def test_latin_sample_seed():
N, problem = problem_setup()
sample1 = latin.sample(problem, N, seed=None)
sample2 = latin.sample(problem, N, seed=123)
np.testing.assert_equal(np.any(np.not_equal(sample1, sample2)), True)
def make_samples_for_row(row,
model,
fixed_parameters,
parameter_bounds={},
analysis_type='sobol',
N=1000,
**kwargs):
'''
Supporting function for sample_and_integrate. Do not run.
row must be a pandas Series
:meta private:
'''
param_names = model['params']
base_value = row[param_names]
to_permute = [p for p in param_names if p not in fixed_parameters]
to_keep = [p for p in param_names if p in fixed_parameters]
if parameter_bounds:
bounds = np.array([parameter_bounds[p] for p in to_permute])
else:
bounds = [[base_value[p] / 10, base_value[p] * 10] for p in to_permute]
problem = {
'num_vars': len(to_permute),
'names': to_permute,
'bounds': bounds,
}
if analysis_type == 'sobol':
new_values = saltelli.sample(problem, N, **kwargs)
elif analysis_type == 'fast':
new_values = fast_sampler.sample(problem, N, **kwargs)
elif analysis_type == 'delta':
new_values = latin.sample(problem, N)
elif analysis_type == 'rbd-fast':
new_values = latin.sample(problem, N)
else:
raise Exception(
'Could not find analyzer. analysis_type must be sobol, fast, rbd-fast or delta.'
)
new_values = pd.DataFrame(new_values, columns=to_permute)
fixed_parametersped_values = np.repeat(
base_value[to_keep].values[np.newaxis, :], len(new_values), 0)
samples = np.concatenate((new_values, fixed_parametersped_values), axis=1)
samples = pd.DataFrame(samples, columns=to_permute + to_keep)
samples = samples[param_names]
return samples, problem
def redraw(self, random_method: str):
"""Redraw the random number with the given method
:param random_method: the random method to use
"""
problem = {
"num_vars": self.num_dim,
"names": list(range(self.num_dim)),
"bounds": [[0, 1]] * self.num_dim,
}
if random_method == "pseudo_random":
seq = np.random.random((self.bucket_size, 2))
elif random_method == "sobol_sequence":
seq = sobol_sequence.sample(self.bucket_size, 2)
elif random_method == "saltelli":
seq = saltelli.sample(problem, self.bucket_size, calc_second_order=False)
elif random_method == "latin_hypercube":
seq = latin.sample(problem, self.bucket_size)
elif random_method == "finite_differences":
seq = finite_diff.sample(problem, self.bucket_size)
elif random_method == "fast":
seq = fast_sampler.sample(problem, self.bucket_size, M=45)
else:
raise ValueError(f"Unknown random method {random_method}")
self.random_draws[random_method] = seq
def test_regression_hdmr_case1():
problem = {
'num_vars': 5,
'names': ['x1', 'x2', 'x3', 'x4', 'x5'],
'bounds': [[0, 1] * 5]
}
X = latin.sample(problem, 10000)
Y = linear_model_1.evaluate(X)
options = {
'maxorder': 2,
'maxiter': 100,
'm': 2,
'K': 1,
'R': 10000,
'alpha': 0.95,
'lambdax': 0.01,
'print_to_console': False
}
Si = hdmr.analyze(problem, X, Y, **options)
assert_allclose(Si['Sa'][0:problem['num_vars']], [0.20] * 5,
atol=5e-2,
rtol=1e-1)
assert_allclose(Si['ST'][0:problem['num_vars']], [0.20] * 5,
atol=5e-2,
rtol=1e-1)
def fix_group_ranking(input_path, variable, output_path, samples, values,
partial_order, index_product, problem, x_fix,
x_fix_adjust, num_pce, seed, sample_range,
product_uniform, filename):
# Calculate the corresponding number of bootstrap with use of group_fix
x_sample = latin.sample(problem, sample_range, seed=88).T
np.savetxt(f'{output_path}metric_samples.txt', x_sample)
# if reduce parameters, change samples
if (variable.num_vars()) == 11:
x_sample = adjust_sampling(x_sample, index_product, x_fix)
conf_uncond, error_dict, pool_res, y_uncond = {}, {}, {}, {}
rand = np.random.randint(
0, x_sample.shape[1], size=(500, x_sample.shape[1]))
ci_bounds = [0.025, 0.975]
import pickle
approx_list_all = pickle.load(
open(f'{output_path}{filename}-approx-list.pkl', "rb"))
for key, value in partial_order.items():
print(key, value)
#pce_list = []
pce_list = approx_list_all[key]
num_pce = len(pce_list)
cv_temp = np.zeros(num_pce)
y_temp = np.zeros(shape=(num_pce, x_sample.shape[1]))
_, sample_size = key.split('_')[0], int(key.split('_')[1])
