miplearn/problems/maxcut.py

@@ -1,111 +0,0 @@
-#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020-2025, UChicago Argonne, LLC. All rights reserved.
-#  Released under the modified BSD license. See COPYING.md for more details.
-
-from dataclasses import dataclass
-from typing import List, Union, Optional, Any
-
-import gurobipy as gp
-import networkx as nx
-import numpy as np
-from gurobipy import quicksum
-from networkx import Graph
-from scipy.stats.distributions import rv_frozen
-
-from miplearn.io import read_pkl_gz
-from miplearn.problems import _gurobipy_set_params
-from miplearn.solvers.gurobi import GurobiModel
-
-
-@dataclass
-class MaxCutData:
-    graph: Graph
-    weights: np.ndarray
-
-
-class MaxCutGenerator:
-    """
-    Random instance generator for the Maximum Cut Problem.
-
-    The generator operates in two modes. When `fix_graph=True`, a single random
-    Erdős-Rényi graph $G_{n,p}$ is generated during initialization, with parameters $n$
-    and $p$ drawn from their respective probability distributions. For each instance,
-    only edge weights are randomly sampled from the set {1, -1}, while the graph
-    structure remains fixed.
-
-    When `fix_graph=False`, both the graph structure and edge weights are randomly
-    generated for each instance.
-    """
-
-    def __init__(
-        self,
-        n: rv_frozen,
-        p: rv_frozen,
-        fix_graph: bool,
-    ):
-        """
-        Initialize the problem generator.
-
-        Parameters
-        ----------
-        n: rv_discrete
-            Probability distribution for the number of nodes.
-        p: rv_continuous
-            Probability distribution for the graph density.
-        fix_graph: bool
-            Controls graph generation for instances. If false, a new random graph is
-            generated for each instance. If true, the same graph is reused across instances.
-        """
-        assert isinstance(n, rv_frozen), "n should be a SciPy probability distribution"
-        assert isinstance(p, rv_frozen), "p should be a SciPy probability distribution"
-        self.n = n
-        self.p = p
-        self.fix_graph = fix_graph
-        self.graph = None
-        if fix_graph:
-            self.graph = self._generate_graph()
-
-    def generate(self, n_samples: int) -> List[MaxCutData]:
-        def _sample() -> MaxCutData:
-            if self.graph is not None:
-                graph = self.graph
-            else:
-                graph = self._generate_graph()
-            m = graph.number_of_edges()
-            weights = np.random.randint(2, size=(m,)) * 2 - 1
-            return MaxCutData(graph, weights)
-
-        return [_sample() for _ in range(n_samples)]
-
-    def _generate_graph(self) -> Graph:
-        return nx.generators.random_graphs.binomial_graph(self.n.rvs(), self.p.rvs())
-
-def build_maxcut_model_gurobipy(
-    data: Union[str, MaxCutData],
-    params: Optional[dict[str, Any]] = None,
-) -> GurobiModel:
-    # Initialize model
-    model = gp.Model()
-    _gurobipy_set_params(model, params)
-
-    # Read data
-    data = _maxcut_read(data)
-    nodes = list(data.graph.nodes())
-    edges = list(data.graph.edges())
-
-    # Add decision variables
-    x = model.addVars(nodes, vtype=gp.GRB.BINARY, name="x")
-
-    # Add the objective function
-    model.setObjective(quicksum(
-        - data.weights[i] * x[e[0]] * (1 - x[e[1]]) for (i, e) in enumerate(edges)
-    ))
-
-    model.update()
-    return GurobiModel(model)
-
-def _maxcut_read(data: Union[str, MaxCutData]) -> MaxCutData:
-    if isinstance(data, str):
-        data = read_pkl_gz(data)
-    assert isinstance(data, MaxCutData)
-    return data

