PySSL: Semi-Supervised Learning Framework#

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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}")

Expected output:

Baseline (10 labels): 0.533
SSL accuracy: 0.887

Documentation#

Getting Started

API Reference

Development

Indices and Tables#