[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, 10 deletions

diff --git a/test/test_optim.py b/test/test_optim.py
index 41f9cbe..67e9d13 100644
--- a/test/test_optim.py
+++ b/test/test_optim.py
@@ -32,9 +32,7 @@ def test_conditional_gradient(nx):
     def fb(G):
         return 0.5 * nx.sum(G ** 2)
 
-    ab = nx.from_numpy(a)
-    bb = nx.from_numpy(b)
-    Mb = nx.from_numpy(M, type_as=ab)
+    ab, bb, Mb = nx.from_numpy(a, b, M)
 
     reg = 1e-1
 
@@ -74,9 +72,7 @@ def test_conditional_gradient_itermax(nx):
     def fb(G):
         return 0.5 * nx.sum(G ** 2)
 
-    ab = nx.from_numpy(a)
-    bb = nx.from_numpy(b)
-    Mb = nx.from_numpy(M, type_as=ab)
+    ab, bb, Mb = nx.from_numpy(a, b, M)
 
     reg = 1e-1
 
@@ -118,9 +114,7 @@ def test_generalized_conditional_gradient(nx):
     reg1 = 1e-3
     reg2 = 1e-1
 
-    ab = nx.from_numpy(a)
-    bb = nx.from_numpy(b)
-    Mb = nx.from_numpy(M, type_as=ab)
+    ab, bb, Mb = nx.from_numpy(a, b, M)
 
     G, log = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True, log=True)
     Gb, log = ot.optim.gcg(ab, bb, Mb, reg1, reg2, fb, df, verbose=True, log=True)
@@ -142,9 +136,12 @@ def test_line_search_armijo(nx):
     pk = np.array([[-0.25, 0.25], [0.25, -0.25]])
     gfk = np.array([[23.04273441, 23.0449082], [23.04273441, 23.0449082]])
     old_fval = -123
+
+    xkb, pkb, gfkb = nx.from_numpy(xk, pk, gfk)
+
     # Should not throw an exception and return 0. for alpha
     alpha, a, b = ot.optim.line_search_armijo(
-        lambda x: 1, nx.from_numpy(xk), nx.from_numpy(pk), nx.from_numpy(gfk), old_fval
+        lambda x: 1, xkb, pkb, gfkb, old_fval
     )
     alpha_np, anp, bnp = ot.optim.line_search_armijo(
         lambda x: 1, xk, pk, gfk, old_fval