def test_optimal_sample_with_groups():
'''
Tests that the combinatorial optimisation approach matches
that of the brute force approach
'''
param_file = "SALib/tests/test_param_file_w_groups_prime.txt"
problem = read_param_file(param_file)
N = 10
num_levels = 8
grid_jump = 4
k_choices = 4
num_params = problem['num_vars']
sample = sample_oat(problem,
N,
num_levels,
grid_jump)
actual = return_max_combo(sample,
N,
num_params,
k_choices)
desired = find_optimum_combination(sample,
N,
num_params,
k_choices)
assert_equal(actual, desired)
def test_optimised_trajectories_with_groups():
"""
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 = "SALib/tests/test_param_file_w_groups_prime.txt"
problem = read_param_file(param_file)
num_levels = 4
grid_jump = num_levels / 2
k_choices = 4
num_params = problem['num_vars']
groups = problem['groups']
input_sample = sample_groups(problem, N, num_levels, grid_jump)
# From gurobi optimal trajectories
actual = return_max_combo(input_sample, N, num_params, k_choices, groups)
desired = find_optimum_combination(input_sample, N, num_params, k_choices,
groups)
assert_equal(actual, desired)
def test_optimised_trajectories_with_groups():
"""
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 = "SALib/tests/test_param_file_w_groups_prime.txt"
problem = read_param_file(param_file)
num_levels = 4
grid_jump = num_levels / 2
k_choices = 4
num_params = problem['num_vars']
groups = problem['groups']
input_sample = sample_groups(problem, N, num_levels, grid_jump)
# From gurobi optimal trajectories
actual = return_max_combo(input_sample,
N,
num_params,
k_choices,
groups)
desired = find_optimum_combination(input_sample,
N,
num_params,
k_choices,
groups)
assert_equal(actual, desired)
def test_optimised_trajectories_without_groups():
"""
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 = "SALib/tests/test_params.txt"
problem = read_param_file(param_file)
num_levels = 4
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
actual = return_max_combo(input_sample,
N,
num_params,
k_choices,
groups)
desired = find_optimum_combination(input_sample,
N,
num_params,
k_choices,
groups)
assert_equal(actual, desired)
def test_optimal_combinations():
N = 6
param_file = "SALib/tests/test_params.txt"
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)
actual = return_max_combo(morris_sample, N, num_params, k_choices)
desired = find_optimum_combination(morris_sample, N, num_params, k_choices)
assert_equal(actual, desired)
def test_optimal_sample_with_groups():
'''
Tests that the combinatorial optimisation approach matches
that of the brute force approach
'''
param_file = "SALib/tests/test_param_file_w_groups_prime.txt"
problem = read_param_file(param_file)
N = 10
num_levels = 8
grid_jump = 4
k_choices = 4
num_params = problem['num_vars']
sample = sample_oat(problem, N, num_levels, grid_jump)
actual = return_max_combo(sample, N, num_params, k_choices)
desired = find_optimum_combination(sample, N, num_params, k_choices)
assert_equal(actual, desired)
def test_optimal_combinations():
N = 6
param_file = "SALib/tests/test_params.txt"
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)
actual = return_max_combo(morris_sample,
N,
num_params,
k_choices)
desired = find_optimum_combination(morris_sample,
N,
num_params,
k_choices)
assert_equal(actual, desired)
def test_optimised_trajectories_without_groups():
"""
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 = "SALib/tests/test_params.txt"
problem = read_param_file(param_file)
num_levels = 4
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
actual = return_max_combo(input_sample, N, num_params, k_choices, groups)
desired = find_optimum_combination(input_sample, N, num_params, k_choices,
groups)
assert_equal(actual, desired)
