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Module_N_AG.py
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Module_N_AG.py
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import torch, time, warnings, os
import numpy as np
from Module_N_C import N_C
from sklearn.metrics import r2_score
from Vars import *
class N_AG(N_C):
def fit(self,
epochs,
lr_halflife,
filename,
save: bool = True,
save_every: int = 1,
reg_lambda: float = 0,
initial_lr: float = 1e-3,
loss_fn: str = 'mse',
optimizer: str = 'radam',
print_every: int = 10,
valid_measure: str = 'mse'):
################## model info ##################
vars = locals()
if 'self' in vars.keys(): del vars['self']
if '__class__' in vars.keys(): del vars['__class__']
self.model_info.update(vars)
self.model_info = {key: value for key, value in sorted(self.model_info.items())}
################## model info ##################
self.setup_optimizer(initial_lr=initial_lr)
self.setup_loss_fn(loss_fn)
for key, value in self.model_info.items():
print(f"{key.upper()}: {value}")
print()
for module in self.children():
print(module)
print()
tLoss_yDDot_history = np.empty(epochs)
vLoss_yDDot_history = np.empty(epochs)
model_parameter_group = np.empty(epochs, dtype=object)
model_validation_loss = np.full(epochs, 1e30)
TrainingStartTime = time.time()
for epoch in range(self.model_info['epochs']):
# Training step
tLabel = []
tPrediction = []
epoch_computation_time = 0
for trainbatch_idx, trainbatch in enumerate(self.train_dataloader):
batch_start = time.time()
# Forward
if self.model_info['metamodeling']:
train_x, train_params, train_y, train_yDot, train_yDDot = trainbatch
train_x = train_x.cuda()
train_params = train_params.cuda()
train_y = train_y.cuda()
train_yDot = train_yDot.cuda()
train_yDDot = train_yDDot.cuda()
with torch.no_grad():
train_x = (train_x - self.mean_x) / self.std_x
train_params = (train_params - self.mean_params) / self.std_params
train_y = (train_y - self.mean_y) / self.std_y
train_yDot = (train_yDot) * (self.std_x / self.std_y)
train_yDDot = (train_yDDot) * (self.std_x ** 2 / self.std_y)
train_x.requires_grad_(True)
pred_y = self.forward(torch.cat((train_x, train_params), dim=1))
pred_yDot = []
pred_yDDot = []
for k in range(len(y)):
pred_yDot.append(torch.autograd.grad(outputs=pred_y[:, k], inputs=train_x,
grad_outputs=torch.ones_like(pred_y[:, k]),
create_graph=True, retain_graph=True)[0])
pred_yDDot.append(torch.autograd.grad(outputs=pred_yDot[k], inputs=train_x,
grad_outputs=torch.ones_like(pred_yDot[k]),
create_graph=True, retain_graph=True)[0])
pred_yDot = torch.cat(pred_yDot, dim=1)
pred_yDDot = torch.cat(pred_yDDot, dim=1)
train_x.requires_grad_(False)
else:
train_x, train_y, train_yDot, train_yDDot = trainbatch
train_x = train_x.cuda()
train_y = train_y.cuda()
train_yDot = train_yDot.cuda()
train_yDDot = train_yDDot.cuda()
with torch.no_grad():
train_x = (train_x - self.mean_x) / self.std_x
train_y = (train_y - self.mean_y) / self.std_y
train_yDot = (train_yDot) * (self.std_x / self.std_y)
train_yDDot = (train_yDDot) * (self.std_x ** 2 / self.std_y)
train_x.requires_grad_(True)
pred_y = self.forward(train_x)
pred_yDot = []
pred_yDDot = []
for k in range(len(y)):
