SwissFit¶
SwissFit¶
SwissFit is a general-purpose library for fitting models to data with Gaussian-distributed noise. The design of SwissFit is inspired by Peter Lepage’s lsqfit and operates in a similar manner. As such, it builds on top of the GVar library and extensively utilizes the powerful numerical tools of SciPy and Vegas to estimate model parameters and their associated uncertainties.
SwissFit currently supports the following.
- lsqfit-style least squares fitting, including priors. Priors can be transformed to represent constraints. Quality of fit and model selection criteria directly available from fit.
- Fully integrated with GVar, which allows fit parameters to be propagated into a secondary analysis with full automatic error propagation.
- Bayesian model averaging in the flavor of PRD103(2021)114502 and PRD109(2024)014510.
- Markov Chain Monte Carlo (MCMC-based) parameter estimation via Peter Lepage’s Vegas library. Similar to the MCMC estimation already available in lsqfit.
- Support for integrating radial basis function networks and feedforward neural networks in least-squares model function
- Optimization with SciPy’s least_squares optimization methods (trust region reflective, Levenberg-Marquardt, dogbox), SciPy’s “minimize” local optimization methods (BFGS, Nelder-Mead, conjugate gradient, etc.), and SciPy’s basin hopping global optimization algorithm
- Basic support for surrogate-based empirical Bayes in the flavor of arXiv:2402.04175
The following are planned or already in the works for SwissFit
- Optimization with stochastic gradient descent, specifically Adam and its Nesterov-accelerated counterpart
- Options for other Markov Chain Monte Carlo algorithms, such as Hamiltonian Monte Carlo
- Empirical Bayes via Scikit-learn’s Bayesian optimization module
- Support for some form of non-parameteric regression
- Much more!
SwissFit is currently in beta. Help us get to a v1.0.0 release by providing feedback and letting me know if you run into problems! Thank you for considering to use SwissFit for whatever problem that you are trying to tackle!
Requirements and installation¶
To install SwissFit, open up a terminal and enter the following commands.
# Upgrade pip - optional, but recommended
pip3 install --upgrade pip
# Install SwissFit
pip3 install swissfit
That’s all! Upon installation, the following packages will also been installed. Note that SwissFit requires Python>=3.10 to run.
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