Merge pull request #64 from tylerjthomas9/class-weight-dict

Allow Naive passing of Julia `class_weight` dictionary

Project.toml

@@ -4,24 +4,29 @@ authors = ["Thibaut Lienart, Anthony Blaom"]
 version = "0.6.0"
 
 [deps]
-MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
 PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 
 [compat]
+Aqua = "0.8"
 MLJBase = "1"
 MLJModelInterface = "1.4"
+MLJTestInterface = "0.2"
 PythonCall = "0.9"
+StableRNGs = "1"
+Statistics = "1"
 Tables = "1.10"
+Test = "1"
 julia = "1.6"
 
 [extras]
+Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 MLJTestInterface = "72560011-54dd-4dc2-94f3-c5de45b75ecd"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["MLJBase", "MLJTestInterface", "StableRNGs", "Test"]
+test = ["Aqua", "MLJBase", "MLJTestInterface", "StableRNGs", "Test"]

src/macros.jl

@@ -103,6 +103,17 @@ function _sk_finalize(m, clean_expr, fit_expr, expr)
     esc(expr)
 end
 
+"""
+    _prepare_param(obj)
+
+Prepare model parameters for passing Python constructor
+"""
+_prepare_param(obj) = obj
+
+function _prepare_param(obj::Dict)
+    return SK.PythonCall.pydict(obj)
+end
+
 # =================================
 # Specifics for SUPERVISED MODELS
 # =================================
@@ -142,9 +153,10 @@ function _skmodel_fit_reg(modelname, params, save_std::Bool=false)
             pyisnull(parent) && ski!(parent, skmod)
             # retrieve the effective ScikitLearn constructor
             skconstr = getproperty(parent, mdl)
-            # build the scikitlearn model passing all the parameters
-            skmodel = skconstr(
-                        $((Expr(:kw, p, :(model.$p)) for p in params)...))
+            param_dict = Dict{Symbol, Any}(
+                [(p => _prepare_param(getfield(model, p))) for p in $params]
+            )
+            skmodel = skconstr(; param_dict...)
             # --------------------------------------------------------------
             # fit and organise results
             X_py = ScikitLearnAPI.numpy.array(Xmatrix)
@@ -180,8 +192,10 @@ function _skmodel_fit_clf(modelname, params)
             parent = eval(sksym)
             pyisnull(parent) && ski!(parent, skmod)
             skconstr = getproperty(parent, mdl)
-            skmodel = skconstr(
-                        $((Expr(:kw, p, :(model.$p)) for p in params)...))
+            param_dict = Dict{Symbol, Any}(
+                [(p => _prepare_param(getfield(model, p))) for p in $params]
+            )
+            skmodel = skconstr(; param_dict...)
             fitres  = SK.fit!(skmodel, Xmatrix, yplain)
             report = (; names)
             if ScikitLearnAPI.pyhasattr(fitres, "coef_")
@@ -264,8 +278,10 @@ function _skmodel_fit_uns(modelname, params)
             parent = eval(sksym)
             pyisnull(parent) && ski!(parent, skmod)
             skconstr = getproperty(parent, mdl)
-            skmodel = skconstr(
-                        $((Expr(:kw, p, :(model.$p)) for p in params)...))
+            param_dict = Dict{Symbol, Any}(
+                [(p => _prepare_param(getfield(model, p))) for p in $params]
+            )
+            skmodel = skconstr(; param_dict...)
             fitres  = SK.fit!(skmodel, Xmatrix)
             # TODO: we may want to use the report later on
             report  = NamedTuple()

test/extras.jl

@@ -6,3 +6,11 @@
     f, = MB.fit(m, 1, X)
     @test f !== nothing
 end
+
+@testset "i63" begin
+    X, y = MB.make_blobs(500, 3, rng=555)
+    w = Dict(1=>0.2, 2=>0.7, 3=>0.1)
+    m = RandomForestClassifier(class_weight=w)
+    f, = MB.fit(m, 1, X, y)
+    @test f !== nothing
+end

test/runtests.jl

@@ -1,3 +1,4 @@
+using Aqua
 using StableRNGs
 using MLJScikitLearnInterface
 using Test
@@ -27,3 +28,5 @@ println("\nclustering");              include("models/clustering.jl")
 println("\nfeature importance tests");  include("feature_importance_tests.jl")
 println("\ngeneric interface tests");   include("generic_api_tests.jl")
 println("\nExtra tests from bug reports"); include("extras.jl")
+
+Aqua.test_all(MLJScikitLearnInterface, ambiguities=false)