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Type4Py: Deep similarity learning-based type inference for Python
- The neural model is trained on the ManyTypes4Py dataset and achieves 79.2\% accuracy considering the prediction of type annotations for parameters, return values, and variables.
- It has a Flask app that exposes an endpoint to query the pre-trained model and return predicted type information for a given Python source file.
- A VS Code extension that provides ML-based type auto-completion for Python and assists developers to gradually add type annotations to their codebases (has more than 1800 installs)
LIBTwinSVM: A Library for Twin Support Vector Machines
- A simple and user-friendly Graphical User Interface
- An improved and fast optimizer (clipDCD) is implemented in C++.
- Supports Linear, RBF, and Rectangular kernel.
- The OVA and OVO estimators are compatible with scikit-learn tools such as GridSearchCV, cross_val_score, etc.
- The classification results can be logged during the grid search process to not lose results in case of power failure.
- A feature-rich visualization tool to show decision boundaries and geometrical interpretation of TwinSVMs.
- The best-fitted classifier can be saved on the disk after the grid search process.
- The pre-trained models can be loaded and evaluated on the test samples.
LightTwinSVM: Simple and Fast Implementation of Standard TwinSVM Classifier
- A simple console program for running TwinSVM classifier.
- Fast optimization algorithm. The clipDCD algorithm is implemented in C++.
- Binary and Multi-class classification (One-vs-All & One-vs-One) are supported.
- Linear, RBF kernel and Rectangular are supported.
- The classifier can be evaluated using either K-fold cross-validation or Training/Test split.
- It supports grid search over C and gamma parameters.
- A detailed classification result will be saved in a spreadsheet file.