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Changelog

[2.0.0a1] (2025-10-08)

This is an alpha release of v2.0.0. There will still be changes before the release of v2.0.0, likely including new hyperparameter defaults.

What's Changed

Major changes

Multiple features (update backend to 2.0) (#999)

This PR updates the backend to SymbolicRegression.jl 2.0.0-alpha.8 and exposes several major new features:

  • N-ary operators: Support for operators with arbitrary arity (not just unary/binary)
    • Added 3-arity operators: fma (fused multiply-add), clamp, etc.
    • This can be used via a new operators parameter dictionary: operators={1: ["sin"], 2: ["+", "*"], 3: ["clamp"]}
  • Equation guesses: Pass initial equation guesses to guide the search using the guesses parameter to fit
    • For example: guesses=["sin(x0 * 2.1 - 0.5)", "x0 * 3.0 + x2"] provides two guesses
    • Control injection rate with fraction_replaced_guesses
  • Advanced autodiff backends: Experimental support for Mooncake.jl and Enzyme.jl
    • Enzyme.jl support via autodiff_backend="Enzyme" (fragile/experimental)
    • Mooncake.jl (experimental - currently disabled pending upstream fix)
  • Feature node mutation: New mutation operator that directly modifies which features are used
    • Control mutation weight with weight_mutate_feature
  • Worker management:
    • worker_imports: specify Julia packages to import on workers
    • worker_timeout: control timeout for worker processes

Automatic batching for big data (#1045)

Other changes

  • docs: add vector expression example by @MilesCranmer in #1041
  • [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci[bot] in #1008
  • chore(deps): bump actions/checkout from 4 to 5 by @dependabot[bot] in #1009
  • ci: update Dockerfile image by @MilesCranmer in #1022
  • test: update docker versions by @MilesCranmer in #1023
  • chore(deps): bump actions/setup-python from 5 to 6 by @dependabot[bot] in #1027
  • deps: update min python to 3.9 by @MilesCranmer in #1052
  • deps: bump juliacall requirement by @dependabot[bot] in #1035
  • chore(deps): bump actions/checkout from 4 to 5 by @dependabot[bot] in #1049
  • chore(deps): bump github/codeql-action from 3 to 4 by @dependabot[bot] in #1050
  • chore(deps): bump actions/setup-python from 5 to 6 by @dependabot[bot] in #1051
  • chore(deps): update beartype requirement from <0.22,>=0.19 to >=0.19,<0.23 by @dependabot[bot] in #1047
  • chore: update pyjuliacall requirement in environment.yml by @MilesCranmer in #1054

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v1.5.9...v2.0.0a1

[1.5.9] (2025-07-15)

What's Changed

  • [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci[bot] in #853
  • Fix type error in feature selection code by @MilesCranmer in #952
  • chore(deps): update juliacall requirement from <0.9.26,>=0.9.24 to >=0.9.24,<0.9.27 by @dependabot[bot] in #980

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v1.5.8...v1.5.9

[1.5.8] (2025-05-20)

What's Changed

  • fix: compat with python 3.8 by removing beartype by @MilesCranmer in #935
  • ci: update workflows to test 3.13 by @MilesCranmer in #929
  • style: fix newline in warning by @MilesCranmer in #931
  • ci: switch to codecov by @MilesCranmer in #932
  • deps: fix local conda env versions by @MilesCranmer in #933

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v1.5.7...v1.5.8

[1.5.7] (2025-05-19)

What's Changed

  • Enable negative losses by @MilesCranmer in #928
  • Recommend TemplateExpressionSpec over ParametricExpressionSpec @MilesCranmer in #920
  • Fix multi-output template expressions by @MilesCranmer in #921
  • build: switch to hatchling by @MilesCranmer in #888
  • chore(deps): bump juliacall from 0.9.24 to 0.9.25 by @dependabot in #925
  • fix: turn off double warning for ParametricExpressionSpec by @MilesCranmer in #930

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v1.5.6...v1.5.7

[1.5.6] (2025-05-04)

What's Changed

  • Added paper contribution and image by @manuel-morales-a in #824
  • fix: pickling of inv by @MilesCranmer in #910
  • Automated update to backend: v1.10.0 by @github-actions in #890

New Contributors

  • @manuel-morales-a made their first contribution in #824

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v1.5.5...v1.5.6

[1.5.5] (2025-04-02)

What's Changed

  • fix: typing extensions dependency by @MilesCranmer in #885

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v1.5.4...v1.5.5

[1.5.4] (2025-04-01)

What's Changed

  • Compat with older Python by @MilesCranmer in #884

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v1.5.3...v1.5.4

[1.5.3] (2025-03-28)

What's Changed

  • fix: change sympy mappings ordering by @romanovzky in #868

New Contributors

  • @romanovzky made their first contribution in #868

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v1.5.2...v1.5.3

[1.5.2] (2025-03-05)

What's Changed

  • fix: mapping of cbrt by @MilesCranmer in #858

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v1.5.1...v1.5.2

[1.5.1] (2025-03-01)

What's Changed

  • fix: comparison operator parsing by @MilesCranmer in #845

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v1.5.0...v1.5.1

[1.5.0] (2025-02-25)

Backend Changes

Major Changes

  • Change behavior of batching to resample only every iteration; not every eval in astroautomata/SymbolicRegression.jl#421
    • This result in a speed improvement for code with batching=true
    • It should also result in improved search results with batching, because comparison within a single population is more stable during evolution. In other words, there is no lucky batch phenomenon.
    • This also refactors the batching interface to be cleaner. There is a SubDataset <: Dataset rather than passing around an array idx explicitly.
    • Note that other than the slight behaviour change, this is otherwise backwards compatible - the old way to write custom loss functions that take idx will still be handled.

