Fix DA cost correction when cost limit is set to Inf (#593)

* Introduce the test that actually fails with cost = nan * Update cost correction algorithm * Better explanation in the code for how missing_labels and label_match interact * All close to check semi-supervised mode * Update RELEASE file * Suppress runtime warning from numpy about using Inf in the multiplication

Author: kachayev

RELEASES.md

@@ -1,5 +1,11 @@
 # Releases
 
+## 0.9.3
+
+#### Closed issues
+- Fixed an issue with cost correction for mismatched labels in `ot.da.BaseTransport` fit methods. This fix addresses the original issue introduced PR #587 (PR #593)
+
+
 ## 0.9.2
 *December 2023*
 

ot/da.py

@@ -13,6 +13,7 @@
 # License: MIT License
 
 import numpy as np
+import warnings
 
 from .backend import get_backend
 from .bregman import sinkhorn, jcpot_barycenter
@@ -499,12 +500,27 @@ class label
  if self.limit_max != np.infty:
  self.limit_max = self.limit_max * nx.max(self.cost_)
 
- # zeros where source label is missing (masked with -1)
- missing_labels = ys + nx.ones(ys.shape, type_as=ys)
- missing_labels = nx.repeat(missing_labels[:, None], ys.shape[0], 1)
- # zeros where labels match
- label_match = ys[:, None] - yt[None, :]
- self.cost_ = nx.maximum(self.cost_, nx.abs(label_match) * nx.abs(missing_labels) * self.limit_max)
+ # missing_labels is a (ns, nt) matrix of {0, 1} such that
+ # the cells (i, j) has 0 iff either ys[i] or yt[j] is masked
+ missing_ys = (ys == -1) + nx.zeros(ys.shape, type_as=ys)
+ missing_yt = (yt == -1) + nx.zeros(yt.shape, type_as=yt)
+ missing_labels = missing_ys[:, None] @ missing_yt[None, :]
+ # labels_match is a (ns, nt) matrix of {True, False} such that
+ # the cells (i, j) has False if ys[i] != yt[i]
+ label_match = (ys[:, None] - yt[None, :]) != 0
+ # cost correction is a (ns, nt) matrix of {-Inf, float, Inf} such
+ # that he cells (i, j) has -Inf where there's no correction necessary
+ # by 'correction' we mean setting cost to a large value when
+ # labels do not match
+ # we suppress potential RuntimeWarning caused by Inf multiplication
+ # (as we explicitly cover potential NANs later)
+ with warnings.catch_warnings():
+ warnings.simplefilter('ignore', category=RuntimeWarning)
+ cost_correction = label_match * missing_labels * self.limit_max
+ # this operation is necessary because 0 * Inf = NAN
+ # thus is irrelevant when limit_max is finite
+ cost_correction = nx.nan_to_num(cost_correction, -np.infty)
+ self.cost_ = nx.maximum(self.cost_, cost_correction)
 
  # distribution estimation
  self.mu_s = self.distribution_estimation(Xs)

test/test_da.py

@@ -89,7 +89,9 @@ def test_sinkhorn_lpl1_transport_class(nx):
  # test its computed
  otda.fit(Xs=Xs, ys=ys, Xt=Xt)
  assert hasattr(otda, "cost_")
+ assert not np.any(np.isnan(nx.to_numpy(otda.cost_))), "cost is finite"
  assert hasattr(otda, "coupling_")
+ assert np.all(np.isfinite(nx.to_numpy(otda.coupling_))), "coupling is finite"
 
  # test dimensions of coupling
  assert_equal(otda.cost_.shape, ((Xs.shape[0], Xt.shape[0])))
@@ -148,7 +150,7 @@ def test_sinkhorn_lpl1_transport_class(nx):
  n_semisup = nx.sum(otda_semi.cost_)
 
  # check that the cost matrix norms are indeed different
- assert n_unsup != n_semisup, "semisupervised mode not working"
+ assert np.allclose(n_unsup, n_semisup, atol=1e-7), "semisupervised mode is not working"
 
  # check that the coupling forbids mass transport between labeled source
  # and labeled target samples
@@ -238,7 +240,7 @@ def test_sinkhorn_l1l2_transport_class(nx):
  n_semisup = nx.sum(otda_semi.cost_)
 
  # check that the cost matrix norms are indeed different
- assert n_unsup != n_semisup, "semisupervised mode not working"
+ assert np.allclose(n_unsup, n_semisup, atol=1e-7), "semisupervised mode is not working"
 
