Make MaxWeightStableSetGenerator return data class

miplearn/problems/stab.py

@@ -1,7 +1,9 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
 #  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
-from typing import List, Dict
+
+from dataclasses import dataclass
+from typing import List
 
 import networkx as nx
 import numpy as np
@@ -14,26 +16,10 @@ from scipy.stats.distributions import rv_frozen
 from miplearn.instance.base import Instance
 
 
-class ChallengeA:
-    def __init__(
-        self,
-        seed: int = 42,
-        n_training_instances: int = 500,
-        n_test_instances: int = 50,
-    ) -> None:
-        np.random.seed(seed)
-        self.generator = MaxWeightStableSetGenerator(
-            w=uniform(loc=100.0, scale=50.0),
-            n=randint(low=200, high=201),
-            p=uniform(loc=0.05, scale=0.0),
-            fix_graph=True,
-        )
-
-        np.random.seed(seed + 1)
-        self.training_instances = self.generator.generate(n_training_instances)
-
-        np.random.seed(seed + 2)
-        self.test_instances = self.generator.generate(n_test_instances)
+@dataclass
+class MaxWeightStableSetData:
+    graph: Graph
+    weights: np.ndarray
 
 
 class MaxWeightStableSetInstance(Instance):
@@ -132,14 +118,14 @@ class MaxWeightStableSetGenerator:
         if fix_graph:
             self.graph = self._generate_graph()
 
-    def generate(self, n_samples: int) -> List[MaxWeightStableSetInstance]:
-        def _sample() -> MaxWeightStableSetInstance:
+    def generate(self, n_samples: int) -> List[MaxWeightStableSetData]:
+        def _sample() -> MaxWeightStableSetData:
             if self.graph is not None:
                 graph = self.graph
             else:
                 graph = self._generate_graph()
             weights = self.w.rvs(graph.number_of_nodes())
-            return MaxWeightStableSetInstance(graph, weights)
+            return MaxWeightStableSetData(graph, weights)
 
         return [_sample() for _ in range(n_samples)]
 

tests/problems/test_stab.py

@@ -29,8 +29,8 @@ def test_stab_generator_fixed_graph() -> None:
         p=uniform(loc=0.05, scale=0.0),
         fix_graph=True,
     )
-    instances = gen.generate(1_000)
-    weights = np.array([instance.weights for instance in instances])
+    data = gen.generate(1_000)
+    weights = np.array([d.weights for d in data])
     weights_avg_actual = np.round(np.average(weights, axis=0))
     weights_avg_expected = [55.0] * 10
     assert list(weights_avg_actual) == weights_avg_expected
@@ -46,8 +46,8 @@ def test_stab_generator_random_graph() -> None:
         p=uniform(loc=0.5, scale=0.0),
         fix_graph=False,
     )
-    instances = gen.generate(1_000)
-    n_nodes = [instance.graph.number_of_nodes() for instance in instances]
-    n_edges = [instance.graph.number_of_edges() for instance in instances]
+    data = gen.generate(1_000)
+    n_nodes = [d.graph.number_of_nodes() for d in data]
+    n_edges = [d.graph.number_of_edges() for d in data]
     assert np.round(np.mean(n_nodes)) == 35.0
     assert np.round(np.mean(n_edges), -1) == 300.0

tests/test_benchmark.py

@@ -7,7 +7,10 @@ import os.path
 from scipy.stats import randint
 
 from miplearn.benchmark import BenchmarkRunner
-from miplearn.problems.stab import MaxWeightStableSetGenerator
+from miplearn.problems.stab import (
+    MaxWeightStableSetInstance,
+    MaxWeightStableSetGenerator,
+)
 from miplearn.solvers.learning import LearningSolver
 
 
@@ -15,8 +18,14 @@ def test_benchmark() -> None:
     for n_jobs in [1, 4]:
         # Generate training and test instances
         generator = MaxWeightStableSetGenerator(n=randint(low=25, high=26))
-        train_instances = generator.generate(5)
-        test_instances = generator.generate(3)
+        train_instances = [
+            MaxWeightStableSetInstance(data.graph, data.weights)
+            for data in generator.generate(5)
+        ]
+        test_instances = [
+            MaxWeightStableSetInstance(data.graph, data.weights)
+            for data in generator.generate(3)
+        ]
 
         # Solve training instances
         training_solver = LearningSolver()