#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Fri Jun 12 10:30:17 2020 @author: ljia This script constructs simple preimages to test preimage methods and find bugs and shortcomings in them. """ def xp_simple_preimage(): import numpy as np """**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': 10, # 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. Compute the Gram matrix and distance matrix.**""" from gklearn.utils.utils import get_graph_kernel_by_name # Get a graph kernel instance. graph_kernel = get_graph_kernel_by_name(kernel_options['name'], node_labels=dataset.node_labels, edge_labels=dataset.edge_labels, node_attrs=dataset.node_attrs, edge_attrs=dataset.edge_attrs, ds_infos=dataset.get_dataset_infos(keys=['directed']), kernel_options=kernel_options) # Compute Gram matrix. gram_matrix, run_time = graph_kernel.compute(dataset.graphs, **kernel_options) # Compute distance matrix. from gklearn.utils import compute_distance_matrix dis_mat, _, _, _ = compute_distance_matrix(gram_matrix) print('done.') """**4. Find the candidate graph.**""" from gklearn.preimage.utils import compute_k_dis # Number of the nearest neighbors. k_neighbors = 10 # For each graph G in dataset, compute the distance between its image \Phi(G) and the mean of its neighbors' images. dis_min = np.inf # the minimum distance between possible \Phi(G) and the mean of its neighbors. for idx, G in enumerate(dataset.graphs): # Find the k nearest neighbors of G. dis_list = dis_mat[idx] # distance between \Phi(G) and image of each graphs. idx_sort = np.argsort(dis_list) # sort distances and get the sorted indices. idx_nearest = idx_sort[1:k_neighbors+1] # indices of the k-nearest neighbors. dis_k_nearest = [dis_list[i] for i in idx_nearest] # k-nearest distances, except the 0. G_k_nearest = [dataset.graphs[i] for i in idx_nearest] # k-nearest neighbors. # Compute the distance between \Phi(G) and the mean of its neighbors. dis_tmp = compute_k_dis(idx, # the index of G in Gram matrix. idx_nearest, # the indices of the neighbors [1 / k_neighbors] * k_neighbors, # coefficients for neighbors. gram_matrix, withterm3=False) # Check if the new distance is smallers. if dis_tmp < dis_min: dis_min = dis_tmp G_cand = G G_neighbors = G_k_nearest print('The minimum distance is', dis_min) """**5. Run median preimage generator.**""" from gklearn.preimage import MedianPreimageGenerator # Set the dataset as the k-nearest neighbors. dataset.load_graphs(G_neighbors) # 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_graph(mpg.set_median) draw_graph(mpg.gen_median) draw_graph(G_cand) # 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() if __name__ == '__main__': xp_simple_preimage()