#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Mar 26 18:48:27 2020 @author: ljia """ import numpy as np import networkx as nx import os from gklearn.dataset import DATASET_META, DataFetcher, DataLoader class Dataset(object): def __init__(self, inputs=None, root='datasets', filename_targets=None, targets=None, mode='networkx', clean_labels=True, reload=False, verbose=False, **kwargs): if inputs is None: self._graphs = None self._targets = None self._node_labels = None self._edge_labels = None self._node_attrs = None self._edge_attrs = None # If inputs is a list of graphs. elif isinstance(inputs, list): node_labels = kwargs.get('node_labels', None) node_attrs = kwargs.get('node_attrs', None) edge_labels = kwargs.get('edge_labels', None) edge_attrs = kwargs.get('edge_attrs', None) self.load_graphs(inputs, targets=targets) self.set_labels(node_labels=node_labels, node_attrs=node_attrs, edge_labels=edge_labels, edge_attrs=edge_attrs) if clean_labels: self.clean_labels() elif isinstance(inputs, str): # If inputs is predefined dataset name. if inputs in DATASET_META: self.load_predefined_dataset(inputs, root=root, clean_labels=clean_labels, reload=reload, verbose=verbose) # If inputs is a file name. else: self.load_dataset(inputs, filename_targets=filename_targets, clean_labels=clean_labels, **kwargs) else: raise TypeError('The "inputs" argument cannot be recoganized. "Inputs" can be a list of graphs, a predefined dataset name, or a file name of a dataset.') self._substructures = None self._node_label_dim = None self._edge_label_dim = None self._directed = None self._dataset_size = None self._total_node_num = None self._ave_node_num = None self._min_node_num = None self._max_node_num = None self._total_edge_num = None self._ave_edge_num = None self._min_edge_num = None self._max_edge_num = None self._ave_node_degree = None self._min_node_degree = None self._max_node_degree = None self._ave_fill_factor = None self._min_fill_factor = None self._max_fill_factor = None self._node_label_nums = None self._edge_label_nums = None self._node_attr_dim = None self._edge_attr_dim = None self._class_number = None def load_dataset(self, filename, filename_targets=None, clean_labels=True, **kwargs): self._graphs, self._targets, label_names = DataLoader(filename, filename_targets=filename_targets, **kwargs).data self._node_labels = label_names['node_labels'] self._node_attrs = label_names['node_attrs'] self._edge_labels = label_names['edge_labels'] self._edge_attrs = label_names['edge_attrs'] if clean_labels: self.clean_labels() def load_graphs(self, graphs, targets=None): # this has to be followed by set_labels(). self._graphs = graphs self._targets = targets # self.set_labels_attrs() # @todo def load_predefined_dataset(self, ds_name, root='datasets', clean_labels=True, reload=False, verbose=False): path = DataFetcher(name=ds_name, root=root, reload=reload, verbose=verbose).path if DATASET_META[ds_name]['database'] == 'tudataset': ds_file = os.path.join(path, ds_name + '_A.txt') fn_targets = None else: load_files = DATASET_META[ds_name]['load_files'] ds_file = os.path.join(path, load_files[0]) fn_targets = os.path.join(path, load_files[1]) if len(load_files) == 2 else None self._graphs, self._targets, label_names = DataLoader(ds_file, filename_targets=fn_targets).data self._node_labels = label_names['node_labels'] self._node_attrs = label_names['node_attrs'] self._edge_labels = label_names['edge_labels'] self._edge_attrs = label_names['edge_attrs'] if clean_labels: self.clean_labels() def set_labels(self, node_labels=[], node_attrs=[], edge_labels=[], edge_attrs=[]): self._node_labels = node_labels self._node_attrs = node_attrs self._edge_labels = edge_labels self._edge_attrs = edge_attrs def set_labels_attrs(self, node_labels=None, node_attrs=None, edge_labels=None, edge_attrs=None): # @todo: remove labels which have only one possible values. if node_labels is None: self._node_labels = self._graphs[0].graph['node_labels'] # # graphs are considered node unlabeled if all nodes have the same label. # infos.update({'node_labeled': is_nl if node_label_num > 1 else False}) if node_attrs is None: self._node_attrs = self._graphs[0].graph['node_attrs'] # for G in Gn: # for n in G.nodes(data=True): # if 'attributes' in n[1]: # return len(n[1]['attributes']) # return 0 if edge_labels is None: self._edge_labels = self._graphs[0].graph['edge_labels'] # # graphs