relative+absolute loss

Author: tvayer

ot/optim.py

@@ -135,7 +135,7 @@ def do_linesearch(cost, G, deltaG, Mi, f_val,
 
 
 def cg(a, b, M, reg, f, df, G0=None, numItermax=200,
-       stopThr=1e-9, verbose=False, log=False, **kwargs):
+       stopThr=1e-9, stopThr2=1e-9, verbose=False, log=False, **kwargs):
     """
     Solve the general regularized OT problem with conditional gradient
 
@@ -173,7 +173,9 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200,
     numItermax : int, optional
         Max number of iterations
     stopThr : float, optional
-        Stop threshol on error (>0)
+        Stop threshol on the relative variation (>0)
+    stopThr2 : float, optional
+        Stop threshol on the absolute variation (>0)
     verbose : bool, optional
         Print information along iterations
     log : bool, optional
@@ -249,8 +251,9 @@ def cost(G):
         if it >= numItermax:
             loop = 0
 
-        delta_fval = (f_val - old_fval) / abs(f_val)
-        if abs(delta_fval) < stopThr:
+        abs_delta_fval = abs(f_val - old_fval)
+        relative_delta_fval = abs_delta_fval / abs(f_val)
+        if relative_delta_fval < stopThr and abs_delta_fval < stopThr2:
             loop = 0
 
         if log:
@@ -259,8 +262,8 @@ def cost(G):
         if verbose:
             if it % 20 == 0:
                 print('{:5s}|{:12s}|{:8s}'.format(
-                    'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32)
-            print('{:5d}|{:8e}|{:8e}'.format(it, f_val, delta_fval))
+                    'It.', 'Loss', 'Relative variation loss', 'Absolute variation loss') + '\n' + '-' * 32)
+            print('{:5d}|{:8e}|{:8e}|{:8e}'.format(it, f_val, relative_delta_fval, abs_delta_fval))
 
     if log:
         return G, log
@@ -269,7 +272,7 @@ def cost(G):
 
 
 def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10,
-        numInnerItermax=200, stopThr=1e-9, verbose=False, log=False):
+        numInnerItermax=200, stopThr=1e-9, stopThr2=1e-9, verbose=False, log=False):
     """
     Solve the general regularized OT problem with the generalized conditional gradient
 
@@ -312,7 +315,9 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10,
     numInnerItermax : int, optional
         Max number of iterations of Sinkhorn
     stopThr : float, optional
-        Stop threshol on error (>0)
+        Stop threshol on the relative variation (>0)
+    stopThr2 : float, optional
+        Stop threshol on the absolute variation (>0)
     verbose : bool, optional
         Print information along iterations
     log : bool, optional
@@ -386,8 +391,10 @@ def cost(G):
         if it >= numItermax:
             loop = 0
 
-        delta_fval = (f_val - old_fval) / abs(f_val)
-        if abs(delta_fval) < stopThr:
+        abs_delta_fval = abs(f_val - old_fval)
+        relative_delta_fval = abs_delta_fval / abs(f_val)
+
+        if relative_delta_fval < stopThr and abs_delta_fval < stopThr2:
             loop = 0
 
         if log:
@@ -396,8 +403,8 @@ def cost(G):
         if verbose:
             if it % 20 == 0:
                 print('{:5s}|{:12s}|{:8s}'.format(
-                    'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32)
-            print('{:5d}|{:8e}|{:8e}'.format(it, f_val, delta_fval))
+                    'It.', 'Loss', 'Relative variation loss', 'Absolute variation loss') + '\n' + '-' * 32)
+            print('{:5d}|{:8e}|{:8e}|{:8e}'.format(it, f_val, relative_delta_fval, abs_delta_fval))
 
     if log:
         return G, log