https://github.com/pymanopt/pymanopt/issues/77>
   - pip install flake8 pytest "pytest-cov<2.6"
+  - pip install -U "sklearn"
   - pip install .
 # command to run tests + check syntax style
 services:

README.md

@@ -29,6 +29,7 @@ It provides the following solvers:
 * Non regularized free support Wasserstein barycenters [20].
 * Unbalanced OT with KL relaxation distance and barycenter [10, 25].
 * Screening Sinkhorn Algorithm for OT [26].
+* JCPOT algorithm for multi-source domain adaptation with target shift [27].
 
 Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder.
 
@@ -257,3 +258,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t
 [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http://cbcl.mit.edu/wasserstein/)  Advances in Neural Information Processing Systems (NIPS).
 
 [26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport), Advances in Neural Information Processing Systems 33 (NeurIPS).
+
+[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi-source Domain Adaptation under Target Shift](http://proceedings.mlr.press/v89/redko19a.html), Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019.

examples/plot_otda_classes.py

@@ -17,7 +17,6 @@
 import matplotlib.pylab as pl
 import ot
 
-
 ##############################################################################
 # Generate data
 # -------------

examples/plot_otda_jcpot.py

@@ -0,0 +1,171 @@
+# -*- 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 <[email protected]>
+#          Ievgen Redko <[email protected]>
+#
+# 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=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')
+
+##############################################################################
+# 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-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()