  # check that the coupling forbids mass transport between labeled source
  # and labeled target samples
@@ -331,7 +333,7 @@ def test_sinkhorn_transport_class(nx):
  n_semisup = nx.sum(otda_semi.cost_)
 
  # check that the cost matrix norms are indeed different
- assert n_unsup != n_semisup, "semisupervised mode not working"
+ assert np.allclose(n_unsup, n_semisup, atol=1e-7), "semisupervised mode is not working"
 
  # check that the coupling forbids mass transport between labeled source
  # and labeled target samples
@@ -371,6 +373,10 @@ def test_unbalanced_sinkhorn_transport_class(nx):
  # test dimensions of coupling
  assert_equal(otda.cost_.shape, ((Xs.shape[0], Xt.shape[0])))
  assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))
+ assert not np.any(np.isnan(nx.to_numpy(otda.cost_))), "cost is finite"
+
+ # test coupling
+ assert np.all(np.isfinite(nx.to_numpy(otda.coupling_))), "coupling is finite"
 
  # test transform
  transp_Xs = otda.transform(Xs=Xs)
@@ -409,19 +415,22 @@ def test_unbalanced_sinkhorn_transport_class(nx):
  # test unsupervised vs semi-supervised mode
  otda_unsup = ot.da.SinkhornTransport()
  otda_unsup.fit(Xs=Xs, Xt=Xt)
+ assert not np.any(np.isnan(nx.to_numpy(otda_unsup.cost_))), "cost is finite"
  n_unsup = nx.sum(otda_unsup.cost_)
 
  otda_semi = ot.da.SinkhornTransport()
  otda_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt)
+ assert not np.any(np.isnan(nx.to_numpy(otda_semi.cost_))), "cost is finite"
  assert_equal(otda_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0])))
  n_semisup = nx.sum(otda_semi.cost_)
 
  # check that the cost matrix norms are indeed different
- assert n_unsup != n_semisup, "semisupervised mode not working"
+ assert np.allclose(n_unsup, n_semisup, atol=1e-7), "semisupervised mode is not working"
 
  # check everything runs well with log=True
  otda = ot.da.SinkhornTransport(log=True)
  otda.fit(Xs=Xs, ys=ys, Xt=Xt)
+ assert not np.any(np.isnan(nx.to_numpy(otda.cost_))), "cost is finite"
  assert len(otda.log_.keys()) != 0
 
 
@@ -448,7 +457,9 @@ def test_emd_transport_class(nx):
 
  # test dimensions of coupling
  assert_equal(otda.cost_.shape, ((Xs.shape[0], Xt.shape[0])))
+ assert not np.any(np.isnan(nx.to_numpy(otda.cost_))), "cost is finite"
  assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))
+ assert np.all(np.isfinite(nx.to_numpy(otda.coupling_))), "coupling is finite"
 
  # test margin constraints
  mu_s = unif(ns)
@@ -495,15 +506,22 @@ def test_emd_transport_class(nx):
  # test unsupervised vs semi-supervised mode
  otda_unsup = ot.da.EMDTransport()
  otda_unsup.fit(Xs=Xs, ys=ys, Xt=Xt)
+ assert_equal(otda_unsup.cost_.shape, ((Xs.shape[0], Xt.shape[0])))
+ assert not np.any(np.isnan(nx.to_numpy(otda_unsup.cost_))), "cost is finite"
+ assert_equal(otda_unsup.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))
+ assert np.all(np.isfinite(nx.to_numpy(otda_unsup.coupling_))), "coupling is finite"
  n_unsup = nx.sum(otda_unsup.cost_)
 
  otda_semi = ot.da.EMDTransport()
  otda_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt)
  assert_equal(otda_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0])))
+ assert not np.any(np.isnan(nx.to_numpy(otda_semi.cost_))), "cost is finite"
+ assert_equal(otda_semi.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))
+ assert np.all(np.isfinite(nx.to_numpy(otda_semi.coupling_))), "coupling is finite"
  n_semisup = nx.sum(otda_semi.cost_)
 
  # check that the cost matrix norms are indeed different
- assert n_unsup != n_semisup, "semisupervised mode not working"
+ assert np.allclose(n_unsup, n_semisup, atol=1e-7), "semisupervised mode is not working"
 
  # check that the coupling forbids mass transport between labeled source
  # and labeled target samples