def predict_and_plot(hyperp, options, filepaths):
#=== Mesh Properties ===#
options.mesh_point_1 = [-1, -1]
options.mesh_point_2 = [1, 1]
# options.nx = 15
# options.ny = 15
options.nx = 50
options.ny = 50
options.num_obs_points = 10
options.order_fe_space = 1
options.order_meta_space = 1
options.num_nodes = (options.nx + 1) * (options.ny + 1)
#=== Construct Mesh ===#
fe_space, meta_space,\
nodes, dof_fe, dof_meta = construct_mesh(options)
#=== Load Observation Indices ===#
if options.obs_type == 'full':
obs_dimensions = options.parameter_dimensions
if options.obs_type == 'obs':
obs_dimensions = options.num_obs_points
print('Loading Boundary Indices')
df_obs_indices = pd.read_csv(filepaths.project.obs_indices + '.csv')
obs_indices = df_obs_indices.to_numpy()
#=== Data and Latent Dimensions of Autoencoder ===#
input_dimensions = obs_dimensions
latent_dimensions = options.parameter_dimensions
#=== Prepare Data ===#
data = DataHandler(hyperp, options, filepaths, obs_indices,
options.parameter_dimensions, obs_dimensions,
options.parameter_dimensions)
data.load_data_test()
if options.add_noise == True:
data.add_noise_qoi_test()
parameter_test = data.poi_test
state_obs_test = data.qoi_test
#=== Load Trained Neural Network ===#
nn = VAE(hyperp, options, input_dimensions, latent_dimensions, None, None,
positivity_constraint_log_exp)
nn.load_weights(filepaths.trained_nn)
#=== Selecting Samples ===#
# sample_number = 1
sample_number = 128
parameter_test_sample = np.expand_dims(parameter_test[sample_number, :], 0)
state_obs_test_sample = np.expand_dims(state_obs_test[sample_number, :], 0)
#=== Saving Specific Sample ===#
df_poi_specific = pd.DataFrame(
{'poi_specific': parameter_test_sample.flatten()})
df_poi_specific.to_csv(filepaths.poi_specific + '.csv', index=False)
df_qoi_specific = pd.DataFrame(
{'qoi_specific': state_obs_test_sample.flatten()})
df_qoi_specific.to_csv(filepaths.qoi_specific + '.csv', index=False)
#=== Predictions ===#
start_time_nn = time.time()
posterior_mean_pred, posterior_cov_pred = nn.encoder(state_obs_test_sample)
elapsed_time_nn = time.time() - start_time_nn
print('Time taken for neural network inference: %.4f' % (elapsed_time_nn))
posterior_pred_draw = nn.reparameterize(posterior_mean_pred,
posterior_cov_pred)
posterior_mean_pred = posterior_mean_pred.numpy().flatten()
posterior_cov_pred = posterior_cov_pred.numpy().flatten()
posterior_pred_draw = posterior_pred_draw.numpy().flatten()
if options.model_aware == 1:
state_obs_pred_draw = nn.decoder(np.expand_dims(
posterior_pred_draw, 0))
state_obs_pred_draw = state_obs_pred_draw.numpy().flatten()
#=== Plotting Prediction ===#
print('================================')
print(' Plotting Predictions ')
print('================================')
#=== Plot FEM Functions ===#
# cross_section_y = 0.5
cross_section_y = 0.0
plot_parameter_min = 0
plot_parameter_max = 6.0
plot_variance_min = 0
plot_variance_max = 1.3
filename_extension = '_%d.png' % (sample_number)
plot_fem_function_fenics_2d(
meta_space, parameter_test_sample, cross_section_y, '',
filepaths.figure_parameter_test + filename_extension, (5, 5),
(plot_parameter_min, plot_parameter_max), True, 'r')
plot_fem_function_fenics_2d(
meta_space, posterior_mean_pred, cross_section_y, '',
filepaths.figure_posterior_mean + filename_extension, (5, 5),
(plot_parameter_min, plot_parameter_max), True, 'k')
