Merge pull request #133 from little-nem/fgw_fix

[MRG] Fix Fused Gromov Wasserstein implementation

1 files changed, 24 insertions, 24 deletions

diff --git a/ot/gromov.py b/ot/gromov.py
index 9869341..43780a4 100644
--- a/ot/gromov.py
+++ b/ot/gromov.py
@@ -433,8 +433,7 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5,
 

     where :

     - M is the (ns,nt) metric cost matrix

-    - :math:`f` is the regularization term ( and df is its gradient)

-    - a and b are source and target weights (sum to 1)

+    - p and q are source and target weights (sum to 1)

     - L is a loss function to account for the misfit between the similarity matrices

 

     The algorithm used for solving the problem is conditional gradient as discussed in  [24]_

@@ -453,17 +452,13 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5,
         Distribution in the target space

     loss_fun : str, optional

         Loss function used for the solver

-    max_iter : int, optional

-        Max number of iterations

-    tol : float, optional

-        Stop threshold on error (>0)

-    verbose : bool, optional

-        Print information along iterations

-    log : bool, optional

-        record log if True

+    alpha : float, optional

+        Trade-off parameter (0 < alpha < 1)

     armijo : bool, optional

         If True the steps of the line-search is found via an armijo research. Else closed form is used.

         If there is convergence issues use False.

+    log : bool, optional

+        record log if True

     **kwargs : dict

         parameters can be directly passed to the ot.optim.cg solver

 

@@ -493,11 +488,11 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5,
         return gwggrad(constC, hC1, hC2, G)

 

     if log:

-        res, log = cg(p, q, M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, log=True, **kwargs)

+        res, log = cg(p, q, (1 - alpha) * M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, log=True, **kwargs)

         log['fgw_dist'] = log['loss'][::-1][0]

         return res, log

     else:

-        return cg(p, q, M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs)

+        return cg(p, q, (1 - alpha) * M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs)

 

 

 def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, armijo=False, log=False, **kwargs):

@@ -515,8 +510,7 @@ def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5
 

     where :

     - M is the (ns,nt) metric cost matrix

-    - :math:`f` is the regularization term ( and df is its gradient)

-    - a and b are source and target weights (sum to 1)

+    - p and q are source and target weights (sum to 1)

     - L is a loss function to account for the misfit between the similarity matrices

     The algorithm used for solving the problem is conditional gradient as discussed in  [1]_

 

@@ -534,17 +528,13 @@ def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5
         Distribution in the target space.

     loss_fun : str, optional

         Loss function used for the solver.

-    max_iter : int, optional

-        Max number of iterations

-    tol : float, optional

-        Stop threshold on error (>0)

-    verbose : bool, optional

-        Print information along iterations

-    log : bool, optional

-        Record log if True.

+    alpha : float, optional

+        Trade-off parameter (0 < alpha < 1)

     armijo : bool, optional

         If True the steps of the line-search is found via an armijo research.

         Else closed form is used. If there is convergence issues use False.

+    log : bool, optional

+        Record log if True.

     **kwargs : dict

         Parameters can be directly pased to the ot.optim.cg solver.

 

@@ -573,7 +563,7 @@ def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5
     def df(G):

         return gwggrad(constC, hC1, hC2, G)

 

-    res, log = cg(p, q, M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, log=True, **kwargs)

+    res, log = cg(p, q, (1 - alpha) * M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, log=True, **kwargs)

     if log:

         log['fgw_dist'] = log['loss'][::-1][0]

         log['T'] = res

@@ -994,6 +984,16 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_
         Whether to fix the structure of the barycenter during the updates

     fixed_features : bool

         Whether to fix the feature of the barycenter during the updates

+    loss_fun : str

+        Loss function used for the solver either 'square_loss' or 'kl_loss'

+    max_iter : int, optional

+        Max number of iterations

+    tol : float, optional

+        Stop threshol on error (>0).

+    verbose : bool, optional

+        Print information along iterations.

+    log : bool, optional

+        Record log if True.

     init_C : ndarray, shape (N,N), optional

         Initialization for the barycenters' structure matrix. If not set

         a random init is used.

@@ -1082,7 +1082,7 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_
                 T_temp = [t.T for t in T]

                 C = update_sructure_matrix(p, lambdas, T_temp, Cs)

 

-        T = [fused_gromov_wasserstein((1 - alpha) * Ms[s], C, Cs[s], p, ps[s], loss_fun, alpha,

+        T = [fused_gromov_wasserstein(Ms[s], C, Cs[s], p, ps[s], loss_fun, alpha,

                                       numItermax=max_iter, stopThr=1e-5, verbose=verbose) for s in range(S)]

 

         # T is N,ns