Types

Node{T} <: AbstractExpressionNode{T}

Node defines a symbolic expression stored in a binary tree. A single Node instance is one "node" of this tree, and has references to its children. By tracing through the children nodes, you can evaluate or print a given expression.

Fields

• degree::UInt8: Degree of the node. 0 for constants, 1 for unary operators, 2 for binary operators.
• constant::Bool: Whether the node is a constant.
• val::T: Value of the node. If degree==0, and constant==true, this is the value of the constant. It has a type specified by the overall type of the Node (e.g., Float64).
• feature::UInt16: Index of the feature to use in the case of a feature node. Only used if degree==0 and constant==false. Only defined if degree == 0 && constant == false.
• op::UInt8: If degree==1, this is the index of the operator in operators.unaops. If degree==2, this is the index of the operator in operators.binops. In other words, this is an enum of the operators, and is dependent on the specific OperatorEnum object. Only defined if degree >= 1
• l::Node{T}: Left child of the node. Only defined if degree >= 1. Same type as the parent node.
• r::Node{T}: Right child of the node. Only defined if degree == 2. Same type as the parent node. This is to be passed as the right argument to the binary operator.

Constructors

Node([T]; val=nothing, feature=nothing, op=nothing, l=nothing, r=nothing, children=nothing, allocator=default_allocator)
Node{T}(; val=nothing, feature=nothing, op=nothing, l=nothing, r=nothing, children=nothing, allocator=default_allocator)

Create a new node in an expression tree. If T is not specified in either the type or the first argument, it will be inferred from the value of val passed or l and/or r. If it cannot be inferred from these, it will default to Float32.

The children keyword can be used instead of l and r and should be a tuple of children. This is to permit the use of splatting in constructors.

You may also construct nodes via the convenience operators generated by creating an OperatorEnum.

You may also choose to specify a default memory allocator for the node other than simply Node{T}() in the allocator keyword argument.

@extend_operators options

Extends all operators defined in this options object to work on the AbstractExpressionNode type. While by default this is already done for operators defined in Base when you create an options and pass define_helper_functions=true, this does not apply to the user-defined operators. Thus, to do so, you must apply this macro to the operator enum in the same module you have the operators defined.

convert(::Type{<:AbstractExpressionNode{T1}}, n::AbstractExpressionNode{T2}) where {T1,T2}

Convert a AbstractExpressionNode{T2} to a AbstractExpressionNode{T1}. This will recursively convert all children nodes to AbstractExpressionNode{T1}, using convert(T1, tree.val) at constant nodes.

Arguments

• ::Type{AbstractExpressionNode{T1}}: Type to convert to.
• tree::AbstractExpressionNode{T2}: AbstractExpressionNode to convert.

set_node!(tree::AbstractExpressionNode{T}, new_tree::AbstractExpressionNode{T}) where {T}

Set every field of tree equal to the corresponding field of new_tree.

copy_node(tree::AbstractExpressionNode; break_sharing::Val{BS}=Val(false)) where {BS}

Copy a node, recursively copying all children nodes. This is more efficient than the built-in copy.

If break_sharing is set to Val(true), sharing in a tree will be ignored.

Expression{T, N, D} <: AbstractExpression{T, N}

(Experimental) Defines a high-level, user-facing, expression type that encapsulates an expression tree (like Node) along with associated metadata for evaluation and rendering.

Fields

• tree::N: The root node of the raw expression tree.
• metadata::Metadata{D}: A named tuple of settings for the expression, such as the operators and variable names.

Constructors

• Expression(tree::AbstractExpressionNode, metadata::NamedTuple): Construct from the fields
• @parse_expression(expr, operators=operators, variable_names=variable_names, node_type=Node): Parse a Julia expression with a given context and create an Expression object.

Usage

This type is intended for end-users to interact with and manipulate expressions at a high level, abstracting away the complexities of the underlying expression tree operations.

ParametricExpression{T,N<:ParametricNode{T},D<:NamedTuple} <: AbstractExpression{T,N}

(Experimental) An expression to store parameters for a tree

A type of expression node that also stores a parameter index

TemplateExpression{T,F,N,E,TS,D} <: AbstractStructuredExpression{T,F,N,E,D}

A symbolic expression that allows the combination of multiple sub-expressions in a structured way, with constraints on variable usage.

TemplateExpression is designed for symbolic regression tasks where domain-specific knowledge or constraints must be imposed on the model's structure.

Constructor

• TemplateExpression(trees; structure, operators, variable_names)
  • trees: A NamedTuple holding the sub-expressions (e.g., f = Expression(...), g = Expression(...)).
  • structure: A TemplateStructure which holds functions that define how the sub-expressions are combined in different contexts.
  • operators: An OperatorEnum that defines the allowed operators for the sub-expressions.
  • variable_names: An optional Vector of String that defines the names of the variables in the dataset.

