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Diffstat (limited to 'ot/gromov.py')
| -rw-r--r-- | ot/gromov.py | 474 |
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diff --git a/ot/gromov.py b/ot/gromov.py new file mode 100644 index 0000000..421ed3f --- /dev/null +++ b/ot/gromov.py @@ -0,0 +1,474 @@ +
+# -*- coding: utf-8 -*-
+"""
+Gromov-Wasserstein transport method
+===================================
+
+
+"""
+
+# Author: Erwan Vautier <erwan.vautier@gmail.com>
+# Nicolas Courty <ncourty@irisa.fr>
+#
+# License: MIT License
+
+import numpy as np
+
+from .bregman import sinkhorn
+from .utils import dist
+
+
+def square_loss(a, b):
+ """
+ Returns the value of L(a,b)=(1/2)*|a-b|^2
+ """
+
+ return 0.5 * (a - b)**2
+
+
+def kl_loss(a, b):
+ """
+ Returns the value of L(a,b)=a*log(a/b)-a+b
+ """
+
+ return a * np.log(a / b) - a + b
+
+
+def tensor_square_loss(C1, C2, T):
+ """
+ Returns the value of \mathcal{L}(C1,C2) \otimes T with the square loss
+ function as the loss function of Gromow-Wasserstein discrepancy.
+
+ Where :
+
+ C1 : Metric cost matrix in the source space
+ C2 : Metric cost matrix in the target space
+ T : A coupling between those two spaces
+
+ The square-loss function L(a,b)=(1/2)*|a-b|^2 is read as :
+ L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with :
+ f1(a)=(a^2)/2
+ f2(b)=(b^2)/2
+ h1(a)=a
+ h2(b)=b
+
+ Parameters
+ ----------
+ C1 : ndarray, shape (ns, ns)
+ Metric cost matrix in the source space
+ C2 : ndarray, shape (nt, nt)
+ Metric costfr matrix in the target space
+ T : np.ndarray(ns,nt)
+ Coupling between source and target spaces
+
+
+ Returns
+ -------
+ tens : (ns*nt) ndarray
+ \mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result
+
+
+ """
+
+ C1 = np.asarray(C1, dtype=np.float64)
+ C2 = np.asarray(C2, dtype=np.float64)
+ T = np.asarray(T, dtype=np.float64)
+
+ def f1(a):
+ return (a**2) / 2
+
+ def f2(b):
+ return (b**2) / 2
+
+ def h1(a):
+ return a
+
+ def h2(b):
+ return b
+
+ tens = -np.dot(h1(C1), T).dot(h2(C2).T)
+ tens -= tens.min()
+
+ return np.array(tens)
+
+
+def tensor_kl_loss(C1, C2, T):
+ """
+ Returns the value of \mathcal{L}(C1,C2) \otimes T with the square loss
+ function as the loss function of Gromow-Wasserstein discrepancy.
+
+ Where :
+
+ C1 : Metric cost matrix in the source space
+ C2 : Metric cost matrix in the target space
+ T : A coupling between those two spaces
+
+ The square-loss function L(a,b)=(1/2)*|a-b|^2 is read as :
+ L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with :
+ f1(a)=a*log(a)-a
+ f2(b)=b
+ h1(a)=a
+ h2(b)=log(b)
+
+ Parameters
+ ----------
+ C1 : ndarray, shape (ns, ns)
+ Metric cost matrix in the source space
+ C2 : ndarray, shape (nt, nt)
+ Metric costfr matrix in the target space
+ T : np.ndarray(ns,nt)
+ Coupling between source and target spaces
+
+
+ Returns
+ -------
+ tens : (ns*nt) ndarray
+ \mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result
+
+ References
+ ----------
+
+ .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." International Conference on Machine Learning (ICML). 2016.