#from pyapprox.utilities import get_random_k_fold_sample_indices
print(f'------------Training samples: {sample_size}--------------')
for i in range(num_pce):
poly = pce_list[i]
y_temp[i, :] = poly(x_sample).flatten()
cv_temp[i] = np.sqrt(poly.variance())[0] / poly.mean()[0]
y_uncond[key] = y_temp
# Note Now this does not use the same cross validation folds
# as used to build the PCE but this should be ok
conf_uncond[key] = uncond_cal(y_uncond[key], ci_bounds, rand)
conf_uncond[key]['cv'] = cv_temp[i].mean()
conf_uncond[key]['cv_low'], conf_uncond[key]['cv_up'] = \
np.quantile(cv_temp, ci_bounds)
error_dict[key], pool_res = group_fix(
value, pce_list, x_sample, y_uncond[key], x_fix_adjust, rand, {},
file_exist=True)
# End for
# # separate confidence intervals into separate dicts and write results
save_path = f'{output_path}{product_uniform}/'
if not os.path.exists(save_path): os.mkdir(save_path)
# convert the result into dataframe
key_outer = list(error_dict.keys())
f_names = list(error_dict[key_outer[0]].keys())
for ele in f_names:
dict_measure = {key: error_dict[key][ele] for key in key_outer}
df = to_df(partial_order, dict_measure)
df.to_csv(f'{save_path}/{ele}.csv')
with open(f'{save_path}y_uncond_stats.json', 'w') as fp:
json.dump(conf_uncond, fp, indent=2)
def prepare_sens_analysis(storm_name_list=[]):
""" Function to prepare the sensitivity analysis for a country.
Arguments:
*storm_name_list* (list) -- list of storms to include in sensitivity analysis. If kept empty, default storms will be used.
Returns:
*param_values* (list) -- list of 5000 combinations of parameter values to be used in sensitivity analysis.
*storm_name_list* (list) -- list of storms to include in sensitivity analysis.
"""
# set parameters for sensitivity analysis
problem = {
'num_vars': 5,
'names': ['c2', 'c3', 'c4', 'lu1', 'lu2'],
'bounds': [[0, 100], [0, 100], [0, 100], [0, 50], [0, 50]]
}
param_values = latin.sample(problem, 5000)
# rescale parameters for vulnerability curves (should add up to 100)
rescale = param_values[:, :3]
for i in range(len(rescale)):
inb = (rescale[i] * 100) / sum(rescale[i])
param_values[i, :3] = inb
# select storms to assess
if len(storm_name_list) == 0:
storm_name_list = [
'19991203', '19900125', '20090124', '20070118', '19991226'
]
return param_values, storm_name_list
def sample_parameters(problem: dict, seed: int, method: str):
if method == "FAST":
return fast_sampler.sample(problem, seed)
if method == "sobol":
return saltelli.sample(problem, seed)
if (method == "RBD_FAST") or (method == "DMIM"):
return latin.sample(problem, seed)
def test_rbd_to_df():
params = ['x1', 'x2', 'x3']
problem = {
'num_vars':
3,
'names':
params,
'groups':
None,
'bounds': [[-3.14159265359, 3.14159265359],
[-3.14159265359, 3.14159265359],
[-3.14159265359, 3.14159265359]]
}
param_values = latin.sample(problem, 1000)
Y = Ishigami.evaluate(param_values)
Si = rbd_fast.analyze(problem, Y, param_values, print_to_console=False)
Si_df = Si.to_df()
assert isinstance(Si_df, pd.DataFrame), \
"RBD Si: Expected DataFrame, got {}".format(type(Si_df))
assert set(Si_df.index) == set(params), "Incorrect index in DataFrame"
col_names = set(['S1'])
assert set(Si_df.columns) == col_names, \
"Unexpected column names in DataFrame. Expected {}, got {}".format(
col_names, Si_df.columns)
def test_regression_rbd_fast():
param_file = 'src/SALib/test_functions/params/Ishigami.txt'
problem = read_param_file(param_file)
param_values = latin.sample(problem, 10000)
Y = Ishigami.evaluate(param_values)
Si = rbd_fast.analyze(problem, param_values, Y, print_to_console=False)
assert_allclose(Si['S1'], [0.31, 0.44, 0.00], atol=5e-2, rtol=1e-1)
def test_regression_rbd_fast():
param_file = 'src/SALib/test_functions/params/Ishigami.txt'
problem = read_param_file(param_file)
param_values = latin.sample(problem, 10000)
Y = Ishigami.evaluate(param_values)
Si = rbd_fast.analyze(problem, param_values, Y, print_to_console=False)
assert_allclose(Si['S1'], [0.31, 0.44, 0.00], atol=5e-2, rtol=1e-1)
def fit(self, X, y):
"""
Fits the regressor to the data `(X, y)` and performs a sensitivity analysis on the result of the regression.
