Merge pull request #31 from panagiotisanagnostou/new_features

Inicorporation of the t-SNE method in projection based algorithms and documentation update

CONTRIBUTING.md

@@ -230,10 +230,4 @@ generation in the repository folder docs.
 
 ## Contact
 
-For more information or any questions on the documentation you can contact us in
-the email 
-
-## Attribution
-
-This guide is based on the **contributing-gen
-**. [Make your own](https://github.com/bttger/contributing-gen)!
+For more information or any questions you can contact the maintainer [Panagiotis Anagnostou](mailto:[email protected]).

README.md

@@ -4,6 +4,7 @@
 [![codecov](https://codecov.io/gh/panagiotisanagnostou/HiPart/branch/main/graph/badge.svg?token=FHoZrLjqfj)](https://codecov.io/gh/panagiotisanagnostou/HiPart)
 [![Codacy Badge](https://app.codacy.com/project/badge/Grade/60c751d914474e288b369461e6e3466a)](https://www.codacy.com/gh/panagiotisanagnostou/HiPart/dashboard?utm_source=github.com&utm_medium=referral&utm_content=panagiotisanagnostou/HiPart&utm_campaign=Badge_Grade)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://github.com/panagiotisanagnostou/HiPart/blob/main/LICENSE)
+[![DOI](https://joss.theoj.org/papers/10.21105/joss.05024/status.svg)](https://doi.org/10.21105/joss.05024)
 
 HiPart: Hierarchical divisive clustering toolbox
 ================================================
@@ -19,15 +20,15 @@ pip install HiPart
 
 Simple Example Execution
 ------------------------
-The example bellow is the simplest form of the package's execution. Shortly, it shows the creation of synthetic clustering dataset containing 6 clusters. Afterwards it is clustered with the dePDDP algorithm and only the cluster labels are returned.
+The example bellow is the simplest form of the package's execution. Shortly, it shows the creation of synthetic clustering dataset containing 6 clusters. Afterwards it is clustered with the DePDDP algorithm and only the cluster labels are returned.
 
 ```python
-from HiPart.clustering import dePDDP
+from HiPart.clustering import DePDDP
 from sklearn.datasets import make_blobs
 
 X, y = make_blobs(n_samples=1500, centers=6, random_state=0)
 
-clustered_class = dePDDP(max_clusters_number=6).fit_predict(X)
+clustered_class = DePDDP(max_clusters_number=6).fit_predict(X)
 ```
 
 Users can find complete execution examples for all the algorithms of the HiPart package in the [clustering_example](https://github.com/panagiotisanagnostou/HiPart/blob/main/examples/clustering_example.py) file of the repository. Also, the users can find a KernelPCA method usage example in the [clustering_with_kpca_example](https://github.com/panagiotisanagnostou/HiPart/blob/main/examples/clustering_with_kpca_example.py) file of the repository. Finally, the file [interactive_visualization_example](https://github.com/panagiotisanagnostou/HiPart/blob/main/examples/interactive_visualization_example.py) contains an example execution of the interactive visualization. The instructions for the interactive visualization GUI can be found with the execution of this visualization.
@@ -40,15 +41,18 @@ Citation
 --------
 
 ```bibtex
-@misc{anagnostou2022hipart,
-  doi = {10.48550/ARXIV.2209.08680},
-  url = {https://arxiv.org/abs/2209.08680},
-  author = {Anagnostou, Panagiotis and Tasoulis, Sotiris and Plagianakos, Vassilis and Tasoulis, Dimitris},
-  keywords = {Machine Learning (stat.ML), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
+@article{Anagnostou2023HiPart,
   title = {HiPart: Hierarchical Divisive Clustering Toolbox},
-  publisher = {arXiv},
-  year = {2022},
-}
+  author = {Panagiotis Anagnostou and Sotiris Tasoulis and Vassilis P. Plagianakos and Dimitris Tasoulis},
+  year = {2023},
+  journal = {Journal of Open Source Software},
+  publisher = {The Open Journal},
+  volume = {8},
+  number = {84},
+  pages = {5024},
+  doi = {10.21105/joss.05024},
+  url = {https://doi.org/10.21105/joss.05024}
+} 
 ```
 
