def simulator(theta):
N_samples = theta.shape[0]
x = torch.zeros(N_samples, conj_model.N, dim)
for i in range(N_samples):
model_tmp = MultivariateNormal(theta[i],
conj_model.model.covariance_matrix)
x[i, :, :] = model_tmp.rsample(sample_shape=(conj_model.N, ))
# return calc_summary_stats(x), theta #/math.sqrt(5) # div with std of prior to nomarlize data
return func.calc_summary_stats(x)
x_test, theta_test = func.run_model_sim(N_prior_pred_test, seed + 2,
conj_model, analytical_posterior,
conj_model.model.covariance_matrix,
dim, True)
# Generate test data for obs data set
print(conj_model.model_sim(theta_test).shape)
N_test_obs_data = 1000
x_test_obs_data = torch.zeros(N_test_obs_data, 5)
theta_test_obs_data = torch.zeros(N_test_obs_data, dim)
for i in range(N_test_obs_data):
x_test_obs_data[i, :] = func.calc_summary_stats(x_o)
theta_test_obs_data[i, :] = conj_model.model.loc
# Set up networks for the likelihood model
# Base dist for posterior model
flow_lik, flow_post = func.set_up_networks()
hyper_params = [0.001, 0.002, 0.95, 0.7] # lr_like, lr_post, gamma_post, gamma
if lambda_val > 0:
hyper_params[-1] = lambda_val
if hp_tuning >= 2:
hyper_params = func.sample_hp("snpla", hp_tuning)
# Load all utility functions for all methods
import mv_gaussian.low_dim_w_summary_stats.functions as func
# Set model and generate data
x_o, conj_model, analytical_posterior = func.set_up_model(seed)
# sample data from posterior
torch.manual_seed(seed)
analytical_posterior_samples = conj_model.sample_analytical_posterior(analytical_posterior, 1000)
np.savetxt('mv_gaussian/low_dim_w_summary_stats/data/post_samples_analytical_' + str(dim) + '_' + str(seed) + '_' +
str(seed_data) + '.csv', analytical_posterior_samples.detach().numpy(), delimiter=",")
np.savetxt('mv_gaussian/low_dim_w_summary_stats/data/x_o_summary_stats_true_' + str(dim) + '_' + str(seed) + '_' +
str(seed_data) + '.csv', func.calc_summary_stats(x_o).detach().numpy(), delimiter=",")
# Sample data sets for ground-truth model
N_test_obs_data = 1000
x_test_obs_data = torch.zeros(N_test_obs_data, 5)
theta_test_obs_data = torch.zeros(N_test_obs_data, dim)
for i in range(N_test_obs_data):
x_test_obs_data[i, :] = func.calc_summary_stats(conj_model.sample())
np.savetxt('mv_gaussian/low_dim_w_summary_stats/data/x_o_summary_stats_samples_true_' + str(dim) + '_' + str(seed) +
'_' + str(seed_data) + '.csv', x_test_obs_data.detach().numpy(), delimiter=",")
# Sample data from prior pred
N_prior_pred_test = 1000
def simulator(theta):
N_samples = theta.shape[0]
x = torch.zeros(N_samples, conj_model.N, dim)
for i in range(N_samples):
model_tmp = MultivariateNormal(theta[i],
conj_model.model.covariance_matrix)
x[i, :, :] = model_tmp.rsample(sample_shape=(conj_model.N, ))
# return calc_summary_stats(x), theta #/math.sqrt(5) # div with std of prior to nomarlize data
return func.calc_summary_stats(x)
s_x_o = func.calc_summary_stats(x_o)
# check simulator and prior
simulator, prior = prepare_for_sbi(simulator, conj_model.prior)
# function that builds the network
def build_custom_post_net(batch_theta, batch_x):
flow_lik, flow_post = func.set_up_networks()
return flow_post
inference = SNRE_B(simulator, prior)
learning_rate = 0.0005 # default value
N_prior_pred_test = 1000
x_test, theta_test = func.run_model_sim(N_prior_pred_test, seed + 2, conj_model, analytical_posterior,
conj_model.model.covariance_matrix, dim, True)
# Generate test data for obs data set
print(conj_model.model_sim(theta_test).shape)
N_test_obs_data = 1000
x_test_obs_data = torch.zeros(N_test_obs_data, 5)
theta_test_obs_data = torch.zeros(N_test_obs_data, dim)
for i in range(N_test_obs_data):
x_test_obs_data[i, :] = func.calc_summary_stats(x_o)
theta_test_obs_data[i, :] = conj_model.model.loc
# Set up networks for the likelihood model
# Base dist for posterior model
flow_lik, flow_post = func.set_up_networks()
optimizer_lik = torch.optim.Adam(flow_lik.parameters())
optimizer_post = torch.optim.Adam(flow_post.parameters(), lr=0.002)
decay_rate_post = 0.95 # no adaptation of Adam's base rate
nbr_rounds = 10
prob_prior_decay_rate = 0.7
prob_prior = spa.calc_prob_prior(nbr_rounds, prob_prior_decay_rate)