Add a few more BFS-based algorithms

.pre-commit-config.yaml

@@ -4,6 +4,11 @@
 # To run: `pre-commit run --all-files`
 # To update: `pre-commit autoupdate`
 #  - &flake8_dependencies below needs updated manually
+ci:
+    # See: https://pre-commit.ci/#configuration
+    autofix_prs: false
+    autoupdate_schedule: monthly
+    skip: [no-commit-to-branch]
 fail_fast: true
 default_language_version:
     python: python3
@@ -20,12 +25,13 @@ repos:
       - id: mixed-line-ending
       - id: trailing-whitespace
   - repo: https://github.com/abravalheri/validate-pyproject
-    rev: v0.12.1
+    rev: v0.12.2
     hooks:
       - id: validate-pyproject
         name: Validate pyproject.toml
+  # I don't yet trust ruff to do what autoflake does
   - repo: https://github.com/myint/autoflake
-    rev: v2.0.1
+    rev: v2.0.2
     hooks:
       - id: autoflake
         args: [--in-place]
@@ -44,36 +50,47 @@ repos:
       - id: auto-walrus
         args: [--line-length, "100"]
   - repo: https://github.com/psf/black
-    rev: 23.1.0
+    rev: 23.3.0
     hooks:
       - id: black
       # - id: black-jupyter
+  - repo: https://github.com/charliermarsh/ruff-pre-commit
+    rev: v0.0.261
+    hooks:
+      - id: ruff
+        args: [--fix-only, --show-fixes]
   - repo: https://github.com/PyCQA/flake8
     rev: 6.0.0
     hooks:
       - id: flake8
         additional_dependencies: &flake8_dependencies
         # These versions need updated manually
         - flake8==6.0.0
-        - flake8-comprehensions==3.10.1
-        - flake8-bugbear==23.2.13
-        - flake8-simplify==0.19.3
+        - flake8-bugbear==23.3.23
+        - flake8-simplify==0.20.0
   - repo: https://github.com/asottile/yesqa
     rev: v1.4.0
     hooks:
       - id: yesqa
         additional_dependencies: *flake8_dependencies
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.2.2
+    rev: v2.2.4
     hooks:
       - id: codespell
         types_or: [python, rst, markdown]
         additional_dependencies: [tomli]
         files: ^(graphblas_algorithms|docs)/
   - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: v0.0.253
+    rev: v0.0.261
     hooks:
       - id: ruff
+  # `pyroma` may help keep our package standards up to date if best practices change.
+  # This is probably a "low value" check though and safe to remove if we want faster pre-commit.
+  - repo: https://github.com/regebro/pyroma
+    rev: "4.2"
+    hooks:
+      - id: pyroma
+        args: [-n, "10", .]
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v4.4.0
     hooks:

README.md

@@ -117,6 +117,10 @@ dispatch pattern shown above.
 - Community
   - inter_community_edges
   - intra_community_edges
+- Components
+  - is_connected
+  - is_weakly_connected
+  - node_connected_component
 - Core
   - k_truss
 - Cuts
@@ -147,11 +151,15 @@ dispatch pattern shown above.
   - is_k_regular
   - is_regular
 - Shortest Paths
+  - all_pairs_bellman_ford_path_length
+  - all_pairs_shortest_path_length
   - floyd_warshall
   - floyd_warshall_predecessor_and_distance
-  - single_source_bellman_ford_path_length
-  - all_pairs_bellman_ford_path_length
   - has_path
+  - negative_edge_cycle
+  - single_source_bellman_ford_path_length
+  - single_source_shortest_path_length
+  - single_target_shortest_path_length
 - Simple Paths
   - is_simple_path
 - S Metric
@@ -162,5 +170,8 @@ dispatch pattern shown above.
   - is_tournament
   - score_sequence
   - tournament_matrix
+- Traversal
+  - bfs_layers
+  - descendants_at_distance
 - Triads
   - is_triad

graphblas_algorithms/algorithms/__init__.py

@@ -3,6 +3,7 @@
 from .centrality import *
 from .cluster import *
 from .community import *
+from .components import *
 from .core import *
 from .cuts import *
 from .dag import *
@@ -16,4 +17,5 @@
 from .smetric import *
 from .structuralholes import *
 from .tournament import *
+from .traversal import *
 from .triads import *

