""" Obtain all kinds of attributes of a graph dataset.
This file is for old version of graphkit-learn.
"""
[docs]def get_dataset_attributes(Gn,
target=None,
attr_names=[],
node_label=None,
edge_label=None):
"""Returns the structure and property information of the graph dataset Gn.
Parameters
----------
Gn : List of NetworkX graph
List of graphs whose information will be returned.
target : list
The list of classification targets corresponding to Gn. Only works for
classification problems.
attr_names : list
List of strings which indicate which informations will be returned. The
possible choices includes:
'substructures': sub-structures Gn contains, including 'linear', 'non
linear' and 'cyclic'.
'node_labeled': whether vertices have symbolic labels.
'edge_labeled': whether egdes have symbolic labels.
'is_directed': whether graphs in Gn are directed.
'dataset_size': number of graphs in Gn.
'ave_node_num': average number of vertices of graphs in Gn.
'min_node_num': minimum number of vertices of graphs in Gn.
'max_node_num': maximum number of vertices of graphs in Gn.
'ave_edge_num': average number of edges of graphs in Gn.
'min_edge_num': minimum number of edges of graphs in Gn.
'max_edge_num': maximum number of edges of graphs in Gn.
'ave_node_degree': average vertex degree of graphs in Gn.
'min_node_degree': minimum vertex degree of graphs in Gn.
'max_node_degree': maximum vertex degree of graphs in Gn.
'ave_fill_factor': average fill factor (number_of_edges /
(number_of_nodes ** 2)) of graphs in Gn.
'min_fill_factor': minimum fill factor of graphs in Gn.
'max_fill_factor': maximum fill factor of graphs in Gn.
'node_label_num': number of symbolic vertex labels.
'edge_label_num': number of symbolic edge labels.
'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.
node_label : string
Node attribute used as label. The default node label is atom. Mandatory
when 'node_labeled' or 'node_label_num' is required.
edge_label : string
Edge attribute used as label. The default edge label is bond_type.
Mandatory when 'edge_labeled' or 'edge_label_num' is required.
Return
------
attrs : dict
Value for each property.
"""
import networkx as nx
import numpy as np
attrs = {}
def get_dataset_size(Gn):
return len(Gn)
def get_all_node_num(Gn):
return [nx.number_of_nodes(G) for G in Gn]
def get_ave_node_num(all_node_num):
return np.mean(all_node_num)
def get_min_node_num(all_node_num):
return np.amin(all_node_num)
def get_max_node_num(all_node_num):
return np.amax(all_node_num)
def get_all_edge_num(Gn):
return [nx.number_of_edges(G) for G in Gn]
def get_ave_edge_num(all_edge_num):
return np.mean(all_edge_num)
def get_min_edge_num(all_edge_num):
return np.amin(all_edge_num)
def get_max_edge_num(all_edge_num):
return np.amax(all_edge_num)
def is_node_labeled(Gn):
return False if node_label is None else True
def get_node_label_num(Gn):
nl = set()
for G in Gn:
nl = nl | set(nx.get_node_attributes(G, node_label).values())
return len(nl)
def is_edge_labeled(Gn):
return False if edge_label is None else True
def get_edge_label_num(Gn):
el = set()
for G in Gn:
el = el | set(nx.get_edge_attributes(G, edge_label).values())
return len(el)
def is_directed(Gn):
return nx.is_directed(Gn[0])
def get_ave_node_degree(Gn):
return np.mean([np.mean(list(dict(G.degree()).values())) for G in Gn])
def get_max_node_degree(Gn):
return np.amax([np.mean(list(dict(G.degree()).values())) for G in Gn])
def get_min_node_degree(Gn):
return np.amin([np.mean(list(dict(G.degree()).values())) for G in Gn])
