docs: update homepage

Author: MilesCranmer

CONTRIBUTORS.md

@@ -6,7 +6,7 @@ In this guide you will get an overview of the contribution workflow from opening
 
 ## New contributor guide
 
-To get an overview of the project, read PySR's [README](README.md). The [PySR docs](https://astroautomata.com/PySR/) give additional information.
+To get an overview of the project, read PySR's [README](README.md). The [PySR docs](https://ai.damtp.cam.ac.uk/pysr/) give additional information.
 Here are some resources to help you get started with open source contributions in general:
 
 - [Finding ways to contribute to open source on GitHub](https://docs.github.com/en/get-started/exploring-projects-on-github/finding-ways-to-contribute-to-open-source-on-github)
@@ -39,7 +39,7 @@ Scan through our [existing issues](https://github.com/MilesCranmer/PySR/issues)
 2. Create a working branch and start with your changes!
 
 3. (Optional) If you would like to make changes to PySR itself, skip to step 4. However, if you are interested in making changes to the _symbolic regression code_ itself,
-check out the [guide](https://astroautomata.com/PySR/backend/) on modifying a custom SymbolicRegression.jl library.
+check out the [guide](https://ai.damtp.cam.ac.uk/pysr/backend/) on modifying a custom SymbolicRegression.jl library.
 In this case, you might instead be interested in making suggestions to the [SymbolicRegression.jl](http://github.com/MilesCranmer/SymbolicRegression.jl) library.
 
 4. You can install your local version of PySR with `python setup.py install`, and run tests with `python -m pysr test main`.

README.md

@@ -11,7 +11,7 @@ https://github.com/MilesCranmer/PySR/assets/7593028/c8511a49-b408-488f-8f18-b174
 
 | **Docs** | **Forums** | **Paper** | **colab demo** |
 |:---:|:---:|:---:|:---:|
-|[![Documentation](https://github.com/MilesCranmer/PySR/actions/workflows/docs.yml/badge.svg)](https://astroautomata.com/PySR/)|[![Discussions](https://img.shields.io/badge/discussions-github-informational)](https://github.com/MilesCranmer/PySR/discussions)|[![Paper](https://img.shields.io/badge/arXiv-2305.01582-b31b1b)](https://arxiv.org/abs/2305.01582)|[![Colab](https://img.shields.io/badge/colab-notebook-yellow)](https://colab.research.google.com/github/MilesCranmer/PySR/blob/master/examples/pysr_demo.ipynb)|
+|[![Documentation](https://github.com/MilesCranmer/PySR/actions/workflows/docs.yml/badge.svg)](https://ai.damtp.cam.ac.uk/pysr/)|[![Discussions](https://img.shields.io/badge/discussions-github-informational)](https://github.com/MilesCranmer/PySR/discussions)|[![Paper](https://img.shields.io/badge/arXiv-2305.01582-b31b1b)](https://arxiv.org/abs/2305.01582)|[![Colab](https://img.shields.io/badge/colab-notebook-yellow)](https://colab.research.google.com/github/MilesCranmer/PySR/blob/master/examples/pysr_demo.ipynb)|
 
 | **pip** | **conda** | **Stats** |
 | :---: | :---: | :---: |
@@ -20,14 +20,14 @@ https://github.com/MilesCranmer/PySR/assets/7593028/c8511a49-b408-488f-8f18-b174
 </div>
 
 If you find PySR useful, please cite the paper [arXiv:2305.01582](https://arxiv.org/abs/2305.01582).
-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)!
+If you've finished a project with PySR, please submit a PR to showcase your work on the [research showcase page](https://ai.damtp.cam.ac.uk/pysr/papers)!
 
