我应该如何使用julia训练SVM？

debugcn 发表于 Dev

本尼·链

是否有人有在Julia（1.4.1）中训练支持向量机（SVM）的经验？

我尝试了LIBSVM接口，但是gituhub页面上的示例给出了一个错误：

# Load Fisher's classic iris data
iris = dataset("datasets", "iris")
# LIBSVM handles multi-class data automatically using a one-against-one strategy
labels = convert(Vector, iris[:Species])
# First dimension of input data is features; second is instances
instances = convert(Array, iris[:, 1:4])'
# Train SVM on half of the data using default parameters. See documentation
# of svmtrain for options
model = svmtrain(instances[:, 1:2:end], labels[1:2:end]);```

ERROR: MethodError: no method matching LIBSVM.SupportVectors(::Int32, ::Array{Int32,1}, ::CategoricalArray{String,1,UInt8,String,CategoricalValue{String,UInt8},Union{}}, ::Array{Float64,2}, ::Array{Int32,1}, ::Array{LIBSVM.SVMNode,1})
Closest candidates are:
LIBSVM.SupportVectors(::Int32, ::Array{Int32,1}, ::Array{T,1}, ::AbstractArray{U,2}, ::Array{Int32,1}, ::Array{LIBSVM.SVMNode,1}) where {T, U} at /home/benny/.julia/packages/LIBSVM/5Z99T/src/LIBSVM.jl:18
LIBSVM.SupportVectors(::LIBSVM.SVMModel, ::Any, ::Any) at /home/benny/.julia/packages/LIBSVM/5Z99T/src/LIBSVM.jl:27

安德烈·奥斯金（Andrey Oskin）

看来LIBSVM.jl文档已经过时了，并且软件包没有得到适当的更新，因此值得一个问题（或者至少是请求更新自述文件的请求）。

您看到的错误与程序包本身无关，而是在当前版本的DataFrames.jlandRDatasets.jl labels列中不再存在Vector（与LIBSVM.jl开发之时一样），而是在CategoricalArray中。您可以通过将CategoricalArray转换为normal来避免此问题Vector{String}。完整的示例如下所示

using RDatasets, LIBSVM
using StatsBase, Printf # `mean` and `printf` are no longer in Base, and should be used explicitly

# Load Fisher's classic iris data
iris = dataset("datasets", "iris")

# LIBSVM handles multi-class data automatically using a one-against-one strategy
labels = string.(convert(Vector, iris[:Species]))

# First dimension of input data is features; second is instances
instances = convert(Array, iris[:, 1:4])'

# Train SVM on half of the data using default parameters. See documentation
# of svmtrain for options
model = svmtrain(instances[:, 1:2:end], labels[1:2:end]);

# Test model on the other half of the data.
(predicted_labels, decision_values) = svmpredict(model, instances[:, 2:2:end]);

# Compute accuracy
@printf "Accuracy: %.2f%%\n" mean((predicted_labels .== labels[2:2:end]))*100

或者，您可以使用MLJ.jl或ScikitLearn.jl，它们应该正确地自动包装LIBSVM.jl。

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