Other changes

Frontend Changes

  • Update backend to v1.8.0 by @MilesCranmer in #833
  • test: update deprecated sklearn test syntax by @MilesCranmer in #834
  • chore(deps): bump juliacall from 0.9.23 to 0.9.24 by @dependabot in #815
  • use standard library logging by @MilesCranmer in #835
  • Remove warning about many features, as not really relevant anymore by @MilesCranmer in #837
  • chore(deps): update beartype requirement from <0.20,>=0.19 to >=0.19,<0.21 by @dependabot in #838
  • chore(deps): update jax[cpu] requirement from <0.5,>=0.4 to >=0.4,<0.6 by @dependabot in #810

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v1.4.0...v1.5.0

[1.4.0] (2025-02-13)

What's Changed

#823 adds support for parameters in template expressions, allowing you to learn expressions under a template, that have custom coefficients which can be optimized.

Along with this, the TemplateExpressionSpec API has changed. (The old API will continue to function, but will not have parametric expressions available).

spec = TemplateExpressionSpec(
    "fx = f(x); p[1] + p[2] * fx + p[3] * fx^2",
    expressions=["f"],
    variable_names=["x"],
    parameters={"p": 3},
)

This would learn three parameters, for the expression $y = p_1 + p_2 f(x) + p_3 f(x)^2.$

You can have multiple parameter vectors, and these parameter vectors can also be indexed by categorical features. For example:

### Learn different parameters for each class:
spec = TemplateExpressionSpec(
    "p1[category] * f(x1, x2) + p2[1] * g(x1^2)",
    expressions=["f", "g"],
    variable_names=["x1", "x2", "category"],
    parameters={"p1": 3, "p2": 1},
)

This will learn an equation of the form: $$y = \alpha_c,f(x_1,x_2) + \beta g(x_1 ^2)$$ where $c$ is the category, $\alpha_c$ is a learned parameter specific to each category, and $\beta$ is a normal scalar category. Note that unlike ParametricExpressionSpec, this feature of TemplateExpressionSpec would have you pass the category variable in X rather than as a category keyword (floating point versions of the categories). This difference means that in a TemplateExpressionSpec, you can actually have multiple categories!

  • Added support for expression-level loss functions via loss_function_expression, which allows you to specify custom loss functions that operate on the full expression object rather than just its evaluated output. This is particularly useful when working with template expressions.

  • Note that the old template expression syntax using function-style definitions is deprecated. Use the new, cleaner syntax instead:

### # Old:
### spec = TemplateExpressionSpec(
###     function_symbols=["f", "g"],
###     combine="((; f, g), (x1, x2, x3)) -> sin(f(x1, x2)) + g(x3)"
### )

### New:
spec = TemplateExpressionSpec(
    "sin(f(x1, x2)) + g(x3)"
    expressions=["f", "g"],
    variable_names=["x1", "x2", "x3"],
)

Full Changelog: v1.3.1...v1.4.0

[1.3.1] (2024-12-27)

What's Changed

  • Automated update to backend: v1.5.1 by @github-actions in #790

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v1.3.0...v1.3.1

[1.3.0] (2024-12-15)

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 $x &gt; 1$:

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: https://github.com/MilesCranmer/PySR/compare/v1.2.0...v1.3.0

[1.2.0] (2024-12-14)

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: https://github.com/MilesCranmer/PySR/compare/v1.1.0...v1.2.0

Backend Changelog: https://github.com/MilesCranmer/SymbolicRegression.jl/compare/v1.2.0...v1.4.0

[1.1.0] (2024-12-09)

What's Changed

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

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v1.0.2...v1.1.0

[1.0.2] (2024-12-07)

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: https://github.com/MilesCranmer/PySR/compare/v1.0.1...v1.0.2

[1.0.1] (2024-12-06)

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: https://github.com/MilesCranmer/PySR/compare/v1.0.0...v1.0.1

[1.0.0] (2024-12-01)

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/

[0.19.4] (2024-08-23)

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: https://github.com/MilesCranmer/PySR/compare/v0.19.3...v0.19.4

[0.19.3] (2024-07-29)

What's Changed

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

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.19.2...v0.19.3

[0.19.2] (2024-07-15)

What's Changed

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

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.19.1...v0.19.2

[0.19.1] (2024-07-15)

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: https://github.com/MilesCranmer/PySR/compare/v0.19.0...v0.19.1

[0.19.0] (2024-06-22)

What's Changed

  • BREAKING: Disable automatic sympy simplification by @MilesCranmer in #658
  • Build: update numpy version by @MilesCranmer in #650
  • Build: bump docker/build-push-action from 5 to 6 by @dependabot in #652

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.18.5...v0.19.0

[0.18.5] (2024-06-16)

