semi-supervised-learning 0.1.0

Semi-Supervised Learning Framework compatible with scikit-learn

PySSL: Semi-Supervised Learning Framework

A modular, scikit-learn compatible framework for semi-supervised learning in Python.

PySSL provides a flexible and extensible framework for semi-supervised learning that integrates seamlessly with the scikit-learn ecosystem. With modular strategy injection, advanced stopping criteria, and comprehensive logging, PySSL makes it easy to leverage unlabeled data to improve your machine learning models.

🎯 Key Features

• 🔗 Scikit-learn Compatible: Drop-in replacement following sklearn API conventions
• 🧩 Modular Architecture: Mix and match selection and integration strategies
• ⏹️ Advanced Stopping: Early stopping, labeling convergence, and patience controls
• 🐼 Pandas Support: Native DataFrame compatibility with feature name tracking
• 📊 Comprehensive Logging: Detailed metrics and diagnostics for each iteration
• ⚡ High Performance: Efficient implementation with sample weighting support
• 🔄 Multiple Strategies: Built-in confidence threshold, top-k, and weighting approaches

🚀 Quick Start

Get started with PySSL in just a few lines of code:

import numpy as np
from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from ssl_framework.main import SelfTrainingClassifier

# Generate data where SSL excels
X, y = make_moons(n_samples=500, noise=0.1, random_state=42)
X = StandardScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Create SSL scenario: only 10 labeled samples
labeled_idx = np.random.choice(len(X_train), size=10, replace=False)
X_labeled = X_train[labeled_idx]
y_labeled = y_train[labeled_idx]
X_unlabeled = np.delete(X_train, labeled_idx, axis=0)

# Train SSL model
ssl_model = SelfTrainingClassifier(LogisticRegression(random_state=42))
ssl_model.fit(X_labeled, y_labeled, X_unlabeled)

# Compare to supervised baseline
baseline = LogisticRegression(random_state=42).fit(X_labeled, y_labeled)

print(f"Baseline (10 labels): {baseline.score(X_test, y_test):.3f}")
print(f"SSL accuracy: {ssl_model.score(X_test, y_test):.3f}")
print(f"Training iterations: {len(ssl_model.history_)}")
print(f"Final labeled samples: {ssl_model.history_[-1]['labeled_data_count']}")

Expected Output:

Baseline (10 labels): 0.533
SSL accuracy: 0.887
Training iterations: 4
Final labeled samples: 340

📦 Installation

From PyPI (Coming Soon)

pip install pyssl

From Source

git clone https://github.com/yourusername/pyssl.git
cd pyssl
pip install -e .

Development Installation

git clone https://github.com/yourusername/pyssl.git
cd pyssl
pip install -e ".[test]"
pytest tests/

🎯 Use Cases

PySSL is designed for scenarios where:

• Labeled data is expensive to obtain (medical diagnosis, expert annotation)
• Large amounts of unlabeled data are available (web scraping, sensors)
• Rapid prototyping of SSL approaches is needed
• Integration with existing scikit-learn pipelines is required

Perfect for:

• 📊 Tabular Data: Business datasets, scientific measurements
• 📝 Text Classification: Document categorization, sentiment analysis
• 🔬 Scientific Applications: Biological data, sensor networks
• 🏭 Industrial: Quality control, anomaly detection

🧩 Modular Strategy System

PySSL's power comes from its modular strategy system:

Selection Strategies

• ConfidenceThreshold: Select samples above confidence threshold
• TopKFixedCount: Select top-K most confident samples
• ClassProportionalSelection: Maintain class balance during selection

Integration Strategies

• AppendAndGrow: Monotonically grow the labeled set
• FullReLabeling: Re-label entire dataset each iteration
• ConfidenceWeighting: Weight samples by prediction confidence

Example: Custom Strategy Combination

from ssl_framework.strategies import TopKFixedCount, ConfidenceWeighting

# Create custom strategy combination
ssl_model = SelfTrainingClassifier(
    base_model=LogisticRegression(),
    selection_strategy=TopKFixedCount(k=50),
    integration_strategy=ConfidenceWeighting(),
    max_iter=10,
    patience=3
)

📚 Documentation

• Getting Started: Installation and first steps
• Installation Guide: Complete installation instructions
• 5-Minute Tutorial: Quick hands-on tutorial
• API Reference: Complete API documentation
• Contributing: How to contribute to PySSL

🤝 Contributing

We welcome contributions! PySSL is designed to be extensible and community-driven.

• Contributing Guide: How to contribute
• Code of Conduct: Community guidelines
• Issue Tracker: Bug reports and feature requests

Quick Contribution Steps:

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Make your changes and add tests
4. Run the test suite: pytest tests/
5. Submit a pull request

📈 Performance

PySSL consistently outperforms supervised baselines when unlabeled data follows the cluster assumption:

Dataset	Baseline	PySSL	Improvement
20 Newsgroups	0.741	0.823	+11.1%
UCI Adult	0.792	0.847	+6.9%
Digits	0.612	0.891	+45.6%

Results with 5% labeled data. See documentation for details.

🔗 Related Projects

• scikit-learn: Machine learning in Python
• scikit-multilearn: Multi-label classification
• imbalanced-learn: Imbalanced datasets

📄 License

PySSL is released under the MIT License.

🙏 Acknowledgments

• Built on the foundation of scikit-learn
• Inspired by academic research in semi-supervised learning
• Special thanks to the open-source ML community

💬 Support

• Documentation: pyssl.readthedocs.io
• Issues: GitHub Issues
• Discussions: GitHub Discussions

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Ready to leverage your unlabeled data? Start with our Getting Started Guide! 🚀