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New translations dataset.py (Chinese Simplified)

l10n_v0.2.x
linlin 5 years ago
parent
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      lang/zh/gklearn/utils/dataset.py

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lang/zh/gklearn/utils/dataset.py View File

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#!/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
from gklearn.utils.graph_files import load_dataset
import os


class Dataset(object):
def __init__(self, filename=None, filename_targets=None, **kwargs):
if filename is None:
self.__graphs = None
self.__targets = None
self.__node_labels = None
self.__edge_labels = None
self.__node_attrs = None
self.__edge_attrs = None
else:
self.load_dataset(filename, filename_targets=filename_targets, **kwargs)
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, **kwargs):
self.__graphs, self.__targets, label_names = load_dataset(filename, filename_targets=filename_targets, **kwargs)
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']
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):
current_path = os.path.dirname(os.path.realpath(__file__)) + '/'
if ds_name == 'Acyclic':
ds_file = current_path + '../../datasets/Acyclic/dataset_bps.ds'
self.__graphs, self.__targets, label_names = load_dataset(ds_file)
elif ds_name == 'AIDS':
ds_file = current_path + '../../datasets/AIDS/AIDS_A.txt'
self.__graphs, self.__targets, label_names = load_dataset(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'
self.__graphs, self.__targets, label_names = load_dataset(ds_file, filename_targets=fn_targets)
elif ds_name == 'COIL-DEL':
ds_file = current_path + '../../datasets/COIL-DEL/COIL-DEL_A.txt'
self.__graphs, self.__targets, label_names = load_dataset(ds_file)
elif ds_name == 'COIL-RAG':
ds_file = current_path + '../../datasets/COIL-RAG/COIL-RAG_A.txt'
self.__graphs, self.__targets, label_names = load_dataset(ds_file)
elif ds_name == 'COLORS-3':
ds_file = current_path + '../../datasets/COLORS-3/COLORS-3_A.txt'
self.__graphs, self.__targets, label_names = load_dataset(ds_file)
elif ds_name == 'Cuneiform':
ds_file = current_path + '../../datasets/Cuneiform/Cuneiform_A.txt'
self.__graphs, self.__targets, label_names = load_dataset(ds_file)
elif ds_name == 'DD':
ds_file = current_path + '../../datasets/DD/DD_A.txt'
self.__graphs, self.__targets, label_names = load_dataset(ds_file)
elif ds_name == 'ENZYMES':
ds_file = current_path + '../../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'
self.__graphs, self.__targets, label_names = load_dataset(ds_file)
elif ds_name == 'Fingerprint':
ds_file = current_path + '../../datasets/Fingerprint/Fingerprint_A.txt'
self.__graphs, self.__targets, label_names = load_dataset(ds_file)
elif ds_name == 'FRANKENSTEIN':
ds_file = current_path + '../../datasets/FRANKENSTEIN/FRANKENSTEIN_A.txt'
self.__graphs, self.__targets, label_names = load_dataset(ds_file)
elif ds_name == 'Letter-high': # node non-symb
ds_file = current_path + '../../datasets/Letter-high/Letter-high_A.txt'
self.__graphs, self.__targets, label_names = load_dataset(ds_file)
elif ds_name == 'Letter-low': # node non-symb
ds_file = current_path + '../../datasets/Letter-low/Letter-low_A.txt'
self.__graphs, self.__targets, label_names = load_dataset(ds_file)
elif ds_name == 'Letter-med': # node non-symb
ds_file = current_path + '../../datasets/Letter-med/Letter-med_A.txt'
self.__graphs, self.__targets, label_names = load_dataset(ds_file)
elif ds_name == 'MAO':
ds_file = current_path + '../../datasets/MAO/dataset.ds'
self.__graphs, self.__targets, label_names = load_dataset(ds_file)
elif ds_name == 'Monoterpenoides':
ds_file = current_path + '../../datasets/Monoterpenoides/dataset_10+.ds'
self.__graphs, self.__targets, label_names = load_dataset(ds_file)
elif ds_name == 'MUTAG':
ds_file = current_path + '../../datasets/MUTAG/MUTAG_A.txt'
self.__graphs, self.__targets, label_names = load_dataset(ds_file)
elif ds_name == 'NCI1':
ds_file = current_path + '../../datasets/NCI1/NCI1_A.txt'
self.__graphs, self.__targets, label_names = load_dataset(ds_file)
elif ds_name == 'NCI109':
ds_file = current_path + '../../datasets/NCI109/NCI109_A.txt'
self.__graphs, self.__targets, label_names = load_dataset(ds_file)
elif ds_name == 'PAH':
ds_file = current_path + '../../datasets/PAH/dataset.ds'
self.__graphs, self.__targets, label_names = load_dataset(ds_file)
elif ds_name == 'SYNTHETIC':
pass
elif ds_name == 'SYNTHETICnew':
ds_file = current_path + '../../datasets/SYNTHETICnew/SYNTHETICnew_A.txt'
self.__graphs, self.__targets, label_names = load_dataset(ds_file)
elif ds_name == 'Synthie':
pass
else:
raise Exception('The dataset name "', ds_name, '" is not pre-defined.')
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']
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

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