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Implement
.generators.ego.ego_graph (#61)
* Implement `.generators.ego.ego_graph` Also, clean up shared BFS functions and move to `_bfs.py`. * use external images in README so they render on PyPI Support and test against Python 3.11 Change development status to Beta (was Alpha).
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,150 @@ | ||
| """BFS routines used by other algorithms""" | ||
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| import numpy as np | ||
| from graphblas import Matrix, Vector, binary, replace, unary | ||
| from graphblas.semiring import any_pair | ||
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| def _get_cutoff(n, cutoff): | ||
| if cutoff is None or cutoff >= n: | ||
| return n # Everything | ||
| return cutoff + 1 # Inclusive | ||
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| def _plain_bfs(G, source, *, cutoff=None): | ||
| index = G._key_to_id[source] | ||
| A = G.get_property("offdiag") | ||
| n = A.nrows | ||
| v = Vector(bool, n, name="bfs_plain") | ||
| q = Vector(bool, n, name="q") | ||
| v[index] = True | ||
| q[index] = True | ||
| any_pair_bool = any_pair[bool] | ||
| cutoff = _get_cutoff(n, cutoff) | ||
| for _i in range(1, cutoff): | ||
| q(~v.S, replace) << any_pair_bool(q @ A) | ||
| if q.nvals == 0: | ||
| break | ||
| v(q.S) << True | ||
| return v | ||
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| def _bfs_level(G, source, cutoff=None, *, transpose=False, dtype=int): | ||
| if dtype == bool: | ||
| dtype = int | ||
| index = G._key_to_id[source] | ||
| A = G.get_property("offdiag") | ||
| if transpose and G.is_directed(): | ||
| A = A.T # TODO: should we use "AT" instead? | ||
| n = A.nrows | ||
| v = Vector(dtype, n, name="bfs_level") | ||
| q = Vector(bool, n, name="q") | ||
| v[index] = 0 | ||
| q[index] = True | ||
| any_pair_bool = any_pair[bool] | ||
| cutoff = _get_cutoff(n, cutoff) | ||
| for i in range(1, cutoff): | ||
| q(~v.S, replace) << any_pair_bool(q @ A) | ||
| if q.nvals == 0: | ||
| break | ||
| v(q.S) << i | ||
| return v | ||
|
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| def _bfs_levels(G, nodes, cutoff=None, *, dtype=int): | ||
| if dtype == bool: | ||
| dtype = int | ||
| A = G.get_property("offdiag") | ||
| n = A.nrows | ||
| if nodes is None: | ||
| # TODO: `D = Vector.from_scalar(0, n, dtype).diag()` | ||
| D = Vector(dtype, n, name="bfs_levels_vector") | ||
| D << 0 | ||
| D = D.diag(name="bfs_levels") | ||
| else: | ||
| ids = G.list_to_ids(nodes) | ||
| D = Matrix.from_coo( | ||
| np.arange(len(ids), dtype=np.uint64), | ||
| ids, | ||
| 0, | ||
| dtype, | ||
| nrows=len(ids), | ||
| ncols=n, | ||
| name="bfs_levels", | ||
| ) | ||
| Q = unary.one[bool](D).new(name="Q") | ||
| any_pair_bool = any_pair[bool] | ||
| cutoff = _get_cutoff(n, cutoff) | ||
| for i in range(1, cutoff): | ||
| Q(~D.S, replace) << any_pair_bool(Q @ A) | ||
| if Q.nvals == 0: | ||
| break | ||
| D(Q.S) << i | ||
| return D | ||
|
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||
|
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| # TODO: benchmark this and the version commented out below | ||
| def _plain_bfs_bidirectional(G, source): | ||
| # Bi-directional BFS w/o symmetrizing the adjacency matrix | ||
| index = G._key_to_id[source] | ||
| A = G.get_property("offdiag") | ||
| # XXX: should we use `AT` if available? | ||
| n = A.nrows | ||
| v = Vector(bool, n, name="bfs_plain") | ||
| q_out = Vector(bool, n, name="q_out") | ||
| q_in = Vector(bool, n, name="q_in") | ||
| v[index] = True | ||
| q_in[index] = True | ||
| any_pair_bool = any_pair[bool] | ||
| is_out_empty = True | ||
| is_in_empty = False | ||
| for _i in range(1, n): | ||
| # Traverse out-edges from the most recent `q_in` and `q_out` | ||
| if is_out_empty: | ||
| q_out(~v.S) << any_pair_bool(q_in @ A) | ||
| else: | ||
| q_out << binary.any(q_out | q_in) | ||
| q_out(~v.S, replace) << any_pair_bool(q_out @ A) | ||
| is_out_empty = q_out.nvals == 0 | ||
| if not is_out_empty: | ||
| v(q_out.S) << True | ||
| elif is_in_empty: | ||
| break | ||
| # Traverse in-edges from the most recent `q_in` and `q_out` | ||
| if is_in_empty: | ||
| q_in(~v.S) << any_pair_bool(A @ q_out) | ||
