Merge pull request #374 from WenjieDu/dev

Add SaitsEmbedding, fix failed CI on macOS with Python3.7, and update docs
WenjieDu · Apr 30, 2024 · bfc4d6b · bfc4d6b
2 parents ef9a9b6 + f332a0c
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diff --git a/.github/workflows/testing_ci.yml b/.github/workflows/testing_ci.yml
@@ -19,7 +19,7 @@ jobs:
         strategy:
             fail-fast: false
             matrix:
-                os: [ubuntu-latest, windows-latest, macOS-latest]
+                os: [ubuntu-latest, windows-latest, macOS-13]
                 python-version: ["3.7", "3.11"]
 
         steps:

diff --git a/.github/workflows/testing_daily.yml b/.github/workflows/testing_daily.yml
@@ -14,7 +14,7 @@ jobs:
         strategy:
             fail-fast: false
             matrix:
-                os: [ubuntu-latest, windows-latest, macOS-latest]
+                os: [ubuntu-latest, windows-latest, macOS-13]
                 python-version: ["3.7", "3.11"]
 
         steps:

diff --git a/README.md b/README.md
@@ -82,7 +82,7 @@ The rest of this readme file is organized as follows:
 
 ## ❖ Available Algorithms
 PyPOTS supports imputation, classification, clustering, forecasting, and anomaly detection tasks on multivariate partially-observed
-time series with missing values. The table below shows the availability of each algorithm in PyPOTS for different tasks.
+time series with missing values. The table below shows the availability of each algorithm (sorted by Year) in PyPOTS for different tasks.
 The symbol ✅ indicates the algorithm is available for the corresponding task (note that models will be continuously updated
 in the future to handle tasks that are not currently supported. Stay tuned❗️).
 The task types are abbreviated as follows: **`IMPU`**: Imputation; **`FORE`**: Forecasting;
@@ -93,8 +93,10 @@ The paper references are all listed at the bottom of this readme file.
 This functionality is implemented with the [Microsoft NNI](https://github.com/microsoft/nni) framework. You may want to refer to our time-series
 imputation survey repo [Awesome_Imputation](https://github.com/WenjieDu/Awesome_Imputation) to see how to config and
 tune the hyperparameters.
-🔥 Note that Transformer, Crossformer, PatchTST, DLinear, ETSformer, FEDformer, Informer, Autoformer are not proposed as imputation methods in their original papers,
-and they cannot accept POTS as input. **To make them applicable on POTS data, we apply the embedding strategy and training approach (ORT+MIT)
+
+🔥 Note that Transformer, iTransformer, FreTS, Crossformer, PatchTST, DLinear, ETSformer, FiLM, FEDformer, Informer, Autoformer
+are not proposed as imputation methods in their original papers, and they cannot accept POTS as input.
+**To make them applicable on POTS data, we apply the embedding strategy and training approach (ORT+MIT)
 the same as we did in [SAITS paper](https://arxiv.org/pdf/2202.08516).**
 
 | **Type**      | **Algo**               | **IMPU** | **FORE** | **CLAS** | **CLUS** | **ANOD** | **Year** |

diff --git a/pypots/classification/brits/__init__.py b/pypots/classification/brits/__init__.py
@@ -7,6 +7,11 @@
 In Advances in Neural Information Processing Systems, volume 31. Curran Associates, Inc., 2018.
 <https://papers.nips.cc/paper_files/paper/2018/file/734e6bfcd358e25ac1db0a4241b95651-Paper.pdf>`_
 
+Notes
+-----
+This implementation is inspired by the official one https://github.com/caow13/BRITS.
+The bugs in the original implementation are fixed here.
+
 """
 
 # Created by Wenjie Du <wenjay.du@gmail.com>

diff --git a/pypots/classification/brits/core.py b/pypots/classification/brits/core.py
@@ -1,14 +1,6 @@
 """
-The implementation of BRITS for the partially-observed time-series classification task.
-
-Refer to the paper "Cao, W., Wang, D., Li, J., Zhou, H., Li, L., & Li, Y. (2018).
-BRITS: Bidirectional Recurrent Imputation for Time Series. NeurIPS 2018."
-
-Notes
------
-Partial implementation uses code from https://github.com/caow13/BRITS. The bugs in the original implementation
-are fixed here.
-
+The core wrapper assembles the submodules of BRITS classification model
+and takes over the forward progress of the algorithm.
 """
 
 # Created by Wenjie Du <wenjay.du@gmail.com>

diff --git a/pypots/classification/brits/data.py b/pypots/classification/brits/data.py
@@ -1,5 +1,5 @@
 """
-Dataset class for model BRITS.
+Dataset class for the classification model BRITS.
 """
 
 # Created by Wenjie Du <wenjay.du@gmail.com>

diff --git a/pypots/classification/brits/model.py b/pypots/classification/brits/model.py
@@ -1,15 +1,6 @@
 """
 The implementation of BRITS for the partially-observed time-series classification task.
 
