Failure on MacOS for Julia 1.3, 1.4: Intel MKL Error

[ablaom]

Further to #211 :

I have introduce CI on this branch for julia 1.3 and julia 1.4, where testing in now failing for MacOS.

The error is triggered by testing of the wrapped scikit-learn clustering models. According to the travis logs, the conda installations for scikit-learn are the same for linux and macOS, excect that macOS has an additional package llvm-openmp-4.0.1 installed.

Any help at all on this one would be appreciated. In particular, should I regard this as julia 1.3/1.4 error?

Here is the tail of the stack trace

Intel MKL ERROR: Parameter 6 was incorrect on entry to DLASWP.
540Intel MKL ERROR: Parameter 6 was incorrect on entry to DLASWP.
541
542Intel MKL ERROR: Parameter 6 was incorrect on entry to DLASWP.
543
544Intel MKL ERROR: Parameter 6 was incorrect on entry to DLASWP.
545
546Intel MKL ERROR: Parameter 6 was incorrect on entry to DLASWP.
547
548
549Intel MKL ERROR: Parameter 6 was incorrect on entry to DLASWP.
550Intel MKL ERROR: Parameter 6 was incorrect on entry to DLASWP.
551
552signal (11): Segmentation fault: 11
553in expression starting at /Users/travis/build/alan-turing-institute/MLJModels.jl/test/ScikitLearn/clustering.jl:139
554thread_team_ctxt_commit_callback at /Users/travis/.julia/conda/3/lib/libmkl_intel_thread.dylib (unknown line)
555mkl_lapack_thread_team_ctxt_commit_task at /Users/travis/.julia/conda/3/lib/libmkl_core.dylib (unknown line)
556mkl_lapack_dgetrf at /Users/travis/.julia/conda/3/lib/libmkl_intel_thread.dylib (unknown line)
557Allocations: 388377569 (Pool: 388284978; Big: 92591); GC: 274
558ERROR: Package MLJModels errored during testing
559Stacktrace:
560 [1] pkgerror(::String, ::Vararg{String,N} where N) at /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.3/Pkg/src/Types.jl:113
561 [2] #test#131(::Bool, ::Nothing, ::Cmd, ::Cmd, ::typeof(Pkg.Operations.test), ::Pkg.Types.Context, ::Array{Pkg.Types.PackageSpec,1}) at /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.3/Pkg/src/Operations.jl:1372
562 [3] #test at ./none:0 [inlined]
563 [4] #test#62(::Bool, ::Nothing, ::Cmd, ::Cmd, ::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::typeof(Pkg.API.test), ::Pkg.Types.Context, ::Array{Pkg.Types.PackageSpec,1}) at /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.3/Pkg/src/API.jl:253
564 [5] (::Pkg.API.var"#kw##test")(::NamedTuple{(:coverage,),Tuple{Bool}}, ::typeof(Pkg.API.test), ::Pkg.Types.Context, ::Array{Pkg.Types.PackageSpec,1}) at ./none:0
565 [6] #test#58 at /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.3/Pkg/src/API.jl:233 [inlined]
566 [7] (::Pkg.API.var"#kw##test")(::NamedTuple{(:coverage,),Tuple{Bool}}, ::typeof(Pkg.API.test)) at ./none:0
567 [8] top-level scope at none:1

[tlienart]

Adding some considerations: commenting out the clustering tests is not enough, the regression and classification tests also fail horribly; there's something seriously wrong here.

