v1.3.0

What's Changed

• Expanded support for differential operators via backend 1.5.0 by @MilesCranmer in #782

e.g., say we wish to integrate $\frac{1}{x^2 \sqrt{x^2 - 1}}$ for $x > 1$:

import numpy as np
from pysr import PySRRegressor, TemplateExpressionSpec

x = np.random.uniform(1, 10, (1000,))  # Integrand sampling points
y = 1 / (x**2 * np.sqrt(x**2 - 1))     # Evaluation of the integrand

expression_spec = TemplateExpressionSpec(
    ["f"], "((; f), (x,)) -> D(f, 1)(x)"
)

model = PySRRegressor(
    binary_operators=["+", "-", "*", "/"],
    unary_operators=["sqrt"],
    expression_spec=expression_spec,
    maxsize=20,
)
model.fit(x[:, np.newaxis], y)

which should correctly find $\frac{\sqrt{x^2 - 1}}{x}$.

Full Changelog: v1.2.0...v1.3.0

v1.2.0

What's Changed

• Compatibility with new scikit-learn API and test suite by @MilesCranmer in #776
• Add differential operators and input stream specification by @MilesCranmer in #780
  • (Note: the differential operators aren't yet in a stable state, and are not yet documented. However, they do work!)
  • This PR also adds various GC allocation improvements in the backend.

Frontend Changelog: v1.1.0...v1.2.0

Backend Changelog: MilesCranmer/SymbolicRegression.jl@v1.2.0...v1.4.0

v1.1.0

What's Changed

• Automated update to backend: v1.2.0 by @github-actions in #770

Full Changelog: v1.0.2...v1.1.0

v1.0.2

What's Changed

• logger fixes: close streams and persist during warm start by @BrotherHa in #763
• Let sympy use log2(x) instead of log(x)/log(2) by @nerai in #712

New Contributors

• @BrotherHa made their first contribution in #763
• @nerai made their first contribution in #712

Full Changelog: v1.0.1...v1.0.2

v1.0.1

What's Changed

• Automated update to backend: v1.1.0 by @github-actions in #762
• Fall back to eager registry when needed by @DilumAluthge in #765

New Contributors

• @DilumAluthge made their first contribution in #765

Full Changelog: v1.0.0...v1.0.1

v1.0.0

PySR v1.0.0 Release Notes

PySR 1.0.0 introduces new features for imposing specific functional forms and finding parametric expressions. It also includes TensorBoard support, along with significant updates to the core algorithm, including some important bug fixes. The default hyperparameters have also been updated based on extensive tuning, with a maxsize of 30 rather than 20.

Major New Features

Expression Specifications

PySR 1.0.0 introduces new ways to specify the structure of equations through "Expression Specifications", that expose the new backend feature of AbstractExpression:

Template Expressions

TemplateExpressionSpec allows you to define a specific structure for your equations. For example:

expression_spec = TemplateExpressionSpec(["f", "g"], "((; f, g), (x1, x2, x3)) -> sin(f(x1, x2)) + g(x3)")

Parametric Expressions

ParametricExpressionSpec enables fitting expressions that can adapt to different categories of data with per-category parameters:

expression_spec = ParametricExpressionSpec(max_parameters=2)
model = PySRRegressor(
    expression_spec=expression_spec
    binary_operators=["+", "*", "-", "/"],
)
model.fit(X, y, category=category)  # Pass category labels

Improved Logging with TensorBoard

The new TensorBoardLoggerSpec enables logging of the search process, as well as hyperparameter recording, which exposes the AbstractSRLogger feature of the backend:

logger_spec = TensorBoardLoggerSpec(
    log_dir="logs/run",
    log_interval=10,  # Log every 10 iterations
)
model = PySRRegressor(logger_spec=logger_spec)

Features logged include:

• Loss curves over time at each complexity level
• Population statistics
• Pareto "volume" logging (measures performance over all complexities with a single scalar)
• The min loss over time

Algorithm Improvements

Updated Default Parameters

The default hyperparameters have been significantly revised based on testing:

• Increased default maxsize from 20 to 30, as I noticed that many people use the defaults, and this maxsize would allow for more accurate expressions.
• New mutation operator weights optimized for better performance, along the new mutation "rotate tree."
• Improved search parameters tuned using Pareto front volume calculations.
• Default niterations increased from 40 to 100, also to support better accuracy (at the expense of slightly longer default search times).

Core Changes

• New output organization: Results are now stored in outputs/<run_id>/ rather than in the directory of execution.
• Improved performance with better parallelism handling
• Support for Python 3.10+
• Updated Julia backend to version 1.10+
• Fix for aliasing issues in crossover operations

Breaking Changes

• Minimum Python version is now 3.10, and minimum Julia version is 1.10
• Output file structure has changed to use directories
• Parameter name updates:
  • equation_file → output_directory + run_id
  • Added clearer naming for parallelism options, such as parallelism="serial" rather than the old multithreading=False, procs=0 which was unclear

Documentation

The documentation has a new home at https://ai.damtp.cam.ac.uk/pysr/

v0.19.4

What's Changed

• Create load_all_packages to install Julia extensions by @MilesCranmer in #688
• Apptainer definition file for PySR by @wkharold in #687
• JuliaCall 0.9.23 by @MilesCranmer in #703
  • build(deps): bump juliacall from 0.9.21 to 0.9.22 by @dependabot in #695

New Contributors

• @wkharold made their first contribution in #687

Full Changelog: v0.19.3...v0.19.4

v0.19.3

What's Changed

• build(deps): bump juliacall from 0.9.20 to 0.9.21 by @dependabot in #678

Full Changelog: v0.19.2...v0.19.3

v0.19.2

What's Changed

• Avoid automatic upgrade to Julia 1.11 by @MilesCranmer in #671

Full Changelog: v0.19.1...v0.19.2

v0.19.1

What's Changed

• Bump docker/setup-qemu-action from 2 to 3 by @dependabot in #506
• fix: from pysr import * by @MilesCranmer in #670

Full Changelog: v0.19.0...v0.19.1