Extend compat for StatisticalMeasures to include v0.3

.github/workflows/ci.yml

@@ -18,7 +18,7 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.6' 
+          - '1.10'
           - '1' # automatically expands to the latest stable 1.x release of Julia.
         os:
           - ubuntu-latest

Project.toml

@@ -1,7 +1,7 @@
 name = "FeatureSelection"
 uuid = "33837fe5-dbff-4c9e-8c2f-c5612fe2b8b6"
 authors = ["Anthony D. Blaom <[email protected]>", "Samuel Okon <[email protected]"]
-version = "0.2.3"
+version = "0.2.4"
 
 [deps]
 MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
@@ -11,15 +11,15 @@ Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 [compat]
 Aqua = "0.8"
 Distributions = "0.25"
-julia = "1.6"
+julia = "1.10"
 MLJBase = "1.4"
 MLJTuning = "0.8"
 MLJDecisionTreeInterface = "0.4"
 MLJScikitLearnInterface = "0.6"
 MLJModelInterface = "1.10"
 ScientificTypesBase = "3"
 StableRNGs = "1"
-StatisticalMeasures = "0.1, 0.2"
+StatisticalMeasures = "0.1, 0.2, 0.3"
 Tables = "1.2"
 Test = "1.6"
 

test/models/featureselector.jl

@@ -12,21 +12,21 @@
     # Test feature selection with `features=Symbol[]`
     namesX   = MLJBase.schema(X).names |> collect
     selector = FeatureSelector()
-    f,       = MLJBase.fit(selector, 1, X)
+    f,       = MLJBase.fit(selector, 0, X)
     @test f == namesX
     Xt = MLJBase.transform(selector, f, MLJBase.selectrows(X, 1:2))
     @test Set(MLJBase.schema(Xt).names) == Set(namesX)
     @test length(Xt.Zn) == 2
 
     # Test on selecting features if `features` keyword is defined
     selector = FeatureSelector(features=[:Zn, :Crim])
-    f,       = MLJBase.fit(selector, 1, X)
+    f,       = MLJBase.fit(selector, 0, X)
     @test MLJBase.transform(selector, f, MLJBase.selectrows(X, 1:2)) ==
             MLJBase.select(X, 1:2, [:Zn, :Crim])
 
     # test on ignoring a feature, even if it's listed in the `features`
     selector.ignore = true
-    f,   = MLJBase.fit(selector, 1, X)
+    f,   = MLJBase.fit(selector, 0, X)
     Xnew = MLJBase.transform(selector, f, X)
     @test MLJBase.transform(selector, f, MLJBase.selectrows(X, 1:2)) ==
          MLJBase.select(X, 1:2, [:x3, :x4])
@@ -35,7 +35,7 @@
     selector = FeatureSelector(features=[:x1, :mickey_mouse])
     @test_throws(
         ArgumentError,
-        MLJBase.fit(selector, 1, X)
+        MLJBase.fit(selector, 0, X)
     )
     selector.ignore = true
     @test_logs(
@@ -50,13 +50,13 @@
     selector = FeatureSelector(features= x-> x == (:x1))
    @test_throws(
         ArgumentError,
-        MLJBase.fit(selector, 1, X)
+        MLJBase.fit(selector, 0, X)
     )
     selector.ignore = true
     selector.features = x-> x in [:Zn, :Crim, :x3, :x4]
      @test_throws(
         ArgumentError,
-        MLJBase.fit(selector, 1, X)
+        MLJBase.fit(selector, 0, X)
     )
 
     # Test model Metadata
@@ -67,4 +67,4 @@ end
 #  To be added with FeatureSelectorRule X = (n1=["a", "b", "a"], n2=["g", "g", "g"], n3=[7, 8, 9],
 #               n4 =UInt8[3,5,10],  o1=[4.5, 3.6, 4.0], )
 # MLJBase.schema(X)
-# Xc = coerce(X,  :n1=>Multiclass, :n2=>Multiclass)
+# Xc = coerce(X,  :n1=>Multiclass, :n2=>Multiclass)

test/models/rfe.jl

@@ -62,9 +62,9 @@ const DTM = DummyTestModels
     selector_mach3 = machine(selector3, Xt, y)
     selector_mach4 = machine(selector4, Xt, y)
 
-    fit!(selector_mach)
-    fit!(selector_mach2)
-    fit!(selector_mach3)
+    fit!(selector_mach, verbosity=0)
+    fit!(selector_mach2, verbosity=0)
+    fit!(selector_mach3, verbosity=0)
     @test_logs(
         (:warn, "n_features > number of features in training data, hence no feature will be eliminated."),
         match_mode=:any,
@@ -149,7 +149,7 @@ end
     svm = SVR(kernel="linear")
     rfe = RecursiveFeatureElimination(model=svm, n_features=5)
     mach = machine(rfe, Xs, ys)
-    fit!(mach)
+    fit!(mach, verbosity=0)
 
     rfecv = RecursiveFeatureElimination(model=svm)
     tuning_rfe_model = TunedModel(
@@ -160,7 +160,7 @@ end
         range=range(rfecv, :n_features, values=1:10)
     )
     self_tuning_rfe_mach = machine(tuning_rfe_model, Xs, ys)
-    fit!(self_tuning_rfe_mach)
+    fit!(self_tuning_rfe_mach, verbosity=0)
 
     # Compare results
     # Convert MLJ RFE scores to rankings