+
+ """
+
+ C1 = np.asarray(C1, dtype=np.float64)
+ C2 = np.asarray(C2, dtype=np.float64)
+ T = np.asarray(T, dtype=np.float64)
+
+ def f1(a):
+ return a * np.log(a + 1e-15) - a
+
+ def f2(b):
+ return b
+
+ def h1(a):
+ return a
+
+ def h2(b):
+ return np.log(b + 1e-15)
+
+ tens = -np.dot(h1(C1), T).dot(h2(C2).T)
+ tens -= tens.min()
+
+ return np.array(tens)
+
+
+def update_square_loss(p, lambdas, T, Cs):
+ """
+ Updates C according to the L2 Loss kernel with the S Ts couplings calculated at each iteration
+
+
+ Parameters
+ ----------
+ p : np.ndarray(N,)
+ weights in the targeted barycenter
+ lambdas : list of the S spaces' weights
+ T : list of S np.ndarray(ns,N)
+ the S Ts couplings calculated at each iteration
+ Cs : Cs : list of S np.ndarray(ns,ns)
+ Metric cost matrices
+
+ Returns
+ ----------
+ C updated
+
+
+ """
+ tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) for s in range(len(T))])
+ ppt = np.outer(p, p)
+
+ return(np.divide(tmpsum, ppt))
+
+
+def update_kl_loss(p, lambdas, T, Cs):
+ """
+ Updates C according to the KL Loss kernel with the S Ts couplings calculated at each iteration
+
+
+ Parameters
+ ----------
+ p : np.ndarray(N,)
+ weights in the targeted barycenter
+ lambdas : list of the S spaces' weights
+ T : list of S np.ndarray(ns,N)
+ the S Ts couplings calculated at each iteration
+ Cs : Cs : list of S np.ndarray(ns,ns)
+ Metric cost matrices
+
+ Returns
+ ----------
+ C updated
+
+
+ """
+ tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) for s in range(len(T))])
+ ppt = np.outer(p, p)
+
+ return(np.exp(np.divide(tmpsum, ppt)))
+
+
+def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr=1e-9, verbose=False, log=False):
+ """
+ Returns the gromov-wasserstein coupling between the two measured similarity matrices
+
+ (C1,p) and (C2,q)
+
+ The function solves the following optimization problem:
+
+ .. math::
+ \GW = arg\min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T))
+
+ s.t. \GW 1 = p
+
+ \GW^T 1= q
+
+ \GW\geq 0
+
+ Where :
+
+ C1 : Metric cost matrix in the source space
+ C2 : Metric cost matrix in the target space
+ p : distribution in the source space
+ q : distribution in the target space
+ L : loss function to account for the misfit between the similarity matrices
+ H : entropy
+
+
+ Parameters
+ ----------
+ C1 : ndarray, shape (ns, ns)
+ Metric cost matrix in the source space
+ C2 : ndarray, shape (nt, nt)
+ Metric costfr matrix in the target space
+ p : np.ndarray(ns,)
+ distribution in the source space
+ q : np.ndarray(nt)
+ distribution in the target space
+ loss_fun : loss function used for the solver either 'square_loss' or 'kl_loss'
+ epsilon : float
+ Regularization term >0
+ numItermax : int, optional
+ Max number of iterations
+ stopThr : float, optional
+ Stop threshold on error (>0)
+ verbose : bool, optional
+ Print information along iterations
+ log : bool, optional
+ record log if True
+ forcing : np.ndarray(N,2)
+ list of forced couplings (where N is the number of forcing)
+
+ Returns
+ -------
+ T : coupling between the two spaces that minimizes :
+ \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T))
+
+ """
+
+ C1 = np.asarray(C1, dtype=np.float64)
+ C2 = np.asarray(C2, dtype=np.float64)
+
+ T = np.outer(p, q) # Initialization
+
+ cpt = 0
+ err = 1
+
+ while (err > stopThr and cpt < numItermax):
+
+ Tprev = T
+
+ if loss_fun == 'square_loss':
+ tens = tensor_square_loss(C1, C2, T)
+
+ elif loss_fun == 'kl_loss':
+ tens = tensor_kl_loss(C1, C2, T)
+
+ T = sinkhorn(p, q, tens, epsilon)
+
+ if cpt % 10 == 0:
+ # we can speed up the process by checking for the error only all the 10th iterations
+ err = np.linalg.norm(T - Tprev)
+
+ if log:
+ 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 = cpt + 1
+
+ if log:
+ return T, log
+ else:
+ return T
+
+
+def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr=1e-9, verbose=False, log=False):
+ """
+ Returns the gromov-wasserstein discrepancy between the two measured similarity matrices
+
