#  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.

from miplearn import LearningSolver, BranchPriorityComponent
from miplearn.problems.knapsack import KnapsackInstance


def _get_instance():
    return KnapsackInstance(
        weights=[23., 26., 20., 18.],
        prices=[505., 352., 458., 220.],
        capacity=67.,
    )


def test_solver():
    instance = _get_instance()
    for mode in ["exact", "heuristic"]:
        for internal_solver in ["cplex", "gurobi"]:
            solver = LearningSolver(time_limit=300,
                                    gap_tolerance=1e-3,
                                    threads=1,
                                    solver=internal_solver,
                                    mode=mode,
                                   )
            results = 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()
            solver.solve(instance)

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