|
- #!/usr/bin/env python3
- # -*- coding: utf-8 -*-
- """
- Created on Wed Oct 16 14:20:06 2019
-
- @author: ljia
- """
- import numpy as np
- from tqdm import tqdm
-
- from scipy import optimize
- import cvxpy as cp
-
- import sys
- sys.path.insert(0, "../")
- from pygraph.utils.graphfiles import loadDataset
- from ged import GED, get_nb_edit_operations
- from utils import kernel_distance_matrix
-
- def fit_GED_to_kernel_distance(Gn, gkernel, itr_max):
- # c_vi, c_vr, c_vs, c_ei, c_er, c_es or parts of them.
- edit_costs = [0.2, 0.2, 0.2, 0.2, 0.2, 0]
- idx_nonzeros = [i for i, item in enumerate(edit_costs) if item != 0]
-
- # compute distances in feature space.
- dis_k_mat, _, _, _ = kernel_distance_matrix(Gn, gkernel=gkernel)
- dis_k_vec = []
- for i in range(len(dis_k_mat)):
- for j in range(i, len(dis_k_mat)):
- dis_k_vec.append(dis_k_mat[i, j])
- dis_k_vec = np.array(dis_k_vec)
-
- residual_list = []
- edit_cost_list = []
-
- for itr in range(itr_max):
- print('iteration', itr)
- ged_all = []
- n_edit_operations = [[] for i in range(len(idx_nonzeros))]
- # compute GEDs and numbers of edit operations.
- edit_cost_constant = [i for i in edit_costs]
- edit_cost_list.append(edit_cost_constant)
- for i in tqdm(range(len(Gn)), desc='computing GEDs', file=sys.stdout):
- # for i in range(len(Gn)):
- for j in range(i, len(Gn)):
- dis, pi_forward, pi_backward = GED(Gn[i], Gn[j], lib='gedlibpy',
- cost='CONSTANT', method='IPFP',
- edit_cost_constant=edit_cost_constant, stabilizer='min',
- repeat=30)
- ged_all.append(dis)
- n_eo_tmp = get_nb_edit_operations(Gn[i],
- Gn[j], pi_forward, pi_backward)
- for idx, item in enumerate(idx_nonzeros):
- n_edit_operations[idx].append(n_eo_tmp[item])
-
- residual = np.sqrt(np.sum(np.square(np.array(ged_all) - dis_k_vec)))
- residual_list.append(residual)
-
- # "fit" geds to distances in feature space by tuning edit costs using the
- # Least Squares Method.
- nb_cost_mat = np.array(n_edit_operations).T
- edit_costs_new, residual = get_better_costs(nb_cost_mat, dis_k_vec)
-
- print(residual)
- for i in range(len(edit_costs_new)):
- if edit_costs_new[i] < 0:
- if edit_costs_new[i] > -1e-6:
- edit_costs_new[i] = 0
- else:
- raise ValueError('The edit cost is negative.')
-
- for idx, item in enumerate(idx_nonzeros):
- edit_costs[item] = edit_costs_new[idx]
-
- return edit_costs, residual_list, edit_cost_list
-
-
- def get_better_costs(nb_cost_mat, dis_k_vec):
- # # method 1: simple least square method.
- # edit_costs_new, residual, _, _ = np.linalg.lstsq(nb_cost_mat, dis_k_vec,
- # rcond=None)
-
- # # method 2: least square method with x_i >= 0.
- # edit_costs_new, residual = optimize.nnls(nb_cost_mat, dis_k_vec)
-
- # method 3: solve as a quadratic program with constraints: x_i >= 0, sum(x) = 1.
- P = np.dot(nb_cost_mat.T, nb_cost_mat)
- q_T = -2 * np.dot(dis_k_vec.T, nb_cost_mat)
- G = -1 * np.identity(nb_cost_mat.shape[1])
- h = np.array([0 for i in range(nb_cost_mat.shape[1])])
- A = np.array([1 for i in range(nb_cost_mat.shape[1])])
- b = 1
- x = cp.Variable(nb_cost_mat.shape[1])
- prob = cp.Problem(cp.Minimize(cp.quad_form(x, P) + q_T@x),
- [G@x <= h,
- A@x == b])
- prob.solve()
- edit_costs_new = x.value
- residual = prob.value - np.dot(dis_k_vec.T, dis_k_vec)
-
- # p = program(minimize(norm2(nb_cost_mat*x-dis_k_vec)),[equals(sum(x),1),geq(x,0)])
- return edit_costs_new, residual
-
-
- if __name__ == '__main__':
- from utils import remove_edges
- ds = {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt',
- 'extra_params': {}} # node/edge symb
- Gn, y_all = loadDataset(ds['dataset'], extra_params=ds['extra_params'])
- # Gn = Gn[0:10]
- remove_edges(Gn)
- gkernel = 'marginalizedkernel'
- itr_max = 10
- edit_costs, residual_list, edit_cost_list = \
- fit_GED_to_kernel_distance(Gn, gkernel, itr_max)
|
