Re-using best equation strings #707
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KevinG1002
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Glad to hear you are liking it! :) If you run using MLJ, SymbolicRegression
# ...
fit!(mach)
equations = report(mach).equationsThis is a vector of callable expression objects. You can call these with: eq = equations[end] # Most accurate expression is last one
# Note that the default selection is `report(mach).best_idx`
formatted_X = MLJ.matrix(X)' # Convert to matrix, and transpose to column-major
options = SymbolicRegression.MLJInterfaceModule.get_options(model) # Equivalent of `Options(...)`
eq(formatted_X, options)This will evaluate the resultant equation on Sorry that you need to call |
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Hello,
New user here (SymbolicRegression.jl) and I absolutely love it!
I was wondering if anyone knew a way to turn the equation strings that are obtained after n-iterations into a callable function so that, for example:
returned_equ = (((x₅ * -50) * x₁) + -2)could be easily turned into a callable likef = turn_to_callable(equation_str)withf(x_1, x_2, ..., x_n) = ....yielding the result of the computation in the equation string (a bit similar to whatPolynomials.jldoes). I am essentially looking for something that could be easily applied to any new vector[x_1, x_2, ..., x_n]without going throughpredict(mach, x_test).Perhaps, to simplify things, how does
predict(mach, x_test)work exactly?Beta Was this translation helpful? Give feedback.
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