def prior_string_AC(self, prior_type, mean, variance, corr):
mean_string = value_to_string(mean)
variance_string = value_to_string(variance)
corr_string = value_to_string(corr)
return '%s_%s_%s_%s' % (prior_type, mean_string, variance_string,
corr_string)
def prior_string_blp(self, prior_type, mean, gamma, delta):
mean_string = value_to_string(mean)
gamma_string = value_to_string(gamma)
delta_string = value_to_string(delta)
return '%s_%s_%s_%s' % (prior_type, mean_string, gamma_string,
delta_string)
def prior_string(self, prior_type, mean):
mean_string = value_to_string(mean)
return '%s_%s' % (prior_type, mean_string)
def __init__(self, options):
################
# Case Name #
################
#=== Key Strings ===#
project_name = 'poisson_linear_2d_'
data_options = 'n%d' %(options.parameter_dimensions)
directory_dataset =\
'../../../../../datasets/fenics/poisson_linear_2d/' +\
'n%d/'%(options.parameter_dimensions)
#=== Data Type ===#
if options.obs_type == 'full':
obs_string = 'full'
if options.obs_type == 'obs':
obs_string = 'obs_o%d'%(options.num_obs_points)
if options.add_noise == 1:
noise_level_string = value_to_string(options.noise_level)
noise_string = 'ns%s_%d'%(noise_level_string,options.num_obs_points)
else:
noise_string = 'ns0'
data_string = data_options + '_' + obs_string + '_' + noise_string + '_'
#=== Prior Properties ===#
if options.prior_type_blp_reg == True:
prior_string_reg = self.prior_string_blp('blp',
options.prior_mean_blp_reg,
options.prior_gamma_blp_reg,
options.prior_delta_blp_reg)
if options.prior_type_blp_train == True:
prior_string_train = self.prior_string_blp('blp',
options.prior_mean_blp_train,
options.prior_gamma_blp_train,
options.prior_delta_blp_train)
if options.prior_type_blp_test == True:
prior_string_test = self.prior_string_blp('blp',
options.prior_mean_blp_test,
options.prior_gamma_blp_test,
options.prior_delta_blp_test)
if options.prior_type_AC_reg == True:
prior_string_reg = self.prior_string_AC('AC',
options.prior_mean_AC_reg,
options.prior_variance_AC_reg,
options.prior_corr_AC_reg)
if options.prior_type_AC_train == True:
prior_string_train = self.prior_string_AC('AC',
options.prior_mean_AC_train,
options.prior_variance_AC_train,
options.prior_corr_AC_train)
if options.prior_type_AC_test == True:
prior_string_test = self.prior_string_AC('AC',
options.prior_mean_AC_test,
options.prior_variance_AC_test,
options.prior_corr_AC_test)
#=== Case String ===#
self.case_name = project_name + data_string + prior_string_train
################
# Datasets #
################
#=== Parameters ===#
self.obs_indices = directory_dataset +\
project_name + 'obs_indices_' +\
'o%d_'%(options.num_obs_points) + data_options
self.poi_train = directory_dataset +\
project_name +\
'parameter_train_' +\
'd%d_'%(options.num_data_train_load) + data_options + '_' + prior_string_train
self.poi_test = directory_dataset +\
project_name +\
'parameter_test_' +\
'd%d_'%(options.num_data_test_load) + data_options + '_' + prior_string_test
self.poi_specific = directory_dataset +\
project_name + 'parameter_specific_' + data_options
if options.obs_type == 'full':
self.qoi_train = directory_dataset +\
project_name +\
'state_' + options.obs_type + '_train_' +\
'd%d_'%(options.num_data_train_load) + data_options + '_' + prior_string_train
self.qoi_test = directory_dataset +\
project_name +\
'state_' + options.obs_type + '_test_' +\
'd%d_'%(options.num_data_test_load) + data_options + '_' + prior_string_test
self.qoi_specific = directory_dataset +\
project_name + 'state_' + options.obs_type + '_specific_' +\
data_options
if options.obs_type == 'obs':
self.qoi_train = directory_dataset +\
project_name +\
'state_' + options.obs_type + '_train_' +\