WARNING: replacing module TestScikitLearn.
[ Info: Precompiling RDatasets [ce6b1742-4840-55fa-b093-852dadbb1d8b]
ARD: Test Failed at /Users/tlienart/.julia/dev/MLJModels/test/ScikitLearn/linear-regressors.jl:18
  Expression: isapprox((f[1]).score(X, y), 0.998859, rtol = 1.0e-5)
   Evaluated: isapprox(-24375.10250848991, 0.998859; rtol = 1.0e-5)
Stacktrace:
 [1] top-level scope at /Users/tlienart/.julia/dev/MLJModels/test/ScikitLearn/linear-regressors.jl:18
 [2] top-level scope at /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.5/Test/src/Test.jl:1114
 [3] top-level scope at /Users/tlienart/.julia/dev/MLJModels/test/ScikitLearn/linear-regressors.jl:16
ARD: Test Failed at /Users/tlienart/.julia/dev/MLJModels/test/ScikitLearn/linear-regressors.jl:19
  Expression: isapprox(norm(predict(m, f, X) .- y) / norm(y), 0.032693, rtol = 1.0e-5)
   Evaluated: isapprox(151.13364588982455, 0.032693; rtol = 1.0e-5)
Stacktrace:
 [1] top-level scope at /Users/tlienart/.julia/dev/MLJModels/test/ScikitLearn/linear-regressors.jl:19
 [2] top-level scope at /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.5/Test/src/Test.jl:1114
 [3] top-level scope at /Users/tlienart/.julia/dev/MLJModels/test/ScikitLearn/linear-regressors.jl:16
BayesianRidge: Error During Test at /Users/tlienart/.julia/dev/MLJModels/test/ScikitLearn/linear-regressors.jl:25
  Got exception outside of a @test
  PyError ($(Expr(:escape, :(ccall(#= /Users/tlienart/.julia/packages/PyCall/zqDXB/src/pyfncall.jl:43 =# @pysym(:PyObject_Call), PyPtr, (PyPtr, PyPtr, PyPtr), o, pyargsptr, kw))))) <class 'numpy.linalg.LinAlgError'>
  LinAlgError('SVD did not converge')
    File "/Users/tlienart/.julia/conda/3/lib/python3.7/site-packages/sklearn/linear_model/bayes.py", line 215, in fit
      U, S, Vh = linalg.svd(X, full_matrices=False)
    File "/Users/tlienart/.julia/conda/3/lib/python3.7/site-packages/scipy/linalg/decomp_svd.py", line 132, in svd
      raise LinAlgError("SVD did not converge")
  