# get fill factor, the number of non-zero entries in the adjacency matrix.
def get_ave_fill_factor(Gn):
return np.mean([nx.number_of_edges(G) / (nx.number_of_nodes(G)
* nx.number_of_nodes(G)) for G in Gn])
def get_max_fill_factor(Gn):
return np.amax([nx.number_of_edges(G) / (nx.number_of_nodes(G)
* nx.number_of_nodes(G)) for G in Gn])
def get_min_fill_factor(Gn):
return np.amin([nx.number_of_edges(G) / (nx.number_of_nodes(G)
* nx.number_of_nodes(G)) for G in Gn])
def get_substructures(Gn):
subs = set()
for G in Gn:
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 is_directed(Gn):
for G in Gn:
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(target):
return len(set(target))
def get_node_attr_dim(Gn):
for G in Gn:
for n in G.nodes(data=True):
if 'attributes' in n[1]:
return len(n[1]['attributes'])
return 0
def get_edge_attr_dim(Gn):
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
if attr_names == []:
attr_names = [
'substructures',
'node_labeled',
'edge_labeled',
'is_directed',
'dataset_size',
'ave_node_num',
'min_node_num',
'max_node_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_num',
'edge_label_num',
'node_attr_dim',
'edge_attr_dim',
'class_number',
]
# dataset size
if 'dataset_size' in attr_names:
attrs.update({'dataset_size': get_dataset_size(Gn)})
# graph node number
if any(i in attr_names
for i in ['ave_node_num', 'min_node_num', 'max_node_num']):
all_node_num = get_all_node_num(Gn)
if 'ave_node_num' in attr_names:
attrs.update({'ave_node_num': get_ave_node_num(all_node_num)})
if 'min_node_num' in attr_names:
attrs.update({'min_node_num': get_min_node_num(all_node_num)})
if 'max_node_num' in attr_names:
attrs.update({'max_node_num': get_max_node_num(all_node_num)})
# graph edge number
if any(i in attr_names for i in
['ave_edge_num', 'min_edge_num', 'max_edge_num']):
all_edge_num = get_all_edge_num(Gn)
if 'ave_edge_num' in attr_names:
attrs.update({'ave_edge_num': get_ave_edge_num(all_edge_num)})
if 'max_edge_num' in attr_names:
attrs.update({'max_edge_num': get_max_edge_num(all_edge_num)})
if 'min_edge_num' in attr_names:
attrs.update({'min_edge_num': get_min_edge_num(all_edge_num)})
# label number
if any(i in attr_names for i in ['node_labeled', 'node_label_num']):
is_nl = is_node_labeled(Gn)
node_label_num = get_node_label_num(Gn)
if 'node_labeled' in attr_names:
# graphs are considered node unlabeled if all nodes have the same label.
attrs.update({'node_labeled': is_nl if node_label_num > 1 else False})
if 'node_label_num' in attr_names:
attrs.update({'node_label_num': node_label_num})
if any(i in attr_names for i in ['edge_labeled', 'edge_label_num']):
is_el = is_edge_labeled(Gn)
edge_label_num = get_edge_label_num(Gn)
if 'edge_labeled' in attr_names:
# graphs are considered edge unlabeled if all edges have the same label.
attrs.update({'edge_labeled': is_el if edge_label_num > 1 else False})
if 'edge_label_num' in attr_names:
attrs.update({'edge_label_num': edge_label_num})
if 'is_directed' in attr_names:
attrs.update({'is_directed': is_directed(Gn)})
if 'ave_node_degree' in attr_names:
attrs.update({'ave_node_degree': get_ave_node_degree(Gn)})
if 'max_node_degree' in attr_names:
attrs.update({'max_node_degree': get_max_node_degree(Gn)})
if 'min_node_degree' in attr_names:
attrs.update({'min_node_degree': get_min_node_degree(Gn)})
if 'ave_fill_factor' in attr_names:
attrs.update({'ave_fill_factor': get_ave_fill_factor(Gn)})
if 'max_fill_factor' in attr_names:
attrs.update({'max_fill_factor': get_max_fill_factor(Gn)})
if 'min_fill_factor' in attr_names:
attrs.update({'min_fill_factor': get_min_fill_factor(Gn)})
if 'substructures' in attr_names:
