| Name | npeccv6 JSON |
| Version |
0.1.10
JSON |
| download |
| home_page | None |
| Summary | Python package for root recognition and robot controll |
| upload_time | 2024-12-23 17:38:49 |
| maintainer | None |
| docs_url | None |
| author | Hubert Waleńczak |
| requires_python | <3.12,>=3.11 |
| license | None |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
## Project Name: NPECCV6
This project is a comprehensive package for advanced data processing, predictive modeling, postprocessing of plant roots, and integration with Azure Machine Learning services. Below is an overview of the project structure and key details.
### Folder Structure
```bash
├── Azure_scripts/ # Scripts for interacting with Azure ML
├── dist/ # Distributable Python packages
├── docs/ # Documentation source and build files
├── tests/ # Test cases for the project
├── Dockerfile # Docker container configuration
├── pyproject.toml # Project configuration file
├── README.md # Project README file
└── npeccv6/ # Main package folder
├── __init__.py # Package initialization
├── api.py # API functions for package operations
├── azure_scripts/ # Azure-specific scripts for pipeline
├── create_model.py # Model creation logic
├── hyperparametetuning.py # Hyperparameter tuning functionality
├── log/ # Log files
├── mlruns/ # MLflow experiment tracking files
├── model_func.py # Core model-related functions
├── model_history.json # Saved model history
├── postprocessing.py # Postprocessing functions
├── predict.py # Prediction workflow
├── preprocessing.py # Data preprocessing functionality
├── register.py # Model registration functions
├── scoring.py # Model scoring utilities
├── train.py # Model training logic
├── user_data/ # User data for interacting with api
└── utils.py # General utility functions
```
### Getting Started
#### Installation
1. Clone the repository:
```bash
git clone <repository_url>
cd <repository_name>
```
2. Install the package using pip:
```bash
pip install dist/npeccv6-0.1.1-py3-none-any.whl
```
3. Install additional dependencies if required:
```bash
poetry install
```
### How to Use the CLI with Folder Structure
w
### Features
- Model Training and Scoring: Comprehensive scripts (train.py, scoring.py) for training and evaluating machine learning models.
- Data Preprocessing: Utilities for data cleaning, normalization, and augmentation (preprocessing.py).
- Azure ML Integration: Scripts to set up and interact with Azure ML resources (azure_scripts/).
- Logging: Centralized logging system for debugging and tracking (log/).
- Prediction and Postprocessing: Ready-to-use prediction pipeline (predict.py) and result enhancement tools (postprocessing.py).
### Documentation
Find the complete project documentation in the docs/ folder. Built documentation is available in the docs/build/html/ directory.
For API only documentation and interactions start fastapi
```bash
cd npeccv6
poetry run fastapi run api.py
```
and visit address shown in terminal. It sould begin with 127.0.0.1
### Contribution
1. Fork the repository and create your feature branch:
```bash
git checkout -b feature/new-feature
```
2. Commit your changes and push to the branch:
```bash
git commit -am 'Add new feature'
git push origin feature/new-feature
```
3. Create a pull request.
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