def test_catch_inputs_not_in_zero_one_range():
input_1 = [[0, 1 / 3.], [0, 1.], [2 / 3., 1.]]
input_2 = [[0, 1 / 3.], [2 / 3., 1 / 3.], [2 / 3., 1.]]
input_3 = [[2 / 3., 0], [2 / 3., 2 / 3.], [0, 2 / 3.]]
input_4 = [[1 / 3., 1.], [1., 1.], [1, 1 / 3.]]
input_5 = [[1 / 3., 1.], [1 / 3., 1 / 3.], [1, 1 / 3.]]
input_6 = [[1 / 3., 2 / 3.], [1 / 3., 0], [1., 0]]
input_sample = np.concatenate(
[input_1, input_2, input_3, input_4, input_5, input_6])
problem = {'num_vars': 2, 'groups': None}
k_choices = 4
N = 10
input_sample *= 10
compute_optimised_trajectories(problem, input_sample, N, k_choices)
def test_raise_error_if_k_gt_N():
"""
Check that an error is raised if `k_choices` is greater than (or equal to) `N`
"""
N = 4
param_file = "SALib/tests/test_params.txt"
problem = read_param_file(param_file)
num_levels = 4
grid_jump = num_levels / 2
k_choices = 6
morris_sample = sample_oat(problem, N, num_levels, grid_jump)
compute_optimised_trajectories(problem, morris_sample, N, k_choices)
def test_raise_error_if_k_gt_N():
"""
Check that an error is raised if `k_choices` is greater than (or equal to) `N`
"""
N = 4
param_file = "SALib/tests/test_params.txt"
problem = read_param_file(param_file)
num_levels = 4
grid_jump = num_levels / 2
k_choices = 6
morris_sample = sample_oat(problem, N, num_levels, grid_jump)
compute_optimised_trajectories(problem,
morris_sample,
N,
k_choices)
def test_find_optimum_trajectories():
input_1 = [[0, 1 / 3.], [0, 1.], [2 / 3., 1.]]
input_2 = [[0, 1 / 3.], [2 / 3., 1 / 3.], [2 / 3., 1.]]
input_3 = [[2 / 3., 0], [2 / 3., 2 / 3.], [0, 2 / 3.]]
input_4 = [[1 / 3., 1.], [1., 1.], [1, 1 / 3.]]
input_5 = [[1 / 3., 1.], [1 / 3., 1 / 3.], [1, 1 / 3.]]
input_6 = [[1 / 3., 2 / 3.], [1 / 3., 0], [1., 0]]
input_sample = np.concatenate(
[input_1, input_2, input_3, input_4, input_5, input_6])
N = 6
problem = {'num_vars': 2, 'groups': None}
k_choices = 4
output = compute_optimised_trajectories(problem, input_sample, N,
k_choices)
expected = np.concatenate([input_1, input_3, input_4, input_6])
np.testing.assert_equal(output, expected)
def test_size_of_trajectories_with_groups():
'''
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 = "SALib/tests/test_param_file_w_groups_prime.txt"
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
grid_jump = 4
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,
grid_jump)
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,
grid_jump)
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)
def test_size_of_trajectories_with_groups():
'''
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 = "SALib/tests/test_param_file_w_groups_prime.txt"
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
grid_jump = 4
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, grid_jump)
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, grid_jump)
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)