+ (C1,p) and (C2,q)
+
+ The function solves the following optimization problem:
+
+ .. math::
+ \GW_Dist = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T))
+
+
+ Where :
+
+ C1 : Metric cost matrix in the source space
+ C2 : Metric cost matrix in the target space
+ p : distribution in the source space
+ q : distribution in the target space
+ L : loss function to account for the misfit between the similarity matrices
+ H : entropy
+
+
+ Parameters
+ ----------
+ C1 : ndarray, shape (ns, ns)
+ Metric cost matrix in the source space
+ C2 : ndarray, shape (nt, nt)
+ Metric costfr matrix in the target space
+ p : np.ndarray(ns,)
+ distribution in the source space
+ q : np.ndarray(nt)
+ distribution in the target space
+ loss_fun : loss function used for the solver either 'square_loss' or 'kl_loss'
+ epsilon : float
+ Regularization term >0
+ numItermax : int, optional
+ Max number of iterations
+ stopThr : float, optional
+ Stop threshold on error (>0)
+ verbose : bool, optional
+ Print information along iterations
+ log : bool, optional
+ record log if True
+ forcing : np.ndarray(N,2)
+ list of forced couplings (where N is the number of forcing)
+
+ Returns
+ -------
+ T : coupling between the two spaces that minimizes :
+ \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T))
+
+ """
+
+ if log:
+ gw, logv = gromov_wasserstein(
+ C1, C2, p, q, loss_fun, epsilon, numItermax, stopThr, verbose, log)
+ else:
+ gw = gromov_wasserstein(C1, C2, p, q, loss_fun,
+ epsilon, numItermax, stopThr, verbose, log)
+
+ if loss_fun == 'square_loss':
+ gw_dist = np.sum(gw * tensor_square_loss(C1, C2, gw))
+
+ elif loss_fun == 'kl_loss':
+ gw_dist = np.sum(gw * tensor_kl_loss(C1, C2, gw))
+
+ if log:
+ return gw_dist, logv
+ else:
+ return gw_dist
+
+
+def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, numItermax=1000, stopThr=1e-9, verbose=False, log=False):
+ """
+ Returns the gromov-wasserstein barycenters of S measured similarity matrices
+
+ (Cs)_{s=1}^{s=S}
+
+ The function solves the following optimization problem:
+
+ .. math::
+ C = argmin_C\in R^NxN \sum_s \lambda_s GW(C,Cs,p,ps)
+
+
+ Where :
+
+ Cs : metric cost matrix
+ ps : distribution
+
+ Parameters
+ ----------
+ N : Integer
+ Size of the targeted barycenter
+ Cs : list of S np.ndarray(ns,ns)
+ Metric cost matrices
+ ps : list of S np.ndarray(ns,)
+ sample weights in the S spaces
+ p : np.ndarray(N,)
+ weights in the targeted barycenter
+ lambdas : list of the S spaces' weights
+ L : tensor-matrix multiplication function based on specific loss function
+ update : function(p,lambdas,T,Cs) that updates C according to a specific Kernel
+ with the S Ts couplings calculated at each iteration
+ epsilon : float
+ Regularization term >0
+ numItermax : int, optional
+ Max number of iterations
+ stopThr : float, optional
+ Stop threshol on error (>0)
+ verbose : bool, optional
+ Print information along iterations
+ log : bool, optional
+ record log if True
+
+ Returns
+ -------
+ C : Similarity matrix in the barycenter space (permutated arbitrarily)
+
+ """
+
+ S = len(Cs)
+
+ Cs = [np.asarray(Cs[s], dtype=np.float64) for s in range(S)]
+ lambdas = np.asarray(lambdas, dtype=np.float64)
+
+ # Initialization of C : random SPD matrix
+ xalea = np.random.randn(N, 2)
+ C = dist(xalea, xalea)
+ C /= C.max()
+
+ cpt = 0
+ err = 1
+
+ error = []
+
+ while(err > stopThr and cpt < numItermax):
+
+ Cprev = C
+
+ T = [gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, epsilon,
+ numItermax, 1e-5, verbose, log) for s in range(S)]
+
+ if loss_fun == 'square_loss':
+ C = update_square_loss(p, lambdas, T, Cs)
+
+ elif loss_fun == 'kl_loss':
+ C = update_kl_loss(p, lambdas, T, Cs)
+
+ if cpt % 10 == 0:
+ # we can speed up the process by checking for the error only all the 10th iterations
+ err = np.linalg.norm(C - Cprev)
+ error.append(err)
+
+ if log:
+ 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 = cpt + 1
+
+ return C
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