:param X: Training data
:param y: Target values
:return: `self`
"""
from numpy import argpartition
N = len(X[0])
if (self.domain is None) or (self.probs is None):
self._avg_fucn(X, y)
if self.regressor is None:
from sklearn.svm import SVR
self.regressor = SVR()
self.regressor.fit(self.domain, self.probs)
bounds = [[
min(self.domain[:, idx]) - self.margin,
max(self.domain[:, idx]) + self.margin
] for idx in range(N)]
problem = dict(num_vars=N,
names=['x%d' % idx for idx in range(N)],
bounds=bounds)
res = []
if self.method == 'sobol':
from SALib.sample import saltelli
from SALib.analyze import sobol
param_values = saltelli.sample(problem, self.num_smpl)
y_ = self.regressor.predict(param_values)
res = sobol.analyze(problem, y_)['ST']
self.weights_ = res
elif self.method == 'morris':
from SALib.sample import morris as mrs
from SALib.analyze import morris
param_values = mrs.sample(problem,
self.num_smpl,
num_levels=self.num_levels)
y_ = self.regressor.predict(param_values)
res = morris.analyze(problem,
param_values,
y_,
num_levels=self.num_levels)['mu_star']
self.weights_ = res
elif self.method == 'delta-mmnt':
from SALib.sample import latin
from SALib.analyze import delta
param_values = latin.sample(problem, self.num_smpl)
y_ = self.regressor.predict(param_values)
res = delta.analyze(problem,
param_values,
y_,
num_resamples=self.num_resmpl)['delta']
self.weights_ = res
self.top_features_ = argpartition(
res, -self.n_features_to_select)[-self.n_features_to_select:]
return self
def test_latin_sample_trivial(self):
problem = {'num_vars': 1, 'bounds': [[0, 1]], 'names': ['var1']}
actual = sample(problem, 10, seed=42)
expected = np.array([[0.8601115], [0.27319939], [0.03745401],
[0.60580836], [0.78661761], [0.97080726],
[0.35986585], [0.19507143], [0.41560186],
[0.51559945]])
np.testing.assert_allclose(actual, expected)
def RBD_FAST_analysis(network, num_samples, prob_def):
print("Sampling via RBD-FAST...")
samples = latin.sample(prob_def, num_samples)
X = samples
samples = np.split(samples, samples.shape[1], axis=1)
samples = [s.squeeze() for s in samples]
values = {n: torch.tensor(s) for n, s in zip(prob_def["names"], samples)}
print("Running GrFN..")
Y = network.run(values).numpy()
print("Analyzing via RBD ...")
return rbd_fast.analyze(prob_def, Y, X, print_to_console=True)
def populate():
# 'parameters' dictionary stores each line in the model
par_keys = list(parameters.keys())
# init problem definiton
seed = int(opts['seed'])
levels = int(opts['p_levels'])
problem = {
'names': opts['par_name'],
'num_vars': len(opts['par_name']),
'bounds': [],
}
# define bounds following the model configuration
for line in range(len(par_keys)):
if parameters[line][0] == 'par':
if parameters[line][3] == 'range':
lower = float(parameters[par_keys[line]][4])
upper = float(parameters[par_keys[line]][5])
if parameters[line][3] == 'factor':
lower = float(parameters[line][2]) * (
1 - float(parameters[par_keys[line]][4]))
upper = float(parameters[line][2]) * (
1 + float(parameters[par_keys[line]][5]))
problem['bounds'].append([lower, upper])
# create samples to simulate
if opts['method'] == 'sobol':
models = saltelli.sample(problem=problem,
N=levels,
calc_second_order=True,
seed=seed)
elif opts['method'] == 'fast':
models = fast_sampler.sample(problem=problem, N=levels, seed=seed)
elif opts['method'] == 'rbd-fast' or opts['method'] == 'delta' or opts[
'method'] == 'dgsm':
models = latin.sample(problem=problem, N=levels, seed=seed)
elif opts['method'] == 'morris':
models = morris_sample(problem=problem, N=levels)
elif opts['method'] == 'frac':
models = ff_sample(problem, seed=seed)
else:
error_msg = 'Wrong method name.'
print(error_msg)
raise ValueError(error_msg)
population = {}
# add samples to population dict
population['problem', 'samples'] = models
# add problem definition to population dict
population['problem', 'definition'] = problem
return population
def analyze(self):
"""Initiate the analysis, and stores the result at data directory.
