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import os
import sys
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
from .embed import *
from .attention import *
from .encoder import *
from .decoder import *
from .variance import *
############################################
# Added for GluNet package
############################################
import optuna
import darts
from torch.utils.tensorboard import SummaryWriter
sys.path.append(os.path.join(os.path.dirname(__file__), '../..'))
from gluformer.utils.training import ExpLikeliLoss, \
EarlyStop, \
modify_collate, \
adjust_learning_rate
from utils.darts_dataset import SamplingDatasetDual
############################################
class Gluformer(nn.Module):
def __init__(self, d_model, n_heads, d_fcn, r_drop,
activ, num_enc_layers, num_dec_layers,
distil, len_seq, len_pred, num_dynamic_features,
num_static_features, label_len):
super(Gluformer, self).__init__()
# Set prediction length
self.len_pred = len_pred
self.label_len = label_len
# Embedding
# note: d_model // 2 == 0
self.enc_embedding = DataEmbedding(d_model, r_drop, num_dynamic_features, num_static_features)
self.dec_embedding = DataEmbedding(d_model, r_drop, num_dynamic_features, num_static_features)
# Encoding
self.encoder = Encoder(
[
EncoderLayer(
att=MultiheadAttention(d_model=d_model, n_heads=n_heads,
d_keys=d_model//n_heads, mask_flag=False,
r_att_drop=r_drop),
d_model=d_model,
d_fcn=d_fcn,
r_drop=r_drop,
activ=activ) for l in range(num_enc_layers)
],
[
ConvLayer(
d_model) for l in range(num_enc_layers-1)
] if distil else None,
norm_layer=torch.nn.LayerNorm(d_model)
)
# Decoding
self.decoder = Decoder(
[
DecoderLayer(
self_att=MultiheadAttention(d_model=d_model, n_heads=n_heads,
d_keys=d_model//n_heads, mask_flag=True,
r_att_drop=r_drop),
cross_att=MultiheadAttention(d_model=d_model, n_heads=n_heads,
d_keys=d_model//n_heads, mask_flag=False,
r_att_drop=r_drop),
d_model=d_model,
d_fcn=d_fcn,
r_drop=r_drop,
activ=activ) for l in range(num_dec_layers)
],
norm_layer=torch.nn.LayerNorm(d_model)
)
# Output
D_OUT = 1
self.projection = nn.Linear(d_model, D_OUT, bias=True)
# Train variance
self.var = Variance(d_model, r_drop, len_seq)
def forward(self, x_id, x_enc, x_mark_enc, x_dec, x_mark_dec):
enc_out = self.enc_embedding(x_id, x_enc, x_mark_enc)
var_out = self.var(enc_out)
enc_out = self.encoder(enc_out)
dec_out = self.dec_embedding(x_id, x_dec, x_mark_dec)
dec_out = self.decoder(dec_out, enc_out)
dec_out = self.projection(dec_out)
return dec_out[:, -self.len_pred:, :], var_out # [B, L, D], log variance
############################################
# Added for GluNet package
############################################
def fit(self,
train_dataset: SamplingDatasetDual,
val_dataset: SamplingDatasetDual,
learning_rate: float = 1e-3,
batch_size: int = 32,
epochs: int = 100,
num_samples: int = 100,
device: str = 'cuda',
model_path: str = None,
trial: optuna.trial.Trial = None,
logger: SummaryWriter = None,):
"""
Fit the model to the data, using Optuna for hyperparameter tuning.
Parameters
----------
train_dataset: SamplingDatasetPast
Training dataset.
val_dataset: SamplingDatasetPast
Validation dataset.
learning_rate: float
Learning rate for Adam.
batch_size: int
Batch size.
epochs: int
Number of epochs.
num_samples: int
Number of samples for infinite mixture
device: str
Device to use.
model_path: str
Path to save the model.
trial: optuna.trial.Trial
Trial for hyperparameter tuning.
logger: SummaryWriter
Tensorboard logger for logging.
