Revert "Updated benchmark files. Table & Plot left"

Author: PyDataBlog

.gitignore

@@ -12,5 +12,4 @@
 .idea/*
 .vscode/*
 test/experiments.jl
-/extras/.ipynb_checkpoints/*
-.Rproj.user
+/extras/.ipynb_checkpoints/*

Project.toml

@@ -1,7 +1,7 @@
 name = "ParallelKMeans"
 uuid = "42b8e9d4-006b-409a-8472-7f34b3fb58af"
 authors = ["Bernard Brenyah", "Andrey Oskin"]
-version = "1.0.0"
+version = "0.2.2"
 
 [deps]
 Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"

README.md

@@ -2,8 +2,7 @@
 
 [![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://PyDataBlog.github.io/ParallelKMeans.jl/stable)
 [![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://PyDataBlog.github.io/ParallelKMeans.jl/dev)
-[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
-[![Build Status](https://github.com/PyDataBlog/ParallelKMeans.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/PyDataBlog/ParallelKMeans.jl/actions)
+[![Build Status](https://github.com/PyDataBlog/ParallelKMeans.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/PyDataBlog/ParallelKMeans.jl/actions/workflows/CI.yml/badge.svg)
 [![codecov](https://codecov.io/gh/PyDataBlog/ParallelKMeans.jl/branch/master/graph/badge.svg?token=799USS6BPH)](https://codecov.io/gh/PyDataBlog/ParallelKMeans.jl)
 [![FOSSA Status](https://app.fossa.com/api/projects/git%2Bgithub.com%2FPyDataBlog%2FParallelKMeans.jl.svg?type=shield)](https://app.fossa.com/projects/git%2Bgithub.com%2FPyDataBlog%2FParallelKMeans.jl?ref=badge_shield)
 [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/PyDataBlog/ParallelKMeans.jl/master)

docs/src/benchmark_image.png

@@ -79,15 +79,14 @@ pkg> free ParallelKMeans
 - [X] Support for weighted K-means.
 - [X] Support of MLJ Random generation hyperparameter.
 - [X] Implementation of [Mini-batch KMeans variant](https://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf)
-- [X] Add contribution guidelines.
 - [ ] Support for other distance metrics supported by [Distances.jl](https://github.com/JuliaStats/Distances.jl#supported-distances).
 - [ ] Implementation of [Geometric methods to accelerate k-means algorithm](http://cs.baylor.edu/~hamerly/papers/sdm2016_rysavy_hamerly.pdf).
 - [ ] Native support for tabular data inputs outside of MLJModels' interface.
+- [ ] GPU support?
 - [ ] Distributed calculations support.
-- [ ] Further optimization of code base.
-- [ ] Improved Documentation with more tutorials.
+- [ ] Optimization of code base.
+- [ ] Improved Documentation
 - [ ] More benchmark tests.
-- [ ] GPU support?
 
 ## How To Use
 
@@ -128,7 +127,7 @@ r.converged             # whether the procedure converged
 - [Elkan()](https://www.aaai.org/Papers/ICML/2003/ICML03-022.pdf) - Recommended for high dimensional data.
 - [Yinyang()](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/ding15.pdf) - Recommended for large dimensions and/or large number of clusters.
 - [Coreset()](http://proceedings.mlr.press/v51/lucic16-supp.pdf) - Recommended for very fast clustering of very large datasets, when extreme accuracy is not important.
-- [MiniBatch()](https://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf) - Recommended for extremely large datasets, when extreme accuracy is not important. *Experimental Implementation*
+- [MiniBatch()](https://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf) - Recommended for extremely large datasets, when extreme accuracy is not important.
 - [Geometric()](http://cs.baylor.edu/~hamerly/papers/sdm2016_rysavy_hamerly.pdf) - (Coming soon)
 
 ### Practical Usage Examples
@@ -188,17 +187,17 @@ Currently, the benchmark speed tests are based on the search for optimal number
 
 _________________________________________________________________________________________________________
 
-|1 million sample (secs)|100k sample (secs)|10k sample (secs)|1k sample (secs)|package                 |language   |process   |
-|-----------------------|------------------|-----------------|----------------|------------------------|-----------|----------|
-|282.7                  |15.27             |0.7324           |0.01682         |Knor                    |R          |full scan |
-|854                    |87                |6.11             |0.000719        |Sklearn KMeans          |Python     |full scan |
-|11.2                   |1.41              |0.000317         |0.000141        |Sklearn MiniBatch Kmeans|Python     |stochastic|
-|254.481                |18.517            |0.000794956      |0.000031211     |Mlpack                  |C++ Wrapper|full scan |
-|653.178                |45.468            |0.000824115      |0.000017301     |Clustering.jl           |Julia      |full scan |
-|19.955                 |2.758             |0.000166957      |0.000009206     |ParallelKMeans Lloyd    |Julia      |full scan |
-|11.234                 |1.654             |0.000109074      |0.000012819     |ParallelKMeans Hamerly  |Julia      |full scan |
-|19.394                 |1.436             |0.000109262      |0.000013726     |ParallelKMeans Elkan    |Julia      |full scan |
-|14.080                 |0.000972914       |0.000095325      |0.000009802     |ParallelKMeans YingYang |Julia      |full scan |
+|1 million sample (secs)|100k sample (secs)|10k sample (secs)|1k sample (secs)|package                |language   |
+|:---------------------:|:----------------:|:---------------:|:--------------:|:---------------------:|:---------:|
+|       538.53100       |     33.15700     |     0.74238     |    0.01710     |     Clustering.jl     |   Julia   |
+|       220.35700       |     20.93600     |     0.82430     |    0.02639     |        mlpack         |C++ Wrapper|
+|       20.55400        |     2.91300      |     0.17559     |    0.00609     |         Lloyd         |   Julia   |
+|       11.51800        |     0.96637      |     0.09990     |    0.00635     |        Hamerly        |   Julia   |
+|       14.01900        |     1.13100      |     0.07912     |    0.00646     |         Elkan         |   Julia   |
+|        9.97000        |     1.14600      |     0.10834     |    0.00704     |       Yinyang         |   Julia   |
+|      1,430.00000      |    146.00000     |     5.77000     |    0.34400     |    Sklearn KMeans     |  Python   |
+|       30.10000        |     3.75000      |     0.61300     |    0.20100     |Sklearn MiniBatchKMeans|  Python   |
+|       218.20000       |     15.51000     |     0.73370     |    0.01947     |         Knor          |     R     |
 
 _________________________________________________________________________________________________________
 
@@ -215,14 +214,15 @@ ________________________________________________________________________________
 - 0.1.8 Minor cleanup
 - 0.1.9 Added travis support for Julia 1.5
 - 0.2.0 Updated MLJ Interface
-- 0.2.1 Initial Mini-batch implementation
-- 0.2.2 Updated MLJInterface
-- 1.0.0 Stable public release
+- 0.2.1 Mini-batch implementation
 
 ## Contributing
 
 Ultimately, we see this package as potentially the one-stop-shop for everything related to KMeans algorithm and its speed up variants. We are open to new implementations and ideas from anyone interested in this project.
-This project adopts the [ColPrac community guidelines](https://github.com/SciML/ColPrac).
+
+Detailed contribution guidelines will be added in upcoming releases.
+
+<!--- TODO: Contribution Guidelines --->
 
 ```@index
 ```