pred_yDot.append(torch.autograd.grad(outputs=pred_y[:, k], inputs=train_x,
grad_outputs=torch.ones_like(pred_y[:, k]),
create_graph=True, retain_graph=True)[0])
pred_yDDot.append(torch.autograd.grad(outputs=pred_yDot[k], inputs=train_x,
grad_outputs=torch.ones_like(pred_yDot[k]),
create_graph=True, retain_graph=True)[0])
pred_yDot = torch.cat(pred_yDot, dim=1)
pred_yDDot = torch.cat(pred_yDDot, dim=1)
train_x.requires_grad_(False)
# Loss
loss_y = self.loss_fn(pred_y, train_y)
loss_yDot = self.loss_fn(pred_yDot, train_yDot)
loss_yDDot = self.loss_fn(pred_yDDot, train_yDDot)
# Regularizer
regularizer = 0
if reg_lambda:
for name, param in self.named_parameters():
if 'weight' in name:
regularizer += torch.square(param)
regularizer *= reg_lambda
# Backward
for param in self.parameters():
param.grad = None
(loss_y + loss_yDot + loss_yDDot + regularizer).backward()
self.optimizer1.step()
batch_end = time.time()
batch_computation_time = batch_end - batch_start
epoch_computation_time += batch_computation_time
# Save
batch_labels = torch.cat((train_y, train_yDot, train_yDDot), dim=1).detach()
batch_preds = torch.cat((pred_y, pred_yDot, pred_yDDot), dim=1).detach()
tLabel.append(batch_labels.cpu())
tPrediction.append(batch_preds.cpu())
# Print
if (epoch + 1) % print_every == 0 and int(len(self.train_dataloader) / 3) != 0 and (
trainbatch_idx + 1) % int(len(self.train_dataloader) / 3) == 0:
print(
f"Batch {trainbatch_idx + 1}/{len(self.train_dataloader)} loss: {loss_y:.6f}, {loss_yDot:.6f}, {loss_yDDot:.6f}")
tLabel = torch.cat(tLabel, dim=0)
tPrediction = torch.cat(tPrediction, dim=0)
tLoss_y = self.MSE((tLabel - tPrediction)[:, :len(y)])
tLoss_yDot = self.MSE((tLabel - tPrediction)[:, 1 * len(y):2 * len(y)])
tLoss_yDDot = self.MSE((tLabel - tPrediction)[:, 2 * len(y):3 * len(y)])
tLoss_yDDot_ = self.MSE(
(tLabel - tPrediction)[:, 2 * len(y):3 * len(y)] * (self.std_y.cpu() / self.std_x.cpu() ** 2))
# Validation step
vLabel = []
vPrediction = []
self.eval()
for validbatch in self.valid_dataloader:
if self.model_info['metamodeling']:
valid_x, valid_params, valid_y, valid_yDot, valid_yDDot = validbatch
valid_x = valid_x.cuda()
valid_params = valid_params.cuda()
valid_y = valid_y.cuda()
valid_yDot = valid_yDot.cuda()
valid_yDDot = valid_yDDot.cuda()
with torch.no_grad():
valid_x = (valid_x - self.mean_x) / self.std_x
valid_params = (valid_params - self.mean_params) / self.std_params
valid_y = (valid_y - self.mean_y) / self.std_y
valid_yDot = (valid_yDot) * (self.std_x / self.std_y)
valid_yDDot = (valid_yDDot) * (self.std_x ** 2 / self.std_y)
valid_x.requires_grad_(True)
pred_y = self.forward(torch.cat((valid_x, valid_params), dim=1))
pred_yDot = []
pred_yDDot = []
for k in range(len(y)):
pred_yDot.append(torch.autograd.grad(outputs=pred_y[:, k], inputs=valid_x,
grad_outputs=torch.ones_like(pred_y[:, k]),
create_graph=True, retain_graph=True)[0])
pred_yDDot.append(torch.autograd.grad(outputs=pred_yDot[k], inputs=valid_x,
grad_outputs=torch.ones_like(pred_yDot[k]),
create_graph=True, retain_graph=True)[0])
pred_yDot = torch.cat(pred_yDot, dim=1)
pred_yDDot = torch.cat(pred_yDDot, dim=1)
valid_x.requires_grad_(False)
else:
valid_x, valid_y, valid_yDot, valid_yDDot = validbatch
valid_x = valid_x.cuda()
