Add SegRNN Implementation

README.md

@@ -126,6 +126,7 @@ The paper references and links are all listed at the bottom of this file.
 | Neural Net    | iTransformer🧑‍🔧[^24]                                                                                                           |    ✅     |          |          |          |          | `2024 - ICLR`      |
 | Neural Net    | ModernTCN[^38]                                                                                                                   |    ✅     |          |          |          |          | `2024 - ICLR`      |
 | Neural Net    | ImputeFormer🧑‍🔧[^34]                                                                                                           |    ✅     |          |          |          |          | `2024 - KDD`       |
+| Neural Net    | SegRNN[^42]                                                                                                                      |    ✅     |          |          |          |          | `2023 - arXiv`     |
 | Neural Net    | SAITS[^1]                                                                                                                        |    ✅     |          |          |          |          | `2023 - ESWA`      |
 | Neural Net    | FreTS🧑‍🔧[^23]                                                                                                                  |    ✅     |          |          |          |          | `2023 - NeurIPS`   |
 | Neural Net    | Koopa🧑‍🔧[^29]                                                                                                                  |    ✅     |          |          |          |          | `2023 - NeurIPS`   |
@@ -509,3 +510,6 @@ Time-Series.AI</a>
 [^41]: Xu, Z., Zeng, A., & Xu, Q. (2024).
 [FITS: Modeling Time Series with 10k parameters](https://openreview.net/forum?id=bWcnvZ3qMb).
 *ICLR 2024*.
+[^42]: Lin, S., Lin, W., Wu, W., Zhao, F., Mo, R., & Zhang, H. (2023). 
+[Segrnn: Segment recurrent neural network for long-term time series forecasting](https://github.com/lss-1138/SegRNN)
+*arXiv 2023*.

pypots/classification/__init__.py

@@ -6,10 +6,12 @@
 # License: BSD-3-Clause
 
 from .brits import BRITS
+from .csai import CSAI
 from .grud import GRUD
 from .raindrop import Raindrop
 
 __all__ = [
+    "CSAI",
     "BRITS",
     "GRUD",
     "Raindrop",

pypots/classification/csai/__init__.py

@@ -0,0 +1,20 @@
+"""
+The package including the modules of CSAI.
+
+Refer to the paper
+`Linglong Qian, Zina Ibrahim, Hugh Logan Ellis, Ao Zhang, Yuezhou Zhang, Tao Wang, Richard Dobson.
+Knowledge Enhanced Conditional Imputation for Healthcare Time-series.
+In Arxiv, 2024.
+<https://arxiv.org/abs/2312.16713>`_
+
+Notes
+-----
+This implementation is inspired by the official one the official implementation https://github.com/LinglongQian/CSAI.
+
+"""
+
+from .model import CSAI
+
+__all__ = [
+    "CSAI",
+]

