# 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.problems.knapsack import KnapsackInstance2
import numpy as np


def test_solver():
    instance = KnapsackInstance2(weights=[23., 26., 20., 18.],
                                 prices=[505., 352., 458., 220.],
                                 capacity=67.)
    solver = LearningSolver()
    solver.solve(instance)
    solver.fit()
    solver.solve(instance)

def test_solve_save_load():
    instance = KnapsackInstance2(weights=[23., 26., 20., 18.],
                                 prices=[505., 352., 458., 220.],
                                 capacity=67.)
    solver = LearningSolver()
    solver.solve(instance)
    solver.fit()
    solver.save("/tmp/knapsack_train.bin")
    prev_x_train_len = len(solver.x_train)
    prev_y_train_len = len(solver.y_train)
    
    solver = LearningSolver()
    solver.load("/tmp/knapsack_train.bin")
    assert len(solver.x_train) == prev_x_train_len
    assert len(solver.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)]
    solver = LearningSolver()
    solver.parallel_solve(instances, n_jobs=3)
    assert len(solver.x_train[0]) == 10
    assert len(solver.y_train[0]) == 10
    
def test_solver_random_branch_priority():
    instance = KnapsackInstance2(weights=[23., 26., 20., 18.],
                                 prices=[505., 352., 458., 220.],
                                 capacity=67.)
    solver = LearningSolver(branch_priority=[1, 2, 3, 4])
    solver.solve(instance, tee=True)