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[MRG] Fix Fused Gromov Wasserstein implementation #133

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Merged
rflamary merged 4 commits into from
Mar 19, 2020
Merged

[MRG] Fix Fused Gromov Wasserstein implementation #133

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1173353
fix fgw alpha parameter implementation
little-nem Mar 9, 2020
20f9abd
clean and complete the document of fgw related functions
little-nem Mar 9, 2020
18fa98f
fixing trailing and before arithmetic operation whitespace issues
little-nem Mar 10, 2020
bb15cdd
Merge branch 'master' into fgw_fix
rflamary Mar 13, 2020
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48 changes: 24 additions & 24 deletions ot/gromov.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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]_
Expand All @@ -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

Expand Down Expand Up @@ -493,11 +488,11 @@ def df(G):
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):
Expand All @@ -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]_

Expand All @@ -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.

Expand Down Expand Up @@ -573,7 +563,7 @@ def f(G):
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
Expand Down Expand Up @@ -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.
Expand Down Expand Up @@ -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
Expand Down