Spaces:
Running
Running
File size: 3,374 Bytes
009c407 841d7fc cf8bf07 3629549 4d9bd75 49c1f08 e987d40 49c1f08 cf8bf07 e987d40 49c1f08 e987d40 49c1f08 cfca8a4 cb14d73 5af7e2e dc9d777 81463ee 5691c27 81463ee 1115381 b5a1925 8c58028 5908dc9 dc9d777 8c58028 b5a1925 8c58028 cb14d73 ecc127c 009c407 ecc127c 17f6afe ecc127c 5908dc9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
# PySR.jl
[](https://pysr.readthedocs.io/en/latest/?badge=latest)
[](https://badge.fury.io/py/pysr)
[](https://travis-ci.com/MilesCranmer/PySR)
**Symbolic regression built on Julia, and interfaced by Python.
Uses regularized evolution, simulated annealing, and gradient-free optimization.**
[Cite this software](https://github.com/MilesCranmer/PySR/blob/master/CITATION.md)
[Documentation](https://pysr.readthedocs.io/en/latest)
Symbolic regression is a very interpretable machine learning algorithm
for low-dimensional problems: these tools search equation space
to find algebraic relations that approximate a dataset.
One can also
extend these approaches to higher-dimensional
spaces by using a neural network as proxy, as explained in
https://arxiv.org/abs/2006.11287, where we apply
it to N-body problems. Here, one essentially uses
symbolic regression to convert a neural net
to an analytic equation. Thus, these tools simultaneously present
an explicit and powerful way to interpret deep models.
*Backstory:*
Previously, we have used
[eureqa](https://www.creativemachineslab.com/eureqa.html),
which is a very efficient and user-friendly tool. However,
eureqa is GUI-only, doesn't allow for user-defined
operators, has no distributed capabilities,
and has become proprietary (and recently been merged into an online
service). Thus, the goal
of this package is to have an open-source symbolic regression tool
as efficient as eureqa, while also exposing a configurable
python interface.
# Installation
PySR uses both Julia and Python, so you need to have both installed.
Install Julia - see [downloads](https://julialang.org/downloads/), and
then instructions for [mac](https://julialang.org/downloads/platform/#macos)
and [linux](https://julialang.org/downloads/platform/#linux_and_freebsd).
(Don't use the `conda-forge` version; it doesn't seem to work properly.)
Then, at the command line,
install the `Optim` and `SpecialFunctions` packages via:
```bash
julia -e 'import Pkg; Pkg.add("Optim"); Pkg.add("SpecialFunctions")'
```
For python, you need to have Python 3, numpy, sympy, and pandas installed.
You can install this package from PyPI with:
```bash
pip install pysr
```
# Quickstart
```python
import numpy as np
from pysr import pysr
# 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"])
...
print(equations)
```
which gives:
```
Complexity MSE Equation
0 5 1.947431 plus(-1.7420927, mult(x0, x0))
1 8 0.486858 plus(-1.8710494, plus(cos(x3), mult(x0, x0)))
2 11 0.000000 plus(plus(mult(x0, x0), cos(x3)), plus(-2.0, cos(x3)))
```
The newest version of PySR also returns three additional columns:
- `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.
|