# -*- coding: utf-8 -*-
"""
Gromov-Wasserstein and Fused-Gromov-Wasserstein conditional gradient solvers.
"""
# Author: Erwan Vautier <erwan.vautier@gmail.com>
# Nicolas Courty <ncourty@irisa.fr>
# Rémi Flamary <remi.flamary@unice.fr>
# Titouan Vayer <titouan.vayer@irisa.fr>
# Cédric Vincent-Cuaz <cedvincentcuaz@gmail.com>
#
# License: MIT License
import numpy as np
import warnings
from ..utils import dist, UndefinedParameter, list_to_array
from ..optim import cg, line_search_armijo, solve_1d_linesearch_quad
from ..utils import check_random_state, unif
from ..backend import get_backend, NumpyBackend
from ._utils import init_matrix, gwloss, gwggrad
from ._utils import update_square_loss, update_kl_loss, update_feature_matrix
[docs]
def gromov_wasserstein(C1, C2, p=None, q=None, loss_fun='square_loss', symmetric=None, log=False, armijo=False, G0=None,
max_iter=1e4, tol_rel=1e-9, tol_abs=1e-9, **kwargs):
r"""
Returns the Gromov-Wasserstein transport between :math:`(\mathbf{C_1}, \mathbf{p})` and :math:`(\mathbf{C_2}, \mathbf{q})`.
The function solves the following optimization problem using Conditional Gradient:
.. math::
\mathbf{T}^* \in \mathop{\arg \min}_\mathbf{T} \quad \sum_{i,j,k,l}
L(\mathbf{C_1}_{i,k}, \mathbf{C_2}_{j,l}) \mathbf{T}_{i,j} \mathbf{T}_{k,l}
s.t. \ \mathbf{\gamma} \mathbf{1} &= \mathbf{p}
\mathbf{\gamma}^T \mathbf{1} &= \mathbf{q}
\mathbf{\gamma} &\geq 0
Where :
- :math:`\mathbf{C_1}`: Metric cost matrix in the source space
- :math:`\mathbf{C_2}`: Metric cost matrix in the target space
- :math:`\mathbf{p}`: distribution in the source space
- :math:`\mathbf{q}`: distribution in the target space
- `L`: loss function to account for the misfit between the similarity matrices
.. note:: This function is backend-compatible and will work on arrays
from all compatible backends. But the algorithm uses the C++ CPU backend
which can lead to copy overhead on GPU arrays.
.. note:: All computations in the conjugate gradient solver are done with
numpy to limit memory overhead.
.. note:: This function will cast the computed transport plan to the data
type of the provided input :math:`\mathbf{C}_1`. Casting to an integer
tensor might result in a loss of precision. If this behaviour is
unwanted, please make sure to provide a floating point input.
Parameters
----------
C1 : array-like, shape (ns, ns)
Metric cost matrix in the source space
C2 : array-like, shape (nt, nt)
Metric cost matrix in the target space
p : array-like, shape (ns,), optional
Distribution in the source space.
If let to its default value None, uniform distribution is taken.
q : array-like, shape (nt,), optional
Distribution in the target space.
If let to its default value None, uniform distribution is taken.
loss_fun : str, optional
loss function used for the solver either 'square_loss' or 'kl_loss'
symmetric : bool, optional
Either C1 and C2 are to be assumed symmetric or not.
If let to its default None value, a symmetry test will be conducted.
Else if set to True (resp. False), C1 and C2 will be assumed symmetric (resp. asymmetric).
verbose : bool, optional
Print information along iterations
log : bool, optional
record log if True
armijo : bool, optional
If True the step of the line-search is found via an armijo research. Else closed form is used.
If there are convergence issues use False.
G0: array-like, shape (ns,nt), optional
If None the initial transport plan of the solver is pq^T.
Otherwise G0 must satisfy marginal constraints and will be used as initial transport of the solver.
max_iter : int, optional
Max number of iterations
tol_rel : float, optional
Stop threshold on relative error (>0)
tol_abs : float, optional
Stop threshold on absolute error (>0)
**kwargs : dict
parameters can be directly passed to the ot.optim.cg solver
Returns
-------
T : array-like, shape (`ns`, `nt`)
Coupling between the two spaces that minimizes:
:math:`\sum_{i,j,k,l} L(\mathbf{C_1}_{i,k}, \mathbf{C_2}_{j,l}) \mathbf{T}_{i,j} \mathbf{T}_{k,l}`
log : dict
Convergence information and loss.
References
----------
.. [12] Gabriel Peyré, Marco Cuturi, and Justin Solomon,
"Gromov-Wasserstein averaging of kernel and distance matrices."
International Conference on Machine Learning (ICML). 2016.
.. [13] Mémoli, Facundo. Gromov–Wasserstein distances and the
metric approach to object matching. Foundations of computational
mathematics 11.4 (2011): 417-487.
.. [47] Chowdhury, S., & Mémoli, F. (2019). The gromov–wasserstein
distance between networks and stable network invariants.
Information and Inference: A Journal of the IMA, 8(4), 757-787.
