How do I specify a precomputed kernel in SVM using LIBSVM or MLJ?

Specific Domains Machine Learning
3

It’s long time, find this way

snippets based on MLJ SVM tutorial

import MLJ: fit!,predict

using MLJ
using Plots
using PrettyPrinting
using Random
using KernelFunctions

#define kernelmethods
k1=PolynomialKernel(; degree=2, c=1)
k2 = SqExponentialKernel() ∘ ScaleTransform(1.5)

# make data
n1=n2=10
Random.seed!(3203)
X = randn(20, 2)
y=vcat(fill(-1, n1), fill(1, n2))
xs,ys=X[:,1],X[:,2]
#scatter(X[:,1],X[:,2],group=y,label=false)

X = MLJ.table(X)
y = categorical(y);

# work flow 
@time SVC = @load SVC pkg=LIBSVM

svc_mdl = SVC(kernel=k2) #<=== kernel is here

svc = machine(svc_mdl, X, y)

fit!(svc);

ypred =predict(svc, X)

misclassification_rate(ypred, y)
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