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| [//]: # (Logo:) | |
| <img src="https://raw.githubusercontent.com/MilesCranmer/PySR/master/pysr_logo.svg" width="400" /> | |
| **PySR: parallel symbolic regression built on Julia, and interfaced by Python.** | |
| Uses regularized evolution, simulated annealing, and gradient-free optimization. | |
| | **Docs** | **pip** | | |
| |---|---| | |
| |[](https://pysr.readthedocs.io/en/latest/?badge=latest)|[](https://badge.fury.io/py/pysr)| | |
| (pronounced like *py* as in python, and then *sur* as in surface) | |
| [Cite this software](https://github.com/MilesCranmer/PySR/blob/master/CITATION.md) | |
| ### Test status: | |
| | **Linux** | **Windows** | **macOS** | **Docker** | **Coverage** | | |
| |---|---|---|---|---| | |
| |[](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)|[](https://github.com/MilesCranmer/PySR/actions/workflows/CI_docker.yml)|[](https://coveralls.io/github/MilesCranmer/PySR)| | |
| Check out [SymbolicRegression.jl](https://github.com/MilesCranmer/SymbolicRegression.jl) for | |
| the pure-Julia backend of this package. | |
| 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 | |
| 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.) | |
| You can install PySR with: | |
| ```bash | |
| pip3 install pysr | |
| python3 -c 'import pysr; pysr.install()' | |
| ``` | |
| The second line will install and update the required Julia packages, including | |
| `PyCall.jl`. | |
| Most common issues at this stage are solved | |
| by [tweaking the Julia package server](https://github.com/MilesCranmer/PySR/issues/27). | |
| to use up-to-date packages. | |
| # Quickstart | |
| 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( | |
| niterations=5, | |
| populations=8, | |
| binary_operators=["+", "*"], | |
| unary_operators=[ | |
| "cos", | |
| "exp", | |
| "sin", | |
| "inv(x) = 1/x", # Custom operator (julia syntax) | |
| ], | |
| model_selection="best", | |
| loss="loss(x, y) = (x - y)^2", # Custom loss function (julia syntax) | |
| ) | |
| ``` | |
| This will set up the model for 5 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 \<enter\>. | |
| 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 MSE Complexity | |
| 0 0.000000 3.5082064 2.710828e+01 1 | |
| 1 0.964260 (x0 * x0) 3.940544e+00 3 | |
| 2 0.030096 (-0.47978288 + (x0 * x0)) 3.710349e+00 5 | |
| 3 0.840770 ((x0 * x0) + cos(x3)) 1.600564e+00 6 | |
| 4 0.928380 ((x0 * x0) + (2.5313091 * cos(x3))) 2.499724e-01 8 | |
| 5 >>>> 13.956461 ((-0.49999997 + (x0 * x0)) + (2.5382001 * cos(... 1.885665e-13 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`), and even JAX and PyTorch format | |
| (both of which are differentiable). | |
| There are several other useful features such as denoising (e.g., `denoising=True`), | |
| feature selection (e.g., `select_k_features=3`). | |
| For a summary of features and options, see [this docs page](https://pysr.readthedocs.io/en/latest/docs/options/). | |
| You can see the full API at [this page](https://pysr.readthedocs.io/en/latest/docs/api-documentation/). | |
| # 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. | |