npeccv6 0.1.10

Python package for root recognition and robot controll

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

├── 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:

git clone <repository_url>
cd <repository_name>

2. Install the package using pip:

pip install dist/npeccv6-0.1.1-py3-none-any.whl

3. Install additional dependencies if required:

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

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:

git checkout -b feature/new-feature

2. Commit your changes and push to the branch:

git commit -am 'Add new feature'
git push origin feature/new-feature

3. Create a pull request.