simple_ml

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Production-ready machine learning algorithms for Eiffel with Design by Contract.

Part of the Simple Eiffel ecosystem.

Status

✅ Production Ready — v1.1.0

• 125 tests passing, 100% pass rate
• 10 ML algorithms with regularization, kernel methods, ensemble learning
• Design by Contract throughout
• SCOOP concurrent-ready

Quick Start

model := create {LINEAR_REGRESSION_MODEL}.make
    .set_learning_rate (0.01)
    .set_max_iterations (100)

model.train (training_X, training_y)
prediction := model.predict (test_X)

For complete documentation, see our docs site.

Features

• Supervised Learning - Linear/Logistic Regression, SVM, KNN, Decision Trees, Random Forests, Neural Networks
• Regularization - L1, L2, ElasticNet for linear models (v1.1.0+)
• Kernel Methods - RBF and Polynomial kernels for SVM (v1.1.0+)
• Ensemble Methods - Gradient Boosting classifier (v1.1.0+)
• Model Selection - K-fold cross-validation, grid search (v1.1.0+)
• Probabilistic - Naive Bayes classifier (v1.1.0+)
• Design by Contract - Every algorithm fully specified with require/ensure/invariant
• SCOOP Ready - Void-safe (void_safety="all") with concurrent support

For details, see the User Guide.

Installation

# Add to your ECF:
<library name="simple_ml" location="$SIMPLE_EIFFEL/simple_ml/simple_ml.ecf"/>

License

MIT License - See LICENSE file

Support

• Docs: https://simple-eiffel.github.io/simple_ml/
• GitHub: https://github.com/simple-eiffel/simple_ml
• Issues: https://github.com/simple-eiffel/simple_ml/issues