"""
arr = [C1, C2]
if p is not None:
arr.append(list_to_array(p))
else:
p = unif(C1.shape[0], type_as=C1)
if q is not None:
arr.append(list_to_array(q))
else:
q = unif(C2.shape[0], type_as=C1)
if G0 is not None:
G0_ = G0
arr.append(G0)
nx = get_backend(*arr)
p0, q0, C10, C20 = p, q, C1, C2
p = nx.to_numpy(p0)
q = nx.to_numpy(q0)
C1 = nx.to_numpy(C10)
C2 = nx.to_numpy(C20)
if symmetric is None:
symmetric = np.allclose(C1, C1.T, atol=1e-10) and np.allclose(C2, C2.T, atol=1e-10)
if G0 is None:
G0 = p[:, None] * q[None, :]
else:
G0 = nx.to_numpy(G0_)
# Check marginals of G0
np.testing.assert_allclose(G0.sum(axis=1), p, atol=1e-08)
np.testing.assert_allclose(G0.sum(axis=0), q, atol=1e-08)
# cg for GW is implemented using numpy on CPU
np_ = NumpyBackend()
constC, hC1, hC2 = init_matrix(C1, C2, p, q, loss_fun, np_)
def f(G):
return gwloss(constC, hC1, hC2, G, np_)
if symmetric:
def df(G):
return gwggrad(constC, hC1, hC2, G, np_)
else:
constCt, hC1t, hC2t = init_matrix(C1.T, C2.T, p, q, loss_fun, np_)
def df(G):
return 0.5 * (gwggrad(constC, hC1, hC2, G, np_) + gwggrad(constCt, hC1t, hC2t, G, np_))
if armijo:
def line_search(cost, G, deltaG, Mi, cost_G, **kwargs):
return line_search_armijo(cost, G, deltaG, Mi, cost_G, nx=np_, **kwargs)
else:
def line_search(cost, G, deltaG, Mi, cost_G, **kwargs):
return solve_gromov_linesearch(G, deltaG, cost_G, hC1, hC2, M=0., reg=1., nx=np_, **kwargs)
if not nx.is_floating_point(C10):
warnings.warn(
"Input structure matrix consists of integer. The transport plan will be "
"casted accordingly, possibly resulting in a loss of precision. "
"If this behaviour is unwanted, please make sure your input "
"structure matrix consists of floating point elements.",
stacklevel=2
)
if log:
res, log = cg(p, q, 0., 1., f, df, G0, line_search, log=True, numItermax=max_iter, stopThr=tol_rel, stopThr2=tol_abs, **kwargs)
log['gw_dist'] = nx.from_numpy(log['loss'][-1], type_as=C10)
log['u'] = nx.from_numpy(log['u'], type_as=C10)
log['v'] = nx.from_numpy(log['v'], type_as=C10)
return nx.from_numpy(res, type_as=C10), log
else:
return nx.from_numpy(cg(p, q, 0., 1., f, df, G0, line_search, log=False, numItermax=max_iter, stopThr=tol_rel, stopThr2=tol_abs, **kwargs), type_as=C10)
[docs]
def gromov_wasserstein2(C1, C2, p=None, q=None, loss_fun='square_loss', symmetric=None, log=False, armijo=False, G0=None,
max_iter=1e4, tol_rel=1e-9, tol_abs=1e-9, **kwargs):
r"""
Returns the Gromov-Wasserstein loss :math:`\mathbf{GW}` between :math:`(\mathbf{C_1}, \mathbf{p})` and :math:`(\mathbf{C_2}, \mathbf{q})`.
To recover the Gromov-Wasserstein distance as defined in [13] compute :math:`d_{GW} = \frac{1}{2} \sqrt{\mathbf{GW}}`.
The function solves the following optimization problem using Conditional Gradient:
.. math::
\mathbf{GW} = \min_\mathbf{T} \quad \sum_{i,j,k,l}
L(\mathbf{C_1}_{i,k}, \mathbf{C_2}_{j,l}) \mathbf{T}_{i,j} \mathbf{T}_{k,l}
s.t. \ \mathbf{\gamma} \mathbf{1} &= \mathbf{p}
\mathbf{\gamma}^T \mathbf{1} &= \mathbf{q}
\mathbf{\gamma} &\geq 0
Where :
- :math:`\mathbf{C_1}`: Metric cost matrix in the source space
- :math:`\mathbf{C_2}`: Metric cost matrix in the target space
- :math:`\mathbf{p}`: distribution in the source space
- :math:`\mathbf{q}`: distribution in the target space
- `L`: loss function to account for the misfit between the similarity
matrices
Note that when using backends, this loss function is differentiable wrt the
matrices (C1, C2) and weights (p, q) for quadratic loss using the gradients from [38]_.
.. note:: This function is backend-compatible and will work on arrays
from all compatible backends. But the algorithm uses the C++ CPU backend
which can lead to copy overhead on GPU arrays.
.. note:: All computations in the conjugate gradient solver are done with
numpy to limit memory overhead.
.. note:: This function will cast the computed transport plan to the data
type of the provided input :math:`\mathbf{C}_1`. Casting to an integer
tensor might result in a loss of precision. If this behaviour is
unwanted, please make sure to provide a floating point input.
Parameters
----------
C1 : array-like, shape (ns, ns)
Metric cost matrix in the source space
C2 : array-like, shape (nt, nt)
Metric cost matrix in the target space
p : array-like, shape (ns,), optional
Distribution in the source space.
If let to its default value None, uniform distribution is taken.
q : array-like, shape (nt,), optional
Distribution in the target space.
If let to its default value None, uniform distribution is taken.
loss_fun : str
loss function used for the solver either 'square_loss' or 'kl_loss'
symmetric : bool, optional
Either C1 and C2 are to be assumed symmetric or not.
If let to its default None value, a symmetry test will be conducted.
Else if set to True (resp. False), C1 and C2 will be assumed symmetric (resp. asymmetric).
verbose : bool, optional
Print information along iterations
log : bool, optional
record log if True
armijo : bool, optional
If True the step of the line-search is found via an armijo research. Else closed form is used.
If there are convergence issues use False.
G0: array-like, shape (ns,nt), optional
If None the initial transport plan of the solver is pq^T.
Otherwise G0 must satisfy marginal constraints and will be used as initial transport of the solver.
max_iter : int, optional
Max number of iterations
tol_rel : float, optional
Stop threshold on relative error (>0)
tol_abs : float, optional
Stop threshold on absolute error (>0)
**kwargs : dict
parameters can be directly passed to the ot.optim.cg solver
Returns
-------
gw_dist : float
Gromov-Wasserstein distance
log : dict
convergence information and Coupling matrix
References
----------
.. [12] Gabriel Peyré, Marco Cuturi, and Justin Solomon,
"Gromov-Wasserstein averaging of kernel and distance matrices."