Generates:
Analysis result at 'acbm/data/output/delta.txt'.
"""
X = latin.sample(self.problem, self.n_samples, seed=self.seed_sample)
Y = ACBM.evaluate(X)
si = delta.analyze(self.problem, X, Y, seed=self.seed_analyze)
pickle.dump(si, open(self.path_output + 'delta.txt', 'wb'))
def test_regression_delta():
param_file = 'src/SALib/test_functions/params/Ishigami.txt'
problem = read_param_file(param_file)
param_values = latin.sample(problem, 10000)
Y = Ishigami.evaluate(param_values)
Si = delta.analyze(problem, param_values, Y, num_resamples=10,
conf_level=0.95, print_to_console=True)
assert_allclose(Si['delta'], [0.210, 0.358, 0.155], atol=5e-2, rtol=1e-1)
assert_allclose(Si['S1'], [0.31, 0.44, 0.00], atol=5e-2, rtol=1e-1)
def test_regression_delta():
param_file = 'src/SALib/test_functions/params/Ishigami.txt'
problem = read_param_file(param_file)
param_values = latin.sample(problem, 10000)
Y = Ishigami.evaluate(param_values)
Si = delta.analyze(problem, param_values, Y, num_resamples=10,
conf_level=0.95, print_to_console=True)
assert_allclose(Si['delta'], [0.210, 0.358, 0.155], atol=5e-2, rtol=1e-1)
assert_allclose(Si['S1'], [0.31, 0.44, 0.00], atol=5e-2, rtol=1e-1)
def main(name, c):
name = name.lower()
model, par, parameters, bounds, samples_to_take = setups(name)
problem = {
'num_vars': len(parameters),
'names': parameters,
'bounds': bounds
}
params = sample(problem, samples_to_take)
# Create queues
done = Queue()
paramsdiv = []
p = []
remaining = 0
for i in range(0, PROC_NUM):
x = params.shape[0] / PROC_NUM
remaining += x - int(x)
x = int(x)
y = params[x * i:x *
(i + 1), :] #filling the y with the x parameter sets
if i == PROC_NUM - 1 and remaining != 0:
y = np.append(y, params[-int(remaining):, :], axis=0)
paramsdiv.append(y)
p.append(
Process(target=evaluate,
args=(paramsdiv[i], model, par, c, done, i)))
p[i].start()
print(np.asarray(paramsdiv).shape)
Ydiv = np.zeros(PROC_NUM).tolist() #no of processes
count = 0
while True:
temp, no = done.get()
Ydiv[no] = temp.tolist()
count += 1
if count == PROC_NUM:
break
Y = []
for i in range(0, PROC_NUM):
Y += Ydiv[i]
Y = np.asarray(Y)
print(Y.shape)
#Y = evaluate(params, model, par, c)
write_file(Y, parameters, name, c, params)
def rbd_fast(self):
"""RBD-FAST sensitivity analysis of the objective function.
This function estimates the sensitivity with the RBD-FAST method of the
objective function with changes in the parameters using SALib:
https://salib.readthedocs.io/en/latest/api.html#rbd-fast-random-balance-designs-fourier-amplitude-sensitivity-test
Returns:
dict: sensitivity values of parameters; dict has keys 'S1'
"""
X, y, problem = self._sensitivity_prep()
n_sample = 2000
param_values = latin.sample(problem, n_sample)
X_s, y_s = self._closest_points(problem, X, y, param_values)
Si = rbd_fast.analyze(problem, X_s, y_s)
return Si
def test_nonuniform_scale_samples_truncnorm():
"""
Test the rescaling of samples for truncated normal distribution
"""
problem = {
'num_vars': 1,
'dists': ['truncnorm'],
'bounds': [[0, 3.14, 2, 1]],
'names': ['x1']
}
actual = latin.sample(problem, 10, seed=42)
expected = np.array([[2.68693037], [1.34115848], [0.39811064],
[2.09477163], [2.49999031], [3.028063], [1.5564238],
[1.11686499], [1.68414443], [1.9022482]])
np.testing.assert_allclose(actual, expected)
def _gen_muestrea(símismo, n, ops):
if símismo.método == 'sobol':
return saltelli.sample(problem=símismo.problema, N=n, **ops)
elif símismo.método == 'fast':
return fast_sampler.sample(problem=símismo.problema, N=n, **ops)
elif símismo.método == 'morris':
return morris_muestra.sample(problem=símismo.problema, N=n, **ops)
elif símismo.método == 'dmim':
return latin.sample(problem=símismo.problema, N=n)
elif símismo.método == 'dgsm':
return saltelli.sample(problem=símismo.problema, N=n)
elif símismo.método == 'ff':
return ff_muestra.sample(problem=símismo.problema)
else:
raise ValueError('Método de análisis de sensibilidad "{}" no reconocido.'.format(símismo.método))
def delta(self):
"""Morris Method sensitivity of the objective function.