tests/problems/test_maxcut.py

@@ -1,57 +0,0 @@
-#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020-2025, UChicago Argonne, LLC. All rights reserved.
-#  Released under the modified BSD license. See COPYING.md for more details.
-import random
-
-import numpy as np
-
-from miplearn.problems.maxcut import MaxCutGenerator, build_maxcut_model_gurobipy
-from scipy.stats import randint, uniform
-
-def _set_seed():
-    random.seed(42)
-    np.random.seed(42)
-
-def test_maxcut_generator_not_fixed() -> None:
-    _set_seed()
-    gen = MaxCutGenerator(
-        n=randint(low=5, high=6),
-        p=uniform(loc=0.5, scale=0.0),
-        fix_graph=False,
-    )
-    data = gen.generate(3)
-    assert len(data) == 3
-    assert list(data[0].graph.nodes()) == [0, 1, 2, 3, 4]
-    assert list(data[0].graph.edges()) == [(0, 2), (0, 3), (0, 4), (2, 3), (2, 4), (3, 4)]
-    assert data[0].weights.tolist() == [-1, 1, -1, -1, -1, 1]
-    assert list(data[1].graph.nodes()) == [0, 1, 2, 3, 4]
-    assert list(data[1].graph.edges()) == [(0, 1), (0, 3), (0, 4), (1, 4), (3, 4)]
-    assert data[1].weights.tolist() == [-1, -1, -1, 1, -1]
-
-def test_maxcut_generator_fixed() -> None:
-    random.seed(42)
-    np.random.seed(42)
-    gen = MaxCutGenerator(
-        n=randint(low=5, high=6),
-        p=uniform(loc=0.5, scale=0.0),
-        fix_graph=True,
-    )
-    data = gen.generate(3)
-    assert len(data) == 3
-    assert list(data[0].graph.nodes()) == [0, 1, 2, 3, 4]
-    assert list(data[0].graph.edges()) == [(0, 2), (0, 3), (0, 4), (2, 3), (2, 4), (3, 4)]
-    assert data[0].weights.tolist() == [-1, 1, -1, -1, -1, 1]
-    assert list(data[1].graph.nodes()) == [0, 1, 2, 3, 4]
-    assert list(data[1].graph.edges()) == [(0, 2), (0, 3), (0, 4), (2, 3), (2, 4), (3, 4)]
-    assert data[1].weights.tolist() == [-1, -1, -1, 1, -1, -1]
-
-def test_maxcut_model():
-    _set_seed()
-    data = MaxCutGenerator(
-        n=randint(low=20, high=21),
-        p=uniform(loc=0.5, scale=0.0),
-        fix_graph=True,
-    ).generate(1)[0]
-    model = build_maxcut_model_gurobipy(data)
-    model.optimize()
-    assert model.inner.ObjVal == -26

tests/problems/test_setcover.py

@@ -2,7 +2,7 @@
 #  Copyright (C) 2020-2022, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-from tempfile import TemporaryDirectory
+from tempfile import NamedTemporaryFile
 
 import numpy as np
 from scipy.stats import randint, uniform
@@ -86,8 +86,8 @@ def test_set_cover() -> None:
         build_setcover_model_gurobipy(data),
     ]:
         assert isinstance(model, AbstractModel)
-        with TemporaryDirectory() as tempdir:
+        with NamedTemporaryFile() as tempfile:
-            with H5File(f"{tempdir}/data.h5", "w") as h5:
+            with H5File(tempfile.name) as h5:
                 model.optimize()
                 model.extract_after_mip(h5)
                 assert h5.get_scalar("mip_obj_value") == 11.0

tests/problems/test_stab.py

@@ -1,7 +1,7 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
 #  Copyright (C) 2020-2022, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
-from tempfile import TemporaryDirectory
+from tempfile import NamedTemporaryFile
 
