Add model M-RNN, and update the docs

README.md

@@ -146,25 +146,26 @@ mae = cal_mae(imputation, X_intact, indicating_mask)  # calculate mean absolute
 ## ❖ Available Algorithms
 PyPOTS supports imputation, classification, clustering, and forecasting tasks on multivariate time series with missing values. The currently available algorithms of four tasks are cataloged in the following table with four partitions. The paper references are all listed at the bottom of this readme file. Please refer to them if you want more details.
 
-|   ***`Imputation`***   |      🚥      |                                                                                        🚥                                                                                         |    🚥    |
-|:----------------------:|:------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:|
-|        **Type**        |  **Abbr.**   |                                                                    **Full name of the algorithm/model/paper**                                                                     | **Year** |
-|       Neural Net       |    SAITS     |                                                               Self-Attention-based Imputation for Time Series [^1]                                                                |   2023   |
-|       Neural Net       | Transformer  | Attention is All you Need [^2];<br>Self-Attention-based Imputation for Time Series [^1];<br><sub>Note: proposed in [^2], and re-implemented as an imputation model in [^1].</sub> |   2017   |
-|       Neural Net       |    BRITS     |                                                              Bidirectional Recurrent Imputation for Time Series [^3]                                                              |   2018   |
-|         Naive          |     LOCF     |                                                                         Last Observation Carried Forward                                                                          |    -     |
-| ***`Classification`*** |      🚥      |                                                                                        🚥                                                                                         |    🚥    |
-|        **Type**        |  **Abbr.**   |                                                                    **Full name of the algorithm/model/paper**                                                                     | **Year** |
-|       Neural Net       |    BRITS     |                                                              Bidirectional Recurrent Imputation for Time Series [^3]                                                              |   2018   |
-|       Neural Net       |    GRU-D     |                                                  Recurrent Neural Networks for Multivariate Time Series with Missing Values [^4]                                                  |   2018   |
-|       Neural Net       |   Raindrop   |                                                    Graph-Guided Network for Irregularly Sampled Multivariate Time Series [^5]                                                     |   2022   |
-|   ***`Clustering`***   |      🚥      |                                                                                        🚥                                                                                         |    🚥    |
-|        **Type**        |  **Abbr.**   |                                                                    **Full name of the algorithm/model/paper**                                                                     | **Year** |
-|       Neural Net       |     CRLI     |                                                      Clustering Representation Learning on Incomplete time-series data [^6]                                                       |   2021   |
-|       Neural Net       |    VaDER     |                                                                  Variational Deep Embedding with Recurrence [^7]                                                                  |   2019   |
-|  ***`Forecasting`***   |      🚥      |                                                                                        🚥                                                                                         |    🚥    |
-|        **Type**        |  **Abbr.**   |                                                                    **Full name of the algorithm/model/paper**                                                                     | **Year** |
-|     Probabilistic      |     BTTF     |                                                                    Bayesian Temporal Tensor Factorization [^8]                                                                    |   2021   |
+|   ***`Imputation`***   |     🚥      |                                                                                        🚥                                                                                         |    🚥    |
+|:----------------------:|:-----------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:|
+|        **Type**        |  **Abbr.**  |                                                                    **Full name of the algorithm/model/paper**                                                                     | **Year** |
+|       Neural Net       |    SAITS    |                                                               Self-Attention-based Imputation for Time Series [^1]                                                                |   2023   |
+|       Neural Net       | Transformer | Attention is All you Need [^2];<br>Self-Attention-based Imputation for Time Series [^1];<br><sub>Note: proposed in [^2], and re-implemented as an imputation model in [^1].</sub> |   2017   |
+|       Neural Net       |    BRITS    |                                                              Bidirectional Recurrent Imputation for Time Series [^3]                                                              |   2018   |
+|       Neural Net       |    M-RNN    |                                                                  Multi-directional Recurrent Neural Network [^9]                                                                  |   2019   |
+|         Naive          |    LOCF     |                                                                         Last Observation Carried Forward                                                                          |    -     |
+| ***`Classification`*** |     🚥      |                                                                                        🚥                                                                                         |    🚥    |
+|        **Type**        |  **Abbr.**  |                                                                    **Full name of the algorithm/model/paper**                                                                     | **Year** |
+|       Neural Net       |    BRITS    |                                                              Bidirectional Recurrent Imputation for Time Series [^3]                                                              |   2018   |
+|       Neural Net       |    GRU-D    |                                                  Recurrent Neural Networks for Multivariate Time Series with Missing Values [^4]                                                  |   2018   |
+|       Neural Net       |  Raindrop   |                                                    Graph-Guided Network for Irregularly Sampled Multivariate Time Series [^5]                                                     |   2022   |
+|   ***`Clustering`***   |     🚥      |                                                                                        🚥                                                                                         |    🚥    |
+|        **Type**        |  **Abbr.**  |                                                                    **Full name of the algorithm/model/paper**                                                                     | **Year** |
+|       Neural Net       |    CRLI     |                                                      Clustering Representation Learning on Incomplete time-series data [^6]                                                       |   2021   |
+|       Neural Net       |    VaDER    |                                                                  Variational Deep Embedding with Recurrence [^7]                                                                  |   2019   |
+|  ***`Forecasting`***   |     🚥      |                                                                                        🚥                                                                                         |    🚥    |
+|        **Type**        |  **Abbr.**  |                                                                    **Full name of the algorithm/model/paper**                                                                     | **Year** |
+|     Probabilistic      |    BTTF     |                                                                    Bayesian Temporal Tensor Factorization [^8]                                                                    |   2021   |
 
