semvec/pypi-packages huggingface dataset:
sys-config-inspector 0.0.1
This package helps to do the basic sysem check and generate html report
Requires Python: >=3.8
Keywords: python,sys_config_inspector,sys_config_inspector,inspector,config-inspector,sys config inspector


# sys-config-inspector

- This Package is help the Developer to check weather all the pre-requigement file are avaliable in deployment environment (or) not



| Support Events | events names |Type     |
| :-------- | :------- | :------------------------- |
| `bash` | `'bash'` | `Used to execute the Bash cmd using sub process` |
| `file_check` | `'file_check'` | `Used to check the file location is exist or not` |
| `s3_download` | `'s3_download'` | `Used to download file from s3 location to local` |
| `file_overwrite` | `'file_overwrite'` | `Used to overwrite a mentioned file` |
| `json_file_content_check` | `'json_file_content_check'` | `Used to check the json chnages` |



## Dependent Python Packages 
| Package Name | Description |
| :-------- | :-------- |
| `rich` | |
| `jinja2` | **For Report Generation**|
| `PyYAML` | |
| `subprocess` | |
qsarify 0.1.1
QSARify: A tool for QSAR model development
Keywords: QSAR,cheminformatics,machine learning

# qsarify

qsarify is a library of tools for the analysis of QSAR/QSPR datasets and models. This library is intended to be used to produce models which relate a set of calculated chemical descriptors to a given numeric endpoint. Many great tools will take the geometry or string data of a given chemical and compute **descriptors**, which are numeric measures of the properties of these, but you can generate some of these with another one of my scripts, [Free Descriptors](https://github.com/StephenSzwiec/free_descriptors).

# Dependencies

- Python 3
- [numpy](https://numpy.org/)
- [pandas](https://pandas.pydata.org/)
- [scikit-learn](https://scikit-learn.org)
- [matplotlib](https://matplotlib.org)


# Installation

`pip install qsarify`

# What is included right now?

- Data preprocessing tools: `data_tools`
- Dimensionality reduction via clustering: `clustering`
- Feature selection:
	- Single threaded: `feature_selection_single`
	- Multi-threaded: `feature_selection_multi`
- Model Export and Visualization: `model_export`
- Cross Valiidation: `cross_validation`

# How to use

The best way to learn how to use this library is to look at the example notebook in the `examples` folder. This notebook will walk you through the workflow of using this library to build a QSAR model.

# Future Plans

- Massively parallel feature selection methods:
	- CUDA acceleration
	- MPI acceleration
- Include Shannon Entropy as a dimensionality reduction metric in clustering
- Embedded kernel methods
- More visualization tools
- More cross validation tools
- Feature selection tools for categorical data

# Contributing


If you would like to contribute to this project, please feel free to fork this repository and submit a pull request. Otherwise, you may also submit an issue. I will try to respond to issues as quickly as possible.

# License


This project is licensed under the GNU GPLv3 license. See the LICENSE file for more details.

# Citation

If you use this library in your work, please cite it as follows:

Szwiec, Stephen. (2023). qsarify: A high performance library for QSAR model development.

BibTex:
```
@misc{szwiec2023qsarify,
  author = {Szwiec, Stephen},
  title = {qsarify: A high performance library for QSAR model development},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/stephenszwiec/qsarify}},
  }
```