'o%d_d%d_' %(options.num_obs_points, options.num_data_train_load) +\
data_options + '_' + prior_string_train
self.qoi_test = directory_dataset +\
project_name +\
'state_' + options.obs_type + '_test_' +\
'o%d_d%d_' %(options.num_obs_points, options.num_data_test_load) +\
data_options + '_' + prior_string_test
self.qoi_specific = directory_dataset +\
project_name + 'state_' + options.obs_type + '_specific_' +\
'o%d_'%(options.num_obs_points) +\
data_options
#############
# Prior #
#############
#=== Prior ===#
self.prior_string_reg = prior_string_reg
self.prior_mean = directory_dataset +\
'prior_mean_' + data_options + '_' + prior_string_reg
self.prior_covariance = directory_dataset +\
'prior_covariance_' + data_options + '_' + prior_string_reg
self.prior_covariance_inverse = directory_dataset +\
'prior_covariance_inverse_' + data_options + '_' + prior_string_reg
self.prior_covariance_cholesky = directory_dataset +\
'prior_covariance_cholesky_' + data_options + '_' + prior_string_reg
self.prior_covariance_cholesky_inverse = directory_dataset +\
'prior_covariance_cholesky_inverse_' + data_options + '_' + prior_string_reg
###################
# FEM Objects #
###################
#=== Pre-Matrices ===#
self.forward_matrix = directory_dataset +\
'forward_matrix_' + data_options
self.mass_matrix = directory_dataset +\
'mass_matrix_' + data_options
self.load_vector = directory_dataset +\
'load_vector_' + data_options
#=== Mesh ===# For plotting FEM function
mesh_name = 'mesh_square_2D_n%d' %(options.parameter_dimensions)
mesh_directory = '../../../../../Datasets/Mesh/' + mesh_name + '/'
self.mesh_nodes = mesh_directory + mesh_name + '_nodes.csv'
self.mesh_elements = mesh_directory + mesh_name + '_elements.csv'
def prior_string_matern(self, prior_type, kern_type, cov_length):
cov_length_string = value_to_string(cov_length)
return '%s_%s_%s'%(prior_type, kern_type, cov_length)
def __init__(self, hyperp, options, project_paths):
####################
# From Project #
####################
#=== Project ===#
self.project = project_paths
#=== Datasets ===#
self.poi_train = project_paths.poi_train
self.poi_test = project_paths.poi_test
self.poi_specific = project_paths.poi_specific
self.qoi_train = project_paths.qoi_train
self.qoi_test = project_paths.qoi_test
self.qoi_specific = project_paths.qoi_specific
#=== Prior ===#
self.prior_string_reg = project_paths.prior_string_reg
self.prior_mean = project_paths.prior_mean
self.prior_covariance = project_paths.prior_covariance
self.prior_covariance_inverse = project_paths.prior_covariance_inverse
self.prior_covariance_cholesky = project_paths.prior_covariance_cholesky
self.prior_covariance_cholesky_inverse = project_paths.prior_covariance_cholesky_inverse
#=== Case Name ===#
self.case_name = project_paths.case_name
#=== Neural Network Architecture and Regularization ===#
autoencoder_type = 'VAE'
if options.resnet == True:
resnet = 'res_'
else:
resnet = 'nores_'
if options.model_aware == True:
forward_model_type = 'maware_'
if options.model_augmented == True:
forward_model_type = 'maug_'
#=== File Name ===#
penalty_js_string = value_to_string(hyperp.penalty_js)
if "posterior_diagonal_covariance" in options:
self.nn_name = autoencoder_type + '_' + forward_model_type + resnet +\
self.prior_string_reg +\
'_hle%d_hld%d_hne%d_hnd%d_%s_pjs%s_d%d_b%d_e%d' %(
hyperp.num_hidden_layers_encoder, hyperp.num_hidden_layers_decoder,
hyperp.num_hidden_nodes_encoder, hyperp.num_hidden_nodes_decoder,
hyperp.activation,
penalty_js_string,
hyperp.num_data_train, hyperp.batch_size, hyperp.num_epochs)
if "posterior_full_covariance" in options:
self.nn_name = autoencoder_type + '_full_' + forward_model_type + resnet +\
self.prior_string_reg +\
