SymbolicRegression.jl searches for symbolic expressions which optimize a particular objective.
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Contents:
- Quickstart
- Constructing expressions
- Exporting to SymbolicUtils.jl
- Contributors ✨
- Code structure
- Search options
Install in Julia with:
using Pkg
Pkg.add("SymbolicRegression")Let's see an example:
import SymbolicRegression: SRRegressor, machine, fit!, predict, report
# Dataset with two named features:
X = (a = rand(500), b = rand(500))
# and one target:
y = @. 2 * cos(X.a * 23.5) - X.b ^ 2
# with some noise:
y = y .+ randn(500) .* 1e-3
model = SRRegressor(
niterations=50,
binary_operators=[+, -, *],
unary_operators=[cos],
)Now, let's create and train this model on our data:
mach = machine(model, X, y)
fit!(mach)You will notice that expressions are printed
using the column names of our table. If,
instead of a table-like object,
a simple array is passed
(e.g., X=randn(100, 2)),
x1, ..., xn will be used for variable names.
Let's look at the expressions discovered:
report(mach)Finally, we can make predictions with the expressions on new data:
predict(mach, X)This will make predictions using the expression
selected by model.selection_method,
which by default is a mix of accuracy and complexity.
You can override this selection and select an equation from the Pareto front manually with:
predict(mach, (data=X, idx=2))where here we choose to evaluate the second equation.
For fitting multiple outputs, one can use MultitargetSRRegressor
(and pass an array of indices to idx in predict for selecting specific equations).
For a full list of options available to each regressor, see the API page.
Inputs can be matrices, NamedTuples of vectors, or any Tables.jl-compatible
table (such as a DataFrame).
These same functions are also available from
MLJ, via
import MLJ: machine, fit!, predict, report, which is useful if you want
compatibility with the broader MLJ ecosystem (pipelines, tuning, etc.).
The heart of SymbolicRegression.jl is the
equation_search function.
This takes a 2D array and attempts
to model a 1D array using analytic functional forms.
Note: unlike the interface above,
this assumes column-major input of shape [features, rows].
import SymbolicRegression: Options, equation_search
X = randn(2, 100)
y = 2 * cos.(X[2, :]) + X[1, :] .^ 2 .- 2
options = Options(
binary_operators=[+, *, /, -],
unary_operators=[cos, exp],
populations=20
)
hall_of_fame = equation_search(
X, y, niterations=40, options=options,
parallelism=:multithreading
)You can view the resultant equations in the dominating Pareto front (best expression seen at each complexity) with:
import SymbolicRegression: calculate_pareto_frontier
dominating = calculate_pareto_frontier(hall_of_fame)This is a vector of PopMember type - which contains the expression along with the cost.
We can get the expressions with:
trees = [member.tree for member in dominating]Each of these equations is an Expression{T} type for some constant type T (like Float32).
These expression objects are callable – you can simply pass in data:
tree = trees[end]
output = tree(X)Expressions are represented under-the-hood as the Node type which is developed
in the DynamicExpressions.jl package.
The Expression type wraps this and includes metadata about operators and variable names.
You can manipulate and construct expressions directly. For example:
using SymbolicRegression: Options, Expression, Node
options = Options(;
binary_operators=[+, -, *, /], unary_operators=[cos, exp, sin]
)
operators = options.operators
variable_names = ["x1", "x2", "x3"]
x1, x2, x3 = [Expression(Node(Float64; feature=i); operators, variable_names) for i=1:3]
tree = cos(x1 - 3.2 * x2) - x1 * x1This tree has Float64 constants, so the type of the entire tree
will be promoted to Node{Float64}.
We can convert all constants (recursively) to Float32:
float32_tree = convert(Expression{Float32}, tree)We can then evaluate this tree on a dataset:
X = rand(Float32, 3, 100)
tree(X)This callable format is the easy-to-use version which will
automatically set all values to NaN if there were any
Inf or NaN during evaluation. You can call the raw evaluation
method with eval_tree_array:
output, did_succeed = eval_tree_array(tree, X)where did_succeed explicitly declares whether the evaluation was successful.
We can view the equations in the dominating Pareto frontier with:
dominating = calculate_pareto_frontier(hall_of_fame)We can convert the best equation to SymbolicUtils.jl with the following function:
import SymbolicRegression: node_to_symbolic
eqn = node_to_symbolic(dominating[end].tree)
println(simplify(eqn*5 + 3))We can also print out the full pareto frontier like so:
import SymbolicRegression: compute_complexity, string_tree
println("Complexity\tMSE\tEquation")
for member in dominating
complexity = compute_complexity(member, options)
loss = member.loss
string = string_tree(member.tree, options)
println("$(complexity)\t$(loss)\t$(string)")
endWe are eager to welcome new contributors! If you have an idea for a new feature, don't hesitate to share it on the issues page or forums.
SymbolicRegression.jl is organized roughly as follows. Rounded rectangles indicate objects, and rectangles indicate functions.
