def main(args):
# Notebook experiment settings
experiment_name = args.experiment_name
experiment_results_folder = args.results_folder
results_path = os.path.join("../", experiment_results_folder)
data_folder = args.data_folder
data_file = args.data_file
extra_data_file = args.extra_data_file
file_name_test = f"{experiment_name}_test.pickle"
file_path_test = os.path.join(results_path, file_name_test)
print(f"Saving: {file_name_test}")
with open(file_path_test, 'wb') as f:
pickle.dump("test", f)
# Data settings
obs_cols = args.obs_cols
# Load data
csv_path = os.path.join(data_folder, data_file)
donkey_df = pd.read_csv(csv_path, parse_dates=[4, 11])
csv_path = os.path.join(data_folder, extra_data_file)
extra_df = pd.read_csv(csv_path, parse_dates=[4, 12])
# Data prep
train_idx, test_idx = get_split_idx_on_day(donkey_df)
# Create full data
test_data = donkey_df.loc[test_idx, obs_cols]
train_data = pd.concat((donkey_df.loc[train_idx, obs_cols], extra_df.loc[:, obs_cols]))
# Normalize data
obs_scaler = StandardScaler().fit(train_data)
scaled_train_data = obs_scaler.transform(train_data)
# Create conditional variable
hours = pd.concat((donkey_df.loc[train_idx, :], extra_df.loc[:, :])).merge_date.dt.hour.values
hours = np.expand_dims(hours, 1)
scaled_test_data = obs_scaler.transform(test_data)
statsmods = KDEMultivariateConditional(endog=scaled_train_data, exog=hours, indep_type='o', dep_type='cc',
bw='cv_ml')
results_dict = {'model': statsmods, }
file_name = f"{experiment_name}.pickle"
file_path = os.path.join(results_path, file_name)
print(f"Saving: {file_name}")
with open(file_path, 'wb') as f:
pickle.dump(results_dict, f)
def main(args):
# Notebook experiment settings
experiment_name = args.experiment_name
experiment_results_folder = args.results_folder
results_path = os.path.join("../", experiment_results_folder)
data_folder = args.data_folder
data_file = args.data_file
extra_data_file = args.extra_data_file
file_name_test = f"{experiment_name}_test.pickle"
file_path_test = os.path.join(results_path, file_name_test)
print(f"Saving: {file_name_test}")
with open(file_path_test, 'wb') as f:
pickle.dump("test", f)
# Data settings
obs_cols = ['user_location_latitude', 'user_location_longitude']
semisup_context_cols = ['hour_sin', 'hour_cos']
joint_cols = obs_cols + semisup_context_cols
# Load data
csv_path = os.path.join(data_folder, data_file)
donkey_df = pd.read_csv(csv_path, parse_dates=[4, 11])
csv_path = os.path.join(data_folder, extra_data_file)
extra_df = pd.read_csv(csv_path, parse_dates=[4, 12])
# Data prep
train_idx, test_idx = get_split_idx_on_day(donkey_df)
# Create full data
test_data = donkey_df.loc[test_idx, joint_cols]
train_data = pd.concat(
(donkey_df.loc[train_idx, joint_cols], extra_df.loc[:, joint_cols]))
# Normalize data
obs_scaler = StandardScaler().fit(train_data)
scaled_train_data = obs_scaler.transform(train_data)
scaled_test_data = obs_scaler.transform(test_data)
fastKDE = fastKDE(scaled_train_data.T)
print(fastKDE)
results_dict = {'model': fastKDE}
file_name = f"{experiment_name}.pickle"
file_path = os.path.join(results_path, file_name)
print(f"Saving: {file_name}")
with open(file_path, 'wb') as f:
pickle.dump(results_dict, f)
def main(args):
# cuda
cuda_exp = args.cuda_exp == "true"
# Notebook experiment settings
experiment_name = args.experiment_name
experiment_results_folder = args.results_folder
results_path = os.path.join("../", experiment_results_folder)
data_folder = args.data_folder
data_file = args.data_file
extra_data_file = args.extra_data_file
