firstname-to-nationality 1.1.11

Nationality Prediction from Firstname using Python 3.13 and scikit-learn

Firstname to Nationality - Python 3.13 Implementation

A name-to-nationality prediction library for Python 3.13+ using machine learning libraries.

🚀 Features

This library provides the following capabilities:

• ✅ Python 3.13+ Compatible: Uses Python features and type hints
• ✅ ML Stack: Built with scikit-learn for performance and compatibility
• ✅ City-Based Prediction: Use geopy geocoding for city-based nationality prediction
• ✅ Type Safety: Full type hints and dataclasses throughout
• ✅ Error Handling: Robust error handling and fallbacks
• ✅ Dev Container Ready: Includes VS Code dev container configuration
• ✅ Flexible Training: Easy model training with your own data
• ✅ Batch Processing: Efficient batch prediction support

📦 Installation

Using the Dev Container (Recommended)

1. Open in VS Code
2. When prompted, click "Reopen in Container"
3. The dev container will build automatically with Python 3.13

Manual Installation

# Ensure you have Python 3.13+
python --version

# Install dependencies
pip install -r requirements.txt

# Install the package
pip install -e .

🔧 Quick Start

Basic Name-Based Prediction

from firstname_to_nationality import FirstnameToNationality

# Initialize the predictor
predictor = FirstnameToNationality()

# Predict nationality for a single name
result = predictor.predict_single("Giuseppe Rossi", top_n=3)
print(result)  # [('Italian', 0.85), ('Spanish', 0.12), ...]

# Batch prediction
names = ["John Smith", "Maria Rodriguez", "Zhang Wei"]
results = predictor(names, top_n=2)

for name, predictions in results:
    nationality, confidence = predictions[0]
    print(f"{name} → {nationality} ({confidence:.2f})")

City-Based Prediction (New!)

from firstname_to_nationality import CityToNationality

# Initialize the city-based predictor
predictor = CityToNationality()

# Predict with city information (more accurate)
result = predictor("Maria Garcia", cities="Barcelona")
print(result)  # Spanish (from Barcelona, Spain)

# Fallback to name-based prediction if no city
result = predictor("Maria Garcia")
print(result)  # Uses ML model on name

# Batch prediction with cities
names = ["John Smith", "Luigi Ferrari", "Zhang Wei"]
cities = ["London", "Milan", "Beijing"]
results = predictor(names, cities=cities)

for item in results:
    name = item["name"]
    pred = item["predictions"][0]
    print(f"{name} from {item['city']} → {pred['nationality']} ({pred['country_code']})")

🧪 Examples

Run the example scripts:

# Basic name-based prediction
python example.py

# Country code mapping
python example_country.py

# City-based prediction with geocoding
python example_city.py

🔥 Training Your Own Model

Using Sample Data

python nationality_trainer.py

Using Your Own Data

Create a CSV file with name and nationality columns:

name,nationality
John Smith,American
Giuseppe Rossi,Italian
Hiroshi Tanaka,Japanese

Then train:

python nationality_trainer.py your_data.csv

Creating a Dictionary

python nationality_trainer.py --dict

🏗️ Architecture

The implementation consists of:

• FirstnameToNationality: Main predictor class with scikit-learn backend
• FirstnameToCountry: Maps nationalities to country codes
• CityToNationality: City-based prediction with geocoding fallback to name-based
• NamePreprocessor: Advanced name preprocessing and normalization
• PredictionResult: Type-safe prediction results using dataclasses
• Model Pipeline: TF-IDF vectorization + Logistic Regression

📁 File Structure

The implementation uses these file paths:

• firstname_to_nationality/best-model.pt: Model checkpoint file
• firstname_to_nationality/firstname_nationalities.pkl: Name-to-nationality dictionary

� Usage Examples

Basic Usage

from firstname_to_nationality import FirstnameToNationality
predictor = FirstnameToNationality()
results = predictor(["John Smith"])

Advanced Features

# Type-safe single predictions
result = predictor.predict_single("John Smith", top_n=3)

# Training interface
predictor.train(names, nationalities, save_model=True)

# Dictionary management
predictor.save_dictionary(name_dict)

🐳 Development with Docker

Dev Container

The repository includes a complete dev container setup for VS Code:

# Open in VS Code
code .
# Click "Reopen in Container" when prompted

Manual Docker

# Build
docker build -f .devcontainer/Dockerfile -t firstname-to-nationality .

# Run
docker run -it --rm -v $(pwd):/workspace firstname-to-nationality

⚡ Performance

The implementation offers:

• Fast training with scikit-learn
• Memory efficiency
• Batch processing support
• Python optimizations

🧬 Dependencies

Core Requirements:

• Python 3.13+
• scikit-learn >= 1.3.0
• numpy >= 1.25.0
• pandas >= 2.0.0
• joblib >= 1.3.0
• geopy >= 2.3.0 (for city-based predictions)

Development:

• pytest, black, isort, pylint, mypy

🤝 Contributing

1. Use the dev container for consistent environment
2. Follow type hints throughout
3. Run tests: pytest
4. Format code: black . && isort .
5. Check types: mypy firstname_to_nationality/

Automated Release Process

This repository uses a fully automated release workflow:

1. Push your code to the main branch
2. Version is automatically bumped based on conventional commit messages
3. GitHub release is created automatically with AI-generated release notes
4. Package is published to PyPI automatically

For more details, see .github/WORKFLOW_SETUP.md.

Commit Message Format

Use conventional commits for automatic version bumping:

• fix: description → Patch version bump (1.0.0 → 1.0.1)
• feat: description → Minor version bump (1.0.0 → 1.1.0)
• feat!: description → Major version bump (1.0.0 → 2.0.0)

Setting Up GitHub Actions Workflows

If you're a maintainer and need to set up the auto-version-bump workflow, see .github/WORKFLOW_SETUP.md for detailed instructions on configuring the required GitHub App for authentication.

📄 License

MIT License

� Implementation Details

This is a complete implementation with:

• ✅ Consistent method signatures
• ✅ Reliable file handling
• ✅ Robust prediction results
• ✅ Efficient model format
• ✅ Minimal dependencies

🎯 Roadmap

• Transformer-based models support
• REST API server
• Web interface
• Multi-language support
• Advanced evaluation metrics