  Stacktrace:
   [1] pyerr_check at /Users/tlienart/.julia/packages/PyCall/zqDXB/src/exception.jl:60 [inlined]
   [2] pyerr_check at /Users/tlienart/.julia/packages/PyCall/zqDXB/src/exception.jl:64 [inlined]
   [3] _handle_error(::String) at /Users/tlienart/.julia/packages/PyCall/zqDXB/src/exception.jl:81
   [4] macro expansion at /Users/tlienart/.julia/packages/PyCall/zqDXB/src/exception.jl:95 [inlined]
   [5] #110 at /Users/tlienart/.julia/packages/PyCall/zqDXB/src/pyfncall.jl:43 [inlined]
   [6] disable_sigint at ./c.jl:446 [inlined]
   [7] __pycall! at /Users/tlienart/.julia/packages/PyCall/zqDXB/src/pyfncall.jl:42 [inlined]
   [8] _pycall!(::PyCall.PyObject, ::PyCall.PyObject, ::Tuple{Array{Float64,2},Array{Float64,1}}, ::Int64, ::Ptr{Nothing}) at /Users/tlienart/.julia/packages/PyCall/zqDXB/src/pyfncall.jl:29
   [9] _pycall!(::PyCall.PyObject, ::PyCall.PyObject, ::Tuple{Array{Float64,2},Array{Float64,1}}, ::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at /Users/tlienart/.julia/packages/PyCall/zqDXB/src/pyfncall.jl:11
   [10] (::PyCall.PyObject)(::Array{Float64,2}, ::Vararg{Any,N} where N; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at /Users/tlienart/.julia/packages/PyCall/zqDXB/src/pyfncall.jl:86
   [11] (::PyCall.PyObject)(::Array{Float64,2}, ::Vararg{Any,N} where N) at /Users/tlienart/.julia/packages/PyCall/zqDXB/src/pyfncall.jl:86
   [12] fit!(::PyCall.PyObject, ::Array{Float64,2}, ::Vararg{Any,N} where N; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at /Users/tlienart/.julia/packages/ScikitLearn/uiLep/src/Skcore.jl:100
   [13] fit!(::PyCall.PyObject, ::Array{Float64,2}, ::Array{Float64,1}) at /Users/tlienart/.julia/packages/ScikitLearn/uiLep/src/Skcore.jl:100
   [14] fit(::MLJModels.ScikitLearn_.BayesianRidgeRegressor, ::Int64, ::Array{Float64,2}, ::Array{Float64,1}) at /Users/tlienart/.julia/dev/MLJModels/src/ScikitLearn/ScikitLearn.jl:157
   [15] top-level scope at /Users/tlienart/.julia/dev/MLJModels/test/ScikitLearn/linear-regressors.jl:27
   [16] top-level scope at /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.5/Test/src/Test.jl:1114
   [17] top-level scope at /Users/tlienart/.julia/dev/MLJModels/test/ScikitLearn/linear-regressors.jl:26
   [18] include(::Function, ::Module, ::String) at ./Base.jl:380
   [19] include at ./Base.jl:368 [inlined]
   [20] include(::String) at /Users/tlienart/.julia/dev/MLJModels/test/ScikitLearn/ScikitLearn.jl:1
   [21] top-level scope at /Users/tlienart/.julia/dev/MLJModels/test/ScikitLearn/ScikitLearn.jl:10
   [22] include(::String) at ./client.jl:441
   [23] top-level scope at /Users/tlienart/.julia/dev/MLJModels/test/runtests.jl:45
   [24] top-level scope at /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.5/Test/src/Test.jl:1114
   [25] top-level scope at /Users/tlienart/.julia/dev/MLJModels/test/runtests.jl:45
   [26] include_string(::Function, ::Module, ::String, ::String) at ./loading.jl:1087
   [27] include_string at ./loading.jl:1095 [inlined]
   [28] include_string(::Module, ::String, ::String, ::Int64) at /Users/tlienart/.julia/packages/CodeTools/kosGY/src/eval.jl:30
   [29] (::Atom.var"#188#192"{String,Int64,String,Bool})() at /Users/tlienart/.julia/packages/Atom/wlPiw/src/eval.jl:111
   [30] withpath(::Atom.var"#188#192"{String,Int64,String,Bool}, ::String) at /Users/tlienart/.julia/packages/CodeTools/kosGY/src/utils.jl:30
   [31] withpath(::Function, ::String) at /Users/tlienart/.julia/packages/Atom/wlPiw/src/eval.jl:9
   [32] #187 at /Users/tlienart/.julia/packages/Atom/wlPiw/src/eval.jl:110 [inlined]
   [33] with_logstate(::Atom.var"#187#191"{String,Int64,String,Bool}, ::Base.CoreLogging.LogState) at ./logging.jl:398
   [34] with_logger at ./logging.jl:505 [inlined]
   [35] #186 at /Users/tlienart/.julia/packages/Atom/wlPiw/src/eval.jl:109 [inlined]
   [36] hideprompt(::Atom.var"#186#190"{String,Int64,String,Bool}) at /Users/tlienart/.julia/packages/Atom/wlPiw/src/repl.jl:140
   [37] macro expansion at /Users/tlienart/.julia/packages/Atom/wlPiw/src/eval.jl:108 [inlined]
   [38] macro expansion at /Users/tlienart/.julia/packages/Media/ItEPc/src/dynamic.jl:24 [inlined]
   [39] eval(::String, ::Int64, ::String, ::String, ::Bool) at /Users/tlienart/.julia/packages/Atom/wlPiw/src/eval.jl:105
   [40] macro expansion at /Users/tlienart/.julia/packages/Atom/wlPiw/src/eval.jl:39 [inlined]
   [41] (::Atom.var"#172#173")() at ./task.jl:358
  
** On entry to DLASCL, parameter number  4 had an illegal value
** On entry to DLASCL, parameter number  4 had an illegal value
LinReg: Error During Test at /Users/tlienart/.julia/dev/MLJModels/test/ScikitLearn/linear-regressors.jl:135
  Got exception outside of a @test
  PyError ($(Expr(:escape, :(ccall(#= /Users/tlienart/.julia/packages/PyCall/zqDXB/src/pyfncall.jl:43 =# @pysym(:PyObject_Call), PyPtr, (PyPtr, PyPtr, PyPtr), o, pyargsptr, kw))))) <class 'numpy.linalg.LinAlgError'>
  LinAlgError('SVD did not converge in Linear Least Squares')
    File "/Users/tlienart/.julia/conda/3/lib/python3.7/site-packages/sklearn/linear_model/base.py", line 503, in fit
      linalg.lstsq(X, y)
    File "/Users/tlienart/.julia/conda/3/lib/python3.7/site-packages/scipy/linalg/basic.py", line 1241, in lstsq
      raise LinAlgError("SVD did not converge in Linear Least Squares")
  

and more of the same

I'm wondering whether there's not a clash between Conda versions somehow

[tlienart]

We should compare

• one of the last run that worked https://travis-ci.com/github/alan-turing-institute/MLJModels.jl/builds/150576942
• the first run after that that didn't https://travis-ci.com/github/alan-turing-institute/MLJModels.jl/jobs/292247108

cc @OkonSamuel

[tlienart]

