v1.5.2

What's Changed

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

Full Changelog: v1.5.1...v1.5.2

v1.5.1

What's Changed

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

Full Changelog: v1.5.0...v1.5.1

v1.5.0

Backend Changes

Major Changes

• Change behavior of batching to resample only every iteration; not every eval in MilesCranmer/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

• feat: better error for mismatched eltypes by @MilesCranmer in MilesCranmer/SymbolicRegression.jl#414
• CompatHelper: bump compat for Optim to 1, (keep existing compat) by @github-actions in MilesCranmer/SymbolicRegression.jl#403
• feat: explicitly monitor errors in workers by @MilesCranmer in MilesCranmer/SymbolicRegression.jl#417
• feat: allow recording crossovers by @MilesCranmer in MilesCranmer/SymbolicRegression.jl#415
• add script for converting record to graphml by @MilesCranmer in MilesCranmer/SymbolicRegression.jl#416
• ci: redistribute part 1 of test suite by @MilesCranmer in MilesCranmer/SymbolicRegression.jl#424
• refactor: rename to .cost by @MilesCranmer in MilesCranmer/SymbolicRegression.jl#423
• fix: batched dataset for optimisation by @MilesCranmer in MilesCranmer/SymbolicRegression.jl#426
• refactor: task local storage instead of thread local by @MilesCranmer in MilesCranmer/SymbolicRegression.jl#427

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: v1.4.0...v1.5.0

v1.4.0

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

v1.3.1

What's Changed

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

Full Changelog: v1.3.0...v1.3.1

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 $x &gt; 1$:

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: 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