I have a function inside a package that I am writing, that looks like this:

using ScikitLearn
function logistic_skl(points::AbstractMatrix{<:Real}, labels::AbstractVector{Bool})
@sk_import linear_model: LogisticRegression
log_reg = fit!(LogisticRegression(penalty="l2"), points', labels)
w = vec(log_reg.coef_)
b = only(log_reg.intercept_)
return w, b
end

But the package containing this function (say MyPkg.jl) now fails to pre-compile, with the following error (pointing to the function above):

ERROR: LoadError: LoadError: syntax: unsupported `const` declaration on local variable around /home/cossio/.julia/packages/ScikitLearn/Kn82b/src/Skcore.jl:187
Stacktrace:
[1] top-level scope at /home/cossio/jl/MyPkg.jl/src/logistic_sklearn.jl:5
[2] include(::Function, ::Module, ::String) at ./Base.jl:380
[3] include at ./Base.jl:368 [inlined]
[4] include(::String) at /home/cossio/jl/MyPkg.jl/src/MyPkg.jl:1
[5] top-level scope at /home/cossio/jl/MyPkg.jl/src/MyPkg.jl:26
[6] include(::Function, ::Module, ::String) at ./Base.jl:380
[7] include(::Module, ::String) at ./Base.jl:368
[8] top-level scope at none:2
[9] eval at ./boot.jl:331 [inlined]
[10] eval(::Expr) at ./client.jl:467
[11] top-level scope at ./none:3
in expression starting at /home/cossio/jl/MyPkg.jl/src/logistic_sklearn.jl:5
in expression starting at /home/cossio/jl/MyPkg.jl/src/MyPkg.jl:26

@sk_import linear_model: LogisticRegression
function logistic_skl(points::AbstractMatrix{<:Real}, labels::AbstractVector{Bool})
log_reg = fit!(LogisticRegression(penalty="l2"), points', labels)
w = vec(log_reg.coef_)
b = only(log_reg.intercept_)
return w, b
end

I get a segmentation fault:

signal (11): Segmentation fault
in expression starting at /home/cossio/jl/MyPkg.jl/test/logistic.jl:22
PyObject_Call at /home/cossio/.julia/conda/3/lib/libpython3.8.so.1.0 (unknown line)
Allocations: 383287667 (Pool: 382734699; Big: 552968); GC: 213
ERROR: Package MyPkg errored during testing (received signal: 11)

The referred line 22 in the test file simply calls logistic_skl with some data.

Here is the data I am using. Note that if I run these lines of code from a console, it works fine. The problem comes when this is inside a package.

@sk_import linear_model: LogisticRegression
function logistic_skl(points::AbstractMatrix{<:Real}, labels::AbstractVector{Bool})
log_reg = fit!(LogisticRegression(penalty="l2"), points', labels)
w = vec(log_reg.coef_)
b = only(log_reg.intercept_)
return w, b
end
# Generate data.
n = 2 # dimensionality of data
N = 10 # number of positive examples
M = 10 # number of negative examples
points = randn(n, N + M) .+ [fill(5, n, N) fill(-5, n, M)]
labels = [trues(N); falses(M)]
# logistic regression
w, b = logistic_skl(points, labels)

using ScikitLearn, PyCall
const LogisticRegression = PyNULL()
function __init__()
@eval global LogisticRegression = pyimport("linear_model")
end

Gives this error:

Got exception outside of a @test
LoadError: InitError: PyError (PyImport_ImportModule
The Python package linear_model could not be found by pyimport. Usually this means
that you did not install linear_model in the Python version being used by PyCall.

using ScikitLearn, PyCall
const LogisticRegression = PyNULL()
function __init__()
@eval @sk_import linear_model: LogisticRegression
end
function logistic_skl(points::AbstractMatrix{<:Real}, labels::AbstractVector{Bool})
log_reg = fit!(LogisticRegression(penalty="l2"), points', labels)
w = vec(log_reg.coef_)
b = only(log_reg.intercept_)
return w, b
end

However, I do get a warning from modifying the const:

WARNING: redefinition of constant LogisticRegression. This may fail, cause incorrect answers, or produce other errors.