plot_fem_function_fenics_2d(
meta_space, posterior_pred_draw, cross_section_y, '',
filepaths.figure_parameter_pred + filename_extension, (5, 5),
(plot_parameter_min, plot_parameter_max), False, '')
if options.obs_type == 'full':
plot_fem_function_fenics_2d(
meta_space, state_obs_test_sample, cross_section_y, 'True State',
filepaths.figure_state_test + filename_extension, (5, 5))
plot_fem_function_fenics_2d(
meta_space, state_obs_pred_draw, cross_section_y,
'State Prediction',
filepaths.figure_state_pred + filename_extension, (5, 5))
#=== Plot Cross-Section with Error Bounds ===#
plot_cross_section(
meta_space, parameter_test_sample, posterior_mean_pred,
posterior_cov_pred, (-1, 1), cross_section_y, '',
filepaths.figure_parameter_cross_section + filename_extension,
(plot_parameter_min, plot_parameter_max))
#=== Plot Variation ===#
plot_fem_function_fenics_2d(
meta_space, np.exp(posterior_cov_pred), cross_section_y, '',
filepaths.figure_posterior_covariance + filename_extension, (5, 5),
(plot_variance_min, plot_variance_max), False, '')
print('Predictions plotted')
def predict_and_plot(hyperp, options, filepaths):
#=== Load Observation Indices ===#
if options.obs_type == 'full':
obs_dimensions = options.parameter_dimensions
if options.obs_type == 'obs':
obs_dimensions = options.num_obs_points
print('Loading Boundary Indices')
df_obs_indices = pd.read_csv(filepaths.project.obs_indices + '.csv')
obs_indices = df_obs_indices.to_numpy()
#=== Data and Latent Dimensions of Autoencoder ===#
input_dimensions = obs_dimensions
latent_dimensions = options.parameter_dimensions
#=== Prepare Data ===#
data = DataHandler(hyperp, options, filepaths,
options.parameter_dimensions, obs_dimensions)
data.load_data_test()
if options.add_noise == 1:
data.add_noise_qoi_test()
parameter_test = data.poi_test
state_obs_test = data.qoi_test
#=== Load Trained Neural Network ===#
nn = VAE(hyperp, options,
input_dimensions, latent_dimensions,
None, None,
positivity_constraint_log_exp)
nn.load_weights(filepaths.trained_nn)
#=== Selecting Samples ===#
sample_number = 105
parameter_test_sample = np.expand_dims(parameter_test[sample_number,:], 0)
state_obs_test_sample = np.expand_dims(state_obs_test[sample_number,:], 0)
#=== Predictions ===#
parameter_pred_sample, _ = nn.encoder(state_obs_test_sample)
state_obs_pred_sample = nn.decoder(parameter_test_sample)
parameter_pred_sample = parameter_pred_sample.numpy().flatten()
state_obs_pred_sample = state_obs_pred_sample.numpy().flatten()
#=== Plotting Prediction ===#
print('================================')
print(' Plotting Predictions ')
print('================================')
#=== Load Mesh ===#
nodes, elements, _, _, _, _, _, _ = load_mesh(filepaths.project)
#=== Plot FEM Functions ===#
plot_fem_function(filepaths.figure_parameter_test,
'True Parameter', 7.0,
nodes, elements,
parameter_test_sample)
plot_fem_function(filepaths.figure_parameter_pred,
'Parameter Prediction', 7.0,
nodes, elements,
parameter_pred_sample)
if options.obs_type == 'full':
plot_fem_function(filepaths.figure_state_test,
'True State', 2.6,
nodes, elements,
state_obs_test_sample)
plot_fem_function(filepaths.figure_state_pred,
'State Prediction', 2.6,
nodes, elements,
state_obs_pred_sample)
print('Predictions plotted')
def predict_and_plot(hyperp, options, filepaths):
#=== Mesh Properties ===#