International Conference on Machine Learning (ICML). 2016.
.. [13] Mémoli, Facundo. Gromov–Wasserstein distances and the
metric approach to object matching. Foundations of computational
mathematics 11.4 (2011): 417-487.
.. [38] C. Vincent-Cuaz, T. Vayer, R. Flamary, M. Corneli, N. Courty, Online
Graph Dictionary Learning, International Conference on Machine Learning
(ICML), 2021.
.. [47] Chowdhury, S., & Mémoli, F. (2019). The gromov–wasserstein
distance between networks and stable network invariants.
Information and Inference: A Journal of the IMA, 8(4), 757-787.
"""
# simple get_backend as the full one will be handled in gromov_wasserstein
nx = get_backend(C1, C2)
# init marginals if set as None
if p is None:
p = unif(C1.shape[0], type_as=C1)
if q is None:
q = unif(C2.shape[0], type_as=C1)
T, log_gw = gromov_wasserstein(
C1, C2, p, q, loss_fun, symmetric, log=True, armijo=armijo, G0=G0,
max_iter=max_iter, tol_rel=tol_rel, tol_abs=tol_abs, **kwargs)
log_gw['T'] = T
gw = log_gw['gw_dist']
if loss_fun == 'square_loss':
gC1 = 2 * C1 * nx.outer(p, p) - 2 * nx.dot(T, nx.dot(C2, T.T))
gC2 = 2 * C2 * nx.outer(q, q) - 2 * nx.dot(T.T, nx.dot(C1, T))
elif loss_fun == 'kl_loss':
gC1 = nx.log(C1 + 1e-15) * nx.outer(p, p) - nx.dot(T, nx.dot(nx.log(C2 + 1e-15), T.T))
gC2 = nx.dot(T.T, nx.dot(C1, T)) / (C2 + 1e-15) + nx.outer(q, q)
gw = nx.set_gradients(gw, (p, q, C1, C2),
(log_gw['u'] - nx.mean(log_gw['u']),
log_gw['v'] - nx.mean(log_gw['v']), gC1, gC2))
if log:
return gw, log_gw
else:
return gw
[docs]
def fused_gromov_wasserstein(M, C1, C2, p=None, q=None, loss_fun='square_loss', symmetric=None, alpha=0.5,
armijo=False, G0=None, log=False, max_iter=1e4, tol_rel=1e-9, tol_abs=1e-9, **kwargs):
r"""
Returns the Fused Gromov-Wasserstein transport between :math:`(\mathbf{C_1}, \mathbf{Y_1}, \mathbf{p})` and :math:`(\mathbf{C_2}, \mathbf{Y_2}, \mathbf{q})`
with pairwise distance matrix :math:`\mathbf{M}` between node feature matrices :math:`\mathbf{Y_1}` and :math:`\mathbf{Y_2}` (see :ref:`[24] <references-fused-gromov-wasserstein>`).
The function solves the following optimization problem using Conditional Gradient:
.. math::
\mathbf{T}^* \in\mathop{\arg\min}_\mathbf{T} \quad (1 - \alpha) \langle \mathbf{T}, \mathbf{M} \rangle_F +
\alpha \sum_{i,j,k,l} L(\mathbf{C_1}_{i,k}, \mathbf{C_2}_{j,l}) \mathbf{T}_{i,j} \mathbf{T}_{k,l}
s.t. \ \mathbf{T} \mathbf{1} &= \mathbf{p}
\mathbf{T}^T \mathbf{1} &= \mathbf{q}
\mathbf{T} &\geq 0
Where :
- :math:`\mathbf{M}`: metric cost matrix between features across domains
- :math:`\mathbf{C_1}`: Metric cost matrix in the source space
- :math:`\mathbf{C_2}`: Metric cost matrix in the target space
- :math:`\mathbf{p}`: distribution in the source space
- :math:`\mathbf{q}`: distribution in the target space
- `L`: loss function to account for the misfit between the similarity and feature matrices
- :math:`\alpha`: trade-off parameter
.. note:: This function is backend-compatible and will work on arrays
from all compatible backends. But the algorithm uses the C++ CPU backend
which can lead to copy overhead on GPU arrays.
.. note:: All computations in the conjugate gradient solver are done with
numpy to limit memory overhead.
.. note:: This function will cast the computed transport plan to the data
type of the provided input :math:`\mathbf{M}`. Casting to an integer
tensor might result in a loss of precision. If this behaviour is
unwanted, please make sure to provide a floating point input.
Parameters
----------
M : array-like, shape (ns, nt)
Metric cost matrix between features across domains
C1 : array-like, shape (ns, ns)
Metric cost matrix representative of the structure in the source space
C2 : array-like, shape (nt, nt)
Metric cost matrix representative of the structure in the target space
p : array-like, shape (ns,), optional
Distribution in the source space.
If let to its default value None, uniform distribution is taken.
q : array-like, shape (nt,), optional
Distribution in the target space.
If let to its default value None, uniform distribution is taken.
loss_fun : str, optional
Loss function used for the solver
symmetric : bool, optional
Either C1 and C2 are to be assumed symmetric or not.
If let to its default None value, a symmetry test will be conducted.
Else if set to True (resp. False), C1 and C2 will be assumed symmetric (resp. asymmetric).
alpha : float, optional
Trade-off parameter (0 < alpha < 1)
armijo : bool, optional
If True the step of the line-search is found via an armijo research. Else closed form is used.
If there are convergence issues use False.
G0: array-like, shape (ns,nt), optional
If None the initial transport plan of the solver is pq^T.
