Source code for pytools.graph

__copyright__ = """
Copyright (C) 2009-2013 Andreas Kloeckner
Copyright (C) 2020 Matt Wala
Copyright (C) 2020 James Stevens
"""

__license__ = """
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""


__doc__ = """
Graph Algorithms
=========================

.. autofunction:: a_star
.. autofunction:: compute_sccs
.. autoclass:: CycleError
.. autofunction:: compute_topological_order
.. autofunction:: compute_transitive_closure
.. autofunction:: contains_cycle
.. autofunction:: compute_induced_subgraph

Type Variables Used
-------------------

.. class:: T

    Any type.
"""

from typing import (TypeVar, Mapping, Iterable, List, Optional, Any, Callable,
                    Set, MutableSet, Dict, Iterator, Tuple)


T = TypeVar("T")


# {{{ a_star

[docs]def a_star( # pylint: disable=too-many-locals initial_state, goal_state, neighbor_map, estimate_remaining_cost=None, get_step_cost=lambda x, y: 1 ): """ With the default cost and heuristic, this amounts to Dijkstra's algorithm. """ from heapq import heappop, heappush if estimate_remaining_cost is None: def estimate_remaining_cost(x): # pylint: disable=function-redefined if x != goal_state: return 1 else: return 0 class AStarNode: __slots__ = ["state", "parent", "path_cost"] def __init__(self, state, parent, path_cost): self.state = state self.parent = parent self.path_cost = path_cost inf = float("inf") init_remcost = estimate_remaining_cost(initial_state) assert init_remcost != inf queue = [(init_remcost, AStarNode(initial_state, parent=None, path_cost=0))] visited_states = set() while queue: _, top = heappop(queue) visited_states.add(top.state) if top.state == goal_state: result = [] it = top while it is not None: result.append(it.state) it = it.parent return result[::-1] for state in neighbor_map[top.state]: if state in visited_states: continue remaining_cost = estimate_remaining_cost(state) if remaining_cost == inf: continue step_cost = get_step_cost(top, state) estimated_path_cost = top.path_cost+step_cost+remaining_cost heappush(queue, (estimated_path_cost, AStarNode(state, top, path_cost=top.path_cost + step_cost))) raise RuntimeError("no solution")
# }}} # {{{ compute SCCs with Tarjan's algorithm
[docs]def compute_sccs(graph: Mapping[T, Iterable[T]]) -> List[List[T]]: to_search = set(graph.keys()) visit_order: Dict[T, int] = {} scc_root = {} sccs = [] while to_search: top = next(iter(to_search)) call_stack: List[Tuple[T, Iterator[T], Optional[T]]] = [(top, iter(graph[top]), None)] visit_stack = [] visiting = set() scc: List[T] = [] while call_stack: top, children, last_popped_child = call_stack.pop() if top not in visiting: # Unvisited: mark as visited, initialize SCC root. count = len(visit_order) visit_stack.append(top) visit_order[top] = count scc_root[top] = count visiting.add(top) to_search.discard(top) # Returned from a recursion, update SCC. if last_popped_child is not None: scc_root[top] = min( scc_root[top], scc_root[last_popped_child]) for child in children: if child not in visit_order: # Recurse. call_stack.append((top, children, child)) call_stack.append((child, iter(graph[child]), None)) break if child in visiting: scc_root[top] = min( scc_root[top], visit_order[child]) else: if scc_root[top] == visit_order[top]: scc = [] while visit_stack[-1] != top: scc.append(visit_stack.pop()) scc.append(visit_stack.pop()) for item in scc: visiting.remove(item) sccs.append(scc) return sccs
# }}} # {{{ compute topological order
[docs]class CycleError(Exception): """ Raised when a topological ordering cannot be computed due to a cycle. :attr node: Node in a directed graph that is part of a cycle. """ def __init__(self, node): self.node = node
class HeapEntry: """ Helper class to compare associated keys while comparing the elements in heap operations. Only needs to define :func:`pytools.graph.__lt__` according to <https://github.com/python/cpython/blob/8d21aa21f2cbc6d50aab3f420bb23be1d081dac4/Lib/heapq.py#L135-L138>. """ def __init__(self, node, key): self.node = node self.key = key def __lt__(self, other): return self.key < other.key
