mirror of
https://github.com/wassname/pytorch-ts.git
synced 2026-08-19 12:30:46 +08:00
76 lines
2.1 KiB
Python
76 lines
2.1 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
|
|
from pts.modules import DistributionOutput, MeanScaler, NOPScaler, FeatureEmbedder
|
|
|
|
|
|
class DeepARNetwork(nn.Module):
|
|
def __init__(
|
|
self,
|
|
num_layers: int,
|
|
num_cells: int,
|
|
cell_type: str,
|
|
history_length: int,
|
|
context_length: int,
|
|
prediction_length: int,
|
|
distr_output: DistributionOutput,
|
|
dropout_rate: float,
|
|
cardinality: List[int],
|
|
embedding_dimension: List[int],
|
|
lags_seq: List[int],
|
|
scaling: bool = True,
|
|
dtype: np.dtype = np.float32,
|
|
) -> None:
|
|
super().__init__()
|
|
self.num_layers = num_layers
|
|
self.num_cells = num_cells
|
|
self.cell_type = cell_type
|
|
self.history_length = history_length
|
|
self.context_length = context_length
|
|
self.prediction_length = prediction_length
|
|
self.dropout_rate = dropout_rate
|
|
self.cardinality = cardinality
|
|
self.embedding_dimension = embedding_dimension
|
|
self.num_cat = len(cardinality)
|
|
self.scaling = scaling
|
|
self.dtype = dtype
|
|
|
|
self.lags_seq = lags_seq
|
|
|
|
self.distr_output = distr_output
|
|
rnn = {"LSTM": nn.LSTM, "GRU": nn.GRU}[
|
|
self.cell_type
|
|
]
|
|
self.rnn = rnn(
|
|
input_size=1,
|
|
hidden_size=num_cells,
|
|
num_layers=num_layers,
|
|
dropout=dropout_rate,
|
|
batch_first=True
|
|
)
|
|
|
|
# TODO
|
|
# self.target_shape = distr_output.event_shape
|
|
|
|
self.proj_distr_args = distr_output.get_args_proj()
|
|
|
|
self.embedder = FeatureEmbedder(
|
|
cardinalities=cardinality,
|
|
embedding_dims=embedding_dimension
|
|
)
|
|
|
|
if scaling:
|
|
self.scaler = MeanScaler(keepdim=True)
|
|
else:
|
|
self.scaler = NOPScaler(keepdim=True)
|
|
|
|
@staticmethod
|
|
def get_lagged_subsequences(
|
|
sequence: torch.Tensor,
|
|
sequence_length: int,
|
|
indices: List[int],
|
|
subsequences_length: int = 1,
|
|
) -> torch.Tensor
|
|
|
|
class DeepARTrainingNetwork(DeepARNetwork):
|
|
pass |