valid_y = valid_y.cuda()
valid_yDot = valid_yDot.cuda()
valid_yDDot = valid_yDDot.cuda()
with torch.no_grad():
valid_x = (valid_x - self.mean_x) / self.std_x
valid_y = (valid_y - self.mean_y) / self.std_y
valid_yDot = (valid_yDot) * (self.std_x / self.std_y)
valid_yDDot = (valid_yDDot) * (self.std_x ** 2 / self.std_y)
valid_x.requires_grad_(True)
pred_y = self.forward(valid_x)
pred_yDot = []
pred_yDDot = []
for k in range(len(y)):
pred_yDot.append(torch.autograd.grad(outputs=pred_y[:, k], inputs=valid_x,
grad_outputs=torch.ones_like(pred_y[:, k]),
create_graph=True, retain_graph=True)[0])
pred_yDDot.append(torch.autograd.grad(outputs=pred_yDot[k], inputs=valid_x,
grad_outputs=torch.ones_like(pred_yDot[k]),
create_graph=True, retain_graph=True)[0])
pred_yDot = torch.cat(pred_yDot, dim=1)
pred_yDDot = torch.cat(pred_yDDot, dim=1)
valid_x.requires_grad_(False)
# Save
batch_labels = torch.cat((valid_y, valid_yDot, valid_yDDot), dim=1).detach()
batch_preds = torch.cat((pred_y, pred_yDot, pred_yDDot), dim=1).detach()
vLabel.append(batch_labels.cpu())
vPrediction.append(batch_preds.cpu())
self.train()
vLabel = torch.cat(vLabel, dim=0)
vPrediction = torch.cat(vPrediction, dim=0)
vLoss_y = self.MSE((vLabel - vPrediction)[:, :len(y)])
vLoss_yDot = self.MSE((vLabel - vPrediction)[:, len(y):2 * len(y)])
vLoss_yDDot = self.MSE((vLabel - vPrediction)[:, 2 * len(y):3 * len(y)])
vLoss_yDDot_ = self.MSE(
(vLabel - vPrediction)[:, 2 * len(y):3 * len(y)] * (self.std_y.cpu() / self.std_x.cpu() ** 2))
if valid_measure == 'mse':
vLoss = self.MSE(vLabel - vPrediction)
elif valid_measure == 'rms':
vLoss = self.RMS(vLabel, vPrediction)
try:
R2value = r2_score(vLabel, vPrediction, multioutput='raw_values')
except ValueError:
R2value = torch.zeros(vLabel.shape[1])
warnings.warn(f"R2 calculation encountered NaN")
tLoss_yDDot_history[epoch] = tLoss_yDDot_.item()
vLoss_yDDot_history[epoch] = vLoss_yDDot_.item()
if (epoch + 1) % print_every == 0:
print(f"Epoch {epoch + 1} (done in {epoch_computation_time:.4f} sec)")
print(f"Learning rate={self.optimizer1.param_groups[0]['lr']:.3e}")
print(f"Normalized loss1 = Train:{tLoss_y.item():.6f}, Valid:{vLoss_y.item():.6f}")
print(f"Normalized loss2 = Train:{tLoss_yDot.item():.6f}, Valid:{vLoss_yDot.item():.6f}")
print(f"Normalized loss3 = Train:{tLoss_yDDot.item():.6f}, Valid:{vLoss_yDDot.item():.6f}")
print(f"Validation measure({valid_measure.upper()}) = {vLoss.item():.6f}")
for outidx, r2value in enumerate(R2value):
print(f"R2({(y + yDot + yDDot)[outidx]})={r2value:.6f}")
print()
# Save state dict
if (epoch + 1) % save_every == 0:
model_parameter = self.state_dict()
for name, param in model_parameter.items():
model_parameter[name] = param.cpu()
model_validation_loss[epoch] = vLoss.item()
model_parameter_group[epoch] = model_parameter
# LR decay
if (epoch + 1) % lr_halflife == 0:
old_LR = self.optimizer1.param_groups[0]['lr']
self.optimizer1.param_groups[0]['lr'] /= 2
new_LR = self.optimizer1.param_groups[0]['lr']
print(f"LR decayed, {old_LR:.3e} -> {new_LR:.3e}")
print()
# End of training epoch
TrainingEndTime = time.time()
Hr, Min, Sec = self.Sec2Time(TrainingEndTime - TrainingStartTime)
print(f"Training finished in {Hr}hr {Min}min {Sec}sec.")
