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 print_indices(S, problem, calc_second_order, file):
# taken from the SAlib source code and modified to print to file
# https://github.com/SALib/SALib/blob/3bc2ddfb50f091e5e5dd1ed4e7fae05853f150e5/SALib/analyze/sobol.py#L240
# Output to console
if not problem.get('groups'):
title = 'Parameter'
names = problem['names']
D = problem['num_vars']
else:
title = 'Group'
_, names = compute_groups_matrix(problem['groups'])
D = len(names)
print('%s S1 S1_conf ST ST_conf' % title, file=file)
for j in range(D):
print('%s %f %f %f %f' % (names[j], S['S1'][
j], S['S1_conf'][j], S['ST'][j], S['ST_conf'][j]), file=file)
if calc_second_order:
print('\n%s_1 %s_2 S2 S2_conf' % (title, title), file=file)
for j in range(D):
for k in range(j + 1, D):
print("%s %s %f %f" % (names[j], names[k],
S['S2'][j, k], S['S2_conf'][j, k]), file=file)
def _sample_groups(problem, N, num_levels, grid_jump):
"""Generate trajectories for groups
Returns an :math:`N(g+1)`-by-:math:`k` array of `N` trajectories,
where :math:`g` is the number of groups and :math:`k` is the number
of factors
Arguments
---------
problem : dict
The problem definition
N : int
The number of trajectories to generate
num_levels : int
The number of grid levels
grid_jump : int
The grid jump size
Returns
-------
numpy.ndarray
"""
group_membership, _ = compute_groups_matrix(problem['groups'])
if group_membership is None:
raise ValueError("Please define the matrix group_membership.")
if not isinstance(group_membership, np.matrixlib.defmatrix.matrix):
raise TypeError("Matrix group_membership should be formatted \
as a numpy matrix")
num_params = group_membership.shape[0]
num_groups = group_membership.shape[1]
sample = np.zeros((N * (num_groups + 1), num_params))
sample = np.array([
generate_trajectory(group_membership, num_levels, grid_jump)
for n in range(N)
])
return sample.reshape((N * (num_groups + 1), num_params))
def _sample_morris(problem: Dict, number_trajectories: int,
num_levels: int = 4) -> np.ndarray:
"""Generate trajectories for groups
Returns an :math:`N(g+1)`-by-:math:`k` array of `N` trajectories,
where :math:`g` is the number of groups and :math:`k` is the number
of factors
Parameters
---------
problem : dict
The problem definition
number_trajectories : int
The number of trajectories to generate
num_levels : int, default=4
The number of grid levels
Returns
-------
numpy.ndarray
"""
groups = _check_groups(problem)
group_membership, _ = compute_groups_matrix(groups)
_check_group_membership(group_membership)
num_params = group_membership.shape[0]
num_groups = group_membership.shape[1]
sample_morris = [_generate_trajectory(group_membership, num_levels)
for _ in range(number_trajectories)]
sample_morris = np.array(sample_morris)
return sample_morris.reshape((number_trajectories * (num_groups + 1),
num_params))
def test_compute_groups_from_parameter_file():
'''
Tests that a group file is read correctly
'''
actual_matrix, actual_unique_names = \
compute_groups_matrix(['Group 1', 'Group 2', 'Group 2'], 3)
assert_equal(actual_matrix, np.matrix('1,0;0,1;0,1', dtype=np.int))
assert_equal(actual_unique_names, ['Group 1', 'Group 2'])
def test_compute_groups_from_parameter_file():
'''
Tests that a group file is read correctly
'''
actual_matrix, actual_unique_names = \
compute_groups_matrix(['Group 1', 'Group 2', 'Group 2'])
assert_equal(actual_matrix, np.array(
[[1, 0], [0, 1], [0, 1]], dtype=np.int))
assert_equal(actual_unique_names, ['Group 1', 'Group 2'])
def test_compute_groups_from_parameter_file():
"""
Tests that a group file is read correctly
"""
actual_matrix, actual_unique_names = \
compute_groups_matrix(['Group 1', 'Group 2', 'Group 2'])
assert_equal(actual_matrix, np.array([[1, 0], [0, 1], [0, 1]],
dtype=np.int))
assert_equal(actual_unique_names, ['Group 1', 'Group 2'])
def test_group_in_param_file_read(setup_param_file_with_groups):
'''
Tests that groups in a parameter file are read correctly
'''
parameter_file = setup_param_file_with_groups
problem = read_param_file(parameter_file)
groups, group_names = compute_groups_matrix(problem['groups'])
assert_equal(problem['names'], ["Test 1", "Test 2", "Test 3"])
