def run_ards(test_fuction: str,
N: int,
noise_std: bool,
random: bool,
M: int = 5,
K: int = 2):
name = 'rbf'
store_name = test_fuction + '.{0:d}.{1:.3f}.{2:d}'.format(M, noise_std, N)
if random:
store_name += '.random'
else:
store_name += '.rom'
store = data.Store(BASE_PATH / store_name)
model.run.GPs(module=model.run.Module.GPY_,
name=name,
store=store,
M_Used=-1,
parameters=None,
optimize=True,
test=True,
sobol=True,
optimizer_options={
'optimizer': 'bfgs',
'max_iters': 5000,
'gtol': 1E-16
},
make_ard=True)
def predict_roms(M: int, N: int, random: bool, noisy: bool):
store_name = 'sin.u1.'
CDF_scale = 2 * pi
CDF_loc = pi
function_with_parameters = function.CallableWithParameters(
function=function.ishigami, parameters={
'a': 0,
'b': 0
})
store_name = store_name + '{0:d}.{1:d}'.format(N, M)
store_name += '.random' if random else '.rom'
noise_std = 0.0001 if noisy else 0
store = data.Store(store_dir(store_name, noise_std, CDF_scale),
data.Store.InitMode.READ_META_ONLY)
fold = data.Fold(store, 0)
rom = model.gpy_.ROM.from_ROM(fold=fold, name='rom', suffix='.test.full')
model_theta = rom.sobol.parameters_read.Theta
data_theta = function.linear_matrix_from_meta(store)
pre_function_with_parameters = (function.CallableWithParameters(
function=function.linear, parameters={'matrix': data_theta})
if random else None)
test_store = scalar_function_of_normal(
store_name=store_name + "\\test",
N=N,
M=M,
X_std=1.0,
noise_std=noise_std,
CDF_scale=CDF_scale,
CDF_loc=CDF_loc,
pre_function_with_parameters=pre_function_with_parameters,
function_with_parameters=function_with_parameters)
fold.set_test_data(df=test_store.data.df)
rom.sobol.gp.test()
result = matmul(model_theta, data_theta.T)
print(result)
def _collect_std(test_function: str, N: int, noise_std: float, random: bool, M: int):
store = data.Store(store_path(test_function, N, noise_std, random, M))
destination = store.dir / "results"
shutil.rmtree(destination, ignore_errors=True)
destination.mkdir(mode=0o777, parents=True, exist_ok=False)
result = 0.0
for k in range(K):
fold = data.Fold(store, k)
result += fold.standard.df.iloc[-1, -1]/K
savetxt(fname=(destination / "std.csv"), X=atleast_2d(result), delimiter=",")
def _run_test(test_function: str, N: int, noise_std: float, random: bool, M: int):
store = data.Store(store_path(test_function, N, noise_std, random, M))
Mu = choose_Mu(test_function)
gp_optimizer_options = {'optimizer': 'bfgs', 'max_iters': 5000, 'gtol': 1E-16}
kernel_parameters = model.gpy_.Kernel.ExponentialQuadratic.Parameters(lengthscale=full((1, 1), 2.5**(M/5), dtype=float))
parameters = model.gpy_.GP.DEFAULT_PARAMETERS._replace(kernel=kernel_parameters, e_floor=1E-6, e=0.003)
name = 'rom.reduced'
model.run.GPs(module=model.run.Module.GPY_, name=name, store=store, M_Used=Mu, parameters=parameters, optimize=True, test=True, sobol=True,
optimizer_options=gp_optimizer_options)
model.run.GPs(module=model.run.Module.GPY_, name=name, store=store, M_Used=Mu, parameters=None, optimize=True, test=True, sobol=True,
optimizer_options=gp_optimizer_options, make_ard=True)
def test_random3(test_fuction: str, N: int, noise_std: float, M: int = 5, K: int = 2 ):
random = True
name = 'derotated.rbf'
gp_optimizer_options = {'optimizer': 'bfgs', 'max_iters': 5000, 'gtol': 1E-16}
kernel_parameters = model.gpy_.Kernel.ExponentialQuadratic.Parameters(lengthscale=full((1, 1), 2.5**(M/5), dtype=float))
parameters = model.gpy_.GP.DEFAULT_PARAMETERS._replace(kernel=kernel_parameters, e_floor=1E-6, e=0.003)
store_name = test_fuction + '.{0:d}.{1:.3f}.{2:d}'.format(M, noise_std, N)
store_name += '.random'
store_name = BASE_PATH / store_name
store = data.Store(store_name)
lin_trans = loadtxt(store.dir / "InverseRotation.csv")
for k in range(K):
fold = data.Fold(store, k)
replace_X_with_U(fold, transpose(lin_trans))
model.run.GPs(module=model.run.Module.GPY_, name=name, store=store, M_Used=-1, parameters=parameters, optimize=True, test=True, sobol=True,
optimizer_options=gp_optimizer_options)
model.run.GPs(module=model.run.Module.GPY_, name=name, store=store, M_Used=-1, parameters=None, optimize=True, test=True, sobol=True,
optimizer_options=gp_optimizer_options, make_ard=True)
sobol_options = {'semi_norm': model.base.Sobol.SemiNorm.DEFAULT_META, 'N_exploit': 3, 'N_explore': 4096, 'options': {'gtol': 1.0E-16}}
def linear_transformation(model_dir: Path) -> NP.Matrix:
with open(model_dir / "__meta__.json", mode='r') as file:
meta = load(file)
function_with_parameters = meta['origin']['functions_with_parameters'][
0].split("; matrix=")
if len(function_with_parameters) > 1:
function_with_parameters = eval(function_with_parameters[-1][:-1])
return array(function_with_parameters)
else:
return eye(meta['data']['M'], dtype=float)
if __name__ == '__main__':
store = data.Store((BASE_PATH / NOISELESS_DIR) / "sin.u1.5000.5.random",
data.Store.InitMode.READ_META_ONLY)
fold = data.Fold(store, 0)
rom = model.gpy_.ROM.from_ROM(fold=fold, name='rom', suffix='.test.full')
model_theta = rom.sobol.parameters_read.Theta
data_theta = function.linear_matrix_from_meta(store)
print(model_theta)
print(data_theta)
"""
def rename(dir_: Path):
for p in dir_.iterdir():
if p.is_dir():
if p.name == "rom..optimized":
p.replace(p.parent / "rom.optimized")
else:
rename(p)
"""