Fix bugs for enum and add test for median preimage generator.

gklearn/ged/env/common_types.py

@@ -6,12 +6,13 @@ Created on Thu Mar 19 18:17:38 2020
 @author: ljia
 """
 
-from enum import Enum, auto
+from enum import Enum, unique
 
+@unique
 class AlgorithmState(Enum):
     """can be used to specify the state of an algorithm.
     """
-    CALLED = auto # The algorithm has been called.
-    INITIALIZED = auto # The algorithm has been initialized.
-    CONVERGED = auto # The algorithm has converged.
-    TERMINATED = auto # The algorithm has terminated.
+    CALLED = 1 # The algorithm has been called.
+    INITIALIZED = 2 # The algorithm has been initialized.
+    CONVERGED = 3 # The algorithm has converged.
+    TERMINATED = 4 # The algorithm has terminated.

gklearn/ged/median/median_graph_estimator.py

@@ -302,7 +302,7 @@ class MedianGraphEstimator(object): # @todo: differ dummy_node from undifined no
 		self.__median_id = gen_median_id
 		self.__state = AlgorithmState.TERMINATED
 		
-		# Get ExchangeGraph representations of the input graphs.
+		# Get NetworkX graph representations of the input graphs.
 		graphs = {}
 		for graph_id in graph_ids:
 			# @todo: get_nx_graph() function may need to be modified according to the coming code.

gklearn/tests/test_median_preimage_generator.py

@@ -0,0 +1,71 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Jan 14 15:39:29 2020
+
+@author: ljia
+"""
+import multiprocessing
+import functools
+from gklearn.preimage.utils import generate_median_preimages_by_class
+
+		
+def test_median_preimage_generator():
+	"""MAO, Treelet, using CONSTANT, symbolic only.
+	"""
+	from gklearn.utils.kernels import polynomialkernel
+	# set parameters.
+	ds_name = 'MAO' #
+	mpg_options = {'fit_method': 'k-graphs',
+				   'init_ecc': [4, 4, 2, 1, 1, 1], #
+				   'ds_name': ds_name,
+				   'parallel': True, # False
+				   'time_limit_in_sec': 0,
+				   'max_itrs': 3, # 
+				   'max_itrs_without_update': 3,
+				   'epsilon_residual': 0.01,
+				   'epsilon_ec': 0.1,
+				   'verbose': 2}
+	pkernel = functools.partial(polynomialkernel, d=4, c=1e+7)
+	kernel_options = {'name': 'Treelet', #
+				      'sub_kernel': pkernel,
+ 					  'parallel': 'imap_unordered', 
+                      # 'parallel': None, 
+					  'n_jobs': multiprocessing.cpu_count(),
+					  'normalize': True,
+					  'verbose': 2}
+	ged_options = {'method': 'IPFP',
+				   'initialization_method': 'RANDOM', # 'NODE'
+				   'initial_solutions': 1, # 1
+				   'edit_cost': 'CONSTANT', # 
+				   'attr_distance': 'euclidean',
+				   'ratio_runs_from_initial_solutions': 1,
+				   'threads': multiprocessing.cpu_count(),
+				   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'}
+	mge_options = {'init_type': 'MEDOID',
+				   'random_inits': 10,
+				   'time_limit': 600,
+				   'verbose': 2,
+				   'refine': False}
+	save_results = True
+	dir_save = ds_name + '.' + kernel_options['name'] + '.symb.pytest/'
+	irrelevant_labels = {'node_attrs': ['x', 'y', 'z'], 'edge_labels': ['bond_stereo']} # 
+	edge_required = False #
+	
+	# print settings.
+	print('parameters:')
+	print('dataset name:', ds_name)
+	print('mpg_options:', mpg_options)
+	print('kernel_options:', kernel_options)
+	print('ged_options:', ged_options)
+	print('mge_options:', mge_options)
+	print('save_results:', save_results)
+	print('irrelevant_labels:', irrelevant_labels)
+	print()
+	
+	# generate preimages.
+	for fit_method in ['k-graphs', 'expert', 'random']:
+		print('\n-------------------------------------')
+		print('fit method:', fit_method, '\n')
+		mpg_options['fit_method'] = fit_method
+		generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required, cut_range=range(0, 4))

gklearn/utils/utils.py

@@ -1,7 +1,7 @@
 import networkx as nx
 import numpy as np
 from copy import deepcopy
-from enum import Enum, auto
+from enum import Enum, unique
 #from itertools import product
 
 # from tqdm import tqdm
@@ -467,8 +467,9 @@ def get_mlti_dim_edge_attrs(G, attr_names):
 		attributes.append(tuple(attrs[aname] for aname in attr_names))
 	return attributes
 
-
+@unique
 class SpecialLabel(Enum):
-    """can be used to define special labels.
-    """
-    DUMMY = auto # The dummy label.
+	"""can be used to define special labels.
+	"""
+	DUMMY = 1 # The dummy label.
+	# DUMMY = auto # enum.auto does not exist in Python 3.5.