Otherwise G0 must satisfy marginal constraints and will be used as initial transport of the solver.
log : bool, optional
record log if True
max_iter : int, optional
Max number of iterations
tol_rel : float, optional
Stop threshold on relative error (>0)
tol_abs : float, optional
Stop threshold on absolute error (>0)
**kwargs : dict
parameters can be directly passed to the ot.optim.cg solver
Returns
-------
T : array-like, shape (`ns`, `nt`)
Optimal transportation matrix for the given parameters.
log : dict
Log dictionary return only if log==True in parameters.
.. _references-fused-gromov-wasserstein:
References
----------
.. [24] Vayer Titouan, Chapel Laetitia, Flamary Rémi, Tavenard Romain
and Courty Nicolas "Optimal Transport for structured data with
application on graphs", International Conference on Machine Learning
(ICML). 2019.
.. [47] Chowdhury, S., & Mémoli, F. (2019). The gromov–wasserstein
distance between networks and stable network invariants.
Information and Inference: A Journal of the IMA, 8(4), 757-787.
"""
arr = [C1, C2, M]
if p is not None:
arr.append(list_to_array(p))
else:
p = unif(C1.shape[0], type_as=M)
if q is not None:
arr.append(list_to_array(q))
else:
q = unif(C2.shape[0], type_as=M)
if G0 is not None:
G0_ = G0
arr.append(G0)
nx = get_backend(*arr)
p0, q0, C10, C20, M0, alpha0 = p, q, C1, C2, M, alpha
p = nx.to_numpy(p0)
q = nx.to_numpy(q0)
C1 = nx.to_numpy(C10)
C2 = nx.to_numpy(C20)
M = nx.to_numpy(M0)
alpha = nx.to_numpy(alpha0)
if symmetric is None:
symmetric = np.allclose(C1, C1.T, atol=1e-10) and np.allclose(C2, C2.T, atol=1e-10)
if G0 is None:
G0 = p[:, None] * q[None, :]
else:
G0 = nx.to_numpy(G0_)
# Check marginals of G0
np.testing.assert_allclose(G0.sum(axis=1), p, atol=1e-08)
np.testing.assert_allclose(G0.sum(axis=0), q, atol=1e-08)
# cg for GW is implemented using numpy on CPU
np_ = NumpyBackend()
constC, hC1, hC2 = init_matrix(C1, C2, p, q, loss_fun, np_)
def f(G):
return gwloss(constC, hC1, hC2, G, np_)
if symmetric:
def df(G):
return gwggrad(constC, hC1, hC2, G, np_)
else:
constCt, hC1t, hC2t = init_matrix(C1.T, C2.T, p, q, loss_fun, np_)
def df(G):
return 0.5 * (gwggrad(constC, hC1, hC2, G, np_) + gwggrad(constCt, hC1t, hC2t, G, np_))
if armijo:
def line_search(cost, G, deltaG, Mi, cost_G, **kwargs):
return line_search_armijo(cost, G, deltaG, Mi, cost_G, nx=np_, **kwargs)
else:
def line_search(cost, G, deltaG, Mi, cost_G, **kwargs):
return solve_gromov_linesearch(G, deltaG, cost_G, hC1, hC2, M=(1 - alpha) * M, reg=alpha, nx=np_, **kwargs)
if not nx.is_floating_point(M0):
warnings.warn(
"Input feature matrix consists of integer. The transport plan will be "
"casted accordingly, possibly resulting in a loss of precision. "
"If this behaviour is unwanted, please make sure your input "
"feature matrix consists of floating point elements.",
stacklevel=2
)
if log:
res, log = cg(p, q, (1 - alpha) * M, alpha, f, df, G0, line_search, log=True, numItermax=max_iter, stopThr=tol_rel, stopThr2=tol_abs, **kwargs)
log['fgw_dist'] = nx.from_numpy(log['loss'][-1], type_as=M0)
log['u'] = nx.from_numpy(log['u'], type_as=M0)
log['v'] = nx.from_numpy(log['v'], type_as=M0)
return nx.from_numpy(res, type_as=M0), log
else:
return nx.from_numpy(cg(p, q, (1 - alpha) * M, alpha, f, df, G0, line_search, log=False, numItermax=max_iter, stopThr=tol_rel, stopThr2=tol_abs, **kwargs), type_as=M0)
[docs]
def fused_gromov_wasserstein2(M, C1, C2, p=None, q=None, loss_fun='square_loss', symmetric=None, alpha=0.5,
armijo=False, G0=None, log=False, max_iter=1e4, tol_rel=1e-9, tol_abs=1e-9, **kwargs):
r"""
Returns the Fused Gromov-Wasserstein distance between :math:`(\mathbf{C_1}, \mathbf{Y_1}, \mathbf{p})` and :math:`(\mathbf{C_2}, \mathbf{Y_2}, \mathbf{q})`
with pairwise distance matrix :math:`\mathbf{M}` between node feature matrices :math:`\mathbf{Y_1}` and :math:`\mathbf{Y_2}` (see :ref:`[24] <references-fused-gromov-wasserstein>`).
The function solves the following optimization problem using Conditional Gradient:
.. math::
\mathbf{FGW} = \mathop{\min}_\mathbf{T} \quad (1 - \alpha) \langle \mathbf{T}, \mathbf{M} \rangle_F +
\alpha \sum_{i,j,k,l} L(\mathbf{C_1}_{i,k}, \mathbf{C_2}_{j,l}) \mathbf{T}_{i,j} \mathbf{T}_{k,l}
s.t. \ \mathbf{T} \mathbf{1} &= \mathbf{p}
\mathbf{T}^T \mathbf{1} &= \mathbf{q}
\mathbf{T} &\geq 0
Where :
- :math:`\mathbf{M}`: metric cost matrix between features across domains
- :math:`\mathbf{C_1}`: Metric cost matrix in the source space
- :math:`\mathbf{C_2}`: Metric cost matrix in the target space
- :math:`\mathbf{p}`: distribution in the source space
- :math:`\mathbf{q}`: distribution in the target space
- `L`: loss function to account for the misfit between the similarity and feature matrices
- :math:`\alpha`: trade-off parameter
Note that when using backends, this loss function is differentiable wrt the
matrices (C1, C2, M) and weights (p, q) for quadratic loss using the gradients from [38]_.