(if you can't see this diagram being rendered, try pasting it into mermaid-js.github.io/mermaid-live-editor)
flowchart TB
op([Options])
d([Dataset])
op --> ES
d --> ES
subgraph ES[equation_search]
direction TB
IP[sr_spawner]
IP --> p1
IP --> p2
subgraph p1[Thread 1]
direction LR
pop1([Population])
pop1 --> src[s_r_cycle]
src --> opt[optimize_and_simplify_population]
opt --> pop1
end
subgraph p2[Thread 2]
direction LR
pop2([Population])
pop2 --> src2[s_r_cycle]
src2 --> opt2[optimize_and_simplify_population]
opt2 --> pop2
end
pop1 --> hof
pop2 --> hof
hof([HallOfFame])
hof --> migration
pop1 <-.-> migration
pop2 <-.-> migration
migration[migrate!]
end
ES --> output([HallOfFame])
The HallOfFame objects store the expressions with the lowest loss seen at each complexity.
The dependency structure of the code itself is as follows:
stateDiagram-v2
AdaptiveParsimony --> Mutate
AdaptiveParsimony --> Population
AdaptiveParsimony --> RegularizedEvolution
AdaptiveParsimony --> SearchUtils
AdaptiveParsimony --> SingleIteration
AdaptiveParsimony --> SymbolicRegression
CheckConstraints --> Mutate
CheckConstraints --> SymbolicRegression
Complexity --> CheckConstraints
Complexity --> HallOfFame
Complexity --> LossFunctions
Complexity --> MLJInterface
Complexity --> Mutate
Complexity --> PopMember
Complexity --> Population
Complexity --> SearchUtils
Complexity --> SingleIteration
Complexity --> SymbolicRegression
Complexity --> Tracing
ConstantOptimization --> ExpressionBuilder
ConstantOptimization --> Mutate
ConstantOptimization --> SingleIteration
Core --> AdaptiveParsimony
Core --> CheckConstraints
Core --> Complexity
Core --> ConstantOptimization
Core --> DimensionalAnalysis
Core --> ExpressionBuilder
Core --> ExpressionBuilder
Core --> HallOfFame
Core --> InterfaceDynamicExpressions
Core --> LossFunctions
Core --> MLJInterface
Core --> Migration
Core --> Mutate
Core --> MutationFunctions
Core --> PopMember
Core --> Population
Core --> RegularizedEvolution
Core --> SearchUtils
Core --> SingleIteration
Core --> SymbolicRegression
Core --> Tracing
Dataset --> Core
DimensionalAnalysis --> LossFunctions
ExpressionBuilder --> SymbolicRegression
HallOfFame --> ExpressionBuilder
HallOfFame --> MLJInterface
HallOfFame --> SearchUtils
HallOfFame --> SingleIteration
HallOfFame --> SymbolicRegression
HallOfFame --> deprecates
InterfaceDynamicExpressions --> ExpressionBuilder
InterfaceDynamicExpressions --> HallOfFame
InterfaceDynamicExpressions --> LossFunctions
InterfaceDynamicExpressions --> SymbolicRegression
InterfaceDynamicQuantities --> Dataset
InterfaceDynamicQuantities --> MLJInterface
LossFunctions --> ConstantOptimization
LossFunctions --> ExpressionBuilder
LossFunctions --> ExpressionBuilder
LossFunctions --> Mutate
LossFunctions --> PopMember
LossFunctions --> Population
LossFunctions --> SingleIteration
LossFunctions --> SymbolicRegression
MLJInterface --> SymbolicRegression
Migration --> SymbolicRegression
Mutate --> RegularizedEvolution
MutationFunctions --> ExpressionBuilder
MutationFunctions --> Mutate
MutationFunctions --> Population
MutationFunctions --> SymbolicRegression
MutationFunctions --> deprecates
MutationWeights --> Core
MutationWeights --> Options
MutationWeights --> OptionsStruct
Operators --> Core
Operators --> Options
Options --> Core
OptionsStruct --> Core
OptionsStruct --> Options
OptionsStruct --> Options
PopMember --> ConstantOptimization
PopMember --> ExpressionBuilder
PopMember --> HallOfFame
PopMember --> Migration
PopMember --> Mutate
PopMember --> Population
PopMember --> SearchUtils
PopMember --> SingleIteration
PopMember --> SymbolicRegression
Population --> ExpressionBuilder
Population --> Migration
Population --> RegularizedEvolution
Population --> SearchUtils
Population --> SingleIteration
Population --> SymbolicRegression
ProgramConstants --> Core
ProgramConstants --> Dataset
ProgramConstants --> Operators
ProgressBars --> SearchUtils
ProgressBars --> SymbolicRegression
RegularizedEvolution --> SingleIteration
SearchUtils --> SymbolicRegression
SingleIteration --> SymbolicRegression
Tracing --> Mutate
Tracing --> RegularizedEvolution
Tracing --> SingleIteration
Tracing --> SymbolicRegression
Utils --> ConstantOptimization
Utils --> Dataset
Utils --> DimensionalAnalysis
Utils --> HallOfFame
Utils --> InterfaceDynamicExpressions
Utils --> MLJInterface
Utils --> Migration
Utils --> Operators
Utils --> Options
Utils --> PopMember
Utils --> Population
Utils --> RegularizedEvolution
Utils --> SearchUtils
Utils --> SingleIteration
Utils --> SymbolicRegression
Utils --> Tracing
Bash command to generate dependency structure from src directory (requires vim-stream):
echo 'stateDiagram-v2'
IFS=$'\n'
for f in *.jl; do
for line in $(cat $f | grep -e 'import \.\.' -e 'import \.' -e 'using \.' -e 'using \.\.'); do
echo $(echo $line | vims -s 'dwf:d$' -t '%s/^\.*//g' '%s/Module//g') $(basename "$f" .jl);
done;
done | vims -l 'f a--> ' | sort