New class structure in the package and incorporation of distance matrix clustering

README.md

@@ -2,7 +2,7 @@
 [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/HiPart)](https://pypi.org/project/HiPart/)
 [![example workflow](https://github.com/panagiotisanagnostou/HiPart/actions/workflows/python-app.yml/badge.svg)](https://github.com/panagiotisanagnostou/HiPart/blob/main/.github/workflows/python-app.yml)
 [![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)
+[![Codacy Badge](https://app.codacy.com/project/badge/Grade/60c751d914474e288b369461e6e3466a)](https://app.codacy.com/gh/panagiotisanagnostou/HiPart/dashboard?utm_source=gh&utm_medium=referral&utm_content=&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)
 
@@ -31,7 +31,17 @@ X, y = make_blobs(n_samples=1500, centers=6, random_state=0)
 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.
+The HiPart package offers a comprehensive suite of examples to guide users in utilizing its various algorithms. These examples are conveniently located in the repository's examples directory.
+
+For a general understanding of the package's capabilities, users can refer to the [clustering_example](https://github.com/panagiotisanagnostou/HiPart/blob/main/examples/clustering_example.py) file. This file serves as a foundational guide, providing complete examples of the package's algorithms in action.
+
+Additionally, for those interested in incorporating KernelPCA methods, the [clustering_with_kpca_example](https://github.com/panagiotisanagnostou/HiPart/blob/main/examples/clustering_with_kpca_example.py) file is an invaluable resource. It offers a detailed example of how to apply KernelPCA within the context of the HiPart package.
+
+Recognizing the importance of clustering via similarity or dissimilarity matrices, such as distance matrices, the HiPart package includes the [clustering_with_distance_matrix_example](https://github.com/panagiotisanagnostou/HiPart/blob/main/examples/distance_matrix_example.py) file. This specific example demonstrates the use of the DePDDP algorithm with a distance matrix, offering a practical application scenario.
+
+Lastly, the package features an interactive visualization component, which is exemplified in the [interactive_visualization_example](https://github.com/panagiotisanagnostou/HiPart/blob/main/examples/interactive_visualization_example.py) file. This example not only showcases the execution of the interactive visualization but also provides comprehensive instructions for navigating the visualization GUI. 
+
+These resources collectively ensure that users of the HiPart package have a well-rounded and practical understanding of its functionalities and applications.
 
 Documentation
 -------------

docs/HiPart.rst

@@ -2,6 +2,7 @@ Package contents
 ================
 
 .. toctree::
+   :maxdepth: 2
    :hidden:
 
 .. automodule:: HiPart

docs/conf.py

@@ -21,7 +21,7 @@
 author = 'Panagiotis Anagnostou'
 
 # The full version, including alpha/beta/rc tags
-release = '0.4.2'
+release = '1.0.0'
 
 # -- General configuration ---------------------------------------------------
 

docs/index.rst

@@ -13,7 +13,7 @@ Welcome to HiPart's documentation!
 .. image:: https://codecov.io/gh/panagiotisanagnostou/HiPart/branch/main/graph/badge.svg?token=FHoZrLjqfj
    :target: https://codecov.io/gh/panagiotisanagnostou/HiPart
 .. image:: https://app.codacy.com/project/badge/Grade/60c751d914474e288b369461e6e3466a
-   :target: https://www.codacy.com/gh/panagiotisanagnostou/HiPart/dashboard?utm_source=github.com&utm_medium=referral&utm_content=panagiotisanagnostou/HiPart&utm_campaign=Badge_Grade
+   :target: https://app.codacy.com/gh/panagiotisanagnostou/HiPart/dashboard?utm_source=gh&utm_medium=referral&utm_content=&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
@@ -25,7 +25,7 @@ HiPart is a package created for the implementation of hierarchical divisive clus
 
 Installation
 ------------
-For the installation of the package, the only necessary actions and requirements are a version of Python higher or equal to 3.7 and the execution of the following command.
+For the installation of the package, the only necessary actions and requirements are a version of Python higher or equal to 3.8 and the execution of the following command.
 
