Source code for graspy.match.faq

# Copyright 2019 NeuroData (
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import numpy as np
import math
from scipy.optimize import linear_sum_assignment
from scipy.optimize import minimize_scalar
from sklearn.utils import check_array
from .skp import SinkhornKnopp

[docs]class FastApproximateQAP: """ Fast Approximate QAP Algorithm (FAQ) The FAQ algorithm solves the Quadratic Assignment Problem (QAP) finding an alignment of the vertices of two graphs which minimizes the number of induced edge disagreements [1]. Parameters ---------- n_init : int, positive (default = 1) Number of random initializations of the starting permutation matrix that the FAQ algorithm will undergo. n_init automatically set to 1 if init_method = 'barycenter' init_method : string (default = 'barycenter') The initial position chosen "barycenter" : the non-informative “flat doubly stochastic matrix,” :math:`J=1*1^T /n` , i.e the barycenter of the feasible region "rand" : some random point near :math:`J, (J+K)/2`, where K is some random doubly stochastic matrix max_iter : int, positive (default = 30) Integer specifying the max number of FW iterations. FAQ typically converges with modest number of iterations shuffle_input : bool (default = True) Gives users the option to shuffle the nodes of A matrix to avoid results from inputs that were already matched eps : float (default = 0.1) A positive, threshold stopping criteria such that FW continues to iterate while Frobenius norm of :math:`(P_{i}-P_{i+1}) > eps` gmp : bool (default = False) Gives users the option to the Graph Matching Problem (GMP) rather than the Quadratic Assignment (QAP). This is accomplished through trivial negation of the objective function. Attributes ---------- perm_inds_ : array, size (n,) where n is the number of vertices in the graphs fitted. The indices of the optimal permutation on the nodes of B, found via FAQ, to best minimize the objective function :math:`f(P) = trace(A^T PBP^T )`. score_ : float The objective function value of for the optimal permutation found. References ---------- .. [1] J. T. Vogelstein, J. M. Conroy, V. Lyzinski, L. J. Podrazik, S. G. Kratzer, E. T. Harley, D. E. Fishkind, R. J. Vogelstein, and C. E. Priebe, “Fast approximate quadratic programming for graph matching,” PLOS one, vol. 10, no. 4, p. e0121002, 2015. """ def __init__( self, n_init=1, init_method="barycenter", max_iter=30, shuffle_input=True, eps=0.1, gmp=False, ): if n_init > 0 and type(n_init) is int: self.n_init = n_init else: msg = '"n_init" must be a positive integer' raise TypeError(msg) if init_method == "rand": self.init_method = "rand" elif init_method == "barycenter": self.init_method = "barycenter" self.n_init = 1 else: msg = 'Invalid "init_method" parameter string' raise ValueError(msg) if max_iter > 0 and type(max_iter) is int: self.max_iter = max_iter else: msg = '"max_iter" must be a positive integer' raise TypeError(msg) if type(shuffle_input) is bool: self.shuffle_input = shuffle_input else: msg = '"shuffle_input" must be a boolean' raise TypeError(msg) if eps > 0 and type(eps) is float: self.eps = eps else: msg = '"eps" must be a positive float' raise TypeError(msg) if type(gmp) is bool: self.gmp = gmp else: msg = '"gmp" must be a boolean' raise TypeError(msg)
[docs] def fit(self, A, B): """ Fits the model with two assigned adjacency matrices Parameters --------- A : 2d-array, square, positive A square, positive adjacency matrix B : 2d-array, square, positive A square, positive adjacency matrix Returns ------- self : returns an instance of self """ A = check_array(A, copy=True, ensure_2d=True) B = check_array(B, copy=True, ensure_2d=True) if A.shape[0] != B.shape[0]: msg = "Matrix entries must be of equal size" raise ValueError(msg) elif A.shape[0] != A.shape[1] or B.shape[0] != B.shape[1]: msg = "Matrix entries must be square" raise ValueError(msg) elif (A >= 0).all() == False or (B >= 0).all() == False: msg = "Matrix entries must be greater than or equal to zero" raise ValueError(msg) n = A.shape[0] # number of vertices in graphs if self.shuffle_input: node_shuffle_input = np.random.permutation(n) A = A[np.ix_(node_shuffle_input, node_shuffle_input)] # shuffle_input to avoid results from inputs that were already matched obj_func_scalar = 1 if self.gmp: obj_func_scalar = -1 At = np.transpose(A) # A transpose Bt = np.transpose(B) # B transpose score = math.inf perm_inds = np.zeros(n) for i in range(self.n_init): # setting initialization matrix if self.init_method == "rand": sk = SinkhornKnopp() K = np.random.rand( n, n ) # generate a nxn matrix where each entry is a random integer [0,1] for i in range(10): # perform 10 iterations of Sinkhorn balancing K = J = np.ones((n, n)) / float( n ) # initialize J, a doubly stochastic barycenter P = (K + J) / 2 elif self.init_method == "barycenter": P = np.ones((n, n)) / float(n) grad_P = math.inf # gradient of P n_iter = 0 # number of FW iterations # OPTIMIZATION WHILE LOOP BEGINS while grad_P > self.eps and n_iter < self.max_iter: delta_f = ( A @ P @ Bt + At @ P @ B ) # computing the gradient of f(P) = -tr(APB^tP^t) rows, cols = linear_sum_assignment( obj_func_scalar * delta_f ) # run hungarian algorithm on gradient(f(P)) Q = np.zeros((n, n)) Q[rows, cols] = 1 # initialize search direction matrix Q def f(x): # computing the original optimization function return obj_func_scalar * np.trace( At
[docs] @ (x * P + (1 - x) * Q) @ B @ np.transpose(x * P + (1 - x) * Q) ) alpha = minimize_scalar( f, bounds=(0, 1), method="bounded" ).x # computing the step size P_i1 = alpha * P + (1 - alpha) * Q # Update P grad_P = np.linalg.norm(P - P_i1) P = P_i1 n_iter += 1 # end of FW optimization loop row, perm_inds_new = linear_sum_assignment( -P ) # Project onto the set of permutation matrices score_new = np.trace( np.transpose(A) @ B[np.ix_(perm_inds_new, perm_inds_new)] ) # computing objective function value if score_new < score: # minimizing score = score_new if self.shuffle_input: perm_inds = np.array([0] * n) perm_inds[node_shuffle_input] = perm_inds_new else: perm_inds = perm_inds_new if self.shuffle_input: node_unshuffle_input = np.array(range(n)) node_unshuffle_input[node_shuffle_input] = np.array(range(n)) A = A[np.ix_(node_unshuffle_input, node_unshuffle_input)] score = np.trace(np.transpose(A) @ B[np.ix_(perm_inds, perm_inds)]) self.perm_inds_ = perm_inds # permutation indices self.score_ = score # objective function value return self
def fit_predict(self, A, B): """ Fits the model with two assigned adjacency matrices, returning optimal permutation indices Parameters --------- A : 2d-array, square, positive A square, positive adjacency matrix B : 2d-array, square, positive A square, positive adjacency matrix Returns ------- perm_inds_ : 1-d array, some shuffling of [0, n_vert) The optimal permutation indices to minimize the objective function """, B) return self.perm_inds_