Implement TravelingSalesmanPerturber

tests/components/lazy/test_mem.py

@@ -22,7 +22,7 @@ def test_mem_component(
     for h5 in [tsp_gp_h5, tsp_pyo_h5]:
         clf = Mock(wraps=DummyClassifier())
         comp = MemorizingLazyComponent(clf=clf, extractor=default_extractor)
-        comp.fit(tsp_gp_h5)
+        comp.fit(h5)
 
         # Should call fit method with correct arguments
         clf.fit.assert_called()
@@ -43,7 +43,7 @@ def test_mem_component(
         # Call before-mip
         stats: Dict[str, Any] = {}
         model = Mock()
-        comp.before_mip(tsp_gp_h5[0], model, stats)
+        comp.before_mip(h5[0], model, stats)
 
         # Should call predict with correct args
         clf.predict.assert_called()

tests/fixtures/gen_tsp.py

@@ -7,6 +7,7 @@ from miplearn.collectors.basic import BasicCollector
 from miplearn.io import write_pkl_gz
 from miplearn.problems.tsp import (
     TravelingSalesmanGenerator,
+    TravelingSalesmanPerturber,
     build_tsp_model_gurobipy,
     build_tsp_model_pyomo,
 )
@@ -16,12 +17,19 @@ gen = TravelingSalesmanGenerator(
     x=uniform(loc=0.0, scale=1000.0),
     y=uniform(loc=0.0, scale=1000.0),
     n=randint(low=20, high=21),
-    gamma=uniform(loc=1.0, scale=0.25),
-    fix_cities=True,
+    gamma=uniform(loc=1.0, scale=0.0),
     round=True,
 )
 
-data = gen.generate(3)
+# Generate a reference instance with fixed cities
+reference_instance = gen.generate(1)[0]
+
+# Generate perturbed instances with same cities but different distance scaling
+perturber = TravelingSalesmanPerturber(
+    gamma=uniform(loc=1.0, scale=0.25),
+    round=True,
+)
+data = perturber.perturb(reference_instance, 3)
 
 params = {"seed": 42, "threads": 1}
 

tests/problems/test_tsp.py

@@ -17,56 +17,30 @@ def test_tsp_generator() -> None:
     gen = TravelingSalesmanGenerator(
         x=uniform(loc=0.0, scale=1000.0),
         y=uniform(loc=0.0, scale=1000.0),
-        n=randint(low=3, high=4),
+        n=randint(low=5, high=6),
         gamma=uniform(loc=1.0, scale=0.25),
-        fix_cities=True,
         round=True,
     )
-    data = gen.generate(2)
+    data = gen.generate(1)
     assert data[0].distances.tolist() == [
-        [0.0, 591.0, 996.0],
-        [591.0, 0.0, 765.0],
-        [996.0, 765.0, 0.0],
-    ]
-    assert data[1].distances.tolist() == [
-        [0.0, 556.0, 853.0],
-        [556.0, 0.0, 779.0],
-        [853.0, 779.0, 0.0],
+        [0.0, 525.0, 950.0, 392.0, 382.0],
+        [525.0, 0.0, 752.0, 761.0, 178.0],
+        [950.0, 752.0, 0.0, 809.0, 721.0],
+        [392.0, 761.0, 809.0, 0.0, 700.0],
+        [382.0, 178.0, 721.0, 700.0, 0.0],
     ]
 
-
-def test_tsp() -> None:
-    data = TravelingSalesmanData(
-        n_cities=6,
-        distances=squareform(
-            pdist(
-                [
-                    [0.0, 0.0],
-                    [1.0, 0.0],
-                    [2.0, 0.0],
-                    [3.0, 0.0],
-                    [0.0, 1.0],
-                    [3.0, 1.0],
-                ]
-            )
-        ),
-    )
-    model = build_tsp_model_gurobipy(data)
+    model = build_tsp_model_gurobipy(data[0])
     model.optimize()
     assert model.inner.getAttr("x", model.inner.getVars()) == [
-        1.0,
         0.0,
         0.0,
         1.0,
-        0.0,
         1.0,
-        0.0,
-        0.0,
-        0.0,
         1.0,
         0.0,
-        0.0,
-        0.0,
         1.0,
         1.0,
+        0.0,
+        0.0,
     ]