[FIX] Refactor the function utils.cost_normalization to work with multiple backends (#472)

* [FEAT] Add the 'median' method to the backend base class and implements this method in the Numpy, Pytorch, Jax and Cupy backends

* [TEST] Modify the 'cost_normalization' test to multiple backends

* [REFACTOR] Refactor the 'utils.cost_normalization' function for multiple backends

* [TEST] Update backend tests for method 'median'

* [DEBUG] Fix the error in the test in the 'median' method with PyTorch backend

* [TEST] Add the edge case where the 'median' method is not yet implemented in the Tensorflow backend.

* [FEAT] Implement the 'median' method in the Tensorflow backend using Numpy

* [DEBUG] For compatibility reasons, the median method in the Pytorch backend change using numpy

* [DEBUG] The 'median' method checks the Pytorch version to decide whether to use torch.quantile or numpy

* Add changes to RELEASES.md

---------

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

Author: framunoz

RELEASES.md

@@ -6,6 +6,7 @@
 - Make alpha parameter in Fused Gromov Wasserstein differentiable (PR #463)
 - Added the sparsity-constrained OT solver to `ot.smooth` and added ` projection_sparse_simplex` to `ot.utils` (PR #459)
 - Add tests on GPU for master branch and approved PR (PR #473)
+- Add `median` method to all inherited classes of `backend.Backend` (PR #472)
 
 #### Closed issues
 
@@ -16,6 +17,7 @@
 - Faster Bures-Wasserstein distance with NumPy backend (PR #468)
 - Fix issue backend for ot.sliced_wasserstein_sphere ot.sliced_wasserstein_sphere_unif (PR #471)
 - Fix issue with ot.barycenter_stabilized when used with PyTorch tensors and log=True (RP #474)
+- Fix `utils.cost_normalization` function issue to work with multiple backends (PR #472)
 
 ## 0.9.0
 

ot/backend.py

@@ -574,6 +574,16 @@ def mean(self, a, axis=None):
         """
         raise NotImplementedError()
 
+    def median(self, a, axis=None):
+        r"""
+        Computes the median of a tensor along given dimensions.
+
+        This function follows the api from :any:`numpy.median`
+
+        See: https://numpy.org/doc/stable/reference/generated/numpy.median.html
+        """
+        raise NotImplementedError()
+
     def std(self, a, axis=None):
         r"""
         Computes the standard deviation of a tensor along given dimensions.
@@ -1123,6 +1133,9 @@ def argmin(self, a, axis=None):
     def mean(self, a, axis=None):
         return np.mean(a, axis=axis)
 
+    def median(self, a, axis=None):
+        return np.median(a, axis=axis)
+
     def std(self, a, axis=None):
         return np.std(a, axis=axis)
 
@@ -1482,6 +1495,9 @@ def argmin(self, a, axis=None):
     def mean(self, a, axis=None):
         return jnp.mean(a, axis=axis)
 
+    def median(self, a, axis=None):
+        return jnp.median(a, axis=axis)
+
     def std(self, a, axis=None):
         return jnp.std(a, axis=axis)
 
@@ -1899,6 +1915,22 @@ def mean(self, a, axis=None):
         else:
             return torch.mean(a)
 
+    def median(self, a, axis=None):
+        from packaging import version
+        # Since version 1.11.0, interpolation is available
+        if version.parse(torch.__version__) >= version.parse("1.11.0"):
+            if axis is not None:
+                return torch.quantile(a, 0.5, interpolation="midpoint", dim=axis)
+            else:
+                return torch.quantile(a, 0.5, interpolation="midpoint")
+
+        # Else, use numpy
+        warnings.warn("The median is being computed using numpy and the array has been detached "
+                      "in the Pytorch backend.")
+        a_ = self.to_numpy(a)
+        a_median = np.median(a_, axis=axis)
+        return self.from_numpy(a_median, type_as=a)
+
     def std(self, a, axis=None):
         if axis is not None:
             return torch.std(a, dim=axis, unbiased=False)
@@ -2289,6 +2321,9 @@ def argmin(self, a, axis=None):
     def mean(self, a, axis=None):
         return cp.mean(a, axis=axis)
 