Last commit ok: 61febaa on Feb 26

First commit nok: d9944ca on Feb 28

List of differences:

Maybe relevant

• CompilerSupportLibraries_jll v0.2.0+1 (added)
• OpenSpecFun_jll v0.5.3+1 -> OpenSpecFun_jll v0.5.3+2

Probably irrelevant

• CSV v0.5.24 -> v0.5.25
• HTTP v0.8.9 -> v0.8.12
• LaTeXStrings v1.0.3 -> 1.1
• MbedTLS 0.7 -> 1.0
• MbedTLS_jll v2.16.0+1
• Rmath v0.6.0 -> Rmath v0.6.1
• Rmath_jll v0.2.2+0

[tlienart]

The openspecfun calls the compilersupportlibs, note that this change was made on February 26 which is also the date where we had our last working push... let's try putting a compat bound on that

[tlienart]

Ok this is almost certainly the culprit, you can see that the compat of that thing is Julia 1.3 which would also explain why it fails on 1.3/1.4... now where the heck is this loaded

[tlienart]

Steps to fix the problem:

• activate MLJModels ] activate .
• ] dev OpenSpecFun_jll will add v0.5.3+3

in your terminal, go wherever that is, for me cd ~/.julia/dev/OpenSpecFun_jll

git reset --hard OpenSpecFun-v0.5.3+1

• ] up (in MLJModels)
• kill Julia (just to be sure)
• ] activate .
• then do the tests

Log

(@v1.5) pkg> activate .
s Activating environment at `~/.julia/dev/MLJModels/Project.toml`

