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_raise_error_if_k_gt_N(setup_function):
"""Check that an error is raised if `k_choices` is greater than
(or equal to) `N`
"""
N = 4
param_file = setup_function
problem = read_param_file(param_file)
num_levels = 4
k_choices = 6
morris_sample = _sample_oat(problem, N, num_levels)
with raises(ValueError):
_compute_optimised_trajectories(problem,
morris_sample,
N,
k_choices,
local_optimization=False)
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)
def test_size_of_trajectories_with_groups(setup_param_groups_prime):
'''
Tests that the number of trajectories produced is computed
correctly (i.e. that the size of the trajectories is a function
of the number of groups, rather than the number of variables
when groups are used.
There are seven variables and three groups.
With N=10:
1. the sample ignoring groups (i.e. the call to `sample_oat')
should be of size N*(D+1)-by-D.
2. the sample with groups should be of size N*(G+1)-by-D
When k=4:
3. the optimal sample ignoring groups should be of size k*(D+1)-by-D
4. the optimal sample with groups should be of size k*(G+1)-by-D
'''
param_file = setup_param_groups_prime
group_problem = read_param_file(param_file)
no_group_problem = read_param_file(param_file)
no_group_problem['groups'] = None
N = 11
num_levels = 8
k_choices = 4
num_params = group_problem['num_vars']
num_groups = 3
# Test 1. dimensions of sample ignoring groups
sample = _sample_oat(no_group_problem,
N,
num_levels)
size_x, size_y = sample.shape
assert_equal(size_x, N * (num_params + 1))
assert_equal(size_y, num_params)
# Test 2. dimensions of sample with groups
group_sample = _sample_groups(group_problem,
N,
num_levels)
size_x, size_y = group_sample.shape
assert_equal(size_x, N * (num_groups + 1))
assert_equal(size_y, num_params)
# Test 3. dimensions of optimal sample without groups
optimal_sample_without_groups = \
_compute_optimised_trajectories(no_group_problem,
sample,
N,
k_choices)
size_x, size_y = optimal_sample_without_groups.shape
assert_equal(size_x, k_choices * (num_params + 1))
assert_equal(size_y, num_params)
# Test 4. dimensions of optimal sample with groups
optimal_sample_with_groups = _compute_optimised_trajectories(group_problem,
group_sample,
N,
k_choices)
size_x, size_y = optimal_sample_with_groups.shape
assert_equal(size_x, k_choices * (num_groups + 1))
assert_equal(size_y, num_params)