 
 ## ❖ Citing PyPOTS
@@ -254,6 +255,8 @@ Thank you all for your attention! 😃
 [^6]: Ma, Q., Chen, C., Li, S., & Cottrell, G. W. (2021). [Learning Representations for Incomplete Time Series Clustering](https://ojs.aaai.org/index.php/AAAI/article/view/17070). *AAAI 2021*.
 [^7]: 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](https://academic.oup.com/gigascience/article/8/11/giz134/5626377). *GigaScience*.
 [^8]: Chen, X., & Sun, L. (2021). [Bayesian Temporal Factorization for Multidimensional Time Series Prediction](https://arxiv.org/abs/1910.06366). *IEEE transactions on pattern analysis and machine intelligence*.
+[^9]: Yoon, J., Zame, W. R., & van der Schaar, M. (2019). [Estimating Missing Data in Temporal Data Streams Using Multi-Directional Recurrent Neural Networks](https://ieeexplore.ieee.org/document/8485748). *IEEE Transactions on Biomedical Engineering*.
+
 
 <details>
 <summary>🏠 Visits</summary>

docs/index.rst

@@ -128,6 +128,7 @@ Task                           Type             Algorithm
 Imputation                     Neural Network   SAITS (Self-Attention-based Imputation for Time Series)                    2022   :cite:`du2023SAITS`
 Imputation                     Neural Network   Transformer                                                                2017   :cite:`vaswani2017Transformer`, :cite:`du2023SAITS`
 Imputation, Classification     Neural Network   BRITS (Bidirectional Recurrent Imputation for Time Series)                 2018   :cite:`cao2018BRITS`
+Imputation                     Neural Network   M-RNN (Multi-directional Recurrent Neural Network)                         2019   :cite:`yoon2019MRNN`
 Imputation                     Naive            LOCF (Last Observation Carried Forward)                                    /      /
 Classification                 Neural Network   GRU-D                                                                      2018   :cite:`che2018GRUD`
 Classification                 Neural Network   Raindrop                                                                   2022   :cite:`zhang2022Raindrop`
@@ -136,6 +137,7 @@ Clustering                     Neural Network   VaDER (Variational Deep Embeddin
 Forecasting                    Probabilistic    BTTF (Bayesian Temporal Tensor Factorization)                              2021   :cite:`chen2021BTMF`
 ============================== ================ =========================================================================  ====== =========
 
+[^9]: Yoon, J., Zame, W. R., & van der Schaar, M. (2019). [Estimating Missing Data in Temporal Data Streams Using Multi-Directional Recurrent Neural Networks](https://ieeexplore.ieee.org/document/8485748). *IEEE Transactions on Biomedical Engineering*.
 