 **Contents**:
 
 - [Why PySR?](#why-pysr)
 - [Installation](#installation)
 - [Quickstart](#quickstart)
-- [→ Documentation](https://astroautomata.com/PySR)
+- [→ Documentation](https://ai.damtp.cam.ac.uk/PySR)
 - [Contributors](#contributors-)
 
 <div align="center">
@@ -254,16 +254,16 @@ model = PySRRegressor.from_file("hall_of_fame.2022-08-10_100832.281.pkl")
 
 There are several other useful features such as denoising (e.g., `denoise=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).
-There are also tips for tuning PySR on [this page](https://astroautomata.com/PySR/tuning).
+For examples of these and other features, see the [examples page](https://ai.damtp.cam.ac.uk/pysr/examples).
+For a detailed look at more options, see the [options page](https://ai.damtp.cam.ac.uk/pysr/options).
+You can also see the full API at [this page](https://ai.damtp.cam.ac.uk/pysr/api).
+There are also tips for tuning PySR on [this page](https://ai.damtp.cam.ac.uk/pysr/tuning).
 
 ### 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](https://astroautomata.com/PySR/api/).
+For details on what each parameter does, check out the [API page](https://ai.damtp.cam.ac.uk/pysr/api/).
 
 ```python
 model = PySRRegressor(

docs/backend.md

@@ -33,9 +33,9 @@ The main search code can be found in `src/SymbolicRegression.jl`.
 
 Here are some tips:
 
--  The documentation for the backend is given [here](https://astroautomata.com/SymbolicRegression.jl/dev/).
+-  The documentation for the backend is given [here](https://ai.damtp.cam.ac.uk/symbolicregression/dev/).
 - Throughout the package, you will often see template functions which typically use a symbol `T` (such as in the string `where {T<:Real}`). Here, `T` is simply the datatype of the input data and stored constants, such as `Float32` or `Float64`. Writing functions in this way lets us write functions generic to types, while still having access to the specific type specified at compilation time.
-- Expressions are stored as binary trees, using the `Node{T}` type, described [here](https://astroautomata.com/SymbolicRegression.jl/dev/types/#SymbolicRegression.CoreModule.EquationModule.Node).
+- Expressions are stored as binary trees, using the `Node{T}` type, described [here](https://ai.damtp.cam.ac.uk/symbolicregression/dev/types/#SymbolicRegression.CoreModule.EquationModule.Node).
 - For reference, the main loop itself is found in the `equation_search` function inside [`src/SymbolicRegression.jl`](https://github.com/MilesCranmer/SymbolicRegression.jl/blob/master/src/SymbolicRegression.jl).
 - Parts of the code which are typically edited by users include:
     - [`src/CheckConstraints.jl`](https://github.com/MilesCranmer/SymbolicRegression.jl/blob/master/src/CheckConstraints.jl), particularly the function `check_constraints`. This function checks whether a given expression satisfies constraints, such as having a complexity lower than `maxsize`, and whether it contains any forbidden nestings of functions.
@@ -70,6 +70,6 @@ For more information on `juliapkg.json`, see [`pyjuliapkg`](https://github.com/J
 
 ## Additional notes
 
-If you get comfortable enough with the backend, you might consider using the Julia package directly: the API is given on the [SymbolicRegression.jl documentation](https://astroautomata.com/SymbolicRegression.jl/dev/).
+If you get comfortable enough with the backend, you might consider using the Julia package directly: the API is given on the [SymbolicRegression.jl documentation](https://ai.damtp.cam.ac.uk/symbolicregression/dev/).
 
 If you make a change that you think could be useful to other users, don't hesitate to open a pull request on either the PySR or SymbolicRegression.jl repositories! Contributions are very appreciated.

docs/examples.md

@@ -345,7 +345,7 @@ a real number from the loss function). But, you don't need to worry about this,
 make sure to return a scalar number of type `L`.
 
 The `tree` argument is the current expression being evaluated. You can read
-about the `tree` fields [here](https://astroautomata.com/SymbolicRegression.jl/stable/types/).
+about the `tree` fields [here](https://ai.damtp.cam.ac.uk/symbolicregression/stable/types/).
 
 For example, let's fix a symbolic form of an expression,
 as a rational function. i.e., $P(X)/Q(X)$ for polynomials $P$ and $Q$.

docs/options.md

@@ -276,7 +276,7 @@ model = PySRRegressor(..., weights=weights, elementwise_loss="myloss(x, y, w) =
 model.fit(..., weights=weights)
 ```
 
