Multi-variable RandomForestRegressor in Julia

[shayandavoodii]

I'm trying to train a RandomForestRegressor using DecisionTree.jl
and RandomizedSearchCV (contained in ScikitLearn.jl) in Julia. Primary datasets like x_train and y_train etc. are provided in my google drive as well, So you can test it on your machine. The code is as follows:

using CSV
using DataFrames

using ScikitLearn: fit!, predict
using ScikitLearn.GridSearch: RandomizedSearchCV
using DecisionTree

x = CSV.read("x.csv", DataFrames.DataFrame)
x_test = CSV.read("x_test.csv", DataFrames.DataFrame)
y_train = CSV.read("y_train.csv", DataFrames.DataFrame)

mod = RandomForestRegressor()

param_dist = Dict("n_trees"=>[50 , 100, 200, 300],
                  "max_depth"=> [3, 5, 6 ,8 , 9 ,10])

model = RandomizedSearchCV(mod, param_dist, n_iter=10, cv=5)

fit!(model, Matrix(x), Matrix(DataFrames.dropmissing(y_train)))

predict(x_test)

This throws a MethodError like this:

ERROR: MethodError: no method matching fit!(::RandomForestRegressor, ::Matrix{Float64}, ::Matrix{Float64})
Closest candidates are:
  fit!(::ScikitLearn.Models.FixedConstant, ::Any, ::Any) at C:\Users\Shayan\.julia\packages\ScikitLearn\ssekP\src\models\constant_model.jl:26
  fit!(::ScikitLearn.Models.ConstantRegressor, ::Any, ::Any) at C:\Users\Shayan\.julia\packages\ScikitLearn\ssekP\src\models\constant_model.jl:10
  fit!(::ScikitLearn.Models.LinearRegression, ::AbstractArray{XT}, ::AbstractArray{yT}) where {XT, yT} at C:\Users\Shayan\.julia\packages\ScikitLearn\ssekP\src\models\linear_regression.jl:27
  ...
Stacktrace:
 [1] _fit!(self::RandomizedSearchCV, X::Matrix{Float64}, y::Matrix{Float64}, parameter_iterable::Vector{Any})
   @ ScikitLearn.Skcore C:\Users\Shayan\.julia\packages\ScikitLearn\ssekP\src\grid_search.jl:332
 [2] fit!(self::RandomizedSearchCV, X::Matrix{Float64}, y::Matrix{Float64})
   @ ScikitLearn.Skcore C:\Users\Shayan\.julia\packages\ScikitLearn\ssekP\src\grid_search.jl:748
 [3] top-level scope
   @ c:\Users\Shayan\Desktop\AUT\Thesis\test.jl:17

If you're curious about the shape of the data:

julia> size(x)
(1550, 70)

julia> size(y_train)
(1550, 10)

How can I solve this problem?

PS: Also I tried:

julia> fit!(model, Matrix{Any}(x), Matrix{Any}(DataFrames.dropmissing(y_train)))

ERROR: MethodError: no method matching fit!(::RandomForestRegressor, ::Matrix{Any}, ::Matrix{Any})
Closest candidates are:
  fit!(::ScikitLearn.Models.FixedConstant, ::Any, ::Any) at C:\Users\Shayan\.julia\packages\ScikitLearn\ssekP\src\models\constant_model.jl:26
  fit!(::ScikitLearn.Models.ConstantRegressor, ::Any, ::Any) at C:\Users\Shayan\.julia\packages\ScikitLearn\ssekP\src\models\constant_model.jl:10
  fit!(::ScikitLearn.Models.LinearRegression, ::AbstractArray{XT}, ::AbstractArray{yT}) where {XT, yT} at C:\Users\Shayan\.julia\packages\ScikitLearn\ssekP\src\models\linear_regression.jl:27
  ...
Stacktrace:
 [1] _fit!(self::RandomizedSearchCV, X::Matrix{Any}, y::Matrix{Any}, parameter_iterable::Vector{Any})
   @ ScikitLearn.Skcore C:\Users\Shayan\.julia\packages\ScikitLearn\ssekP\src\grid_search.jl:332
 [2] fit!(self::RandomizedSearchCV, X::Matrix{Any}, y::Matrix{Any})
   @ ScikitLearn.Skcore C:\Users\Shayan\.julia\packages\ScikitLearn\ssekP\src\grid_search.jl:748
 [3] top-level scope
   @ c:\Users\Shayan\Desktop\AUT\Thesis\MyWork\Thesis.jl:327

[ablaom]

I'm afraid DecisionTree.jl does not support multi-targets.

[shayandavoodii]

I'm afraid DecisionTree.jl does not support multi-targets.

Thank you for your response. Will DecisionTree.jl support that kind of model?

[ablaom]

I do not know of any such plans. If you have the expertise and would like to contribute a PR to add that feature, I think we can find someone to review.

[ablaom]

Closing in favour of #30.