What's Changed

New features

  • Per-variable custom complexities by @MilesCranmer in #649

    model.fit(X, y, complexity_of_variables=[1, 3])
# run a search with feature 1 having complexity 1 and feature 2 with complexity 3

  • Automatically suggest similar parameters by @MilesCranmer in #620

Other

  • Bump julia-actions/cache from 1 to 2 by @dependabot in #621
  • Update pysr_demo.ipynb by @VishalJ99 in #624
  • [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci in #612
  • Bump docker/login-action from 2 to 3 by @dependabot in #509
  • More extensive typing stubs and associated refactoring by @MilesCranmer in #609

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.18.4...v0.18.5

Backend changes

New features

Other

Full Changelog: https://github.com/MilesCranmer/SymbolicRegression.jl/compare/v0.24.4...v0.24.5

New Contributors

  • @VishalJ99 made their first contribution in #624

[0.18.4] (2024-05-04)

Frontend changes

  • Add dimensionless constants mode; update Python version constraints; upgrade juliacall to 0.9.20 (#608) (@MilesCranmer)
  • Fix sign typo in example docs (#611) (@hvaara)

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.18.3...v0.18.4

Backend changes

Full Changelog: https://github.com/MilesCranmer/SymbolicRegression.jl/compare/v0.24.2...v0.24.4

New Contributors

  • @hvaara made their first contribution in #611

[0.18.3] (2024-04-26)

Frontend changes

  • Automated update to backend: v0.24.3 by @github-actions in #605

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.18.2...v0.18.3

Backend changes

Full Changelog: https://github.com/MilesCranmer/SymbolicRegression.jl/compare/v0.24.1...v0.24.2

[0.18.2] (2024-04-15)

Frontend changes

  • Add missing greater operator in sympy mapping by @MilesCranmer in #590
  • Bump julia-actions/setup-julia from 1 to 2 by @dependabot in #591
  • [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci in #537
  • Automated update to backend: v0.24.2 by @MilesCranmer in #598

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.18.1...v0.18.2

Backend changes

Merged pull requests:

Closed issues:

Full Changelog: https://github.com/MilesCranmer/SymbolicRegression.jl/compare/v0.24.1...v0.24.2

[0.18.1] (2024-03-26)

What's Changed

  • Revert GitHub-based registry for backend by @MilesCranmer in #587

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.18.0...v0.18.1

[0.18.0] (2024-03-24)

Frontend changes

  • fix TypeError when a variable name matches a builtin python function by @tomjelen in #558
  • Update to backend: v0.24.0 by @MilesCranmer in #564
  • Fix extensions not being added to package env by @MilesCranmer in #579
  • Bump backend version and switch to GitHub-based registry by @MilesCranmer in #580

Backend changes

Filtered to only include relevant ones for Python frontend. Also note that not all backend features, like graph-based expressions/program synthesis, are supported yet, so I don't mention those changes yet.

  • (BREAKING) The swap_operands mutation contributed by @foxtran now has a default weight of 0.1 rather than 0.0.

  • (BREAKING) The Dataset struct has had many of its field declared immutable, as a safety precaution.

    • If you had relied on the mutability of the struct to set parameters after initializing it, or had changed any properties of the dataset within a loss function (which actually would break assumptions outside the loss function anyways), you will need to modify your code. Note you can always copy fields of the dataset to variables and then modify those variables

  • LoopVectorization.jl has been moved to a package extension. PySR will install automatically at first use of turbo=True rather than by default, which means faster install time and startup time.

    • Note that LoopVectorization will no longer result in improved performance in Julia 1.11 and thus turbo=True will have no effect on that version (due to internal changes in Julia), which is why I have instead done the following:

  • Bumper.jl support added. Passing bumper=true to PySRRegressor() will result in faster performance.

    • Uses bump allocation (see rust package bumpalo for a good explanation) in the expression evaluation which can get speeds equivalent to LoopVectorization and sometimes even better due to better management of allocations rather than relying on garbage collection. Seems like a pretty good alternative, and doesn't rely on manipulating Julia internals for performance (astroautomata/SymbolicRegression.jl#287)

  • Various fixes to distributed compute; confirmed Slurm support again!

  • Now prefer to use new keyword-based constructors for nodes:

    Node{T}(feature=...)        # leaf referencing a particular feature column
Node{T}(val=...)            # constant value leaf
Node{T}(op=1, l=x1)         # operator unary node, using the 1st unary operator
Node{T}(op=1, l=x1, r=1.5)  # binary unary node, using the 1st binary operator

    rather than the previous constructors Node(op, l, r) and Node(T; val=...) (though those will still work; just with a depwarn). If you did any construction of nodes manually, note the new syntax. (Old syntax will still work though)

  • Formatting overhaul of backend (astroautomata/SymbolicRegression.jl#278)

  • Upgraded Optim to 1.9

  • Upgraded DynamicQuantities to 0.13

  • Upgraded DynamicExpressions to 0.16

  • The main search loop in the backend has been greatly refactored for readability and improved type inference. It now looks like this (down from a monolithic ~1000 line function)

    function _equation_search(
    datasets::Vector{D}, ropt::RuntimeOptions, options::Options, saved_state
) where {D<:Dataset}
    _validate_options(datasets, ropt, options)
    state = _create_workers(datasets, ropt, options)
    _initialize_search!(state, datasets, ropt, options, saved_state)
    _warmup_search!(state, datasets, ropt, options)
    _main_search_loop!(state, datasets, ropt, options)
    _tear_down!(state, ropt, options)
    return _format_output(state, ropt)
end

Backend changes: https://github.com/MilesCranmer/SymbolicRegression.jl/compare/v0.23.1...v0.24.1

New Contributors

  • @tomjelen made their first contribution in #558

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.17.4...v0.18.0

[0.17.4] (2024-03-21)

Small patch to Julia version to avoid buggy libgomp in 1.10.1 and 1.10.2.