.. note:: This function is backend-compatible and will work on arrays
from all compatible backends. But the algorithm uses the C++ CPU backend
which can lead to copy overhead on GPU arrays.
.. note:: All computations in the conjugate gradient solver are done with
numpy to limit memory overhead.
.. note:: This function will cast the computed transport plan to the data
type of the provided input :math:`\mathbf{M}`. Casting to an integer
tensor might result in a loss of precision. If this behaviour is
unwanted, please make sure to provide a floating point input.
Parameters
----------
M : array-like, shape (ns, nt)
Metric cost matrix between features across domains
C1 : array-like, shape (ns, ns)
Metric cost matrix representative of the structure in the source space.
C2 : array-like, shape (nt, nt)
Metric cost matrix representative of the structure in the target space.
p : array-like, shape (ns,), optional
Distribution in the source space.
If let to its default value None, uniform distribution is taken.
q : array-like, shape (nt,), optional
Distribution in the target space.
If let to its default value None, uniform distribution is taken.
loss_fun : str, optional
Loss function used for the solver.
symmetric : bool, optional
Either C1 and C2 are to be assumed symmetric or not.
If let to its default None value, a symmetry test will be conducted.
Else if set to True (resp. False), C1 and C2 will be assumed symmetric (resp. asymmetric).
alpha : float, optional
Trade-off parameter (0 < alpha < 1)
armijo : bool, optional
If True the step of the line-search is found via an armijo research.
Else closed form is used. If there are convergence issues use False.
G0: array-like, shape (ns,nt), optional
If None the initial transport plan of the solver is pq^T.
Otherwise G0 must satisfy marginal constraints and will be used as initial transport of the solver.
log : bool, optional
Record log if True.
max_iter : int, optional
Max number of iterations
tol_rel : float, optional
Stop threshold on relative error (>0)
tol_abs : float, optional
Stop threshold on absolute error (>0)
**kwargs : dict
Parameters can be directly passed to the ot.optim.cg solver.
Returns
-------
fgw-distance : float
Fused Gromov-Wasserstein distance for the given parameters.
log : dict
Log dictionary return only if log==True in parameters.
.. _references-fused-gromov-wasserstein2:
References
----------
.. [24] Vayer Titouan, Chapel Laetitia, Flamary Rémi, Tavenard Romain
and Courty Nicolas
"Optimal Transport for structured data with application on graphs"
International Conference on Machine Learning (ICML). 2019.
.. [38] C. Vincent-Cuaz, T. Vayer, R. Flamary, M. Corneli, N. Courty, Online
Graph Dictionary Learning, International Conference on Machine Learning
(ICML), 2021.
.. [47] Chowdhury, S., & Mémoli, F. (2019). The gromov–wasserstein
distance between networks and stable network invariants.
Information and Inference: A Journal of the IMA, 8(4), 757-787.
"""
nx = get_backend(C1, C2, M)
# init marginals if set as None
if p is None:
p = unif(C1.shape[0], type_as=M)
if q is None:
q = unif(C2.shape[0], type_as=M)
T, log_fgw = fused_gromov_wasserstein(
M, C1, C2, p, q, loss_fun, symmetric, alpha, armijo, G0, log=True,
max_iter=max_iter, tol_rel=tol_rel, tol_abs=tol_abs, **kwargs)
fgw_dist = log_fgw['fgw_dist']
log_fgw['T'] = T
# compute separate terms for gradients and log
lin_term = nx.sum(T * M)
log_fgw['quad_loss'] = (fgw_dist - (1 - alpha) * lin_term)
log_fgw['lin_loss'] = lin_term * (1 - alpha)
gw_term = log_fgw['quad_loss'] / alpha
if loss_fun == 'square_loss':
gC1 = 2 * C1 * nx.outer(p, p) - 2 * nx.dot(T, nx.dot(C2, T.T))
gC2 = 2 * C2 * nx.outer(q, q) - 2 * nx.dot(T.T, nx.dot(C1, T))
elif loss_fun == 'kl_loss':
gC1 = nx.log(C1 + 1e-15) * nx.outer(p, p) - nx.dot(T, nx.dot(nx.log(C2 + 1e-15), T.T))
gC2 = nx.dot(T.T, nx.dot(C1, T)) / (C2 + 1e-15) + nx.outer(q, q)
if isinstance(alpha, int) or isinstance(alpha, float):
fgw_dist = nx.set_gradients(fgw_dist, (p, q, C1, C2, M),
(log_fgw['u'] - nx.mean(log_fgw['u']),
log_fgw['v'] - nx.mean(log_fgw['v']),
alpha * gC1, alpha * gC2, (1 - alpha) * T))
else:
fgw_dist = nx.set_gradients(fgw_dist, (p, q, C1, C2, M, alpha),
(log_fgw['u'] - nx.mean(log_fgw['u']),
log_fgw['v'] - nx.mean(log_fgw['v']),
alpha * gC1, alpha * gC2, (1 - alpha) * T,
gw_term - lin_term))
if log:
return fgw_dist, log_fgw
else:
return fgw_dist
[docs]
def solve_gromov_linesearch(G, deltaG, cost_G, C1, C2, M, reg,
alpha_min=None, alpha_max=None, nx=None, **kwargs):
"""
Solve the linesearch in the FW iterations for any inner loss that decomposes as in Proposition 1 in :ref:`[12] <references-solve-linesearch>`.
Parameters
----------
G : array-like, shape(ns,nt)
The transport map at a given iteration of the FW
deltaG : array-like (ns,nt)
Difference between the optimal map found by linearization in the FW algorithm and the value at a given iteration
cost_G : float
Value of the cost at `G`
C1 : array-like (ns,ns), optional
Transformed Structure matrix in the source domain.
