symbolfit 0.1.0

Automatic parametric modeling with symbolic regression

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An API to automate parametric modeling with symbolic regression, originally developed for data analysis in the experimental high-energy physics community, but also applicable beyond.

Symbolfit takes binned data with measurement/systematic uncertainties as input, utilizes PySR to perform a machine-search for batches of functional forms that model the data, parameterizes these functions, and utilizes LMFIT to re-optimize the functions and provide uncertainty estimation, all in one go. It is designed to maximize automatation with minimal human input. Each run produces a batch of functions with uncertainty estimation, which are evaluated, saved, and plotted automatically into readable output files, ready for downstream tasks.

• Installation
• Getting Started
• Documentation
• Citation

Installation

Prerequisite

Install Julia (backend for PySR)

curl -fsSL https://install.julialang.org | sh

then check if installed properly

julia --version

Installation via PyPI

# With Python>=3.9
pip install symbolfit

Installation via conda

conda create --name symbolfit_env python=3.9
conda activate symbolfit_env
conda install conda-forge::symbolfit

Getting Started

To run an example fit (or simply python fit_example.py):

from symbolfit.symbolfit import *

dataset = importlib.import_module('examples.datasets.toy_dataset_1.dataset')
pysr_config = importlib.import_module('examples.pysr_configs.pysr_config_1').pysr_config

model = SymbolFit(
    	x = dataset.x,
    	y = dataset.y,
    	y_up = dataset.y_up,
    	y_down = dataset.y_down,
    	pysr_config = pysr_config,
    	max_complexity = 60,
    	input_rescale = True,
    	scale_y_by = 'mean',
    	max_stderr = 20,
    	fit_y_unc = True,
    	random_seed = None,
    	loss_weights = None
)

model.fit()

After the fit, save results to csv:

model.save_to_csv(output_dir = 'output_dir/')

and plot results to pdf:

model.plot_to_pdf(
    	output_dir = 'output_dir/',
    	bin_widths_1d = dataset.bin_widths_1d,
    	#bin_edges_2d = dataset.bin_edges_2d,
    	plot_logy = False,
    	plot_logx = False,
        sampling_95quantile = False
)

Candidate functions with full substitutions can be printed in prompt:

model.print_candidate(candidate_number = 10)

Each run will produce a batch of candidate functions and will automatically save all results to five output files:

1. candidates.csv: saves all candidate functions and evaluations in a csv table.
2. candidates_reduced.csv: saves a reduced version for essential information without intermediate results.
3. candidates.pdf: plots all candidate functions with associated uncertainties one by one for fit quality evaluation.
4. candidates_sampling.pdf: plots all candidate functions with total uncertainty coverage generated by sampling parameters.
5. candidates_gof.pdf: plots the goodness-of-fit scores.
6. candidates_correlation.pdf: plots the correlation matrices for the parameters of each candidate function.

Documentation

The documentation can be found here for more info and demonstrations.

Citation

If you find this useful in your research, please consider citing Symbolfit:

Coming soon!

and PySR:

@misc{cranmerInterpretableMachineLearning2023,
    title = {Interpretable {Machine} {Learning} for {Science} with {PySR} and {SymbolicRegression}.jl},
    url = {http://arxiv.org/abs/2305.01582},
    doi = {10.48550/arXiv.2305.01582},
    urldate = {2023-07-17},
    publisher = {arXiv},
    author = {Cranmer, Miles},
    month = may,
    year = {2023},
    note = {arXiv:2305.01582 [astro-ph, physics:physics]},
    keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Computer Science - Symbolic Computation, Physics - Data Analysis, Statistics and Probability},
}