New translations test_median_graph_estimator.py (Chinese Simplified)

lang/zh/gklearn/ged/median/test_median_graph_estimator.py

@@ -0,0 +1,159 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Mar 16 17:26:40 2020
+
+@author: ljia
+"""
+	
+def test_median_graph_estimator():
+	from gklearn.utils import load_dataset
+	from gklearn.ged.median import MedianGraphEstimator, constant_node_costs
+	from gklearn.gedlib import librariesImport, gedlibpy
+	from gklearn.preimage.utils import get_same_item_indices
+	import multiprocessing
+
+	# estimator parameters.
+	init_type = 'MEDOID'
+	num_inits = 1
+	threads = multiprocessing.cpu_count()
+	time_limit = 60000
+	
+	# algorithm parameters.
+	algo = 'IPFP'
+	initial_solutions = 1
+	algo_options_suffix = ' --initial-solutions ' + str(initial_solutions) + ' --ratio-runs-from-initial-solutions 1 --initialization-method NODE '
+
+	edit_cost_name = 'LETTER2'
+	edit_cost_constants = [0.02987291, 0.0178211, 0.01431966, 0.001, 0.001]
+	ds_name = 'Letter_high'
+	
+	# Load dataset.
+	# dataset = '../../datasets/COIL-DEL/COIL-DEL_A.txt'
+	dataset = '../../../datasets/Letter-high/Letter-high_A.txt'
+	Gn, y_all, label_names = load_dataset(dataset)
+	y_idx = get_same_item_indices(y_all)
+	for i, (y, values) in enumerate(y_idx.items()):
+		Gn_i = [Gn[val] for val in values]
+		break
+	
+	# Set up the environment.
+	ged_env = gedlibpy.GEDEnv()
+	# gedlibpy.restart_env()
+	ged_env.set_edit_cost(edit_cost_name, edit_cost_constant=edit_cost_constants)
+	for G in Gn_i:
+		ged_env.add_nx_graph(G, '')
+	graph_ids = ged_env.get_all_graph_ids()
+	set_median_id = ged_env.add_graph('set_median')
+	gen_median_id = ged_env.add_graph('gen_median')
+	ged_env.init(init_option='EAGER_WITHOUT_SHUFFLED_COPIES')
+	
+	# Set up the estimator.
+	mge = MedianGraphEstimator(ged_env, constant_node_costs(edit_cost_name))
+	mge.set_refine_method(algo, '--threads ' + str(threads) + ' --initial-solutions ' + str(initial_solutions) + ' --ratio-runs-from-initial-solutions 1')
+	
+	mge_options = '--time-limit ' + str(time_limit) + ' --stdout 2 --init-type ' + init_type
+	mge_options += ' --random-inits ' + str(num_inits) + ' --seed ' + '1'  + ' --update-order TRUE --refine FALSE --randomness PSEUDO  --parallel TRUE '# @todo: std::to_string(rng())
+	
+	# Select the GED algorithm.
+	algo_options = '--threads ' + str(threads) + algo_options_suffix
+	mge.set_options(mge_options)
+	mge.set_label_names(node_labels=label_names['node_labels'],
+					  edge_labels=label_names['edge_labels'], 
+					  node_attrs=label_names['node_attrs'], 
+					  edge_attrs=label_names['edge_attrs'])
+	mge.set_init_method(algo, algo_options)
+	mge.set_descent_method(algo, algo_options)
+	
+	# Run the estimator.
+	mge.run(graph_ids, set_median_id, gen_median_id)
+	
+	# Get SODs.
+	sod_sm = mge.get_sum_of_distances('initialized')
+	sod_gm = mge.get_sum_of_distances('converged')
+	print('sod_sm, sod_gm: ', sod_sm, sod_gm)
+	
+	# Get median graphs.
