def run_rbfs(test_fuction: str, N: int, noise_std: bool, random: bool, M: int = 5, K: int = 2 ): name = 'rbf' kernel_parameters = model.gpy_.Kernel.ExponentialQuadratic.Parameters(lengthscale=full((1, 1), 0.2, dtype=float)) parameters = model.gpy_.GP.DEFAULT_PARAMETERS._replace(kernel=kernel_parameters, e_floor=1E-6, e=0.003) CDF_scale = 2 * pi CDF_loc = pi if test_fuction == 'sin.1': function_with_parameters = function.CallableWithParameters(function.ishigami, parameters={'a': 0.0, 'b': 0.0}) elif test_fuction == 'sin.2': function_with_parameters = function.CallableWithParameters(function.ishigami, parameters={'a': 2.0, 'b': 0.0}) elif test_fuction == 'ishigami': function_with_parameters = function.callable_with_parameters(function.ishigami) else: CDF_scale = 1.0 CDF_loc = 0.0 function_with_parameters = function.callable_with_parameters(function.sobol_g) store_name = test_fuction + '.{0:d}.{1:.3f}.{2:d}'.format(M, noise_std, N) if random: pre_function_with_parameters = function.CallableWithParameters(function=function.linear, parameters={'matrix': ortho_group.rvs(M)}) store_name += '.random' else: pre_function_with_parameters = None store_name += '.rom' store_name = BASE_PATH / store_name store = scalar_function_of_normal(store_name=store_name, 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) data.Fold.into_K_folds(parent=store, K=K, shuffled_before_folding=False, standard=data.Store.Standard.mean_and_std, replace_empty_test_with_data_=True) model.run.GPs(module=model.run.Module.GPY_, name=name, store=store, M_Used=-1, parameters=parameters, optimize=True, test=True, sobol=True) sobol_options = {'semi_norm': model.base.Sobol.SemiNorm.DEFAULT_META, 'N_exploit': 3, 'N_explore': 4096, 'options': {'gtol': 1.0E-12}} rom_options = {'iterations': 6, 'guess_identity_after_iteration': 2, 'sobol_optimizer_options': sobol_options, 'gp_initializer': model.base.ROM.GP_Initializer.ORIGINAL, 'gp_optimizer_options': model.run.Module.GPY_.value.GP.DEFAULT_OPTIMIZER_OPTIONS} model.run.ROMs(module=model.run.Module.GPY_, name='rom', store=store, source_gp_name=name, Mu=-1, Mx=-1, optimizer_options=rom_options)
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 run_rbfs(test_fuction: str, N: int, noise_std: bool, random: bool, M: int = 5, K: int = 2 ): name = 'rbf' kernel_parameters = model.gpy_.Kernel.ExponentialQuadratic.Parameters(lengthscale=full((1, 1), 2.0**(M/5), dtype=float)) parameters = model.gpy_.GP.DEFAULT_PARAMETERS._replace(kernel=kernel_parameters, e_floor=1E-6, e=0.003) CDF_scale = 2 * pi CDF_loc = pi if test_fuction == 'sin.1': function_with_parameters = function.CallableWithParameters(function.ishigami, parameters={'a': 0.0, 'b': 0.0}) elif test_fuction == 'sin.2': function_with_parameters = function.CallableWithParameters(function.ishigami, parameters={'a': 2.0, 'b': 0.0}) elif test_fuction == 'ishigami': function_with_parameters = function.callable_with_parameters(function.ishigami) else: CDF_scale = 1.0 CDF_loc = 0.0 function_with_parameters = function.callable_with_parameters(function.sobol_g) store_name = test_fuction + '.{0:d}.{1:.3f}.{2:d}'.format(M, noise_std, N) if random: pre_function_with_parameters = function.CallableWithParameters(function=function.linear, parameters={'matrix': ortho_group.rvs(M)}) store_name += '.random' else: pre_function_with_parameters = None store_name += '.rom' store_name = BASE_PATH / store_name store = scalar_function_of_normal(store_name=store_name, 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) data.Fold.into_K_folds(parent=store, K=K, shuffled_before_folding=False, standard=data.Store.Standard.mean_and_std, replace_empty_test_with_data_=True) 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={'optimizer': 'bfgs', 'max_iters': 5000, 'gtol': 1E-16})
def test_random(test_fuction: str, N: int, noise_std: float, M: int = 5, K: int = 2 ): random = True name = '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) CDF_scale = 2 * pi CDF_loc = pi if test_fuction == 'sin.1': function_with_parameters = function.CallableWithParameters(function.ishigami, parameters={'a': 0.0, 'b': 0.0}) elif test_fuction == 'sin.2': function_with_parameters = function.CallableWithParameters(function.ishigami, parameters={'a': 2.0, 'b': 0.0}) elif test_fuction == 'ishigami': function_with_parameters = function.callable_with_parameters(function.ishigami) else: CDF_scale = 1.0 CDF_loc = 0.0 function_with_parameters = function.CallableWithParameters(function.sobol_g, parameters={'m_very_important': 2, 'm_important': 3, 'm_unimportant': 4}) store_name = test_fuction + '.{0:d}.{1:.3f}.