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https://github.com/wassname/pytorch-ts.git
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192 lines
7.3 KiB
Python
192 lines
7.3 KiB
Python
from typing import List
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import torch
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import torch.nn as nn
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import numpy as np
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from pts.modules import DistributionOutput, MeanScaler, NOPScaler, FeatureEmbedder
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class DeepARNetwork(nn.Module):
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def __init__(
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self,
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num_layers: int,
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num_cells: int,
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cell_type: str,
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history_length: int,
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context_length: int,
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prediction_length: int,
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distr_output: DistributionOutput,
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dropout_rate: float,
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cardinality: List[int],
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embedding_dimension: List[int],
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lags_seq: List[int],
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scaling: bool = True,
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dtype: np.dtype = np.float32,
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) -> None:
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super().__init__()
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self.num_layers = num_layers
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self.num_cells = num_cells
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self.cell_type = cell_type
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self.history_length = history_length
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self.context_length = context_length
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self.prediction_length = prediction_length
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self.dropout_rate = dropout_rate
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self.cardinality = cardinality
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self.embedding_dimension = embedding_dimension
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self.num_cat = len(cardinality)
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self.scaling = scaling
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self.dtype = dtype
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self.lags_seq = lags_seq
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self.distr_output = distr_output
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rnn = {"LSTM": nn.LSTM, "GRU": nn.GRU}[self.cell_type]
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self.rnn = rnn(input_size=1,
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hidden_size=num_cells,
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num_layers=num_layers,
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dropout=dropout_rate,
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batch_first=True)
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# TODO
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# self.target_shape = distr_output.event_shape
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self.proj_distr_args = distr_output.get_args_proj()
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self.embedder = FeatureEmbedder(cardinalities=cardinality,
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embedding_dims=embedding_dimension)
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if scaling:
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self.scaler = MeanScaler(keepdim=True)
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else:
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self.scaler = NOPScaler(keepdim=True)
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@staticmethod
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def get_lagged_subsequences(
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sequence: torch.Tensor,
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sequence_length: int,
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indices: List[int],
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subsequences_length: int = 1,
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) -> torch.Tensor:
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"""
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Returns lagged subsequences of a given sequence.
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Parameters
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----------
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sequence : Tensor
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the sequence from which lagged subsequences should be extracted.
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Shape: (N, T, C).
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sequence_length : int
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length of sequence in the T (time) dimension (axis = 1).
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indices : List[int]
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list of lag indices to be used.
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subsequences_length : int
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length of the subsequences to be extracted.
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Returns
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--------
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lagged : Tensor
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a tensor of shape (N, S, C, I), where S = subsequences_length and
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I = len(indices), containing lagged subsequences. Specifically,
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lagged[i, j, :, k] = sequence[i, -indices[k]-S+j, :].
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"""
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assert max(indices) + subsequences_length <= sequence_length, (
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f"lags cannot go further than history length, found lag {max(indices)} "
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f"while history length is only {sequence_length}")
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assert all(lag_index >= 0 for lag_index in indices)
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lagged_values = []
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for lag_index in indices:
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begin_index = -lag_index - subsequences_length
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end_index = -lag_index if lag_index > 0 else None
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lagged_values.append(sequence[:, begin_index:end_index, ...])
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return torch.stack(lagged_values, dim=-1)
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@staticmethod
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def weighted_average(tensor: torch.Tensor,
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weights: Optional[torch.Tensor] = None,
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dim=None):
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if weights is not None:
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weighted_tensor = tensor * weights
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sum_weights = torch.max(torch.ones_like(weights.sum(dim=dim)),
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weights.sum(dim=dim))
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return weighted_tensor.sum(dim=dim) / sum_weights
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else:
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return tensor.mean(dim=dim)
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def unroll_encoder(
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self,
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feat_static_cat: torch.Tensor, # (batch_size, num_features)
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feat_static_real: torch.Tensor, # (batch_size, num_features)
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past_time_feat: torch.Tensor, # (batch_size, history_length, num_features)
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past_target: torch.Tensor, # (batch_size, history_length, *target_shape)
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past_observed_values: torch.Tensor, # (batch_size, history_length, *target_shape)
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future_time_feat: Optional[
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torch.Tensor]=None, # (batch_size, prediction_length, num_features)
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future_target: Optional[
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torch.Tensor]=None, # (batch_size, prediction_length, *target_shape)
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) -> Tuple[torch.Tensor, List, torch.Tensor, torch.Tensor]:
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if future_time_feat is None or future_target is None:
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time_feat = past_time_feat[:,self.history_length - self.context_length:,...]
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sequence = past_target
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sequence_length = self.history_length
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subsequences_length = self.context_length
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else:
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time_feat = torch.cat(
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(
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past_time_feat[:,self.history_length - self.context_length:,...],
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future_time_feat,
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),
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dim=1)
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sequence = torch.cat((past_target, future_target), dim=1)
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sequence_length = self.history_length + self.prediction_length
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subsequences_length = self.context_length + self.prediction_length
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lags = self.get_lagged_subsequences(
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sequence=sequence,
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sequence_length=sequence_length,
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indices=self.lags_seq,
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subsequences_length=subsequences_length)
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# scale is computed on the context length last units of the past target
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# scale shape is (batch_size, 1, *target_shape)
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_, scale = self.scaler(
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past_target[:,self.context_length:,...],
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past_observed_values[:,self.context_length:,...]
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)
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# (batch_size, num_features)
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embedded_cat = self.embedder(feat_static_cat)
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# in addition to embedding features, use the log scale as it can help
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# prediction too
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# (batch_size, num_features + prod(target_shape))
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static_feat = torch.cat((
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embedded_cat,
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feat_static_real,
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scale.log()
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if len(self.target_shape) == 0
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else scale.squeeze(1).log()
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), dim=1)
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# (batch_size, subsequences_length, num_features + 1)
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repeated_static_feat = static_feat.expand(-1, subsequences_length, -1)
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# (batch_size, sub_seq_len, *target_shape, num_lags)
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lags_scaled = lags / scale.unsqueeze(-1)
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# from (batch_size, sub_seq_len, *target_shape, num_lags)
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# to (batch_size, sub_seq_len, prod(target_shape) * num_lags)
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input_lags = lags_scaled.reshape((-1, subsequences_length, len(self.lags_seq) * prod(self.target_shape)))
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# unroll encoder
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outputs, state = self.rnn(inputs)
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# outputs: (batch_size, seq_len, num_cells)
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# state: list of (batch_size, num_cells) tensors
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# scale: (batch_size, 1, *target_shape)
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# static_feat: (batch_size, num_features + prod(target_shape))
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return outputs, state, scale, static_feat
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class DeepARTrainingNetwork(DeepARNetwork):
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pass |