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.17.3...v0.17.4

[0.17.3] (2024-03-20)

What's Changed

  • Bump juliacall from 0.9.15 to 0.9.19 by @dependabot in #569
    • Upstreamed patching of seval to support multiple expressions
  • remove repeated operator by @RaulPL in #573

New Contributors

  • @RaulPL made their first contribution in #573

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.17.2...v0.17.3

[0.17.2] (2024-03-12)

What's Changed

  • All cell state in bio image paper by @chris-soelistyo in #560
  • Refactor update_backend.yml workflow by @sefffal in #562
  • Limit to Julia 1.6.7-1.10.0 and 1.10.3+ by @MilesCranmer in #565

New Contributors

  • @chris-soelistyo made their first contribution in #560
  • @sefffal made their first contribution in #562

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.17.1...v0.17.2

[0.17.1] (2024-02-13)

What's Changed

  • Fix y_units bug by @MilesCranmer in #545

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.17.0...v0.17.1

[0.17.0] (2024-02-12)

What's Changed

  • Bump docker/build-push-action from 3 to 5 by @dependabot in #510
  • Bump actions/cache from 3 to 4 by @dependabot in #526
  • Update colab notebook to use juliaup by @MilesCranmer in #531
  • Bump peter-evans/create-pull-request from 5 to 6 by @dependabot in #539
  • (BREAKING) Rewrite Julia interface with PyJulia -> JuliaCall; other changes by @MilesCranmer @cjdoris @mkitti in #535

Detailed changes from #535

  • (BREAKING) Changed PyJulia with JuliaCall
    • Need to change eval -> seval
    • Manually converting to Vector when calling SymbolicRegression.jl functions (otherwise would get passed as PyList{Any}; see JuliaPy/PythonCall.jl#441)
    • Wrapped equation_search code with jl.PythonCall.GC.disable() to avoid multithreading-related segfaults (JuliaPy/PythonCall.jl#298)
    • Manually convert np.str_ to str before passing to variable_names, otherwise it becomes a PyArray and not a String (might be worth adding a workaround, it seems like PyJulia does this automatically)
  • (BREAKING) Julia is now installed automatically when you import pysr (via JuliaCall)
  • (BREAKING) The user no longer needs to run python -m pysr install. The install process is done by JuliaCall at import time.
    • Removed code related to pysr.install() and python -m pysr install because JuliaCall now handles this.
    • python -m pysr install will not give a warning and do nothing.
  • (BREAKING) Remove the feynman problems dataset. Didn't seem good to have a dataset within a library itself.
  • (BREAKING) Deprecated julia_project argument (ignored; no effect). The user now needs to set this up by customizing juliapkg.json. See updated documentation for instructions.
  • (BREAKING) Switch from python -m pysr.test [test] to python -m pysr test [test].
  • Switches to pyproject.toml for building rather than setup.py. However, setup.py install should still work.
  • Dependencies are now managed by pyjuliapkg rather than the custom code we made. Simplifies things a lot!
  • Rather than storing the raw julia variables in PySRRegressor, I am now storing a serialized version of them. This means you can now pickle the search state and warm-start the search from a file, in another Python process!
    • Not breaking! Because self.raw_julia_state_ will deserialize it automatically for you
  • SymbolicRegression is now available to import from PySR:
from pysr import SymbolicRegression as SR
x1 = SR.Node(feature=1)  # Create expressions manually
  • SymbolicRegression options are accessible in <model>.julia_options_ (generated from a serialized format for pickle safety) so that the user can call a variety of functions in SymbolicRegression.jl directly.
  • Deprecated various kwargs to match SymbolicRegression.jl (old names will still work, so this is not breaking):
    • ncyclesperiteration => ncycles_per_iteration
    • loss => elementwise_loss
    • full_objective => loss_function
  • Fixes Jupyter printing by automatically loading the juliacall.ipython extension at import time
  • Adds Zygote.jl to environment by default
  • Does unittesting on an example Jupyter notebook

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.16.9...v0.17.0

[0.16.9] (2024-01-05)

What's Changed

  • Swap operands mutation by @foxtran in #512

New Contributors

  • @foxtran made their first contribution in #512

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.16.8...v0.16.9

[0.16.8] (2023-12-31)

What's Changed

  • Install typing_extensions for compatibility with Python 3.7 by @MilesCranmer in #497
  • Create dependabot.yml by @MilesCranmer in #500
  • Fix docker CI nightly by @MilesCranmer in #499
  • Enforce upper bound compats by @MilesCranmer in #498