# Regularization settings
if args.noise_reg_scheduler == "constant":
noise_reg_schedule = constant_regularization_schedule
elif args.noise_reg_scheduler == "sqrt":
noise_reg_schedule = square_root_noise_schedule
elif args.noise_reg_scheduler == "rot":
noise_reg_schedule = rule_of_thumb_noise_schedule
else:
noise_reg_schedule = constant_regularization_schedule
noise_reg_sigma = args.noise_reg_sigma # Used as sigma in rule of thumb and as noise in const
l2_reg = args.l2_reg
initial_lr = args.initial_lr
lr_decay = args.lr_decay
# Data settings
obs_cols = args.obs_cols
semisup_context_cols = args.semisup_context_cols
context_cols = semisup_context_cols
# Training settings
epochs = args.epochs
batch_size = args.batch_size
clipped_adam = args.clipped_adam
# Dimensions of problem
problem_dim = len(args.obs_cols)
context_dim = len(context_cols)
# Flow settings
flow_depth = args.flow_depth
c_net_depth = args.c_net_depth
c_net_h_dim = args.c_net_h_dim
batchnorm_momentum = args.batchnorm_momentum
# Define context conditioner
context_n_depth = args.context_n_depth
context_n_h_dim = args.context_n_h_dim
rich_context_dim = args.rich_context_dim
settings_dict = {
"epochs": epochs,
"batch_size": batch_size,
"problem_dim": problem_dim,
"context_dim": context_dim,
"flow_depth": flow_depth,
"c_net_depth": c_net_depth,
"c_net_h_dim": c_net_h_dim,
"context_n_depth": context_n_depth,
"context_n_h_dim": context_n_h_dim,
"rich_context_dim": rich_context_dim,
"obs_cols": obs_cols,
"context_cols": context_cols,
"semisup_context_cols": semisup_context_cols,
"batchnorm_momentum": batchnorm_momentum,
"l2_reg": l2_reg,
"clipped_adam": clipped_adam,
"initial_lr": initial_lr,
"lr_decay": lr_decay
}
print(f"Settings:\n{settings_dict}")
# Load data
csv_path = os.path.join(data_folder, data_file)
donkey_df = pd.read_csv(csv_path, parse_dates=[4, 11])
csv_path = os.path.join(data_folder, extra_data_file)
extra_df = pd.read_csv(csv_path, parse_dates=[4, 12])
# Save the test train split. We do use seed but this way we have it.
train_idx, test_idx = get_split_idx_on_day(donkey_df)
run_idxs = {'train': train_idx, 'test': test_idx}
train_dataloader, test_dataloader, obs_scaler, semisup_context_scaler = searchlog_no_weather_day_split2(
sup_df=donkey_df,
unsup_df=extra_df,
obs_cols=obs_cols,
semisup_context_cols=semisup_context_cols,
batch_size=batch_size,
cuda_exp=True)
# Define stuff for reqularization
data_size = len(train_dataloader)
data_dim = problem_dim + context_dim
# Define normalizing flow
normalizing_flow = conditional_normalizing_flow_factory3(
flow_depth=flow_depth,
problem_dim=problem_dim,
c_net_depth=c_net_depth,
c_net_h_dim=c_net_h_dim,
context_dim=context_dim,
context_n_h_dim=context_n_h_dim,
context_n_depth=context_n_depth,
rich_context_dim=rich_context_dim,
cuda=cuda_exp,
batchnorm_momentum=batchnorm_momentum)
# Setup Optimizer
if clipped_adam is None:
if l2_reg is None:
optimizer = optim.Adam(normalizing_flow.modules.parameters(),
lr=initial_lr)
else:
optimizer = optim.Adam(normalizing_flow.modules.parameters(),
lr=initial_lr,
weight_decay=l2_reg)
else:
if l2_reg is None:
optimizer = ClippedAdam(normalizing_flow.modules.parameters(),
lr=initial_lr,
clip_norm=clipped_adam)
else:
optimizer = ClippedAdam(normalizing_flow.modules.parameters(),
lr=initial_lr,
weight_decay=l2_reg,
clip_norm=clipped_adam)
if lr_decay is not None:
scheduler = optim.lr_scheduler.ExponentialLR(optimizer=optimizer,
gamma=lr_decay,
last_epoch=-1)
# Setup regularization
h = noise_reg_schedule(data_size, data_dim, noise_reg_sigma)