are considered edge unlabeled if all edges have the same label. # infos.update({'edge_labeled': is_el if edge_label_num > 1 else False}) if edge_attrs is None: self._edge_attrs = self._graphs[0].graph['edge_attrs'] # for G in Gn: # if nx.number_of_edges(G) > 0: # for e in G.edges(data=True): # if 'attributes' in e[2]: # return len(e[2]['attributes']) # return 0 def get_dataset_infos(self, keys=None, params=None): """Computes and returns the structure and property information of the graph dataset. Parameters ---------- keys : list, optional A list of strings which indicate which informations will be returned. The possible choices includes: 'substructures': sub-structures graphs contains, including 'linear', 'non linear' and 'cyclic'. 'node_label_dim': whether vertices have symbolic labels. 'edge_label_dim': whether egdes have symbolic labels. 'directed': whether graphs in dataset are directed. 'dataset_size': number of graphs in dataset. 'total_node_num': total number of vertices of all graphs in dataset. 'ave_node_num': average number of vertices of graphs in dataset. 'min_node_num': minimum number of vertices of graphs in dataset. 'max_node_num': maximum number of vertices of graphs in dataset. 'total_edge_num': total number of edges of all graphs in dataset. 'ave_edge_num': average number of edges of graphs in dataset. 'min_edge_num': minimum number of edges of graphs in dataset. 'max_edge_num': maximum number of edges of graphs in dataset. 'ave_node_degree': average vertex degree of graphs in dataset. 'min_node_degree': minimum vertex degree of graphs in dataset. 'max_node_degree': maximum vertex degree of graphs in dataset. 'ave_fill_factor': average fill factor (number_of_edges / (number_of_nodes ** 2)) of graphs in dataset. 'min_fill_factor': minimum fill factor of graphs in dataset. 'max_fill_factor': maximum fill factor of graphs in dataset. 'node_label_nums': list of numbers of symbolic vertex labels of graphs in dataset. 'edge_label_nums': list number of symbolic edge labels of graphs in dataset. 'node_attr_dim': number of dimensions of non-symbolic vertex labels. Extracted from the 'attributes' attribute of graph nodes. 'edge_attr_dim': number of dimensions of non-symbolic edge labels. Extracted from the 'attributes' attribute of graph edges. 'class_number': number of classes. Only available for classification problems. 'all_degree_entropy': the entropy of degree distribution of each graph. 'ave_degree_entropy': the average entropy of degree distribution of all graphs. All informations above will be returned if `keys` is not given. params: dict of dict, optional A dictinary which contains extra parameters for each possible element in ``keys``. Return ------ dict Information of the graph dataset keyed by `keys`. """ infos = {} if keys == None: keys = [ 'substructures', 'node_label_dim', 'edge_label_dim', 'directed', 'dataset_size', 'total_node_num', 'ave_node_num', 'min_node_num', 'max_node_num', 'total_edge_num', 'ave_edge_num', 'min_edge_num', 'max_edge_num', 'ave_node_degree', 'min_node_degree', 'max_node_degree', 'ave_fill_factor', 'min_fill_factor', 'max_fill_factor', 'node_label_nums', 'edge_label_nums', 'node_attr_dim', 'edge_attr_dim', 'class_number', 'all_degree_entropy', 'ave_degree_entropy' ] # dataset size if 'dataset_size' in keys: if self._dataset_size is None: self._dataset_size = self._get_dataset_size() infos['dataset_size'] = self._dataset_size # graph node number if any(i in keys for i in ['total_node_num', 'ave_node_num', 'min_node_num', 'max_node_num']): all_node_nums = self._get_all_node_nums() if 'total_node_num' in keys: if self._total_node_num is None: self._total_node_num = self._get_total_node_num(all_node_nums) infos['total_node_num'] = self._total_node_num if 'ave_node_num' in keys: if self._ave_node_num is None: self._ave_node_num = self._get_ave_node_num(all_node_nums) infos['ave_node_num'] = self._ave_node_num if 'min_node_num' in keys: if self._min_node_num is None: self._min_node_num = self._get_min_node_num(all_node_nums) infos['min_node_num'] = self._min_node_num if 'max_node_num' in keys: if self._max_node_num is None: self._max_node_num = self._get_max_node_num(all_node_nums) infos['max_node_num'] = self._max_node_num # graph edge number if any(i in keys for i in ['total_edge_num', 'ave_edge_num', 'min_edge_num', 'max_edge_num']): all_edge_nums = self._get_all_edge_nums() if 'total_edge_num' in