Fix windows test failures (#262)

* dummy push to check current test success on windows

* update test pipeline to v1

* syntax

* info

* Project.toml

* info statements

* Pycall version wasnt issue

* see if issue is in fit! or just calling the pycall object

* try noise_learn false

* add MWE at start of Emulator testing

* typo, remove noise_learn = false

* trim MWE

* escapes

* update python packages

* update python packages for lin/mac

* update 1.1.1 -> 1.3.2

* format

* update init

* try update test versions

* try resolve syntax error?

* try resolve syntax error?

* try resolve syntax error?

* indent

* update versions of some actions, and checkout actions@main

* rm info statements

* rm info

* test 1.5.1

* rm / change info statements

.github/workflows/Tests.yml

@@ -1,12 +1,8 @@
 name: Tests
-
-on:
-  pull_request:
-  push:
-    tags: '*'
-
+on: [push]
 jobs:
-  test-linux:
+  test-linux-v1:
+    name: test-linux-v1
     timeout-minutes: 60
     strategy:
       fail-fast: true
@@ -15,17 +11,17 @@ jobs:
 
     steps:
     - name: Cancel Previous Runs
-      uses: styfle/cancel-workflow-action@0.8.0
+      uses: styfle/cancel-workflow-action@0.12.1
       with:
         access_token: ${{ github.token }}
 
     - name: Checkout
-      uses: actions/checkout@v2
+      uses: actions/checkout@main
 
     - name: Set up Julia
-      uses: julia-actions/setup-julia@v1
+      uses: julia-actions/setup-julia@v2
       with:
-        version: 1
+        version: '1'
 
     - name: Install Julia Project Packages
       # we add this ENV varaible to force PyCall to download and use Conda rather than
@@ -56,12 +52,13 @@ jobs:
       if: success()
 
     - name: Submit coverage
-      uses: codecov/codecov-action@v1.0.7
+      uses: codecov/codecov-action@v5
       with:
         token: ${{secrets.CODECOV_TOKEN}}
       if: success()
 
-  test-macos:
+  test-macos-v1:
+    name: test-macos-v1
     timeout-minutes: 60
     strategy:
       fail-fast: true
@@ -70,18 +67,18 @@ jobs:
 
     steps:
     - name: Cancel Previous Runs
-      uses: styfle/cancel-workflow-action@0.8.0
+      uses: styfle/cancel-workflow-action@0.12.1
       with:
         access_token: ${{ github.token }}
 
     - name: Checkout
-      uses: actions/checkout@v2
+      uses: actions/checkout@main
 
     - name: Set up Julia
-      uses: julia-actions/setup-julia@v1
+      uses: julia-actions/setup-julia@v2
       with:
-        version: 1.9.0
-                 # later versions causes hanging in package building
+        version: 1
+
     - name: Install Julia Project Packages
       env:
         PYTHON: ""
@@ -101,7 +98,8 @@ jobs:
       run: |
         LD_LIBRARY_PATH=$HOME/.julia/conda/3/lib julia --project -e 'using Pkg; Pkg.test()'
 
-  test-windows:
+  test-windows-v1:
+    name: test-windows-v1
     timeout-minutes: 60
     strategy:
       fail-fast: true
@@ -110,15 +108,15 @@ jobs:
 
     steps:
     - name: Cancel Previous Runs
-      uses: styfle/cancel-workflow-action@0.8.0
+      uses: styfle/cancel-workflow-action@0.12.1
       with:
         access_token: ${{ github.token }}
 
     - name: Checkout
-      uses: actions/checkout@v2
+      uses: actions/checkout@main
 
     - name: Set up Julia
-      uses: julia-actions/setup-julia@v1
+      uses: julia-actions/setup-julia@v2
       with:
         version: 1
 

src/Emulator.jl

@@ -149,6 +149,7 @@ function Emulator(
 
         training_pairs = PairedDataContainer(training_inputs, decorrelated_training_outputs)
         # [4.] build an emulator
+
         build_models!(machine_learning_tool, training_pairs)
     else
         if decorrelate || !isa(machine_learning_tool, VectorRandomFeatureInterface)
@@ -160,7 +161,6 @@ function Emulator(
         build_models!(machine_learning_tool, training_pairs, regularization_matrix = obs_noise_cov)
     end
 