noise_reg = NoiseRegularizer(discrete_dims=None, h=h, cuda=cuda_exp)
# Train and test sizes
n_train = train_dataloader.dataset.shape[0]
n_test = test_dataloader.dataset.shape[0]
# Training loop
full_train_losses = []
train_losses = []
test_losses = []
no_noise_losses = []
for epoch in range(1, epochs + 1):
normalizing_flow.modules.train()
train_epoch_loss = 0
for k, batch in enumerate(train_dataloader):
# Add noise reg to two moons
batch = noise_reg.add_noise(batch)
x = batch[:, :problem_dim]
context = batch[:, problem_dim:]
# Condition the flow on the sampled covariate and calculate -log_prob = loss
conditioned_flow_dist = normalizing_flow.condition(context)
loss = -conditioned_flow_dist.log_prob(x).sum()
# Calculate gradients and take an optimizer step
normalizing_flow.modules.zero_grad()
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_epoch_loss += loss.item()
full_train_losses.append(train_epoch_loss / n_train)
# save every 10 epoch to log and eval
if epoch % 10 == 0 or epoch == epochs - 1:
normalizing_flow.modules.eval()
train_losses.append(train_epoch_loss / n_train)
no_noise_epoch_loss = 0
for k, batch in enumerate(train_dataloader):
# Add noise reg to two moons
x = batch[:, :problem_dim]
context = batch[:, problem_dim:]
# Condition the flow on the sampled covariate and calculate -log_prob = loss
conditioned_flow_dist = normalizing_flow.condition(context)
loss = -conditioned_flow_dist.log_prob(x).sum()
no_noise_epoch_loss += loss.item()
no_noise_losses.append(no_noise_epoch_loss / n_train)
test_epoch_loss = 0
for j, batch in enumerate(test_dataloader):
# Sample covariates and use them to sample from conditioned two_moons
x = batch[:, :problem_dim]
context = batch[:, problem_dim:]
# Condition the flow on the sampled covariate and calculate -log_prob = loss
conditioned_flow_dist = normalizing_flow.condition(context)
test_loss = -conditioned_flow_dist.log_prob(x).sum()
test_epoch_loss += test_loss.item()
test_losses.append(test_epoch_loss / n_test)
# Take scheduler step if needed
if lr_decay is not None:
scheduler.step()
# Plot Epoch results if epoch == epochs-1:
if epoch == epochs - 1:
normalizing_flow.modules.eval()
print(
f"Epoch {epoch}: train loss: {train_losses[-1]} no noise loss:{no_noise_losses[-1]} test_loss: {test_losses[-1]}"
)
experiment_dict = {
'train': train_losses,
'test': test_losses,
'no_noise_losses': no_noise_losses
}
results_dict = {
'model': normalizing_flow,
'settings': settings_dict,
'logs': experiment_dict,
'data_split': run_idxs
}
file_name = f"{experiment_name}.pickle"
file_path = os.path.join(results_path, file_name)
print(f"Saving: {file_name}")
with open(file_path, 'wb') as f:
pickle.dump(results_dict, f)
def main(args):
# cuda
cuda_exp = args.cuda_exp == "true"
# Notebook experiment settings
experiment_name = args.experiment_name
experiment_results_folder = args.results_folder
results_path = os.path.join("../", experiment_results_folder)
data_folder = args.data_folder
data_file = args.data_file
extra_data_file = args.extra_data_file
# Regularization settings
if args.noise_reg_scheduler == "constant":
noise_reg_schedule = constant_regularization_schedule
elif args.noise_reg_scheduler == "sqrt":
noise_reg_schedule = square_root_noise_schedule
elif args.noise_reg_scheduler == "rot":
noise_reg_schedule = rule_of_thumb_noise_schedule
else:
noise_reg_schedule = constant_regularization_schedule
noise_reg_sigma = args.noise_reg_sigma # Used as sigma in rule of thumb and as noise in const
