SymbolicRegression.jl | The Julia Language Ecosystem Directory

Bot releases are hidden (Show)

SymbolicRegression.jl - v0.24.1

Published by MilesCranmer 7 months ago

What's Changed

CompatHelper: bump compat for MLJModelInterface to 1.9, (keep existing compat) by @github-actions in https://github.com/MilesCranmer/SymbolicRegression.jl/pull/295
CompatHelper: bump compat for ProgressBars to 1, (keep existing compat) by @github-actions in https://github.com/MilesCranmer/SymbolicRegression.jl/pull/294
Ensure we load ClusterManagers.jl on workers by @MilesCranmer in https://github.com/MilesCranmer/SymbolicRegression.jl/pull/297
Move test dependencies to test folder by @MilesCranmer in https://github.com/MilesCranmer/SymbolicRegression.jl/pull/298

Full Changelog: https://github.com/MilesCranmer/SymbolicRegression.jl/compare/v0.24.0...v0.24.1

SymbolicRegression.jl - v0.24.0

Published by MilesCranmer 7 months ago

What's Changed

Experimental support for program synthesis / graph-like expressions instead of trees (https://github.com/MilesCranmer/SymbolicRegression.jl/pull/271)
- BREAKING: many types now have a third type parameter, declaring the type of node. For example, PopMember{T,L} is now PopMember{T,L,N} for N the type of expression.
- Can now specify a node_type in creation of Options. This node_type <: AbstractExpressionNode can be a GraphNode which will result in expressions that care share nodes – and therefore have a lower complexity.
- Two new mutations: form_connection and break_connection – which control the merging and breaking of shared nodes in expressions. These are experimental.
BREAKING: The Dataset struct has had many of its field declared immutable (for memory safety). If you had relied on the mutability of the struct to set parameters after initializing it, you will need to modify your code.
BREAKING: LoopVectorization.jl moved to a package extension. Need to install it separately (https://github.com/MilesCranmer/SymbolicRegression.jl/pull/287).
DEPRECATED: 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).

Bumper.jl support added. Passing bumper=true to Options() will result in using bump-allocation for evaluation which can get speeds equivalent to LoopVectorization and sometimes even better due to better management of allocations. (https://github.com/MilesCranmer/SymbolicRegression.jl/pull/287)
Upgraded Optim.jl to 1.9.
Upgraded DynamicQuantities to 0.13
Upgraded DynamicExpressions to 0.16
The main search loop 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

Full Changelog: https://github.com/MilesCranmer/SymbolicRegression.jl/compare/v0.23.3...v0.24.0

SymbolicRegression.jl - v0.23.1

Published by MilesCranmer 10 months ago

What's Changed

Implement swap operands mutation for binary operators by @foxtran in https://github.com/MilesCranmer/SymbolicRegression.jl/pull/276