pySuStaIn

SuStaIn algorithm in Python, with the option to describe the subtype progression patterns using either the event-based model or the piecewise linear z-score model.

Papers

• The SuStaIn algorithm: Young et al. 2018
• The event-based model: Fonteijn et al. 2012, (with Gaussian mixture modelling Young et al. 2014)
• The piecewise linear z-score model: Young et al. 2018

Dependencies

• Python >= 3.5
• NumPy >= 1.18
• SciPy
• Matplotlib
• Scikit-learn for cross-validation
• kde_ebm for mixture modelling (KDE and GMM included)
• pathos for parallelization

Parallelisation

• Added parallelized startpoints

Running different SuStaIn implementations

sustainType can be set to:

• "mixture_GMM" : SuStaIn with an event-based model progression pattern, with Gaussian mixture modelling of normal/abnormal.
• "mixture_KDE": SuStaIn with an event-based model progression pattern, with Kernel Density Estimation (KDE) mixture modelling of normal/abnormal.
• "zscore": SuStaIn with a piecewise linear z-score model progression pattern.

See simrun.py for examples of how to run these different implementations.

SuStaIn Tutorial

See the jupyter notebook for a tutorial on how to use SuStaIn using simulated data.