python实现有向无环图(DAG)

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我是靠谱客的博主爱撒娇柠檬，最近开发中收集的这篇文章主要介绍python实现有向无环图(DAG)，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

摘自dagobah项目dag

from collections import OrderedDict, defaultdict
from copy import copy, deepcopy
class DAG(object):
""" Directed acyclic graph implementation. """
def __init__(self):
""" Construct a new DAG with no nodes or edges. """
self.reset_graph()
def add_node(self, node_name, graph=None):
""" Add a node if it does not exist yet, or error out. """
if not graph:
graph = self.graph
if node_name in graph:
raise KeyError('node %s already exists' % node_name)
graph[node_name] = set()
def add_node_if_not_exists(self, node_name, graph=None):
try:
self.add_node(node_name, graph=graph)
except KeyError:
pass
def delete_node(self, node_name, graph=None):
""" Deletes this node and all edges referencing it. """
if not graph:
graph = self.graph
if node_name not in graph:
raise KeyError('node %s does not exist' % node_name)
graph.pop(node_name)
for node, edges in graph.items():
if node_name in edges:
edges.remove(node_name)
def delete_node_if_exists(self, node_name, graph=None):
try:
self.delete_node(node_name, graph=graph)
except KeyError:
pass
def add_edge(self, ind_node, dep_node, graph=None):
""" Add an edge (dependency) between the specified nodes. """
if not graph:
graph = self.graph
if ind_node not in graph or dep_node not in graph:
raise KeyError('one or more nodes do not exist in graph')
test_graph = deepcopy(graph)
test_graph[ind_node].add(dep_node)
is_valid, message = self.validate(test_graph)
if is_valid:
graph[ind_node].add(dep_node)
else:
raise Exception()
def delete_edge(self, ind_node, dep_node, graph=None):
""" Delete an edge from the graph. """
if not graph:
graph = self.graph
if dep_node not in graph.get(ind_node, []):
raise KeyError('this edge does not exist in graph')
graph[ind_node].remove(dep_node)
def rename_edges(self, old_task_name, new_task_name, graph=None):
""" Change references to a task in existing edges. """
if not graph:
graph = self.graph
for node, edges in graph.items():
if node == old_task_name:
graph[new_task_name] = copy(edges)
del graph[old_task_name]
else:
if old_task_name in edges:
edges.remove(old_task_name)
edges.add(new_task_name)
def predecessors(self, node, graph=None):
""" Returns a list of all predecessors of the given node """
if graph is None:
graph = self.graph
return [key for key in graph if node in graph[key]]
def downstream(self, node, graph=None):
""" Returns a list of all nodes this node has edges towards. """
if graph is None:
graph = self.graph
if node not in graph:
raise KeyError('node %s is not in graph' % node)
return list(graph[node])
def all_downstreams(self, node, graph=None):
"""Returns a list of all nodes ultimately downstream
of the given node in the dependency graph, in
topological order."""
if graph is None:
graph = self.graph
nodes = [node]
nodes_seen = set()
i = 0
while i < len(nodes):
downstreams = self.downstream(nodes[i], graph)
for downstream_node in downstreams:
if downstream_node not in nodes_seen:
nodes_seen.add(downstream_node)
nodes.append(downstream_node)
i += 1
return list(
filter(
lambda node: node in nodes_seen,
self.topological_sort(graph=graph)
)
)
def all_leaves(self, graph=None):
""" Return a list of all leaves (nodes with no downstreams) """
if graph is None:
graph = self.graph
return [key for key in graph if not graph[key]]
def from_dict(self, graph_dict):
""" Reset the graph and build it from the passed dictionary.
The dictionary takes the form of {node_name: [directed edges]}
"""
self.reset_graph()
for new_node in graph_dict.keys():
self.add_node(new_node)
for ind_node, dep_nodes in graph_dict.items():
if not isinstance(dep_nodes, list):
raise TypeError('dict values must be lists')
for dep_node in dep_nodes:
self.add_edge(ind_node, dep_node)
def reset_graph(self):
""" Restore the graph to an empty state. """
self.graph = OrderedDict()
def ind_nodes(self, graph=None):
""" Returns a list of all nodes in the graph with no dependencies. """
if graph is None:
graph = self.graph
dependent_nodes = set(
node for dependents in graph.values() for node in dependents
)
return [node for node in graph.keys() if node not in dependent_nodes]
def validate(self, graph=None):
""" Returns (Boolean, message) of whether DAG is valid. """
graph = graph if graph is not None else self.graph
if len(self.ind_nodes(graph)) == 0:
return False, 'no independent nodes detected'
try:
self.topological_sort(graph)
except ValueError:
return False, 'failed topological sort'
return True, 'valid'
def topological_sort(self, graph=None):
""" Returns a topological ordering of the DAG.
Raises an error if this is not possible (graph is not valid).
"""
if graph is None:
graph = self.graph
result = []
in_degree = defaultdict(lambda: 0)
for u in graph:
for v in graph[u]:
in_degree[v] += 1
ready = [node for node in graph if not in_degree[node]]
while ready:
u = ready.pop()
result.append(u)
for v in graph[u]:
in_degree[v] -= 1
if in_degree[v] == 0:
ready.append(v)
if len(result) == len(graph):
return result
else:
raise ValueError('graph is not acyclic')
def size(self):
return len(self.graph)
if __name__ == '__main__':
dag = DAG()
dag.add_node("a")
dag.add_node("b")
dag.add_node("c")
dag.add_node("d")
dag.add_edge("a", "b")
dag.add_edge("a", "d")
dag.add_edge("b", "c")
print(dag.topological_sort())
print(dag.graph)
print(dag.all_downstreams("b"))