-Built-in loss (faster) (see [losses](https://astroautomata.com/SymbolicRegression.jl/dev/losses/)).
+Built-in loss (faster) (see [losses](https://ai.damtp.cam.ac.uk/symbolicregression/dev/losses/)).
 This one computes the L3 norm:
 
 ```python

examples/pysr_demo.ipynb

@@ -321,7 +321,7 @@
     "id": "qvgVbOoSFtQY"
    },
    "source": [
-    "A full list of operators is given here: https://astroautomata.com/PySR/operators,\n",
+    "A full list of operators is given here: https://ai.damtp.cam.ac.uk/pysr/operators,\n",
     "but we can also use any binary or unary operator in `julia`, or define our own as arbitrary functions.\n",
     "\n",
     "Say that we want a command to do quartic powers:\n",
@@ -1498,7 +1498,7 @@
     "id": "S5dO61g1bDhk"
    },
    "source": [
-    "The full list of PySR parameters can be found here: https://astroautomata.com/PySR/api"
+    "The full list of PySR parameters can be found here: https://ai.damtp.cam.ac.uk/pysr/api"
    ]
   }
  ],

mkdocs.yml

@@ -36,7 +36,7 @@ nav:
   - interactive-docs.md
 
 extra:
-  homepage: https://astroautomata.com/PySR
+  homepage: https://ai.damtp.cam.ac.uk/pysr
 
 extra_css:
   - stylesheets/extra.css

pysr/sr.py

@@ -76,7 +76,7 @@ def _process_constraints(binary_operators, unary_operators, constraints):
                     "One typical constraint is to use `constraints={..., '^': (-1, 1)}`, which "
                     "will allow arbitrary-complexity base (-1) but only powers such as "
                     "a constant or variable (1). "
-                    "For more tips, please see https://astroautomata.com/PySR/tuning/"
+                    "For more tips, please see https://ai.damtp.cam.ac.uk/pysr/tuning/"
                 )
             constraints[op] = (-1, -1)
         if op in ["plus", "sub", "+", "-"]:
@@ -221,7 +221,7 @@ class PySRRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):
     Most default parameters have been tuned over several example equations,
     but you should adjust `niterations`, `binary_operators`, `unary_operators`
     to your requirements. You can view more detailed explanations of the options
-    on the [options page](https://astroautomata.com/PySR/options) of the
+    on the [options page](https://ai.damtp.cam.ac.uk/pysr/options) of the
     documentation.
 
     Parameters
@@ -241,7 +241,7 @@ class PySRRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):
         most accurate model.
     binary_operators : list[str]
         List of strings for binary operators used in the search.
-        See the [operators page](https://astroautomata.com/PySR/operators/)
+        See the [operators page](https://ai.damtp.cam.ac.uk/pysr/operators/)
         for more details.
         Default is `["+", "-", "*", "/"]`.
     unary_operators : list[str]
@@ -943,7 +943,7 @@ def __init__(
                 elif k == "julia_project":
                     warnings.warn(
                         "The `julia_project` parameter has been deprecated. To use a custom "
-                        "julia project, please see `https://astroautomata.com/PySR/backend`.",
+                        "julia project, please see `https://ai.damtp.cam.ac.uk/pysr/backend`.",
                         FutureWarning,
                     )
                 elif k == "julia_kwargs":
@@ -2046,7 +2046,7 @@ def fit(
         if X.shape[0] > 10000 and not self.batching:
             warnings.warn(
                 "Note: you are running with more than 10,000 datapoints. "
-                "You should consider turning on batching (https://astroautomata.com/PySR/options/#batching). "
+                "You should consider turning on batching (https://ai.damtp.cam.ac.uk/pysr/options/#batching). "
                 "You should also reconsider if you need that many datapoints. "
                 "Unless you have a large amount of noise (in which case you "
                 "should smooth your dataset first), generally < 10,000 datapoints "