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"""
Graph data problem designed for graph2graph learning
"""
from typing import List
import networkx as nx
class Node:
"""
Node
"""
# def __init__(self, ID=None):
# """
#
# :param id:
# :param object:
# :param rep:
# :param state:
# :param prob:
# """
#
# self.ID = ID
@property
def ID(self):
"""
:return:
:rtype:
"""
pass
@ID.setter
def ID(self, id):
"""
:param id:
:type id:
:return:
:rtype:
"""
pass
def __hash__(self):
"""
:return:
"""
return hash(self.ID)
def __eq__(self, another):
"""
:param another:
:return:
"""
return self.ID == another.ID
class Edge(object):
"""
Edge
"""
def __init__(self, start=None, end=None):
"""
:param object:
:param rep:
:param state:
:param prob:
"""
self.start = start
self.end = end
class Graph(object):
"""
Graph
"""
def __init__(self, g=None):
"""
init graph
"""
super().__init__()
if g is None:
self.g = nx.Graph()
self.node_id_base = 0
else:
import copy
assert (isinstance(g, nx.Graph) or isinstance(g, Graph))
if isinstance(g, Graph):
self.g = copy.deepcopy(g.g)
self.node_id_base = g.node_id_base
else:
self.g = copy.deepcopy(g)
self.node_id_base = max(g.nodes, default=0) + 1
def nodes(self):
"""
:return:
"""
for node in self.g.nodes:
yield self.get_node(node)
def edges(self):
"""
:return:
"""
for s, e in self.g.edges():
edge = self.g[s][e]["Edge"]
s_node = self.get_node(s)
e_node = self.get_node(e)
yield (s_node, edge, e_node)
def has_node(self, node):
"""
:param node:
:return:
"""
return node.ID in self.g
def has_edge(self, node1, node2):
"""
:param node1:
:param node2:
:return:
"""
return node2.ID in self.g[node1.ID]
def number_of_nodes(self):
"""
:return:
"""
return nx.number_of_nodes(self.g)
def number_of_edges(self):
"""
:return:
"""
return nx.number_of_edges(self.g)
def get_node(self, node_id):
"""
:param node_id:
:return:
"""
return self.g.nodes[node_id]["Node"]
def remove_node(self, node):
"""
:param node:
:return:
"""
self.g.remove_node(node.ID)
def remove_edge(self, edge):
"""
:param node:
:return:
"""
self.g.remove_edge(edge.start, edge.end)
def remove_edge_between(self, node1, node2):
"""
:param node1:
:type node1:
:param node2:
:type node2:
:return:
:rtype:
"""
if self.g.has_edge(node1.ID, node2.ID):
self.g.remove_edge(node1.ID, node2.ID)
def get_edge(self, node1, node2):
"""
:param node1_id:
:param node2_id:
:return:
"""
if isinstance(node1, Node):
node1 = node1.ID
if isinstance(node2, Node):
node2 = node2.ID
"""
if type(node1) is not int:
node1 = node1.ID
if type(node2) is not int:
node2 = node2.ID
"""
try:
edge = self.g[node1][node2]["Edge"]
except KeyError as e:
raise Exception("There is no edge between node {0} and {1}".format(node1, node2))
return edge
def add_node(self, n, reuse_id=False):
"""
:param n:
:param id:
:return:
"""
if reuse_id:
node_id = n.ID
else:
node_id = self.node_id_base
self.node_id_base += 1
n.ID = node_id
self.g.add_node(node_id, Node=n)
return n
def add_edge(self, ni, nj, e):
"""
:param ni:
:param eij:
:param nj:
:return:
"""
if not isinstance(ni, Node):
ni = self.get_node(ni)
if not isinstance(nj, Node):
nj = self.get_node(nj)
e.start = ni.ID
e.end = nj.ID
self.g.add_edge(ni.ID, nj.ID, Edge=e)
def neighbors(self, node):
"""
:param ni:
:return:
"""
for nj in self.g[node.ID]:
eij = self.g[node.ID][nj]["Edge"]
yield eij, self.get_node(nj)
def connected_components(self):
"""
:return:
"""
components = nx.algorithms.components.connected_components(self.g)
for component in components:
yield [self.get_node(x) for x in component]
