https://github.com/codemeasandwich/aiemail

multi-label email classification
https://github.com/codemeasandwich/aiemail

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multi-label email classification

• Host: GitHub
• URL: https://github.com/codemeasandwich/aiemail
• Owner: codemeasandwich
• Created: 2024-03-20T17:15:37.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2024-04-23T16:51:41.000Z (over 2 years ago)
• Last Synced: 2025-02-13T04:44:40.065Z (over 1 year ago)
• Language: Python
• Size: 260 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

          
# Multi-Label Email Classification System

This repository contains the code for a multi-label email classification system. The system is designed to classify emails based on multiple dependent variables (labels) such as Type 2, Type 3, and Type 4. It provides a modular and extensible architecture that allows for easy modification and addition of preprocessing steps, machine learning models, and evaluation metrics.

## Features

- Separation of concerns (SoC) architecture with components for preprocessing, embeddings, modeling, etc.

- Supports two design decisions for multi-label classification:

  1. Chained Multi-outputs Approach: Trains a single model instance on chained labels (e.g., Type 2, Type 2+3, Type 2+3+4)

  2. Hierarchical Modeling Approach: Trains multiple model instances on filtered data based on the classes of preceding labels

- Encapsulates input data using a `Data` class for consistent access across models

- Implements multiple machine learning models with a consistent interface for training, prediction, and evaluation

- Provides a main controller (`main.py`) for orchestrating the preprocessing, embedding, modeling, and evaluation steps

## Repository Structure

- `main.py`: Main controller script for running the email classification system

- `preprocess.py`: Contains functions for data preprocessing, including de-duplication, noise removal, and translation

- `embeddings.py`: Implements functions for generating embeddings from text data (e.g., TF-IDF)

- `modelling/`: Directory containing modules related to modeling

  - `modelling.py`: Defines functions for model training, prediction, and evaluation

  - `data_model.py`: Implements the `Data` class for encapsulating input data

- `model/`: Directory containing implementations of various machine learning models

  - `base.py`: Defines the abstract base class for all models

  - `randomforest.py`: Implements the Random Forest model

  - `sgd.py`: Implements the Stochastic Gradient Descent (SGD) model

  - `adaboost.py`: Implements the AdaBoost model

  - `voting.py`: Implements the Voting Classifier model

  - `hist_gb.py`: Implements the Histogram-based Gradient Boosting model

  - `random_trees_ensembling.py`: Implements the Random Trees Embedding model

- `data/`: Directory containing the input data files

  - `AppGallery.csv`: Input data file for the AppGallery domain

  - `Purchasing.csv`: Input data file for the Purchasing domain

- `Config.py`: Configuration file for storing constants and settings

## Usage

1. Install the required dependencies (TODO: Create `requirements.txt` file).

2. Place the input data files (`AppGallery.csv` and `Purchasing.csv`) in the `data/` directory.

3. Modify the `Config.py` file to adjust any configuration settings if needed.

4. Run the `main.py` script to execute the email classification system.

## Contributing

Contributions to this project are welcome. If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.

## License

This project is licensed under the [MIT License](LICENSE).