 Acknowledgments
@@ -60,3 +64,4 @@ Collaborators
 Dimitris Tasoulis [:email:](mailto:[email protected])
 Panagiotis Anagnostou [:email:](mailto:[email protected])
 Sotiris Tasoulis [:email:](mailto:[email protected])
+Vassilis Plagianakos [:email:](mailto:[email protected])

docs/index.rst

@@ -16,6 +16,8 @@ Welcome to HiPart's documentation!
    :target: https://www.codacy.com/gh/panagiotisanagnostou/HiPart/dashboard?utm_source=github.com&utm_medium=referral&utm_content=panagiotisanagnostou/HiPart&utm_campaign=Badge_Grade
 .. image:: https://img.shields.io/badge/License-MIT-yellow.svg
    :target: https://github.com/panagiotisanagnostou/HiPart/blob/main/LICENSE
+.. image:: https://joss.theoj.org/papers/10.21105/joss.05024/status.svg
+   :target: https://doi.org/10.21105/joss.05024
 
 HiPart: Hierarchical divisive clustering toolbox
 ------------------------------------------------
@@ -50,14 +52,17 @@ Citation
 
    .. code-block:: bibtex
 
-      @misc{anagnostou2022hipart,
-        doi = {10.48550/ARXIV.2209.08680},
-        url = {https://arxiv.org/abs/2209.08680},
-        author = {Anagnostou, Panagiotis and Tasoulis, Sotiris and Plagianakos, Vassilis and Tasoulis, Dimitris},
-        keywords = {Machine Learning (stat.ML), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
+      @article{Anagnostou2023HiPart,
         title = {HiPart: Hierarchical Divisive Clustering Toolbox},
-        publisher = {arXiv},
-        year = {2022},
+        author = {Panagiotis Anagnostou and Sotiris Tasoulis and Vassilis P. Plagianakos and Dimitris Tasoulis},
+        year = {2023},
+        journal = {Journal of Open Source Software},
+        publisher = {The Open Journal},
+        volume = {8},
+        number = {84},
+        pages = {5024},
+        doi = {10.21105/joss.05024},
+        url = {https://doi.org/10.21105/joss.05024}
       }
 
 
@@ -66,14 +71,6 @@ Acknowledgments
 This project has received funding from the Hellenic Foundation for Research and Innovation (HFRI), under grant agreement No 1901.
 
 
-Collaborators
--------------
-Dimitris Tasoulis [:email:]([email protected])
-Panagiotis Anagnostou [:email:]([email protected])
-Sotiris Tasoulis [:email:]([email protected])
-
-
-
 .. toctree::
    :maxdepth: 2
    :hidden:

examples/interactive_visualization_example.py

@@ -23,4 +23,4 @@
 ).fit(X)
 
 # interactive visualization execution
-int_viz.main(depddp)
+int_viz.main(depddp)

paper_scripts/__utilities.py

@@ -51,8 +51,6 @@
     subprocess.check_call([sys.executable, "-m", "pip", "install", "h5py"])
     import h5py
 import numpy as np
-import re
-import string
 import time
 import traceback
 import warnings

setup.py

@@ -3,7 +3,7 @@
 with open("README.md", "r", encoding="utf-8") as fh:
     long_description = fh.read()
 
-__version__ = "0.3.1"
+__version__ = "0.3.2"
 
 setuptools.setup(
     name="HiPart",
@@ -32,7 +32,9 @@
         "dash>=2.0",
     ],
     project_urls={
-        "Bug Tracker": "https://github.com/panagiotisanagnostou/HiPart",
+        "Documentation": "https://hipart.readthedocs.io/en/latest/",
+        "Source Code": "https://github.com/panagiotisanagnostou/HiPart/",
+        "Bug Tracker": "https://github.com/panagiotisanagnostou/HiPart/issues",
     },
     classifiers=[
         "Development Status :: 4 - Beta",

src/HiPart/__init__.py

@@ -37,7 +37,7 @@
 from KDEpy.TreeKDE import TreeKDE
 from KDEpy.FFTKDE import FFTKDE
 
-__version__ = "0.3.1"
+__version__ = "0.3.2"
 __author__ = "Panagiotis Anagnostou"
 
 TreeKDE = TreeKDE

src/HiPart/__utility_functions.py

@@ -31,6 +31,7 @@
 import warnings
 
 from KDEpy import FFTKDE
+from sklearn.manifold import TSNE
 from sklearn.decomposition import PCA, KernelPCA, FastICA
 
 
@@ -88,6 +89,12 @@ def execute_decomposition_method(
             **decomposition_args
         )
         two_dimensions = ica.fit_transform(data_matrix)
+    elif decomposition_method == "tsne":
+        tsne = TSNE(
+            n_components=n_of_dimentions,
+            **decomposition_args
+        )
+        two_dimensions = tsne.fit_transform(data_matrix)
     else:
         raise ValueError(
             ": The decomposition method ("