Switch tests to simpler Knapsack encoding; remove outdated test

miplearn/tests/test_branching.py

@@ -3,16 +3,16 @@
 # Written by Alinson S. Xavier <axavier@anl.gov>
 
 from miplearn import BranchPriorityComponent, LearningSolver
-from miplearn.problems.knapsack import MultiKnapsackInstance
+from miplearn.problems.knapsack import KnapsackInstance
 import numpy as np
 import tempfile
 
 def _get_instances():
     return [
-        MultiKnapsackInstance(
-            weights=np.array([[23., 26., 20., 18.]]),
-            prices=np.array([505., 352., 458., 220.]),
-            capacities=np.array([67.])
+        KnapsackInstance(
+            weights=[23., 26., 20., 18.],
+            prices=[505., 352., 458., 220.],
+            capacity=67.,
         ),
     ] * 2
 
@@ -23,11 +23,11 @@ def test_branching():
     for instance in instances:
         component.after_solve(None, instance, None)
     component.fit(None)
-    for key in [0, 1, 2, 3]:
+    for key in ["default"]:
         assert key in component.x_train.keys()
         assert key in component.y_train.keys()
-        assert component.x_train[key].shape == (2,  9)
-        assert component.y_train[key].shape == (2,  1)
+        assert component.x_train[key].shape == (8,  4)
+        assert component.y_train[key].shape == (8,  1)
         
         
 def test_branch_priority_save_load():
@@ -36,14 +36,14 @@ def test_branch_priority_save_load():
     solver.parallel_solve(_get_instances(), n_jobs=2)
     solver.fit()
     comp = solver.components["branch-priority"]
-    assert comp.x_train[0].shape == (2, 9)
-    assert comp.y_train[0].shape == (2, 1)
-    assert 0 in comp.predictors.keys()
+    assert comp.x_train["default"].shape == (8, 4)
+    assert comp.y_train["default"].shape == (8, 1)
+    assert "default" in comp.predictors.keys()
     solver.save_state(state_file.name)
     
     solver = LearningSolver(components={"branch-priority": BranchPriorityComponent()})
     solver.load_state(state_file.name)
     comp = solver.components["branch-priority"]
-    assert comp.x_train[0].shape == (2, 9)
-    assert comp.y_train[0].shape == (2, 1)
-    assert 0 in comp.predictors.keys()
+    assert comp.x_train["default"].shape == (8, 4)
+    assert comp.y_train["default"].shape == (8, 1)
+    assert "default" in comp.predictors.keys()

miplearn/tests/test_solver.py

@@ -3,17 +3,17 @@
 # Written by Alinson S. Xavier <axavier@anl.gov>
 
 from miplearn import LearningSolver
-from miplearn.problems.knapsack import MultiKnapsackInstance
+from miplearn.problems.knapsack import KnapsackInstance
 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.])
+    return KnapsackInstance(
+        weights=[23., 26., 20., 18.],
+        prices=[505., 352., 458., 220.],
+        capacity=67.,
     )
 
 def test_solver():
@@ -50,8 +50,8 @@ def test_parallel_solve():
     solver = LearningSolver()
     results = solver.parallel_solve(instances, n_jobs=3)
     assert len(results) == 10
-    assert len(solver.components["warm-start"].x_train[0]) == 10
-    assert len(solver.components["warm-start"].y_train[0]) == 10
+    assert len(solver.components["warm-start"].x_train["default"]) == 40
+    assert len(solver.components["warm-start"].y_train["default"]) == 40
     
 def test_solver_random_branch_priority():
     instance = _get_instance()

miplearn/tests/test_transformer.py

@@ -1,44 +0,0 @@
-# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
-# 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 MultiKnapsackInstance
-import numpy as np
-import pyomo.environ as pe
-
-def test_transform_with_categories():
-    transformer = PerVariableTransformer()
-    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
-    solver.solve(model)
-
-    var_split = transformer.split_variables(instance, model)
-    var_split_expected = {
-        0: [(model.x, 0)],
-        1: [(model.x, 1)],
-        2: [(model.x, 2)],
-        3: [(model.x, 3)],
-    }
-    assert var_split == var_split_expected
-
-    var_index_pairs = var_split[0]
-    x_actual = transformer.transform_instance(instance, var_index_pairs)
-    x_expected = np.hstack([
-        instance.get_instance_features(),
-        instance.get_variable_features(model.x, 0),
-    ])
-    assert (x_expected == x_actual).all()
-
-    solver.solve(model)
-
-    y_actual = transformer.transform_solution(var_index_pairs)
-    y_expected = np.array([[0., 1.]])
-    assert y_actual.tolist() == y_expected.tolist()

miplearn/tests/test_warmstart.py

@@ -3,17 +3,17 @@
 # Written by Alinson S. Xavier <axavier@anl.gov>
 
 from miplearn import WarmStartComponent, LearningSolver
-from miplearn.problems.knapsack import MultiKnapsackInstance
+from miplearn.problems.knapsack import KnapsackInstance
 import numpy as np
 import tempfile
 
 
 def _get_instances():
     return [
-        MultiKnapsackInstance(
-            weights=np.array([[23., 26., 20., 18.]]),
-            prices=np.array([505., 352., 458., 220.]),
-            capacities=np.array([67.])
+        KnapsackInstance(
+            weights=[23., 26., 20., 18.],
+            prices=[505., 352., 458., 220.],
+            capacity=67.,
         ),
     ] * 2
 
@@ -24,14 +24,14 @@ def test_warm_start_save_load():
     solver.parallel_solve(_get_instances(), n_jobs=2)
     solver.fit()
     comp = solver.components["warm-start"]
-    assert comp.x_train[0].shape == (2, 9)
-    assert comp.y_train[0].shape == (2, 2)
-    assert 0 in comp.predictors.keys()
+    assert comp.x_train["default"].shape == (8, 4)
+    assert comp.y_train["default"].shape == (8, 2)
+    assert "default" in comp.predictors.keys()
     solver.save_state(state_file.name)
     
     solver = LearningSolver(components={"warm-start": WarmStartComponent()})
     solver.load_state(state_file.name)
     comp = solver.components["warm-start"]
-    assert comp.x_train[0].shape == (2, 9)
-    assert comp.y_train[0].shape == (2, 2)
-    assert 0 in comp.predictors.keys()
+    assert comp.x_train["default"].shape == (8, 4)
+    assert comp.y_train["default"].shape == (8, 2)
+    assert "default" in comp.predictors.keys()