From 7af8c2147d61349f4d99ca33318a8a125e4569aa Mon Sep 17 00:00:00 2001
From: haoran010 <62598274+haoran010@users.noreply.github.com>
Date: Mon, 25 Oct 2021 10:47:22 +0200
Subject: [MRG] Regularization path for l2 UOT (#274)
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* add reg path

* debug examples and verify pep8

* pep8 and move the reg path examples in unbalanced folder

Co-authored-by: haoran010 <haoran.wu@insa-rennes.fr>
Co-authored-by: Rémi Flamary <remi.flamary@gmail.com>
---
 test/test_regpath.py | 64 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 64 insertions(+)
 create mode 100644 test/test_regpath.py

(limited to 'test/test_regpath.py')

diff --git a/test/test_regpath.py b/test/test_regpath.py
new file mode 100644
index 0000000..967c27b
--- /dev/null
+++ b/test/test_regpath.py
@@ -0,0 +1,64 @@
+"""Tests for module regularization path"""
+
+# Author: Haoran Wu <haoran.wu@univ-ubs.fr>
+#
+# License: MIT License
+
+import numpy as np
+import ot
+
+
+def test_fully_relaxed_path():
+
+    n_source = 50   # nb source samples (gaussian)
+    n_target = 40   # nb target samples (gaussian)
+
+    mu = np.array([0, 0])
+    cov = np.array([[1, 0], [0, 2]])
+
+    np.random.seed(0)
+    xs = ot.datasets.make_2D_samples_gauss(n_source, mu, cov)
+    xt = ot.datasets.make_2D_samples_gauss(n_target, mu, cov)
+
+    # source and target distributions
+    a = ot.utils.unif(n_source)
+    b = ot.utils.unif(n_target)
+
+    # loss matrix
+    M = ot.dist(xs, xt)
+    M /= M.max()
+
+    t, _, _ = ot.regpath.regularization_path(a, b, M, reg=1e-8,
+                                             semi_relaxed=False)
+
+    G = t.reshape((n_source, n_target))
+    np.testing.assert_allclose(a, G.sum(1), atol=1e-05)
+    np.testing.assert_allclose(b, G.sum(0), atol=1e-05)
+
+
+def test_semi_relaxed_path():
+
+    n_source = 50   # nb source samples (gaussian)
+    n_target = 40   # nb target samples (gaussian)
+
+    mu = np.array([0, 0])
+    cov = np.array([[1, 0], [0, 2]])
+
+    np.random.seed(0)
+    xs = ot.datasets.make_2D_samples_gauss(n_source, mu, cov)
+    xt = ot.datasets.make_2D_samples_gauss(n_target, mu, cov)
+
+    # source and target distributions
+    a = ot.utils.unif(n_source)
+    b = ot.utils.unif(n_target)
+
+    # loss matrix
+    M = ot.dist(xs, xt)
+    M /= M.max()
+
+    t, _, _ = ot.regpath.regularization_path(a, b, M, reg=1e-8,
+                                             semi_relaxed=True)
+
+    G = t.reshape((n_source, n_target))
+    np.testing.assert_allclose(a, G.sum(1), atol=1e-05)
+    np.testing.assert_allclose(b, G.sum(0), atol=1e-10)
-- 
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