Add FITS

pypots/imputation/fits/__init__.py

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+"""
+The package including the modules of FITS.
+
+Refer to the paper
+`Zhijian Xu, Ailing Zeng, and Qiang Xu.
+FITS: Modeling Time Series with 10k parameters.
+In The Twelfth International Conference on Learning Representations, 2024.
+<https://openreview.net/pdf?id=bWcnvZ3qMb>`_
+
+Notes
+-----
+This implementation is inspired by the official one https://github.com/VEWOXIC/FITS
+
+"""
+
+# Created by Wenjie Du <wenjay.du@gmail.com>
+# License: BSD-3-Clause
+
+
+from .model import FITS
+
+__all__ = [
+    "FITS",
+]

pypots/imputation/fits/core.py

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+"""
+The core wrapper assembles the submodules of FITS 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.nn as nn
+
+from ...nn.functional import nonstationary_norm, nonstationary_denorm
+from ...nn.modules.fits import BackboneFITS
+from ...nn.modules.saits import SaitsLoss, SaitsEmbedding
+
+
+class _FITS(nn.Module):
+    def __init__(
+        self,
+        n_steps: int,
+        n_features: int,
+        cut_freq: int,
+        individual: bool,
+        ORT_weight: float = 1,
+        MIT_weight: float = 1,
+        apply_nonstationary_norm: bool = False,
+    ):
+        super().__init__()
+
+        self.n_steps = n_steps
+        self.apply_nonstationary_norm = apply_nonstationary_norm
+
+        self.saits_embedding = SaitsEmbedding(
+            n_features * 2,
+            n_features,
+            with_pos=False,
+        )
+        self.backbone = BackboneFITS(
+            n_steps,
+            n_features,
+            0,  # n_pred_steps is not used in the imputation task
+            cut_freq,
+            individual,
+        )
+
+        # for the imputation task, the output dim is the same as input dim
+        self.output_projection = nn.Linear(n_features, n_features)
+        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"]
+
+        if self.apply_nonstationary_norm:
+            # Normalization from Non-stationary Transformer
+            X, means, stdev = nonstationary_norm(X, missing_mask)
+
+        # WDU: the original FITS 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(X, missing_mask)
+
+        # FITS encoder processing
+        enc_out = self.backbone(enc_out)
+        if self.apply_nonstationary_norm:
+            # De-Normalization from Non-stationary Transformer
+            enc_out = nonstationary_denorm(enc_out, means, stdev)
+
+        # project back the original data space
+        reconstruction = self.output_projection(enc_out)
+
+        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/fits/data.py

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+"""
+Dataset class for FITS.
+"""
+
+# Created by Wenjie Du <wenjay.du@gmail.com>
+# License: BSD-3-Clause
+
+from typing import Union
+
+from ..saits.data import DatasetForSAITS
+
+
+class DatasetForFITS(DatasetForSAITS):
+    """Actually FITS 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)