Merge pull request #312 from WenjieDu/dev

Fixing some bugs, updating the docs and requirements

README.md

@@ -113,7 +113,13 @@ Considering the future workload, PyPOTS tutorials are released in a single repo,
 and you can find them in [BrewPOTS](https://github.com/WenjieDu/BrewPOTS).
 Take a look at it now, and learn how to brew your POTS datasets!
 
-☕️ Welcome to the universe of PyPOTS. Enjoy it and have fun!
+<p align="center">
+<a href="https://pypots.com/ecosystem/">
+    <img src="https://pypots.com/figs/pypots_logos/Ecosystem/PyPOTS_Ecosystem_Pipeline.png" width="95%"/>
+</a>
+<br>
+<b> ☕️ Welcome to the universe of PyPOTS. Enjoy it and have fun!</b>
+</p>
 
 
 ## ❖ Installation
@@ -165,7 +171,7 @@ X = X.reshape(num_samples, 48, -1)
 X_ori = X  # keep X_ori for validation
 X = mcar(X, 0.1)  # randomly hold out 10% observed values as ground truth
 dataset = {"X": X}  # X for model input
-print(X.shape)  # (11988, 48, 37), 11988 samples, 48 time steps, 37 features
+print(X.shape)  # (11988, 48, 37), 11988 samples and each sample has 48 time steps, 37 features
 
 # Model training. This is PyPOTS showtime.
 saits = SAITS(n_steps=48, n_features=37, n_layers=2, d_model=256, d_inner=128, n_heads=4, d_k=64, d_v=64, dropout=0.1, epochs=10)
@@ -213,6 +219,16 @@ This functionality is implemented with the [Microsoft NNI](https://github.com/mi
 
