Split test_solver.py

src/python/miplearn/solvers/tests/__init__.py

@@ -2,3 +2,12 @@
 #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
+from miplearn.problems.knapsack import KnapsackInstance
+
+
+def _get_instance():
+    return KnapsackInstance(
+        weights=[23., 26., 20., 18.],
+        prices=[505., 352., 458., 220.],
+        capacity=67.,
+    )

src/python/miplearn/solvers/tests/test_internal_solver.py

@@ -2,23 +2,11 @@
 #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-import pickle
-import tempfile
-
 import pyomo.environ as pe
-from miplearn import BranchPriorityComponent
-from miplearn import LearningSolver
-from miplearn.problems.knapsack import KnapsackInstance
 from miplearn.solvers.cplex import CPLEXSolver
 from miplearn.solvers.gurobi import GurobiSolver
 
-
+from . import _get_instance
-def _get_instance():
-    return KnapsackInstance(
-        weights=[23., 26., 20., 18.],
-        prices=[505., 352., 458., 220.],
-        capacity=67.,
-    )
 
 
 def test_internal_solver_warm_starts():
@@ -84,51 +72,3 @@ def test_internal_solver():
         solver.solve_lp()
         assert model.x[0].value == 0.5
 
-
-def test_learning_solver():
-    instance = _get_instance()
-    for mode in ["exact", "heuristic"]:
-        for internal_solver in ["cplex", "gurobi", GurobiSolver]:
-            solver = LearningSolver(time_limit=300,
-                                    gap_tolerance=1e-3,
-                                    threads=1,
-                                    solver=internal_solver,
-                                    mode=mode)
-
-            solver.solve(instance)
-            assert instance.solution["x"][0] == 1.0
-            assert instance.solution["x"][1] == 0.0
-            assert instance.solution["x"][2] == 1.0
-            assert instance.solution["x"][3] == 1.0
-            assert instance.lower_bound == 1183.0
-            assert instance.upper_bound == 1183.0
-
-            assert round(instance.lp_solution["x"][0], 3) == 1.000
-            assert round(instance.lp_solution["x"][1], 3) == 0.923
-            assert round(instance.lp_solution["x"][2], 3) == 1.000
-            assert round(instance.lp_solution["x"][3], 3) == 0.000
-            assert round(instance.lp_value, 3) == 1287.923
-
-            solver.fit([instance])
-            solver.solve(instance)
-
-            # Assert solver is picklable
-            with tempfile.TemporaryFile() as file:
-                pickle.dump(solver, file)
-
-
-def test_parallel_solve():
-    instances = [_get_instance() for _ in range(10)]
-    solver = LearningSolver()
-    results = solver.parallel_solve(instances, n_jobs=3)
-    assert len(results) == 10
-    for instance in instances:
-        assert len(instance.solution["x"].keys()) == 4
-
-
-def test_add_components():
-    solver = LearningSolver(components=[])
-    solver.add(BranchPriorityComponent())
-    solver.add(BranchPriorityComponent())
-    assert len(solver.components) == 1
-    assert "BranchPriorityComponent" in solver.components

src/python/miplearn/solvers/tests/test_learning_solver.py

@@ -0,0 +1,61 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+import pickle
+import tempfile
+
+from miplearn import BranchPriorityComponent
+from miplearn import LearningSolver
+from miplearn.solvers.gurobi import GurobiSolver
+
+from . import _get_instance
+
+
+def test_learning_solver():
+    instance = _get_instance()
+    for mode in ["exact", "heuristic"]:
+        for internal_solver in ["cplex", "gurobi", GurobiSolver]:
+            solver = LearningSolver(time_limit=300,
+                                    gap_tolerance=1e-3,
+                                    threads=1,
+                                    solver=internal_solver,
+                                    mode=mode)
+
+            solver.solve(instance)
+            assert instance.solution["x"][0] == 1.0
+            assert instance.solution["x"][1] == 0.0
+            assert instance.solution["x"][2] == 1.0
+            assert instance.solution["x"][3] == 1.0
+            assert instance.lower_bound == 1183.0
+            assert instance.upper_bound == 1183.0
+
+            assert round(instance.lp_solution["x"][0], 3) == 1.000
+            assert round(instance.lp_solution["x"][1], 3) == 0.923
+            assert round(instance.lp_solution["x"][2], 3) == 1.000
+            assert round(instance.lp_solution["x"][3], 3) == 0.000
+            assert round(instance.lp_value, 3) == 1287.923
+
+            solver.fit([instance])
+            solver.solve(instance)
+
+            # Assert solver is picklable
+            with tempfile.TemporaryFile() as file:
+                pickle.dump(solver, file)
+
+
+def test_parallel_solve():
+    instances = [_get_instance() for _ in range(10)]
+    solver = LearningSolver()
+    results = solver.parallel_solve(instances, n_jobs=3)
+    assert len(results) == 10
+    for instance in instances:
+        assert len(instance.solution["x"].keys()) == 4
+
+
+def test_add_components():
+    solver = LearningSolver(components=[])
+    solver.add(BranchPriorityComponent())
+    solver.add(BranchPriorityComponent())
+    assert len(solver.components) == 1
+    assert "BranchPriorityComponent" in solver.components