    .. code-block:: sh
 
@@ -44,7 +44,17 @@ The example bellow is the simplest form of the package's execution. Shortly, it
 
       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.
+The HiPart package offers a comprehensive suite of examples to guide users in utilizing its various algorithms. These examples are conveniently located in the repository's examples directory.
+
+For a general understanding of the package's capabilities, users can refer to the `clustering_example <https://github.com/panagiotisanagnostou/HiPart/blob/main/examples/clustering_example.py>`_ file. This file serves as a foundational guide, providing complete examples of the package's algorithms in action.
+
+Additionally, for those interested in incorporating KernelPCA methods, the `clustering_with_kpca_example <https://github.com/panagiotisanagnostou/HiPart/blob/main/examples/clustering_with_kpca_example.py>`_ file is an invaluable resource. It offers a detailed example of how to apply KernelPCA within the context of the HiPart package.
+
+Recognizing the importance of clustering via similarity or dissimilarity matrices, such as distance matrices, the HiPart package includes the `clustering_with_distance_matrix_example <https://github.com/panagiotisanagnostou/HiPart/blob/main/examples/distance_matrix_example.py>`_ file. This specific example demonstrates the use of the DePDDP algorithm with a distance matrix, offering a practical application scenario.
+
+Lastly, the package features an interactive visualization component, which is exemplified in the `interactive_visualization_example <https://github.com/panagiotisanagnostou/HiPart/blob/main/examples/interactive_visualization_example.py>`_ file. This example not only showcases the execution of the interactive visualization but also provides comprehensive instructions for navigating the visualization GUI.
+
+These resources collectively ensure that users of the HiPart package have a well-rounded and practical understanding of its functionalities and applications.
 
 
 Citation
@@ -76,10 +86,10 @@ Contents
 -------------
 
 .. toctree::
-   :maxdepth: 2
+   :maxdepth: 3
 
    self
-   modules
+   HiPart
    examples
 
 * :ref:`genindex`

examples/clustering_example.py

@@ -8,9 +8,9 @@
 from sklearn.metrics import adjusted_rand_score as ari
 from sklearn.metrics import normalized_mutual_info_score as nmi
 
+import HiPart.visualizations as viz
 import matplotlib.pyplot as plt
 import time
-import HiPart.visualizations as viz
 
 # %% Example data creation
 # number of cluster in the data

examples/clustering_with_kpca_example.py

@@ -1,11 +1,11 @@
-import HiPart.visualizations as viz
-import matplotlib.pyplot as plt
-import numpy as np
-
 from HiPart.clustering import DePDDP
 from sklearn.decomposition import KernelPCA
 from sklearn.datasets import make_circles
 
+import HiPart.visualizations as viz
+import matplotlib.pyplot as plt
+import numpy as np
+
 
 def plot_manafolds(X, y, vals, gamma, title):
     """

examples/distance_matrix_example.py

@@ -0,0 +1,47 @@
+from HiPart.clustering import DePDDP
+from sklearn.datasets import make_blobs
+from sklearn.metrics import adjusted_rand_score as ari
+from sklearn.metrics import normalized_mutual_info_score as nmi
+from scipy.spatial import distance_matrix
+
+import HiPart.visualizations as viz
+import HiPart.interactive_visualization as iviz
+import time
+
+# %% Example data creation
+# number of cluster in the data
+clusters = 5
+
+X, y = make_blobs(
+    n_samples=500,
+    centers=5,
+    cluster_std=.8,
+    random_state=123,
+)
+print("Example data shape: {}\n".format(X.shape))
+
+# Calculate distance matrix
+dist_matrix = distance_matrix(X, X)
+
+# %% dePDDP algorithm execution
+# timer for the execution time in the form of tic-toc
+tic = time.perf_counter()
+depddp = DePDDP(
+    decomposition_method="mds",
+    max_clusters_number=clusters,
+    bandwidth_scale=0.5,
+    percentile=0.1,
+    distance_matrix=True,
+    random_state=12,
+).fit(dist_matrix)
+toc = time.perf_counter()
+
+# results evaluation in terms of execution time, MNI and ARI metrics
+print("depddp_time= {val:.5f}".format(val=toc - tic))
+print("depddp_mni= {val:.5f}".format(val=nmi(y, depddp.labels_)))
+print("depddp_ari= {val:.5f}\n".format(val=ari(y, depddp.labels_)))
+
+# scatter visualization
+viz.split_visualization(depddp).show()
+# interactive visualization
+iviz.main(depddp)

setup.py

@@ -3,7 +3,7 @@
 with open("README.md", "r", encoding="utf-8") as fh:
     long_description = fh.read()
 
-__version__ = "0.4.2"
+__version__ = "1.0.0"
 
 setuptools.setup(
     name="HiPart",

src/HiPart/__init__.py

@@ -37,7 +37,7 @@
 from KDEpy.TreeKDE import TreeKDE
 from KDEpy.FFTKDE import FFTKDE
 
-__version__ = "0.4.2"
+__version__ = "1.0.0"
 __author__ = "Panagiotis Anagnostou"
 
 TreeKDE = TreeKDE