[MRG] Domain adaptation and unbalanced solvers with backend support (#343)

* First draft

* Add matrix inverse and square root to backend

* Eigen decomposition for older versions of pytorch (1.8.1 and older)

* Corrected eigen decomposition for pytorch 1.8.1 and older

* Spectral theorem is a thing

* Optimization

* small optimization

* More functions converted

* pep8

* remove a warning and prepare torch meshgrid for future torch release (which will change default indexing)

* dots and pep8

* Meshgrid corrected for older version and prepared for future versions changes

* New backend functions

* Base transport

* LinearTransport

* All transport classes + pep8

* PR added to release file

* Jcpot barycenter test

* unbalanced with backend

* pep8

* bug solve

* test of domain adaptation with backends

* solve bug for tic toc & macos

* solving scipy deprecation warning

* solving scipy deprecation warning attempt2

* solving scipy deprecation warning attempt3

* A warning is triggered when a float->int conversion is detected

* bug solve

* docs

* release file updated

* Better handling of float->int conversion in EMD

* Corrected test for is_floating_point

* docs

* release file updated

* cupy does not allow implicit cast

* fromnumpy

* added test

* test da tf jax

* test unbalanced with no provided histogram

* using type_as argument in unif function correctly

* pep8

* transport plan cast in emd changed behaviour, now trying to cast as histogram's dtype, defaulting to cost matrix

Co-authored-by: Rémi Flamary <remi.flamary@gmail.com>

1 files changed, 7 insertions, 12 deletions

diff --git a/test/test_ot.py b/test/test_ot.py
index 3e2d845..bb258e2 100644
--- a/test/test_ot.py
+++ b/test/test_ot.py
@@ -47,8 +47,7 @@ def test_emd_backends(nx):
 
     G = ot.emd(a, a, M)
 
-    ab = nx.from_numpy(a)
-    Mb = nx.from_numpy(M)
+    ab, Mb = nx.from_numpy(a, M)
 
     Gb = ot.emd(ab, ab, Mb)
 
@@ -68,8 +67,7 @@ def test_emd2_backends(nx):
 
     val = ot.emd2(a, a, M)
 
-    ab = nx.from_numpy(a)
-    Mb = nx.from_numpy(M)
+    ab, Mb = nx.from_numpy(a, M)
 
     valb = ot.emd2(ab, ab, Mb)
 
@@ -90,8 +88,7 @@ def test_emd_emd2_types_devices(nx):
     for tp in nx.__type_list__:
         print(nx.dtype_device(tp))
 
-        ab = nx.from_numpy(a, type_as=tp)
-        Mb = nx.from_numpy(M, type_as=tp)
+        ab, Mb = nx.from_numpy(a, M, type_as=tp)
 
         Gb = ot.emd(ab, ab, Mb)
 
@@ -117,8 +114,7 @@ def test_emd_emd2_devices_tf():
 
     # Check that everything stays on the CPU
     with tf.device("/CPU:0"):
-        ab = nx.from_numpy(a)
-        Mb = nx.from_numpy(M)
+        ab, Mb = nx.from_numpy(a, M)
         Gb = ot.emd(ab, ab, Mb)
         w = ot.emd2(ab, ab, Mb)
         nx.assert_same_dtype_device(Mb, Gb)
@@ -126,8 +122,7 @@ def test_emd_emd2_devices_tf():
 
     if len(tf.config.list_physical_devices('GPU')) > 0:
         # Check that everything happens on the GPU
-        ab = nx.from_numpy(a)
-        Mb = nx.from_numpy(M)
+        ab, Mb = nx.from_numpy(a, M)
         Gb = ot.emd(ab, ab, Mb)
         w = ot.emd2(ab, ab, Mb)
         nx.assert_same_dtype_device(Mb, Gb)
@@ -310,8 +305,8 @@ def test_free_support_barycenter_backends(nx):
 
     X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init)
 
-    measures_locations2 = [nx.from_numpy(x) for x in measures_locations]
-    measures_weights2 = [nx.from_numpy(x) for x in measures_weights]
+    measures_locations2 = nx.from_numpy(*measures_locations)
+    measures_weights2 = nx.from_numpy(*measures_weights)
     X_init2 = nx.from_numpy(X_init)
 
     X2 = ot.lp.free_support_barycenter(measures_locations2, measures_weights2, X_init2)