Example

Let's create an example TemplateExpression that combines two sub-expressions f(x1, x2) and g(x3):

# Define operators and variable names
options = Options(; binary_operators=(+, *, /, -), unary_operators=(sin, cos))
operators = options.operators
variable_names = ["x1", "x2", "x3"]

# Create sub-expressions
x1 = Expression(Node{Float64}(; feature=1); operators, variable_names)
x2 = Expression(Node{Float64}(; feature=2); operators, variable_names)
x3 = Expression(Node{Float64}(; feature=3); operators, variable_names)

# Create TemplateExpression
example_expr = (; f=x1, g=x3)
st_expr = TemplateExpression(
    example_expr;
    structure=TemplateStructure{(:f, :g)}(nt -> sin(nt.f) + nt.g * nt.g),
    operators,
    variable_names,
)

We can also define constraints on which variables each sub-expression is allowed to access:

variable_constraints = (; f=[1, 2], g=[3])
st_expr = TemplateExpression(
    example_expr;
    structure=TemplateStructure(
        nt -> sin(nt.f) + nt.g * nt.g; variable_constraints
    ),
    operators,
    variable_names,
)

When fitting a model in SymbolicRegression.jl, you would provide the TemplateExpression as the expression_type argument, and then pass expression_options=(; structure=TemplateStructure(...)) as additional options. The variable_constraints will constraint f to only have access to x1 and x2, and g to only have access to x3.

TemplateStructure{K,S,N,E,C} <: Function

A struct that defines a prescribed structure for a TemplateExpression, including functions that define the result of combining sub-expressions in different contexts.

The K parameter is used to specify the symbols representing the inner expressions. If not declared using the constructor TemplateStructure{K}(...), the keys of the variable_constraints NamedTuple will be used to infer this.

Fields

• combine: Optional function taking a NamedTuple of function keys => expressions, returning a single expression. Fallback method used by get_tree on a TemplateExpression to generate a single Expression.
• combine_vectors: Optional function taking a NamedTuple of function keys => vectors, returning a single vector. Used for evaluating the expression tree. You may optionally define a method with a second argument X for if you wish to include the data matrix X (of shape [num_features, num_rows]) in the computation.
• combine_strings: Optional function taking a NamedTuple of function keys => strings, returning a single string. Used for printing the expression tree.
• variable_constraints: Optional NamedTuple that defines which variables each sub-expression is allowed to access. For example, requesting f(x1, x2) and g(x3) would be equivalent to (; f=[1, 2], g=[3]).

Population(pop::Array{PopMember{T,L}, 1})

Create population from list of PopMembers.

Population(dataset::Dataset{T,L};
           population_size, nlength::Int=3, options::AbstractOptions,
           nfeatures::Int)

Create random population and score them on the dataset.

Population(X::AbstractMatrix{T}, y::AbstractVector{T};
           population_size, nlength::Int=3,
           options::AbstractOptions, nfeatures::Int,
           loss_type::Type=Nothing)

Create random population and score them on the dataset.

PopMember(t::AbstractExpression{T}, score::L, loss::L)

Create a population member with a birth date at the current time. The type of the Node may be different from the type of the score and loss.

Arguments

• t::AbstractExpression{T}: The tree for the population member.
• score::L: The score (normalized to a baseline, and offset by a complexity penalty)
• loss::L: The raw loss to assign.

PopMember(
    dataset::Dataset{T,L},
    t::AbstractExpression{T},
    options::AbstractOptions
)

Create a population member with a birth date at the current time. Automatically compute the score for this tree.

Arguments

• dataset::Dataset{T,L}: The dataset to evaluate the tree on.
• t::AbstractExpression{T}: The tree for the population member.
• options::AbstractOptions: What options to use.

HallOfFame{T<:DATA_TYPE,L<:LOSS_TYPE}

List of the best members seen all time in .members, with .members[c] being the best member seen at complexity c. Including only the members which actually have been set, you can run .members[exists].

Fields

• members::Array{PopMember{T,L},1}: List of the best members seen all time. These are ordered by complexity, with .members[1] the member with complexity 1.
• exists::Array{Bool,1}: Whether the member at the given complexity has been set.

Dataset{T<:DATA_TYPE,L<:LOSS_TYPE}

Fields

• X::AbstractMatrix{T}: The input features, with shape (nfeatures, n).
• y::AbstractVector{T}: The desired output values, with shape (n,).
• index::Int: The index of the output feature corresponding to this dataset, if any.
• n::Int: The number of samples.
• nfeatures::Int: The number of features.
• weights::Union{AbstractVector,Nothing}: If the dataset is weighted, these specify the per-sample weight (with shape (n,)).
• extra::NamedTuple: Extra information to pass to a custom evaluation function. Since this is an arbitrary named tuple, you could pass any sort of dataset you wish to here.
• avg_y: The average value of y (weighted, if weights are passed).
• use_baseline: Whether to use a baseline loss. This will be set to false if the baseline loss is calculated to be Inf.
• baseline_loss: The loss of a constant function which predicts the average value of y. This is loss-dependent and should be updated with update_baseline_loss!.
• variable_names::Array{String,1}: The names of the features, with shape (nfeatures,).
• display_variable_names::Array{String,1}: A version of variable_names but for printing to the terminal (e.g., with unicode versions).
• y_variable_name::String: The name of the output variable.
• X_units: Unit information of X. When used, this is a vector of DynamicQuantities.Quantity{<:Any,<:Dimensions} with shape (nfeatures,).
• y_units: Unit information of y. When used, this is a single DynamicQuantities.Quantity{<:Any,<:Dimensions}.
• X_sym_units: Unit information of X. When used, this is a vector of DynamicQuantities.Quantity{<:Any,<:SymbolicDimensions} with shape (nfeatures,).
• y_sym_units: Unit information of y. When used, this is a single DynamicQuantities.Quantity{<:Any,<:SymbolicDimensions}.

update_baseline_loss!(dataset::Dataset{T,L}, options::AbstractOptions) where {T<:DATA_TYPE,L<:LOSS_TYPE}

Update the baseline loss of the dataset using the loss function specified in options.