This function estimates the sensitivity with the Morris Method of the
objective function with changes in the parameters using SALib:
https://salib.readthedocs.io/en/latest/api.html#delta-moment-independent-measure
Returns:
dict: sensitivity values of parameters; dict has keys 'delta',
'delta_conf', 'S1', and 'S1_conf'
"""
X, y, problem = self._sensitivity_prep()
n_sample = 2000
param_values = latin.sample(problem, n_sample)
X_s, y_s = self._closest_points(problem, X, y, param_values)
Si = delta.analyze(problem, X_s, y_s)
return Si
def coupled_montecarlo_setup(smps=2, firstTimeStep=166):
# Get and Save Latin Hypercube Matrix
problem = get_problem()
smps_matrix = latin.sample(problem, smps)
# Normalized matrix (will be re-scaled by phd-model-process)
saveLHSmatrix(smps_matrix)
mc_upper = problem['upper']
for sample_nr in range(1, smps + 1):
sample_vector = getInputVector(sample_nr - 1, smps_matrix)
run_coupled_sample(sample_nr,
firstTimeStep,
sample_vector=sample_vector,
mc_upper=mc_upper,
couple=True,
montecarlo_couple=True)
def redraw(self, random_method):
problem = {'num_vars': 2, 'names': ['x', 'y'], 'bounds': [[0, 1]] * 2}
if random_method == 'pseudo_random':
seq = np.random.random((NUM_DATA_POINTS, 2))
elif random_method == 'sobol_sequence':
seq = sobol_sequence.sample(NUM_DATA_POINTS, 2)
elif random_method == 'saltelli':
seq = saltelli.sample(problem,
NUM_DATA_POINTS,
calc_second_order=False)
elif random_method == 'latin_hypercube':
seq = latin.sample(problem, NUM_DATA_POINTS)
elif random_method == 'finite_differences':
seq = finite_diff.sample(problem, NUM_DATA_POINTS)
elif random_method == 'fast':
seq = fast_sampler.sample(problem, NUM_DATA_POINTS, M=45)
self.random_draws[random_method] = seq
def fix_increase_sample(input_path, variable, output_path, samples, values,
partial_order, index_product, problem, x_fix, x_fix_adjust,
num_pce, seed, sample_range, product_uniform, filename):
# if reduce parameters, change samples
key = list(partial_order.keys())[0]
_, sample_size = key.split('_')
value = partial_order[key]
import pickle
approx_list_all = pickle.load(
open(f'{output_path}{filename}-approx-list.pkl', "rb"))
poly_list = [approx_list_all[key][0]]
conf_uncond, error_dict, pool_res, y_uncond = {}, {}, {}, {}
ci_bounds = [0.025, 0.975]
nstart, nstop, nstep = sample_range
for n in range(nstart, nstop + 1, nstep):
print(n)
x_sample = latin.sample(problem, n, seed=88)
x_sample = x_sample.T
# if reduce parameters, change samples
if (variable.num_vars()) == 11:
x_sample = adjust_sampling(x_sample, index_product, x_fix)
# Calculate the corresponding number of bootstrap with use pf group_fix
rand = np.random.randint(0, x_sample.shape[1], size=(500, x_sample.shape[1]))
# add the calculation of y_uncond
y_uncond_temp = poly_list[0](x_sample).T
conf_uncond[str(n)] = uncond_cal(y_uncond_temp, ci_bounds, rand)