 import networkx as nx
 import numpy as np
@@ -25,8 +25,8 @@ def test_stab() -> None:
         build_stab_model_pyomo(data),
     ]:
         assert isinstance(model, AbstractModel)
-        with TemporaryDirectory() as tempdir:
+        with NamedTemporaryFile() as tempfile:
-            with H5File(f"{tempdir}/data.h5", "w") as h5:
+            with H5File(tempfile.name) as h5:
                 model.optimize()
                 model.extract_after_mip(h5)
                 assert h5.get_scalar("mip_obj_value") == -2.0

tests/test_h5.py

@@ -1,7 +1,7 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
 #  Copyright (C) 2020-2022, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
-from tempfile import TemporaryDirectory
+from tempfile import NamedTemporaryFile
 from typing import Any
 
 import numpy as np
@@ -11,8 +11,8 @@ from miplearn.h5 import H5File
 
 
 def test_h5() -> None:
-    with TemporaryDirectory() as tempdir:
+    file = NamedTemporaryFile()
-        with H5File(f"{tempdir}/data.h5", "w") as h5:
+    h5 = H5File(file.name)
     _assert_roundtrip_scalar(h5, "A")
     _assert_roundtrip_scalar(h5, True)
     _assert_roundtrip_scalar(h5, 1)

tests/test_solvers.py

@@ -2,7 +2,7 @@
 #  Copyright (C) 2020-2022, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-from tempfile import TemporaryDirectory
+from tempfile import NamedTemporaryFile
 from typing import Callable, Any
 
 import numpy as np
@@ -49,8 +49,8 @@ def _test_solver(build_model: Callable, data: Any) -> None:
 
 
 def _test_extract(model: AbstractModel) -> None:
-    with TemporaryDirectory() as tempdir:
+    with NamedTemporaryFile() as tempfile:
-        with H5File(f"{tempdir}/data.h5", "w") as h5:
+        with H5File(tempfile.name) as h5:
 
             def test_scalar(key: str, expected_value: Any) -> None:
                 actual_value = h5.get_scalar(key)
@@ -129,6 +129,7 @@ def _test_extract(model: AbstractModel) -> None:
             test_scalar("mip_obj_value", 11.0)
             mip_wallclock_time = h5.get_scalar("mip_wallclock_time")
             assert mip_wallclock_time is not None
+            assert mip_wallclock_time > 0
             if model._supports_node_count:
                 count = h5.get_scalar("mip_node_count")
                 assert count is not None
@@ -144,8 +145,8 @@ def _test_extract(model: AbstractModel) -> None:
 
 
 def _test_add_constr(model: AbstractModel) -> None:
-    with TemporaryDirectory() as tempdir:
+    with NamedTemporaryFile() as tempfile:
-        with H5File(f"{tempdir}/data.h5", "w") as h5:
+        with H5File(tempfile.name) as h5:
             model.add_constrs(
                 np.array([b"x[2]", b"x[3]"], dtype="S"),
                 np.array([[0, 1], [1, 0]]),
@@ -160,8 +161,8 @@ def _test_add_constr(model: AbstractModel) -> None:
 
 
 def _test_fix_vars(model: AbstractModel) -> None:
-    with TemporaryDirectory() as tempdir:
+    with NamedTemporaryFile() as tempfile:
-        with H5File(f"{tempdir}/data.h5", "w") as h5:
+        with H5File(tempfile.name) as h5:
             model.fix_variables(
                 var_names=np.array([b"x[2]", b"x[3]"], dtype="S"),
                 var_values=np.array([0, 0]),
@@ -174,8 +175,8 @@ def _test_fix_vars(model: AbstractModel) -> None:
 
 
 def _test_infeasible(model: AbstractModel) -> None:
-    with TemporaryDirectory() as tempdir:
+    with NamedTemporaryFile() as tempfile:
-        with H5File(f"{tempdir}/data.h5", "w") as h5:
+        with H5File(tempfile.name) as h5:
             model.fix_variables(
                 var_names=np.array([b"x[0]", b"x[3]"], dtype="S"),
                 var_values=np.array([0, 0]),