l2_reg = args.l2_reg
initial_lr = args.initial_lr
lr_factor = args.lr_factor
lr_patience = args.lr_patience
min_lr = args.min_lr
# Data settings
obs_cols = args.obs_cols
semisup_context_cols = args.semisup_context_cols
sup_context_cols = args.sup_context_cols
if sup_context_cols is None:
context_cols = semisup_context_cols
else:
context_cols = semisup_context_cols + sup_context_cols
# Training settings
epochs = args.epochs
batch_size = args.batch_size
clipped_adam = args.clipped_adam
# Dimensions of problem
problem_dim = len(args.obs_cols)
context_dim = len(context_cols)
# Flow settings
flow_depth = args.flow_depth
c_net_depth = args.c_net_depth
c_net_h_dim = args.c_net_h_dim
batchnorm_momentum = args.batchnorm_momentum
# Define context conditioner
context_n_depth = args.context_n_depth
context_n_h_dim = args.context_n_h_dim
rich_context_dim = args.rich_context_dim
settings_dict = {
"epochs": epochs,
"batch_size": batch_size,
"problem_dim": problem_dim,
"context_dim": context_dim,
"flow_depth": flow_depth,
"c_net_depth": c_net_depth,
"c_net_h_dim": c_net_h_dim,
"context_n_depth": context_n_depth,
"context_n_h_dim": context_n_h_dim,
"rich_context_dim": rich_context_dim,
"obs_cols": obs_cols,
"context_cols": context_cols,
"semisup_context_cols": semisup_context_cols,
"sup_context_context_cols": sup_context_cols,
"batchnorm_momentum": batchnorm_momentum,
"l2_reg": l2_reg,
"clipped_adam": clipped_adam,
"initial_lr": initial_lr,
"lr_factor": lr_factor,
"lr_patience": lr_patience,
"min_lr": min_lr,
"noise_reg_sigma": noise_reg_sigma
}
print(f"Settings:\n{settings_dict}")
# Load data
csv_path = os.path.join(data_folder, data_file)
donkey_df = pd.read_csv(csv_path, parse_dates=[4, 11])
csv_path = os.path.join(data_folder, extra_data_file)
extra_df = pd.read_csv(csv_path, parse_dates=[4, 12])
# Save the test train split. We do use seed but this way we have it.
train_idx, test_idx = get_split_idx_on_day(donkey_df)
run_idxs = {'train': train_idx, 'test': test_idx}
train_dataloader, test_dataloader, extra_dataloader, obs_scaler, semisup_context_scaler, sup_context_scaler = searchlog_semisup_day_split(
sup_df=donkey_df,
unsup_df=extra_df,
obs_cols=obs_cols,
semisup_context_cols=semisup_context_cols,
sup_context_cols=sup_context_cols,
batch_size=batch_size,
cuda_exp=True)
# Define stuff for reqularization
data_size = len(train_dataloader)
data_dim = problem_dim + context_dim
# Define normalizing flow
normalizing_flow = conditional_normalizing_flow_factory3(
flow_depth=flow_depth,
problem_dim=problem_dim,
c_net_depth=c_net_depth,
c_net_h_dim=c_net_h_dim,
context_dim=context_dim,
context_n_h_dim=context_n_h_dim,
context_n_depth=context_n_depth,
rich_context_dim=rich_context_dim,
cuda=cuda_exp,
batchnorm_momentum=batchnorm_momentum)
# Setup Optimizer
if clipped_adam is None:
if l2_reg is None:
optimizer = optim.Adam(normalizing_flow.modules.parameters(),
lr=initial_lr)
else:
optimizer = optim.Adam(normalizing_flow.modules.parameters(),
lr=initial_lr,
weight_decay=l2_reg)
else:
if l2_reg is None:
optimizer = ClippedAdam(normalizing_flow.modules.parameters(),
lr=initial_lr,
clip_norm=clipped_adam)
else:
optimizer = ClippedAdam(normalizing_flow.modules.parameters(),
lr=initial_lr,
weight_decay=l2_reg,
clip_norm=clipped_adam)
if lr_factor is not None:
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer=optimizer,
factor=lr_factor,
patience=lr_patience,
min_lr=min_lr,
verbose=True)
# Setup regularization
h = noise_reg_schedule(data_size, data_dim, noise_reg_sigma)