-
     return Emulator{FT}(
         machine_learning_tool,
         training_pairs,

src/GaussianProcess.jl

@@ -10,8 +10,8 @@ using ScikitLearn
 const pykernels = PyNULL()
 const pyGP = PyNULL()
 function __init__()
-    copy!(pykernels, pyimport_conda("sklearn.gaussian_process.kernels", "scikit-learn=1.1.1"))
-    copy!(pyGP, pyimport_conda("sklearn.gaussian_process", "scikit-learn=1.1.1"))
+    copy!(pykernels, pyimport_conda("sklearn.gaussian_process.kernels", "scikit-learn=1.5.1"))
+    copy!(pyGP, pyimport_conda("sklearn.gaussian_process", "scikit-learn=1.5.1"))
 end
 
 #exports (from Emulator)
@@ -260,25 +260,20 @@ function build_models!(
         kern = kern + white
         println("Learning additive white noise")
     end
-
     regularization_noise = gp.alg_reg_noise
 
     for i in 1:N_models
         kernel_i = deepcopy(kern)
         data_i = output_values[i, :]
         m = pyGP.GaussianProcessRegressor(kernel = kernel_i, n_restarts_optimizer = 10, alpha = regularization_noise)
-
         # ScikitLearn.fit! arguments:
         # input_values:    (N_samples × input_dim)
         # data_i:    (N_samples,)
+
+        @info("Training kernel $(i), ")
         ScikitLearn.fit!(m, input_values, data_i)
-        if i == 1
-            println(m.kernel.hyperparameters)
-            print("Completed training of: ")
-        end
-        println(i, ", ")
         push!(models, m)
-        println(m.kernel)
+        @info(m.kernel)
     end
 end
 

test/Emulator/MWE.jl

@@ -0,0 +1,29 @@
+using PyCall
+using ScikitLearn
+const pykernels = PyNULL()
+const pyGP = PyNULL()
+
+function init()
+    copy!(pykernels, pyimport_conda("sklearn.gaussian_process.kernels", "scikit-learn=1.3.2"))
+    copy!(pyGP, pyimport_conda("sklearn.gaussian_process", "scikit-learn=1.3.2"))
+end
+
+function minimal_failing_example()
+    # some kind of kernel
+    kernel = pykernels.ConstantKernel(constant_value = 1.0)
+
+    # some data
+    n = 20                                       # number of training points
+    x = reshape(2.0 * π * rand(n), 1, n)         # predictors/features: 1 x n
+    y = reshape(sin.(x) + 0.05 * randn(n)', 1, n) # predictands/targets: 1 x n
+
+    # the model
+    m = pyGP.GaussianProcessRegressor(kernel = kernel)
+
+    # call fit!
+    ScikitLearn.fit!(m, x, y)
+    @info "fit successful"
+end
+
+init()
+minimal_failing_example()

test/Emulator/runtests.jl

@@ -1,3 +1,5 @@
+include("MWE.jl")
+
 # Import modules
 using Random
 using Test

test/GaussianProcess/runtests.jl

@@ -8,7 +8,7 @@ using LinearAlgebra
 using PyCall
 using ScikitLearn
 const pykernels = PyNULL()
-copy!(pykernels, pyimport_conda("sklearn.gaussian_process.kernels", "scikit-learn=1.1.1"))
+copy!(pykernels, pyimport_conda("sklearn.gaussian_process.kernels", "scikit-learn=1.5.1"))
 
 
 using CalibrateEmulateSample.Emulators
@@ -127,9 +127,7 @@ using CalibrateEmulateSample.DataContainers
     se = pykernels.RBF(1.0)
     GPkernel = var * se
 
-
     gp3 = GaussianProcess(gppackage; kernel = GPkernel, noise_learn = true, prediction_type = pred_type)
-
     em3 = Emulator(
         gp3,
         iopairs,
@@ -138,7 +136,6 @@ using CalibrateEmulateSample.DataContainers
         standardize_outputs = false,
         retained_svd_frac = 1.0,
     )
-
     @test_logs (:warn,) (:warn,) Emulator(
         gp3,
         iopairs,
@@ -159,7 +156,6 @@ using CalibrateEmulateSample.DataContainers
 
     Emulators.optimize_hyperparameters!(em3)
 
-
     #gp3 = GaussianProcess(iopairs, gppackage; GPkernel=GPkernel, obs_noise_cov=nothing,
     #               normalized=false, noise_learn=true,
     #	   retained_svd_frac=1.0, standardize=false,

test/runtests.jl

@@ -6,6 +6,7 @@ if TEST_PLOT_OUTPUT
     using Plots
 end
 
+
 function include_test(_module)
     println("Starting tests for $_module")
     t = @elapsed include(joinpath(_module, "runtests.jl"))