Implement MultiKnapsackGenerator and MultiKnapsackInstance

miplearn/tests/test_solver.py

@@ -3,25 +3,28 @@
 # Written by Alinson S. Xavier <axavier@anl.gov>
 
 from miplearn import LearningSolver
-from miplearn.problems.knapsack import KnapsackInstance2
+from miplearn.problems.knapsack import MultiKnapsackInstance
 from miplearn.branching import BranchPriorityComponent
 from miplearn.warmstart import WarmStartComponent
 import numpy as np
 
 
+def _get_instance():
+    return MultiKnapsackInstance(
+        weights=np.array([[23., 26., 20., 18.]]),
+        prices=np.array([505., 352., 458., 220.]),
+        capacities=np.array([67.])
+    )
+
 def test_solver():
-    instance = KnapsackInstance2(weights=[23., 26., 20., 18.],
-                                 prices=[505., 352., 458., 220.],
-                                 capacity=67.)
+    instance = _get_instance()
     solver = LearningSolver()
     solver.solve(instance)
     solver.fit()
     solver.solve(instance)
 
 def test_solve_save_load_state():
-    instance = KnapsackInstance2(weights=[23., 26., 20., 18.],
-                                 prices=[505., 352., 458., 220.],
-                                 capacity=67.)
+    instance = _get_instance()
     components_before = {
         "warm-start": WarmStartComponent(),
         "branch-priority": BranchPriorityComponent(),
@@ -43,10 +46,7 @@ def test_solve_save_load_state():
     assert len(solver.components["warm-start"].y_train) == prev_y_train_len
 
 def test_parallel_solve():
-    instances = [KnapsackInstance2(weights=np.random.rand(5),
-                                   prices=np.random.rand(5),
-                                   capacity=3.0)
-                 for _ in range(10)]
+    instances = [_get_instance() for _ in range(10)]
     solver = LearningSolver()
     results = solver.parallel_solve(instances, n_jobs=3)
     assert len(results) == 10
@@ -54,9 +54,7 @@ def test_parallel_solve():
     assert len(solver.components["warm-start"].y_train[0]) == 10
     
 def test_solver_random_branch_priority():
-    instance = KnapsackInstance2(weights=[23., 26., 20., 18.],
-                                 prices=[505., 352., 458., 220.],
-                                 capacity=67.)
+    instance = _get_instance()
     components = {
         "warm-start": BranchPriorityComponent(initial_priority=np.array([1, 2, 3, 4])),
     }

miplearn/tests/test_transformer.py

@@ -2,59 +2,19 @@
 # Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
 # Written by Alinson S. Xavier <axavier@anl.gov>
 
+from miplearn import LearningSolver
 from miplearn.transformers import PerVariableTransformer
-from miplearn.problems.knapsack import KnapsackInstance, KnapsackInstance2
+from miplearn.problems.knapsack import MultiKnapsackInstance
 import numpy as np
 import pyomo.environ as pe
 
-
-def test_transform():
-    transformer = PerVariableTransformer()
-    instance = KnapsackInstance(weights=[23., 26., 20., 18.],
-                                prices=[505., 352., 458., 220.],
-                                capacity=67.)
-    model = instance.to_model()
-    solver = pe.SolverFactory('gurobi')
-    solver.options["threads"] = 1
-    solver.solve(model)
-
-    var_split = transformer.split_variables(instance, model)
-    var_split_expected = {
-        "default": [
-            (model.x, 0),
-            (model.x, 1),
-            (model.x, 2),
-            (model.x, 3)
-        ]
-    }
-    assert var_split == var_split_expected
-    var_index_pairs = [(model.x, i) for i in range(4)]
-
-    x_actual = transformer.transform_instance(instance, var_index_pairs)
-    x_expected = np.array([
-        [67., 21.75, 23., 505.],
-        [67., 21.75, 26., 352.],
-        [67., 21.75, 20., 458.],
-        [67., 21.75, 18., 220.],
-    ])
-    assert x_expected.tolist() == np.round(x_actual, decimals=2).tolist()
-
-    solver.solve(model)
-    y_actual = transformer.transform_solution(var_index_pairs)
-    y_expected = np.array([
-        [0., 1.],
-        [1., 0.],
-        [0., 1.],
-        [0., 1.],
-    ])
-    assert y_actual.tolist() == y_expected.tolist()
-
-
 def test_transform_with_categories():
     transformer = PerVariableTransformer()
-    instance = KnapsackInstance2(weights=[23., 26., 20., 18.],
-                                 prices=[505., 352., 458., 220.],
-                                 capacity=67.)
+    instance = MultiKnapsackInstance(
+        weights=np.array([[23., 26., 20., 18.]]),
+        prices=np.array([505., 352., 458., 220.]),
+        capacities=np.array([67.])
+    )
     model = instance.to_model()
     solver = pe.SolverFactory('gurobi')
     solver.options["threads"] = 1
@@ -71,10 +31,11 @@ def test_transform_with_categories():
 
     var_index_pairs = var_split[0]
     x_actual = transformer.transform_instance(instance, var_index_pairs)
-    x_expected = np.array([
-        [23., 26., 20., 18., 505., 352., 458., 220.]
+    x_expected = np.hstack([
+        instance.get_instance_features(),
+        instance.get_variable_features(model.x, 0),
     ])
-    assert x_expected.tolist() == np.round(x_actual, decimals=2).tolist()
+    assert (x_expected == x_actual).all()
 
     solver.solve(model)