+    def median(self, a, axis=None):
+        return cp.median(a, axis=axis)
+
     def std(self, a, axis=None):
         return cp.std(a, axis=axis)
 
@@ -2678,6 +2713,13 @@ def argmin(self, a, axis=None):
     def mean(self, a, axis=None):
         return tnp.mean(a, axis=axis)
 
+    def median(self, a, axis=None):
+        warnings.warn("The median is being computed using numpy and the array has been detached "
+                      "in the Tensorflow backend.")
+        a_ = self.to_numpy(a)
+        a_median = np.median(a_, axis=axis)
+        return self.from_numpy(a_median, type_as=a)
+
     def std(self, a, axis=None):
         return tnp.std(a, axis=axis)
 

ot/utils.py

@@ -359,16 +359,18 @@ def cost_normalization(C, norm=None):
         The input cost matrix normalized according to given norm.
     """
 
+    nx = get_backend(C)
+
     if norm is None:
         pass
     elif norm == "median":
-        C /= float(np.median(C))
+        C /= float(nx.median(C))
     elif norm == "max":
-        C /= float(np.max(C))
+        C /= float(nx.max(C))
     elif norm == "log":
-        C = np.log(1 + C)
+        C = nx.log(1 + C)
     elif norm == "loglog":
-        C = np.log1p(np.log1p(C))
+        C = nx.log(1 + nx.log(1 + C))
     else:
         raise ValueError('Norm %s is not a valid option.\n'
                          'Valid options are:\n'

test/test_backend.py

@@ -221,6 +221,8 @@ def test_empty_backend():
         nx.argmin(M)
     with pytest.raises(NotImplementedError):
         nx.mean(M)
+    with pytest.raises(NotImplementedError):
+        nx.median(M)
     with pytest.raises(NotImplementedError):
         nx.std(M)
     with pytest.raises(NotImplementedError):
@@ -519,6 +521,10 @@ def test_func_backends(nx):
         lst_b.append(nx.to_numpy(A))
         lst_name.append('mean')
 
+        A = nx.median(Mb)
+        lst_b.append(nx.to_numpy(A))
+        lst_name.append('median')
+
         A = nx.std(Mb)
         lst_b.append(nx.to_numpy(A))
         lst_name.append('std')

test/test_utils.py

@@ -270,25 +270,31 @@ def test_clean_zeros():
     assert len(b) == n - nz2
 
 
-def test_cost_normalization():
+def test_cost_normalization(nx):
 
     C = np.random.rand(10, 10)
+    C1 = nx.from_numpy(C)
 
     # does nothing
-    M0 = ot.utils.cost_normalization(C)
-    np.testing.assert_allclose(C, M0)
+    M0 = ot.utils.cost_normalization(C1)
+    M1 = nx.to_numpy(M0)
+    np.testing.assert_allclose(C, M1)
 
-    M = ot.utils.cost_normalization(C, 'median')
-    np.testing.assert_allclose(np.median(M), 1)
+    M = ot.utils.cost_normalization(C1, 'median')
+    M1 = nx.to_numpy(M)
+    np.testing.assert_allclose(np.median(M1), 1)
 
-    M = ot.utils.cost_normalization(C, 'max')
-    np.testing.assert_allclose(M.max(), 1)
+    M = ot.utils.cost_normalization(C1, 'max')
+    M1 = nx.to_numpy(M)
+    np.testing.assert_allclose(M1.max(), 1)
 
-    M = ot.utils.cost_normalization(C, 'log')
-    np.testing.assert_allclose(M.max(), np.log(1 + C).max())
+    M = ot.utils.cost_normalization(C1, 'log')
+    M1 = nx.to_numpy(M)
+    np.testing.assert_allclose(M1.max(), np.log(1 + C).max())
 
-    M = ot.utils.cost_normalization(C, 'loglog')
-    np.testing.assert_allclose(M.max(), np.log(1 + np.log(1 + C)).max())
+    M = ot.utils.cost_normalization(C1, 'loglog')
+    M1 = nx.to_numpy(M)
+    np.testing.assert_allclose(M1.max(), np.log(1 + np.log(1 + C)).max())
 
 
 def test_check_params():