keys: if self._total_edge_num is None: self._total_edge_num = self._get_total_edge_num(all_edge_nums) infos['total_edge_num'] = self._total_edge_num if 'ave_edge_num' in keys: if self._ave_edge_num is None: self._ave_edge_num = self._get_ave_edge_num(all_edge_nums) infos['ave_edge_num'] = self._ave_edge_num if 'max_edge_num' in keys: if self._max_edge_num is None: self._max_edge_num = self._get_max_edge_num(all_edge_nums) infos['max_edge_num'] = self._max_edge_num if 'min_edge_num' in keys: if self._min_edge_num is None: self._min_edge_num = self._get_min_edge_num(all_edge_nums) infos['min_edge_num'] = self._min_edge_num # label number if 'node_label_dim' in keys: if self._node_label_dim is None: self._node_label_dim = self._get_node_label_dim() infos['node_label_dim'] = self._node_label_dim if 'node_label_nums' in keys: if self._node_label_nums is None: self._node_label_nums = {} for node_label in self._node_labels: self._node_label_nums[node_label] = self._get_node_label_num(node_label) infos['node_label_nums'] = self._node_label_nums if 'edge_label_dim' in keys: if self._edge_label_dim is None: self._edge_label_dim = self._get_edge_label_dim() infos['edge_label_dim'] = self._edge_label_dim if 'edge_label_nums' in keys: if self._edge_label_nums is None: self._edge_label_nums = {} for edge_label in self._edge_labels: self._edge_label_nums[edge_label] = self._get_edge_label_num(edge_label) infos['edge_label_nums'] = self._edge_label_nums if 'directed' in keys or 'substructures' in keys: if self._directed is None: self._directed = self._is_directed() infos['directed'] = self._directed # node degree if any(i in keys for i in ['ave_node_degree', 'max_node_degree', 'min_node_degree']): all_node_degrees = self._get_all_node_degrees() if 'ave_node_degree' in keys: if self._ave_node_degree is None: self._ave_node_degree = self._get_ave_node_degree(all_node_degrees) infos['ave_node_degree'] = self._ave_node_degree if 'max_node_degree' in keys: if self._max_node_degree is None: self._max_node_degree = self._get_max_node_degree(all_node_degrees) infos['max_node_degree'] = self._max_node_degree if 'min_node_degree' in keys: if self._min_node_degree is None: self._min_node_degree = self._get_min_node_degree(all_node_degrees) infos['min_node_degree'] = self._min_node_degree # fill factor if any(i in keys for i in ['ave_fill_factor', 'max_fill_factor', 'min_fill_factor']): all_fill_factors = self._get_all_fill_factors() if 'ave_fill_factor' in keys: if self._ave_fill_factor is None: self._ave_fill_factor = self._get_ave_fill_factor(all_fill_factors) infos['ave_fill_factor'] = self._ave_fill_factor if 'max_fill_factor' in keys: if self._max_fill_factor is None: self._max_fill_factor = self._get_max_fill_factor(all_fill_factors) infos['max_fill_factor'] = self._max_fill_factor if 'min_fill_factor' in keys: if self._min_fill_factor is None: self._min_fill_factor = self._get_min_fill_factor(all_fill_factors) infos['min_fill_factor'] = self._min_fill_factor if 'substructures' in keys: if self._substructures is None: self._substructures = self._get_substructures() infos['substructures'] = self._substructures if 'class_number' in keys: if self._class_number is None: self._class_number = self._get_class_number() infos['class_number'] = self._class_number if 'node_attr_dim' in keys: if self._node_attr_dim is None: self._node_attr_dim = self._get_node_attr_dim() infos['node_attr_dim'] = self._node_attr_dim if 'edge_attr_dim' in keys: if self._edge_attr_dim is None: self._edge_attr_dim = self._get_edge_attr_dim() infos['edge_attr_dim'] = self._edge_attr_dim # entropy of degree distribution. if 'all_degree_entropy' in keys: if params is not None and ('all_degree_entropy' in params) and ('base' in params['all_degree_entropy']): base = params['all_degree_entropy']['base'] else: base = None infos['all_degree_entropy'] = self._compute_all_degree_entropy(base=base) if 'ave_degree_entropy' in keys: if params is not None and ('ave_degree_entropy' in params) and ('base' in params['ave_degree_entropy']): base = params['ave_degree_entropy']['base'] else: base = None infos['ave_degree_entropy'] = np.mean(self._compute_all_degree_entropy(base=base)) return infos def print_graph_infos(self, infos): from collections import OrderedDict keys = list(infos.keys()) print(OrderedDict(sorted(infos.items(), key=lambda i: keys.index(i[0])))) def