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.16.7...v0.16.8

[0.16.7] (2023-12-31)

What's Changed

  • [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci in #495
  • Warn the user on Python 3.12 by @MilesCranmer in #496

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.16.6...v0.16.7

[0.16.6] (2023-12-24)

What's Changed

  • [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci in #488
  • Add parameter for specifying --heap-size-hint on spawned Julia processes by @MilesCranmer in #493

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.16.5...v0.16.6

[0.16.5] (2023-12-14)

What's Changed

  • Add more piecewise operators by @MilesCranmer in #486

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.16.4...v0.16.5

[0.16.4] (2023-12-13)

What's Changed

  • Requesting addition of paper to research examples by @tmengel in #415
  • Incorporate pre-commit hooks by @MilesCranmer in #425
  • Refactor sympy and export functionality by @MilesCranmer in #427
  • Refactor utility functions in sr.py by @MilesCranmer in #428
  • [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci in #431
  • Add paper "Discovery of a Planar Black Hole Mass Scaling Relation for Spiral Galaxies" by @ZehaoJin in #437
  • [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci in #440
  • Added "min" and "max" sympy mapping by @tanweer-mahdi in #473
  • Added "round" operator in the Sympy mappings by @tanweer-mahdi in #474
  • [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci in #446
  • Automated update to backend: v0.22.5 by @MilesCranmer in #482

New Contributors

  • @tmengel made their first contribution in #415
  • @ZehaoJin made their first contribution in #437
  • @tanweer-mahdi made their first contribution in #473

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.16.3...v0.16.4

[0.16.3] (2023-08-21)

What's Changed

  • Automated update to backend: v0.22.4 by @MilesCranmer in #413
    • Fixes world age issue

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.16.2...v0.16.3

[0.16.2] (2023-08-17)

What's Changed

  • Automated update to backend: v0.22.3 by @MilesCranmer in #409

Backend changes

Backend changes: https://github.com/MilesCranmer/SymbolicRegression.jl/compare/v0.22.2...v0.22.3

Frontend changes: https://github.com/MilesCranmer/PySR/compare/v0.16.1...v0.16.2

[0.16.1] (2023-08-10)

What's Changed

  • Automated update to backend: v0.22.2 by @MilesCranmer in #404

Backend changes

Backend Changelog: Diff since v0.22.1 PySR Changelog: https://github.com/MilesCranmer/PySR/compare/v0.16.0...v0.16.1

[0.16.0] (2023-08-07)

What's Changed

  • Backend version update in #400. Includes:
    • Algorithmic improvements to batching
    • Code quality improvements (some method ambiguities, old exports)

Backend changes

  • (Algorithm modification) Evaluate on fixed batch when building per-population hall of fame in astroautomata/SymbolicRegression.jl#243
    • This only affects searches that use batching=true. It results in improved searches on large datasets, as the "winning expression" is not biased towards an expression that landed on a lucky batch.
    • Note that this only occurs within an iteration. Evaluation on the entire dataset still happens at the end of an iteration and those loss measurements are used for absolute comparison between expressions.
  • (Algorithm modification) Deprecates the fast_cycle feature in astroautomata/SymbolicRegression.jl#243. Use of this parameter will have no effect.
    • Was removed to ease maintenance burden and because it doesn't have a use. This feature was created early on in development as a way to get parallelism within a population. It is no longer useful as you can parallelize across populations.
  • Add Aqua.jl to test suite in astroautomata/SymbolicRegression.jl#245 for code quality control
  • CompatHelper: bump compat for DynamicExpressions to 0.12, (keep existing compat) in astroautomata/SymbolicRegression.jl#242
    • Is able to avoids method invalidations when using operators to construct expressions manually by modifying a global constant mapping of operator => index, rather than @eval-ing new operators.
    • This only matters if you were using operators to build trees, like x1 + x2. All internal search code uses Node() explicitly to build expressions, so did not rely on method invalidation at any point.

Backend Changelog: https://github.com/MilesCranmer/SymbolicRegression.jl/compare/v0.21.5...v0.22.1

PySR Changelog: https://github.com/MilesCranmer/PySR/compare/v0.15.4...v0.16.0

[0.15.4] (2023-08-04)

What's Changed

  • Warn user when using power laws by @MilesCranmer in #399
    • This seems like the most common configuration mistake in PySR: using the ^ operator without setting constraints, leading to extremely complex expressions with poor generalization properties. Thus, this warning will let the user know about it if they set up ^ without constraints.

