Timepass Trainer

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Timepass Trainer

A Python project demonstrating logistic regression training using stochastic gradient descent (SGD) with live progress visualization and rich terminal output.

Overview

Timepass Trainer generates a synthetic classification dataset and trains a logistic regression model incrementally using SGDClassifier from scikit-learn. The training process includes:

  • Partial fitting over multiple epochs
  • Real-time progress bar display using the rich library
  • Periodic logging of accuracy during training
  • Final evaluation metrics displayed in a formatted table

This project highlights incremental learning, interactive terminal UI, and evaluation reporting with ease.

Features

  • Synthetic dataset creation (1000 samples, 20 features, 15 informative)
  • Logistic Regression via SGDClassifier with random seeding
  • Training over 50 epochs with live progress bar and log messages
  • Calculation and display of final accuracy, log loss, precision, and recall
  • Cleaner output with convergence warnings suppressed
  • Built with Python 3.11+, numpy, scikit-learn, and rich

Installation

Clone the repository and install dependencies:

git clone https://github.com/yourusername/timepass-trainer.git
cd timepass-trainer
python3 -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install -r requirements.txt

Usage

Run the training script:

python main.py

During execution, observe:

  • A progress bar updating with each epoch
  • Logged accuracy every 10 epochs
  • A summary table of training metrics after completion

Code Overview

  • main.py: Core training logic including data generation, model training loop, progress reporting, and final metrics display.
  • requirements.txt & pyproject.toml: Define necessary Python dependencies.
  • .gitignore, .python-version: Project environment and ignored files config.

Dependencies

  • Python 3.11 or higher
  • numpy >= 2.3.2
  • scikit-learn >= 1.7.1
  • rich >= 14.1.0

Install dependencies using pip install -r requirements.txt.

Contributing

Contributions, issue reports, and pull requests are welcome! Please ensure code style conformity and add tests where relevant.

License

This project is licensed under the MIT License. See the LICENSE file for details.