Update to use to_coo/from_coo instead of to_values/from_values

.github/workflows/test.yml

@@ -33,7 +33,7 @@ jobs:
           conda install -c conda-forge python-graphblas scipy pandas \
             pytest-cov pytest-randomly black flake8-comprehensions flake8-bugbear
             # matplotlib lxml pygraphviz pydot sympy  # Extra networkx deps we don't need yet
-          pip install git+https://github.com/jim22k/networkx.git@nx-sparse --no-deps
+          pip install git+https://github.com/networkx/networkx.git@main --no-deps
           pip install -e . --no-deps
       - name: Style checks
         run: |

.pre-commit-config.yaml

@@ -2,6 +2,8 @@
 #
 # Before first use: `pre-commit install`
 # To run: `pre-commit run --all-files`
+# To update: `pre-commit autoupdate`
+#  - &flake8_dependencies below needs updated manually
 fail_fast: true
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
@@ -26,7 +28,7 @@ repos:
       - id: isort
         language_version: python3
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.1.0
+    rev: v3.2.2
     hooks:
       - id: pyupgrade
         args: [--py38-plus]
@@ -45,9 +47,10 @@ repos:
     hooks:
       - id: flake8
         additional_dependencies: &flake8_dependencies
+        # These versions need updated manually
         - flake8==5.0.4
-        - flake8-comprehensions==3.10.0
-        - flake8-bugbear==22.9.23
+        - flake8-comprehensions==3.10.1
+        - flake8-bugbear==22.10.27
   - repo: https://github.com/asottile/yesqa
     rev: v1.4.0
     hooks:

graphblas_algorithms/algorithms/cluster.py

@@ -241,7 +241,7 @@ def square_clustering(G, node_ids=None):
     A, degrees = G.get_properties("A degrees+")  # TODO" how to handle self-edges?
     # P2 from https://arxiv.org/pdf/2007.11111.pdf; we'll also use it as scratch
     if node_ids is not None:
-        v = Vector.from_values(node_ids, True, size=degrees.size)
+        v = Vector.from_coo(node_ids, True, size=degrees.size)
         Asubset = binary.second(v & A).new(name="A_subset")
     else:
         Asubset = A
@@ -298,10 +298,10 @@ def generalized_degree(G, *, mask=None):
     else:
         Tri(A.S) << 0
     Tri(Tri.S, binary.second) << plus_pair(Tri @ A.T)
-    rows, cols, vals = Tri.to_values()
+    rows, cols, vals = Tri.to_coo()
     # The column index indicates the number of triangles an edge participates in.
     # The largest this can be is `A.ncols - 1`.  Values is count of edges.
-    return Matrix.from_values(
+    return Matrix.from_coo(
         rows,
         vals,
         np.ones(vals.size, dtype=int),

graphblas_algorithms/algorithms/core.py

@@ -28,7 +28,7 @@ def k_truss(G: Graph, k) -> Graph:
             S = C
 
     # Remove isolate nodes
-    indices, _ = C.reduce_rowwise(monoid.any).to_values()
+    indices, _ = C.reduce_rowwise(monoid.any).to_coo()
     Ktruss = C[indices, indices].new()
 
     # Convert back to networkx graph with correct node ids

graphblas_algorithms/algorithms/dag.py

@@ -10,7 +10,7 @@ def descendants(G, source):
         raise KeyError(f"The node {source} is not in the graph")
     index = G._key_to_id[source]
     A = G._A
-    q = Vector.from_values(index, True, size=A.nrows, name="q")
+    q = Vector.from_coo(index, True, size=A.nrows, name="q")
     rv = q.dup(name="descendants")
     for _ in range(A.nrows):
         q(~rv.S, replace) << lor_pair(q @ A)
@@ -26,7 +26,7 @@ def ancestors(G, source):
         raise KeyError(f"The node {source} is not in the graph")
     index = G._key_to_id[source]
     A = G._A
-    q = Vector.from_values(index, True, size=A.nrows, name="q")
+    q = Vector.from_coo(index, True, size=A.nrows, name="q")
     rv = q.dup(name="descendants")
     for _ in range(A.nrows):
         q(~rv.S, replace) << lor_pair(A @ q)

graphblas_algorithms/algorithms/shortest_paths/generic.py

@@ -11,9 +11,9 @@ def has_path(G, source, target):
     if src == dst:
         return True
     A = G._A
-    q_src = Vector.from_values(src, True, size=A.nrows, name="q_src")
+    q_src = Vector.from_coo(src, True, size=A.nrows, name="q_src")
     seen_src = q_src.dup(name="seen_src")
-    q_dst = Vector.from_values(dst, True, size=A.nrows, name="q_dst")
+    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)