'_hle%d_hld%d_hne%d_hnd%d_%s_pjs%s_d%d_b%d_e%d' %(
hyperp.num_hidden_layers_encoder, hyperp.num_hidden_layers_decoder,
hyperp.num_hidden_nodes_encoder, hyperp.num_hidden_nodes_decoder,
hyperp.activation,
penalty_js_string,
hyperp.num_data_train, hyperp.batch_size, hyperp.num_epochs)
if "posterior_iaf" in options:
if options.iaf_lstm_update == True:
iaf_type_string = 'IAFLSTM_'
else:
iaf_type_string = 'IAF_'
self.nn_name = autoencoder_type + iaf_type_string + forward_model_type + resnet +\
self.prior_string_reg +\
'_hle%d_hld%d_hne%d_hnd%d_%s_hli%d_hni%d_%s_pjs%s_d%d_b%d_e%d'\
%(hyperp.num_hidden_layers_encoder, hyperp.num_hidden_layers_decoder,
hyperp.num_hidden_nodes_encoder, hyperp.num_hidden_nodes_decoder,
hyperp.activation,
hyperp.num_iaf_transforms, hyperp.num_hidden_nodes_iaf,
hyperp.activation_iaf,
penalty_js_string,
hyperp.num_data_train, hyperp.batch_size, hyperp.num_epochs)
#=== Filename ===#
self.case_and_nn_name = self.case_name + '/' + self.nn_name
def __init__(self, options):
################
# Case Name #
################
#=== Key Strings ===#
project_name = 'advection_diffusion_2d_'
if options.flow_navier_stokes == True:
flow_string = 'navier_stokes'
if options.flow_darcy == True:
flow_string = 'darcy'
if options.time_stepping_erk4 == True:
time_stepping_string = 'erk4'
if options.time_stepping_lserk4 == True:
time_stepping_string = 'lserk4'
if options.time_stepping_implicit == True:
time_stepping_string = 'imp'
num_nodes_string = 'n%d' % (options.parameter_dimensions)
data_options = num_nodes_string + '_' +\
flow_string + '_' +\
time_stepping_string
directory_dataset = '../../../../../datasets/fenics/advection_diffusion_2d/' +\
num_nodes_string + '/' + flow_string + '_' + time_stepping_string + '/'
#=== Data Type ===#
if options.obs_type == 'full':
obs_string = 'full'
if options.obs_type == 'obs':
obs_string = 'obs_o%d' % (options.num_obs_points)
if options.add_noise == 1:
noise_level_string = value_to_string(options.noise_level)
noise_string = 'ns%s_%d' % (noise_level_string,
options.num_obs_points)
else:
noise_string = 'ns0'
data_string = data_options + '_' + obs_string + '_' + noise_string + '_'
#=== Prior Properties ===#
if options.prior_type_blp_reg == True:
prior_string_reg = self.prior_string_blp(
'blp', options.prior_mean_blp_reg, options.prior_gamma_blp_reg,
options.prior_delta_blp_reg)
if options.prior_type_blp_train == True:
prior_string_train = self.prior_string_blp(
'blp', options.prior_mean_blp_train,
options.prior_gamma_blp_train, options.prior_delta_blp_train)
if options.prior_type_blp_test == True:
prior_string_test = self.prior_string_blp(
'blp', options.prior_mean_blp_test,
options.prior_gamma_blp_test, options.prior_delta_blp_test)
if options.prior_type_ac_reg == True:
prior_string_reg = self.prior_string_ac(
'ac', options.prior_mean_ac_reg, options.prior_variance_ac_reg,
options.prior_corr_ac_reg)
if options.prior_type_ac_train == True:
prior_string_train = self.prior_string_ac(
'ac', options.prior_mean_ac_train,
options.prior_variance_ac_train, options.prior_corr_ac_train)
if options.prior_type_ac_test == True:
prior_string_test = self.prior_string_ac(
'ac', options.prior_mean_ac_test,
options.prior_variance_ac_test, options.prior_corr_ac_test)
#=== Case String ===#
self.case_name = project_name + data_string + prior_string_train
################
# Datasets #
################
#=== Parameters ===#
self.obs_indices = directory_dataset +\
project_name + 'obs_indices_' +\
'o%d_'%(options.num_obs_points) + num_nodes_string
self.poi_train = directory_dataset +\
project_name +\
'parameter_train_' +\
'd%d_'%(options.num_data_train_load) + num_nodes_string + '_' + prior_string_train
self.poi_test = directory_dataset +\
project_name +\
