Machine fit! from the model not working

[davidbp]

Hello,

While trying to replicate the current the "getting started" tutorial in the docs I get an error trying to fit a machine:

This is the code I tried

using MLJ
using Pkg
using RDatasets
using DecisionTree

iris = dataset("datasets", "iris");
X, y = iris[:,1:4], iris[:,5];

#show((Pkg.status("MLJ"))

Pkg.status("MLJ")
println("size(X) = ", size(X))


## Define machine and train it
println("Define tree model")
tree_model = DecisionTree.DecisionTreeClassifier(max_depth=4)
println("Train model")
tree_machine = machine(tree_model, X, y)

This is what I get

    Status `~/.julia/environments/v1.1/Project.toml`
  [add582a8] MLJ v0.2.3
size(X) = (150, 4)
Define tree model
Train model
ERROR: LoadError: MethodError: no method matching machine(::DecisionTreeClassifier, ::DataFrame, ::CategoricalArray{String,1,UInt8,String,CategoricalString{UInt8},Union{}})
Closest candidates are:
  machine(!Matched::MLJBase.Model, ::Any...) at /Users/david/.julia/packages/MLJ/HXakA/src/machines.jl:165
  machine(!Matched::MLJBase.Model, ::Any, !Matched::AbstractNode) at /Users/david/.julia/packages/MLJ/HXakA/src/networks.jl:353
  machine(!Matched::MLJBase.Model, !Matched::AbstractNode, ::Any) at /Users/david/.julia/packages/MLJ/HXakA/src/networks.jl:354
Stacktrace:
 [1] top-level scope at none:0
 [2] include at ./boot.jl:326 [inlined]
 [3] include_relative(::Module, ::String) at ./loading.jl:1038
 [4] include(::Module, ::String) at ./sysimg.jl:29
 [5] exec_options(::Base.JLOptions) at ./client.jl:267
 [6] _start() at ./client.jl:436
in expression starting at /Users/david/Documents/git_stuff/julia_tutorials/MLJ/minimal_working_example_problem.jl:19```

[ablaom]

tree_model = DecisionTree.DecisionTreeClassifier(max_depth=4)

This is not an MLJ Model but an unwrapped struct from the DecisionTree package with the same name that you have exposed to Main. To use the MLJ model, delete using DecisionTree - which you do not need (or replace with import DecisionTree) and replace the above line with

@load DecisionTreeClassifier
tree_model = DecisionTreeClassifier(max_depth=4)

[davidbp]

Hello Anthony,

You are right, I was trying to create a machine with a type that is not a MLJBase.Model.

In any case the fit method returns an error

This

using MLJ
using RDatasets

iris = dataset("datasets", "iris");
X, y = iris[:,1:4], iris[:,5];
println("size(X) = ", size(X))

## Define machine and train it 
println("Define tree model")
@load DecisionTreeClassifier
tree_model = DecisionTreeClassifier(max_depth=2)

#tree_model = DecisionTree.DecisionTreeClassifier(max_depth=4)
println("Define Machine")
tree_machine = machine(tree_model, X, y)

println("Train model")
train_ind, test_ind = partition(eachindex(y), 0.7, shuffle=true);
fit!(tree_machine, rows=train_ind)

prints

size(X) = (150, 4)
Define tree model
import MLJModels ✔
import DecisionTree ✔
import MLJModels.DecisionTree_.DecisionTreeClassifier ✔
Define Machine
Train model
[ Info: Training Machine{DecisionTreeClassifier} @ 1…72.
ERROR: LoadError: MethodError: no method matching build_tree(::CategoricalArray{String,1,UInt8,String,CategoricalString{UInt8},Union{}}, ::Array{Float64,2}, ::Float64, ::Int64, ::Int64, ::Int64, ::Float64)
Closest candidates are:
  build_tree(!Matched::Array{T<:Float64,1}, ::Array{S,2}, ::Any, ::Any, ::Any, ::Any, ::Any; rng) where {S, T<:Float64} at /Users/david/.julia/packages/DecisionTree/nfB7O/src/regression/main.jl:27
  build_tree(!Matched::Array{T,1}, ::Array{S,2}, ::Any, ::Any, ::Any, ::Any, ::Any; rng) where {S, T} at /Users/david/.julia/packages/DecisionTree/nfB7O/src/classification/main.jl:83
  build_tree(!Matched::Array{T<:Float64,1}, ::Array{S,2}, ::Any, ::Any, ::Any, ::Any) where {S, T<:Float64} at /Users/david/.julia/packages/DecisionTree/nfB7O/src/regression/main.jl:27
  ...
Stacktrace:
 [1] fit(::DecisionTreeClassifier, ::Int64, ::DataFrame, ::CategoricalArray{String,1,UInt8,String,CategoricalString{UInt8},Union{}}) at /Users/david/.julia/packages/MLJModels/p7doS/src/DecisionTree.jl:110
 [2] #fit!#3(::Array{Int64,1}, ::Int64, ::Bool, ::Function, ::Machine{DecisionTreeClassifier}) at /Users/david/.julia/packages/MLJ/HXakA/src/machines.jl:131
 [3] (::getfield(StatsBase, Symbol("#kw##fit!")))(::NamedTuple{(:rows,),Tuple{Array{Int64,1}}}, ::typeof(fit!), ::Machine{DecisionTreeClassifier}) at ./none:0
 [4] top-level scope at none:0
 [5] include at ./boot.jl:326 [inlined]
 [6] include_relative(::Module, ::String) at ./loading.jl:1038
 [7] include(::Module, ::String) at ./sysimg.jl:29
 [8] exec_options(::Base.JLOptions) at ./client.jl:267
 [9] _start() at ./client.jl:436
in expression starting at /Users/david/Documents/git_stuff/julia_tutorials/MLJ/minimal_working_example_problem.jl:26

[ablaom]

I'm pretty sure this will go away if you update MLJ to the latest tagged release. It is caused by a compatibility problem with CategoricalArrays.

Let me know if it doesn't