Merge ee0dfbe into 55ee9e6

.github/workflows/ci.yml

@@ -9,7 +9,7 @@ on:
 
 env:
   PYTHON: Conda 
-  LD_LIBRARY_PATH: $LD_LIBRARY_PATH:$HOME/.julia/conda/3/lib
+  LD_LIBRARY_PATH: /home/runner/.julia/conda/3/x86_64/lib # used by linux os only
 
 jobs:
   test:
@@ -36,6 +36,9 @@ jobs:
           arch: ${{ matrix.julia-arch }}
       - uses: julia-actions/cache@v1 # https://github.com/julia-actions/cache
       - uses: julia-actions/julia-buildpkg@latest
+      - name: "Export LD_LIBRARY_PATH envrioment variable"
+        if: ${{matrix.os == 'ubuntu-latest' && matrix.julia-version == '1.6'}}
+        run: export LD_LIBRARY_PATH=$LD_LIBRARY_PATH
       - uses: julia-actions/julia-runtest@latest
       - uses: julia-actions/julia-uploadcodecov@v0.1
         continue-on-error: true

Project.toml

@@ -21,7 +21,7 @@ VersionParsing = "81def892-9a0e-5fdd-b105-ffc91e053289"
 
 [compat]
 Compat = "2.2, 3, 4"
-Conda = "1.5.2, 1.6, 1.7"
+Conda = "1.8"
 DataFrames = "0.20, 0.21, 0.22, 1"
 IterTools = "1.2, 1.3"
 MacroTools = "0.5"

README.md

@@ -35,6 +35,15 @@ To install ScikitLearn.jl, type `]add ScikitLearn` at the REPL.
 
 To import Python models (optional), ScikitLearn.jl requires [the scikit-learn Python library](https://cstjean.github.io/ScikitLearn.jl/dev/man/models/#Installation-and-importing-Python-models-1), which will be installed automatically when needed. Most of the examples use [PyPlot.jl](https://github.com/stevengj/PyPlot.jl)
 
+## Known issue
+
+On Linux builds, importing python models via `@sk_import` is known to fail for Julia v<0.8.4 when the `PYTHON` enviroment variable from `PyCall.jl` is set to `""` or `conda`. This is becuase the version libstdcxx loaded by Julia v<0.8.4 isn't compatible with the version of scikit-learn installed via Conda.
+The easiest and recommended way to resolve this is to upgrade to Julia v>=1.8.4. If you must stick with your current julia version you can also resolve this issue by pre-appending your system's `LD_LIBRARY_PATH` enviroment variable as shown below
+```bash
+ROOT_ENV=`julia -e "using Conda; print(Conda.ROOTENV)`
+export LD_LIBRARY_PATH=$ROOT_ENV"/lib":$LD_LIBRARY_PATH
+```
+
 ## Documentation
 
 See the [manual](https://cstjean.github.io/ScikitLearn.jl/dev/) and

src/Skcore.jl

@@ -166,55 +166,74 @@ function import_sklearn()
     global mkl_checked
     global libstdcxx_solved
 
-    @static if Sys.isapple()
-      mod = try
+    mod = try
+        @static if Sys.isapple()
             if PyCall.conda && !mkl_checked
                 try
                     # check for existence of mkl-service. 
                     # Numpy, sklearn, etc. requires either `mkl` or `no-mkl` service to run
                     # By default Conda comes with mkl
-                    # For this package to run on MacOS the `no-mkl` versions of Numpy, sklearn is needed   
-                    pyimport("mkl")
+                    # For this package to run on MacOS, the `no-mkl` versions of Numpy, sklearn are needed   
+                    pyimport("mkl") # jumps to catch block if "mkl" doesn't exist on the users Mac
 