'parameter_test_' +\
'd%d_'%(options.num_data_test_load) + num_nodes_string + '_' + prior_string_test
self.poi_specific = directory_dataset +\
project_name + 'parameter_blob_' + num_nodes_string
if options.obs_type == 'full':
self.qoi_train = directory_dataset +\
project_name +\
'state_' + options.obs_type + '_train_' +\
'd%d_'%(options.num_data_train_load) + data_options + '_' + prior_string_train
self.qoi_test = directory_dataset +\
project_name +\
'state_' + options.obs_type + '_test_' +\
'd%d_'%(options.num_data_test_load) + data_options + '_' + prior_string_test
self.qoi_specific = directory_dataset +\
project_name + 'state_' + options.obs_type + '_blob_' +\
'o%d_'%(options.num_obs_points) +\
data_options
if options.obs_type == 'obs':
self.qoi_train = directory_dataset +\
project_name +\
'state_' + options.obs_type + '_train_' +\
'o%d_d%d_' %(options.num_obs_points, options.num_data_train_load) +\
data_options + '_' + prior_string_train
self.qoi_test = directory_dataset +\
project_name +\
'state_' + options.obs_type + '_test_' +\
'o%d_d%d_' %(options.num_obs_points, options.num_data_test_load) +\
data_options + '_' + prior_string_test
self.qoi_specific = directory_dataset +\
project_name + 'state_' + options.obs_type + '_blob_' +\
'o%d_'%(options.num_obs_points) +\
data_options
#############
# Prior #
#############
#=== Prior ===#
self.prior_string_reg = prior_string_reg
self.prior_mean = directory_dataset +\
'prior_mean_' + num_nodes_string + '_' + prior_string_reg
self.prior_covariance = directory_dataset +\
'prior_covariance_' + num_nodes_string + '_' + prior_string_reg
self.prior_covariance_cholesky = directory_dataset +\
'prior_covariance_cholesky_' + num_nodes_string + '_' + prior_string_reg
self.prior_covariance_cholesky_inverse = directory_dataset +\
'prior_covariance_cholesky_inverse_' + num_nodes_string + '_' + prior_string_reg
###################
# FEM Objects #
###################
#=== FEM Operators ===#
self.fem_operator_spatial = directory_dataset +\
'fem_operator_spatial_' + num_nodes_string
self.fem_operator_implicit_ts = directory_dataset +\
'fem_operator_implicit_ts_' + num_nodes_string
self.fem_operator_implicit_ts_rhs = directory_dataset +\
'fem_operator_implicit_ts_rhs_' + num_nodes_string
def __init__(self, options):
################
# Case Name #
################
#=== Defining Filenames ===#
data_options = 'm%d_n%d' % (options.mesh_dimensions,
options.parameter_dimensions)
directory_dataset = '../../../../../datasets/simple_1d/'
if hasattr(options, 'continuous_linear_sin'
) and options.continuous_linear_sin == 1:
project_name = 'continuous_linear_sin_1d'
if hasattr(options, 'continuous_linear_uniform'
) and options.continuous_linear_uniform == 1:
project_name = 'continuous_linear_uniform_1d'
directory_dataset += project_name + '/' + data_options + '/'
#=== Data Properties ===#
if options.obs_type == 'full':
obs_string = 'full'
if options.obs_type == 'obs':
obs_string = 'obs_o%d' % (options.num_obs_points)
if options.add_noise == 1:
noise_level_string = value_to_string(options.noise_level)
noise_string = 'ns%s_%d' % (noise_level_string,
options.num_obs_points)
else:
noise_string = 'ns0'
data_string = data_options + '_' + obs_string + '_' + noise_string + '_'
#=== Prior Properties ===#
if options.prior_type_identity_reg == True:
prior_string_reg = self.prior_string(
'identity', options.prior_mean_identity_reg)
if options.prior_type_identity_train == True:
prior_string_train = self.prior_string(
'identity', options.prior_mean_identity_train)
if options.prior_type_identity_test == True:
prior_string_test = self.prior_string(
'identity', options.prior_mean_identity_test)
if options.prior_type_laplacian_reg == True:
