ERROR: PyError ($(Expr(:escape, :(ccall(#= /home/tas/.julia/packages/PyCall/ttONZ/src/pyfncall.jl:44 =# @pysym(:PyObject_Call), PyPtr, (PyPtr, PyPtr, PyPtr), o, pyargsptr, kw))))) <class 'TypeError'>
TypeError("Cannot clone object '<PyCall.jlwrap RandomForestClassifier\nn_trees: 350\nn_subfeatures: 30\npartial_sampling: 0.4\nmax_depth: -1\nmin_samples_leaf: 1\nmin_samples_split: 2\nmin_purity_increase: 0.001\nclasses: ensemble: >' (type <class 'PyCall.jlwrap'>): it does not seem to be a scikit-learn estimator as it does not implement a 'get_params' methods.")
File "/home/tas/.julia/conda/3/lib/python3.7/site-packages/sklearn/feature_selection/frommodel.py", line 195, in fit
self.estimator = clone(self.estimator)
File "/home/tas/.julia/conda/3/lib/python3.7/site-packages/sklearn/base.py", line 60, in clone
% (repr(estimator), type(estimator)))
Stacktrace:
[1] pyerr_check at /home/tas/.julia/packages/PyCall/ttONZ/src/exception.jl:60 [inlined]
[2] pyerr_check at /home/tas/.julia/packages/PyCall/ttONZ/src/exception.jl:64 [inlined]
[3] macro expansion at /home/tas/.julia/packages/PyCall/ttONZ/src/exception.jl:84 [inlined]
[4] __pycall!(::PyCall.PyObject, ::Ptr{PyCall.PyObject_struct}, ::PyCall.PyObject, ::Ptr{Nothing}) at /home/tas/.julia/packages/PyCall/ttONZ/src/pyfncall.jl:44
[5] _pycall!(::PyCall.PyObject, ::PyCall.PyObject, ::Tuple{Array{Float64,2},Array{String,1}}, ::Int64, ::Ptr{Nothing}) at /home/tas/.julia/packages/PyCall/ttONZ/src/pyfncall.jl:29
[6] _pycall!(::PyCall.PyObject, ::PyCall.PyObject, ::Tuple{Array{Float64,2},Array{String,1}}, ::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at /home/tas/.julia/packages/PyCall/ttONZ/src/pyfncall.jl:11
[7] #call#111(::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::PyCall.PyObject, ::Array{Float64,2}, ::Vararg{Any,N} where N) at /home/tas/.julia/packages/PyCall/ttONZ/src/pyfncall.jl:89
[8] (::PyCall.PyObject)(::Array{Float64,2}, ::Vararg{Any,N} where N) at /home/tas/.julia/packages/PyCall/ttONZ/src/pyfncall.jl:89
[9] #fit!#31(::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::Function, ::PyCall.PyObject, ::Array{Float64,2}, ::Vararg{Any,N} where N) at /home/tas/.julia/packages/ScikitLearn/bo2Pt/src/Skcore.jl:100
[10] fit!(::PyCall.PyObject, ::Array{Float64,2}, ::Array{String,1}) at /home/tas/.julia/packages/ScikitLearn/bo2Pt/src/Skcore.jl:100
[11] top-level scope at none:0
PS: There is no issue if I use RandomForestClassifier from ScikitLearn.
I'm trying to use SelectFromModel with RadomForestClassifier. There is support for that on ScickitLearn in Julia?
I get the following error:
PS: There is no issue if I use RandomForestClassifier from ScikitLearn.