Merge pull request #401 from WenjieDu/(feat)add_micn

Add MICN modules and implement it as an imputation model

pypots/imputation/__init__.py

@@ -29,6 +29,7 @@
 from .scinet import SCINet
 from .revinscinet import RevIN_SCINet
 from .koopa import Koopa
+from .micn import MICN
 
 # naive imputation methods
 from .locf import LOCF
@@ -60,6 +61,7 @@
  "SCINet",
  "RevIN_SCINet",
  "Koopa",
+ "MICN",
  # naive imputation methods
  "LOCF",
  "Mean",

pypots/imputation/micn/__init__.py

@@ -0,0 +1,24 @@
+"""
+The package of the partially-observed time-series imputation model MICN.
+
+Refer to the paper
+`Huiqiang Wang, Jian Peng, Feihu Huang, Jince Wang, Junhui Chen, and Yifei Xiao
+"MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting".
+In the Eleventh International Conference on Learning Representations, 2023.
+<https://openreview.net/pdf?id=zt53IDUR1U>`_
+
+Notes
+-----
+This implementation is inspired by the official one https://github.com/wanghq21/MICN
+
+"""
+
+# Created by Wenjie Du <[email protected]>
+# License: BSD-3-Clause
+
+
+from .model import MICN
+
+__all__ = [
+ "MICN",
+]

pypots/imputation/micn/core.py

@@ -0,0 +1,95 @@
+"""
+The core wrapper assembles the submodules of MICN imputation model
+and takes over the forward progress of the algorithm.
+"""
+
+# Created by Wenjie Du <[email protected]>
+# License: BSD-3-Clause
+
+import torch.nn as nn
+
+from ...nn.modules.fedformer.layers import SeriesDecompositionMultiBlock
+from ...nn.modules.micn import BackboneMICN
+from ...nn.modules.saits import SaitsLoss, SaitsEmbedding
+
+
+class _MICN(nn.Module):
+ def __init__(
+ self,
+ n_steps: int,
+ n_features: int,
+ n_layers: int,
+ d_model: int,
+ dropout: float,
+ conv_kernel: list = None,
+ ORT_weight: float = 1,
+ MIT_weight: float = 1,
+ ):
+ super().__init__()
+
+ self.saits_embedding = SaitsEmbedding(
+ n_features * 2,
+ d_model,
+ with_pos=True,
+ dropout=dropout,
+ )
+
+ decomp_kernel = [] # kernel of decomposition operation
+ isometric_kernel = [] # kernel of isometric convolution
+ for ii in conv_kernel:
+ if ii % 2 == 0: # the kernel of decomposition operation must be odd
+ decomp_kernel.append(ii + 1)
+ isometric_kernel.append((n_steps + n_steps + ii) // ii)
+ else:
+ decomp_kernel.append(ii)
+ isometric_kernel.append((n_steps + n_steps + ii - 1) // ii)
+
+ self.decomp_multi = SeriesDecompositionMultiBlock(decomp_kernel)
+ self.backbone = BackboneMICN(
+ n_steps,
+ n_features,
+ n_steps,
+ n_features,
+ n_layers,
+ d_model,
+ decomp_kernel,
+ isometric_kernel,
+ conv_kernel,
+ )
+
+ # for the imputation task, the output dim is the same as input dim
+ 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"]
+
+ seasonal_init, trend_init = self.decomp_multi(X)
+
+ # WDU: the original MICN paper isn't proposed for imputation task. Hence the model doesn't take
+ # the missing mask into account, which means, in the process, the model doesn't know which part of
+ # the input data is missing, and this may hurt the model's imputation performance. Therefore, I apply the
+ # SAITS embedding method to project the concatenation of features and masks into a hidden space, as well as
+ # the output layers to project back from the hidden space to the original space.
+ enc_out = self.saits_embedding(seasonal_init, missing_mask)
+
+ # MICN encoder processing
+ reconstruction = self.backbone(enc_out)
+ reconstruction = reconstruction + trend_init
+
+ imputed_data = missing_mask * X + (1 - missing_mask) * reconstruction
+ 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/micn/data.py

@@ -0,0 +1,24 @@
+"""
+Dataset class for MICN.
+"""
+
+# Created by Wenjie Du <[email protected]>
+# License: BSD-3-Clause
+
+from typing import Union
+
+from ..saits.data import DatasetForSAITS
+
+
+class DatasetForMICN(DatasetForSAITS):
+ """Actually MICN uses the same data strategy as SAITS, needs MIT for training."""
+
+ 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)