Update more sparse arrays

docs/source/mods/min-cost-flow.rst

@@ -89,7 +89,7 @@ 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 matrix of type '<class 'numpy.int64'>'
+          <5x6 sparse array of type '<class 'numpy.int64'>'
                   with 7 stored elements in COOrdinate format>
           >>> print(G)
             (0, 1)        1
@@ -207,7 +207,7 @@ formats.
           >>> obj
           31.0
           >>> sol
-          <5x6 sparse matrix of type '<class 'numpy.float64'>'
+          <5x6 sparse array of type '<class 'numpy.float64'>'
                   with 5 stored elements in COOrdinate format>
           >>> print(sol)
             (0, 1)        1.0

docs/source/mods/qubo.rst

@@ -109,7 +109,7 @@ definition as a SciPy sparse matrix in a comment).
     # weights = [-3, 2, -1, -2, 3]
     # row = [1, 2, 0, 0, 1]
     # col = [1, 2, 1, 2, 2]
-    # Q = sp.coo_matrix((weights, (row, col)), shape=(3, 3))
+    # Q = sp.coo_array((weights, (row, col)), shape=(3, 3))
 
     result = solve_qubo(Q)
 

src/gurobi_optimods/bipartite_matching.py

@@ -215,5 +215,5 @@ def _maximum_bipartite_matching_scipy(adjacency, nodes1, nodes2, create_env):
 
     # Return undirected, unweighted adjacency matrix
     arg = (np.ones(from_arc_result.shape), (from_arc_result, to_arc_result))
-    matching = sp.coo_matrix(arg, dtype=float, shape=G.shape)
+    matching = sp.coo_array(arg, dtype=float, shape=G.shape)
     return matching + matching.T

src/gurobi_optimods/datasets.py

@@ -106,7 +106,7 @@ def _convert_pandas_to_scipy(
     edge_data, node_data, capacity=True, cost=True, demand=True
 ):
     """
-    Convert from a pandas DataFrame to several scipy.sparse.coo_matrix contain
+    Convert from a pandas DataFrame to several scipy.sparse.coo_array contain
     the graph structure, the capacity and cost values per edge, and the demand
     values per node.
     """
@@ -116,17 +116,17 @@ def _convert_pandas_to_scipy(
     a1 = np.array([c[1] for c in coords])
 
     data = np.ones(len(coords), dtype=np.int64)
-    G = sp.coo_matrix((data, (a0, a1)))
+    G = sp.coo_array((data, (a0, a1)))
 
     costs = None
     if cost:
         data = edge_data["cost"].values
-        costs = sp.coo_matrix((data, (a0, a1)))
+        costs = sp.coo_array((data, (a0, a1)))
 
     cap = None
     if capacity:
         data = edge_data["capacity"].values
-        cap = sp.coo_matrix((data, (a0, a1)))
+        cap = sp.coo_array((data, (a0, a1)))
 
     dem = None
     if demand:

src/gurobi_optimods/min_cost_flow.py

@@ -149,7 +149,7 @@ def min_cost_flow_scipy(
         edge_source_result = edge_source[select]
         edge_target_result = edge_target[select]
         arg = (x.X[select], (edge_source_result, edge_target_result))
-        return model.ObjVal, sp.coo_matrix(arg, dtype=float, shape=G.shape)
+        return model.ObjVal, sp.coo_array(arg, dtype=float, shape=G.shape)
 
 
 @optimod()