def test_optimal_sample_with_groups(setup_param_groups_prime):
'''
Tests that the combinatorial optimisation approach matches
that of the brute force approach
'''
param_file = setup_param_groups_prime
problem = read_param_file(param_file)
N = 10
num_levels = 8
k_choices = 4
num_params = problem['num_vars']
sample = _sample_oat(problem,
N,
num_levels)
strategy = GlobalOptimisation()
actual = strategy.return_max_combo(sample,
N,
num_params,
k_choices)
brute_strategy = BruteForce()
desired = brute_strategy.brute_force_most_distant(sample,
N,
num_params,
k_choices)
assert_equal(actual, desired)
def test_optimised_trajectories_groups(setup_param_groups_prime):
"""
Tests that the optimisation problem gives
the same answer as the brute force problem
(for small values of `k_choices` and `N`)
with groups
"""
N = 11
param_file = setup_param_groups_prime
problem = read_param_file(param_file)
num_levels = 4
k_choices = 4
num_params = problem['num_vars']
groups = compute_groups_matrix(problem['groups'], num_params)
input_sample = _sample_groups(problem, N, num_levels)
# From gurobi optimal trajectories
strategy = GlobalOptimisation()
actual = strategy.sample(input_sample,
N,
num_params,
k_choices,
groups)
brute_strategy = BruteForce()
desired = brute_strategy.sample(input_sample,
N,
num_params,
k_choices,
groups)
assert_equal(actual, desired)
def test_optimised_trajectories_without_groups(setup_function):
"""
Tests that the optimisation problem gives
the same answer as the brute force problem
(for small values of `k_choices` and `N`),
particularly when there are two or more identical
trajectories
"""
N = 6
param_file = setup_function
problem = read_param_file(param_file)
k_choices = 4
num_params = problem['num_vars']
groups = problem['groups']
# 6 trajectories, with 5th and 6th identical
input_sample = np.array([[0.33333333, 0.66666667],
[1., 0.66666667],
[1., 0.],
[0., 0.33333333],
[0., 1.],
[0.66666667, 1.],
[0.66666667, 0.33333333],
[0.66666667, 1.],
[0., 1.],
[0.66666667, 1.],
[0.66666667, 0.33333333],
[0., 0.33333333],
[1., 1.],
[1., 0.33333333],
[0.33333333, 0.33333333],
[1., 1.],
[1., 0.33333333],
[0.33333333, 0.33333333]], dtype=np.float32)
# print(input_sample)
# From gurobi optimal trajectories
strategy = GlobalOptimisation()
actual = strategy.return_max_combo(input_sample,
N,
num_params,
k_choices)
local_strategy = BruteForce()
desired = local_strategy.brute_force_most_distant(input_sample,
N,
num_params,
k_choices,
groups)
assert_equal(actual, desired)
def test_optimal_combinations(setup_function):
N = 6
param_file = setup_function
problem = read_param_file(param_file)
num_params = problem['num_vars']
num_levels = 10
grid_jump = num_levels / 2
k_choices = 4
morris_sample = _sample_oat(problem, N, num_levels, grid_jump)
global_strategy = GlobalOptimisation()
actual = global_strategy.return_max_combo(morris_sample, N, num_params,
k_choices)
brute_strategy = BruteForce()
desired = brute_strategy.brute_force_most_distant(morris_sample, N,
num_params, k_choices)
assert_equal(actual, desired)
def test_optimal_combinations(setup_function):
N = 6
param_file = setup_function
problem = read_param_file(param_file)
num_params = problem['num_vars']
num_levels = 10
k_choices = 4
morris_sample = _sample_oat(problem, N, num_levels)
global_strategy = GlobalOptimisation()
actual = global_strategy.return_max_combo(morris_sample,
N,
num_params,
k_choices)
brute_strategy = BruteForce()
desired = brute_strategy.brute_force_most_distant(morris_sample,
N,
num_params,
k_choices)
assert_equal(actual, desired)