Spaces:
Sleeping
Sleeping
| [//]: # (Logo:) | |
| <img src="https://raw.githubusercontent.com/MilesCranmer/PySR/master/docs/images/pysr_logo.svg" width="400" /> | |
| # PySR: High-Performance Symbolic Regression in Python | |
| PySR is built on an extremely optimized pure-Julia backend, and uses regularized evolution, simulated annealing, and gradient-free optimization to search for equations that fit your data. | |
| | **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)| | |
| 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 | |
| | pip (macOS, Linux, Windows) | conda (macOS - only Intel, Linux) | | |
| |---|---| | |
| | 1. Install Julia manually (see [downloads](https://julialang.org/downloads/))<br>2. `pip install pysr`<br>3. `python -c 'import pysr; pysr.install()'` | 1. `conda install -c conda-forge pysr`<br>2. `python -c 'import pysr; pysr.install()'`| | |
| This last step will install and update the required Julia packages, including | |
| `PyCall.jl`. | |
| 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 M1 processors. You might have to scroll down to [upcoming releases](https://julialang.org/downloads/#upcoming_release). | |
| # 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( | |
| niterations=40, | |
| 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 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 \<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 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()`. 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. | |
| 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). | |
| # 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. | |