Mixed Membership Stochastic Block Models

A Python implementation of Mixed Membership Stochastic Block Models for recommendation systems, based on the work by Godoy-Lorite et al. (2016). This library provides an efficient, vectorized implementation suitable for both research and production environments.

Freatures

• Fast, vectorized implementation of MMSBM.
• Support for both simple and cross-validated fitting.
• Parallel processing for multiple sampling runs.
• Comprehensive model statistics and evaluation metrics.
• Compatible with Python 3.6+ through 3.12.

Installation

pip install mmsbm

Quick Start

from mmsbm import MMSBM

# Create a model
model = MMSBM(user_groups=2, item_groups=4)

# Fit and predict
model.fit(train_data)
predictions = model.predict(test_data)

# Get model results
results = model.score()

Detailed Usage

Data Format

The input data should be a pandas DataFrame with exactly 3 columns representing users, items, and ratings:

import pandas as pd
from random import choice

train = pd.DataFrame(
    {
    "users": [f"user{choice(list(range(5)))}" for _ in range(100)],
    "items": [f"item{choice(list(range(10)))}" for _ in range(100)],
    "ratings": [choice(list(range(1, 6))) for _ in range(100)]
    }
)

test = pd.DataFrame(
    {
    "users": [f"user{choice(list(range(5)))}" for _ in range(50)],
    "items": [f"item{choice(list(range(10)))}" for _ in range(50)],
    "ratings": [choice(list(range(1, 6))) for _ in range(50)]
    }
)

Model Configuration

from mmsbm import MMSBM

# Initialize the MMSBM class:
model = MMSBM(
    user_groups=2,      # Number of user groups
    item_groups=4,      # Number of item groups
    iterations=500,     # Number of EM iterations
    sampling=5,         # Number of parallel runs
    seed=1,             # Random seed for reproducibility
    debug=False         # Enable debug logging
)

Training Methods

Simple Fit

mmsbm.fit(train)

Cross-Validation Fit

accuracies = mmsbm.cv_fit(train, folds=5)
print(f"Mean accuracy: {np.mean(accuracies):.3f} ± {np.std(accuracies):.3f}")

Making Predictions

predictions = mmsbm.predict(test)

Model Evaluation

results = model.score()

# Access various metrics
accuracy = results['stats']['accuracy']
mae = results['stats']['mae']

# Access model parameters
theta = results['objects']['theta']  # User group memberships
eta = results['objects']['eta']      # Item group memberships
pr = results['objects']['pr']        # Rating probabilities

Performance Considerations

• Computation is vectorized for efficient processing of large datasets.
• Parallel processing for multiple sampling runs
• Computational complexity scales primarily with the number of unique items, but not users
• Memory usage scales primarily with the number of unique users and items

Running Tests

To run tests do the following:

# Install development dependencies
pip install -e ".[dev]"

# Run tests
python -m pytest tests/*

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

TODO

• Progress bars are not working for jupyter notebooks.
• Include user_groups and item_groups optimization procedure.

References

[1]: Godoy-Lorite, Antonia, et al. "Accurate and scalable social recommendation using mixed-membership stochastic block models." Proceedings of the National Academy of Sciences 113.50 (2016): 14207-14212.