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Commit 6dd93bd

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Add floyd_warshall (#42)
* Add `floyd_warshall` * Optimization: better handle sparsity such as skip empty nodes * Simplify (so this PR can be a reference) * Better name ("Outer", not "temp")
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‎README.md

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@@ -147,6 +147,7 @@ dispatch pattern shown above.
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- is_k_regular
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- is_regular
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- Shortest Paths
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- floyd_warshall
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- has_path
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- Simple Paths
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- is_simple_path
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from .dense import *
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from .generic import *
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from graphblas import Matrix, Vector, binary
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from graphblas.select import offdiag
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from graphblas.semiring import any_plus
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__all__ = ["floyd_warshall"]
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def floyd_warshall(G, is_weighted=False):
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# By using `offdiag` instead of `G._A`, we ensure that D will not become dense.
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# Dense D may be better at times, but not including the diagonal will result in less work.
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# Typically, Floyd-Warshall algorithms sets the diagonal of D to 0 at the beginning.
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# This is unnecessary with sparse matrices, and we set the diagonal to 0 at the end.
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# We also don't iterate over index `i` if either row i or column i are empty.
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if G.is_directed():
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A, row_degrees, column_degrees = G.get_properties("offdiag row_degrees- column_degrees-")
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nonempty_nodes = binary.pair(row_degrees & column_degrees).new(name="nonempty_nodes")
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else:
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A, nonempty_nodes = G.get_properties("offdiag degrees-")
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if A.dtype == bool or not is_weighted:
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dtype = int
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else:
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dtype = A.dtype
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n = A.nrows
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D = Matrix(dtype, nrows=n, ncols=n, name="floyd_warshall")
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if is_weighted:
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D << A
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else:
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D(A.S) << 1 # Like `D << unary.one[int](A)`
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del A
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Row = Matrix(dtype, nrows=1, ncols=n, name="Row")
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Col = Matrix(dtype, nrows=n, ncols=1, name="Col")
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Outer = Matrix(dtype, nrows=n, ncols=n, name="Outer")
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for i in nonempty_nodes:
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Col << D[:, [i]]
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Row << D[[i], :]
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Outer << any_plus(Col @ Row) # Like `col.outer(row, binary.plus)`
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D(binary.min) << offdiag(Outer)
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# Set diagonal values to 0 (this way seems fast).
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# The missing values are implied to be infinity, so we set diagonals explicitly to 0.
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mask = Vector(bool, size=n, name="mask")
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mask << True
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Mask = mask.diag(name="Mask")
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D(Mask.S) << 0
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return D

‎graphblas_algorithms/interface.py

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is_k_regular = nxapi.regular.is_k_regular
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is_regular = nxapi.regular.is_regular
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# Shortest Paths
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floyd_warshall = nxapi.shortest_paths.dense.floyd_warshall
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has_path = nxapi.shortest_paths.generic.has_path
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# Simple Paths
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is_simple_path = nxapi.simple_paths.is_simple_path
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import pytest
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except ImportError: # pragma: no cover (import)
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return
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skip = [
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("test_attributes", {"TestBoruvka", "test_mst.py"}),
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("test_weight_attribute", {"TestBoruvka", "test_mst.py"}),
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]
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multi_attributed = "unable to handle multi-attributed graphs"
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multidigraph = "unable to handle MultiDiGraph"
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freeze = frozenset
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skip = {
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("test_attributes", freeze({"TestBoruvka", "test_mst.py"})): multi_attributed,
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("test_weight_attribute", freeze({"TestBoruvka", "test_mst.py"})): multi_attributed,
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("test_zero_weight", freeze({"TestFloyd", "test_dense.py"})): multidigraph,
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}
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for item in items:
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kset = set(item.keywords)
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for test_name, keywordsin skip:
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for (test_name, keywords), reasonin skip.items():
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if item.name == test_name and keywords.issubset(kset):
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item.add_marker(
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pytest.mark.xfail(reason="unable to handle multi-attributed graphs")
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)
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item.add_marker(pytest.mark.xfail(reason=reason))
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from .dense import *
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from .generic import *
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from graphblas_algorithms import algorithms
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from graphblas_algorithms.classes.digraph import to_graph
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__all__ = ["floyd_warshall"]
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def floyd_warshall(G, weight="weight"):
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G = to_graph(G, weight=weight)
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D = algorithms.floyd_warshall(G, is_weighted=weight is not None)
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return G.matrix_to_dicts(D)

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