noise_reg = NoiseRegularizer(discrete_dims=None, h=h, cuda=cuda_exp)
# Train and test sizes
n_train = train_dataloader.dataset.shape[0]
n_test = test_dataloader.dataset.shape[0]
# Define the possible supervised contexts to marginalize out during unsupervised training
context_val_dict = {}
if "wind_dir_sin" in sup_context_cols and "wind_dir_cos" in sup_context_cols:
wind_dir_arr = np.unique(donkey_df[['wind_dir_sin',
'wind_dir_cos']].values.tolist(),
axis=0)
context_val_dict['wind_dir_sin'] = wind_dir_arr
context_val_dict['wind_dir_cos'] = None
if "windy" in sup_context_cols:
windy_arr = donkey_df['windy'].unique()
context_val_dict['windy'] = windy_arr
if "air_temp" in sup_context_cols:
air_temp_arr = donkey_df['air_temp'].unique()
context_val_dict['air_temp'] = air_temp_arr
if "rain" in sup_context_cols:
rain_arr = donkey_df['rain'].unique()
context_val_dict['rain'] = rain_arr
context_val_arr = [
context_val_dict[col] for col in sup_context_cols
if context_val_dict[col] is not None
]
temp_contexts = np.array(list(itertools.product(*context_val_arr)))
contexts_arr = []
for row in temp_contexts:
cleaned_row = []
for elem in row:
if isinstance(elem, np.ndarray):
for value in elem:
cleaned_row.append(value)
else:
cleaned_row.append(elem)
contexts_arr.append(cleaned_row)
possible_contexts = np.array(contexts_arr)
prior_dict = {
True:
torch.tensor(donkey_df.rain.sum() / len(donkey_df)).float().cuda(),
False:
torch.tensor(1 - donkey_df.rain.sum() / len(donkey_df)).float().cuda()
}
print(len(possible_contexts))
print(possible_contexts)
print(prior_dict)
# Training loop
train_losses = []
test_losses = []
no_noise_losses = []
lr_scheduler_steps = []
for epoch in range(1, epochs + 1):
normalizing_flow.modules.train()
train_epoch_loss = 0
for k, batch in enumerate(train_dataloader):
# Add noise reg to two moons
batch = noise_reg.add_noise(batch)
x = batch[:, :problem_dim]
context = batch[:, problem_dim:]
# Condition the flow on the sampled covariate and calculate -log_prob = loss
conditioned_flow_dist = normalizing_flow.condition(context)
loss = -conditioned_flow_dist.log_prob(x).sum()
# Calculate gradients and take an optimizer step
normalizing_flow.modules.zero_grad()
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_epoch_loss += loss.item()
# Cheeky unsupervised step that's not really logged
for k, batch in enumerate(extra_dataloader):
batch = noise_reg.add_noise(batch)
x = batch[:, :problem_dim]
semisup_context = batch[:, problem_dim:]
loss = 0
for unscaled_sup_context in possible_contexts:
sup_context = sup_context_scaler.transform(
[unscaled_sup_context])
sup_context = torch.tensor(sup_context).float().expand(
(semisup_context.shape[0], len(sup_context[0]))).cuda()
context = torch.cat((semisup_context, sup_context),
dim=1) # Mayb
conditioned_flow_dist = normalizing_flow.condition(context)
loss += -(conditioned_flow_dist.log_prob(x) *
prior_dict[unscaled_sup_context[0]]).sum()
# Calculate gradients and take an optimizer step
normalizing_flow.modules.zero_grad()
optimizer.zero_grad()
loss.backward()
optimizer.step()
normalizing_flow.modules.eval()
with torch.no_grad():
test_epoch_loss = 0
for j, batch in enumerate(test_dataloader):
# Sample covariates and use them to sample from conditioned two_moons
x = batch[:, :problem_dim]
context = batch[:, problem_dim:]
# Condition the flow on the sampled covariate and calculate -log_prob = loss
conditioned_flow_dist = normalizing_flow.condition(context)
test_loss = -conditioned_flow_dist.log_prob(x).sum()
test_epoch_loss += test_loss.item()