remove_labels(self, node_labels=[], edge_labels=[], node_attrs=[], edge_attrs=[]): node_labels = [item for item in node_labels if item in self._node_labels] edge_labels = [item for item in edge_labels if item in self._edge_labels] node_attrs = [item for item in node_attrs if item in self._node_attrs] edge_attrs = [item for item in edge_attrs if item in self._edge_attrs] for g in self._graphs: for nd in g.nodes(): for nl in node_labels: del g.nodes[nd][nl] for na in node_attrs: del g.nodes[nd][na] for ed in g.edges(): for el in edge_labels: del g.edges[ed][el] for ea in edge_attrs: del g.edges[ed][ea] if len(node_labels) > 0: self._node_labels = [nl for nl in self._node_labels if nl not in node_labels] if len(edge_labels) > 0: self._edge_labels = [el for el in self._edge_labels if el not in edge_labels] if len(node_attrs) > 0: self._node_attrs = [na for na in self._node_attrs if na not in node_attrs] if len(edge_attrs) > 0: self._edge_attrs = [ea for ea in self._edge_attrs if ea not in edge_attrs] def clean_labels(self): labels = [] for name in self._node_labels: label = set() for G in self._graphs: label = label | set(nx.get_node_attributes(G, name).values()) if len(label) > 1: labels.append(name) break if len(label) < 2: for G in self._graphs: for nd in G.nodes(): del G.nodes[nd][name] self._node_labels = labels labels = [] for name in self._edge_labels: label = set() for G in self._graphs: label = label | set(nx.get_edge_attributes(G, name).values()) if len(label) > 1: labels.append(name) break if len(label) < 2: for G in self._graphs: for ed in G.edges(): del G.edges[ed][name] self._edge_labels = labels labels = [] for name in self._node_attrs: label = set() for G in self._graphs: label = label | set(nx.get_node_attributes(G, name).values()) if len(label) > 1: labels.append(name) break if len(label) < 2: for G in self._graphs: for nd in G.nodes(): del G.nodes[nd][name] self._node_attrs = labels labels = [] for name in self._edge_attrs: label = set() for G in self._graphs: label = label | set(nx.get_edge_attributes(G, name).values()) if len(label) > 1: labels.append(name) break if len(label) < 2: for G in self._graphs: for ed in G.edges(): del G.edges[ed][name] self._edge_attrs = labels def cut_graphs(self, range_): self._graphs = [self._graphs[i] for i in range_] if self._targets is not None: self._targets = [self._targets[i] for i in range_] self.clean_labels() def trim_dataset(self, edge_required=False): if edge_required: trimed_pairs = [(idx, g) for idx, g in enumerate(self._graphs) if (nx.number_of_nodes(g) != 0 and nx.number_of_edges(g) != 0)] else: trimed_pairs = [(idx, g) for idx, g in enumerate(self._graphs) if nx.number_of_nodes(g) != 0] idx = [p[0] for p in trimed_pairs] self._graphs = [p[1] for p in trimed_pairs] self._targets = [self._targets[i] for i in idx] self.clean_labels() def copy(self): dataset = Dataset() graphs = [g.copy() for g in self._graphs] if self._graphs is not None else None target = self._targets.copy() if self._targets is not None else None node_labels = self._node_labels.copy() if self._node_labels is not None else None node_attrs = self._node_attrs.copy() if self._node_attrs is not None else None edge_labels = self._edge_labels.copy() if self._edge_labels is not None else None edge_attrs = self._edge_attrs.copy() if self._edge_attrs is not None else None dataset.load_graphs(graphs, target) dataset.set_labels(node_labels=node_labels, node_attrs=node_attrs, edge_labels=edge_labels, edge_attrs=edge_attrs) # @todo: clean_labels and add other class members? return dataset def get_all_node_labels(self): node_labels = [] for g in self._graphs: for n in g.nodes(): nl = tuple(g.nodes[n].items()) if nl not in node_labels: node_labels.append(nl) return node_labels def get_all_edge_labels(self): edge_labels = [] for g in self._graphs: for e in g.edges(): el = tuple(g.edges[e].items()) if el not in edge_labels: edge_labels.append(el) return edge_labels def _get_dataset_size(self): return len(self._graphs) def _get_all_node_nums(self): return [nx.number_of_nodes(G) for G in self._graphs] def _get_total_node_nums(self, all_node_nums): return np.sum(all_node_nums) def _get_ave_node_num(self, all_node_nums): return np.mean(all_node_nums) def _get_min_node_num(self, all_node_nums): return np.amin(all_node_nums) def _get_max_node_num(self, all_node_nums): return np.amax(all_node_nums) def _get_all_edge_nums(self): return [nx.number_of_edges(G) for G in