options.hole_single_circle = False
options.hole_two_rectangles = True
options.discretization_domain = 17
options.domain_length = 1
options.domain_width = 1
options.rect_1_point_1 = [0.25, 0.15]
options.rect_1_point_2 = [0.5, 0.4]
options.rect_2_point_1 = [0.6, 0.6]
options.rect_2_point_2 = [0.75, 0.85]
#=== Construct Mesh ===#
Vh, nodes, dof = construct_mesh(options)
#=== Load Observation Indices ===#
obs_dimensions = options.num_obs_points * options.num_time_steps
print('Loading Boundary Indices')
df_obs_indices = pd.read_csv(filepaths.project.obs_indices + '.csv')
obs_indices = df_obs_indices.to_numpy()
#=== Data and Latent Dimensions of Autoencoder ===#
input_dimensions = obs_dimensions
latent_dimensions = options.parameter_dimensions
#=== Prepare Data ===#
data = DataHandler(hyperp, options, filepaths,
options.parameter_dimensions, obs_dimensions)
# data.load_data_specific()
# if options.add_noise == 1:
# data.add_noise_qoi_specific()
# parameter_test = data.poi_specific
# state_obs_test = data.qoi_specific
data.load_data_test()
if options.add_noise == True:
data.add_noise_qoi_test()
parameter_test = data.poi_test
state_obs_test = data.qoi_test
#=== Load Trained Neural Network ===#
nn = VAE(hyperp, options, input_dimensions, latent_dimensions, None, None,
positivity_constraint_log_exp)
nn.load_weights(filepaths.trained_nn)
#=== Selecting Samples ===#
sample_number = 15
parameter_test_sample = np.expand_dims(parameter_test[sample_number, :], 0)
state_obs_test_sample = np.expand_dims(state_obs_test[sample_number, :], 0)
#=== Predictions ===#
posterior_mean_pred, posterior_cov_pred = nn.encoder(state_obs_test_sample)
posterior_pred_draw = nn.reparameterize(posterior_mean_pred,
posterior_cov_pred)
posterior_mean_pred = posterior_mean_pred.numpy().flatten()
posterior_cov_pred = posterior_cov_pred.numpy().flatten()
posterior_pred_draw = posterior_pred_draw.numpy().flatten()
if options.model_aware == 1:
state_obs_pred_draw = nn.decoder(np.expand_dims(
posterior_pred_draw, 0))
state_obs_pred_draw = state_obs_pred_draw.numpy().flatten()
#=== Plotting Prediction ===#
print('================================')
print(' Plotting Predictions ')
print('================================')
#=== Plot FEM Functions ===#
cross_section_y = 0.8
filename_extension = '_%d.png' % (sample_number)
plot_fem_function_fenics_2d(
Vh, parameter_test_sample, cross_section_y, '',
filepaths.figure_parameter_test + filename_extension, (5, 5), (0, 5),
False)
plot_fem_function_fenics_2d(
Vh, posterior_mean_pred, cross_section_y, '',
filepaths.figure_posterior_mean + filename_extension, (5, 5), (0, 5),
True)
plot_fem_function_fenics_2d(
Vh, posterior_pred_draw, cross_section_y, '',
filepaths.figure_parameter_pred + filename_extension, (5, 5), (0, 5),
True)
if options.obs_type == 'full':
plot_fem_function_fenics_2d(
Vh, state_obs_test_sample, cross_section_y, 'True State',
filepaths.figure_state_test + filename_extension, (5, 5))
plot_fem_function_fenics_2d(
Vh, state_obs_pred_draw, cross_section_y, 'State Prediction',
filepaths.figure_state_pred + filename_extension, (5, 5))
#=== Plot Cross-Section with Error Bounds ===#
plot_cross_section(
Vh, parameter_test_sample, posterior_mean_pred, posterior_cov_pred,
(0, 1), cross_section_y, '',
filepaths.figure_parameter_cross_section + filename_extension, (0, 5))
print('Predictions plotted')