# save every 10 epoch to log and eval
if epoch % 10 == 0 or epoch == epochs - 1:
normalizing_flow.modules.eval()
train_losses.append(train_epoch_loss / n_train)
test_losses.append(test_epoch_loss / n_test)
no_noise_epoch_loss = 0
for k, batch in enumerate(train_dataloader):
# Add noise reg to two moons
x = batch[:, :problem_dim]
context = batch[:, problem_dim:]
# Condition the flow on the sampled covariate and calculate -log_prob = loss
conditioned_flow_dist = normalizing_flow.condition(context)
loss = -conditioned_flow_dist.log_prob(x).sum()
no_noise_epoch_loss += loss.item()
no_noise_losses.append(no_noise_epoch_loss / n_train)
# Take scheduler step if needed
if lr_factor is not None:
scheduler.step(test_epoch_loss / n_test)
lr_scheduler_steps.append(epoch)
# Plot Epoch results if epoch == epochs-1:
if epoch == epochs - 1:
normalizing_flow.modules.eval()
print(
f"Epoch {epoch}: train loss: {train_losses[-1]} no noise loss:{no_noise_losses[-1]} test_loss: {test_losses[-1]}"
)
experiment_dict = {
'train': train_losses,
'test': test_losses,
'no_noise_losses': no_noise_losses
}
results_dict = {
'model': normalizing_flow,
'settings': settings_dict,
'logs': experiment_dict,
'data_split': run_idxs,
'lr_steps': lr_scheduler_steps
}
file_name = f"{experiment_name}.pickle"
file_path = os.path.join(results_path, file_name)
print(f"Saving: {file_name}")
with open(file_path, 'wb') as f:
pickle.dump(results_dict, f)
def main(args):
# cuda
cuda_exp = args.cuda_exp == "true"
print(cuda_exp)
# Notebook experiment settings
experiment_name = args.experiment_name
experiment_results_folder = args.results_folder
results_path = os.path.join("../", experiment_results_folder)
data_folder = args.data_folder
data_file = args.data_file
# Regularization settings
if args.noise_reg_scheduler == "constant":
noise_reg_schedule = constant_regularization_schedule
elif args.noise_reg_scheduler == "sqrt":
noise_reg_schedule = square_root_noise_schedule
elif args.noise_reg_scheduler == "rot":
noise_reg_schedule = rule_of_thumb_noise_schedule
else:
noise_reg_schedule = constant_regularization_schedule
noise_reg_sigma = args.noise_reg_sigma # Used as sigma in rule of thumb and as noise in const
# Data settings
obs_cols = args.obs_cols
context_cols = args.context_cols
# Training settings
epochs = args.epochs
batch_size = args.batch_size
# Dimensions of problem
problem_dim = len(args.obs_cols)
context_dim = len(args.context_cols)
# Flow settings
flow_depth = args.flow_depth
c_net_depth = args.c_net_depth
c_net_h_dim = args.c_net_h_dim
# Define context conditioner
context_n_depth = args.context_n_depth
context_n_h_dim = args.context_n_h_dim
rich_context_dim = args.rich_context_dim
settings_dict = {
"epochs": epochs,
"batch_size": batch_size,
"problem_dim": problem_dim,
"context_dim": context_dim,
"flow_depth": flow_depth,
"c_net_depth": c_net_depth,
"c_net_h_dim": c_net_h_dim,
"context_n_depth": context_n_depth,
"context_n_h_dim": context_n_h_dim,
"rich_context_dim": rich_context_dim,
"obs_cols": obs_cols,
"context_cols": context_cols
}
print(f"Settings:\n{settings_dict}")
# Load data
csv_path = os.path.join(data_folder, data_file)
donkey_df = pd.read_csv(csv_path, parse_dates=[4, 11])
train_dataloader, test_dataloader, _, _ = searchlog_day_split(
donkey_df, obs_cols, context_cols, batch_size, cuda_exp)
train_idx, test_idx = get_split_idx_on_day(donkey_df)
run_idxs = {'train': train_idx, 'test': test_idx}
# Define stuff for reqularization
data_size = len(train_dataloader)