[docs]def compute_topological_order(graph: Mapping[T, Iterable[T]], key: Optional[Callable[[T], Any]] = None) -> List[T]: """Compute a topological order of nodes in a directed graph. :arg graph: A :class:`collections.abc.Mapping` representing a directed graph. The dictionary contains one key representing each node in the graph, and this key maps to a :class:`collections.abc.Iterable` of its successor nodes. :arg key: A custom key function may be supplied to determine the order in break-even cases. Expects a function of one argument that is used to extract a comparison key from each node of the *graph*. :returns: A :class:`list` representing a valid topological ordering of the nodes in the directed graph. .. note:: * Requires the keys of the mapping *graph* to be hashable. * Implements `Kahn's algorithm <https://w.wiki/YDy>`__. .. versionadded:: 2020.2 """ # all nodes have the same keys when not provided keyfunc = key if key is not None else (lambda x: 0) from heapq import heapify, heappop, heappush order = [] # {{{ compute nodes_to_num_predecessors nodes_to_num_predecessors = {node: 0 for node in graph} for node in graph: for child in graph[node]: nodes_to_num_predecessors[child] = ( nodes_to_num_predecessors.get(child, 0) + 1) # }}} total_num_nodes = len(nodes_to_num_predecessors) # heap: list of instances of HeapEntry(n) where 'n' is a node in # 'graph' with no predecessor. Nodes with no predecessors are the # schedulable candidates. heap = [HeapEntry(n, keyfunc(n)) for n, num_preds in nodes_to_num_predecessors.items() if num_preds == 0] heapify(heap) while heap: # pick the node with least key node_to_be_scheduled = heappop(heap).node order.append(node_to_be_scheduled) # discard 'node_to_be_scheduled' from the predecessors of its # successors since it's been scheduled for child in graph.get(node_to_be_scheduled, ()): nodes_to_num_predecessors[child] -= 1 if nodes_to_num_predecessors[child] == 0: heappush(heap, HeapEntry(child, keyfunc(child))) if len(order) != total_num_nodes: # any node which has a predecessor left is a part of a cycle raise CycleError(next(iter(n for n, num_preds in nodes_to_num_predecessors.items() if num_preds != 0))) return order
# }}} # {{{ compute transitive closure
[docs]def compute_transitive_closure(graph: Mapping[T, MutableSet[T]]) -> ( Mapping[T, MutableSet[T]]): """Compute the transitive closure of a directed graph using Warshall's algorithm. :arg graph: A :class:`collections.abc.Mapping` representing a directed graph. The dictionary contains one key representing each node in the graph, and this key maps to a :class:`collections.abc.MutableSet` of nodes that are connected to the node by outgoing edges. This graph may contain cycles. This object must be picklable. Every graph node must be included as a key in the graph. :returns: The transitive closure of the graph, represented using the same data type. .. versionadded:: 2020.2 """ # Warshall's algorithm from copy import deepcopy closure = deepcopy(graph) # (assumes all graph nodes are included in keys) for k in graph.keys(): for n1 in graph.keys(): for n2 in graph.keys(): if k in closure[n1] and n2 in closure[k]: closure[n1].add(n2) return closure
# }}} # {{{ check for cycle
[docs]def contains_cycle(graph: Mapping[T, Iterable[T]]) -> bool: """Determine whether a graph contains a cycle. :arg graph: A :class:`collections.abc.Mapping` representing a directed graph. The dictionary contains one key representing each node in the graph, and this key maps to a :class:`collections.abc.Iterable` of nodes that are connected to the node by outgoing edges. :returns: A :class:`bool` indicating whether the graph contains a cycle. .. versionadded:: 2020.2 """ try: compute_topological_order(graph) return False except CycleError: return True
# }}} # {{{ compute induced subgraph
[docs]def compute_induced_subgraph(graph: Mapping[T, Set[T]], subgraph_nodes: Set[T]) -> Mapping[T, Set[T]]: """Compute the induced subgraph formed by a subset of the vertices in a graph. :arg graph: A :class:`collections.abc.Mapping` representing a directed graph. The dictionary contains one key representing each node in the graph, and this key maps to a :class:`collections.abc.Set` of nodes that are connected to the node by outgoing edges. :arg subgraph_nodes: A :class:`collections.abc.Set` containing a subset of the graph nodes in the graph. :returns: A :class:`dict` representing the induced subgraph formed by the subset of the vertices included in `subgraph_nodes`. .. versionadded:: 2020.2 """ new_graph = {} for node, children in graph.items(): if node in subgraph_nodes: new_graph[node] = children & subgraph_nodes return new_graph
# }}} # vim: foldmethod=marker