Julia multithreading from pycall-CodePudding

Say I have a jupyter notebook:

%%julia

using Pkg
Pkg.add("DecisionTree")
using DecisionTree

X = Vector([1.1,2.2,3.3])
Y = Vector([1.1,2.2,3.3])
X = reshape(X, size(X))

X = Float32.(X)
Y = Float32.(Y)
print(typeof(X))
print(typeof(Y))
model = DecisionTree.build_forest(Y, X')

From what I know DecisionTree.jl uses multithreading, which pycall does not support, which results in the error:

RuntimeError: <PyCall.jlwrap (in a Julia function called from Python)
JULIA: TaskFailedException
Stacktrace:
  [1] wait
    @ .\task.jl:334 [inlined]
  [2] threading_run(func::Function)
    @ Base.Threads .\threadingconstructs.jl:38
  [3] macro expansion
    @ .\threadingconstructs.jl:97 [inlined]
  [4] build_forest(labels::Vector{Float32}, features::LinearAlgebra.Adjoint{Float32, Vector{Float32}}, n_subfeatures::Int64, n_trees::Int64, partial_sampling::Float64, max_depth::Int64,

My question is - is there any way to make it work after all?

CodePudding user response：

    nested task error: BoundsError: attempt to access 1×3 adjoint(::Vector{Float32}) with eltype Float32 at index [[2, 1], 1:3]
    Stacktrace:
     [1] throw_boundserror(A::LinearAlgebra.Adjoint{Float32, Vector{Float32}}, I::Tuple{Vector{Int64}, Base.Slice{Base.OneTo{Int64}}})
       @ Base ./abstractarray.jl:691

this is the error, if you run this code in julia

CodePudding user response：

The problem has nothing to do with calling it from Python, but from the fact that you are trying to make a model where the features is a single record with 3 dimensions and the label is a 3 (records) vector. DecisionTrees expects indeed the input to be a column vector of dimension nRecords for the label and a nRecods by nDimensions matrix for the features.

For example:

julia> X = [1.1,2.2,3.3]
3-element Vector{Float64}:
 1.1
 2.2
 3.3

julia> Y = [1.1,2.2,3.3]
3-element Vector{Float64}:
 1.1
 2.2
 3.3

julia> X = reshape(X,3,1) # reshape to a single column **matrix**
3×1 Matrix{Float64}:
 1.1
 2.2
 3.3

julia> model = DecisionTree.build_forest(Y, X)
Ensemble of Decision Trees
Trees:      10
Avg Leaves: 1.0
Avg Depth:  0.0

Also, to make a vector you don't need to specify "Vector". I suggest you to have a look on my tutorial on Julia or on my course on Scientific Programming and Machine Learning with Julia (I completed it just a couple of days ago, I still need to "clean" it before announcing it)