attrs.update({'substructures': get_substructures(Gn)})
if 'class_number' in attr_names:
attrs.update({'class_number': get_class_num(target)})
if 'node_attr_dim' in attr_names:
attrs['node_attr_dim'] = get_node_attr_dim(Gn)
if 'edge_attr_dim' in attr_names:
attrs['edge_attr_dim'] = get_edge_attr_dim(Gn)
from collections import OrderedDict
return OrderedDict(
sorted(attrs.items(), key=lambda i: attr_names.index(i[0])))
[docs]def load_predefined_dataset(ds_name):
import os
from gklearn.utils.graphfiles import loadDataset
current_path = os.path.dirname(os.path.realpath(__file__)) + '/'
if ds_name == 'Acyclic':
ds_file = current_path + '../../datasets/Acyclic/dataset_bps.ds'
graphs, targets = loadDataset(ds_file)
elif ds_name == 'AIDS':
ds_file = current_path + '../../datasets/AIDS/AIDS_A.txt'
graphs, targets = loadDataset(ds_file)
elif ds_name == 'Alkane':
ds_file = current_path + '../../datasets/Alkane/dataset.ds'
fn_targets = current_path + '../../datasets/Alkane/dataset_boiling_point_names.txt'
graphs, targets = loadDataset(ds_file, filename_y=fn_targets)
elif ds_name == 'COIL-DEL':
ds_file = current_path + '../../datasets/COIL-DEL/COIL-DEL_A.txt'
graphs, targets = loadDataset(ds_file)
elif ds_name == 'COIL-RAG':
ds_file = current_path + '../../datasets/COIL-RAG/COIL-RAG_A.txt'
graphs, targets = loadDataset(ds_file)
elif ds_name == 'COLORS-3':
ds_file = current_path + '../../datasets/COLORS-3/COLORS-3_A.txt'
graphs, targets = loadDataset(ds_file)
elif ds_name == 'Cuneiform':
ds_file = current_path + '../../datasets/Cuneiform/Cuneiform_A.txt'
graphs, targets = loadDataset(ds_file)
elif ds_name == 'DD':
ds_file = current_path + '../../datasets/DD/DD_A.txt'
graphs, targets = loadDataset(ds_file)
elif ds_name == 'ENZYMES':
ds_file = current_path + '../../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'
graphs, targets = loadDataset(ds_file)
elif ds_name == 'Fingerprint':
ds_file = current_path + '../../datasets/Fingerprint/Fingerprint_A.txt'
graphs, targets = loadDataset(ds_file)
elif ds_name == 'FRANKENSTEIN':
ds_file = current_path + '../../datasets/FRANKENSTEIN/FRANKENSTEIN_A.txt'
graphs, targets = loadDataset(ds_file)
elif ds_name == 'Letter-high': # node non-symb
ds_file = current_path + '../../datasets/Letter-high/Letter-high_A.txt'
graphs, targets = loadDataset(ds_file)
elif ds_name == 'Letter-low': # node non-symb
ds_file = current_path + '../../datasets/Letter-low/Letter-low_A.txt'
graphs, targets = loadDataset(ds_file)
elif ds_name == 'Letter-med': # node non-symb
ds_file = current_path + '../../datasets/Letter-med/Letter-med_A.txt'
graphs, targets = loadDataset(ds_file)
elif ds_name == 'MAO':
ds_file = current_path + '../../datasets/MAO/dataset.ds'
graphs, targets = loadDataset(ds_file)
elif ds_name == 'Monoterpenoides':
ds_file = current_path + '../../datasets/Monoterpenoides/dataset_10+.ds'
graphs, targets = loadDataset(ds_file)
elif ds_name == 'MUTAG':
ds_file = current_path + '../../datasets/MUTAG/MUTAG_A.txt'
graphs, targets = loadDataset(ds_file)
elif ds_name == 'NCI1':
ds_file = current_path + '../../datasets/NCI1/NCI1_A.txt'
graphs, targets = loadDataset(ds_file)
elif ds_name == 'NCI109':
ds_file = current_path + '../../datasets/NCI109/NCI109_A.txt'
graphs, targets = loadDataset(ds_file)
elif ds_name == 'PAH':
ds_file = current_path + '../../datasets/PAH/dataset.ds'
graphs, targets = loadDataset(ds_file)
elif ds_name == 'SYNTHETIC':
pass
elif ds_name == 'SYNTHETICnew':
ds_file = current_path + '../../datasets/SYNTHETICnew/SYNTHETICnew_A.txt'
graphs, targets = loadDataset(ds_file)
elif ds_name == 'Synthie':
pass
else:
raise Exception('The dataset name "', ds_name, '" is not pre-defined.')
return graphs, targets