def estimation(dataset,
boot_index,
model_params,
model_params_grad,
num_obs_samples,
num_future_steps,
category_tt_split,
num_mc_samples,
output_file,
true_model_params=None):
y, x = dataset
y_complete = y.clone().detach()
y_complete = y_complete[0:-num_future_steps]
category_tt_split = 'session'
y, x, y_future, x_future = train_future_split(y, x, num_future_steps)
y_train, y_test, test_inds = train_test_split(y.cpu(),
x.cpu(),
cat=category_tt_split)
x = x.clone().detach() #torch.tensor(x, dtype=dtype, device=device)
y_train = y_train.clone().detach(
) #torch.tensor(y_train, dtype=dtype, device=device)
y_test = torch.tensor(y_test, dtype=dtype, device=device)
test_inds = torch.tensor(test_inds, dtype=torch.long, device=device)
y_future = y_future.clone().detach(
) #torch.tensor(y_future, dtype=dtype, device=device)
x_future = x_future.clone().detach(
) #torch.tensor(x_future, dtype=dtype, device=device)
y_train = torch.tensor(y, device=device)
data = [y_train, x, y_test, test_inds, y_future, x_future, y_complete]
model = LearningDynamicsModel(dim=dim)
boot_output_file = output_file + '/' + str(boot_index)
os.makedirs(boot_output_file)
os.makedirs(boot_output_file + '/model_structs')
os.makedirs(boot_output_file + '/data')
os.makedirs(boot_output_file + '/plots')
inference = Inference(
data=data,
model=model,
model_params=model_params,
model_params_grad=model_params_grad,
savedir=boot_output_file,
num_obs_samples=num_obs_samples,
num_future_steps=num_future_steps,
num_mc_samples=num_mc_samples,
ppc_window=50,
z_true=z_true,
true_model_params=true_model_params) # pass in just for figures
opt_params = inference.run()
torch.save(opt_params, boot_output_file + '/model_structs/opt_params.npy')
torch.save(dataset, boot_output_file + '/data/dataset.npy')
torch.save(model_params,
boot_output_file + '/model_structs/model_params.npy')
return opt_params
num_future_steps = 1
category_tt_split = 'session'
num_mc_samples = 10
ppc_window = 50
percent_test = .2
features = [
'Bias', 'X1', 'X2', 'Choice t-1', 'RW Side t-1', 'X1 t-1', 'X2 t-1'
]
y_complete = torch.tensor(y.copy(), device=device)
y_complete = y_complete[0:-num_future_steps]
#y, x, y_future, x_future = train_future_split(y, x, num_future_steps)
y_future = y
x_future = x
y_train, y_test, test_inds = train_test_split(y, x, category_tt_split,
percent_test)
x = torch.tensor(x, dtype=dtype, device=device)
y_train = torch.tensor(y_train, dtype=dtype, device=device)
y_test = torch.tensor(y_test, dtype=dtype, device=device)
test_inds = torch.tensor(test_inds, dtype=torch.long, device=device)
y_future = torch.tensor(y_future, dtype=dtype, device=device)
x_future = torch.tensor(x_future, dtype=dtype, device=device)
#data = [y_train, x]
data = [y_train, x, y_test, test_inds, y_future, x_future, y_complete]
datasets.append(data)
model_params_grad = {
'init_latent_loc': False,
'init_latent_log_scale': False,
'transition_log_scale': False,
'log_gamma': True,
# os.mkdir(savedir)
# f = '../data/W066_short.csv'
x, y, rw = read_and_process(num_obs_samples, f, savedir=savedir)
rw = torch.tensor(rw, dtype=dtype, device=device)
dim = x.shape[2]
T = x.shape[0]
init_prior = ([0.0] * dim, [math.log(1.0)] * dim)
transition_scale = [math.log(1.0)] #* dim
num_future_steps = 1
category_tt_split = 'single'
num_mc_samples = 10
y, x, y_future, x_future = train_future_split(y, x, num_future_steps)
y_train, y_test, test_inds = train_test_split(y, x, cat=category_tt_split)
x = torch.tensor(x, dtype=dtype, device=device)
y_train = torch.tensor(y_train, dtype=dtype, device=device)
y_test = torch.tensor(y_test, dtype=dtype, device=device)
test_inds = torch.tensor(test_inds, dtype=torch.long, device=device)
y_future = torch.tensor(y_future, dtype=dtype, device=device)
x_future = torch.tensor(x_future, dtype=dtype, device=device)
#data = [y_train, x]
data = [y_train, x, y_test, test_inds, y_future, x_future]
# declare model here
init_transition_log_scale = [math.log(1.)] # * dim
model = LearningDynamicsModel(init_prior, init_transition_log_scale, dim=3)
inference = Inference(data,
model,
savedir='',
num_obs_samples=num_obs_samples,