Add informer as an imputation model (#335)

* feat: add Informer as an imputation model;

README.md

@@ -203,6 +203,7 @@ This functionality is implemented with the [Microsoft NNI](https://github.com/mi
 |       Neural Net       |   DLinear   |                                                           Are Transformers Effective for Time Series Forecasting? [^17]                                                           |   2023   |
 |       Neural Net       |  ETSformer  |                                                       Exponential Smoothing Transformers for Time-series Forecasting [^19]                                                        |   2023   |
 |       Neural Net       |  FEDformer  |                                                 Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting [^20]                                                  |   2022   |
+|       Neural Net       |  Informer   |                                                   Beyond Efficient Transformer for Long Sequence Time-Series Forecasting [^21]                                                    |   2021   |
 |       Neural Net       | Autoformer  |                                              Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting [^15]                                              |   2021   |
 |       Neural Net       |    CSDI     |                                              Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation [^12]                                              |   2021   |
 |       Neural Net       |   US-GAN    |                                                          Unsupervised GAN for Multivariate Time Series Imputation [^10]                                                           |   2021   |
@@ -332,6 +333,8 @@ PyPOTS community is open, transparent, and surely friendly. Let's work together
 [^18]: Nie, Y., Nguyen, N. H., Sinthong, P., & Kalagnanam, J. (2023). [A time series is worth 64 words: Long-term forecasting with transformers](https://openreview.net/forum?id=Jbdc0vTOcol). *ICLR 2023*
 [^19]: Woo, G., Liu, C., Sahoo, D., Kumar, A., & Hoi, S. (2023). [ETSformer: Exponential Smoothing Transformers for Time-series Forecasting](https://openreview.net/forum?id=5m_3whfo483).  *ICLR 2023*
 [^20]: Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., & Jin, R. (2022). [FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting](https://proceedings.mlr.press/v162/zhou22g.html). *ICML 2022*.
+[^21]: Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., & Zhang, W. (2021). [Informer: Beyond efficient transformer for long sequence time-series forecasting](https://ojs.aaai.org/index.php/AAAI/article/view/17325). *AAAI 2021*.
+
 
 
 <details>

docs/index.rst

@@ -181,6 +181,7 @@ Imputation                     Neural Net       PatchTST (A Time Series is Worth
 Imputation                     Neural Net       DLinear (Are transformers effective for time series forecasting?)                                         2023   :cite:`zeng2023dlinear`
 Imputation                     Neural Net       ETSformer (Exponential Smoothing Transformers for Time-series Forecasting)                                2023   :cite:`woo2023etsformer`
 Imputation                     Neural Net       FEDformer (Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting)                    2022   :cite:`zhou2022fedformer`
+Imputation                     Neural Net       Informer (Beyond Efficient Transformer for Long Sequence Time-Series Forecasting)                         2021   :cite:`zhou2021informer`
 Imputation                     Neural Net       Autoformer (Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting)            2021   :cite:`wu2021autoformer`
 Imputation                     Neural Net       US-GAN (Unsupervised GAN for Multivariate Time Series Imputation)                                         2021   :cite:`miao2021SSGAN`
 Imputation                     Neural Net       CSDI (Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation)                  2021   :cite:`tashiro2021csdi`

docs/pypots.imputation.rst

@@ -73,6 +73,15 @@ pypots.imputation.fedformer
    :show-inheritance:
    :inherited-members:
 
+pypots.imputation.informer
+------------------------------
+
+.. automodule:: pypots.imputation.informer
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :inherited-members:
+
 pypots.imputation.autoformer
 ------------------------------
 

docs/references.bib

@@ -544,3 +544,13 @@ @inproceedings{zhou2022fedformer
 pdf = {https://proceedings.mlr.press/v162/zhou22g/zhou22g.pdf},
 url = {https://proceedings.mlr.press/v162/zhou22g.html},
 }
+
+@inproceedings{zhou2021informer,
+title={Informer: Beyond efficient transformer for long sequence time-series forecasting},
+author={Zhou, Haoyi and Zhang, Shanghang and Peng, Jieqi and Zhang, Shuai and Li, Jianxin and Xiong, Hui and Zhang, Wancai},
+booktitle={Proceedings of the AAAI conference on artificial intelligence},
+volume={35},
+number={12},
+pages={11106--11115},
+year={2021}
+}

pypots/imputation/__init__.py

@@ -16,6 +16,7 @@
 from .etsformer import ETSformer
 from .fedformer import FEDformer
 from .crossformer import Crossformer
+from .informer import Informer
 from .autoformer import Autoformer
 from .dlinear import DLinear
 from .patchtst import PatchTST
@@ -36,6 +37,7 @@
     "TimesNet",
     "PatchTST",
     "DLinear",
+    "Informer",
     "Autoformer",
     "BRITS",
     "MRNN",

pypots/imputation/autoformer/modules/core.py

@@ -10,10 +10,10 @@
 from .submodules import (
     SeasonalLayerNorm,
     AutoformerEncoderLayer,
-    AutoformerEncoder,
     AutoCorrelation,
     AutoCorrelationLayer,
 )
+from ...informer.modules.submodules import InformerEncoder
 from ....nn.modules.transformer.embedding import DataEmbedding
 from ....utils.metrics import calc_mse
 