assert_equal(groups, np.array([[1, 0], [1, 0], [0, 1]], dtype=np.int))
assert_equal(group_names, ['Group 1', 'Group 2'])
def test_group_in_param_file_read(setup_param_file_with_groups):
'''
Tests that groups in a parameter file are read correctly
'''
parameter_file = setup_param_file_with_groups
problem = read_param_file(parameter_file)
groups, group_names = compute_groups_matrix(
problem['groups'])
assert_equal(problem['names'], ["Test 1", "Test 2", "Test 3"])
assert_equal(groups, np.array([[1, 0], [1, 0], [0, 1]], dtype=np.int))
assert_equal(group_names, ['Group 1', 'Group 2'])
def test_group_in_param_file_read():
'''
Tests that groups in a parameter file are read correctly
'''
parameter_file = setup_param_file_with_groups()
problem = read_param_file(parameter_file)
groups, group_names = compute_groups_matrix(problem['groups'],
problem['num_vars'])
assert_equal(problem['names'], ["Test 1", "Test 2", "Test 3"])
assert_equal(groups, np.matrix('1,0;1,0;0,1', dtype=np.int))
assert_equal(group_names, ['Group 1', 'Group 2'])
def _sample_groups(problem, N, num_levels=4):
"""Generate trajectories for groups
Returns an :math:`N(g+1)`-by-:math:`k` array of `N` trajectories,
where :math:`g` is the number of groups and :math:`k` is the number
of factors
Arguments
---------
problem : dict
The problem definition
N : int
The number of trajectories to generate
num_levels : int, default=4
The number of grid levels
Returns
-------
numpy.ndarray
"""
if len(problem['groups']) != problem['num_vars']:
raise ValueError("Groups do not match to number of variables")
group_membership, _ = compute_groups_matrix(problem['groups'])
if group_membership is None:
raise ValueError("Please define the 'group_membership' matrix")
if not isinstance(group_membership, np.ndarray):
raise TypeError("Argument 'group_membership' should be formatted \
as a numpy ndarray")
num_params = group_membership.shape[0]
num_groups = group_membership.shape[1]
sample = np.zeros((N * (num_groups + 1), num_params))
sample = np.array([generate_trajectory(group_membership,
num_levels)
for n in range(N)])
return sample.reshape((N * (num_groups + 1), num_params))
def _sample_groups(problem, N, num_levels, grid_jump):
"""Generate trajectories for groups
Returns an :math:`N(g+1)`-by-:math:`k` array of `N` trajectories,
where :math:`g` is the number of groups and :math:`k` is the number
of factors
Arguments
---------
problem : dict
The problem definition
N : int
The number of trajectories to generate
num_levels : int
The number of grid levels
grid_jump : int
The grid jump size
Returns
-------
numpy.ndarray
"""
group_membership, _ = compute_groups_matrix(problem['groups'])
if group_membership is None:
raise ValueError("Please define the matrix group_membership.")
if not isinstance(group_membership, np.matrixlib.defmatrix.matrix):
raise TypeError("Matrix group_membership should be formatted \
as a numpy matrix")
num_params = group_membership.shape[0]
num_groups = group_membership.shape[1]
sample = np.zeros((N * (num_groups + 1), num_params))
sample = np.array([generate_trajectory(group_membership,
num_levels,
grid_jump)
for n in range(N)])
return sample.reshape((N * (num_groups + 1), num_params))
conf_level=0.95,
print_to_console=False,
num_levels=4,
grid_jump=2,
num_resamples=1000)
Si_CumulativeCO2 = morris.analyze(problem,
param_values,
Morris_Objectives[:, 1],
conf_level=0.95,
print_to_console=False,
num_levels=4,
grid_jump=2,
num_resamples=1000)
num_vars = problem['num_vars']
groups, unique_group_names = compute_groups_matrix(problem['groups'], num_vars)
number_of_groups = len(unique_group_names)
print("{0:<30} {1:>10} {2:>10} {3:>15} {4:>10}".format("Parameter", "Mu_Star",
"Mu", "Mu_Star_Conf",
"Sigma"))
for j in list(range(number_of_groups)):
print("{0:30} {1:10.3f} {2:10.3f} {3:15.3f} {4:10.3f}".format(
Si_OF['names'][j], Si_OF['mu_star'][j], Si_OF['mu'][j],
Si_OF['mu_star_conf'][j], Si_OF['sigma'][j]))
import csv
line1 = Si_OF['mu_star']
line2 = Si_OF['mu_star_conf']
line3 = Si_CumulativeCO2['mu_star']
line4 = Si_CumulativeCO2['mu_star_conf']
with open('MMResults.csv', 'w') as f:
writer = csv.writer(f, delimiter=',')
