Adding a flat_params test, preparing resampling to receive loggers an…

…d making _logmachinemeasures more generic

ext/LoggersExt/mlflow.jl

@@ -18,8 +18,7 @@ function _logmodelparams(client::MLFlow, run::MLFlowRun, model::Model)
     end
 end
 
-function _logmachinemeasures(client::MLFlow, run::MLFlowRun, measures::Vector{T},
-                             measurements::Vector{Float64}) where T<:Measure
+function _logmachinemeasures(client::MLFlow, run::MLFlowRun, measures, measurements)
     measure_names = measures .|> info .|> x -> x.name
     for (name, value) in zip(measure_names, measurements)
         logmetric(client, run, name, value)

src/resampling.jl

@@ -1339,7 +1339,8 @@ end
         operation=predict,
         repeats = 1,
         acceleration=default_resource(),
-        check_measure=true
+        check_measure=true,
+        logger=nothing
     )
 
 Resampling model wrapper, used internally by the `fit` method of
@@ -1374,7 +1375,7 @@ are not to be confused with any weights bound to a `Resampler` instance
 in a machine, used for training the wrapped `model` when supported.
 
 """
-mutable struct Resampler{S} <: Model
+mutable struct Resampler{S, L} <: Model
     model
     resampling::S # resampling strategy
     measure
@@ -1385,6 +1386,7 @@ mutable struct Resampler{S} <: Model
     check_measure::Bool
     repeats::Int
     cache::Bool
+    logger::L
 end
 
 # Some traits are markded as `missing` because we cannot determine
@@ -1423,7 +1425,8 @@ function Resampler(;
     acceleration=default_resource(),
     check_measure=true,
     repeats=1,
-    cache=true
+    cache=true,
+    logger=nothing
 )
     resampler = Resampler(
         model,
@@ -1435,7 +1438,8 @@ function Resampler(;
         acceleration,
         check_measure,
         repeats,
-        cache
+        cache,
+        logger
     )
     message = MLJModelInterface.clean!(resampler)
     isempty(message) || @warn message

test/extensions/loggers.jl

@@ -9,7 +9,10 @@ using ..Models
     experiment_name = "mlflow logger tests"
 
     @testset "outside extension tests" begin
-        @test_throws ErrorException mlflow_logger("http://localhost:5000")
+        # This test is only relevant for Julia 1.9 and above
+        if VERSION >= v"1.9"
+            @test_throws ErrorException mlflow_logger("http://localhost:5000")
+        end
 
         using MLFlowClient
         logger = mlflow_logger("http://localhost:5000";

test/utilities.jl

@@ -171,5 +171,16 @@ end
         "sin, cos, tan, ..."
 end
 
+@testset "flattening parameters" begin
+    t = (a = (ax = (ax1 = 1, ax2 = 2), ay = 3), b = 4)
+    dict_t = Dict(
+        "a__ax__ax1" => 1,
+        "a__ax__ax2" => 2,
+        "a__ay" => 3,
+        "b" => 4,
+    )
+    @test MLJBase.flat_params(t) == dict_t
+end
+
 end # module
 true