graphblas_algorithms/algorithms/centrality/eigenvector.py

@@ -27,7 +27,7 @@ def eigenvector_centrality(G, max_iter=100, tol=1.0e-6, nstart=None, name="eigen
     # Power iteration: make up to max_iter iterations
     A = G._A
     xprev = Vector(float, N, name="x_prev")
-    for _ in range(max_iter):
+    for _i in range(max_iter):
         xprev << x
         x += x @ A
         normalize(x, "L2")

graphblas_algorithms/algorithms/centrality/katz.py

@@ -44,7 +44,7 @@ def katz_centrality(
 
     # Power iteration: make up to max_iter iterations
     xprev = Vector(float, N, name="x_prev")
-    for _ in range(max_iter):
+    for _i in range(max_iter):
         xprev, x = x, xprev
         # x << alpha * semiring(xprev @ A) + beta
         x << semiring(xprev @ A)

graphblas_algorithms/algorithms/components/__init__.py

@@ -0,0 +1,2 @@
+from .connected import *
+from .weakly_connected import *

graphblas_algorithms/algorithms/components/connected.py

@@ -0,0 +1,31 @@
+from graphblas import Vector, replace
+from graphblas.semiring import any_pair
+
+from graphblas_algorithms.algorithms.exceptions import PointlessConcept
+
+
+def is_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)
+
+
+def node_connected_component(G, n):
+    return _plain_bfs(G, n)
+
+
+def _plain_bfs(G, source):
+    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]
+    for _i in range(1, n):
+        q(~v.S, replace) << any_pair_bool(q @ A)
+        if q.nvals == 0:
+            break
+        v(q.S) << True
+    return v

graphblas_algorithms/algorithms/components/weakly_connected.py

@@ -0,0 +1,77 @@
+from graphblas import Vector, binary, replace
+from graphblas.semiring import any_pair
+
+from graphblas_algorithms.algorithms.exceptions import PointlessConcept
+
+
+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)
+
+
+# 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
+
+
+"""
+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
+"""

graphblas_algorithms/algorithms/dag.py

@@ -1,5 +1,5 @@
 from graphblas import Vector, replace
-from graphblas.semiring import lor_pair
+from graphblas.semiring import any_pair
 
 __all__ = ["descendants", "ancestors"]
 
@@ -10,10 +10,12 @@ def descendants(G, source):
         raise KeyError(f"The node {source} is not in the graph")
     index = G._key_to_id[source]
     A = G.get_property("offdiag")
-    q = Vector.from_coo(index, True, size=A.nrows, name="q")
+    q = Vector(bool, size=A.nrows, name="q")
+    q[index] = True
     rv = q.dup(name="descendants")
-    for _ in range(A.nrows):
-        q(~rv.S, replace) << lor_pair(q @ A)
+    any_pair_bool = any_pair[bool]
+    for _i in range(A.nrows):
+        q(~rv.S, replace) << any_pair_bool(q @ A)
         if q.nvals == 0:
             break
         rv(q.S) << True
@@ -26,10 +28,12 @@ def ancestors(G, source):
         raise KeyError(f"The node {source} is not in the graph")
     index = G._key_to_id[source]
     A = G.get_property("offdiag")
-    q = Vector.from_coo(index, True, size=A.nrows, name="q")
+    q = Vector(bool, size=A.nrows, name="q")
+    q[index] = True
     rv = q.dup(name="descendants")
-    for _ in range(A.nrows):
-        q(~rv.S, replace) << lor_pair(A @ q)
+    any_pair_bool = any_pair[bool]
+    for _i in range(A.nrows):
+        q(~rv.S, replace) << any_pair_bool(A @ q)
         if q.nvals == 0:
             break
         rv(q.S) << True

graphblas_algorithms/algorithms/dominating.py

@@ -1,8 +1,8 @@
-from graphblas.semiring import lor_pair
+from graphblas.semiring import any_pair
 
