Use Hyperopt.jl for Pareto Optimization about datadrivendiffeq.jl HOT 1 CLOSED

using Hyperopt

function SInDy_2(X::AbstractArray{S, 2}, DX::AbstractArray{S, 2}, Ψ::Basis, thresholds::AbstractArray ; f_target = (du, θ, ξ)->norm([norm(ξ, 0); norm(du .- θ'ξ, 2)]), p::AbstractArray = [], t::AbstractVector = [], maxiter::Int64 = 10, opt::T = Optimize.STRRidge(),denoise::Bool = false, normalize::Bool = true, convergence_error = eps()) where {T <: Optimize.AbstractOptimizer, S <: Number}
    @assert size(X)[end] == size(DX)[end]
    nx, nm = size(X)
    ny, nm = size(DX)

    θ = Ψ(X, p, t)

    scales = ones(eltype(X), length(Ψ))

    #ξ = zeros(eltype(X), length(Ψ))
    Ξ = zeros(eltype(X), length(Ψ), ny)


    iters = 0

    denoise ? optimal_shrinkage!(θ') : nothing
    normalize ? DataDrivenDiffEq.normalize_theta!(scales, θ) : nothing

    for j in 1:ny
        ho = @hyperopt for i = length(thresholds), sampler = RandomSampler(), t = thresholds
            set_threshold!(opt, t)
            iters_ = sparse_regression!(view(Ξ, :, j), view(θ, :, :), DX[j, :]', maxiter, opt, false, false, convergence_error)
            f_target(view(DX, j, :), view(θ, :, :), view(Ξ, :, j))
        end
        t_, f_ = minimum(ho)
        set_threshold!(opt, t_[1])
        iters_ = sparse_regression!(view(Ξ, :, j), view(θ, :, :), DX[j, :]', maxiter, opt, false, false, convergence_error)
        iters_ > iters ? iters = iters_ : nothing
    end
    return SparseIdentificationResult(Ξ, Ψ, iters, opt, iters < maxiter, DX, X, p = p)
end