conf_uncond[str(n)]['median'] = np.quantile(y_uncond_temp[0][rand], ci_bounds, axis=1).mean(axis=0).mean()
conf_uncond[str(n)]['mean'] = poly_list[0].mean()[0]
error_dict[str(n)], pool_res = group_fix(value, poly_list, x_sample, y_uncond_temp,
x_fix_adjust, rand, {}, file_exist=True)
# End for
# separate confidence intervals into separate dicts and write results
save_path = f'{output_path}{product_uniform}/'
if not os.path.exists(save_path): os.mkdir(save_path)
# convert the result into dataframe
key_outer = list(error_dict.keys())
f_names = list(error_dict[key_outer[0]].keys())
for ele in f_names:
dict_measure = {key: error_dict[key][ele] for key in key_outer}
df = pd.DataFrame.from_dict(dict_measure)
df.to_csv(f'{save_path}/{ele}_adaptive.csv')
df_stats = pd.DataFrame(data=conf_uncond, index=np.arange(nstart, nstop + 1, nstep))
df_stats.to_csv(f'{save_path}/stats_uncond_adaptive.csv')
def test_latin_sample_two_groups(self):
problem = {
'num_vars': 2,
'bounds': [[0, 1], [0, 1]],
'names': ['var1', 'var2'],
'groups': ['group1', 'group2']
}
actual = sample(problem, 10, seed=42)
expected = np.array([[0.17319939,
0.85247564], [0.50205845, 0.21559945],
[0.4601115, 0.59699099], [0.83042422, 0.71834045],
[0.03745401, 0.38661761], [0.7181825, 0.15986585],
[0.68324426,
0.92912291], [0.30580836, 0.09507143],
[0.21560186, 0.47080726], [0.9431945,
0.62123391]])
np.testing.assert_allclose(actual, expected)
def lhs_uniform_sample(num_vars, num_samples):
"""
Create uncorrelated uniform samples on the unit interval
Parameters
----------
num_vars
num_samples
Returns
-------
"""
samples = lhs.sample(
{
'num_vars': num_vars,
'bounds': [[0, 1] for i in range(num_vars)]
}, num_samples)
return samples
def test_latin_sample_no_groups(self):
problem = {
'num_vars': 2,
'bounds': [[0, 1], [0, 1]],
'names': ['var1', 'var2'],
'groups': None
}
actual = sample(problem, 10, seed=42)
expected = np.array([[0.86011150,
0.15247564], [0.27319939, 0.84560700],
[0.03745401,
0.73663618], [0.60580836, 0.51394939],
[0.78661761,
0.46118529], [0.97080726, 0.97851760],
[0.35986585,
0.03042422], [0.19507143, 0.32912291],
[0.41560186, 0.62921446],
[0.51559945, 0.24319450]])
approx(actual, expected)
def test_regression_hdmr_ishigami():
param_file = 'src/SALib/test_functions/params/Ishigami.txt'
problem = read_param_file(param_file)
X = latin.sample(problem, 10000)
Y = Ishigami.evaluate(X)
options = {
'maxorder': 2,
'maxiter': 100,
'm': 4,
'K': 1,
'R': 10000,
'alpha': 0.95,
'lambdax': 0.01,
'print_to_console': False
}
Si = hdmr.analyze(problem, X, Y, **options)
assert_allclose(Si['Sa'][0:problem['num_vars']], [0.31, 0.44, 0.00],
atol=5e-2,
rtol=1e-1)
assert_allclose(Si['ST'][0:problem['num_vars']], [0.55, 0.44, 0.24],
atol=5e-2,
rtol=1e-1)
def sensitivity_analysis(self, N=1000, force=False):
"""Run a RBD-fast sensitivity analysis and return the first order
indices.