prior_string_reg = self.prior_string_laplacian(
'laplacian', options.prior_mean_laplacian_reg)
if options.prior_type_laplacian_train == True:
prior_string_train = self.prior_string_laplacian(
'laplacian', options.prior_mean_laplacian_train)
if options.prior_type_laplacian_test == True:
prior_string_test = self.prior_string_laplacian(
'laplacian', options.prior_mean_laplacian_test)
#=== Case String ===#
self.case_name = project_name + '_' + data_string + prior_string_train
################
# Datasets #
################
#=== Forward Operator ===#
if hasattr(options,
'discrete_polynomial') and options.discrete_polynomial == 1:
self.forward_operator = directory_dataset +\
project_name + '_forward_matrix_' +\
data_options
if hasattr(options, 'continuous_linear_sin'
) and options.continuous_linear_sin == 1:
self.forward_operator = directory_dataset +\
project_name + '_forward_vector_' +\
data_options
if hasattr(options, 'continuous_linear_uniform'
) and options.continuous_linear_uniform == 1:
self.forward_operator = directory_dataset +\
project_name + '_forward_vector_' +\
data_options
#=== Parameters ===#
self.obs_indices = directory_dataset +\
project_name + '_' + 'obs_indices_' +\
'o%d_'%(options.num_obs_points) + data_options
self.poi_train = directory_dataset +\
project_name + '_' + 'parameter_train_' +\
'd%d_'%(options.num_data_train_load) + data_options + '_' + prior_string_train
self.poi_test = directory_dataset +\
project_name + '_' + 'parameter_test_' +\
'd%d_'%(options.num_data_test_load) + data_options + '_' + prior_string_test
self.poi_specific = ''
#=== State ===#
if options.obs_type == 'full':
self.qoi_train = directory_dataset +\
project_name + '_' + 'state_' + options.obs_type + '_train_' +\
'd%d_'%(options.num_data_train_load) + data_options + '_' + prior_string_train
self.qoi_test = directory_dataset +\
project_name + '_' + 'state_' + options.obs_type + '_test_' +\
'd%d_'%(options.num_data_test_load) + data_options + '_' + prior_string_test
self.qoi_specific = ''
if options.obs_type == 'obs':
self.qoi_train = directory_dataset +\
project_name + '_' + 'state_' + options.obs_type + '_train_' +\
'o%d_d%d_' %(options.num_obs_points, options.num_data_train_load) +\
data_options + '_' + prior_string_train
self.qoi_test = directory_dataset +\
project_name + '_' + 'state_' + options.obs_type + '_test_' +\
'o%d_d%d_' %(options.num_obs_points, options.num_data_test_load) +\
data_options + '_' + prior_string_test
self.qoi_specific = ''
#############
# Prior #
#############
#=== Prior ===#
self.prior_string_reg = prior_string_reg
self.prior_mean = directory_dataset +\
'prior_mean_' + data_options + '_' + prior_string_train
self.prior_covariance = directory_dataset +\
'prior_covariance_' + data_options + '_' + prior_string_train
self.prior_covariance_inverse = directory_dataset +\
'prior_covariance_inverse_' + data_options + '_' + prior_string_train
self.prior_covariance_cholesky = directory_dataset +\
'prior_covariance_cholesky_' + data_options + '_' + prior_string_train
self.prior_covariance_cholesky_inverse = directory_dataset +\
'prior_covariance_cholesky_inverse_' + data_options + '_' + prior_string_train
def __init__(self, options):
################
# Case Name #
################
#=== Key Strings ===#
data_options = 'n%d' % (options.parameter_dimensions)
if options.boundary_conditions_dirichlet == True:
project_name = 'screened_poisson_linear_dirichlet_1d_'
directory_dataset =\
'../../../../../datasets/fenics/screened_poisson_linear_dirichlet_1d/' +\
data_options + '/'
if options.boundary_conditions_neumann == True:
project_name = 'screened_poisson_linear_neumann_1d_'
directory_dataset =\
'../../../../../datasets/fenics/screened_poisson_linear_neumann_1d/' +\