"""
# create data loaders, optimizer, loss, and early stopping
collate_fn_custom = modify_collate(num_samples)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
drop_last=True,
collate_fn=collate_fn_custom)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=batch_size,
shuffle=True,
drop_last=True,
collate_fn=collate_fn_custom)
criterion = ExpLikeliLoss(num_samples)
optimizer = torch.optim.Adam(self.parameters(), lr=learning_rate, betas=(0.1, 0.9))
scaler = torch.cuda.amp.GradScaler()
early_stop = EarlyStop(patience=10, delta=0.001)
self.to(device)
# train and evaluate the model
for epoch in range(epochs):
train_loss = []
for i, (past_target_series,
past_covariates,
future_covariates,
static_covariates,
future_target_series) in enumerate(train_loader):
# zero out gradient
optimizer.zero_grad()
# reshape static covariates to be [batch_size, num_static_covariates]
static_covariates = static_covariates.reshape(-1, static_covariates.shape[-1])
# create decoder input: pad with zeros the prediction sequence
dec_inp = torch.cat([past_target_series[:, -self.label_len:, :],
torch.zeros([
past_target_series.shape[0],
self.len_pred,
past_target_series.shape[-1]
])],
dim=1)
future_covariates = torch.cat([past_covariates[:, -self.label_len:, :],
future_covariates], dim=1)
# move to device
dec_inp = dec_inp.to(device)
past_target_series = past_target_series.to(device)
past_covariates = past_covariates.to(device)
future_covariates = future_covariates.to(device)
static_covariates = static_covariates.to(device)
future_target_series = future_target_series.to(device)
# forward pass with autograd
with torch.cuda.amp.autocast():
pred, logvar = self(static_covariates,
past_target_series,
past_covariates,
dec_inp,
future_covariates)
loss = criterion(pred, future_target_series, logvar)
# backward pass
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
# log loss
if logger is not None:
logger.add_scalar('train_loss', loss.item(), epoch * len(train_loader) + i)
train_loss.append(loss.item())
# log loss
if logger is not None:
logger.add_scalar('train_loss_epoch', np.mean(train_loss), epoch)
# evaluate the model
val_loss = []
with torch.no_grad():
for i, (past_target_series,
past_covariates,
future_covariates,
static_covariates,
future_target_series) in enumerate(val_loader):
# reshape static covariates to be [batch_size, num_static_covariates]
static_covariates = static_covariates.reshape(-1, static_covariates.shape[-1])
# create decoder input
dec_inp = torch.cat([past_target_series[:, -self.label_len:, :],
torch.zeros([
past_target_series.shape[0],
self.len_pred,
past_target_series.shape[-1]
])],
dim=1)
future_covariates = torch.cat([past_covariates[:, -self.label_len:, :],
future_covariates], dim=1)
# move to device
dec_inp = dec_inp.to(device)
past_target_series = past_target_series.to(device)
past_covariates = past_covariates.to(device)
future_covariates = future_covariates.to(device)
static_covariates = static_covariates.to(device)
future_target_series = future_target_series.to(device)
# forward pass
pred, logvar = self(static_covariates,
past_target_series,
past_covariates,
dec_inp,
future_covariates)
loss = criterion(pred, future_target_series, logvar)
val_loss.append(loss.item())
# log loss
if logger is not None:
logger.add_scalar('val_loss', loss.item(), epoch * len(val_loader) + i)
# log loss
logger.add_scalar('val_loss_epoch', np.mean(val_loss), epoch)
# check early stopping
early_stop(np.mean(val_loss), self, model_path)
if early_stop.stop:
break
# check pruning
if trial is not None:
trial.report(np.mean(val_loss), epoch)
if trial.should_prune():
raise optuna.exceptions.TrialPruned()
# load best model
if model_path is not None:
self.load_state_dict(torch.load(model_path))
def predict(self, test_dataset: SamplingDatasetDual,
batch_size: int = 32,
num_samples: int = 100,
device: str = 'cuda'):
"""
Predict the future target series given the supplied samples from the dataset.
Parameters
----------
test_dataset : SamplingDatasetInferenceDual
The dataset to use for inference.
batch_size : int, optional
The batch size to use for inference, by default 32
num_samples : int, optional
The number of samples to use for inference, by default 100
Returns
-------
Predictions
The predicted future target series in shape n x len_pred x num_samples, where
n is total number of predictions.
Logvar
The logvariance of the predicted future target series in shape n x len_pred.
"""
# define data loader
collate_fn_custom = modify_collate(num_samples)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=batch_size,
shuffle=False,
drop_last=False,
collate_fn=collate_fn_custom)
# predict
self.train()
# move to device
self.to(device)
predictions = []; logvars = []
for i, (past_target_series,
historic_future_covariates,
future_covariates,
static_covariates) in enumerate(test_loader):
# reshape static covariates to be [batch_size, num_static_covariates]
static_covariates = static_covariates.reshape(-1, static_covariates.shape[-1])
# create decoder input
dec_inp = torch.cat([past_target_series[:, -self.label_len:, :],
torch.zeros([
past_target_series.shape[0],
self.len_pred,
past_target_series.shape[-1]
])],
dim=1)
future_covariates = torch.cat([historic_future_covariates[:, -self.label_len:, :],
future_covariates], dim=1)
# move to device
dec_inp = dec_inp.to(device)
past_target_series = past_target_series.to(device)
historic_future_covariates = historic_future_covariates.to(device)
future_covariates = future_covariates.to(device)
static_covariates = static_covariates.to(device)
# forward pass
pred, logvar = self(static_covariates,
past_target_series,
historic_future_covariates,
dec_inp,
future_covariates)
# transfer in numpy and arrange sample along last axis
pred = pred.cpu().detach().numpy()
logvar = logvar.cpu().detach().numpy()
pred = pred.transpose((1, 0, 2)).reshape((pred.shape[1], -1, num_samples)).transpose((1, 0, 2))
logvar = logvar.transpose((1, 0, 2)).reshape((logvar.shape[1], -1, num_samples)).transpose((1, 0, 2))
predictions.append(pred)
logvars.append(logvar)
predictions = np.concatenate(predictions, axis=0)
logvars = np.concatenate(logvars, axis=0)
return predictions, logvars
############################################
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