Keyword Arguments:
N {int} -- number of sampled used to run the analysis (default: {1000})
Returns:
dict -- first order sensitivity indices
""" # noqa
if self._expensive and not force:
raise ValueError("sensitivity analysis should not be done on "
"expensive function, but only on metamodel or "
"test functions. Use force=True to override that "
"behavior")
X = latin.sample(self._problem, N)
y = self(X)
S1 = rbd_fast.analyze(self._problem, y, X)["S1"]
return dict(sorted([(var, idx)
for var, idx
in zip(self.inputs, S1)],
key=sort_by_values, reverse=True))
def sample(**kwargs):
"""
:param kwargs: dictionary containing the following parameters
problem_file_path or -prob: path to a json that contains the problem with parameters and bounds, String
n_samples or -n: the number of parameter samples that should be returned,
:return: None
"""
n_samples = kwargs.get('n_samples')
problem_file_path = kwargs.get('problem_file_path')
if kwargs.get('output_file_name'):
output_file_name = kwargs.get('output_file_name')
else:
output_file_name = 'hypercube.json'
with open(problem_file_path) as json_file:
problem = json.load(json_file)
latin_hyper_cube = latin.sample(problem=problem, N=n_samples)
latin_hyper_cube = latin_hyper_cube.tolist()
# create list of problems with initial values
problems = []
for pars in latin_hyper_cube:
# transform the necessary parameters into integers
for idx, p in enumerate(pars):
if problem['integer'][idx]:
pars[idx] = round(int(pars[idx]))
new_problem = copy.deepcopy(problem)
new_problem['initial'] = pars
problems.append(new_problem)
with open(output_file_name, 'w') as f:
json.dump(problems, f)
click.echo(
'Sampling {} samples done, check out the samples here: {}'.format(
n_samples, output_file_name))
def random_sample(args):
items = args.dimensions
n = args.n
f = NamedTemporaryFile(delete=False)
for i in range(len(items)):
f.write(str(i) + " 0.501 " + str(items[i] + 0.499) + "\n")
f.close()
# Read the parameter range file and generate samples
problem = read_param_file(f.name)
# Generate samples
param_values = latin.sample(problem, n)
param_rounded = []
for l in param_values:
param_rounded.append([int(round(n, 0)) for n in l])
for i in param_rounded:
print i
def sample_uniforms(model, num_samples=1000, log=[]):
bounds = []
names = []
for name, tuple in model.parameter_distributions.items():
names.append(name)
bounds.append(tuple)
for name, tuple in model.species_distributions.items():
names.append(name)
bounds.append(tuple)
problem = {'num_vars': len(names), 'names': names, 'bounds': bounds}
problem = salib_problem_to_log_space(problem, log)
lhc_samples = latin.sample(problem, num_samples)
lhc_samples = samples_to_normal_space(lhc_samples, problem, log)
samples = parse_samples(lhc_samples,
list(model.parameter_distributions.keys()),
list(model.species_distributions.keys()))
return samples
def random_sample(args): items = args.dimensions n = args.n f = NamedTemporaryFile(delete=False) for i in range(len(items)): f.write(str(i) + " 0.501 " + str(items[i] + 0.499) + "\n") f.close() # Read the parameter range file and generate samples problem = read_param_file(f.name) # Generate samples param_values = latin.sample(problem, n) param_rounded = [] for l in param_values: param_rounded.append([int(round(n, 0)) for n in l]) for i in param_rounded: print i
def test_rbd_to_df():
params = ['x1', 'x2', 'x3']
problem = {
'num_vars': 3,
'names': params,
'groups': None,
'bounds': [[-3.14159265359, 3.14159265359],
[-3.14159265359, 3.14159265359],
[-3.14159265359, 3.14159265359]]
}
param_values = latin.sample(problem, 1000)
Y = Ishigami.evaluate(param_values)
Si = rbd_fast.analyze(problem, param_values, Y, print_to_console=False)
Si_df = Si.to_df()
assert isinstance(Si_df, pd.DataFrame), \
"RBD Si: Expected DataFrame, got {}".format(type(Si_df))
assert set(Si_df.index) == set(params), "Incorrect index in DataFrame"
col_names = set(['S1'])
assert set(Si_df.columns) == col_names, \
"Unexpected column names in DataFrame. Expected {}, got {}".format(
col_names, Si_df.columns)
import sys
sys.path.append('../..')