graphblas_algorithms/algorithms/simple_paths.py

@@ -16,7 +16,7 @@ def is_simple_path(G, nodes):
     if len(indices) != len(nodes) or len(indices) > len(set(indices)):
         return False
     # Check all steps in path at once
-    P = Matrix.from_values(indices[:-1], indices[1:], True, nrows=A.nrows, ncols=A.ncols)
+    P = Matrix.from_coo(indices[:-1], indices[1:], True, nrows=A.nrows, ncols=A.ncols)
     P << binary.second(A & P)
     return P.nvals == len(indices) - 1
     # Alternative

graphblas_algorithms/algorithms/tournament.py

@@ -15,7 +15,7 @@ def is_tournament(G):
 
 def score_sequence(G):
     degrees = G.get_property("row_degrees+")
-    _, values = degrees.to_values(indices=False, sort=False)
+    _, values = degrees.to_coo(indices=False, sort=False)
     values.sort()
     if degrees.nvals != degrees.size:
         values = np.pad(values, (degrees.size - degrees.nvals, 0))

graphblas_algorithms/classes/_utils.py

@@ -78,7 +78,7 @@ def dict_to_vector(self, d, *, size=None, dtype=None, name=None):
         size = len(self)
     key_to_id = self._key_to_id
     indices, values = zip(*((key_to_id[key], val) for key, val in d.items()))
-    return Vector.from_values(indices, values, size=size, dtype=dtype, name=name)
+    return Vector.from_coo(indices, values, size=size, dtype=dtype, name=name)
 
 
 def list_to_vector(self, nodes, dtype=bool, *, size=None, name=None):
@@ -88,7 +88,7 @@ def list_to_vector(self, nodes, dtype=bool, *, size=None, name=None):
         size = len(self)
     key_to_id = self._key_to_id
     index = [key_to_id[key] for key in nodes]
-    return Vector.from_values(index, True, size=size, dtype=dtype, name=name)
+    return Vector.from_coo(index, True, size=size, dtype=dtype, name=name)
 
 
 def list_to_mask(self, nodes, *, size=None, name="mask"):
@@ -122,7 +122,7 @@ def set_to_vector(self, nodes, dtype=bool, *, ignore_extra=False, size=None, nam
             nodes = set(nodes)
         nodes = nodes & key_to_id.keys()
     index = [key_to_id[key] for key in nodes]
-    return Vector.from_values(index, True, size=size, dtype=dtype, name=name)
+    return Vector.from_coo(index, True, size=size, dtype=dtype, name=name)
 
 
 def vector_to_dict(self, v, *, mask=None, fillvalue=None):
@@ -132,7 +132,7 @@ def vector_to_dict(self, v, *, mask=None, fillvalue=None):
     elif fillvalue is not None and v.nvals < v.size:
         v(mask=~v.S) << fillvalue
     id_to_key = self.id_to_key
-    return {id_to_key[index]: value for index, value in zip(*v.to_values(sort=False))}
+    return {id_to_key[index]: value for index, value in zip(*v.to_coo(sort=False))}
 
 
 def vector_to_nodemap(self, v, *, mask=None, fillvalue=None):
@@ -165,7 +165,7 @@ def vector_to_nodeset(self, v):
 
 def vector_to_set(self, v):
     id_to_key = self.id_to_key
-    indices, _ = v.to_values(values=False, sort=False)
+    indices, _ = v.to_coo(values=False, sort=False)
     return {id_to_key[index] for index in indices}
 
 
@@ -227,7 +227,7 @@ def to_networkx(self, edge_attribute="weight"):
         A = self.get_property("L+")
     G.add_nodes_from(self._key_to_id)
     id_to_key = self.id_to_key
-    rows, cols, vals = A.to_values()
+    rows, cols, vals = A.to_coo()
     rows = (id_to_key[row] for row in rows.tolist())
     cols = (id_to_key[col] for col in cols.tolist())
     if edge_attribute is None:

graphblas_algorithms/classes/nodemap.py

@@ -52,7 +52,7 @@ def __getitem__(self, key):
     def __iter__(self):
         # Slow if we iterate over one; fast if we iterate over all
         return map(
-            self.id_to_key.__getitem__, self.vector.to_values(values=False, sort=False)[0].tolist()
+            self.id_to_key.__getitem__, self.vector.to_coo(values=False, sort=False)[0].tolist()
         )
 
     def __len__(self):
@@ -123,7 +123,7 @@ def __getitem__(self, key):
 
     def __iter__(self):
         # Slow if we iterate over one; fast if we iterate over all
-        return iter(self.vector.to_values(values=False, sort=False)[0].tolist())
+        return iter(self.vector.to_coo(values=False, sort=False)[0].tolist())
 
