Merge pull request #26 from WenjieDu/dev

Merge dev into main

README.md

@@ -22,7 +22,7 @@
     </a>
     <!-- Coveralls report -->
     <a alt='Coveralls report' href='https://coveralls.io/github/WenjieDu/PyPOTS'> 
-        <img src='https://coveralls.io/repos/github/WenjieDu/PyPOTS/badge.svg'>
+        <img src='https://img.shields.io/coverallsCoverage/github/WenjieDu/PyPOTS?branch=main&logo=coveralls&labelColor=3F5767'>
     </a>
     <!-- PyPI download number -->
     <a alt='PyPI download number' href='https://pepy.tech/project/pypots'>
@@ -36,6 +36,10 @@
     <a alt='CODE_OF_CONDUCT' href='CODE_OF_CONDUCT.md'> 
         <img src='https://img.shields.io/badge/Contributor%20Covenant-v2.1-4baaaa.svg'>
     </a>
+    <!-- Slack Workspace -->
+    <a alt='Slack Workspace' href='https://join.slack.com/t/pypots-dev/shared_invite/zt-1gq6ufwsi-p0OZdW~e9UW_IA4_f1OfxA'> 
+        <img src='https://img.shields.io/badge/Slack-PyPOTS-grey?logo=slack&labelColor=4A154B&color=62BCE5'>
+    </a>
 </p>
 
 ⦿ `Motivation`: Due to all kinds of reasons like failure of collection sensors, communication error, and unexpected malfunction, missing values are common to see in time series from the real-world environment. This makes partially-observed time series (POTS) a pervasive problem in open-world modeling and prevents advanced data analysis. Although this problem is important, the area of data mining on POTS still lacks a dedicated toolkit. PyPOTS is created to fill in this blank.
@@ -85,7 +89,7 @@ or
 ## ❖ Attention 👀
 The documentation and tutorials are under construction. And a short paper introducing PyPOTS is on the way! 🚀 Stay tuned please!
 
-‼️ PyPOTS is currently under developing. If you like it and look forward to its growth, <ins>please give PyPOTS a star and watch it to keep you posted on its progress and to let me know that its development is meaningful</ins>. If you have any feedback, or want to contribute ideas/suggestions or share time-series related algorithms/papers, please join PyPOTS community and <a alt='GitHub Discussions' href='https://github.com/WenjieDu/PyPOTS/discussions'><img align='center' src='https://img.shields.io/badge/Chat-in_Discussions-green?logo=github&color=60A98D'></a>, or create an issue.
+‼️ PyPOTS is currently under developing. If you like it and look forward to its growth, <ins>please give PyPOTS a star and watch it to keep you posted on its progress and to let me know that its development is meaningful</ins>. If you have any feedback, or want to contribute ideas/suggestions or share time-series related algorithms/papers, please join PyPOTS community and chat on <a alt='Slack Workspace' href='https://join.slack.com/t/pypots-dev/shared_invite/zt-1gq6ufwsi-p0OZdW~e9UW_IA4_f1OfxA'><img align='center' src='https://img.shields.io/badge/Slack-PyPOTS-grey?logo=slack&labelColor=4A154B&color=62BCE5'></a>, or create an issue. If you have any additional questions or have interests in collaboration, please take a look at [my GitHub profile](https://github.com/WenjieDu) and feel free to contact me 😃.
 
 Thank you all for your attention! 😃
 

pypots/data/load_specific_datasets.py

@@ -43,7 +43,7 @@ def preprocess_physionet2012(data):
     def apply_func(df_temp):  # pad and truncate to set the max length of samples as 48
         missing = list(set(range(0, 48)).difference(set(df_temp["Time"])))
         missing_part = pd.DataFrame({"Time": missing})
-        df_temp = df_temp.append(missing_part, ignore_index=False, sort=False)  # pad
+        df_temp = pd.concat([df_temp, missing_part], ignore_index=False, sort=False)  # pad
         df_temp = df_temp.set_index("Time").sort_index().reset_index()
         df_temp = df_temp.iloc[:48]  # truncate
         return df_temp

pypots/tests/unified_data_for_test.py

@@ -5,7 +5,6 @@
 # Created by Wenjie Du <[email protected]>
 # License: GLP-v3
 
-import pandas as pd
 import torch
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler
@@ -75,21 +74,6 @@ def gene_physionet2012():
     # generate samples
     df = load_specific_dataset("physionet_2012")
     X = df["X"]
-    X = X.drop(df["static_features"], axis=1)
-
-    def apply_func(df_temp):
-        missing = list(set(range(0, 48)).difference(set(df_temp["Time"])))
-        missing_part = pd.DataFrame({"Time": missing})
-        df_temp = df_temp.append(missing_part, ignore_index=False, sort=False)
-        df_temp = df_temp.set_index("Time").sort_index().reset_index()
-        df_temp = df_temp.iloc[:48]
-        return df_temp
-
-    X = X.groupby("RecordID").apply(apply_func)
-    X = X.drop("RecordID", axis=1)
-    X = X.reset_index()
-    X = X.drop(["level_1", "Time"], axis=1)
-
     y = df["y"]
     all_recordID = X["RecordID"].unique()
     train_set_ids, test_set_ids = train_test_split(all_recordID, test_size=0.2)