self.count_params()
if save:
idx_argmin = np.argmin(model_validation_loss)
print(f"Saving the best model:")
print(
f"Validation loss({valid_measure.upper()}) {model_validation_loss[idx_argmin]:.6f} at Epoch {idx_argmin + 1} ")
self.model_info['training_time'] = (TrainingEndTime - TrainingStartTime)
self.model_info['yddot_training_loss_history'] = tLoss_yDDot_history
self.model_info['yddot_validation_loss_history'] = vLoss_yDDot_history
self.model_info['model_state_dict'] = model_parameter_group[idx_argmin]
self.model_info = {key: value for key, value in sorted(self.model_info.items())} # arrange
if not os.path.exists('Models'):
os.mkdir("Models")
torch.save(self.model_info, f'Models/{filename}')
# Dictionary txt
f = open(f"Models/{filename.split('.')[0]}.txt", "w")
lines = []
exceptions = ["model_state_dict", ]
for k in self.model_info.keys():
if k in exceptions:
continue
else:
lines.append(f"{k} : {self.model_info[k]}\n")
f.writelines(lines)
f.close()
if model_parameter_group[idx_argmin] == None:
print(f"WARNING: Saved model parameter is None")
def forward_with_normalization(self, x):
self.eval()
x = x.cuda()
if self.metamodeling:
x = (x - torch.cat((self.mean_x, self.mean_params))) / torch.cat((self.std_x, self.std_params))
t = x[:, 0].view(-1, 1)
t.requires_grad_(True)
pred_y = self.forward(torch.cat((t, x[:, 1:]), dim=1))
pred_yDot = []
pred_yDDot = []
for k in range(len(y)):
pred_yDot.append(
torch.autograd.grad(outputs=pred_y[:, k], inputs=t, grad_outputs=torch.ones_like(pred_y[:, k]),
create_graph=True, retain_graph=True)[0])
pred_yDDot.append(
torch.autograd.grad(outputs=pred_yDot[k], inputs=t, grad_outputs=torch.ones_like(pred_yDot[k]),
create_graph=True, retain_graph=True)[0])
pred_yDot = torch.cat(pred_yDot, dim=1)
pred_yDDot = torch.cat(pred_yDDot, dim=1)
t.requires_grad_(False)
else:
x = (x - self.mean_x) / self.std_x
t = x
t.requires_grad_(True)
pred_y = self.forward(t)
pred_yDot = []
pred_yDDot = []
for k in range(len(y)):
pred_yDot.append(
torch.autograd.grad(outputs=pred_y[:, k], inputs=t, grad_outputs=torch.ones_like(pred_y[:, k]),
create_graph=True, retain_graph=True)[0])
pred_yDDot.append(
torch.autograd.grad(outputs=pred_yDot[k], inputs=t, grad_outputs=torch.ones_like(pred_yDot[k]),
create_graph=True, retain_graph=True)[0])
pred_yDot = torch.cat(pred_yDot, dim=1)
pred_yDDot = torch.cat(pred_yDDot, dim=1)
t.requires_grad_(False)
pred_y = (pred_y * self.std_y + self.mean_y).detach().cpu()
pred_yDot = (pred_yDot * (self.std_y / self.std_x)).detach().cpu()
pred_yDDot = (pred_yDDot * (self.std_y / self.std_x ** 2)).detach().cpu()
return pred_y, pred_yDot, pred_yDDot
def forward_without_normalization(self, x):
self.eval()
x = x.cuda()
if self.metamodeling:
x = (x - torch.cat((self.mean_x, self.mean_params))) / torch.cat((self.std_x, self.std_params))
t = x[:, 0].view(-1, 1)
t.requires_grad_(True)
pred_y = self.forward(torch.cat((t, x[:, 1:]), dim=1))
pred_yDot = []
pred_yDDot = []
for k in range(len(y)):
pred_yDot.append(
torch.autograd.grad(outputs=pred_y[:, k], inputs=t, grad_outputs=torch.ones_like(pred_y[:, k]),
create_graph=True, retain_graph=True)[0])
pred_yDDot.append(
torch.autograd.grad(outputs=pred_yDot[k], inputs=t, grad_outputs=torch.ones_like(pred_yDot[k]),
create_graph=True, retain_graph=True)[0])
pred_yDot = torch.cat(pred_yDot, dim=1)
pred_yDDot = torch.cat(pred_yDDot, dim=1)
t.requires_grad_(False)
else:
x = (x - self.mean_x) / self.std_x
t = x
t.requires_grad_(True)
pred_y = self.forward(t)
pred_yDot = []
pred_yDDot = []
for k in range(len(y)):
pred_yDot.append(
torch.autograd.grad(outputs=pred_y[:, k], inputs=t, grad_outputs=torch.ones_like(pred_y[:, k]),
create_graph=True, retain_graph=True)[0])
pred_yDDot.append(
torch.autograd.grad(outputs=pred_yDot[k], inputs=t, grad_outputs=torch.ones_like(pred_yDot[k]),
create_graph=True, retain_graph=True)[0])
pred_yDot = torch.cat(pred_yDot, dim=1)
pred_yDDot = torch.cat(pred_yDDot, dim=1)
t.requires_grad_(False)
pred_y = (pred_y).detach().cpu()
pred_yDot = ((pred_yDot * (self.std_y / self.std_x) - self.mean_yDot) / self.std_yDot).detach().cpu()
pred_yDDot = ((pred_yDDot * (self.std_y / self.std_x ** 2) - self.mean_yDDot) / self.std_yDDot).detach().cpu()
return pred_y, pred_yDot, pred_yDDot