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.15.3...v0.15.4

[0.15.3] (2023-08-02)

What's Changed

  • Use unicode in printing without needing to decode by @MilesCranmer in #398

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.15.2...v0.15.3

[0.15.2] (2023-08-01)

What's Changed

  • Ensure files are read as utf-8 on all operating systems by @MilesCranmer in #396

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.15.1...v0.15.2

[0.15.1] (2023-07-30)

What's Changed

  • Fix compat with old scikit-learn versions by @MilesCranmer in #393

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.15.0...v0.15.1

[0.15.0] (2023-07-28)

What's Changed

  • Backend version update in #389. Includes:
    • Dimensional analysis (see docs examples page)
    • Printing improvements
    • Many misc changes (see below)

Backend Changes

  • astroautomata/SymbolicRegression.jl#228 and astroautomata/SymbolicRegression.jl#230 and astroautomata/SymbolicRegression.jl#231 and astroautomata/SymbolicRegression.jl#235
    • Dimensional analysis (228)
    • Printing improvements (228)
      • By default, only 5 significant digits are now printed, rather than the entire float. You can change this with the print_precision option.
      • In the default printed equations, x₁ is used rather than x1.
      • y = is printed at the start (or y₁ = for multi-output). With units this becomes, for example, y[kg] =.
    • Misc
      • Easier to convert from MLJ interface to SymbolicUtils (via node_to_symbolic(::Node, ::AbstractSRRegressor)) (228)
      • Improved precompilation (228)
      • Various performance and type stability improvements (228)
      • Inlined the recording option to speedup compilation (230)
      • Updated Julia tutorials to use MLJ rather than low-level interface (228)
      • Moved JSON3.jl to extension (231)
      • Use PackageExtensionsCompat.jl over Requires.jl (231)
      • Require LossFunctions.jl to be 0.10 (231)
      • Batching inside optimization loop + batching support for custom objectives by (235)
      • Update docker defaults: Julia=1.9.1; Python=3.10.11 in #371

Backend Changelog: https://github.com/MilesCranmer/SymbolicRegression.jl/compare/v0.20.0...v0.21.0

PySR Changelog: https://github.com/MilesCranmer/PySR/compare/v0.14.3...v0.15.0

[0.14.3] (2023-07-04)

What's Changed

  • Self-repairing PyCall installation to lower entrance barrier for new users by @MilesCranmer and @mkitti in #363

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.14.2...v0.14.3

[0.14.2] (2023-06-20)

What's Changed

  • Recommend user install with --enable-shared by @MilesCranmer in #352
  • Automated update to backend: v0.19.1 by @MilesCranmer in #355

Backend

Diff since v0.19.0

Merged pull requests on backend:

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.14.1...v0.14.2

[0.14.1] (2023-05-28)

What's Changed

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.14.0...v0.14.1

[0.14.0] (2023-05-20)

What's Changed

  • Added CLI to run pysr.install() to install Julia dependencies by @w2ll2am in #298
    • Let's you install PySR with python -m pysr install rather than python -c 'import pysr; pysr.install()'
    • This CLI also has other options available (precompilation, Julia project name, etc.)

New Contributors

  • @w2ll2am made their first contribution in #298

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.13.0...v0.14.0

[0.13.0] (2023-05-12)

What's Changed

  • Test Julia 1.9 by @MilesCranmer in #329
  • Automated update to backend: v0.18.0 by @MilesCranmer in #331

Backend changes

Diff since v0.17.1

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.12.3...v0.13.0

[0.12.3] (2023-04-27)

What's Changed

  • Highlight contributors by @MilesCranmer in #301
  • Automated update to backend: v0.17.1 by @MilesCranmer in #320

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.12.2...v0.12.3

[0.12.2] (2023-04-22)

What's Changed

  • Add paper 'Electron Transfer Rules of Minerals under Pressure…' by @GCaptainNemo in #288
  • Fix colab notebook example by @MilesCranmer in #295
  • Add paper: "Data-Driven Equation Discovery of a Cloud Cover Parameterization" by @agrundner24 in #302
  • Pass through enable_autodiff parameter by @MilesCranmer in #316

New Contributors

  • @GCaptainNemo made their first contribution in #288
  • @agrundner24 made their first contribution in #302

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.12.1...v0.12.2

[0.12.1] (2023-03-25)

What's Changed

  • Allow user to specify full objective functions by @MilesCranmer in #276

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.12.0...v0.12.1

[0.12.0] (2023-03-22)

What's Changed

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.11.17...v0.12.0

[0.11.17] (2023-03-07)

What's Changed

  • Update backend version with warm start fix by @MilesCranmer in #271
    • This means that you can change the dataset or loss function, and warm_start=True will still work, and the losses will be re-computed.

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.11.16...v0.11.17

[0.11.16] (2023-03-01)

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.11.15...v0.11.16

[0.11.15] (2023-02-18)

What's Changed

  • Bump backend version with data race fix by @MilesCranmer in #268
    • Incorporates depth check into constraints, rather than in mutation step.
    • Fixes one instance of a data race (appears to be remaining issues, however)

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.11.14...v0.11.15

[0.11.14] (2023-02-13)

What's Changed

  • Update backend with constraints fix by @MilesCranmer in #265

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.11.13...v0.11.14

[0.11.13] (2023-02-09)

What's Changed

  • Fix latex_table assertion for multi-output by @MilesCranmer in #253
  • Make precompilation optional by @MilesCranmer in #263

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.11.12...v0.11.13

[0.11.12] (2023-01-16)

What's Changed

  • Make docker build multi-stage by @MilesCranmer in #235
  • Create interactive API reference page by @MilesCranmer in #247
  • Bump backend version with stream fix; fixes #250 by @MilesCranmer in #252

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.11.11...v0.11.12

[0.11.11] (2022-11-22)

What's Changed

  • Make Julia startup options configurable; set optimize=3 by @MilesCranmer in #228