self._graphs] def _get_total_edge_nums(self, all_edge_nums): return np.sum(all_edge_nums) def _get_ave_edge_num(self, all_edge_nums): return np.mean(all_edge_nums) def _get_min_edge_num(self, all_edge_nums): return np.amin(all_edge_nums) def _get_max_edge_num(self, all_edge_nums): return np.amax(all_edge_nums) def _get_node_label_dim(self): return len(self._node_labels) def _get_node_label_num(self, node_label): nl = set() for G in self._graphs: nl = nl | set(nx.get_node_attributes(G, node_label).values()) return len(nl) def _get_edge_label_dim(self): return len(self._edge_labels) def _get_edge_label_num(self, edge_label): el = set() for G in self._graphs: el = el | set(nx.get_edge_attributes(G, edge_label).values()) return len(el) def _is_directed(self): return nx.is_directed(self._graphs[0]) def _get_all_node_degrees(self): return [np.mean(list(dict(G.degree()).values())) for G in self._graphs] def _get_ave_node_degree(self, all_node_degrees): return np.mean(all_node_degrees) def _get_max_node_degree(self, all_node_degrees): return np.amax(all_node_degrees) def _get_min_node_degree(self, all_node_degrees): return np.amin(all_node_degrees) def _get_all_fill_factors(self): """Get fill factor, the number of non-zero entries in the adjacency matrix. Returns ------- list[float] List of fill factors for all graphs. """ return [nx.number_of_edges(G) / (nx.number_of_nodes(G) ** 2) for G in self._graphs] def _get_ave_fill_factor(self, all_fill_factors): return np.mean(all_fill_factors) def _get_max_fill_factor(self, all_fill_factors): return np.amax(all_fill_factors) def _get_min_fill_factor(self, all_fill_factors): return np.amin(all_fill_factors) def _get_substructures(self): subs = set() for G in self._graphs: degrees = list(dict(G.degree()).values()) if any(i == 2 for i in degrees): subs.add('linear') if np.amax(degrees) >= 3: subs.add('non linear') if 'linear' in subs and 'non linear' in subs: break if self._directed: for G in self._graphs: if len(list(nx.find_cycle(G))) > 0: subs.add('cyclic') break # else: # # @todo: this method does not work for big graph with large amount of edges like D&D, try a better way. # upper = np.amin([nx.number_of_edges(G) for G in Gn]) * 2 + 10 # for G in Gn: # if (nx.number_of_edges(G) < upper): # cyc = list(nx.simple_cycles(G.to_directed())) # if any(len(i) > 2 for i in cyc): # subs.add('cyclic') # break # if 'cyclic' not in subs: # for G in Gn: # cyc = list(nx.simple_cycles(G.to_directed())) # if any(len(i) > 2 for i in cyc): # subs.add('cyclic') # break return subs def _get_class_num(self): return len(set(self._targets)) def _get_node_attr_dim(self): return len(self._node_attrs) def _get_edge_attr_dim(self): return len(self._edge_attrs) def _compute_all_degree_entropy(self, base=None): """Compute the entropy of degree distribution of each graph. Parameters ---------- base : float, optional The logarithmic base to use. The default is ``e`` (natural logarithm). Returns ------- degree_entropy : float The calculated entropy. """ from gklearn.utils.stats import entropy degree_entropy = [] for g in self._graphs: degrees = list(dict(g.degree()).values()) en = entropy(degrees, base=base) degree_entropy.append(en) return degree_entropy @property def graphs(self): return self._graphs @property def targets(self): return self._targets @property def node_labels(self): return self._node_labels @property def edge_labels(self): return self._edge_labels @property def node_attrs(self): return self._node_attrs @property def edge_attrs(self): return self._edge_attrs def split_dataset_by_target(dataset): from gklearn.preimage.utils import get_same_item_indices graphs = dataset.graphs targets = dataset.targets datasets = [] idx_targets = get_same_item_indices(targets) for key, val in idx_targets.items(): sub_graphs = [graphs[i] for i in val] sub_dataset = Dataset() sub_dataset.load_graphs(sub_graphs, [key] * len(val)) node_labels = dataset.node_labels.copy() if dataset.node_labels is not None else None node_attrs = dataset.node_attrs.copy() if dataset.node_attrs is not None else None edge_labels = dataset.edge_labels.copy() if dataset.edge_labels is not None else None edge_attrs = dataset.edge_attrs.copy() if dataset.edge_attrs is not None else None sub_dataset.set_labels(node_labels=node_labels, node_attrs=node_attrs, edge_labels=edge_labels, edge_attrs=edge_attrs) datasets.append(sub_dataset) # @todo: clean_labels? return datasets