 __all__ = ["is_dominating_set"]
 
 
 def is_dominating_set(G, nbunch):
-    nbrs = lor_pair(nbunch @ G._A).new(mask=~nbunch.S)  # A or A.T?
+    nbrs = any_pair[bool](nbunch @ G._A).new(mask=~nbunch.S)  # A or A.T?
     return nbrs.size - nbunch.nvals - nbrs.nvals == 0

graphblas_algorithms/algorithms/exceptions.py

@@ -14,5 +14,9 @@ class PointlessConcept(GraphBlasAlgorithmException):
     pass
 
 
+class NoPath(GraphBlasAlgorithmException):
+    pass
+
+
 class Unbounded(GraphBlasAlgorithmException):
     pass

graphblas_algorithms/algorithms/link_analysis/hits_alg.py

@@ -30,7 +30,7 @@ def hits(G, max_iter=100, tol=1.0e-8, nstart=None, normalized=True, *, with_auth
         a, h = h, a
         ATA = (A.T @ A).new(name="ATA")  # Authority matrix
         aprev = Vector(float, N, name="a_prev")
-        for _ in range(max_iter):
+        for _i in range(max_iter):
             aprev, a = a, aprev
             a << ATA @ aprev
             normalize(a, "Linf")
@@ -41,7 +41,7 @@ def hits(G, max_iter=100, tol=1.0e-8, nstart=None, normalized=True, *, with_auth
             raise ConvergenceFailure(max_iter)
     else:
         hprev = Vector(float, N, name="h_prev")
-        for _ in range(max_iter):
+        for _i in range(max_iter):
             hprev, h = h, hprev
             a << hprev @ A
             h << A @ a

graphblas_algorithms/algorithms/link_analysis/pagerank_alg.py

@@ -79,7 +79,7 @@ def pagerank(
     # Power iteration: make up to max_iter iterations
     xprev = Vector(float, N, name="x_prev")
     w = Vector(float, N, name="w")
-    for _ in range(max_iter):
+    for _i in range(max_iter):
         xprev, x = x, xprev
 
         # x << alpha * ((xprev * S) @ A + "dangling_weights") + (1 - alpha) * p

graphblas_algorithms/algorithms/shortest_paths/__init__.py

@@ -1,3 +1,4 @@
 from .dense import *
 from .generic import *
+from .unweighted import *
 from .weighted import *

graphblas_algorithms/algorithms/shortest_paths/generic.py

@@ -1,5 +1,5 @@
 from graphblas import Vector, replace
-from graphblas.semiring import lor_pair
+from graphblas.semiring import any_pair
 
 __all__ = ["has_path"]
 
@@ -11,23 +11,26 @@ def has_path(G, source, target):
     if src == dst:
         return True
     A = G.get_property("offdiag")
-    q_src = Vector.from_coo(src, True, size=A.nrows, name="q_src")
+    q_src = Vector(bool, size=A.nrows, name="q_src")
+    q_src[src] = True
     seen_src = q_src.dup(name="seen_src")
-    q_dst = Vector.from_coo(dst, True, size=A.nrows, name="q_dst")
-    seen_dst = q_dst.dup(name="seen_dst")
-    for _ in range(A.nrows // 2):
-        q_src(~seen_src.S, replace) << lor_pair(q_src @ A)
+    q_dst = Vector(bool, size=A.nrows, name="q_dst")
+    q_dst[dst] = True
+    seen_dst = q_dst.dup(name="seen_dst", clear=True)
+    any_pair_bool = any_pair[bool]
+    for _i in range(A.nrows // 2):
+        q_src(~seen_src.S, replace) << any_pair_bool(q_src @ A)
         if q_src.nvals == 0:
             return False
-        if lor_pair(q_src @ q_dst):
+        if any_pair_bool(q_src @ q_dst):
             return True
 
-        q_dst(~seen_dst.S, replace) << lor_pair(A @ q_dst)
+        seen_dst(q_dst.S) << True
+        q_dst(~seen_dst.S, replace) << any_pair_bool(A @ q_dst)
         if q_dst.nvals == 0:
             return False
-        if lor_pair(q_src @ q_dst):
+        if any_pair_bool(q_src @ q_dst):
             return True
 
         seen_src(q_src.S) << True
-        seen_dst(q_dst.S) << True
     return False