 ❖ Citing PyPOTS
 ^^^^^^^^^^^^^^^^

docs/pypots.imputation.rst

@@ -28,6 +28,15 @@ pypots.imputation.brits module
    :show-inheritance:
    :inherited-members:
 
+pypots.imputation.mrnn module
+------------------------------
+
+.. automodule:: pypots.imputation.mrnn
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :inherited-members:
+
 pypots.imputation.locf module
 -----------------------------
 

docs/references.bib

@@ -12,6 +12,18 @@ @article{cao2018BRITS
 keywords = {Computer Science - Machine Learning,Statistics - Machine Learning}
 }
 
+@ARTICLE{yoon2019MRNN,
+author={Yoon, Jinsung and Zame, William R. and van der Schaar, Mihaela},
+journal={IEEE Transactions on Biomedical Engineering},
+title={Estimating Missing Data in Temporal Data Streams Using Multi-Directional Recurrent Neural Networks},
+year={2019},
+volume={66},
+number={5},
+pages={1477-1490},
+doi={10.1109/TBME.2018.2874712}
+}
+
+
 @article{che2018GRUD,
 title = {Recurrent {{Neural Networks}} for {{Multivariate Time Series}} with {{Missing Values}}},
 author = {Che, Zhengping and Purushotham, Sanjay and Cho, Kyunghyun and Sontag, David and Liu, Yan},

pypots/__init__.py

@@ -22,7 +22,7 @@
 #
 # Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer.
 # 'X.Y.dev0' is the canonical version of 'X.Y.dev'
-__version__ = "0.1.0"
+__version__ = "0.1.1"
 
 
 __all__ = [

pypots/imputation/__init__.py

@@ -9,10 +9,12 @@
 from .locf import LOCF
 from .saits import SAITS
 from .transformer import Transformer
+from .mrnn import MRNN
 
 __all__ = [
-    "BRITS",
-    "Transformer",
     "SAITS",
+    "Transformer",
+    "BRITS",
+    "MRNN",
     "LOCF",
 ]

pypots/imputation/mrnn/__init__.py

@@ -0,0 +1,13 @@
+"""
+
+"""
+
+# Created by Wenjie Du <[email protected]>
+# License: GLP-v3
+
+from .model import MRNN
+
+
+__all__ = [
+    "MRNN",
+]

pypots/imputation/mrnn/data.py

@@ -0,0 +1,46 @@
+"""
+Dataset class for model MRNN.
+"""
+
+# Created by Wenjie Du <[email protected]>
+# License: GLP-v3
+
+from typing import Union
+
+from ..brits.data import DatasetForBRITS
+
+
+class DatasetForMRNN(DatasetForBRITS):
+    """Dataset class for BRITS.
+
+    Parameters
+    ----------
+    data : dict or str,
+        The dataset for model input, should be a dictionary including keys as 'X' and 'y',
+        or a path string locating a data file.
+        If it is a dict, X should be array-like of shape [n_samples, sequence length (time steps), n_features],
+        which is time-series data for input, can contain missing values, and y should be array-like of shape
+        [n_samples], which is classification labels of X.
+        If it is a path string, the path should point to a data file, e.g. a h5 file, which contains
+        key-value pairs like a dict, and it has to include keys as 'X' and 'y'.
+
+    return_labels : bool, default = True,
+        Whether to return labels in function __getitem__() if they exist in the given data. If `True`, for example,
+        during training of classification models, the Dataset class will return labels in __getitem__() for model input.
+        Otherwise, labels won't be included in the data returned by __getitem__(). This parameter exists because we
+        need the defined Dataset class for all training/validating/testing stages. For those big datasets stored in h5
+        files, they already have both X and y saved. But we don't read labels from the file for validating and testing
+        with function _fetch_data_from_file(), which works for all three stages. Therefore, we need this parameter for
+        distinction.
+
+    file_type : str, default = "h5py"
+        The type of the given file if train_set and val_set are path strings.
+    """
+
+    def __init__(
+        self,
+        data: Union[dict, str],
+        return_labels: bool = True,
+        file_type: str = "h5py",
+    ):
+        super().__init__(data, return_labels, file_type)