tropy is a Python library for efficiently fitting piecewise linear models using tropical geometry and mean payoff games. It is distributed under the MIT License.

Read the (draft) paper behind this library.

If you already have a working installation of NumPy, the easiest way is to clone this repository.

tropy was tested on Python 3.9 and NumPy 1.25. Graphing capabilities require Matplotlib. tropy still relies on Pandas (for saving weights), scikit-learn (for computing accuracies) and tqdm, even though these dependencies could easily be removed.

The easiest way to play with tropy and to reproduce graphs made in the paper is to use script playground.py. It allows to fit tropical polynomials on provided 3D datasets and to graph the results.

usage: playground.py [-h] [-s [file_path]] [--beta BETA] [--simplified]
                     [--feature-selection no_features]
                     {iris,iris-binary,moons,toy,toy-centers,toy-reverse,toy-centers-reverse,bintoy,bintoy-separated,bintoy-mixed,circular}
                     [degree]

Fitting and plotting tropical piecewise linear classifiers on 3D datasets

positional arguments:
  {...}                 Dataset to classify
  degree                Degree of tropical polynomial

optional arguments:
  -h, --help            Show this help message and exit
  -s [file_path], --save [file_path]
                        Save the figure (.PGF)
  --beta BETA           If specified, Beta value for using 'linear SVM on log paper' trick
  --simplified          Provide a simplified view of the hypersurface, with the decision boundary only
  --feature-selection no_features
                        Experimental: heuristic to generate more relevant
                        monomials based on data. Specify the number of points to sample per class if wanted. Bypasses degree option.

Commands	Results
./playground.py moons 3 ./playground.py moons --feature-selection 5 --simplified
./playground.py circular 3 ./playground.py circular --feature-selection 5
./playground.py bintoy-separated 1 ./playground.py bintoy-separated 1 --beta 10
./playground.py toy-reverse 3 ./playground.py toy-reverse --feature-selection 3

tropy is only able of plotting tropical polynomials for 3-dimensional data, but it can perform classification in much higher dimensions.

To fit a classifier based on a cubic tropical polynomial, for instance:

from tropy.svm import TropicalSVC
model = TropicalSVC()
model.fit(Xtrain, poly_degree = 3)

where Xtrain is a list of NumPy 2D arrays. Each of them corresponds to some data class, and stores data points as columns.

To evaluate model accuracy on test data:

acc = model.accuracy(Xtest)

Methods model.predict, model.export_weights and model.load_weights are also provided.

Module tropy.graph comes with a handful of methods to plot tridimensional tropical hypersurfaces. Feel free to use them for custom purposes.

• tropicalization.py shows that Maslov's dequantization of classical SVMs does not produce good results.
• tightness.py experimentally evaluates the tightness of the norm boundaries derived in Appendix A of the paper.
• iris_multiclass.ipynb shows how one could use tropy on real-world data to build a multi-class classifier.