Make TravelingSalesmanGenerator return data class

miplearn/problems/tsp.py

@@ -1,6 +1,7 @@
 #  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 dataclasses import dataclass
 from typing import List, Tuple, Any, Optional, Dict
 
 import networkx as nx
@@ -17,28 +18,10 @@ from miplearn.solvers.pyomo.base import BasePyomoSolver
 from miplearn.types import ConstraintName
 
 
-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 = TravelingSalesmanGenerator(
-            x=uniform(loc=0.0, scale=1000.0),
-            y=uniform(loc=0.0, scale=1000.0),
-            n=randint(low=350, high=351),
-            gamma=uniform(loc=0.95, scale=0.1),
-            fix_cities=True,
-            round=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 TravelingSalesmanData:
+    n_cities: int
+    distances: np.ndarray
 
 
 class TravelingSalesmanInstance(Instance):
@@ -180,8 +163,8 @@ class TravelingSalesmanGenerator:
             self.fixed_n = None
             self.fixed_cities = None
 
-    def generate(self, n_samples: int) -> List[TravelingSalesmanInstance]:
-        def _sample() -> TravelingSalesmanInstance:
+    def generate(self, n_samples: int) -> List[TravelingSalesmanData]:
+        def _sample() -> TravelingSalesmanData:
             if self.fixed_cities is not None:
                 assert self.fixed_n is not None
                 n, cities = self.fixed_n, self.fixed_cities
@@ -191,7 +174,7 @@ class TravelingSalesmanGenerator:
             distances = np.tril(distances) + np.triu(distances.T, 1)
             if self.round:
                 distances = distances.round()
-            return TravelingSalesmanInstance(n, distances)
+            return TravelingSalesmanData(n, distances)
 
         return [_sample() for _ in range(n_samples)]
 

tests/components/test_primal.py

@@ -13,7 +13,7 @@ from miplearn.classifiers.threshold import Threshold
 from miplearn.components import classifier_evaluation_dict
 from miplearn.components.primal import PrimalSolutionComponent
 from miplearn.features.sample import Sample, MemorySample
-from miplearn.problems.tsp import TravelingSalesmanGenerator
+from miplearn.problems.tsp import TravelingSalesmanGenerator, TravelingSalesmanInstance
 from miplearn.solvers.learning import LearningSolver
 from miplearn.solvers.tests import assert_equals
 
@@ -108,7 +108,8 @@ def test_usage() -> None:
         ]
     )
     gen = TravelingSalesmanGenerator(n=randint(low=5, high=6))
-    instance = gen.generate(1)[0]
+    data = gen.generate(1)
+    instance = TravelingSalesmanInstance(data[0].n_cities, data[0].distances)
     solver.solve(instance)
     solver.fit([instance])
     stats = solver.solve(instance)

tests/problems/test_tsp.py

@@ -14,17 +14,17 @@ from miplearn.solvers.tests import assert_equals
 
 
 def test_generator() -> None:
-    instances = TravelingSalesmanGenerator(
+    data = TravelingSalesmanGenerator(
         x=uniform(loc=0.0, scale=1000.0),
         y=uniform(loc=0.0, scale=1000.0),
         n=randint(low=100, high=101),
         gamma=uniform(loc=0.95, scale=0.1),
         fix_cities=True,
     ).generate(100)
-    assert len(instances) == 100
-    assert instances[0].n_cities == 100
-    assert norm(instances[0].distances - instances[0].distances.T) < 1e-6
-    d = [instance.distances[0, 1] for instance in instances]
+    assert len(data) == 100
+    assert data[0].n_cities == 100
+    assert norm(data[0].distances - data[0].distances.T) < 1e-6
+    d = [d.distances[0, 1] for d in data]
     assert np.std(d) > 0