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75 lines
2.6 KiB
75 lines
2.6 KiB
# MIPLearn: A Machine-Learning Framework for Mixed-Integer Optimization
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# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
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# Written by Alinson S. Xavier <axavier@anl.gov>
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from miplearn import Instance, LearningSolver
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from miplearn.transformers import PerVariableTransformer
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from miplearn.problems.knapsack import KnapsackInstance, KnapsackInstance2
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import numpy as np
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import pyomo.environ as pe
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def test_transform():
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transformer = PerVariableTransformer()
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instance = KnapsackInstance(weights=[23., 26., 20., 18.],
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prices=[505., 352., 458., 220.],
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capacity=67.)
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model = instance.to_model()
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var_split = transformer.split_variables(instance, model)
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var_split_expected = {
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"default": [
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(model.x, 0),
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(model.x, 1),
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(model.x, 2),
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(model.x, 3)
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]
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}
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assert var_split == var_split_expected
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var_index_pairs = [(model.x, i) for i in range(4)]
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x_actual = transformer.transform_instance(instance, var_index_pairs)
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x_expected = np.array([
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[67., 21.75, 23., 505.],
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[67., 21.75, 26., 352.],
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[67., 21.75, 20., 458.],
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[67., 21.75, 18., 220.],
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])
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assert x_expected.tolist() == x_actual.tolist()
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solver = pe.SolverFactory('cplex')
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solver.options["threads"] = 1
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solver.solve(model)
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y_actual = transformer.transform_solution(var_index_pairs)
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y_expected = np.array([1., 0., 1., 1.])
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assert y_actual.tolist() == y_expected.tolist()
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def test_transform_with_categories():
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transformer = PerVariableTransformer()
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instance = KnapsackInstance2(weights=[23., 26., 20., 18.],
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prices=[505., 352., 458., 220.],
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capacity=67.)
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model = instance.to_model()
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var_split = transformer.split_variables(instance, model)
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var_split_expected = {
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0: [(model.x, 0)],
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1: [(model.x, 1)],
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2: [(model.x, 2)],
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3: [(model.x, 3)],
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}
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assert var_split == var_split_expected
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var_index_pairs = var_split[0]
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x_actual = transformer.transform_instance(instance, var_index_pairs)
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x_expected = np.array([[23., 26., 20., 18., 505., 352., 458., 220.]])
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assert x_expected.tolist() == x_actual.tolist()
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solver = pe.SolverFactory('cplex')
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solver.options["threads"] = 1
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solver.solve(model)
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y_actual = transformer.transform_solution(var_index_pairs)
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y_expected = np.array([1.])
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assert y_actual.tolist() == y_expected.tolist() |