For the 'square_loss' and 'kl_loss', we provide hC1 from ot.gromov.init_matrix
C2 : array-like (nt,nt), optional
Transformed Structure matrix in the source domain.
For the 'square_loss' and 'kl_loss', we provide hC2 from ot.gromov.init_matrix
M : array-like (ns,nt)
Cost matrix between the features.
reg : float
Regularization parameter.
alpha_min : float, optional
Minimum value for alpha
alpha_max : float, optional
Maximum value for alpha
nx : backend, optional
If let to its default value None, a backend test will be conducted.
Returns
-------
alpha : float
The optimal step size of the FW
fc : int
nb of function call. Useless here
cost_G : float
The value of the cost for the next iteration
.. _references-solve-linesearch:
References
----------
.. [24] Vayer Titouan, Chapel Laetitia, Flamary Rémi, Tavenard Romain and Courty Nicolas
"Optimal Transport for structured data with application on graphs"
International Conference on Machine Learning (ICML). 2019.
.. [12] Gabriel Peyré, Marco Cuturi, and Justin Solomon,
"Gromov-Wasserstein averaging of kernel and distance matrices."
International Conference on Machine Learning (ICML). 2016.
"""
if nx is None:
G, deltaG, C1, C2, M = list_to_array(G, deltaG, C1, C2, M)
if isinstance(M, int) or isinstance(M, float):
nx = get_backend(G, deltaG, C1, C2)
else:
nx = get_backend(G, deltaG, C1, C2, M)
dot = nx.dot(nx.dot(C1, deltaG), C2.T)
a = - reg * nx.sum(dot * deltaG)
b = nx.sum(M * deltaG) - reg * (nx.sum(dot * G) + nx.sum(nx.dot(nx.dot(C1, G), C2.T) * deltaG))
alpha = solve_1d_linesearch_quad(a, b)
if alpha_min is not None or alpha_max is not None:
alpha = np.clip(alpha, alpha_min, alpha_max)
# the new cost is deduced from the line search quadratic function
cost_G = cost_G + a * (alpha ** 2) + b * alpha
return alpha, 1, cost_G
[docs]
def gromov_barycenters(
N, Cs, ps=None, p=None, lambdas=None, loss_fun='square_loss',
symmetric=True, armijo=False, max_iter=1000, tol=1e-9,
stop_criterion='barycenter', warmstartT=False, verbose=False,
log=False, init_C=None, random_state=None, **kwargs):
r"""
Returns the Gromov-Wasserstein barycenters of `S` measured similarity matrices :math:`(\mathbf{C}_s)_{1 \leq s \leq S}`
The function solves the following optimization problem with block coordinate descent:
.. math::
\mathbf{C}^* = \mathop{\arg \min}_{\mathbf{C}\in \mathbb{R}^{N \times N}} \quad \sum_s \lambda_s \mathrm{GW}(\mathbf{C}, \mathbf{C}_s, \mathbf{p}, \mathbf{p}_s)
Where :
- :math:`\mathbf{C}_s`: metric cost matrix
- :math:`\mathbf{p}_s`: distribution
Parameters
----------
N : int
Size of the targeted barycenter
Cs : list of S array-like of shape (ns, ns)
Metric cost matrices
ps : list of S array-like of shape (ns,), optional
Sample weights in the `S` spaces.
If let to its default value None, uniform distributions are taken.
p : array-like, shape (N,), optional
Weights in the targeted barycenter.
If let to its default value None, uniform distribution is taken.
lambdas : list of float, optional
List of the `S` spaces' weights.
If let to its default value None, uniform weights are taken.
loss_fun : callable, optional
tensor-matrix multiplication function based on specific loss function
symmetric : bool, optional.
Either structures are to be assumed symmetric or not. Default value is True.
Else if set to True (resp. False), C1 and C2 will be assumed symmetric (resp. asymmetric).
armijo : bool, optional
If True the step of the line-search is found via an armijo research.
Else closed form is used. If there are convergence issues use False.
max_iter : int, optional
Max number of iterations
tol : float, optional
Stop threshold on relative error (>0)
stop_criterion : str, optional. Default is 'barycenter'.
Stop criterion taking values in ['barycenter', 'loss']. If set to 'barycenter'
uses absolute norm variations of estimated barycenters. Else if set to 'loss'
uses the relative variations of the loss.
warmstartT: bool, optional
Either to perform warmstart of transport plans in the successive
fused gromov-wasserstein transport problems.s
verbose : bool, optional
Print information along iterations.
log : bool, optional
Record log if True.
init_C : bool | array-like, shape(N,N)
Random initial value for the :math:`\mathbf{C}` matrix provided by user.
random_state : int or RandomState instance, optional
Fix the seed for reproducibility
Returns
-------
C : array-like, shape (`N`, `N`)
Similarity matrix in the barycenter space (permutated arbitrarily)
log : dict
Only returned when log=True. It contains the keys:
- :math:`\mathbf{T}`: list of (`N`, `ns`) transport matrices
- :math:`\mathbf{p}`: (`N`,) barycenter weights
- values used in convergence evaluation.
References
----------
.. [12] Gabriel Peyré, Marco Cuturi, and Justin Solomon,
"Gromov-Wasserstein averaging of kernel and distance matrices."
International Conference on Machine Learning (ICML). 2016.
"""
if stop_criterion not in ['barycenter', 'loss']:
raise ValueError(f"Unknown `stop_criterion='{stop_criterion}'`. Use one of: {'barycenter', 'loss'}.")