+	set_median = ged_env.get_nx_graph(set_median_id)
+	gen_median = ged_env.get_nx_graph(gen_median_id)
+	
+	return set_median, gen_median
+
+
+def test_median_graph_estimator_symb():
+	from gklearn.utils import load_dataset
+	from gklearn.ged.median import MedianGraphEstimator, constant_node_costs
+	from gklearn.gedlib import librariesImport, gedlibpy
+	from gklearn.preimage.utils import get_same_item_indices
+	import multiprocessing
+
+	# estimator parameters.
+	init_type = 'MEDOID'
+	num_inits = 1
+	threads = multiprocessing.cpu_count()
+	time_limit = 60000
+	
+	# algorithm parameters.
+	algo = 'IPFP'
+	initial_solutions = 1
+	algo_options_suffix = ' --initial-solutions ' + str(initial_solutions) + ' --ratio-runs-from-initial-solutions 1 --initialization-method NODE '
+
+	edit_cost_name = 'CONSTANT'
+	edit_cost_constants = [4, 4, 2, 1, 1, 1]
+	ds_name = 'MUTAG'
+	
+	# Load dataset.
+	dataset = '../../../datasets/MUTAG/MUTAG_A.txt'
+	Gn, y_all, label_names = load_dataset(dataset)
+	y_idx = get_same_item_indices(y_all)
+	for i, (y, values) in enumerate(y_idx.items()):
+		Gn_i = [Gn[val] for val in values]
+		break
+	Gn_i = Gn_i[0:10]
+	
+	# Set up the environment.
+	ged_env = gedlibpy.GEDEnv()
+	# gedlibpy.restart_env()
+	ged_env.set_edit_cost(edit_cost_name, edit_cost_constant=edit_cost_constants)
+	for G in Gn_i:
+		ged_env.add_nx_graph(G, '')
+	graph_ids = ged_env.get_all_graph_ids()
+	set_median_id = ged_env.add_graph('set_median')
+	gen_median_id = ged_env.add_graph('gen_median')
+	ged_env.init(init_option='EAGER_WITHOUT_SHUFFLED_COPIES')
+	
+	# Set up the estimator.
+	mge = MedianGraphEstimator(ged_env, constant_node_costs(edit_cost_name))
+	mge.set_refine_method(algo, '--threads ' + str(threads) + ' --initial-solutions ' + str(initial_solutions) + ' --ratio-runs-from-initial-solutions 1')
+	
+	mge_options = '--time-limit ' + str(time_limit) + ' --stdout 2 --init-type ' + init_type
+	mge_options += ' --random-inits ' + str(num_inits) + ' --seed ' + '1'  + ' --update-order TRUE --refine FALSE --randomness PSEUDO --parallel TRUE '# @todo: std::to_string(rng())
+	
+	# Select the GED algorithm.
+	algo_options = '--threads ' + str(threads) + algo_options_suffix
+	mge.set_options(mge_options)
+	mge.set_label_names(node_labels=label_names['node_labels'],
+					  edge_labels=label_names['edge_labels'], 
+					  node_attrs=label_names['node_attrs'], 
+					  edge_attrs=label_names['edge_attrs'])
+	mge.set_init_method(algo, algo_options)
+	mge.set_descent_method(algo, algo_options)
+	
+	# Run the estimator.
+	mge.run(graph_ids, set_median_id, gen_median_id)
+	
+	# Get SODs.
+	sod_sm = mge.get_sum_of_distances('initialized')
+	sod_gm = mge.get_sum_of_distances('converged')
+	print('sod_sm, sod_gm: ', sod_sm, sod_gm)
+	
+	# Get median graphs.
+	set_median = ged_env.get_nx_graph(set_median_id)
+	gen_median = ged_env.get_nx_graph(gen_median_id)
+	
+	return set_median, gen_median
+
+
+if __name__ == '__main__':
+ 	# set_median, gen_median = test_median_graph_estimator()
+ 	set_median, gen_median = test_median_graph_estimator_symb()