 
 ## ❖ Citing PyPOTS
+> [!TIP]
+> **[Updates in Feb 2024]** 😎 Our survey paper [Deep Learning for Multivariate Time Series Imputation: A Survey](https://arxiv.org/abs/2402.04059) has been released on arXiv.
+The code is open source in the GitHub repo [Awesome_Imputation](https://github.com/WenjieDu/Awesome_Imputation).
+We comprehensively review the literature of the state-of-the-art deep-learning imputation methods for time series, 
+provide a taxonomy for them, and discuss the challenges and future directions in this field.
+> 
+> **[Updates in Jun 2023]** 🎉 A short version of the PyPOTS paper is accepted by the 9th SIGKDD international workshop on
+Mining and Learning from Time Series ([MiLeTS'23](https://kdd-milets.github.io/milets2023/))).
+**Additionally**, PyPOTS has been included as a [PyTorch Ecosystem](https://pytorch.org/ecosystem/) project.
+
 The paper introducing PyPOTS is available on arXiv at [this URL](https://arxiv.org/abs/2305.18811),
 and we are pursuing to publish it in prestigious academic venues, e.g. JMLR (track for
 [Machine Learning Open Source Software](https://www.jmlr.org/mloss/)). If you use PyPOTS in your work,
@@ -222,7 +238,7 @@ There are scientific research projects using PyPOTS and referencing in their pap
 Here is [an incomplete list of them](https://scholar.google.com/scholar?as_ylo=2022&q=%E2%80%9CPyPOTS%E2%80%9D&hl=en>).
 
 ``` bibtex
-@article{du2023PyPOTS,
+@article{du2023pypots,
 title={{PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series}},
 author={Wenjie Du},
 year={2023},
@@ -239,17 +255,6 @@ doi={10.48550/arXiv.2305.18811},
 > arXiv, abs/2305.18811.https://arxiv.org/abs/2305.18811
 
 
-> [!TIP]
-> **[Updates in Feb 2024]** 😎 Our survey paper [Deep Learning for Multivariate Time Series Imputation: A Survey](https://arxiv.org/abs/2402.04059) has been released on arXiv.
-The code is open source in the GitHub repo [Awesome_Imputation](https://github.com/WenjieDu/Awesome_Imputation).
-We comprehensively review the literature of the state-of-the-art deep-learning imputation methods for time series, 
-provide a taxonomy for them, and discuss the challenges and future directions in this field.
-> 
-> **[Updates in Jun 2023]** 🎉 A short version of the PyPOTS paper is accepted by the 9th SIGKDD international workshop on
-Mining and Learning from Time Series ([MiLeTS'23](https://kdd-milets.github.io/milets2023/))).
-Besides, PyPOTS has been included as a [PyTorch Ecosystem](https://pytorch.org/ecosystem/) project.
-
-
 ## ❖ Contribution
 You're very welcome to contribute to this exciting project!
 

docs/install.rst

@@ -26,13 +26,14 @@ Alternatively, you can install from the latest source code which may be not offi
 Required Dependencies
 """""""""""""""""""""
 * Python >=3.7
+* h5py
 * numpy
 * scipy
+* pandas
+* matplotlib
+* tensorboard
 * scikit-learn
-* pandas <2.0.0
 * torch >=1.10.0
-* tensorboard
-* h5py
 * tsdb >=0.2
 * pygrinder >=0.2
 
@@ -56,11 +57,6 @@ In addition, note that Python v.3.7 has also been in the end-of-life status sinc
 Hence, we will raise the minimum support Python version to v3.8 in the future.
 Please use Python v3.8 or above if possible also for the security of your development environment.
 
-* **Why we need pandas <2.0.0?**
-
-Because v2 may cause ``ModuleNotFoundError: No module named 'pandas.core.indexes.numeric'``,
-see https://stackoverflow.com/questions/75953279/modulenotfounderror-no-module-named-pandas-core-indexes-numeric-using-metaflo.
-
 * **Why we need PyTorch >=1.10?**
 
 Because of pytorch_sparse, please refer to https://github.com/rusty1s/pytorch_sparse/issues/207#issuecomment-1065549338.

environment-dev.yml

@@ -7,21 +7,20 @@ channels:
     - nodefaults
 
 dependencies:
-    ## basic
-    #- conda-forge::python
-    #- conda-forge::pip
-    #- conda-forge::scipy
-    #- conda-forge::numpy
-    #- conda-forge::scikit-learn
-    #- conda-forge::pandas <2.0.0
-    #- conda-forge::h5py
-    #- conda-forge::tensorboard
-    #- conda-forge::pygrinder >=0.4
-    #- conda-forge::tsdb >=0.2
-    #- conda-forge::matplotlib
-    #- pytorch::pytorch >=1.10.0
+    # basic
+    - conda-forge::pip
+    - conda-forge::h5py
+    - conda-forge::numpy
+    - conda-forge::scipy
+    - conda-forge::python
+    - conda-forge::pandas
+    - conda-forge::matplotlib
+    - conda-forge::tensorboard
+    - conda-forge::scikit-learn
+    - conda-forge::pygrinder >=0.4
+    - conda-forge::tsdb >=0.2
+    - pytorch::pytorch >=1.10.0
     ## Below we install the latest pypots because we need pypots-cli in it for development.
-    ## PyPOTS itself already includes all basic dependencies.
     - conda-forge::pypots
 
     # optional

pypots/utils/visual/data.py

@@ -88,7 +88,10 @@ def plot_data(
             plt.setp(axes[row, 0], ylabel="value")
         if row == -1:
             plt.setp(axes[-1, col], xlabel="time")
-    plt.show()
+
+    logger.info(
+        "Plotting finished. Please invoke matplotlib.pyplot.show() to display the plot."
+    )
 
 
 def plot_missingness(
@@ -166,4 +169,7 @@ def plot_missingness(
     axes[1].set_xlabel(r"Sequence length", fontsize=7)
     axes[1].set_ylabel("Frequency", fontsize=7)
     axes[1].tick_params(axis="both", labelsize=7)
-    plt.show()
+
+    logger.info(
+        "Plotting finished. Please invoke matplotlib.pyplot.show() to display the plot."
+    )

requirements.txt

@@ -1,13 +1,13 @@
 # This requirements.txt file only include the basic dependencies for PyPOTS.
 # Please refer to setup.cfg for more dependency details.
 
+h5py
 numpy
-scikit-learn
-matplotlib
 scipy
-torch>=1.10.0
+pandas
+matplotlib
 tensorboard
-pandas<2.0.0
-pygrinder>=0.4
+scikit-learn
+torch>=1.10.0
 tsdb>=0.2
-h5py
+pygrinder>=0.4

setup.py

@@ -23,6 +23,7 @@
         "Download": "https://github.com/WenjieDu/PyPOTS/archive/main.zip",
     },
     keywords=[
+        "data science",
         "data mining",
         "neural networks",
         "machine learning",
@@ -31,6 +32,7 @@
         "time-series analysis",
         "time series",
         "imputation",
+        "interpolation",
         "classification",
         "clustering",
         "forecasting",
@@ -44,16 +46,16 @@
     packages=find_packages(exclude=["tests"]),
     include_package_data=True,
     install_requires=[
+        "h5py",
         "numpy",
-        "scikit-learn",
-        "matplotlib",
         "scipy",
-        "torch>=1.10.0",
+        "pandas",
+        "matplotlib",
         "tensorboard",
-        "pandas<2.0.0",
-        "pygrinder>=0.4",
+        "scikit-learn",
+        "torch>=1.10.0",
         "tsdb>=0.2",
-        "h5py",
+        "pygrinder>=0.4",
     ],
     python_requires=">=3.7.0",
     setup_requires=["setuptools>=38.6.0"],
@@ -63,13 +65,16 @@
         "Intended Audience :: Developers",
         "Intended Audience :: Education",
         "Intended Audience :: Science/Research",
+        "Intended Audience :: Healthcare Industry",
         "License :: OSI Approved :: BSD License",
         "Operating System :: OS Independent",
         "Programming Language :: Python :: 3",
         "Programming Language :: Python :: 3.7",
         "Programming Language :: Python :: 3.8",
         "Programming Language :: Python :: 3.9",
         "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
         "Topic :: Scientific/Engineering :: Artificial Intelligence",
+        "Topic :: Software Development :: Libraries :: Application Frameworks",
     ],
 )