-                    #following Code runs only if mkl-service exists otherwise jumps to catch branch
-                    @info "Installing non-mkl versions of sci-kit learn via Conda"
-                    #use non-mkl versions of python packages when ENV["PYTHON"]="Conda" or "" is used
-                    #when a different non-conda local python is used everthing works fine
+                    # following Code runs only if mkl-service exists otherwise jumps to catch branch
+                    @info(
+                        "Uninstalling mkl and Installing "*
+                        "non-mkl versions of sci-kit learn via Conda"
+                    )
+                    # Use non-mkl versions of python packages when ENV["PYTHON"]="Conda" 
+                    # or "" is used.
+                    # When a different non-conda local python is used everthing works fine
                     Conda.add("nomkl")
                     Conda.rm("mkl")#This also removes mkl-service
-                    #force reinstall of scikit-learn replacing any previous mkl version
-                    Conda.add("scikit-learn")
-                    Conda.add("openblas")
-                    Conda.add("llvm-openmp", channel = "conda-forge")
-                    mkl_checked = true
-                catch
-                    mkl_checked = true
-                end   
+                catch err
+                    ## This block is reached when `mkl` pkg isn't installed.
+                    @info(
+                        "mkl not found, proceeding to installing non-mkl versions "*
+                        "of sci-kit learn via Conda"
+                    )
+                end
+                # force reinstall of scikit-learn replacing any previous mkl version
+                Conda.add("scikit-learn>=1.2,<1.3", channel="conda-forge")
+                #Conda.add("openblas")
+                #Conda.add("llvm-openmp", channel = "conda-forge")
+                mkl_checked = true
+            end
+            #PyCall.pyimport_conda("sklearn", "scikit-learn>=1.2,<1.3", "conda-forge")
+        else
+            @static if Sys.islinux()
+                if !libstdcxx_solved
+                version = _compatible_libstdcxx_ng_version()
+                Conda.add("conda", channel="anaconda")
+                Conda.add("libstdcxx-ng$version", channel="conda-forge")
+                #=
+                if version == ">=3.4,<12.0" 
+                    # https://github.com/scikit-learn/scikit-learn/pull/23990
+                    Conda.add("scikit-learn>=1.2,<1.3", channel="conda-forge") 
+                end
+                =#
+                libstdcxx_solved = true
+                end
             end
-            Conda.add("llvm-openmp", channel = "conda-forge")
-            PyCall.pyimport_conda("sklearn", "scikit-learn")
-
-        catch
-            @info("scikit-learn isn't properly installed."*
-                    "Please use PyCall default Conda or non-conda local python")
-            rethrow()
         end
-
-    elseif Sys.islinux()
-        if !libstdcxx_solved
-            version = _compatible_libstdcxx_ng_version()
-            Conda.add("conda", channel="anaconda")
-            Conda.add("libstdcxx-ng$version", channel="conda-forge")
-            if version == ">=3.4,<12.0" 
-                # https://github.com/scikit-learn/scikit-learn/pull/23990
-                Conda.add("scikit-learn<1.1", channel="conda-forge") 
+        PyCall.pyimport_conda("sklearn", "scikit-learn>=1.2,<1.3", "conda-forge")
+    catch
+        INFO_MSG = "scikit-learn isn't properly installed."*
+        "Please make sure PyCall is using the default Conda or non-conda local python."
+        @static if Sys.islinux()
+            if libstdcxx_solved
+                INFO_MSG = INFO_MSG * 
+                    "\nIf you are experiencing Conda package conflicts, this may "*
+                    "be because the version libstdcxx loaded by Julia $(Base.VERSION) "*
+                    "isn't compatible with the version of scikit-learn installed by "*
+                    "Conda. To resolve this, we recommend upgrading to Julia v>=1.8.4.\n"*
+                    "If you must stick with your current julia version you can "*
+                    "also resolve this issue by pre-appending `$(CONDA.ROOTENV)/lib` to "*
+                    "your system's `LD_LIBRARY_PATH` enviroment variable."
             end
-            libstdcxx_solved = true
         end
-
-        mod = PyCall.pyimport_conda("sklearn", "scikit-learn")
-    else
-        mod = PyCall.pyimport_conda("sklearn", "scikit-learn")
+        @info(INFO_MSG)
+        rethrow()
     end
 
    version = VersionParsing.vparse(mod.__version__)