enhancement of documentation and version 1.4.0

Author: quentinhaenn

README.md

@@ -20,11 +20,10 @@ Radius clustering is a Python package that implements clustering under radius co
 
 ## Roadmap
 
-- [ ] Version 2.0.0beta:
+- [x] Version 1.4.0:
     - [x] Add support for custom MDS solvers
-    - [ ] Improve documentation and examples
-    - [ ] Add iterative algorithm in both exact and approximate versions, which will allow to use the package in a more flexible way, especially when not knowing the radius beforehand.
-    - [ ] Add more examples and tutorials
+    - [x] Improve documentation and examples
+    - [x] Add more examples and tutorials
 
 ## Installation
 

docs/source/usage.rst

@@ -82,6 +82,10 @@ Custom MDS Solver
 The two default solvers provided by the actual implementation of the `radius_clustering` package are focused on exactness (or proximity to exactness) of the results of a NP-hard problem. So, they may not be suitable for all use cases, especially when performance is a concern.
 If you have your own implementation of a Minimum Dominating Set (MDS) solver, you can use it with the `RadiusClustering` class ny using the :py:func:'RadiusClustering.set_solver' method. It will check that the solver is compatible with the expected input format and return type, and will use it to perform clustering.
 
+.. versionadded:: 1.4.0
+   The :py:func:`RadiusClustering.set_solver` method was added to allow users to set a custom MDS solver.
+   It is *NOT* backward compatible with previous versions of the package, as it comes with new structure and methods to handle custom solvers.
+
 Here's an example of how to implement a custom MDS solver and use it with the `RadiusClustering` class, using NetworkX implementation of the dominating set problem : 
 
 .. code-block:: python
@@ -122,6 +126,6 @@ Here's an example of how to implement a custom MDS solver and use it with the `R
 
 .. attention::
    We cannot guarantee that the custom MDS solver will produce the same results as the default solvers, especially if it is not purposely designed to solve the Minimum Dominating Set problem but rather just finds a dominating set. The results may vary depending on the implementation and the specific characteristics of the dataset.
-   As an example, a benchmark of our solutions and a custom one using NetworkX is available in the `Example Gallery` section of the documentation, which shows that the custom solver may produce different results than the default solvers, especially in terms of the number of clusters and the time taken to compute them.
+   As an example, a benchmark of our solutions and a custom one using NetworkX is available in the `Example Gallery` section of the documentation, which shows that the custom solver may produce different results than the default solvers, especially in terms of the number of clusters and the time taken to compute them (see :ref:`sphx_glr_auto_examples_plot_benchmark_custom.py`).
    However, it can be useful for specific use cases where performance is a concern or when you have a custom implementation that fits your needs better.
 

src/radius_clustering/algorithms.py

@@ -5,11 +5,10 @@
 These functions can be replaced in the `RadiusClustering` class
 to perform clustering using another algorithm.
 
-.. versionadded:: 2.0.0
+.. versionadded:: 1.4.0
     Refactoring the structure of the code to separate the clustering algorithms
     This allows for easier maintenance and extensibility of the codebase.
-    Plus, this allows for the addition of new clustering algorithms
-    such as `Curgraph` added in this version.
+
 """
 from __future__ import annotations
 

src/radius_clustering/radius_clustering.py

@@ -53,10 +53,15 @@ class RadiusClustering(ClusterMixin, BaseEstimator):
     .. note::
         The `random_state_` attribute is not used when the `manner` is set to "exact".
     
-    .. versionchanged:: 2.0.0
+    .. versionchanged:: 1.4.0
         The `RadiusClustering` class has been refactored.
         Clustering algorithms are now separated into their own module
         (`algorithms.py`) to improve maintainability and extensibility.
+    
+    .. versionadded:: 1.4.0
+        The `set_solver` method was added to allow users to set a custom solver
+        for the MDS problem. This allows for flexibility in how the MDS problem is solved
+        and enables users to use their own implementations of MDS clustering algorithms.
 
     .. versionadded:: 1.3.0
 
@@ -292,6 +297,9 @@ def set_solver(self, solver: callable) -> None:
         Set a custom solver for resolving the MDS problem.
         This method allows users to replace the default MDS solver with a custom one.
 
+        An example is provided below and in the example gallery : 
+        :ref:`sphx_glr_auto_examples_plot_benchmark_custom.py`
+
         .. important::
             The custom solver must accept the same parameters as the default solvers
             and return a tuple containing the cluster centers and the execution time.