     def __len__(self):
         return self.vector.nvals
@@ -232,7 +232,7 @@ def __iter__(self):
         # Slow if we iterate over one; fast if we iterate over all
         return map(
             self.id_to_key.__getitem__,
-            self._get_rows().to_values(values=False, sort=False)[0].tolist(),
+            self._get_rows().to_coo(values=False, sort=False)[0].tolist(),
         )
 
     def __len__(self):

graphblas_algorithms/classes/nodeset.py

@@ -46,7 +46,7 @@ def __contains__(self, x):
     def __iter__(self):
         # Slow if we iterate over one; fast if we iterate over all
         return map(
-            self.id_to_key.__getitem__, self.vector.to_values(values=False, sort=False)[0].tolist()
+            self.id_to_key.__getitem__, self.vector.to_coo(values=False, sort=False)[0].tolist()
         )
 
     def __len__(self):

graphblas_algorithms/interface.py

@@ -1,5 +1,3 @@
-import pytest
-
 from . import nxapi
 
 
@@ -93,6 +91,10 @@ def convert_to_nx(obj, *, name=None):
 
     @staticmethod
     def on_start_tests(items):
+        try:
+            import pytest
+        except ImportError:  # pragma: no cover (import)
+            return
         skip = [
             ("test_attributes", {"TestBoruvka", "test_mst.py"}),
             ("test_weight_attribute", {"TestBoruvka", "test_mst.py"}),

graphblas_algorithms/nxapi/boundary.py

@@ -21,7 +21,7 @@ def edge_boundary(G, nbunch1, nbunch2=None, data=False, keys=False, default=None
     v1 = G.set_to_vector(nbunch1, ignore_extra=True)
     v2 = G.set_to_vector(nbunch2, ignore_extra=True)
     result = algorithms.edge_boundary(G, v1, v2, is_weighted=is_multigraph or data)
-    rows, cols, vals = result.to_values(values=is_multigraph or data)
+    rows, cols, vals = result.to_coo(values=is_multigraph or data)
     id_to_key = G.id_to_key
     if data:
         it = zip(

graphblas_algorithms/nxapi/cluster.py

@@ -95,7 +95,7 @@ def _split(L, k):
 # TODO: should this move into algorithms?
 def _square_clustering_split(G, node_ids=None, *, nsplits):
     if node_ids is None:
-        node_ids = G._A.reduce_rowwise(monoid.any).to_values()[0]
+        node_ids = G._A.reduce_rowwise(monoid.any).to_coo()[0]
     result = None
     for chunk_ids in _split(node_ids, nsplits):
         res = algorithms.square_clustering(G, chunk_ids)

graphblas_algorithms/tests/test_match_nx.py

@@ -28,26 +28,17 @@
 
 def isdispatched(func):
     """Can this NetworkX function dispatch to other backends?"""
-    # Haha, there should be a better way to know this
-    registered_algorithms = backends._registered_algorithms
-    try:
-        return (
-            func.__globals__.get("_registered_algorithms") is registered_algorithms
-            and func.__module__.startswith("networkx")
-            and func.__module__ != "networkx.classes.backends"
-            and set(func.__code__.co_freevars) == {"func", "name"}
-        )
-    except Exception:
-        return False
+    return (
+        callable(func) and hasattr(func, "dispatchname") and func.__module__.startswith("networkx")
+    )
 
 
 def dispatchname(func):
     """The dispatched name of the dispatchable NetworkX function"""
     # Haha, there should be a better way to get this
     if not isdispatched(func):
         raise ValueError(f"Function is not dispatched in NetworkX: {func.__name__}")
-    index = func.__code__.co_freevars.index("name")
-    return func.__closure__[index].cell_contents
+    return func.dispatchname
 
 
 def fullname(func):

requirements.txt

@@ -1 +1 @@
-python-graphblas >=2022.10.1
+python-graphblas >=2022.11.0

scripts/bench_pagerank.py

@@ -178,14 +178,14 @@ def main(filename, backend, time, n, verify, alpha, tol, _get_result=False):
 
         start = timeit.default_timer()
         df = pd.read_csv(filename, delimiter="\t", names=["row", "col"])
-        G = Matrix.from_values(df["row"].values, df["col"].values, 1)
+        G = Matrix.from_coo(df["row"].values, df["col"].values, 1)
         stop = timeit.default_timer()
         num_nodes = G.nrows
         num_edges = G.nvals
         if _get_result:
             result = pagerank(G, alpha=alpha, tol=tol)
             result(~result.S) << 0  # Densify just in case
-            return result.to_values()[1]
+            return result.to_coo()[1]
 
     elif backend == "scipy":
         import pandas as pd