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Fix numpy and scipy incompatibilities #149

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Jun 27, 2024
Merged

Fix numpy and scipy incompatibilities #149

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5234b1a
Fix deprecation warning in tests
simonbowly Jun 26, 2024
b019e9a
Fix scipy 1.14 incompatibility
simonbowly Jun 26, 2024
784c3bd
Adjust doctests
simonbowly Jun 27, 2024
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10 changes: 6 additions & 4 deletions docs/source/mods/bipartite-matching.rst
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Expand Up @@ -182,10 +182,12 @@ Solution
.. doctest:: bipartite_matching_sp
:options: +NORMALIZE_WHITESPACE

>>> print(sp.triu(matching))
(0, 7) 1.0
(1, 6) 1.0
(3, 5) 1.0
>>> upper = sp.triu(matching)
>>> for edge, value in zip(zip(*upper.coords), upper.data):
... print(f"{edge}: {value}")
(0, 7): 1.0
(1, 6): 1.0
(3, 5): 1.0

.. group-tab:: networkx

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38 changes: 17 additions & 21 deletions docs/source/mods/max-flow-min-cut.rst
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Expand Up @@ -126,17 +126,15 @@ An example of these inputs with their respective requirements is shown below.
>>> from gurobi_optimods import datasets
>>> G, capacities, _, _ = datasets.simple_graph_scipy()
>>> G.data = capacities.data # Copy capacity data
>>> G
<5x6 sparse array of type '<class 'numpy.int64'>'
with 7 stored elements in COOrdinate format>
>>> print(G)
(0, 1) 2
(0, 2) 2
(1, 3) 1
(2, 3) 1
(2, 4) 2
(3, 5) 2
(4, 5) 2
>>> for edge, value in zip(zip(*G.coords), G.data):
... print(f"{edge}: {value}")
(0, 1): 2
(0, 2): 2
(1, 3): 1
(2, 3): 1
(2, 4): 2
(3, 5): 2
(4, 5): 2

We only need the adjacency matrix for the graph (as a sparse array) where
each each entry contains the capacity of the edge.
Expand Down Expand Up @@ -204,16 +202,14 @@ Let us use the data to solve the maximum flow problem.
>>> obj, sol = max_flow(G, 0, 5, verbose=False)
>>> obj
3.0
>>> sol
<5x6 sparse array of type '<class 'numpy.float64'>'
with 6 stored elements in COOrdinate format>
>>> print(sol)
(0, 1) 1.0
(0, 2) 2.0
(1, 3) 1.0
(2, 4) 2.0
(3, 5) 1.0
(4, 5) 2.0
>>> for edge, value in zip(zip(*sol.coords), sol.data):
... print(f"{edge}: {value}")
(0, 1): 1.0
(0, 2): 2.0
(1, 3): 1.0
(2, 4): 2.0
(3, 5): 1.0
(4, 5): 2.0

The ``max_flow`` function returns the value of the maximum flow as well a
sparse array with the amount of non-zero flow in each edge in the
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70 changes: 34 additions & 36 deletions docs/source/mods/min-cost-flow.rst
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Expand Up @@ -88,33 +88,33 @@ An example of these inputs with their respective requirements is shown below.

>>> from gurobi_optimods import datasets
>>> G, capacities, cost, demands = datasets.simple_graph_scipy()
>>> G
<5x6 sparse array of type '<class 'numpy.int64'>'
with 7 stored elements in COOrdinate format>
>>> print(G)
(0, 1) 1
(0, 2) 1
(1, 3) 1
(2, 3) 1
(2, 4) 1
(3, 5) 1
(4, 5) 1
>>> print(capacities)
(0, 1) 2
(0, 2) 2
(1, 3) 1
(2, 3) 1
(2, 4) 2
(3, 5) 2
(4, 5) 2
>>> print(cost)
(0, 1) 9
(0, 2) 7
(1, 3) 1
(2, 3) 10
(2, 4) 6
(3, 5) 1
(4, 5) 1
>>> for edge, value in zip(zip(*G.coords), G.data):
... print(f"{edge}: {value}")
(0, 1): 1
(0, 2): 1
(1, 3): 1
(2, 3): 1
(2, 4): 1
(3, 5): 1
(4, 5): 1
>>> for edge, value in zip(zip(*capacities.coords), capacities.data):
... print(f"{edge}: {value}")
(0, 1): 2
(0, 2): 2
(1, 3): 1
(2, 3): 1
(2, 4): 2
(3, 5): 2
(4, 5): 2
>>> for edge, value in zip(zip(*cost.coords), cost.data):
... print(f"{edge}: {value}")
(0, 1): 9
(0, 2): 7
(1, 3): 1
(2, 3): 10
(2, 4): 6
(3, 5): 1
(4, 5): 1
>>> print(demands)
[-2 0 -1 1 0 2]

Expand Down Expand Up @@ -206,15 +206,13 @@ formats.
>>> obj, sol = min_cost_flow_scipy(G, capacities, cost, demands, verbose=False)
>>> obj
31.0
>>> sol
<5x6 sparse array of type '<class 'numpy.float64'>'
with 5 stored elements in COOrdinate format>
>>> print(sol)
(0, 1) 1.0
(0, 2) 1.0
(1, 3) 1.0
(2, 4) 2.0
(4, 5) 2.0
>>> for edge, value in zip(zip(*sol.coords), sol.data):
... print(f"{edge}: {value}")
(0, 1): 1.0
(0, 2): 1.0
(1, 3): 1.0
(2, 4): 2.0
(4, 5): 2.0

The ``min_cost_flow_scipy`` function returns the cost of the solution as
well as a ``sp.sparray`` that provides the amount of flow for each
Expand Down
3 changes: 2 additions & 1 deletion src/gurobi_optimods/min_cut.py
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Expand Up @@ -205,10 +205,11 @@ def _min_cut_scipy(G, source, sink, create_env):
return MinCutResult(0.0, (set(), set()), set())

queue = [source]
G = G.tocsr()
while len(queue) > 0:
node = queue.pop()
p1.add(node)
row = G.getrow(node)
row = G[[node]]
# Add successors of `node` that are not in the cutset
queue.extend(
[
Expand Down
2 changes: 1 addition & 1 deletion tests/test_bipartite_matching.py
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Expand Up @@ -34,7 +34,7 @@ def assert_is_unweighted_matching(self, matching):
assert_allclose(matching.data, np.ones(matching.data.shape))
adj = matching.todense()
assert_allclose(adj, adj.T)
self.assertTrue(np.alltrue(adj.sum(axis=0) <= 1))
self.assertTrue(np.all(adj.sum(axis=0) <= 1))

def test_empty(self):
# Matching of an empty graph is empty
Expand Down