def breadth_first_dag(self, start_node):
"""
:return:
"""
dag = DirectedGraph()
for node in self.nodes():
dag.add_node(node.copy(), reuse_id=True)
edges = nx.bfs_edges(self.g, start_node.ID)
orderd_nodes = [start_node.ID] + [v for u, v in edges]
for i, u in enumerate(orderd_nodes):
for j, v in enumerate(orderd_nodes):
if j <= i:
continue
node_u = self.get_node(u)
node_v = self.get_node(v)
if self.has_edge(node_u, node_v):
edge = self.get_edge(node_u, node_v).copy()
dag.add_edge(node_u, node_v, edge)
assert self.number_of_nodes() == dag.number_of_nodes()
assert self.number_of_edges() == dag.number_of_edges()
return dag
def breadth_first_tree(self, start_node):
"""
:return:
"""
dag = DirectedGraph()
dag.add_node(start_node.copy(), reuse_id=True)
edges = nx.bfs_edges(self.g, start_node.ID)
def __get_or_copy_node(u):
try:
node_u = dag.get_node(u)
except:
node_u = self.get_node(u).copy()
dag.add_node(node_u, reuse_id=True)
return node_u
for u, v in edges:
node_u = __get_or_copy_node(u)
node_v = __get_or_copy_node(v)
edge = self.get_edge(u, v)
dag.add_edge(node_u, node_v, edge)
return dag
def __copy__(self):
"""
:return:
"""
copied = type(self)()
copied.g = self.g.copy()
copied.node_id_base = self.node_id_base
return copied
def __deepcopy__(self, memodict={}):
"""
:param memodict:
:type memodict:
:return:
:rtype:
"""
from copy import deepcopy
# copied_g = type(self.g)()
#
copied = type(self)()
memodict[id(self)] = copied
for node in self.nodes():
new_node = deepcopy(node)
new_node = copied.add_node(new_node, reuse_id=True)
assert new_node.ID == node.ID, "Node ID is not copied correctly {0} {1}".format(new_node.ID, node.ID)
for (s_node, edge, e_node) in self.edges():
copied.add_edge(s_node, e_node, deepcopy(edge))
copied.node_id_base = self.node_id_base
return copied
def offsprings(self, node, filter=None):
"""
:param node:
:return:
"""
for node_id in nx.dfs_postorder_nodes(self.g, node.ID):
node = self.get_node(node_id)
if not filter or filter(node):
yield self.get_node(node_id)
def subgraph(self, nodes):
"""
:param nodes:
:return:
"""
node_ids = [n.ID if isinstance(n, Node) else n for n in nodes]
subgraph = self.g.subgraph(node_ids).copy()
result = self.__class__()
result.g = subgraph
return result
def has_path(self, node1, node2):
"""
:param node1:
:param node2:
:return:
"""
return nx.algorithms.shortest_paths.has_path(self.g, node1.ID, node2.ID)
def dual(self):
"""
return the dual graph
the dual graph is the graph with edges corresponding nodes and
nodes corresponding edges
"""
dual = Graph()
edge_node_map = dict()
for edge in self.edges():
node = Node(value=edge.value)
dual.add_node(node)
edge_node_map[(edge.start, edge.end)] = node
edge_node_map[(edge.end, edge.start)] = node
for node in self.nodes():
edges = list(self.g.edges(node.ID))
# since the end node is added
assert len(edges) >= 2, "Edge number should larger than 2 " \
"since the end node is added"
for idx1, (edge1_start, edge1_end) in enumerate(edges):
for (edge2_start, edge2_end) in edges[idx1 + 1:]:
node1 = edge_node_map[(edge1_start, edge1_end)]
node2 = edge_node_map[(edge2_start, edge2_end)]
dual.add_edge(node1, node2, Edge(value=node.value))
return dual
#
# def visualize(self, file_name=None):
# """
#
# :return:
# """
#
# visual_g = type(self.g)()
#
# for node in self.nodes():
# visual_g.add_node(node.ID, label=self.node_label(node))
#
# for node_s, edge, node_e in self.edges():
#
# visual_g.add_edge(node_s.ID, node_e.ID, label=self.edge_label(edge))
#
# from networkx.drawing.nx_agraph import graphviz_layout, to_agraph
#
# A = to_agraph(visual_g)
# if file_name:
# A.draw(file_name, prog="dot")
#
# return A.to_string()
class DirectedGraph(Graph):