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.11.10...v0.11.11

[0.11.10] (2022-11-21)

What's Changed

  • Clean up dockerfile by @MilesCranmer in #223
  • Update backend version with improved resource monitoring by @MilesCranmer in #227

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.11.9...v0.11.10

[0.11.9] (2022-11-05)

What's Changed

  • Refactor testing suite to have CLI by @MilesCranmer in #221

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.11.8...v0.11.9

[0.11.8] (2022-11-04)

What's Changed

  • Fix PyCall not giving traceback by @MilesCranmer in #218
  • Fixed safe operators; make progress bar print to stderr by @MilesCranmer in #219

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.11.7...v0.11.8

[0.11.7] (2022-11-04)

What's Changed

  • Expand nightly conda-forge tests to other Python versions by @MilesCranmer in #212
  • Clean up parameter groupings in docs by @MilesCranmer in #214
  • Add optimization-as-mutation, and adaptive parsimony by @MilesCranmer in #217

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.11.6...v0.11.7

[0.11.6] (2022-10-31)

What's Changed

  • Speed up evaluation with turbo parameter by @MilesCranmer in #208
Space.Balls.-.Ludicrous.Speed-ygE01sOhzz0.mp4

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.11.5...v0.11.6

[0.11.5] (2022-10-24)

What's Changed

  • 30-50% Faster evaluation, and perform explicit version assertion for backend by @MilesCranmer in #205

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.11.4...v0.11.5

[0.11.4] (2022-10-10)

What's Changed

  • Fix conda forge installs by @MilesCranmer in #202

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.11.3...v0.11.4

[0.11.3] (2022-10-06)

What's Changed

  • Faster evaluation for constant sub-expressions (SymbolicRegression.jl#129)
  • Will now check variable names for spaces and other non-alphanumeric characters, aside from underscores. Before this would only raise an issue after a search, when trying to pickle the saved data.

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.11.2...v0.11.3

[0.11.2] (2022-09-28)

(Fix for conda-forge build)

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.11.1...v0.11.2

[0.11.1-1] (2022-09-26)

What's Changed

  • Added Customization page in the docs for tweaking the backend's loss function and constraints.
  • Adding two entries to papers.yml by @JayWadekar in #192
  • Explicitly deprecate Julia <= 1.5 by @MilesCranmer in #194
  • Allow custom shared projects for julia_project by @MilesCranmer @mkitti in #197
    • e.g., this would allow you to run with @my-project and it will set up a shared Julia project under my-project (in the environments dir)

New Contributors

  • @JayWadekar made their first contribution in #192

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.11.0...v0.11.1-1

[0.11.0] (2022-09-11)

What's Changed

  • Update backend #191
    • Includes high-precision constants when precision=64
    • Enables datasets with zero variance (to allow fitting a constant)
    • Changes, e.g., abs(x)^y to x^y, with expressions avoided altogether for invalid input. This is because the former would sometimes give weird functional forms by exploiting the cusp at x=0. Thanks to @johanbluecreek.

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.10.4...v0.11.0

[0.10.4-1] (2022-09-08)

What's Changed

  • Fix install for Julia <=1.6 by @MilesCranmer @mkitti in #188
    • PyJulia will now launch directly into the shared pysr-{version} environment, rather than activating it later.

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.10.3...v0.10.4

[0.10.3] (2022-09-06)

What's Changed

  • Displays a warning message when PyTorch is imported before PyJulia starts. See pytorch/pytorch#78829. The only current solution is to start Julia beforehand.
  • New docs! Using Material-Mkdocs:

Screen Shot 2022-09-06 at 6 06 49 PM

[0.10.2] (2022-09-06)

What's Changed

  • Set JULIA_PROJECT, use Pkg.add once by @mkitti in #186

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.10.1...v0.10.2

[0.10.1] (2022-09-06)

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.10.0...v0.10.1

[0.10.0] (2022-08-14)

What's Changed

  • Easy loading from auto-generated checkpoint files by @MilesCranmer w/ review @tttc3 @Pablo-Lemos in #167
    • Use .from_file to load from the auto-generated .pkl file.
  • LaTeX table generator by @MilesCranmer w/ review @tttc3 @kazewong in #156
    • Generate a LaTeX table of discovered equations with .latex_table()
  • Improved default model selection strategy by @MilesCranmer in #177
    • Old strategy is available as model_selection="score"
  • Add opencontainers image-spec to Dockerfile by @SauravMaheshkar w/ review @MilesCranmer in #166
  • Switch to comma-based csv format by @MilesCranmer in #176

Bug fixes

New Contributors

  • @SauravMaheshkar made their first contribution in #166

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.9.0...v0.10.0

[0.9.0] (2022-06-04)

What's Changed

  • Refactor of PySRRegressor by @tttc3 in #146
    • PySRRegressor is now completely compatible with scikit-learn.
    • PySRRegressor can be stored in a pickle file, even after fitting, and then be reloaded and used with .predict()
    • PySRRegressor.equations -> PySRRegressor.equations_

New Contributors

  • @tttc3 made their first contribution in #146

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.8.7...v0.9.0

[0.8.5] (2022-05-20)

What's Changed

  • Custom complexities for operators, constants, and variables (#138)
  • Early stopping conditions (#134)
    • Based on a certain loss value being achieved
    • Max number of evaluations (for theoretical studies of genetic algorithms, rather than anything practical).
  • Work with specified expression rather than the one given by model_selection, by passing index to the function you wish to use (e.g,. model.predict(X, index=5) would use the 5th equation.).

