Merge pull request #449 from WenjieDu/dev

Make classification GRUD more robust, and update docs

README.md

@@ -161,7 +161,7 @@ And what else? Please read on ;-)
 👈 Time series datasets are taken as coffee beans at PyPOTS, and POTS datasets are incomplete coffee beans with missing parts that have their own meanings.
 To make various public time-series datasets readily available to users,
 <i>Time Series Data Beans (TSDB)</i> is created to make loading time-series datasets super easy!
-Visit [TSDB](https://github.com/WenjieDu/TSDB) right now to know more about this handy tool 🛠, and it now supports a total of 170 open-source datasets!
+Visit [TSDB](https://github.com/WenjieDu/TSDB) right now to know more about this handy tool 🛠, and it now supports a total of 172 open-source datasets!
 
 <a href="https://github.com/WenjieDu/PyGrinder">
     <img src="https://pypots.com/figs/pypots_logos/PyGrinder/logo_FFBG.svg" align="right" width="140" alt="PyGrinder logo"/>

docs/index.rst

@@ -220,7 +220,7 @@ And what else? Please read on ;-)
 👈 Time series datasets are taken as coffee beans at PyPOTS, and POTS datasets are incomplete coffee beans with missing parts that have their own meanings.
 To make various public time-series datasets readily available to users,
 *Time Series Data Beans (TSDB)* is created to make loading time-series datasets super easy!
-Visit `TSDB <https://github.com/WenjieDu/TSDB>`_ right now to know more about this handy tool 🛠, and it now supports a total of 170 open-source datasets!
+Visit `TSDB <https://github.com/WenjieDu/TSDB>`_ right now to know more about this handy tool 🛠, and it now supports a total of 172 open-source datasets!
 
 .. image:: https://pypots.com/figs/pypots_logos/PyGrinder/logo_FFBG.svg
    :width: 150
@@ -298,12 +298,8 @@ please cite it as below and 🌟star `PyPOTS repository <https://github.com/Wenj
    @article{du2023pypots,
    title={{PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series}},
    author={Wenjie Du},
+   journal={arXiv preprint arXiv:2305.18811},
    year={2023},
-   eprint={2305.18811},
-   archivePrefix={arXiv},
-   primaryClass={cs.LG},
-   url={https://arxiv.org/abs/2305.18811},
-   doi={10.48550/arXiv.2305.18811},
    }
 
 or

pypots/classification/grud/data.py

@@ -63,6 +63,8 @@ def __init__(
             self.empirical_mean = torch.sum(
                 self.missing_mask * self.X, dim=[0, 1]
             ) / torch.sum(self.missing_mask, dim=[0, 1])
+            # fill nan with 0, in case some features have no observations
+            self.empirical_mean = torch.nan_to_num(self.empirical_mean, 0)
 
     def _fetch_data_from_array(self, idx: int) -> Iterable:
         """Fetch data according to index.