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PySR searches for symbolic expressions which optimize a particular objective.

https://github.com/MilesCranmer/PySR/assets/7593028/c8511a49-b408-488f-8f18-b1749078268f

## PySR: High-Performance Symbolic Regression in Python and Julia¶

Docs |
Forums |
Paper |
colab demo |
---|---|---|---|

pip |
conda |
Stats |
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If you find PySR useful, please cite the paper arXiv:2305.01582. If you've finished a project with PySR, please submit a PR to showcase your work on the research showcase page!

**Contents**:

### Contributors ✨¶

We are eager to welcome new contributors! Check out our contributors guide for tips 🚀. If you have an idea for a new feature, don't hesitate to share it on the issues or discussions page.

_{Mark Kittisopikul}💻 💡 🚇 📦 📣 👀 🔧 ⚠️ |
_{T Coxon}🐛 💻 🔌 💡 🚇 🚧 👀 🔧 ⚠️ 📓 |
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_{Anthony Blaom, PhD}🚇 💡 👀 |
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_{Eric Hanson}💡 📣 📓 |
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_{Rik Huijzer}💡 🚇 |
_{Hongyu Wang}💡 📣 🔬 |
_{Saurav Maheshkar}🔧 |

**Test status**

Linux |
Windows |
macOS |
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Docker |
Conda |
Coverage |

## Why PySR?¶

PySR is an open-source tool for *Symbolic Regression*: a machine learning
task where the goal is to find an interpretable symbolic expression that optimizes some objective.

Over a period of several years, PySR has been engineered from the ground up to be (1) as high-performance as possible, (2) as configurable as possible, and (3) easy to use. PySR is developed alongside the Julia library SymbolicRegression.jl, which forms the powerful search engine of PySR. The details of these algorithms are described in the PySR paper.

Symbolic regression works best on low-dimensional datasets, but
one can also extend these approaches to higher-dimensional
spaces by using "*Symbolic Distillation*" of Neural Networks, as explained in
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 neural networks.

## Installation¶

pip | conda | docker |
---|---|---|

Everywhere (recommended) | Linux and Intel-based macOS | Everywhere (if all else fails) |

### pip¶

- Install Julia
- Alternatively, my personal preference is to use juliaup, which performs this automatically.

- Then, run:
- Finally, to install Julia dependencies:
(Alternatively, from within Python, you can call

`import pysr; pysr.install()`

)

### conda¶

The PySR build in conda includes all required dependencies, so you can install it by simply running:

from within your target conda environment.

However, note that the conda install does not support precompilation of Julia libraries, so the start time may be slightly slower as the JIT-compilation will be running. (Once the compilation finishes, there will not be a performance difference though.)

### docker build¶

- Clone this repo.
- In the repo, run the build command with:
- You can then start the container with an IPython execution with:

For more details, see the docker section.

### Common issues¶

Common issues tend to be related to Python not finding Julia.
To debug this, try running `python3 -c 'import os; print(os.environ["PATH"])'`

.
If none of these folders contain your Julia binary, then you need to add Julia's `bin`

folder to your `PATH`

environment variable.

**Running PySR on macOS with an M1 processor:** you should use the pip version, and make sure to get the Julia binary for ARM/M-series processors.

## Quickstart¶

You might wish to try the interactive tutorial here, which uses the notebook in `examples/pysr_demo.ipynb`

.

In practice, I highly recommend using IPython rather than Jupyter, as the printing is much nicer. Below is a quick demo here which you can paste into a Python runtime. First, let's import numpy to generate some test data:

```
import numpy as np
X = 2 * np.random.randn(100, 5)
y = 2.5382 * np.cos(X[:, 3]) + X[:, 0] ** 2 - 0.5
```

We have created a dataset with 100 datapoints, with 5 features each. The relation we wish to model is \(2.5382 \cos(x_3) + x_0^2 - 0.5\).

Now, let's create a PySR model and train it. PySR's main interface is in the style of scikit-learn:

```
from pysr import PySRRegressor
model = PySRRegressor(
niterations=40, # < Increase me for better results
binary_operators=["+", "*"],
unary_operators=[
"cos",
"exp",
"sin",
"inv(x) = 1/x",
# ^ Custom operator (julia syntax)
],
extra_sympy_mappings={"inv": lambda x: 1 / x},
# ^ Define operator for SymPy as well
loss="loss(prediction, target) = (prediction - target)^2",
# ^ Custom loss function (julia syntax)
)
```

This will set up the model for 40 iterations of the search code, which contains hundreds of thousands of mutations and equation evaluations.

Let's train this model on our dataset:

Internally, this launches a Julia process which will do a multithreaded search for equations to fit the dataset.

Equations will be printed during training, and once you are satisfied, you may quit early by hitting 'q' and then \<enter>.

After the model has been fit, you can run `model.predict(X)`

to see the predictions on a given dataset using the automatically-selected expression,
or, for example, `model.predict(X, 3)`

to see the predictions of the 3rd equation.