Cs = list_to_array(*Cs)
arr = [*Cs]
if ps is not None:
arr += list_to_array(*ps)
else:
ps = [unif(C.shape[0], type_as=C) for C in Cs]
if p is not None:
arr.append(list_to_array(p))
else:
p = unif(N, type_as=Cs[0])
nx = get_backend(*arr)
S = len(Cs)
if lambdas is None:
lambdas = [1. / S] * S
# Initialization of C : random SPD matrix (if not provided by user)
if init_C is None:
generator = check_random_state(random_state)
xalea = generator.randn(N, 2)
C = dist(xalea, xalea)
C /= C.max()
C = nx.from_numpy(C, type_as=p)
else:
C = init_C
cpt = 0
err = 1e15 # either the error on 'barycenter' or 'loss'
if warmstartT:
T = [None] * S
if stop_criterion == 'barycenter':
inner_log = False
else:
inner_log = True
curr_loss = 1e15
if log:
log_ = {}
log_['err'] = []
if stop_criterion == 'loss':
log_['loss'] = []
while (err > tol and cpt < max_iter):
if stop_criterion == 'barycenter':
Cprev = C
else:
prev_loss = curr_loss
# get transport plans
if warmstartT:
res = [gromov_wasserstein(
C, Cs[s], p, ps[s], loss_fun, symmetric=symmetric, armijo=armijo, G0=T[s],
max_iter=max_iter, tol_rel=1e-5, tol_abs=0., log=inner_log, verbose=verbose, **kwargs)
for s in range(S)]
else:
res = [gromov_wasserstein(
C, Cs[s], p, ps[s], loss_fun, symmetric=symmetric, armijo=armijo, G0=None,
max_iter=max_iter, tol_rel=1e-5, tol_abs=0., log=inner_log, verbose=verbose, **kwargs)
for s in range(S)]
if stop_criterion == 'barycenter':
T = res
else:
T = [output[0] for output in res]
curr_loss = np.sum([output[1]['gw_dist'] for output in res])
# update barycenters
if loss_fun == 'square_loss':
C = update_square_loss(p, lambdas, T, Cs, nx)
elif loss_fun == 'kl_loss':
C = update_kl_loss(p, lambdas, T, Cs, nx)
# update convergence criterion
if stop_criterion == 'barycenter':
err = nx.norm(C - Cprev)
if log:
log_['err'].append(err)
else:
err = abs(curr_loss - prev_loss) / prev_loss if prev_loss != 0. else np.nan
if log:
log_['loss'].append(curr_loss)
log_['err'].append(err)
if verbose:
if cpt % 200 == 0:
print('{:5s}|{:12s}'.format(
'It.', 'Err') + '\n' + '-' * 19)
print('{:5d}|{:8e}|'.format(cpt, err))
cpt += 1
if log:
log_['T'] = T
log_['p'] = p
return C, log_
else:
return C
[docs]
def fgw_barycenters(
N, Ys, Cs, ps=None, lambdas=None, alpha=0.5, fixed_structure=False,
fixed_features=False, p=None, loss_fun='square_loss', armijo=False,
symmetric=True, max_iter=100, tol=1e-9, stop_criterion='barycenter',
warmstartT=False, verbose=False, log=False, init_C=None, init_X=None,
random_state=None, **kwargs):
r"""
Returns the Fused Gromov-Wasserstein barycenters of `S` measurable networks with node features :math:`(\mathbf{C}_s, \mathbf{Y}_s, \mathbf{p}_s)_{1 \leq s \leq S}`
(see eq (5) in :ref:`[24] <references-fgw-barycenters>`), estimated using Fused Gromov-Wasserstein transports from Conditional Gradient solvers.
The function solves the following optimization problem:
.. math::
\mathbf{C}^*, \mathbf{Y}^* = \mathop{\arg \min}_{\mathbf{C}\in \mathbb{R}^{N \times N}, \mathbf{Y}\in \mathbb{Y}^{N \times d}} \quad \sum_s \lambda_s \mathrm{FGW}_{\alpha}(\mathbf{C}, \mathbf{C}_s, \mathbf{Y}, \mathbf{Y}_s, \mathbf{p}, \mathbf{p}_s)
Where :
- :math:`\mathbf{Y}_s`: feature matrix
- :math:`\mathbf{C}_s`: metric cost matrix
- :math:`\mathbf{p}_s`: distribution
Parameters
----------
N : int
Desired number of samples of the target barycenter
Ys: list of array-like, each element has shape (ns,d)
Features of all samples
Cs : list of array-like, each element has shape (ns,ns)
Structure matrices of all samples
ps : list of array-like, each element has shape (ns,), optional
Masses of all samples.
If let to its default value None, uniform distributions are taken.
lambdas : list of float, optional
List of the `S` spaces' weights.
If let to its default value None, uniform weights are taken.
alpha : float, optional
Alpha parameter for the fgw distance.
fixed_structure : bool, optional
Whether to fix the structure of the barycenter during the updates.
fixed_features : bool, optional
Whether to fix the feature of the barycenter during the updates
p : array-like, shape (N,), optional
Weights in the targeted barycenter.
If let to its default value None, uniform distribution is taken.
loss_fun : str, optional
Loss function used for the solver either 'square_loss' or 'kl_loss'
armijo : bool, optional
If True the step of the line-search is found via an armijo research.
Else closed form is used. If there are convergence issues use False.
symmetric : bool, optional
Either structures are to be assumed symmetric or not. Default value is True.
Else if set to True (resp. False), C1 and C2 will be assumed symmetric (resp. asymmetric).
max_iter : int, optional
Max number of iterations
tol : float, optional
Stop threshold on relative error (>0)
stop_criterion : str, optional. Default is 'barycenter'.
