Getting Started

Installation

PySR uses both Julia and Python, so you need to have both installed.

Install Julia - see downloads, and then instructions for mac and linux. (Don't use the conda-forge version; it doesn't seem to work properly.)

You can install PySR with:

pip3 install pysr
python3 -c 'import pysr; pysr.install()'

The second line will install and update the required Julia packages, including PyCall.jl.

Quickstart

import numpy as np
from pysr import pysr, best, get_hof

# Dataset
X = 2*np.random.randn(100, 5)
y = 2*np.cos(X[:, 3]) + X[:, 0]**2 - 2

# Learn equations
equations = pysr(X, y, niterations=5,
        binary_operators=["plus", "mult"],
        unary_operators=["cos", "exp", "sin"])

...# (you can use ctl-c to exit early)

print(best())

which gives:

x0**2 + 2.000016*cos(x3) - 1.9999845

The second and additional calls of pysr will be significantly faster in startup time, since the first call to Julia will compile and cache functions from the symbolic regression backend.

One can also use best_tex to get the LaTeX form, or best_callable to get a function you can call. This uses a score which balances complexity and error; however, one can see the full list of equations with:

print(get_hof())

This is a pandas table, with additional columns:

MSE - the mean square error of the formula
score - a metric akin to Occam's razor; you should use this to help select the "true" equation.
sympy_format - sympy equation.
lambda_format - a lambda function for that equation, that you can pass values through.