(MLJModels) pkg> status
Project MLJModels v0.9.3
     Status `~/.julia/dev/MLJModels/Project.toml`
   324d7699 CategoricalArrays v0.7.7
   b4f34e82 Distances v0.8.2
   31c24e10 Distributions v0.22.6
   a7f614a8 MLJBase v0.12.3 `~/.julia/dev/MLJBase`
   e80e1ace MLJModelInterface v0.2.1 `../MLJModelInterface`
   6f286f6a MultivariateStats v0.7.0
   efe28fd5 OpenSpecFun_jll v0.5.3+1 `~/.julia/dev/OpenSpecFun_jll`
   bac558e1 OrderedCollections v1.1.0
   d96e819e Parameters v0.12.0
   ae029012 Requires v1.0.1
   321657f4 ScientificTypes v0.7.1 `../ScientificTypes`
   2913bbd2 StatsBase v0.32.2
   bd369af6 Tables v1.0.3
   b77e0a4c InteractiveUtils
   37e2e46d LinearAlgebra
   44cfe95a Pkg
   9a3f8284 Random
   10745b16 Statistics
[ Info: Precompiling MLJModels [d491faf4-2d78-11e9-2867-c94bc002c0b7]
[ Info: Model metadata loaded from registry. 
[ Info: Precompiling RDatasets [ce6b1742-4840-55fa-b093-852dadbb1d8b]
[ Info: Training Machine{AffinityPropagation} @ 1…25.
Test Summary:       | Pass  Total
AffinityPropagation |    5      5
[ Info: Training Machine{AgglomerativeClustering} @ 1…41.
Test Summary:           | Pass  Total
AgglomerativeClustering |    4      4
[ Info: Training Machine{Birch} @ 9…06.
Test Summary: | Pass  Total
Birch         |    8      8
[ Info: Training Machine{DBSCAN} @ 9…63.
Test Summary: | Pass  Total
DBSCAN        |    4      4
[ Info: Training Machine{FeatureAgglomeration} @ 1…75.
Test Summary:        | Pass  Total
FeatureAgglomeration |    7      7
[ Info: Training Machine{KMeans} @ 2…15.
Test Summary: | Pass  Total
KMeans        |    6      6
[ Info: Training Machine{MiniBatchKMeans} @ 9…15.
Test Summary: | Pass  Total
MBKMeans      |    6      6
[ Info: Training Machine{MeanShift} @ 9…80.
Test Summary: | Pass  Total
MeanShift     |    5      5
[ Info: Training Machine{OPTICS} @ 5…86.
Test Summary: | Pass  Total
OPTICS        |    4      4
[ Info: Training Machine{SpectralClustering} @ 1…33.
Test Summary:      | Pass  Total
SpectralClustering |    4      4
Pass  Total
SVM-infos     |    7      7
Test Summary: | Pass  Total
SVCs          |    3      3
Test Summary: | Pass  Total
SVRs          |    3      3
Test Summary: | Pass  Total
ARD           |    3      3
Test Summary: | Pass  Total
BayesianRidge |    3      3
Test Summary: | Pass  Total
ElasticNet    |    3      3
Test Summary: | Pass  Total
ElasticNetCV  |    3      3
Test Summary: | Pass  Total
Huber         |    3      3
Test Summary: | Pass  Total
Lars          |    3      3
Test Summary: | Pass  Total
LarsCV        |    3      3
Test Summary: | Pass  Total
Lasso         |    3      3
Test Summary: | Pass  Total
LassoCV       |    3      3
Test Summary: | Pass  Total
LassoLars     |    3      3
Test Summary: | Pass  Total
LassoLarsCV   |    3      3
Test Summary: | Pass  Total
LassoLarsIC   |    3      3
Test Summary: | Pass  Total
LinReg        |    5      5
Test Summary: | Pass  Total
OMP           |    3      3
Test Summary: | Pass  Total
OMPCV         |    3      3
Test Summary: | Pass  Total
OMPCV         |    3      3
Test Summary: | Pass  Total
PassAggr      |    3      3
Test Summary: | Pass  Total
RANSAC        |    3      3
Test Summary: | Pass  Total
Ridge         |    3      3
Test Summary: | Pass  Total
RidgeCV       |    4      4
Test Summary: | Pass  Total
SGDReg        |    3      3
Test Summary: | Pass  Total
TheilSen      |    3      3
Test Summary: | Pass  Total
MTLassoCV     |    4      4
Test Summary: | Pass  Total
MTLassoCV     |    3      3
Test Summary: | Pass  Total
MTElNet       |    3      3
Test Summary: | Pass  Total
MTElNetCV     |    3      3
Test Summary: | Pass  Total
LogRegClf     |    8      8
Test Summary: | Pass  Total
LogRegCVClf   |    8      8
Test Summary: | Pass  Total
PAClf         |    7      7
Test Summary: | Pass  Total
PerceptronClf |    7      7
Test Summary: | Pass  Total
RidgeClf      |    7      7
Test Summary: | Pass  Total
RidgeCVClf    |    7      7
Test Summary: | Pass  Total
SGDClf        |   13     13
Test Summary: | Pass  Total
GPRegressor   |    3      3
Test Summary: | Pass  Total
GPClassif     |    8      8
Test Summary: | Pass  Total
AdaBoostReg   |    3      3
Test Summary: | Pass  Total
BaggingReg    |    3      3
Test Summary: | Pass  Total
XTreeReg      |    3      3
Test Summary: | Pass  Total
GBReg         |    3      3
Test Summary: | Pass  Total
RFReg         |    3      3
Test Summary: | Pass  Total
AdaboostClf   |    8      8
Test Summary: | Pass  Total
BaggingClf    |    8      8
Test Summary: | Pass  Total
GradBoostClf  |    8      8
Test Summary: | Pass  Total
RForestClf    |    8      8
Test Summary: | Pass  Total
XTreeClf      |    8      8
Test Summary: | Pass  Total
LDA           |    8      8
Test Summary: | Pass  Total
QDA           |    8      8
Test Summary: | Pass  Total
DummyReg      |    4      4
Test Summary: | Pass  Total
DummyClf      |    7      7
Test Summary: | Pass  Total
GaussianNBClf |    8      8
Test Summary: | Pass  Total
KNNReg        |    5      5
Test Summary: | Pass  Total
KNNClf        |    8      8
Test Summary: | Pass  Total
BernNBClf     |    7      7
Test Summary: | Pass  Total
MultiNBClf    |    7      7
Test Summary: | Pass  Total
ComplNBClf    |    7      7

This is on a Mac with v1.5

[OkonSamuel]

@tlienart looking at the METADATA file it seems OpenSpecFun_jll.jl is a depency only used by Distributions.jl and SpecialFunctions.jl

[tlienart]

I think possibly RMath? But either way Elliott has commented on a similar issue in the BinaryBuilder package, seems like using MKL_jll before anything else might solve the issue but I haven’t tried

Here : JuliaPackaging/BinaryBuilder.jl#700

[staticfloat]

I tried to reproduce this locally, and I haven't been able to so far. I installed MLJModels.jl#mkl within a pristine environment on my MacOS machine and ran the MLJModels tests on Julia 1.4.0, and even tried commenting out the explicit MKL_jll, but it never failed on my machine. Is there something I'm missing?

