Updated Imputeformer

pypots/imputation/imputeformer/__init__.py

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+"""
+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 Wenjie Du <wenjay.du@gmail.com>
+# License: BSD-3-Clause
+
+
+from .model import Imputeformer
+
+__all__ = [
+    "Imputeformer",
+]

pypots/imputation/imputeformer/core.py

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+"""
+The core wrapper assembles the submodules of Imputeformer imputation model
+and takes over the forward progress of the algorithm.
+"""
+
+# Created by Wenjie Du <wenjay.du@gmail.com>
+# License: BSD-3-Clause
+
+import torch
+import torch.nn as nn
+
+from ...nn.modules.saits import SaitsLoss
+from ...nn.modules.imputeformer import EmbeddedAttentionLayer, ProjectedAttentionLayer, MLP
+from einops import repeat
+
+
+class _Imputeformer(nn.Module):
+    """
+    Spatiotempoarl 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,
+            num_temporal_heads: int,
+            dropout: float = 0.,
+            input_dim: int = 1,
+            output_dim: int = 1,
+            ORT_weight: float = 1,
+            MIT_weight: float = 1,
+    ):
+        super().__init__()
+
+        self.num_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
+        model_dim = d_input_embed + d_learnable_embed
+        self.model_dim = model_dim
+
+        self.num_temporal_heads = num_temporal_heads
+        self.num_layers = n_layers
+        self.input_proj = nn.Linear(input_dim, self.input_embedding_dim)
+        self.dim_proj = d_proj
+
+        self.learnable_embedding = nn.init.xavier_uniform_(
+            nn.Parameter(torch.empty(self.in_steps, self.num_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.num_nodes, self.dim_proj, self.model_dim, num_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, 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
+

pypots/imputation/imputeformer/data.py

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+"""
+Dataset class for the imputation model Imputeformer.
+"""
+
+# Created by Wenjie Du <wenjay.du@gmail.com>
+# 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)