diff --git a/miplearn/tests/test_branching.py b/miplearn/tests/test_branching.py index b39c710..7fbbdd1 100644 --- a/miplearn/tests/test_branching.py +++ b/miplearn/tests/test_branching.py @@ -3,16 +3,16 @@ # Written by Alinson S. Xavier 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() diff --git a/miplearn/tests/test_solver.py b/miplearn/tests/test_solver.py index e5c5ebe..01d5dee 100644 --- a/miplearn/tests/test_solver.py +++ b/miplearn/tests/test_solver.py @@ -3,17 +3,17 @@ # Written by Alinson S. Xavier 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() diff --git a/miplearn/tests/test_transformer.py b/miplearn/tests/test_transformer.py deleted file mode 100644 index 049ceb6..0000000 --- a/miplearn/tests/test_transformer.py +++ /dev/null @@ -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 - -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() diff --git a/miplearn/tests/test_warmstart.py b/miplearn/tests/test_warmstart.py index 1cd193f..12e047c 100644 --- a/miplearn/tests/test_warmstart.py +++ b/miplearn/tests/test_warmstart.py @@ -3,17 +3,17 @@ # Written by Alinson S. Xavier 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()