| else: | ||
| q_in << binary.any(q_out | q_in) | ||
| q_in(~v.S, replace) << any_pair_bool(A @ q_in) | ||
| is_in_empty = q_in.nvals == 0 | ||
| if not is_in_empty: | ||
| v(q_in.S) << True | ||
| elif is_out_empty: | ||
| break | ||
| return v | ||
|
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||
|
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| """ | ||
| def _plain_bfs_bidirectional(G, source): | ||
| # Bi-directional BFS w/o symmetrizing the adjacency matrix | ||
| index = G._key_to_id[source] | ||
| A = G.get_property("offdiag") | ||
| n = A.nrows | ||
| v = Vector(bool, n, name="bfs_plain") | ||
| q = Vector(bool, n, name="q") | ||
| q2 = Vector(bool, n, name="q_2") | ||
| v[index] = True | ||
| q[index] = True | ||
| any_pair_bool = any_pair[bool] | ||
| for _i in range(1, n): | ||
| q2(~v.S, replace) << any_pair_bool(q @ A) | ||
| v(q2.S) << True | ||
| q(~v.S, replace) << any_pair_bool(A @ q) | ||
| if q.nvals == 0: | ||
| if q2.nvals == 0: | ||
| break | ||
| q, q2 = q2, q | ||
| elif q2.nvals != 0: | ||
| q << binary.any(q | q2) | ||
| return v | ||
| """ |
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75 changes: 3 additions & 72 deletions
75
graphblas_algorithms/algorithms/components/weakly_connected.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,77 +1,8 @@ | ||
| from graphblas import Vector, binary, replace | ||
| from graphblas.semiring import any_pair | ||
|
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| from graphblas_algorithms.algorithms.exceptions import PointlessConcept | ||
| from .._bfs import _plain_bfs_bidirectional | ||
| from ..exceptions import PointlessConcept | ||
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| def is_weakly_connected(G): | ||
| if len(G) == 0: | ||
| raise PointlessConcept("Connectivity is undefined for the null graph.") | ||
| return _plain_bfs(G, next(iter(G))).nvals == len(G) | ||
|
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| # TODO: benchmark this and the version commented out below | ||
| def _plain_bfs(G, source): | ||
| # Bi-directional BFS w/o symmetrizing the adjacency matrix | ||
| index = G._key_to_id[source] | ||
| A = G.get_property("offdiag") | ||
| # XXX: should we use `AT` if available? | ||
| n = A.nrows | ||
| v = Vector(bool, n, name="bfs_plain") | ||
| q_out = Vector(bool, n, name="q_out") | ||
| q_in = Vector(bool, n, name="q_in") | ||
| v[index] = True | ||
| q_in[index] = True | ||
| any_pair_bool = any_pair[bool] | ||
| is_out_empty = True | ||
| is_in_empty = False | ||
| for _i in range(1, n): | ||
| # Traverse out-edges from the most recent `q_in` and `q_out` | ||
| if is_out_empty: | ||
| q_out(~v.S) << any_pair_bool(q_in @ A) | ||
| else: | ||
| q_out << binary.any(q_out | q_in) | ||
| q_out(~v.S, replace) << any_pair_bool(q_out @ A) | ||
| is_out_empty = q_out.nvals == 0 | ||
| if not is_out_empty: | ||
| v(q_out.S) << True | ||
| elif is_in_empty: | ||
| break | ||
| # Traverse in-edges from the most recent `q_in` and `q_out` | ||
| if is_in_empty: | ||
| q_in(~v.S) << any_pair_bool(A @ q_out) | ||
| else: | ||
| q_in << binary.any(q_out | q_in) | ||
| q_in(~v.S, replace) << any_pair_bool(A @ q_in) | ||
| is_in_empty = q_in.nvals == 0 | ||
| if not is_in_empty: | ||
| v(q_in.S) << True | ||
| elif is_out_empty: | ||
| break | ||
| return v | ||
|
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||
|
|
||
| """ | ||
| def _plain_bfs(G, source): | ||
| # Bi-directional BFS w/o symmetrizing the adjacency matrix | ||
| index = G._key_to_id[source] | ||
| A = G.get_property("offdiag") | ||
| n = A.nrows | ||
| v = Vector(bool, n, name="bfs_plain") | ||
| q = Vector(bool, n, name="q") | ||
| q2 = Vector(bool, n, name="q_2") | ||
| v[index] = True | ||
| q[index] = True | ||
| any_pair_bool = any_pair[bool] | ||
| for _i in range(1, n): | ||
| q2(~v.S, replace) << any_pair_bool(q @ A) | ||
| v(q2.S) << True | ||
| q(~v.S, replace) << any_pair_bool(A @ q) | ||
| if q.nvals == 0: | ||
| if q2.nvals == 0: | ||
| break | ||
| q, q2 = q2, q | ||
| elif q2.nvals != 0: | ||
| q << binary.any(q | q2) | ||
| return v | ||
| """ | ||
| return _plain_bfs_bidirectional(G, next(iter(G))).nvals == len(G) |
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