"""
Directed Graph
"""
def __init__(self, g=None):
"""
:param edge_identifier:
"""
if g is None:
g = nx.DiGraph()
super().__init__(g=g)
def is_connected(self):
"""
:return:
"""
return nx.algorithms.components.is_weakly_connected(self.g)
def connected_components(self):
"""
:return:
"""
components = nx.algorithms.components.weakly_connected_components(self.g)
for component in components:
yield [self.get_node(x) for x in component]
def is_leaf(self, node):
if len(list(self.children(node))) == 0:
return True
return False
def children(self, node):
"""
:param node:
:return:
"""
for child_id in self.g.successors(node.ID):
child = self.get_node(child_id)
rel = self.get_edge(node, child)
yield child, rel
def offsprings(self, node, filter=None):
"""
:param node:
:return:
"""
yield node
for node_id in nx.descendants(self.g, node.ID):
node = self.get_node(node_id)
if not filter or filter(node):
yield self.get_node(node_id)
def ancestors(self, node, filter=None):
"""
:param node:
:return:
"""
yield node
for node_id in nx.ancestors(self.g, node.ID):
node = self.get_node(node_id)
if not filter or filter(node):
yield self.get_node(node_id)
def parents(self, node):
"""
:param node:
:return:
"""
for parent_id in self.g.predecessors(node.ID):
parent = self.get_node(parent_id)
rel = self.get_edge(parent, node)
yield parent, rel
def topological_sort(self):
"""
:return:
"""
for id in nx.topological_sort(self.g):
yield self.get_node(id)
class LearnableGraph(object):
"""
LearnableGraph
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def node_types(self, orders: List[Node], node_voc_functor):
"""
:return:
:rtype:
"""
import numpy as np
return np.narray([node_voc_functor(x) for x in orders])
def adjmatrix(self, node_orders: List[Node], edge_voc_functor, empty_id=0):
"""
:return:
:rtype:
"""
n_nodes = len(node_orders)
node2index = dict((node, idx) for idx, node in enumerate(node_orders))
import numpy as np
in_a = np.ones([n_nodes, n_nodes], dtype=np.int32) * empty_id
out_a = np.ones([n_nodes, n_nodes], dtype=np.int32) * empty_id
for u, edge, v in self.edges():
u_idx = node2index[u]
v_idx = node2index[v]
e_idx = edge_voc_functor(edge) # zero is empty type
out_a[u_idx][v_idx] = e_idx
in_a[v_idx][u_idx] = e_idx
if not nx.is_directed(self.g):
in_a[u_idx][v_idx] = e_idx
out_a[v_idx][u_idx] = e_idx
return (in_a, out_a)
def to_tensor(self, node_orders: List[Node], node_voc, edge_voc, end_node=None):
"""
:return:
"""
node_types = self.node_types(node_orders, node_voc)
a_in, a_out = self.adjmatrix(node_orders, edge_voc)
if end_node:
node_num = len(node_types)
node_types.resize((node_num + 1,))
node_types[-1] = end_node
a_in.resize((node_num + 1, node_num + 1))
a_out.resize((node_num + 1, node_num + 1))
return node_types, a_in, a_out
def valid_alignment(choices):
"""
:param choices:
:return:
"""
def _inner(i):
if i == n:
yield tuple(result)
return
for elt in sets[i] - seen:
seen.add(elt)
result[i] = elt
for t in _inner(i + 1):
yield t
seen.remove(elt)
sets = [set(seq) for seq in choices]
n = len(sets)
seen = set()
result = [None] * n
for t in _inner(0):
yield t
def is_valid_topology_sort(dag, node_objs):
"""
decide whether the order of nodes in dag2 is a valid topological sort order of dag1
:param dag:
:param pred_node_objs donot contain the start node:
:return:
"""
target_nodes = list(dag.nodes())
target_node_objects = [n.object for n in target_nodes]