Stop criterion taking values in ['barycenter', 'loss']. If set to 'barycenter'
uses absolute norm variations of estimated barycenters. Else if set to 'loss'
uses the relative variations of the loss.
warmstartT: bool, optional
Either to perform warmstart of transport plans in the successive
fused gromov-wasserstein transport problems.
verbose : bool, optional
Print information along iterations.
log : bool, optional
Record log if True.
init_C : array-like, shape (N,N), optional
Initialization for the barycenters' structure matrix. If not set
a random init is used.
init_X : array-like, shape (N,d), optional
Initialization for the barycenters' features. If not set a
random init is used.
random_state : int or RandomState instance, optional
Fix the seed for reproducibility
Returns
-------
X : array-like, shape (`N`, `d`)
Barycenters' features
C : array-like, shape (`N`, `N`)
Barycenters' structure matrix
log : dict
Only returned when log=True. It contains the keys:
- :math:`\mathbf{T}`: list of (`N`, `ns`) transport matrices
- :math:`\mathbf{p}`: (`N`,) barycenter weights
- :math:`(\mathbf{M}_s)_s`: all distance matrices between the feature of the barycenter and the other features :math:`(dist(\mathbf{X}, \mathbf{Y}_s))_s` shape (`N`, `ns`)
- values used in convergence evaluation.
.. _references-fgw-barycenters:
References
----------
.. [24] Vayer Titouan, Chapel Laetitia, Flamary Rémi, Tavenard Romain
and Courty Nicolas
"Optimal Transport for structured data with application on graphs"
International Conference on Machine Learning (ICML). 2019.
"""
if stop_criterion not in ['barycenter', 'loss']:
raise ValueError(f"Unknown `stop_criterion='{stop_criterion}'`. Use one of: {'barycenter', 'loss'}.")
Cs = list_to_array(*Cs)
Ys = list_to_array(*Ys)
arr = [*Cs, *Ys]
if ps is not None:
arr += list_to_array(*ps)
else:
ps = [unif(C.shape[0], type_as=C) for C in Cs]
if p is not None:
arr.append(list_to_array(p))
else:
p = unif(N, type_as=Cs[0])
nx = get_backend(*arr)
S = len(Cs)
if lambdas is None:
lambdas = [1. / S] * S
d = Ys[0].shape[1] # dimension on the node features
if fixed_structure:
if init_C is None:
raise UndefinedParameter('If C is fixed it must be initialized')
else:
C = init_C
else:
if init_C is None:
generator = check_random_state(random_state)
xalea = generator.randn(N, 2)
C = dist(xalea, xalea)
C = nx.from_numpy(C, type_as=ps[0])
else:
C = init_C
if fixed_features:
if init_X is None:
raise UndefinedParameter('If X is fixed it must be initialized')
else:
X = init_X
else:
if init_X is None:
X = nx.zeros((N, d), type_as=ps[0])
else:
X = init_X
Ms = [dist(X, Ys[s]) for s in range(len(Ys))]
if warmstartT:
T = [None] * S
cpt = 0
if stop_criterion == 'barycenter':
inner_log = False
err_feature = 1e15
err_structure = 1e15
err_rel_loss = 0.
else:
inner_log = True
err_feature = 0.
err_structure = 0.
curr_loss = 1e15
err_rel_loss = 1e15
if log:
log_ = {}
if stop_criterion == 'barycenter':
log_['err_feature'] = []
log_['err_structure'] = []
log_['Ts_iter'] = []
else:
log_['loss'] = []
log_['err_rel_loss'] = []
while ((err_feature > tol or err_structure > tol or err_rel_loss > tol) and cpt < max_iter):
if stop_criterion == 'barycenter':
Cprev = C
Xprev = X
else:
prev_loss = curr_loss
# get transport plans
if warmstartT:
res = [fused_gromov_wasserstein(
Ms[s], C, Cs[s], p, ps[s], loss_fun=loss_fun, alpha=alpha, armijo=armijo, symmetric=symmetric,
G0=T[s], max_iter=max_iter, tol_rel=1e-5, tol_abs=0., log=inner_log, verbose=verbose, **kwargs)
for s in range(S)]
else:
res = [fused_gromov_wasserstein(
Ms[s], C, Cs[s], p, ps[s], loss_fun=loss_fun, alpha=alpha, armijo=armijo, symmetric=symmetric,
G0=None, max_iter=max_iter, tol_rel=1e-5, tol_abs=0., log=inner_log, verbose=verbose, **kwargs)
for s in range(S)]
if stop_criterion == 'barycenter':
T = res
else:
T = [output[0] for output in res]
curr_loss = np.sum([output[1]['fgw_dist'] for output in res])
# update barycenters
if not fixed_features:
Ys_temp = [y.T for y in Ys]
X = update_feature_matrix(lambdas, Ys_temp, T, p, nx).T
Ms = [dist(X, Ys[s]) for s in range(len(Ys))]
if not fixed_structure:
if loss_fun == 'square_loss':
C = update_square_loss(p, lambdas, T, Cs, nx)
elif loss_fun == 'kl_loss':
C = update_kl_loss(p, lambdas, T, Cs, nx)
# update convergence criterion
if stop_criterion == 'barycenter':
err_feature, err_structure = 0., 0.
if not fixed_features:
err_feature = nx.norm(X - Xprev)
if not fixed_structure:
err_structure = nx.norm(C - Cprev)
if log:
log_['err_feature'].append(err_feature)
log_['err_structure'].append(err_structure)
log_['Ts_iter'].append(T)
if verbose:
if cpt % 200 == 0:
print('{:5s}|{:12s}'.format(
'It.', 'Err') + '\n' + '-' * 19)
print('{:5d}|{:8e}|'.format(cpt, err_structure))
print('{:5d}|{:8e}|'.format(cpt, err_feature))
else:
err_rel_loss = abs(curr_loss - prev_loss) / prev_loss if prev_loss != 0. else np.nan
if log:
log_['loss'].append(curr_loss)
log_['err_rel_loss'].append(err_rel_loss)
if verbose:
if cpt % 200 == 0:
print('{:5s}|{:12s}'.format(
'It.', 'Err') + '\n' + '-' * 19)
print('{:5d}|{:8e}|'.format(cpt, err_rel_loss))
cpt += 1
if log:
log_['T'] = T
log_['p'] = p
log_['Ms'] = Ms
return X, C, log_
else:
return X, C