You may run:

to print the learned equations:

```
PySRRegressor.equations_ = [
pick score equation loss complexity
0 0.000000 4.4324794 42.354317 1
1 1.255691 (x0 * x0) 3.437307 3
2 0.011629 ((x0 * x0) + -0.28087974) 3.358285 5
3 0.897855 ((x0 * x0) + cos(x3)) 1.368308 6
4 0.857018 ((x0 * x0) + (cos(x3) * 2.4566472)) 0.246483 8
5 >>>> inf (((cos(x3) + -0.19699033) * 2.5382123) + (x0 *... 0.000000 10
]
```

This arrow in the `pick`

column indicates which equation is currently selected by your
`model_selection`

strategy for prediction.
(You may change `model_selection`

after `.fit(X, y)`

as well.)

`model.equations_`

is a pandas DataFrame containing all equations, including callable format
(`lambda_format`

),
SymPy format (`sympy_format`

- which you can also get with `model.sympy()`

), and even JAX and PyTorch format
(both of which are differentiable - which you can get with `model.jax()`

and `model.pytorch()`

).

Note that `PySRRegressor`

stores the state of the last search, and will restart from where you left off the next time you call `.fit()`

, assuming you have set `warm_start=True`

.
This will cause problems if significant changes are made to the search parameters (like changing the operators). You can run `model.reset()`

to reset the state.

You will notice that PySR will save two files:
`hall_of_fame...csv`

and `hall_of_fame...pkl`

.
The csv file is a list of equations and their losses, and the pkl file is a saved state of the model.
You may load the model from the `pkl`

file with:

There are several other useful features such as denoising (e.g., `denoising=True`

),
feature selection (e.g., `select_k_features=3`

).
For examples of these and other features, see the examples page.
For a detailed look at more options, see the options page.
You can also see the full API at this page.
There are also tips for tuning PySR on this page.

### Detailed Example¶

The following code makes use of as many PySR features as possible. Note that is just a demonstration of features and you should not use this example as-is. For details on what each parameter does, check out the API page.

```
model = PySRRegressor(
procs=4,
populations=8,
# ^ 2 populations per core, so one is always running.
population_size=50,
ncyclesperiteration=500,
# ^ Generations between migrations.
niterations=10000000, # Run forever
early_stop_condition=(
"stop_if(loss, complexity) = loss < 1e-6 && complexity < 10"
# Stop early if we find a good and simple equation
),
timeout_in_seconds=60 * 60 * 24,
# ^ Alternatively, stop after 24 hours have passed.
maxsize=50,
# ^ Allow greater complexity.
maxdepth=10,
# ^ But, avoid deep nesting.
binary_operators=["*", "+", "-", "/"],
unary_operators=["square", "cube", "exp", "cos2(x)=cos(x)^2"],
constraints={
"/": (-1, 9),
"square": 9,
"cube": 9,
"exp": 9,
},
# ^ Limit the complexity within each argument.
# "inv": (-1, 9) states that the numerator has no constraint,
# but the denominator has a max complexity of 9.
# "exp": 9 simply states that `exp` can only have
# an expression of complexity 9 as input.
nested_constraints={
"square": {"square": 1, "cube": 1, "exp": 0},
"cube": {"square": 1, "cube": 1, "exp": 0},
"exp": {"square": 1, "cube": 1, "exp": 0},
},
# ^ Nesting constraints on operators. For example,
# "square(exp(x))" is not allowed, since "square": {"exp": 0}.
complexity_of_operators={"/": 2, "exp": 3},
# ^ Custom complexity of particular operators.
complexity_of_constants=2,
# ^ Punish constants more than variables
select_k_features=4,
# ^ Train on only the 4 most important features
progress=True,
# ^ Can set to false if printing to a file.
weight_randomize=0.1,
# ^ Randomize the tree much more frequently
cluster_manager=None,
# ^ Can be set to, e.g., "slurm", to run a slurm
# cluster. Just launch one script from the head node.
precision=64,
# ^ Higher precision calculations.
warm_start=True,
# ^ Start from where left off.
turbo=True,
# ^ Faster evaluation (experimental)
julia_project=None,
# ^ Can set to the path of a folder containing the
# "SymbolicRegression.jl" repo, for custom modifications.
update=False,
# ^ Don't update Julia packages
extra_sympy_mappings={"cos2": lambda x: sympy.cos(x)**2},
# extra_torch_mappings={sympy.cos: torch.cos},
# ^ Not needed as cos already defined, but this
# is how you define custom torch operators.
# extra_jax_mappings={sympy.cos: "jnp.cos"},
# ^ For JAX, one passes a string.
)
```

### Docker¶

You can also test out PySR in Docker, without installing it locally, by running the following command in the root directory of this repo:

This builds an image called `pysr`

for your system's architecture,
which also contains IPython.

You can then run this with:

which will link the current directory to the container's `/data`

directory
and then launch ipython.

If you have issues building for your system's architecture,
you can emulate another architecture by including `--platform linux/amd64`

,
before the `build`

and `run`

commands.