data_dim = problem_dim + context_dim
# Define normalizing flow
normalizing_flow = conditional_normalizing_flow_factory2(
flow_depth=flow_depth,
problem_dim=problem_dim,
c_net_depth=c_net_depth,
c_net_h_dim=c_net_h_dim,
context_dim=context_dim,
context_n_h_dim=context_n_h_dim,
context_n_depth=context_n_depth,
rich_context_dim=rich_context_dim,
cuda=cuda_exp)
# Setup Optimizer
optimizer = optim.Adam(normalizing_flow.modules.parameters(), lr=1e-4)
print("number of params: ",
sum(p.numel() for p in normalizing_flow.modules.parameters()))
# Setup regularization
h = noise_reg_schedule(data_size, data_dim, noise_reg_sigma)
noise_reg = NoiseRegularizer(discrete_dims=None, h=h, cuda=cuda_exp)
print(f"Data size: {train_dataloader.dataset.shape}")
print(f"Noise scale: {h}")
# Train and test sizes
n_train = train_dataloader.dataset.shape[0]
n_test = test_dataloader.dataset.shape[0]
print(f"n_train {n_train}")
print(f"n_test {n_test}")
# Training loop
full_train_losses = []
train_losses = []
test_losses = []
no_noise_losses = []
for epoch in range(1, epochs + 1):
normalizing_flow.modules.train()
train_epoch_loss = 0
for k, batch in enumerate(train_dataloader):
# Add noise reg to two moons
batch = noise_reg.add_noise(batch)
x = batch[:, :problem_dim]
context = batch[:, problem_dim:]
# Condition the flow on the sampled covariate and calculate -log_prob = loss
conditioned_flow_dist = normalizing_flow.condition(context)
loss = -conditioned_flow_dist.log_prob(x).sum()
# Calculate gradients and take an optimizer step
normalizing_flow.modules.zero_grad()
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_epoch_loss += loss.item()
full_train_losses.append(train_epoch_loss / n_train)
# save every 10 epoch to log and eval
if epoch % 10 == 0 or epoch == epochs - 1:
normalizing_flow.modules.eval()
train_losses.append(train_epoch_loss / n_train)
no_noise_epoch_loss = 0
for k, batch in enumerate(train_dataloader):
# Add noise reg to two moons
x = batch[:, :problem_dim]
context = batch[:, problem_dim:]
# Condition the flow on the sampled covariate and calculate -log_prob = loss
conditioned_flow_dist = normalizing_flow.condition(context)
loss = -conditioned_flow_dist.log_prob(x).sum()
no_noise_epoch_loss += loss.item()
no_noise_losses.append(no_noise_epoch_loss / n_train)
test_epoch_loss = 0
for j, batch in enumerate(test_dataloader):
# Sample covariates and use them to sample from conditioned two_moons
x = batch[:, :problem_dim]
context = batch[:, problem_dim:]
# Condition the flow on the sampled covariate and calculate -log_prob = loss
conditioned_flow_dist = normalizing_flow.condition(context)
test_loss = -conditioned_flow_dist.log_prob(x).sum()
test_epoch_loss += test_loss.item()
test_losses.append(test_epoch_loss / n_test)
if epoch % 100 == 0:
print(
f"Epoch {epoch}: train loss: {train_losses[-1]} no noise loss:{no_noise_losses[-1]} test_loss: {test_losses[-1]}"
)
# Plot Epoch results if epoch == epochs-1:
if epoch == epochs - 1:
normalizing_flow.modules.eval()
print(
f"Epoch {epoch}: train loss: {train_losses[-1]} no noise loss:{no_noise_losses[-1]} test_loss: {test_losses[-1]}"
)
experiment_dict = {
'train': train_losses,
'test': test_losses,
'no_noise_losses': no_noise_losses
}
results_dict = {
'model': normalizing_flow,
'settings': settings_dict,
'logs': experiment_dict,
'data_split': run_idxs
}
file_name = f"{experiment_name}.pickle"
file_path = os.path.join(results_path, file_name)
print(f"Saving: {file_name}")
with open(file_path, 'wb') as f:
saved_flow = pickle.dump(results_dict, f)
