Make docs builder copy README

.github/workflows/docs.yml

@@ -34,6 +34,7 @@ jobs:
         run: npm install -g docsify
       - name: "Build API docs"
         run: |
+          cp README.md docs/
           pydoc-markdown --build --site-dir build -vv
           cp docs/build/content/docs/api*.md docs/
           for f in docs/api*.md; do mv "$f" "$f.bkup" && cat "$f.bkup" | sed '1,4d' > "$f" && rm "$f.bkup"; done

docs/README.md

@@ -1,170 +0,0 @@
-[//]: # (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** |
-|---|---|
-|[![Documentation Status](https://readthedocs.org/projects/pysr/badge/?version=latest)](https://pysr.readthedocs.io/en/latest/?badge=latest)|[![PyPI version](https://badge.fury.io/py/pysr.svg)](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 let me know and I may showcase your work here!
-
-
-### Test status:
-| **Linux** | **Windows** | **macOS (intel)** | **Docker** | **Coverage** | 
-|---|---|---|---|---|
-|[![Linux](https://github.com/MilesCranmer/PySR/actions/workflows/CI.yml/badge.svg)](https://github.com/MilesCranmer/PySR/actions/workflows/CI.yml)|[![Windows](https://github.com/MilesCranmer/PySR/actions/workflows/CI_Windows.yml/badge.svg)](https://github.com/MilesCranmer/PySR/actions/workflows/CI_Windows.yml)|[![macOS](https://github.com/MilesCranmer/PySR/actions/workflows/CI_mac.yml/badge.svg)](https://github.com/MilesCranmer/PySR/actions/workflows/CI_mac.yml)|[![Docker](https://github.com/MilesCranmer/PySR/actions/workflows/CI_docker.yml/badge.svg)](https://github.com/MilesCranmer/PySR/actions/workflows/CI_docker.yml)|[![Coverage Status](https://coveralls.io/repos/github/MilesCranmer/PySR/badge.svg?branch=master&service=github)](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.
-
-# 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=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).
-
-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 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.