[WIP] add mass feature to nx.kl_div and harmonize kl computation in t…

…he toolbox (#654)

* add mass feature to nx.kl_div

* test

* test

* fix tipo doc

* fix jax

RELEASES.md

@@ -3,6 +3,7 @@
 ## 0.9.5dev
 
 #### New features
+- Add feature `mass=True` for `nx.kl_div` (PR #654)
 
 #### Closed issues
 

ot/backend.py

@@ -944,16 +944,17 @@ def eigh(self, a):
         """
         raise NotImplementedError()
 
-    def kl_div(self, p, q, eps=1e-16):
+    def kl_div(self, p, q, mass=False, eps=1e-16):
         r"""
-        Computes the Kullback-Leibler divergence.
+        Computes the (Generalized) Kullback-Leibler divergence.
 
         This function follows the api from :any:`scipy.stats.entropy`.
 
         Parameter eps is used to avoid numerical errors and is added in the log.
 
         .. math::
-             KL(p,q) = \sum_i p(i) \log (\frac{p(i)}{q(i)}+\epsilon)
+             KL(p,q) = \langle \mathbf{p}, log(\mathbf{p} / \mathbf{q} + eps \rangle
+             + \mathbb{1}_{mass=True} \langle \mathbf{q} - \mathbf{p}, \mathbf{1} \rangle
 
         See: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.entropy.html
         """
@@ -1352,8 +1353,11 @@ def sqrtm(self, a):
     def eigh(self, a):
         return np.linalg.eigh(a)
 
-    def kl_div(self, p, q, eps=1e-16):
-        return np.sum(p * np.log(p / q + eps))
+    def kl_div(self, p, q, mass=False, eps=1e-16):
+        value = np.sum(p * np.log(p / q + eps))
+        if mass:
+            value = value + np.sum(q - p)
+        return value
 
     def isfinite(self, a):
         return np.isfinite(a)
@@ -1751,8 +1755,11 @@ def sqrtm(self, a):
     def eigh(self, a):
         return jnp.linalg.eigh(a)
 
-    def kl_div(self, p, q, eps=1e-16):
-        return jnp.sum(p * jnp.log(p / q + eps))
+    def kl_div(self, p, q, mass=False, eps=1e-16):
+        value = jnp.sum(p * jnp.log(p / q + eps))
+        if mass:
+            value = value + jnp.sum(q - p)
+        return value
 
     def isfinite(self, a):
         return jnp.isfinite(a)
@@ -2238,8 +2245,11 @@ def sqrtm(self, a):
     def eigh(self, a):
         return torch.linalg.eigh(a)
 
-    def kl_div(self, p, q, eps=1e-16):
-        return torch.sum(p * torch.log(p / q + eps))
+    def kl_div(self, p, q, mass=False, eps=1e-16):
+        value = torch.sum(p * torch.log(p / q + eps))
+        if mass:
+            value = value + torch.sum(q - p)
+        return value
 
     def isfinite(self, a):
         return torch.isfinite(a)
@@ -2639,8 +2649,11 @@ def sqrtm(self, a):
     def eigh(self, a):
         return cp.linalg.eigh(a)
 
-    def kl_div(self, p, q, eps=1e-16):
-        return cp.sum(p * cp.log(p / q + eps))
+    def kl_div(self, p, q, mass=False, eps=1e-16):
+        value = cp.sum(p * cp.log(p / q + eps))
+        if mass:
+            value = value + cp.sum(q - p)
+        return value
 
     def isfinite(self, a):
         return cp.isfinite(a)
@@ -3063,8 +3076,11 @@ def sqrtm(self, a):
     def eigh(self, a):
         return tf.linalg.eigh(a)
 
-    def kl_div(self, p, q, eps=1e-16):
-        return tnp.sum(p * tnp.log(p / q + eps))
+    def kl_div(self, p, q, mass=False, eps=1e-16):
+        value = tnp.sum(p * tnp.log(p / q + eps))
+        if mass:
+            value = value + tnp.sum(q - p)
+        return value
 
     def isfinite(self, a):
         return tnp.isfinite(a)

ot/bregman/_barycenter.py

@@ -364,7 +364,7 @@ def _barycenter_sinkhorn_log(A, M, reg, weights=None, numItermax=1000,
         log = {'err': []}
 