Full Changelog since v0.8.1: https://github.com/MilesCranmer/PySR/compare/v0.8.1...v0.8.5

[0.8.1] (2022-05-08)

What's Changed

  • Enable distributed processing with ClusterManagers.jl from #133

Full Changelog: https://github.com/MilesCranmer/PySR/compare/v0.8.0...v0.8.1

[0.8.0] (2022-05-08)

This new release updates the entire set of default PySR parameters according to the ones presented in #115. These parameters have been tuned over nearly 71,000 trials. See the discussion for further info.

Additional changes:

  • Nested constraints implemented. For example, you can now prevent sin and cos from being repeatedly nested, by using the argument: nested_constraints={"sin": {"sin": 0, "cos": 0}, "cos": {"sin": 0, "cos": 0}}. This argument states that within a sin operator, you can only have a max depth of 0 for other sin or cos. The same is done for cos. The argument nested_constraints={"^": {"+": 2, "*": 1, "^": 0}} states that within a pow operator, you can only have 2 things added, or 1 use of multiplication (i.e., no double products), and zero other pow operators. This helps a lot with finding interpretable expressions!
  • New parsimony algorithm (backend change). This seems to help searches quite a bit, especially when one is searching for more complex expressions. This is turned on by use_frequency_in_tournament which is now the default.
  • Many backend improvements: speed, bug fixes, etc.
  • Improved stability of multi-processing (backend change). Thanks to @CharFox1.
  • Auto-differentiation implemented (backend change). This isn't used by default in any instances right now, but could be used by optimization later. Thanks to @kazewong.
  • Improved testing coverage of weird edge cases.
  • All parameters to PySRRegressor have been cleaned up to be in snake_case rather than CamelCase. The backend is also now almost entirely snake_case for internal functions. +Other readability improvements. Thanks to @bstollnitz and @patrick-kidger for the suggestions.

[0.6.0] (2021-06-01)

PySR Version 0.6.0

Large changes:

  • Exports to JAX, PyTorch, NumPy. All exports have a similar interface. JAX and PyTorch allow the equation parameters to be trained (e.g., as part of some differentiable model). Read https://pysr.readthedocs.io/en/latest/docs/options/#callable-exports-numpy-pytorch-jax for details. Thanks Patrick Kidger for the PyTorch export.
  • Multi-output y input is allowed, and the backend will efficiently batch over each output. A list of dataframes is returned by pysr for these cases. All best_* functions return a list as well.
  • BFGS optimizer introduced + more stable parameter search due to back tracking line search.

Smaller changes since 0.5.16:

  • Expanded tests, coverage calculation for PySR
  • Improved (pre-processing) feature selection with random forest
  • New default parameters for search:
    • annealing=False (no annealing works better with the new code. This is equivalent to alpha=infinity)
    • useFrequency=True (deals with complexity in a smarter way)
    • npopulations = 20 procs*4
    • progress=True (show a progress bar)
    • optimizer_algorithm="BFGS"
    • optimizer_iterations=10
    • optimize_probability=1
    • binary_operators default = ["+", "-", "/", "*"]
    • unary_operators default = []
  • Warnings:
    • Using maxsize > 40 will trigger a warning mentioning how it will be slow and use a lot of memory. Will mention to turn off useFrequency, and perhaps also use warmupMaxsizeBy.
  • Deprecated nrestarts -> optimizer_nrestarts
  • Printing fixed in Jupyter

[0.4.0] (2021-02-01)

With versions v0.4.0/v0.4.0, SymbolicRegression.jl and PySR have now been completely disentangled: PySR is 100% Python code (with some Julia meta-programming), and SymbolicRegression.jl is 100% Julia code.

PySR now works by activating a Julia env that has SymbolicRegression.jl as a dependency, and making calls to it! By default it will set up a Julia project inside the pip install location, and install requirements at the user's confirmation, though you can pass an arbitrary project directory as well (e.g., if you want to use PySR but also tweak the backend). The nice thing about this is that for Python users, all you need to do is install a Julia binary somewhere, and they should be good to go. And for Julia users, you never need to touch the Python side.

The SymbolicRegression.jl backend also sets up workers automatically & internally now, so one never needs to call @everywhere when setting things up. The same is true even with locally-defined functions - these get passed to workers!

With PySR importing the latest Julia code, this also means it gets new simplification routines powered by SymbolicUtils.jl, which seem to help improve the equations discovered.

[0.3.8] (2020-09-27)

Populations don't block eachother, which gives a large speedup especially for large numbers of populations. This was fixed by using RemoteChannel() in Julia.

Some populations happen to take longer than others - perhaps they have very complex equations - and can therefore block others that have finished early. This lets the processor work on the next population to be finished.

[0.3.5] (2020-09-27)

Uses equation from Cranmer et al. (2020) https://arxiv.org/abs/2006.11287 to score equations, and prints this alongside MSE. This makes symbolic regression more robust to noise.

[0.2] (2020-09-21)