text:
import ClassImbalance
import DataFrames
import Random

"""
"""
function calculate_smote_pct_under(
        ;
        pct_over::Real = 0,
        minority_to_majority_ratio::Real = 0,
        )
    if pct_over < 0
        error("pct_over must be >=0")
    end
    if minority_to_majority_ratio <= 0
        error("minority_to_majority_ratio must be >0")
    end
    result = 100*minority_to_majority_ratio*(100+pct_over)/pct_over
    return result
end

"""
"""
function smote(
        features_df::DataFrames.AbstractDataFrame,
        labels_df::DataFrames.AbstractDataFrame,
        feature_names::AbstractVector{Symbol},
        label_name::Symbol;
        majorityclass::AbstractString = "",
        minorityclass::AbstractString = "",
        pct_over::Real = 0,
        minority_to_majority_ratio::Real = 0,
        k::Integer = 5,
        )
    result = smote(
        Random.GLOBAL_RNG,
        features_df,
        labels_df,
        feature_names,
        label_name,
        majorityclass = majorityclass,
        minorityclass = minorityclass,
        pct_over = pct_over,
        minority_to_majority_ratio = minority_to_majority_ratio,
        k = k,
        )
    return result
end

"""
"""
function smote(
        rng::Random.AbstractRNG,
        features_df::DataFrames.AbstractDataFrame,
        labels_df::DataFrames.AbstractDataFrame,
        feature_names::AbstractVector{Symbol},
        label_name::Symbol;
        majorityclass::AbstractString = "",
        minorityclass::AbstractString = "",
        pct_over::Real = 0,
        minority_to_majority_ratio::Real = 0,
        k::Integer = 5,
        )
    if majorityclass == ""
        error("you need to specify majorityclass")
    end
    if minorityclass == ""
        error("you need to specify minorityclass")
    end
    pct_under = calculate_smote_pct_under(
        ;
        pct_over = pct_over,
        minority_to_majority_ratio = minority_to_majority_ratio,
        )
    if size(features_df, 1) != size(labels_df, 1)
        error("size(features_df, 1) != size(labels_df, 1)")
    end
    if !(size(features_df, 1) > 0)
        error("!(size(features_df, 1) > 0)")
    end
    labelsstringarray = labels_df[label_name]
    labelsbinaryarray = fill(Int(0), length(labelsstringarray))
    for i = 1:length(labelsstringarray)
        # Paul's smote code assumes 1 = minority, 0 = majority
        if labelsstringarray[i] == minorityclass
            labelsbinaryarray[i] = 1
        elseif labelsstringarray[i] == majorityclass
            labelsbinaryarray[i] = 0
        else
            error("value in labels column is neither majority nor minority")
        end
    end
    smotedfeatures_df, smotedlabelsbinaryarray = ClassImbalance.smote(
        features_df[feature_names],
        labelsbinaryarray;
        k = k,
        pct_over = pct_over,
        pct_under = pct_under,
        )
    smotedlabelsstringarray = Array{String}(
        undef,
        length(smotedlabelsbinaryarray),
        )
    for i = 1:length(smotedlabelsbinaryarray)
        if smotedlabelsbinaryarray[i] == 1
            smotedlabelsstringarray[i] = minorityclass
        elseif smotedlabelsbinaryarray[i] == 0
            smotedlabelsstringarray[i] = majorityclass
        else
            error("if you see this error, you will be very sad.")
        end
    end
    smotedlabels_df = DataFrames.DataFrame()
    smotedlabels_df[label_name] = smotedlabelsstringarray
    return smotedfeatures_df, smotedlabels_df
end


text:
using JuMP, EAGO

                     m = Model()

                     EAGO.register_eago_operators!(m)

                     @variable(m, -1 <= x[i=1:3] <= 1)

                     @variable(m, -87.82140256746152 <= q <= 82.66761573703552)