     M = - M / reg
-    logA = nx.log(A + 1e-15)
+    logA = nx.log(A + 1e-16)
     log_KU, G = nx.zeros((2, *logA.shape), type_as=A)
     err = 1
     for ii in range(numItermax):
@@ -702,7 +702,7 @@ def _barycenter_debiased_log(A, M, reg, weights=None, numItermax=1000,
         log = {'err': []}
 
     M = - M / reg
-    logA = nx.log(A + 1e-15)
+    logA = nx.log(A + 1e-16)
     log_KU, G = nx.zeros((2, *logA.shape), type_as=A)
     c = nx.zeros(dim, type_as=A)
     err = 1

ot/coot.py

@@ -139,10 +139,6 @@ def co_optimal_transport(X, Y, wx_samp=None, wx_feat=None, wy_samp=None, wy_feat
         Advances in Neural Information Processing ny_sampstems, 33 (2020).
     """
 
-    def compute_kl(p, q):
-        kl = nx.sum(p * nx.log(p + 1.0 * (p == 0))) - nx.sum(p * nx.log(q))
-        return kl
-
     # Main function
 
     if method_sinkhorn not in ["sinkhorn", "sinkhorn_log"]:
@@ -245,9 +241,9 @@ def compute_kl(p, q):
                 coot = coot + alpha_samp * nx.sum(M_samp * pi_samp)
             # Entropic part
             if eps_samp != 0:
-                coot = coot + eps_samp * compute_kl(pi_samp, wxy_samp)
+                coot = coot + eps_samp * nx.kl_div(pi_samp, wxy_samp)
             if eps_feat != 0:
-                coot = coot + eps_feat * compute_kl(pi_feat, wxy_feat)
+                coot = coot + eps_feat * nx.kl_div(pi_feat, wxy_feat)
             list_coot.append(coot)
 
             if err < tol_bcd or abs(list_coot[-2] - list_coot[-1]) < early_stopping_tol:

ot/gromov/_utils.py

@@ -109,7 +109,7 @@ def h2(b):
             return 2 * b
     elif loss_fun == 'kl_loss':
         def f1(a):
-            return a * nx.log(a + 1e-15) - a
+            return a * nx.log(a + 1e-16) - a
 
         def f2(b):
             return b
@@ -118,7 +118,7 @@ def h1(a):
             return a
 
         def h2(b):
-            return nx.log(b + 1e-15)
+            return nx.log(b + 1e-16)
     else:
         raise ValueError(f"Unknown `loss_fun='{loss_fun}'`. Use one of: {'square_loss', 'kl_loss'}.")
 
@@ -502,7 +502,7 @@ def h2(b):
             return 2 * b
     elif loss_fun == 'kl_loss':
         def f1(a):
-            return a * nx.log(a + 1e-15) - a
+            return a * nx.log(a + 1e-16) - a
 
         def f2(b):
             return b
@@ -511,7 +511,7 @@ def h1(a):
             return a
 
         def h2(b):
-            return nx.log(b + 1e-15)
+            return nx.log(b + 1e-16)
     else:
         raise ValueError(f"Unknown `loss_fun='{loss_fun}'`. Use one of: {'square_loss', 'kl_loss'}.")
 

test/test_utils.py

@@ -620,3 +620,19 @@ def test_label_normalization(nx):
     # labels are shifted but the shift if expected
     y_normalized_start = ot.utils.label_normalization(y, start=1)
     np.testing.assert_array_equal(y, y_normalized_start)
+
+
+def test_kl_div(nx):
+    n = 10
+    rng = np.random.RandomState(0)
+    # test on non-negative tensors
+    x = rng.randn(n)
+    x = x - x.min() + 1e-5
+    y = rng.randn(n)
+    y = y - y.min() + 1e-5
+    xb = nx.from_numpy(x)
+    yb = nx.from_numpy(y)
+    kl = nx.kl_div(xb, yb)
+    kl_mass = nx.kl_div(xb, yb, True)
+    recovered_kl = kl_mass - nx.sum(yb - xb)
+    np.testing.assert_allclose(kl, recovered_kl)