data_options + '/'
#=== Data Type ===#
if options.obs_type == 'full':
obs_string = 'full'
if options.obs_type == 'obs':
obs_string = 'obs_o%d' % (options.num_obs_points)
if options.add_noise == 1:
noise_level_string = value_to_string(options.noise_level)
noise_string = 'ns%s_%d' % (noise_level_string,
options.num_obs_points)
else:
noise_string = 'ns0'
data_string = data_options + '_' + obs_string + '_' + noise_string + '_'
#=== Prior Properties ===#
if options.prior_type_lp_reg == True:
prior_string_reg = self.prior_string_lp('lp',
options.prior_mean_lp_reg,
options.prior_gamma_lp_reg,
options.prior_delta_lp_reg)
if options.prior_type_lp_train == True:
prior_string_train = self.prior_string_lp(
'lp', options.prior_mean_lp_train,
options.prior_gamma_lp_train, options.prior_delta_lp_train)
if options.prior_type_lp_test == True:
prior_string_test = self.prior_string_lp(
'lp', options.prior_mean_lp_test, options.prior_gamma_lp_test,
options.prior_delta_lp_test)
#=== Case String ===#
self.case_name = project_name + data_string + prior_string_train
################
# Datasets #
################
#=== Parameters ===#
self.obs_indices = directory_dataset +\
project_name + 'obs_indices_' +\
'o%d_'%(options.num_obs_points) + data_options
self.poi_train = directory_dataset +\
project_name +\
'parameter_train_' +\
'd%d_'%(options.num_data_train_load) + data_options + '_' + prior_string_train
self.poi_test = directory_dataset +\
project_name +\
'parameter_test_' +\
'd%d_'%(options.num_data_test_load) + data_options + '_' + prior_string_test
self.poi_specific = directory_dataset +\
project_name + 'parameter_specific_' + data_options
if options.obs_type == 'full':
self.qoi_train = directory_dataset +\
project_name +\
'state_' + options.obs_type + '_train_' +\
'd%d_'%(options.num_data_train_load) + data_options + '_' + prior_string_train
self.qoi_test = directory_dataset +\
project_name +\
'state_' + options.obs_type + '_test_' +\
'd%d_'%(options.num_data_test_load) + data_options + '_' + prior_string_test
self.qoi_specific = directory_dataset +\
project_name + 'state_' + options.obs_type + '_specific_' +\
data_options
if options.obs_type == 'obs':
self.qoi_train = directory_dataset +\
project_name +\
'state_' + options.obs_type + '_train_' +\
'o%d_d%d_' %(options.num_obs_points, options.num_data_train_load) +\
data_options + '_' + prior_string_train
self.qoi_test = directory_dataset +\
project_name +\
'state_' + options.obs_type + '_test_' +\
'o%d_d%d_' %(options.num_obs_points, options.num_data_test_load) +\
data_options + '_' + prior_string_test
self.qoi_specific = directory_dataset +\
project_name + 'state_' + options.obs_type + '_specific_' +\
'o%d_'%(options.num_obs_points) +\
data_options
#############
# Prior #
#############
#=== Prior ===#
if options.prior_type_identity_reg == True:
self.prior_string_reg = 'identity'
else:
self.prior_string_reg = prior_string_reg
self.prior_mean = directory_dataset +\
'prior_mean_' + data_options + '_' + prior_string_reg
self.prior_covariance = directory_dataset +\
'prior_covariance_' + data_options + '_' + prior_string_reg
self.prior_covariance_inverse = directory_dataset +\
'prior_covariance_inverse_' + data_options + '_' + prior_string_reg
self.prior_covariance_cholesky = directory_dataset +\
'prior_covariance_cholesky_' + data_options + '_' + prior_string_reg
self.prior_covariance_cholesky_inverse = directory_dataset +\
'prior_covariance_cholesky_inverse_' + data_options + '_' + prior_string_reg
###################
# FEM Objects #
###################
#=== Forward Operator ===#
self.forward_matrix = directory_dataset +\
'forward_matrix_' + data_options
self.mass_matrix = directory_dataset +\
'mass_matrix_' + data_options
