symbolfit 0.1.2

Automatic parametric modeling with symbolic regression

Docs	pip	conda	Paper	Colab

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 and pip:

pip install symbolfit

Installation via conda

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

Editable installation for developers

git clone https://github.com/hftsoi/symbolfit.git
cd symbolfit
pip install -e .

Getting Started

To run an example fit, get the example datasets by cloning this repo:

git clone https://github.com/hftsoi/symbolfit.git
cd symbolfit

Then within a python session (or simply do 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_gauss').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 files:

model.save_to_csv(output_dir = 'output_dir/')

and plot results to pdf files:

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 promptly:

model.print_candidate(candidate_number = 10)

Each fit will produce a batch of candidate functions and will automatically save all results to six 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 the candidate functions.

More demonstrations can be found in the documentation.

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},
}