{2:d}'.format(M, noise_std, N) if random: lin_trans = ortho_group.rvs(M) pre_function_with_parameters = function.CallableWithParameters(function=function.linear, parameters={'matrix': lin_trans}) store_name += '.random' else: pre_function_with_parameters = None store_name += '.rom' store_name = BASE_PATH / store_name store = scalar_function_of_normal(store_name=store_name, 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) # lin_trans = transpose(lin_trans) savetxt(store.dir / "InverseRotation.csv", lin_trans) data.Fold.into_K_folds(parent=store, K=K, shuffled_before_folding=False, standard=data.Store.Standard.mean_and_std, replace_empty_test_with_data_=True) 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}} for k in range(K): sobol = model.gpy_.Sobol.from_GP(data.Fold(store, k), 'rbf.ard', 'rbf.ard.derotated') for sobol.m in reversed(range(M)): sobol.Theta_old = lin_trans sobol.write_parameters(sobol.Parameters(Mu=sobol.Mu, Theta=sobol.Theta, D=sobol.Tensor3AsMatrix(sobol.D), S1=None, S=sobol.Tensor3AsMatrix(sobol.S)))
def run_roms(test_fuction: str, N: int, noise_std: float, random: bool, M: int = 5, K: int = 2 ): name = '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) CDF_scale = 2 * pi CDF_loc = pi if test_fuction == 'sin.1': function_with_parameters = function.CallableWithParameters(function.ishigami, parameters={'a': 0.0, 'b': 0.0}) elif test_fuction == 'sin.2': function_with_parameters = function.CallableWithParameters(function.ishigami, parameters={'a': 2.0, 'b': 0.0}) elif test_fuction == 'ishigami': function_with_parameters = function.callable_with_parameters(function.ishigami) else: CDF_scale = 1.0 CDF_loc = 0.0 function_with_parameters = function.CallableWithParameters(function.sobol_g, parameters={'m_very_important': 2, 'm_important': 3, 'm_unimportant': 4}) store_name = test_fuction + '.{0:d}.{1:.3f}.{2:d}'.format(M, noise_std, N) if random: lin_trans = ortho_group.rvs(M) pre_function_with_parameters = function.CallableWithParameters(function=function.linear, parameters={'matrix': lin_trans}) store_name += '.random' else: pre_function_with_parameters = None store_name += '.rom' store_name = BASE_PATH / store_name store = scalar_function_of_normal(store_name=store_name, 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) savetxt(store.dir / "InverseRotation.csv", transpose(lin_trans)) data.Fold.into_K_folds(parent=store, K=K, shuffled_before_folding=False, standard=data.Store.Standard.mean_and_std, replace_empty_test_with_data_=True) 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}} rom_options = {'iterations': 1, 'guess_identity_after_iteration': -1, 'sobol_optimizer_options': sobol_options, 'gp_initializer': model.base.ROM.GP_Initializer.RBF, 'gp_optimizer_options': gp_optimizer_options} model.run.ROMs(module=model.run.Module.GPY_, name='rom', store=store, source_gp_name=name, Mu=-1, Mx=-1, optimizer_options=rom_options, rbf_parameters=parameters)
def run_roms(M: int, N: int, K:int, ishigami: bool, random: bool, noisy: bool): name = 'ard' kernel_parameters = model.gpy_.Kernel.ExponentialQuadratic.Parameters(lengthscale=full((1, M), 0.2, dtype=float)) if ishigami: store_name = 'sin.2.' CDF_scale = 2 * pi CDF_loc = pi function_with_parameters = function.CallableWithParameters(function=function.ishigami, parameters={'a': 2.0, 'b': 0}) else: store_name = 'sin.1.' 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) if random: pre_function_with_parameters = function.CallableWithParameters(function=function.linear, parameters={'matrix': ortho_group.rvs(M)}) store_name += '.random' else: pre_function_with_parameters = None store_name += '.rom' if noisy: noise_std = 0.025 parameters = model.gpy_.GP.DEFAULT_PARAMETERS._replace(kernel=kernel_parameters, e_floor=1E-5, e=0.01) else: noise_std = 0 parameters = model.gpy_.GP.DEFAULT_PARAMETERS._replace(kernel=kernel_parameters, e_floor=1E-5, e=1E-10) store = scalar_function_of_normal(store_name=store_name, 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) data.Fold.into_K_folds(parent=store, K=K, shuffled_before_folding=False, standard=data.Store.Standard.mean_and_std, replace_empty_test_with_data_=True) model.run.GPs(module=model.run.Module.GPY_, name=name, store=store, M_Used=-1, parameters=parameters, optimize=True, test=True, sobol=True) sobol_options = {'semi_norm': model.base.Sobol.SemiNorm.DEFAULT_META, 'N_exploit': 3, 'N_explore': 4096, 'options': {'gtol': 1.0E-16}} rom_options = {'iterations': 4, 'guess_identity_after_iteration': 2, 'sobol_optimizer_options': sobol_options, 'gp_initializer': model.base.ROM.GP_Initializer.CURRENT_WITH_GUESSED_LENGTHSCALE, 'gp_optimizer_options': model.run.Module.GPY_.value.GP.DEFAULT_OPTIMIZER_OPTIONS} model.run.ROMs(module=model.run.Module.GPY_, name='rom', store=store, source_gp_name='ard', Mu=-1, Mx=-1, optimizer_options=rom_options)