-Refer to the paper "Wei Cao, Dong Wang, Jian Li, Hao Zhou, Lei Li, and Yitan Li.
-BRITS: Bidirectional recurrent imputation for time series.
-In Advances in Neural Information Processing Systems, volume 31. Curran Associates, Inc., 2018."
-
-Notes
------
-Partial implementation uses code from https://github.com/caow13/BRITS.
-The bugs in the original implementation are fixed here.
-
 """
 
 # Created by Wenjie Du <wenjay.du@gmail.com>

diff --git a/pypots/classification/grud/__init__.py b/pypots/classification/grud/__init__.py
@@ -7,6 +7,10 @@
 Scientific Reports, 8(1):6085, April 2018.
 <https://www.nature.com/articles/s41598-018-24271-9.pdf>`_
 
+Notes
+-----
+This implementation is inspired by the official one  https://github.com/PeterChe1990/GRU-D
+
 """
 
 # Created by Wenjie Du <wenjay.du@gmail.com>

diff --git a/pypots/classification/grud/core.py b/pypots/classification/grud/core.py
@@ -1,9 +1,6 @@
 """
-The implementation of GRU-D for the partially-observed time-series imputation task.
-
-Refer to the paper "Che, Z., Purushotham, S., Cho, K., Sontag, D.A., & Liu, Y. (2018).
-Recurrent Neural Networks for Multivariate Time Series with Missing Values. Scientific Reports."
-
+The core wrapper assembles the submodules of GRU-D classification model
+and takes over the forward progress of the algorithm.
 """
 
 # Created by Wenjie Du <wenjay.du@gmail.com>

diff --git a/pypots/classification/grud/data.py b/pypots/classification/grud/data.py
@@ -1,5 +1,5 @@
 """
-Dataset class for model GRU-D.
+Dataset class for the classification model GRU-D.
 """
 
 # Created by Wenjie Du <wenjay.du@gmail.com>

diff --git a/pypots/classification/grud/model.py b/pypots/classification/grud/model.py
@@ -1,10 +1,6 @@
 """
 The implementation of GRU-D for the partially-observed time-series classification task.
 
-Refer to the paper "Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu.
-Recurrent Neural Networks for Multivariate Time Series with Missing Values.
-Scientific Reports, 8(1):6085, April 2018."
-
 """
 
 # Created by Wenjie Du <wenjay.du@gmail.com>

diff --git a/pypots/classification/raindrop/__init__.py b/pypots/classification/raindrop/__init__.py
@@ -7,6 +7,10 @@
 In ICLR, 2022.
 <https://openreview.net/forum?id=Kwm8I7dU-l5>`_
 
+Notes
+-----
+This implementation is inspired by the official one the official implementation https://github.com/mims-harvard/Raindrop
+
 """
 
 # Created by Wenjie Du <wenjay.du@gmail.com>

diff --git a/pypots/classification/raindrop/core.py b/pypots/classification/raindrop/core.py
@@ -1,9 +1,6 @@
 """
-The implementation of Raindrop for the partially-observed time-series classification task.
-
-Refer to the paper "Zhang, X., Zeman, M., Tsiligkaridis, T., & Zitnik, M. (2022).
-Graph-Guided Network for Irregularly Sampled Multivariate Time Series. ICLR 2022."
-
+The core wrapper assembles the submodules of Raindrop classification model
+and takes over the forward progress of the algorithm.
 """
 
 

diff --git a/pypots/classification/raindrop/data.py b/pypots/classification/raindrop/data.py
@@ -1,5 +1,5 @@
 """
-Dataset class for model Raindrop.
+Dataset class for the classification model Raindrop.
 """
 
 # Created by Wenjie Du <wenjay.du@gmail.com>

diff --git a/pypots/classification/raindrop/model.py b/pypots/classification/raindrop/model.py
@@ -1,10 +1,6 @@
 """
 The implementation of Raindrop for the partially-observed time-series classification task.
 
-Refer to the paper "Xiang Zhang, Marko Zeman, Theodoros Tsiligkaridis, and Marinka Zitnik.
-Graph-guided network for irregularly sampled multivariate time series.
-In ICLR, 2022."
-
 """
 
 

diff --git a/pypots/classification/template/core.py b/pypots/classification/template/core.py
@@ -1,8 +1,6 @@
 """
-The implementation of YourNewModel for the partially-observed time-series classification task.
-
-Refer to the paper "Your paper citation".
-
+The core wrapper assembles the submodules of YourNewModel classification model
+and takes over the forward progress of the algorithm.
 """
 
 # Created by Your Name <Your contact email> TODO: modify the author information.

diff --git a/pypots/classification/template/data.py b/pypots/classification/template/data.py
@@ -1,5 +1,5 @@
 """
-Dataset class for YourNewModel.
+Dataset class for the classification model YourNewModel.
 
