[//]: # (Logo:)
https://user-images.githubusercontent.com/7593028/188328887-1b6cda72-2f41-439e-ae23-75dd3489bc24.mp4
# PySR: High-Performance Symbolic Regression in Python
PySR uses evolutionary algorithms to search for symbolic expressions which optimize a particular objective.
| **Docs** | **colab** | **pip** | **conda** | **Stats** |
|---|---|---|---|---|
|[](https://astroautomata.com/PySR/)|[](https://colab.research.google.com/github/MilesCranmer/PySR/blob/master/examples/pysr_demo.ipynb)|[](https://badge.fury.io/py/pysr)|[](https://anaconda.org/conda-forge/pysr)|[](https://badge.fury.io/py/pysr)|
(pronounced like *py* as in python, and then *sur* as in surface)
If you find PySR useful, please cite it using the citation information given in [CITATION.md](https://github.com/MilesCranmer/PySR/blob/master/CITATION.md).
If you've finished a project with PySR, please submit a PR to showcase your work on the [Research Showcase page](https://astroautomata.com/PySR/papers)!
### Test status
| **Linux** | **Windows** | **macOS (intel)** |
|---|---|---|
|[](https://github.com/MilesCranmer/PySR/actions/workflows/CI.yml)|[](https://github.com/MilesCranmer/PySR/actions/workflows/CI_Windows.yml)|[](https://github.com/MilesCranmer/PySR/actions/workflows/CI_mac.yml)|
| **Docker** | **Conda** | **Coverage** |
|[](https://github.com/MilesCranmer/PySR/actions/workflows/CI_docker.yml)|[](https://github.com/MilesCranmer/PySR/actions/workflows/CI_conda_forge.yml)|[](https://coveralls.io/github/MilesCranmer/PySR)|
PySR is built on an extremely optimized pure-Julia backend: [SymbolicRegression.jl](https://github.com/MilesCranmer/SymbolicRegression.jl).
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
[2006.11287](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
| pip
(works everywhere) | conda
(Linux and Intel-based macOS) |
|---|---|
| 1. [Install Julia](https://julialang.org/downloads/)
2. Then, run: `pip install -U pysr`
3. Finally, to install Julia packages:
`python -c 'import pysr; pysr.install()'` | `conda install -c conda-forge pysr` |
Common issues tend to be related to Python not finding Julia.
To debug this, try running `python -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.
# Introduction
Let's create a PySR example. First, let's import
numpy to generate some test data:
```python
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:
```python
from pysr import PySRRegressor
model = PySRRegressor(
model_selection="best", # Result is mix of simplicity+accuracy
niterations=40,
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(x, y) = (x - y)^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:
```python
model.fit(X, y)
```
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 \.
After the model has been fit, you can run `model.predict(X)`
to see the predictions on a given dataset.
You may run:
```python
print(model)
```
to print the learned equations:
```python
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:
```python
model = PySRRegressor.from_file("hall_of_fame.2022-08-10_100832.281.pkl")
```
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](https://astroautomata.com/PySR/examples).
For a detailed look at more options, see the [options page](https://astroautomata.com/PySR/options).
You can also see the full API at [this page](https://astroautomata.com/PySR/api).
## 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.
```python
model = PySRRegressor(
procs=4,
populations=8,
# ^ 2 populations per core, so one is always running.
population_size=50,
# ^ Slightly larger populations, for greater diversity.
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.
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:
```bash
docker build --pull --rm -f "Dockerfile" -t pysr "."
```
This builds an image called `pysr`. If you have issues building (for example, on Apple Silicon),
you can emulate an architecture that works by including: `--platform linux/amd64`.
You can then run this with:
```bash
docker run -it --rm -v "$PWD:/data" pysr ipython
```
which will link the current directory to the container's `/data` directory
and then launch ipython.