MIPLearn v0.3

miplearn/problems/setpack.py

@@ -0,0 +1,66 @@
+#  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 dataclasses import dataclass
+from typing import List, Union
+
+import gurobipy as gp
+import numpy as np
+from gurobipy.gurobipy import GRB
+from scipy.stats import uniform, randint
+from scipy.stats.distributions import rv_frozen
+
+from .setcover import SetCoverGenerator
+from miplearn.solvers.gurobi import GurobiModel
+from ..io import read_pkl_gz
+
+
+@dataclass
+class SetPackData:
+    costs: np.ndarray
+    incidence_matrix: np.ndarray
+
+
+class SetPackGenerator:
+    def __init__(
+        self,
+        n_elements: rv_frozen = randint(low=50, high=51),
+        n_sets: rv_frozen = randint(low=100, high=101),
+        costs: rv_frozen = uniform(loc=0.0, scale=100.0),
+        costs_jitter: rv_frozen = uniform(loc=-5.0, scale=10.0),
+        K: rv_frozen = uniform(loc=25.0, scale=0.0),
+        density: rv_frozen = uniform(loc=0.02, scale=0.00),
+        fix_sets: bool = True,
+    ) -> None:
+        self.gen = SetCoverGenerator(
+            n_elements=n_elements,
+            n_sets=n_sets,
+            costs=costs,
+            costs_jitter=costs_jitter,
+            K=K,
+            density=density,
+            fix_sets=fix_sets,
+        )
+
+    def generate(self, n_samples: int) -> List[SetPackData]:
+        return [
+            SetPackData(
+                s.costs,
+                s.incidence_matrix,
+            )
+            for s in self.gen.generate(n_samples)
+        ]
+
+
+def build_setpack_model(data: Union[str, SetPackData]) -> GurobiModel:
+    if isinstance(data, str):
+        data = read_pkl_gz(data)
+    assert isinstance(data, SetPackData)
+    (n_elements, n_sets) = data.incidence_matrix.shape
+    model = gp.Model()
+    x = model.addMVar(n_sets, vtype=GRB.BINARY, name="x")
+    model.addConstr(data.incidence_matrix @ x <= np.ones(n_elements))
+    model.setObjective(-data.costs @ x)
+    model.update()
+    return GurobiModel(model)