pypots/classification/csai/core.py

@@ -0,0 +1,123 @@
+"""
+
+"""
+
+# Created by Linglong Qian, Joseph Arul Raj <[email protected], [email protected]>
+# License: BSD-3-Clause
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ...nn.modules.csai import BackboneBCSAI
+
+# class DiceBCELoss(nn.Module):
+#     def __init__(self, weight=None, size_average=True):
+#         super(DiceBCELoss, self).__init__()
+#         self.bcelogits = nn.BCEWithLogitsLoss()
+
+#     def forward(self, y_score, y_out, targets, smooth=1):
+
+#         #comment out if your model contains a sigmoid or equivalent activation layer
+#         # inputs = F.sigmoid(inputs)       
+
+#         #flatten label and prediction tensors
+#         BCE = self.bcelogits(y_out, targets)
+
+#         y_score = y_score.view(-1)
+#         targets = targets.view(-1)
+#         intersection = (y_score * targets).sum()
+#         dice_loss = 1 - (2.*intersection + smooth)/(y_score.sum() + targets.sum() + smooth)
+
+#         Dice_BCE = BCE + dice_loss
+
+#         return BCE, Dice_BCE
+
+
+class _BCSAI(nn.Module):
+    def __init__(
+            self,
+            n_steps: int,
+            n_features: int,
+            rnn_hidden_size: int,
+            imputation_weight: float,
+            consistency_weight: float,
+            classification_weight: float,
+            n_classes: int,
+            step_channels: int,
+            dropout: float = 0.5,
+            intervals=None,
+    ):
+        super().__init__()
+        self.n_steps = n_steps
+        self.n_features = n_features
+        self.rnn_hidden_size = rnn_hidden_size
+        self.imputation_weight = imputation_weight
+        self.consistency_weight = consistency_weight
+        self.classification_weight = classification_weight
+        self.n_classes = n_classes
+        self.step_channels = step_channels
+        self.intervals = intervals
+
+        # create models
+        self.model = BackboneBCSAI(n_steps, n_features, rnn_hidden_size, step_channels, intervals)
+        self.f_classifier = nn.Linear(self.rnn_hidden_size, n_classes)
+        self.b_classifier = nn.Linear(self.rnn_hidden_size, n_classes)
+        self.imputer = nn.Linear(self.rnn_hidden_size, n_features)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, inputs: dict, training: bool = True) -> dict:
+
+        (
+            imputed_data,
+            f_reconstruction,
+            b_reconstruction,
+            f_hidden_states,
+            b_hidden_states,
+            consistency_loss,
+            reconstruction_loss,
+        ) = self.model(inputs)
+
+        results = {
+            "imputed_data": imputed_data,
+        }
+
+        f_logits = self.f_classifier(self.dropout(f_hidden_states))
+        b_logits = self.b_classifier(self.dropout(b_hidden_states))
+
+        # f_prediction = torch.sigmoid(f_logits)
+        # b_prediction = torch.sigmoid(b_logits)
+
+        f_prediction = torch.softmax(f_logits, dim=1)
+        b_prediction = torch.softmax(b_logits, dim=1)
+        classification_pred = (f_prediction + b_prediction) / 2
+
+        results = {
+            "imputed_data": imputed_data,
+            "classification_pred": classification_pred,
+        }
+
+        # if in training mode, return results with losses
+        if training:
+            # criterion = DiceBCELoss().to(imputed_data.device)
+            results["consistency_loss"] = consistency_loss
+            results["reconstruction_loss"] = reconstruction_loss
+            # print(inputs["labels"].unsqueeze(1))
+            f_classification_loss = F.nll_loss(torch.log(f_prediction), inputs["labels"])
+            b_classification_loss = F.nll_loss(torch.log(b_prediction), inputs["labels"])
+            # f_classification_loss, _ = criterion(f_prediction, f_logits, inputs["labels"].unsqueeze(1).float())
+            # b_classification_loss, _ = criterion(b_prediction, b_logits, inputs["labels"].unsqueeze(1).float())
+            classification_loss = (f_classification_loss + b_classification_loss) 
+
+            loss = (
+                self.consistency_weight * consistency_loss + 
+                self.imputation_weight * reconstruction_loss +
+                self.classification_weight * classification_loss
+            )
+
+            results["loss"] = loss
+            results["classification_loss"] = classification_loss
+            results["f_reconstruction"] = f_reconstruction
+            results["b_reconstruction"] = b_reconstruction
+
+        return results

pypots/classification/csai/data.py

@@ -0,0 +1,39 @@
+"""
+
+"""
+
+# Created by Joseph Arul Raj <[email protected]>
+# License: BSD-3-Clause
+
+from typing import Union
+from ...imputation.csai.data import DatasetForCSAI as DatasetForCSAI_Imputation     
+
+
+
+class DatasetForCSAI(DatasetForCSAI_Imputation):
+    def __init__(self, 
+                 data: Union[dict, str],  
+                 file_type: str = "hdf5",
+                 return_y: bool = True,
+                 removal_percent: float = 0.0,
+                 increase_factor: float = 0.1,
+                 compute_intervals: bool = False,
+                 replacement_probabilities = None,
+                 normalise_mean : list = [],
+                 normalise_std: list = [],
+                 training: bool = True
+    ):
+        super().__init__(
+            data=data,
+            return_X_ori=False,
+            return_y=return_y,
+            file_type=file_type,
+            removal_percent=removal_percent,
+            increase_factor=increase_factor,
+            compute_intervals=compute_intervals,
+            replacement_probabilities=replacement_probabilities,
+            normalise_mean=normalise_mean,
+            normalise_std=normalise_std,
+            training=training
+       )
+