choices = []
for i, node_obj in enumerate(node_objs):
cur_choice = []
for j, target_node in enumerate(target_node_objects):
if target_node == node_obj:
cur_choice.append(j)
if len(cur_choice) == 0:
return False
choices.append(cur_choice)
for align in valid_alignment(choices):
if len(set(align)) != len(align):
continue
node_ids = [target_nodes[i].ID for i in align]
bad_align = False
for id, node_id in enumerate(node_ids):
if nx.descendants(dag.g, node_id).intersection(set(node_ids[:id])):
bad_align = True
break
if nx.ancestors(dag.g, node_id).intersection(set(node_ids[id + 1:])):
bad_align = True
break
if not bad_align:
return True
return False
def not_isomorphic(graph_a, graph_b):
"""
:param graph_a:
:param graph_b:
:return:
"""
return nx.faster_could_be_isomorphic(graph_a.g, graph_b.g)
def dot2image(dot_string, file_name=None, program="dot", format=None, return_img=False):
"""
@param g:
@param file_name:
@return:
"""
from PIL import Image
import os
import tempfile
dot_file = tempfile.NamedTemporaryFile(mode='w', suffix=".dot", delete=False)
dot_file.write(dot_string)
dot_file.close()
if not format:
format = "svg"
if not file_name and return_img:
import tempfile
fout = tempfile.NamedTemporaryFile(suffix="." + format)
file_name = fout.name
return_val = os.system(f'{program} -T {format} "{dot_file.name}" -o "{file_name}"')
assert return_val == 0
if return_img:
return Image.open(file_name)
class GraphVisualizer(object):
"""
BasicGraphVisualizer
"""
def node_label(self, graph, node, *args, **kwargs):
"""
:param node:
:type node:
:return:
:rtype:
"""
return str(node)
def node_style(self, graph, node, *args, **kwargs):
"""
@param graph:
@param node:
@param args:
@param kwargs:
@return:
"""
return {}
def edge_label(self, graph, edge, *args, **kwargs):
"""
:param edge:
:type edge:
:return:
:rtype:
"""
return str(edge)
def edge_style(self, graph, edge, *args, **kwargs):
"""
@param graph:
@param edge:
@param args:
@param kwargs:
@return:
"""
return {}
def visualize(self, graph, file_name=None, return_img=False, format="svg", no_text=False, *args, **kwargs):
"""
@return:
@rtype:
"""
import io
dot_string = io.StringIO()
dot_string.write("strict digraph {\n")
node2index = dict()
for index, node_id in enumerate(graph.g.nodes()):
node = graph.get_node(node_id)
node_label = self.node_label(graph, node, no_text=no_text, *args, **kwargs)
node_style = self.node_style(graph, node, *args, **kwargs)
node2index[node.ID] = index
node_attr = ['label="{0}"'.format(node_label)]
for k, v in node_style.items():
node_attr.append('{0}="{1}"'.format(k, v))
vis_node_label = '{0}\t[{1}]; \n'.format(
index, ", ".join(node_attr)
)
dot_string.write(vis_node_label)
# if simple:
# g.add_node(id2index[node_id], label=node_text, shape=shape)
# else:
# g.add_node(node_id, label=node_text, shape=shape)
for s, e in graph.g.edges():
edge = graph.g[s][e]["Edge"]
edge_label = self.edge_label(graph, edge, *args, **kwargs)
edge_style = self.edge_style(graph, edge, *args, **kwargs)
edge_attr = ['label="{0}"'.format(edge_label)]
for k, v in edge_style.items():
edge_attr.append('{0}="{1}"'.format(k, v))
s = node2index[s]
e = node2index[e]
dot_string.write('{0}\t->\t{1}\t[{2}];\n'.format(
s, e, ", ".join(edge_attr)
))
dot_string.write("}\n")
dot_string = dot_string.getvalue()
result = dot_string
if file_name or return_img:
image = dot2image(dot_string, file_name=file_name, return_img=return_img,
format=format)
if return_img:
result = image
return result
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