| Name | aisafe-xgboost JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | None |
| Summary | An ensemble xgboost model made by bright-rookie. |
| upload_time | 2024-11-03 14:15:12 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | MIT License Copyright (c) 2024 bright-rookie Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice, this permission notice, and all disclaimers shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. MEDICAL DISCLAIMER: THIS SOFTWARE AND ANY ASSOCIATED MATERIALS ARE FOR EDUCATIONAL PURPOSES ONLY AND SHALL NOT BE USED FOR MEDICAL DECISIONS OR CLINICAL PURPOSES. THIS REPOSITORY AND WEBSITE ARE NOT INTENDED FOR CLINICAL USE UNDER ANY CIRCUMSTANCES. THE CONTENT MUST NOT BE USED FOR MEDICAL DECISION-MAKING OR AS A SUBSTITUTE FOR PROFESSIONAL MEDICAL ADVICE. ANY MEDICAL CONCERNS SHOULD BE DIRECTED TO QUALIFIED HEALTHCARE PROFESSIONALS. BRIGHT-ROOKIE AND CONTRIBUTORS ASSUME NO LIABILITY FOR ANY DAMAGES ARISING FROM THE USE OR MISUSE OF THIS REPOSITORY OR WEBSITE. USE OF ANY REPOSITORY CONTENTS AND WEBSITE DATA IS ENTIRELY AT YOUR OWN RISK. NO WARRANTIES ARE PROVIDED REGARDING THE ACCURACY OR COMPLETENESS OF ANY INFORMATION CONTAINED HEREIN. |
| keywords |
child abuse
gradient boosting machine
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# AISAFE-BACKEND
<a target="_blank" href="https://cookiecutter-data-science.drivendata.org/">
<img src="https://img.shields.io/badge/CCDS-Project%20template-328F97?logo=cookiecutter" />
</a>
A backend leveraging gradient boosting machines and multimodal neural networks for early detection of child abuse.
## DISCLAIMER
The information provided by aisafe_back (owned by GitHub user bright-rookie) on this GitHub repository or on the website `aisafe.qbio.page` is for educational and informational purposes only. All code and data within this repository or information garnered from the website are mock data and examples intended solely for demonstration and experimentation with machine learning models. This repository does not contain functional AI models or real medical data, and any outputs generated by the code are purely fictional and lack any basis in real-world medical analysis or diagnostics.
UNDER NO CIRCUMSTANCES should any code, data, or outputs from this repository or the website be used in clinical settings, to make medical decisions, or as a substitute for professional healthcare advice. bright-rookie and contributors to this repository disclaim any liability for damages resulting from the use or misuse of information contained within this repository. Use of this repository and reliance on any content derived from this repository, or the use of the data from the website are entirely at your own risk.
This repository or website may contain links to other websites or external content maintained by third parties. These links are provided for convenience only, and bright-rookie and contributors do not endorse, guarantee, or take responsibility for the accuracy or completeness of any information available through these links. We have not investigated or verified the content of linked websites. Use of such external resources is also entirely at your own risk.
### Professional Disclaimer
This repository or website does not provide real medical or health advice. The code and data are intended solely for instructional purposes in machine learning and should not be used for any medical or diagnostic purposes. The repository or website does not and cannot provide any kind of medical advice or treatment recommendation. For any medical concerns, consult a qualified healthcare professional.
## Installation
```bash
pip install aisafe_xgboost
```
## Usage
`aisafe_xgboost` can be used as either a CLI or a Python package.
To train the model, run the following command:
```bash
safe-train
```
To use the model in python, run the following command:
```python
>>> from aisafe_xgboost import model
>>> prediction, explainability = model(
info = info, # Iterable of Length 4 [Patient Age, Sex, Height Percentile, Weight Percentile]
bruise = bruse, # Iterable of Length 11 [float]
response = response, # Iterable of Length 9 [int]
lab = lab, # Iterable of Length 19 [float]
xray = xray, # Iterable of Length 9 [float]
video = video, # Iterable of Length 30 [float]
)
>>> prediction
0.75
>>> explainability
[("문진정보", 0.5), ("Lab 수치", 0.3), ("X-ray 영상", 0.1), ("진료 영상", 0.1), ("신체 계측치", 0.0), ("멍 정보", 0.0)]
```
## Project Organization
```
├── LICENSE <- Open-source license if one is chosen
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks.
│
├── pyproject.toml <- Project configuration file with package metadata for
│ aisafe and configuration for tools like black
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
│
└── aisafe_xgboost <- Source code for use in this project.
│
├── __init__.py <- Makes aisafe a Python module
│
├── training.py <- For training the xgboost model
│
├── utils.py <- General utility functions for parsing data
│
├── inference.py <- Code to run model inference with trained models
│
├── models
│ ├── *.ubj <- XGBoost models, stored as .ubj files (universal binary format)
│ └── *.npy <- Weights for combinining the xgboost model as an ensemble
│
├── growth data
│ └── *.csv <- Growth Data for Korean male/female children
└── mock_data
└── *.csv <- Mock data for training the model
```
--------
Raw data
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"description": "# AISAFE-BACKEND\n\n<a target=\"_blank\" href=\"https://cookiecutter-data-science.drivendata.org/\">\n <img src=\"https://img.shields.io/badge/CCDS-Project%20template-328F97?logo=cookiecutter\" />\n</a>\n\nA backend leveraging gradient boosting machines and multimodal neural networks for early detection of child abuse. \n\n## DISCLAIMER\n\nThe information provided by aisafe_back (owned by GitHub user bright-rookie) on this GitHub repository or on the website `aisafe.qbio.page` is for educational and informational purposes only. All code and data within this repository or information garnered from the website are mock data and examples intended solely for demonstration and experimentation with machine learning models. 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