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New translations cost_matrices_learner.py (Chinese Simplified)

l10n_v0.2.x
linlin 4 years ago
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1 changed files with 148 additions and 0 deletions
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      lang/zh/gklearn/ged/learning/cost_matrices_learner.py

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lang/zh/gklearn/ged/learning/cost_matrices_learner.py View File

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 7 11:42:48 2020

@author: ljia
"""
import numpy as np
import cvxpy as cp
import time
from gklearn.ged.learning.costs_learner import CostsLearner
from gklearn.ged.util import compute_geds_cml


class CostMatricesLearner(CostsLearner):
def __init__(self, edit_cost='CONSTANT', triangle_rule=False, allow_zeros=True, parallel=False, verbose=2):
super().__init__(parallel, verbose)
self._edit_cost = edit_cost
self._triangle_rule = triangle_rule
self._allow_zeros = allow_zeros
def fit(self, X, y):
if self._edit_cost == 'LETTER':
raise Exception('Cannot compute for cost "LETTER".')
elif self._edit_cost == 'LETTER2':
raise Exception('Cannot compute for cost "LETTER2".')
elif self._edit_cost == 'NON_SYMBOLIC':
raise Exception('Cannot compute for cost "NON_SYMBOLIC".')
elif self._edit_cost == 'CONSTANT': # @todo: node/edge may not labeled.
if not self._triangle_rule and self._allow_zeros:
w = cp.Variable(X.shape[1])
cost_fun = cp.sum_squares(X @ w - y)
constraints = [w >= [0.0 for i in range(X.shape[1])]]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
self.execute_cvx(prob)
edit_costs_new = w.value
residual = np.sqrt(prob.value)
elif self._triangle_rule and self._allow_zeros: # @todo
x = cp.Variable(nb_cost_mat.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat @ x - dis_k_vec)
constraints = [x >= [0.0 for i in range(nb_cost_mat.shape[1])],
np.array([1.0, 0.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 1.0, 0.0, 0.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 0.0, 0.0, 1.0, 0.0, 0.0]).T@x >= 0.01,
np.array([0.0, 0.0, 0.0, 0.0, 1.0, 0.0]).T@x >= 0.01,
np.array([1.0, 1.0, -1.0, 0.0, 0.0, 0.0]).T@x >= 0.0,
np.array([0.0, 0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
self.__execute_cvx(prob)
edit_costs_new = x.value
residual = np.sqrt(prob.value)
elif not self._triangle_rule and not self._allow_zeros: # @todo
x = cp.Variable(nb_cost_mat.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat @ x - dis_k_vec)
constraints = [x >= [0.01 for i in range(nb_cost_mat.shape[1])]]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
self.__execute_cvx(prob)
edit_costs_new = x.value
residual = np.sqrt(prob.value)
elif self._triangle_rule and not self._allow_zeros: # @todo
x = cp.Variable(nb_cost_mat.shape[1])
cost_fun = cp.sum_squares(nb_cost_mat @ x - dis_k_vec)
constraints = [x >= [0.01 for i in range(nb_cost_mat.shape[1])],
np.array([1.0, 1.0, -1.0, 0.0, 0.0, 0.0]).T@x >= 0.0,
np.array([0.0, 0.0, 0.0, 1.0, 1.0, -1.0]).T@x >= 0.0]
prob = cp.Problem(cp.Minimize(cost_fun), constraints)
self.__execute_cvx(prob)
edit_costs_new = x.value
residual = np.sqrt(prob.value)
else:
raise Exception('The edit cost "', self._ged_options['edit_cost'], '" is not supported for update progress.')
self._cost_list.append(edit_costs_new)
def init_geds_and_nb_eo(self, y, graphs):
time0 = time.time()
self._cost_list.append(np.concatenate((self._ged_options['node_label_costs'],
self._ged_options['edge_label_costs'])))
ged_vec, self._nb_eo = self.compute_geds_and_nb_eo(graphs)
self._residual_list.append(np.sqrt(np.sum(np.square(np.array(ged_vec) - y))))
self._runtime_list.append(time.time() - time0)

if self._verbose >= 2:
print('Current node label costs:', self._cost_list[-1][0:len(self._ged_options['node_label_costs'])])
print('Current edge label costs:', self._cost_list[-1][len(self._ged_options['node_label_costs']):])
print('Residual list:', self._residual_list)
def update_geds_and_nb_eo(self, y, graphs, time0):
self._ged_options['node_label_costs'] = self._cost_list[-1][0:len(self._ged_options['node_label_costs'])]
self._ged_options['edge_label_costs'] = self._cost_list[-1][len(self._ged_options['node_label_costs']):]
ged_vec, self._nb_eo = self.compute_geds_and_nb_eo(graphs)
self._residual_list.append(np.sqrt(np.sum(np.square(np.array(ged_vec) - y))))
self._runtime_list.append(time.time() - time0)
def compute_geds_and_nb_eo(self, graphs):
ged_vec, ged_mat, n_edit_operations = compute_geds_cml(graphs, options=self._ged_options, parallel=self._parallel, verbose=(self._verbose > 1))
return ged_vec, np.array(n_edit_operations)
def check_convergency(self):
self._ec_changed = False
for i, cost in enumerate(self._cost_list[-1]):
if cost == 0:
if self._cost_list[-2][i] > self._epsilon_ec:
self._ec_changed = True
break
elif abs(cost - self._cost_list[-2][i]) / cost > self._epsilon_ec:
self._ec_changed = True
break
# if abs(cost - edit_cost_list[-2][i]) > self.__epsilon_ec:
# ec_changed = True
# break
self._residual_changed = False
if self._residual_list[-1] == 0:
if self._residual_list[-2] > self._epsilon_residual:
self._residual_changed = True
elif abs(self._residual_list[-1] - self._residual_list[-2]) / self._residual_list[-1] > self._epsilon_residual:
self._residual_changed = True
self._converged = not (self._ec_changed or self._residual_changed)
if self._converged:
self._itrs_without_update += 1
else:
self._itrs_without_update = 0
self._num_updates_ecs += 1
def print_current_states(self):
print()
print('-------------------------------------------------------------------------')
print('States of iteration', self._itrs + 1)
print('-------------------------------------------------------------------------')
# print('Time spend:', self.__runtime_optimize_ec)
print('Total number of iterations for optimizing:', self._itrs + 1)
print('Total number of updating edit costs:', self._num_updates_ecs)
print('Was optimization of edit costs converged:', self._converged)
print('Did edit costs change:', self._ec_changed)
print('Did residual change:', self._residual_changed)
print('Iterations without update:', self._itrs_without_update)
print('Current node label costs:', self._cost_list[-1][0:len(self._ged_options['node_label_costs'])])
print('Current edge label costs:', self._cost_list[-1][len(self._ged_options['node_label_costs']):])
print('Residual list:', self._residual_list)
print('-------------------------------------------------------------------------')

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