@@ -43,7 +43,7 @@ def __init__(
             dropout=dropout,
             with_pos=False,
         )
-        self.encoder = AutoformerEncoder(
+        self.encoder = InformerEncoder(
             [
                 AutoformerEncoderLayer(
                     AutoCorrelationLayer(

pypots/imputation/autoformer/modules/submodules.py

@@ -285,35 +285,6 @@ def forward(self, x, attn_mask=None):
         return res, attn
 
 
-class AutoformerEncoder(nn.Module):
-    def __init__(self, attn_layers, conv_layers=None, norm_layer=None):
-        super().__init__()
-        self.attn_layers = nn.ModuleList(attn_layers)
-        self.conv_layers = (
-            nn.ModuleList(conv_layers) if conv_layers is not None else None
-        )
-        self.norm = norm_layer
-
-    def forward(self, x, attn_mask=None):
-        attns = []
-        if self.conv_layers is not None:
-            for attn_layer, conv_layer in zip(self.attn_layers, self.conv_layers):
-                x, attn = attn_layer(x, attn_mask=attn_mask)
-                x = conv_layer(x)
-                attns.append(attn)
-            x, attn = self.attn_layers[-1](x)
-            attns.append(attn)
-        else:
-            for attn_layer in self.attn_layers:
-                x, attn = attn_layer(x, attn_mask=attn_mask)
-                attns.append(attn)
-
-        if self.norm is not None:
-            x = self.norm(x)
-
-        return x, attns
-
-
 class AutoformerDecoderLayer(nn.Module):
     """
     Autoformer decoder layer with the progressive decomposition architecture
@@ -372,23 +343,3 @@ def forward(self, x, cross, x_mask=None, cross_mask=None):
             1, 2
         )
         return x, residual_trend
-
-
-class AutoformerDecoder(nn.Module):
-    def __init__(self, layers, norm_layer=None, projection=None):
-        super().__init__()
-        self.layers = nn.ModuleList(layers)
-        self.norm = norm_layer
-        self.projection = projection
-
-    def forward(self, x, cross, x_mask=None, cross_mask=None, trend=None):
-        for layer in self.layers:
-            x, residual_trend = layer(x, cross, x_mask=x_mask, cross_mask=cross_mask)
-            trend = trend + residual_trend
-
-        if self.norm is not None:
-            x = self.norm(x)
-
-        if self.projection is not None:
-            x = self.projection(x)
-        return x, trend

pypots/imputation/fedformer/modules/core.py

@@ -9,11 +9,11 @@
 
 from .submodules import MultiWaveletTransform, FourierBlock
 from ...autoformer.modules.submodules import (
-    AutoformerEncoder,
     AutoformerEncoderLayer,
     AutoCorrelationLayer,
     SeasonalLayerNorm,
 )
+from ...informer.modules.submodules import InformerEncoder
 from ....nn.modules.transformer.embedding import DataEmbedding
 from ....utils.metrics import calc_mse
 
@@ -57,7 +57,7 @@ def __init__(
                 f"Unsupported version: {version}. Please choose from ['Wavelets', 'Fourier']."
             )
 
-        self.encoder = AutoformerEncoder(
+        self.encoder = InformerEncoder(
             [
                 AutoformerEncoderLayer(
                     AutoCorrelationLayer(

pypots/imputation/informer/__init__.py

@@ -0,0 +1,17 @@
+"""
+The package of the partially-observed time-series imputation model Informer.
+
+Refer to the paper "Wu, H., Xu, J., Wang, J., & Long, M. (2021).
+Informer: Decomposition transformers with auto-correlation for long-term series forecasting. NeurIPS 2021.".
+
+"""
+
+# Created by Wenjie Du <wenjay.du@gmail.com>
+# License: BSD-3-Clause
+
+
+from .model import Informer
+
+__all__ = [
+    "Informer",
+]

pypots/imputation/informer/data.py

@@ -0,0 +1,24 @@
+"""
+Dataset class for Informer.
+"""
+
+# Created by Wenjie Du <wenjay.du@gmail.com>
+# License: BSD-3-Clause
+
+from typing import Union
+
+from ..saits.data import DatasetForSAITS
+
+
+class DatasetForInformer(DatasetForSAITS):
+    """Actually Informer uses the same data strategy as SAITS, needs MIT for training."""
+
+    def __init__(
+        self,
+        data: Union[dict, str],
+        return_X_ori: bool,
+        return_labels: bool,
+        file_type: str = "h5py",
+        rate: float = 0.2,
+    ):
+        super().__init__(data, return_X_ori, return_labels, file_type, rate)