README.md

@@ -5,90 +5,58 @@ This is the code implementation of my Bsc Thesis, which has topic of "Unsupervis
 
 ## Getting started
 
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
-
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
-
-## Add your files
-
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-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
+## Installation Prerequisites
+Before you get started,
 
+```bash
+1. System should have IDE or text editor
+2. Python version 3.9.0
+3. PIP
 ```
-cd existing_repo
-git remote add origin https://mygit.th-deg.de/ko11920/thesiscodeimplementation.git
-git branch -M main
-git push -uf origin main
-```
-
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://mygit.th-deg.de/ko11920/thesiscodeimplementation/-/settings/integrations)
-
-## Collaborate with your team
-
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-
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
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-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
+# Set-up an Environment:
 
-***
+Clustering-Tool is built with Python and Streamlit. 
+For the installation of a local development is described below.
 
-# Editing this README
+### For a virtual environment, 
+Independent of which package management tool you use, it is recommended that commands on this page are 
+run in a virtual environment. By doing so, it ensures that the dependencies added for Streamlit will not 
+impact other Python projects you are working on.
 
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
+Tools use for environment management:
+ - [conda](https://www.anaconda.com/products/distribution)
 
-## Suggestions for a good README
-
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
-
-## Name
-Choose a self-explaining name for your project.
-
-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
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-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
-
-## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
+## 1. Clone repository to a destination folder
+```bash
+git clone https://mygit.th-deg.de/kv14423/clustering-tool.git
+```
 
-## Usage
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
+## Set-up
 
-## Support
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
+```bash
+conda create --name <VenvEnvName> python==3.9.0 -y
+```
+```bash
+conda activate <VenvEnvName>
+```
 
-## Roadmap
-If you have ideas for releases in the future, it is a good idea to list them in the README.
+```bash
+pip install -r requirements.txt
+```
 
-## Contributing
-State if you are open to contributions and what your requirements are for accepting them.
+## Code Structure
+For the replication of the results, you need to run those python files.
 
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
+Firstly to run those algorithms defined in the thesis and plots the CVI metrics, run the following pythn files as follow:
 
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
+## 1. Run fine_tune_hyperparameter.py
+After the execution of the script, a new folder is created where CVI metrics of those datasets is created named as <b>cvi_results_of_clustering
 
-## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
+## 2. Run select_optimal_parameters.py
+Using the cvi score of clustering algorithms for each dataset of <b>cvi_results_of_clustering folder, run this script and new folder for composite score is created called <b>composite_score_results which takes the best parameters from the cvi_results.
 
-## License
-For open source projects, say how it is licensed.
+## 3. Run optimal_hyperparameter_plot.py
+This script plots the no of cluster for those cvi indices based on the composite score.
 
-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
+## 4. Run plot_for_all_cvi_from_all_data.py
+This script is used to plot the five CVI indices for the clusterd of whole dataset that shows the cvi-score for all the algorithms used, which represent the best algorithms according to cvi.
\ No newline at end of file

better-implem.py

@@ -706,7 +706,7 @@ def visualize_features_heatmap(daily_aggregates):
 # Example Usage
 if __name__ == "__main__":
     # Step 1: Load the dataset
-    data = pd.read_csv("/home/jvaldes/Desktop/krishna-thesis/thesiscodeimplementation/data/EFH (1).csv")  # Replace with your dataset file
+    data = pd.read_csv("/home/jvaldes/Desktop/krishna-thesis/thesiscodeimplementation/data/eth_data/EFH (8).csv")  # Replace with your dataset file
 
     # Process the data to get daily mean values
     daily_mean_df = process_daily_mean_power(data)

clustering_cvi.csv

 Silhouette Score,Davies-Bouldin Score,Calinski-Harabasz Score,Dunn Index,COP Index,Score Function
-0.23010091440535346,0.8125262754129975,75.67886360829836,0.00191514031834882,0.010523960350357313,0.08340997077066392
+0.3104714677604624,0.852058991814046,233.13824672633373,0.012709898306111338,0.007268193467649197,0.04207614520369935