 TODO: modify the above description for your model's dataset class.
 

diff --git a/pypots/classification/template/model.py b/pypots/classification/template/model.py
@@ -1,8 +1,6 @@
 """
 The implementation of YourNewModel for the partially-observed time-series classification task.
 
-Refer to the paper "Your paper citation".
-
 TODO: modify the above description with your model's information.
 
 """

diff --git a/pypots/clustering/crli/__init__.py b/pypots/clustering/crli/__init__.py
@@ -8,6 +8,10 @@
 <https://ojs.aaai.org/index.php/AAAI/article/view/17070>`_
 
 
+Notes
+-----
+This implementation is inspired by the official one https://github.com/qianlima-lab/CRLI
+
 """
 
 # Created by Wenjie Du <wenjay.du@gmail.com>

diff --git a/pypots/clustering/crli/core.py b/pypots/clustering/crli/core.py
@@ -1,10 +1,6 @@
 """
-The implementation of CRLI (Clustering Representation Learning on Incomplete time-series data) for
-the partially-observed time-series clustering task.
-
-Refer to the paper "Ma, Q., Chen, C., Li, S., & Cottrell, G. W. (2021).
-Learning Representations for Incomplete Time Series Clustering. AAAI 2021."
-
+The core wrapper assembles the submodules of CRLI clustering model
+and takes over the forward progress of the algorithm.
 """
 
 # Created by Wenjie Du <wenjay.du@gmail.com>

diff --git a/pypots/clustering/crli/data.py b/pypots/clustering/crli/data.py
@@ -1,5 +1,5 @@
 """
-Dataset class for model GRU-D.
+Dataset class for the clustering model CRLI.
 """
 
 # Created by Wenjie Du <wenjay.du@gmail.com>

diff --git a/pypots/clustering/crli/model.py b/pypots/clustering/crli/model.py
@@ -2,10 +2,6 @@
 The implementation of CRLI (Clustering Representation Learning on Incomplete time-series data) for
 the partially-observed time-series clustering task.
 
-Refer to the paper "Qianli Ma, Chuxin Chen, Sen Li, and Garrison W. Cottrell.
-Learning Representations for Incomplete Time Series Clustering.
-In AAAI, 35(10):8837–8846, May 2021."
-
 """
 
 # Created by Wenjie Du <wenjay.du@gmail.com>

diff --git a/pypots/clustering/template/core.py b/pypots/clustering/template/core.py
@@ -1,8 +1,6 @@
 """
-The implementation of YourNewModel for the partially-observed time-series clustering task.
-
-Refer to the paper "Your paper citation".
-
+The core wrapper assembles the submodules of YourNewModel clustering model
+and takes over the forward progress of the algorithm.
 """
 
 # Created by Your Name <Your contact email> TODO: modify the author information.

diff --git a/pypots/clustering/template/data.py b/pypots/clustering/template/data.py
@@ -1,5 +1,5 @@
 """
-Dataset class for YourNewModel.
+Dataset class for the clustering model YourNewModel.
 
 TODO: modify the above description for your model's dataset class.
 

diff --git a/pypots/clustering/template/model.py b/pypots/clustering/template/model.py
@@ -1,8 +1,6 @@
 """
 The implementation of YourNewModel for the partially-observed time-series clustering task.
 
-Refer to the paper "Your paper citation".
-
 TODO: modify the above description with your model's information.
 
 """

diff --git a/pypots/clustering/vader/__init__.py b/pypots/clustering/vader/__init__.py
@@ -8,6 +8,10 @@
 GigaScience, 8(11):giz134, November 2019.
 <https://academic.oup.com/gigascience/article-pdf/8/11/giz134/30797160/giz134.pdf>`_
 
+Notes
+-----
+This implementation is inspired by the official one https://github.com/johanndejong/VaDER
+
 """
 
 # Created by Wenjie Du <wenjay.du@gmail.com>

diff --git a/pypots/clustering/vader/core.py b/pypots/clustering/vader/core.py
@@ -1,10 +1,6 @@
 """
-The implementation of VaDER for the partially-observed time-series clustering task.
-
-Refer to the paper "Jong, J.D., Emon, M.A., Wu, P., Karki, R., Sood, M., Godard, P., Ahmad, A., Vrooman, H.A.,
-Hofmann-Apitius, M., & Fröhlich, H. (2019).
-Deep learning for clustering of multivariate clinical patient trajectories with missing values. GigaScience."
-
+The core wrapper assembles the submodules of VaDER clustering model
+and takes over the forward progress of the algorithm.
 """
 
 # Created by Wenjie Du <wenjay.du@gmail.com>

diff --git a/pypots/clustering/vader/data.py b/pypots/clustering/vader/data.py
@@ -1,5 +1,5 @@
 """
-Dataset class for model GRU-D.
+Dataset class for the clustering model VaDER.
 """
 
 # Created by Wenjie Du <wenjay.du@gmail.com>

diff --git a/pypots/clustering/vader/model.py b/pypots/clustering/vader/model.py
@@ -1,11 +1,6 @@
 """
 The implementation of VaDER for the partially-observed time-series clustering task.
 
