Add examples.

Author: jajupmochi

gklearn/examples/median_preimege_generator.py

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+# -*- coding: utf-8 -*-
+"""example_median_preimege_generator.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1PIDvHOcmiLEQ5Np3bgBDdu0kLOquOMQK
+
+**This script demonstrates how to generate a graph preimage using Boria's method.**
+---
+"""
+
+"""**1.   Get dataset.**"""
+
+from gklearn.utils import Dataset, split_dataset_by_target
+
+# Predefined dataset name, use dataset "MAO".
+ds_name = 'MAO'
+# The node/edge labels that will not be used in the computation.
+irrelevant_labels = {'node_attrs': ['x', 'y', 'z'], 'edge_labels': ['bond_stereo']}
+
+# Initialize a Dataset.
+dataset_all = Dataset()
+# Load predefined dataset "MAO".
+dataset_all.load_predefined_dataset(ds_name)
+# Remove irrelevant labels.
+dataset_all.remove_labels(**irrelevant_labels)
+# Split the whole dataset according to the classification targets.
+datasets = split_dataset_by_target(dataset_all)
+# Get the first class of graphs, whose median preimage will be computed.
+dataset = datasets[0]
+len(dataset.graphs)
+
+"""**2.  Set parameters.**"""
+
+import multiprocessing
+
+# Parameters for MedianPreimageGenerator (our method).
+mpg_options = {'fit_method': 'k-graphs', # how to fit edit costs. "k-graphs" means use all graphs in median set when fitting.
+			   'init_ecc': [4, 4, 2, 1, 1, 1], # initial edit costs.
+			   'ds_name': ds_name, # name of the dataset.
+			   'parallel': True, # whether the parallel scheme is to be used.
+			   'time_limit_in_sec': 0, # maximum time limit to compute the preimage. If set to 0 then no limit.
+			   'max_itrs': 100, # maximum iteration limit to optimize edit costs. If set to 0 then no limit.
+			   'max_itrs_without_update': 3, # If the times that edit costs is not update is more than this number, then the optimization stops.
+			   'epsilon_residual': 0.01, # In optimization, the residual is only considered changed if the change is bigger than this number.
+			   'epsilon_ec': 0.1, # In optimization, the edit costs are only considered changed if the changes are bigger than this number.
+			   'verbose': 2 # whether to print out results.
+               }
+# Parameters for graph kernel computation.
+kernel_options = {'name': 'PathUpToH', # use path kernel up to length h.
+				  'depth': 9,
+				  'k_func': 'MinMax',
+				  'compute_method': 'trie',
+				  'parallel': 'imap_unordered', # or None
+				  'n_jobs': multiprocessing.cpu_count(),
+				  'normalize': True, # whether to use normalized Gram matrix to optimize edit costs.
+				  'verbose': 2 # whether to print out results.
+                  }
+# Parameters for GED computation.
+ged_options = {'method': 'IPFP', # use IPFP huristic.
+			   'initialization_method': 'RANDOM', # or 'NODE', etc.
+			   'initial_solutions': 10, # when bigger than 1, then the method is considered mIPFP.
+			   'edit_cost': 'CONSTANT', # use CONSTANT cost.
+			   'attr_distance': 'euclidean', # the distance between non-symbolic node/edge labels is computed by euclidean distance.
+			   'ratio_runs_from_initial_solutions': 1,
+			   'threads': multiprocessing.cpu_count(), # parallel threads. Do not work if mpg_options['parallel'] = False.
+			   'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'
+               }
+# Parameters for MedianGraphEstimator (Boria's method).
+mge_options = {'init_type': 'MEDOID', # how to initial median (compute set-median). "MEDOID" is to use the graph with smallest SOD.
+			   'random_inits': 10, # number of random initialization when 'init_type' = 'RANDOM'.
+			   'time_limit': 600, # maximum time limit to compute the generalized median. If set to 0 then no limit.
+			   'verbose': 2, # whether to print out results.
+			   'refine': False # whether to refine the final SODs or not.
+               }
+print('done.')
+
+"""**3.   Run median preimage generator.**"""
+
+from gklearn.preimage import MedianPreimageGenerator
+
+# Create median preimage generator instance.
+mpg = MedianPreimageGenerator()
+# Add dataset.
+mpg.dataset = dataset
+# Set parameters.
+mpg.set_options(**mpg_options.copy())
+mpg.kernel_options = kernel_options.copy()
+mpg.ged_options = ged_options.copy()
+mpg.mge_options = mge_options.copy()
+# Run.
+mpg.run()
+
+"""**4. Get results.**"""
+
+# Get results.
+import pprint
+pp = pprint.PrettyPrinter(indent=4) # pretty print
+results = mpg.get_results()
+pp.pprint(results)
+
+# Draw generated graphs.
+def draw_graph(graph):
+	import matplotlib.pyplot as plt
+	import networkx as nx
+	plt.figure()
+	pos = nx.spring_layout(graph)
+	nx.draw(graph, pos, node_size=500, labels=nx.get_node_attributes(graph, 'atom_symbol'), font_color='w', width=3, with_labels=True)
+	plt.show()
+	plt.clf()
+	plt.close()
+ 
+draw_graph(mpg.set_median)
+draw_graph(mpg.gen_median)