-
-
Notifications
You must be signed in to change notification settings - Fork 110
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #454 from WenjieDu/dev
Add ImputeFormer, fix RevIN, and update docs
- Loading branch information
Showing
14 changed files
with
1,007 additions
and
32 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,20 @@ | ||
| """ | ||
| The package of the partially-observed time-series imputation model ImputeFormer. | ||
| Refer to the papers | ||
| `Tong Nie, Guoyang Qin, Wei Ma, Yuewen Mei, Jian Sun. | ||
| "ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation" | ||
| KDD 2024. | ||
| <https://doi.org/10.48550/arXiv.2312.01728>`_ | ||
| """ | ||
|
|
||
| # Created by Tong Nie <[email protected]> and Wenjie Du <[email protected]> | ||
| # License: BSD-3-Clause | ||
|
|
||
|
|
||
| from .model import ImputeFormer | ||
|
|
||
| __all__ = [ | ||
| "ImputeFormer", | ||
| ] |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,151 @@ | ||
| """ | ||
| The core wrapper assembles the submodules of ImputeFormer imputation model | ||
| and takes over the forward progress of the algorithm. | ||
| """ | ||
|
|
||
| # Created by Tong Nie <[email protected]> and Wenjie Du <[email protected]> | ||
| # License: BSD-3-Clause | ||
|
|
||
| import torch | ||
| import torch.nn as nn | ||
|
|
||
| from ...nn.modules.imputeformer import ( | ||
| EmbeddedAttentionLayer, | ||
| ProjectedAttentionLayer, | ||
| MLP, | ||
| ) | ||
| from ...nn.modules.saits import SaitsLoss | ||
|
|
||
|
|
||
| class _ImputeFormer(nn.Module): | ||
| """ | ||
| Spatiotemporal Imputation Transformer induced by low-rank factorization, KDD'24. | ||
| Note: | ||
| This is a simplified implementation under the SAITS framework (ORT+MIT). | ||
| The timestamp encoding is also removed for ease of implementation. | ||
| """ | ||
|
|
||
| def __init__( | ||
| self, | ||
| n_steps: int, | ||
| n_features: int, | ||
| n_layers: int, | ||
| d_input_embed: int, | ||
| d_learnable_embed: int, | ||
| d_proj: int, | ||
| d_ffn: int, | ||
| n_temporal_heads: int, | ||
| dropout: float = 0.0, | ||
| input_dim: int = 1, | ||
| output_dim: int = 1, | ||
| ORT_weight: float = 1, | ||
| MIT_weight: float = 1, | ||
| ): | ||
| super().__init__() | ||
|
|
||
| self.n_nodes = n_features | ||
| self.in_steps = n_steps | ||
| self.out_steps = n_steps | ||
| self.input_dim = input_dim | ||
| self.output_dim = output_dim | ||
| self.input_embedding_dim = d_input_embed | ||
| self.learnable_embedding_dim = d_learnable_embed | ||
| self.model_dim = d_input_embed + d_learnable_embed | ||
|
|
||
| self.n_temporal_heads = n_temporal_heads | ||
| self.num_layers = n_layers | ||
| self.input_proj = nn.Linear(input_dim, self.input_embedding_dim) | ||
| self.d_proj = d_proj | ||
| self.d_ffn = d_ffn | ||
|
|
||
| self.learnable_embedding = nn.init.xavier_uniform_( | ||
| nn.Parameter( | ||
| torch.empty(self.in_steps, self.n_nodes, self.learnable_embedding_dim) | ||
| ) | ||
| ) | ||
|
|
||
| self.readout = MLP(self.model_dim, self.model_dim, output_dim, n_layers=2) | ||
|
|
||
| self.attn_layers_t = nn.ModuleList( | ||
| [ | ||
| ProjectedAttentionLayer( | ||
| self.n_nodes, | ||
| self.d_proj, | ||
| self.model_dim, | ||
| self.n_temporal_heads, | ||
| self.model_dim, | ||
| dropout, | ||
| ) | ||
| for _ in range(self.num_layers) | ||
| ] | ||
| ) | ||
|
|
||