-Refer to the paper "Johann de Jong, Mohammad Asif Emon, Ping Wu, Reagon Karki, Meemansa Sood, Patrice Godard,
-Ashar Ahmad, Henri Vrooman, Martin Hofmann-Apitius, and Holger Fröhlich.
-Deep learning for clustering of multivariate clinical patient trajectories with missing values.
-GigaScience, 8(11):giz134, November 2019."
-
 """
 
 # Created by Wenjie Du <wenjay.du@gmail.com>

diff --git a/pypots/forecasting/bttf/__init__.py b/pypots/forecasting/bttf/__init__.py
@@ -7,6 +7,10 @@
 IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1–1, 2021.
 <https://arxiv.org/pdf/1910.06366>`_
 
+Notes
+-----
+This numpy implementation is the same with the official one from https://github.com/xinychen/transdim.
+
 """
 
 # Created by Wenjie Du <wenjay.du@gmail.com>

diff --git a/pypots/forecasting/bttf/core.py b/pypots/forecasting/bttf/core.py
@@ -1,15 +1,5 @@
 """
-The implementation of BTTF (Bayesian Temporal Tensor Factorization) for the partially-observed time-series
-forecasting task.
-
-Refer to the paper "Chen, X., & Sun, L. (2021).
-Bayesian Temporal Factorization for Multidimensional Time Series Prediction.
-IEEE transactions on pattern analysis and machine intelligence."
-
-Notes
------
-This numpy implementation is the same with the official one from https://github.com/xinychen/transdim.
-
+The core wrapper assembles the submodules of BTTF forecasting model.
 """
 
 # Created by Wenjie Du <wenjay.du@gmail.com>

diff --git a/pypots/forecasting/bttf/model.py b/pypots/forecasting/bttf/model.py
@@ -2,14 +2,6 @@
 The implementation of BTTF (Bayesian Temporal Tensor Factorization) for the partially-observed time-series
 forecasting task.
 
-Refer to the paper "Xinyu Chen and Lijun Sun.
-Bayesian Temporal Factorization for Multidimensional Time Series Prediction.
-IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1–1, 2021."
-
-Notes
------
-This numpy implementation is the same with the official one from https://github.com/xinychen/transdim.
-
 """
 
 # Created by Wenjie Du <wenjay.du@gmail.com>

diff --git a/pypots/forecasting/bttf/submodules.py b/pypots/forecasting/bttf/submodules.py
@@ -1,13 +1,4 @@
 """
-The implementation of the modules for BTTF (Bayesian Temporal Tensor Factorization).
-
-Refer to the paper "Chen, X., & Sun, L. (2021).
-Bayesian Temporal Factorization for Multidimensional Time Series Prediction.
-IEEE transactions on pattern analysis and machine intelligence."
-
-Notes
------
-This numpy implementation is the same with the official one from https://github.com/xinychen/transdim.
 
 """
 

diff --git a/pypots/forecasting/csdi/__init__.py b/pypots/forecasting/csdi/__init__.py
@@ -7,6 +7,10 @@
 In NeurIPS, 2021.
 <https://proceedings.neurips.cc/paper_files/paper/2021/file/cfe8504bda37b575c70ee1a8276f3486-Paper.pdf>`_
 
+Notes
+-----
+This implementation is inspired by the official one the official implementation https://github.com/ermongroup/CSDI
+
 """
 
 # Created by Wenjie Du <wenjay.du@gmail.com>

diff --git a/pypots/forecasting/csdi/core.py b/pypots/forecasting/csdi/core.py
@@ -1,3 +1,8 @@
+"""
+The core wrapper assembles the submodules of CSDI forecasting model
+and takes over the forward progress of the algorithm.
+"""
+
 # Created by Wenjie Du <wenjay.du@gmail.com>
 # License: BSD-3-Clause
 

diff --git a/pypots/forecasting/csdi/data.py b/pypots/forecasting/csdi/data.py
@@ -1,5 +1,5 @@
 """
-
+Dataset class for the forecasting model CSDI.
 """
 
 # Created by Wenjie Du <wenjay.du@gmail.com>