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Julia Package #8
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Hi Mike, |
I'm curious. I'll take a look at what you've got. |
I did a first try to implement it at OutlierDetectionTrees.jl. I changed the implementation a little bit to work without explicit feature names, check out IsolationForest.jl. Only a very small wrapper is necessary to make it work with MLJ and OutlierDetection.jl, see models/IForest.jl. A small usage example: import Pkg
Pkg.activate(;temp=true)
Pkg.add("MLJ")
Pkg.add("OutlierDetection")
Pkg.add("OutlierDetectionData")
Pkg.develop(;url="https://github.com/OutlierDetectionJL/OutlierDetectionTrees.jl")
using MLJ
using OutlierDetection
using OutlierDetectionData
using OutlierDetectionTrees
X, y = ODDS.load("thyroid")
train, test = partition(eachindex(y), 0.5, stratify=y, shuffle=true)
detector = ProbabilisticDetector(IForestDetector())
mach = machine(detector, X)
fit!(mach, rows=train)
ŷ = predict(mach, rows=test)
auc(ŷ, y[test]) The results do not appear to be correct, yet. Once the bugs are fixed it should be ready to be registered with MLJ's model registry. What do you think? |
Would like to make this so it can be installed via the Julia package manager.
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