| self.attn_layers_s = nn.ModuleList( | ||
| [ | ||
| EmbeddedAttentionLayer( | ||
| self.model_dim, | ||
| self.learnable_embedding_dim, | ||
| self.d_ffn, | ||
| ) | ||
| for _ in range(self.num_layers) | ||
| ] | ||
| ) | ||
|
|
||
| # apply SAITS loss function to Transformer on the imputation task | ||
| self.saits_loss_func = SaitsLoss(ORT_weight, MIT_weight) | ||
|
|
||
| def forward(self, inputs: dict, training: bool = True) -> dict: | ||
| x, missing_mask = inputs["X"], inputs["missing_mask"] | ||
|
|
||
| # x: (batch_size, in_steps, num_nodes) | ||
| # Note that ImputeFormer is designed for Spatial-Temporal data that has the format [B, S, N, C], | ||
| # where N is the number of nodes and C is an additional feature dimension, | ||
| # We simply add an extra axis here for implementation. | ||
| x = x.unsqueeze(-1) # [b s n c] | ||
| missing_mask = missing_mask.unsqueeze(-1) # [b s n c] | ||
| batch_size = x.shape[0] | ||
| # Whiten missing values | ||
| x = x * missing_mask | ||
| x = self.input_proj(x) # (batch_size, in_steps, num_nodes, input_embedding_dim) | ||
|
|
||
| # Learnable node embedding | ||
| node_emb = self.learnable_embedding.expand( | ||
| batch_size, *self.learnable_embedding.shape | ||
| ) | ||
| x = torch.cat( | ||
| [x, node_emb], dim=-1 | ||
| ) # (batch_size, in_steps, num_nodes, model_dim) | ||
|
|
||
| # Spatial and temporal processing with customized attention layers | ||
| x = x.permute(0, 2, 1, 3) # [b n s c] | ||
| for att_t, att_s in zip(self.attn_layers_t, self.attn_layers_s): | ||
| x = att_t(x) | ||
| x = att_s(x, self.learnable_embedding, dim=1) | ||
|
|
||
| # Readout | ||
| x = x.permute(0, 2, 1, 3) # [b s n c] | ||
| reconstruction = self.readout(x) | ||
| reconstruction = reconstruction.squeeze(-1) # [b s n] | ||
| missing_mask = missing_mask.squeeze(-1) # [b s n] | ||
|
|
||
| # Below is the SAITS processing pipeline: | ||
| # replace the observed part with values from X | ||
| imputed_data = missing_mask * inputs["X"] + (1 - missing_mask) * reconstruction | ||
|
|
||
| # ensemble the results as a dictionary for return | ||
| results = { | ||
| "imputed_data": imputed_data, | ||
| } | ||
|
|
||
| # if in training mode, return results with losses | ||
| if training: | ||
| X_ori, indicating_mask = inputs["X_ori"], inputs["indicating_mask"] | ||
| loss, ORT_loss, MIT_loss = self.saits_loss_func( | ||
| reconstruction, X_ori, missing_mask, indicating_mask | ||
| ) | ||
| results["ORT_loss"] = ORT_loss | ||
| results["MIT_loss"] = MIT_loss | ||
| # `loss` is always the item for backward propagating to update the model | ||
| results["loss"] = loss | ||
|
|
||
| return results |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,22 @@ | ||
| """ | ||
| Dataset class for the imputation model ImputeFormer. | ||
| """ | ||
|
|
||
| # Created by Tong Nie <[email protected]> and Wenjie Du <[email protected]> | ||
| # License: BSD-3-Clause | ||
|
|
||
| from typing import Union | ||
|
|
||
| from ..saits.data import DatasetForSAITS | ||
|
|
||
|
|
||
| class DatasetForImputeFormer(DatasetForSAITS): | ||
| def __init__( | ||
| self, | ||
| data: Union[dict, str], | ||
| return_X_ori: bool, | ||
| return_y: bool, | ||
| file_type: str = "hdf5", | ||
| rate: float = 0.2, | ||
| ): | ||
| super().__init__(data, return_X_ori, return_y, file_type, rate) |
Oops, something went wrong.