[tlienart]

Thanks for trying this Elliott, it may well be that the error now only shows on Travis (cf link in the other issue) though I’m a bit surprised if that’s really the case.

My environment is far from pristine but I’ll try creating one from scratch and testing on my side. Either way I think it would be good to make sure we can control this so that no one encounters undue segfaults !

Thanks again!

cc @ablaom , @azev77 who also encountered this issue

[azev77]

@staticfloat
Below: LGBM works, but ARDRegressor gives an error during fit!.
I don't get this error on my windows PC or w/ Julia 1.2...

using MLJ
X, y =  @load_boston;
train, test = partition(eachindex(y), .7, rng=333);

@load LGBMRegressor
mdl  = LGBMRegressor()
mach = machine(mdl, X, y)
fit!(mach, rows=train)


@load ARDRegressor
mdl  = ARDRegressor()
mach = machine(mdl, X, y)
fit!(mach, rows=train)

PyError ($(Expr(:escape, :(ccall(#= /Users/AZevelev/.julia/packages/PyCall/zqDXB/src/pyfncall.jl:43 =# @pysym(:PyObject_Call), PyPtr, (PyPtr, PyPtr, PyPtr), o, pyargsptr, kw))))) <class 'numpy.linalg.LinAlgError'>
LinAlgError('unrecoverable internal error.')
  File "/Users/AZevelev/.julia/conda/3/lib/python3.7/site-packages/sklearn/linear_model/_bayes.py", line 577, in fit
    sigma_ = update_sigma(X, alpha_, lambda_, keep_lambda, n_samples)
  File "/Users/AZevelev/.julia/conda/3/lib/python3.7/site-packages/sklearn/linear_model/_bayes.py", line 562, in update_sigma
    X[:, keep_lambda].T))
  File "/Users/AZevelev/.julia/conda/3/lib/python3.7/site-packages/sklearn/externals/_scipy_linalg.py", line 99, in pinvh
    s, u = decomp.eigh(a, lower=lower, check_finite=False)
  File "/Users/AZevelev/.julia/conda/3/lib/python3.7/site-packages/scipy/linalg/decomp.py", line 474, in eigh
    raise LinAlgError("unrecoverable internal error.")

pyerr_check at exception.jl:60 [inlined]
pyerr_check at exception.jl:64 [inlined]
_handle_error(::String) at exception.jl:81
macro expansion at exception.jl:95 [inlined]
#110 at pyfncall.jl:43 [inlined]
disable_sigint at c.jl:446 [inlined]
__pycall! at pyfncall.jl:42 [inlined]
_pycall!(::PyCall.PyObject, ::PyCall.PyObject, ::Tuple{Array{Float64,2},Array{Float64,1}}, ::Int64, ::Ptr{Nothing}) at pyfncall.jl:29
_pycall!(::PyCall.PyObject, ::PyCall.PyObject, ::Tuple{Array{Float64,2},Array{Float64,1}}, ::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at pyfncall.jl:11
(::PyCall.PyObject)(::Array{Float64,2}, ::Vararg{Any,N} where N; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at pyfncall.jl:86
(::PyCall.PyObject)(::Array{Float64,2}, ::Vararg{Any,N} where N) at pyfncall.jl:86
fit!(::PyCall.PyObject, ::Array{Float64,2}, ::Vararg{Any,N} where N; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at Skcore.jl:100
fit!(::PyCall.PyObject, ::Array{Float64,2}, ::Array{Float64,1}) at Skcore.jl:100
fit(::ARDRegressor, ::Int64, ::NamedTuple{(:Crim, :Zn, :Indus, :NOx, :Rm, :Age, :Dis, :Rad, :Tax, :PTRatio, :Black, :LStat),NTuple{12,Array{Float64,1}}}, ::Array{Float64,1}) at ScikitLearn.jl:157
fit!(::Machine{ARDRegressor}; rows::Array{Int64,1}, verbosity::Int64, force::Bool) at machines.jl:183
(::StatsBase.var"#fit!##kw")(::NamedTuple{(:rows,),Tuple{Array{Int64,1}}}, ::typeof(fit!), ::Machine{ARDRegressor}) at machines.jl:146
top-level scope at MLJ_FitMachine_Error.jl:18