SymbolicRegression.jl

What's Changed

• https://github.com/MilesCranmer/SymbolicRegression.jl/pull/228 and https://github.com/MilesCranmer/SymbolicRegression.jl/pull/230 and https://github.com/MilesCranmer/SymbolicRegression.jl/pull/231
  • Dimensional analysis (#228)
    • Allows you to (softly) constrain discovered expressions to those that respect physical dimensions
    • Pass vectors of DynamicQuantities.jl Quantity type to the MLJ interface.
    • OR, specify X_units, y_units to low-level equation_search.
  • 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 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)

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

SymbolicRegression v0.20.0

Diff since v0.19.1

Closed issues:

• [Feature]: MLJ integration (#225)

Merged pull requests:

• MLJ Integration (#226) (@MilesCranmer, @OkonSamuel)

SymbolicRegression v0.19.1

Diff since v0.19.0

Merged pull requests:

• CompatHelper: bump compat for StatsBase to 0.34, (keep existing compat) (#202) (@github-actions[bot])
• (Soft deprecation) change varMap to variable_names (#219) (@MilesCranmer)
• (Soft deprecation) rename EquationSearch to equation_search (#222) (@MilesCranmer)
• Fix equation splitting for unicode variables (#223) (@MilesCranmer)

What's Changed

• Time to load improved by 40% with the following changes in https://github.com/MilesCranmer/SymbolicRegression.jl/pull/215
  • Moved SymbolicUtils.jl to extension/Requires.jl
  • Removed StaticArrays.jl as a dependency and implement tiny version of MVector
  • Removed @generated functions

Full Changelog: https://github.com/MilesCranmer/SymbolicRegression.jl/compare/v0.18.0...v0.19.0

SymbolicRegression v0.18.0

Diff since v0.17.1

Merged pull requests:

• Overload ^ if user passes explicitly (#201) (@MilesCranmer)
• Upgrade DynamicExpressions to 0.8; LossFunctions to 0.10 (#206) (@github-actions[bot])
• Show expressions evaluated per second (#209) (@MilesCranmer)
• Cache complexity of expressions whenever possible (#210) (@MilesCranmer)

SymbolicRegression v0.17.1

Diff since v0.17.0

Merged pull requests:

• Faster custom losses (#197) (@MilesCranmer)
• Migrate from SnoopPrecompile to PrecompileTools (#198) (@timholy)

SymbolicRegression v0.17.0

Diff since v0.16.3

Closed issues:

• troubles in pysr.install() (#196)

Merged pull requests:

• Multiple refactors: arbitrary data in Dataset, separate mutation weight conditioning, fix data races, cleaner API (#190) (@MilesCranmer)
• CompatHelper: bump compat for DynamicExpressions to 0.6, (keep existing compat) (#194) (@github-actions[bot])

SymbolicRegression v0.16.3

Diff since v0.16.2

Merged pull requests:

• CompatHelper: bump compat for SymbolicUtils to 1, (keep existing compat) (#168) (@github-actions[bot])

What's Changed

• Turn off simplification when constraints given by @MilesCranmer in https://github.com/MilesCranmer/SymbolicRegression.jl/pull/189

Full Changelog: https://github.com/MilesCranmer/SymbolicRegression.jl/compare/v0.16.1...v0.16.2

Full Changelog: https://github.com/MilesCranmer/SymbolicRegression.jl/compare/v0.16.0...v0.16.1

SymbolicRegression v0.16.0

Diff since v0.15.3

Closed issues:

• Partially fixed trees (#166)
• Settings of addprocs (#180)
• Equation printout should split into multiple lines (#182)

Merged pull requests:

• Force safe closing of threads (#175) (@MilesCranmer)
• Abstract number support (#183) (@MilesCranmer)
• Include datetime in default filename (#185) (@MilesCranmer)

What's Changed

• Re-compute losses for warm start by @MilesCranmer in https://github.com/MilesCranmer/SymbolicRegression.jl/pull/177

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

What's Changed

• Include depth check in check_constraints by @MilesCranmer in https://github.com/MilesCranmer/SymbolicRegression.jl/pull/172
• Fix data race in state saving by @MilesCranmer in https://github.com/MilesCranmer/SymbolicRegression.jl/pull/173

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

What's Changed

• Fix bug in constraint checking by @MilesCranmer in https://github.com/MilesCranmer/SymbolicRegression.jl/pull/171

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

What's Changed

• Fully-customizable training objectives by @MilesCranmer in https://github.com/MilesCranmer/SymbolicRegression.jl/pull/143
• Safely catch non-readable stdin stream by @MilesCranmer in https://github.com/MilesCranmer/SymbolicRegression.jl/pull/169

Full Changelog: https://github.com/MilesCranmer/SymbolicRegression.jl/compare/v0.14.5...v0.15.0

SymbolicRegression v0.14.5

Diff since v0.14.4

Closed issues:

• Large test output (#159)

Merged pull requests:

• Quiet progress bar during CI (#160) (@MilesCranmer)
• Proper SnoopCompilation (#161) (@MilesCranmer)

SymbolicRegression v0.14.4

Diff since v0.14.3

Merged pull requests:

• Refactor monitoring of resources (#158) (@MilesCranmer)

SymbolicRegression v0.14.3

Diff since v0.14.2

Merged pull requests:

• Turn off safe operators for turbo=true (#156) (@MilesCranmer)
• Use ProgressBars.jl instead of copying (#157) (@MilesCranmer)

SymbolicRegression v0.14.2

Diff since v0.14.1

SymbolicRegression v0.14.1

Diff since v0.14.0

Merged pull requests:

• Do optimizations as a low-probability mutation (#154) (@MilesCranmer)