from SALib.analyze import rbd_fast
from SALib.sample import latin
from SALib.test_functions import Ishigami
from SALib.util import read_param_file
# Read the parameter range file and generate samples
problem = read_param_file('../../SALib/test_functions/params/Ishigami.txt')
# Generate samples
param_values = latin.sample(problem, 1000)
# Run the "model" and save the output in a text file
# This will happen offline for external models
Y = Ishigami.evaluate(param_values)
# Perform the sensitivity analysis using the model output
# Specify which column of the output file to analyze (zero-indexed)
Si = rbd_fast.analyze(problem, Y, param_values, print_to_console=False)
# Returns a dictionary with keys 'S1' and 'ST'
# e.g. Si['S1'] contains the first-order index for each parameter, in the
# same order as the parameter file
def sa(model, response, policy={}, method="sobol", nsamples=1000, **kwargs):
if len(model.uncertainties) == 0:
raise ValueError("no uncertainties defined in model")
problem = { 'num_vars' : len(model.uncertainties),
'names' : model.uncertainties.keys(),
'bounds' : [[0.0, 1.0] for u in model.uncertainties],
'groups' : kwargs.get("groups", None) }
# estimate the argument N passed to the sampler that produces the requested
# number of samples
N = _predict_N(method, nsamples, problem["num_vars"], kwargs)
# generate the samples
if method == "sobol":
samples = saltelli.sample(problem, N, **_cleanup_kwargs(saltelli.sample, kwargs))
elif method == "morris":
samples = morris_sampler.sample(problem, N, **_cleanup_kwargs(morris_sampler.sample, kwargs))
elif method == "fast":
samples = fast_sampler.sample(problem, N, **_cleanup_kwargs(fast_sampler.sample, kwargs))
elif method == "ff":
samples = ff_sampler.sample(problem, **_cleanup_kwargs(ff_sampler.sample, kwargs))
elif method == "dgsm":
samples = finite_diff.sample(problem, N, **_cleanup_kwargs(finite_diff.sample, kwargs))
elif method == "delta":
if "samples" in kwargs:
samples = kwargs["samples"]
else:
samples = latin.sample(problem, N, **_cleanup_kwargs(latin.sample, kwargs))
# convert from samples in [0, 1] to uncertainty domain
for i, u in enumerate(model.uncertainties):
samples[:,i] = u.ppf(samples[:,i])
# run the model and collect the responses
responses = np.empty(samples.shape[0])
for i in range(samples.shape[0]):
sample = {k : v for k, v in zip(model.uncertainties.keys(), samples[i])}
responses[i] = evaluate(model, overwrite(sample, policy))[response]
# run the sensitivity analysis method
if method == "sobol":
result = sobol.analyze(problem, responses, **_cleanup_kwargs(sobol.analyze, kwargs))
elif method == "morris":
result = morris_analyzer.analyze(problem, samples, responses, **_cleanup_kwargs(morris_analyzer.analyze, kwargs))
elif method == "fast":
result = fast.analyze(problem, responses, **_cleanup_kwargs(fast.analyze, kwargs))
elif method == "ff":
result = ff_analyzer.analyze(problem, samples, responses, **_cleanup_kwargs(ff_analyzer.analyze, kwargs))
elif method == "dgsm":
result = dgsm.analyze(problem, samples, responses, **_cleanup_kwargs(dgsm.analyze, kwargs))
elif method == "delta":
result = delta.analyze(problem, samples, responses, **_cleanup_kwargs(delta.analyze, kwargs))
# convert the SALib results into a form allowing pretty printing and
# lookups using the parameter name
pretty_result = SAResult(result["names"] if "names" in result else problem["names"])
if "S1" in result:
pretty_result["S1"] = {k : float(v) for k, v in zip(problem["names"], result["S1"])}
if "S1_conf" in result:
pretty_result["S1_conf"] = {k : float(v) for k, v in zip(problem["names"], result["S1_conf"])}
if "ST" in result:
pretty_result["ST"] = {k : float(v) for k, v in zip(problem["names"], result["ST"])}
if "ST_conf" in result:
pretty_result["ST_conf"] = {k : float(v) for k, v in zip(problem["names"], result["ST_conf"])}
if "S2" in result:
pretty_result["S2"] = _S2_to_dict(result["S2"], problem)
if "S2_conf" in result:
pretty_result["S2_conf"] = _S2_to_dict(result["S2_conf"], problem)
if "delta" in result:
pretty_result["delta"] = {k : float(v) for k, v in zip(problem["names"], result["delta"])}
if "delta_conf" in result:
pretty_result["delta_conf"] = {k : float(v) for k, v in zip(problem["names"], result["delta_conf"])}
if "vi" in result:
pretty_result["vi"] = {k : float(v) for k, v in zip(problem["names"], result["vi"])}
if "vi_std" in result:
pretty_result["vi_std"] = {k : float(v) for k, v in zip(problem["names"], result["vi_std"])}
if "dgsm" in result:
pretty_result["dgsm"] = {k : float(v) for k, v in zip(problem["names"], result["dgsm"])}
if "dgsm_conf" in result:
pretty_result["dgsm_conf"] = {k : float(v) for k, v in zip(problem["names"], result["dgsm_conf"])}
if "mu" in result:
pretty_result["mu"] = {k : float(v) for k, v in zip(result["names"], result["mu"])}
if "mu_star" in result:
pretty_result["mu_star"] = {k : float(v) for k, v in zip(result["names"], result["mu_star"])}
if "mu_star_conf" in result:
pretty_result["mu_star_conf"] = {k : float(v) for k, v in zip(result["names"], result["mu_star_conf"])}
if "sigma" in result:
pretty_result["sigma"] = {k : float(v) for k, v in zip(result["names"], result["sigma"])}
return pretty_result