                     add_NL_constraint(m, :(softplus(0.568996230261511 + -0.6444045388073083*softplus(0.8247395355426832 + -0.2815589970674197*softplus(-0.8472674884857732 + 0.12673262140718844*softplus(0.8408524421985439 + -0.03850956721592169*$(x[1]) + 0.8346333861274338*$(x[2]) + 0.3435557949935637*$(x[3])) + 0.22924863555513264*softplus(-0.5206002646111196 + -0.6929418480784393*$(x[1]) + -0.02820767391072332*$(x[2]) + -0.04938482127984356*$(x[3])) + -0.14343275408487788*softplus(0.8250363611878835 + 0.8304207310240672*$(x[1]) + -0.20133411435535464*$(x[2]) + -0.29584435518282115*$(x[3])) + 0.38193743952568937*softplus(0.6278046914235738 + -0.6443229282211105*$(x[1]) + -0.7841344946232534*$(x[2]) + -0.16775025966863577*$(x[3])) + 0.7470214568022899*softplus(0.7249142640471629 + 0.8062020364864311*$(x[1]) + 0.680231814161814*$(x[2]) + 0.03773489408284192*$(x[3]))) + 0.3907345794847368*softplus(-0.8374168336432568 + -0.5200290789256532*softplus(0.8408524421985439 + -0.03850956721592169*$(x[1]) + 0.8346333861274338*$(x[2]) + 0.3435557949935637*$(x[3])) + 0.22613086714596653*softplus(-0.5206002646111196 + -0.6929418480784393*$(x[1]) + -0.02820767391072332*$(x[2]) + -0.04938482127984356*$(x[3])) + 0.5211815007242757*softplus(0.8250363611878835 + 0.8304207310240672*$(x[1]) + -0.20133411435535464*$(x[2]) + -0.29584435518282115*$(x[3])) + -0.3005041955394576*softplus(0.6278046914235738 + -0.6443229282211105*$(x[1]) + -0.7841344946232534*$(x[2]) + -0.16775025966863577*$(x[3])) + 0.6833233841804214*softplus(0.7249142640471629 + 0.8062020364864311*$(x[1]) + 0.680231814161814*$(x[2]) + 0.03773489408284192*$(x[3]))) + 0.23961509129748304*softplus(-0.8184816952777028 + 0.16489767969630487*softplus(0.8408524421985439 + -0.03850956721592169*$(x[1]) + 0.8346333861274338*$(x[2]) + 0.3435557949935637*$(x[3])) + 0.5218375475900632*softplus(-0.5206002646111196 + -0.6929418480784393*$(x[1]) + -0.02820767391072332*$(x[2]) + -0.04938482127984356*$(x[3])) + 0.06864645713679662*softplus(0.8250363611878835 + 0.8304207310240672*$(x[1]) + -0.20133411435535464*$(x[2]) + -0.29584435518282115*$(x[3])) + -0.3485834116426605*softplus(0.6278046914235738 + -0.6443229282211105*$(x[1]) + -0.7841344946232534*$(x[2]) + -0.16775025966863577*$(x[3])) + 0.9488444237137803*softplus(0.7249142640471629 + 0.8062020364864311*$(x[1]) + 0.680231814161814*$(x[2]) + 0.03773489408284192*$(x[3]))) + 0.657728996110658*softplus(0.09567841002846222 + 0.9766053202311835*softplus(0.8408524421985439 + -0.03850956721592169*$(x[1]) + 0.8346333861274338*$(x[2]) + 0.3435557949935637*$(x[3])) + -0.8095561798136077*softplus(-0.5206002646111196 + -0.6929418480784393*$(x[1]) + -0.02820767391072332*$(x[2]) + -0.04938482127984356*$(x[3])) + -0.7033837991140013*softplus(0.8250363611878835 + 0.8304207310240672*$(x[1]) + -0.20133411435535464*$(x[2]) + -0.29584435518282115*$(x[3])) + 0.521625940127187*softplus(0.6278046914235738 + -0.6443229282211105*$(x[1]) + 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-0.20133411435535464*$(x[2]) + -0.29584435518282115*$(x[3])) + -0.3485834116426605*softplus(0.6278046914235738 + -0.6443229282211105*$(x[1]) + -0.7841344946232534*$(x[2]) + -0.16775025966863577*$(x[3])) + 0.9488444237137803*softplus(0.7249142640471629 + 0.8062020364864311*$(x[1]) + 0.680231814161814*$(x[2]) + 0.03773489408284192*$(x[3]))) + -0.6787727389211491*softplus(0.09567841002846222 + 0.9766053202311835*softplus(0.8408524421985439 + -0.03850956721592169*$(x[1]) + 0.8346333861274338*$(x[2]) + 0.3435557949935637*$(x[3])) + -0.8095561798136077*softplus(-0.5206002646111196 + -0.6929418480784393*$(x[1]) + -0.02820767391072332*$(x[2]) + -0.04938482127984356*$(x[3])) + -0.7033837991140013*softplus(0.8250363611878835 + 0.8304207310240672*$(x[1]) + -0.20133411435535464*$(x[2]) + -0.29584435518282115*$(x[3])) + 0.521625940127187*softplus(0.6278046914235738 + -0.6443229282211105*$(x[1]) + -0.7841344946232534*$(x[2]) + -0.16775025966863577*$(x[3])) + 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0.03773489408284192*$(x[3]))) + -0.9816855534058186*softplus(-0.9217298157598419 + -0.1303173404119562*softplus(0.8408524421985439 + -0.03850956721592169*$(x[1]) + 0.8346333861274338*$(x[2]) + 0.3435557949935637*$(x[3])) + 0.9725817523240283*softplus(-0.5206002646111196 + -0.6929418480784393*$(x[1]) + -0.02820767391072332*$(x[2]) + -0.04938482127984356*$(x[3])) + 0.7227834995135853*softplus(0.8250363611878835 + 0.8304207310240672*$(x[1]) + -0.20133411435535464*$(x[2]) + -0.29584435518282115*$(x[3])) + -0.020291466895306698*softplus(0.6278046914235738 + -0.6443229282211105*$(x[1]) + -0.7841344946232534*$(x[2]) + -0.16775025966863577*$(x[3])) + 0.7019902602324835*softplus(0.7249142640471629 + 0.8062020364864311*$(x[1]) + 0.680231814161814*$(x[2]) + 0.03773489408284192*$(x[3]))))) - $q <= 0.0))

                     @objective(m, Min, q)

                     return m

ext: jl

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