99 lines
3.5 KiB
Python
99 lines
3.5 KiB
Python
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# beamsearch.py - breadth-first search with limited queueing
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#
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# Copyright 2016-2019 NetworkX developers.
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#
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# This file is part of NetworkX.
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#
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# NetworkX is distributed under a BSD license; see LICENSE.txt for more
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# information.
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"""Basic algorithms for breadth-first searching the nodes of a graph."""
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import networkx as nx
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from .breadth_first_search import generic_bfs_edges
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__all__ = ['bfs_beam_edges']
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def bfs_beam_edges(G, source, value, width=None):
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"""Iterates over edges in a beam search.
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The beam search is a generalized breadth-first search in which only
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the "best" *w* neighbors of the current node are enqueued, where *w*
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is the beam width and "best" is an application-specific
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heuristic. In general, a beam search with a small beam width might
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not visit each node in the graph.
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Parameters
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----------
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G : NetworkX graph
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source : node
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Starting node for the breadth-first search; this function
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iterates over only those edges in the component reachable from
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this node.
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value : function
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A function that takes a node of the graph as input and returns a
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real number indicating how "good" it is. A higher value means it
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is more likely to be visited sooner during the search. When
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visiting a new node, only the `width` neighbors with the highest
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`value` are enqueued (in decreasing order of `value`).
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width : int (default = None)
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The beam width for the search. This is the number of neighbors
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(ordered by `value`) to enqueue when visiting each new node.
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Yields
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------
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edge
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Edges in the beam search starting from `source`, given as a pair
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of nodes.
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Examples
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--------
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To give nodes with, for example, a higher centrality precedence
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during the search, set the `value` function to return the centrality
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value of the node::
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>>> G = nx.karate_club_graph()
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>>> centrality = nx.eigenvector_centrality(G)
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>>> source = 0
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>>> width = 5
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>>> for u, v in nx.bfs_beam_edges(G, source, centrality.get, width):
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... print((u, v)) # doctest: +SKIP
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"""
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if width is None:
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width = len(G)
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def successors(v):
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"""Returns a list of the best neighbors of a node.
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`v` is a node in the graph `G`.
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The "best" neighbors are chosen according to the `value`
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function (higher is better). Only the `width` best neighbors of
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`v` are returned.
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The list returned by this function is in decreasing value as
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measured by the `value` function.
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"""
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# TODO The Python documentation states that for small values, it
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# is better to use `heapq.nlargest`. We should determine the
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# threshold at which its better to use `heapq.nlargest()`
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# instead of `sorted()[:]` and apply that optimization here.
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#
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# If `width` is greater than the number of neighbors of `v`, all
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# neighbors are returned by the semantics of slicing in
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# Python. This occurs in the special case that the user did not
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# specify a `width`: in this case all neighbors are always
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# returned, so this is just a (slower) implementation of
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# `bfs_edges(G, source)` but with a sorted enqueue step.
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return iter(sorted(G.neighbors(v), key=value, reverse=True)[:width])
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# TODO In Python 3.3+, this should be `yield from ...`
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for e in generic_bfs_edges(G, source, successors):
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yield e
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