Re-organize code to resolve docstring bug

src/FeatureSelection.jl

@@ -10,5 +10,6 @@ const MMI = MLJModelInterface
 include("models/featureselector.jl")
 include("models/rfe.jl")
 include("shared.jl")
+include("type_docstrings.jl")
 
 end # module

src/models/featureselector.jl

@@ -87,82 +87,4 @@ MMI.metadata_model(
     load_path = "FeatureSelection.FeatureSelector"
 )
 
-## Docstring
-"""
-$(MMI.doc_header(FeatureSelector))
-
-Use this model to select features (columns) of a table, usually as
-part of a model `Pipeline`.
-
-
-# Training data
-
-In MLJ or MLJBase, bind an instance `model` to data with
-
-    mach = machine(model, X)
-
-where
-
-- `X`: any table of input features, where "table" is in the sense of Tables.jl
-
-Train the machine using `fit!(mach, rows=...)`.
-
-
-# Hyper-parameters
-
-- `features`: one of the following, with the behavior indicated:
-
-  - `[]` (empty, the default): filter out all features (columns) which
-    were not encountered in training
-
-  - non-empty vector of feature names (symbols): keep only the
-    specified features (`ignore=false`) or keep only unspecified
-    features (`ignore=true`)
-
-  - function or other callable: keep a feature if the callable returns
-    `true` on its name. For example, specifying
-    `FeatureSelector(features = name -> name in [:x1, :x3], ignore =
-    true)` has the same effect as `FeatureSelector(features = [:x1,
-    :x3], ignore = true)`, namely to select all features, with the
-    exception of `:x1` and `:x3`.
-
-- `ignore`: whether to ignore or keep specified `features`, as
-  explained above
-
-
-# Operations
-
-- `transform(mach, Xnew)`: select features from the table `Xnew` as
-  specified by the model, taking features seen during training into
-  account, if relevant
-
-
-# Fitted parameters
-
-The fields of `fitted_params(mach)` are:
-
-- `features_to_keep`: the features that will be selected
-
-
-# Example
-
-```
-using MLJ
-
-X = (ordinal1 = [1, 2, 3],
-     ordinal2 = coerce(["x", "y", "x"], OrderedFactor),
-     ordinal3 = [10.0, 20.0, 30.0],
-     ordinal4 = [-20.0, -30.0, -40.0],
-     nominal = coerce(["Your father", "he", "is"], Multiclass));
-
-selector = FeatureSelector(features=[:ordinal3, ], ignore=true);
-
-julia> transform(fit!(machine(selector, X)), X)
-(ordinal1 = [1, 2, 3],
- ordinal2 = CategoricalValue{Symbol,UInt32}["x", "y", "x"],
- ordinal4 = [-20.0, -30.0, -40.0],
- nominal = CategoricalValue{String,UInt32}["Your father", "he", "is"],)
-
-```
-"""
-FeatureSelector
+# docstring is in "src/type_docstrings.jl"

src/type_docstrings.jl

@@ -0,0 +1,81 @@
+# This file cannot be include before types and all metadata is defined
+
+## Docstring
+"""
+$(MMI.doc_header(FeatureSelector))
+
+Use this model to select features (columns) of a table, usually as
+part of a model `Pipeline`.
+
+
+# Training data
+
+In MLJ or MLJBase, bind an instance `model` to data with
+
+    mach = machine(model, X)
+
+where
+
+- `X`: any table of input features, where "table" is in the sense of Tables.jl
+
+Train the machine using `fit!(mach, rows=...)`.
+
+
+# Hyper-parameters
+
+- `features`: one of the following, with the behavior indicated:
+
+  - `[]` (empty, the default): filter out all features (columns) which
+    were not encountered in training
+
+  - non-empty vector of feature names (symbols): keep only the
+    specified features (`ignore=false`) or keep only unspecified
+    features (`ignore=true`)
+
+  - function or other callable: keep a feature if the callable returns
+    `true` on its name. For example, specifying
+    `FeatureSelector(features = name -> name in [:x1, :x3], ignore =
+    true)` has the same effect as `FeatureSelector(features = [:x1,
+    :x3], ignore = true)`, namely to select all features, with the
+    exception of `:x1` and `:x3`.
+
+- `ignore`: whether to ignore or keep specified `features`, as
+  explained above
+
+
+# Operations
+
+- `transform(mach, Xnew)`: select features from the table `Xnew` as
+  specified by the model, taking features seen during training into
+  account, if relevant
+
+
+# Fitted parameters
+
+The fields of `fitted_params(mach)` are:
+
+- `features_to_keep`: the features that will be selected
+
+
+# Example
+
+```
+using MLJ
+
+X = (ordinal1 = [1, 2, 3],
+     ordinal2 = coerce(["x", "y", "x"], OrderedFactor),
+     ordinal3 = [10.0, 20.0, 30.0],
+     ordinal4 = [-20.0, -30.0, -40.0],
+     nominal = coerce(["Your father", "he", "is"], Multiclass));
+
+selector = FeatureSelector(features=[:ordinal3, ], ignore=true);
+
+julia> transform(fit!(machine(selector, X)), X)
+(ordinal1 = [1, 2, 3],
+ ordinal2 = CategoricalValue{Symbol,UInt32}["x", "y", "x"],
+ ordinal4 = [-20.0, -30.0, -40.0],
+ nominal = CategoricalValue{String,UInt32}["Your father", "he", "is"],)
+
+```
+"""
+FeatureSelector

test/models/featureselector.jl

@@ -62,6 +62,7 @@
     # Test model Metadata
     @test MLJBase.input_scitype(selector) == MLJBase.Table
     @test MLJBase.output_scitype(selector) == MLJBase.Table
+    @test MLJBase.package_name(selector) == "FeatureSelection"
 end
 
 #  To be added with FeatureSelectorRule X = (n1=["a", "b", "a"], n2=["g", "g", "g"], n3=[7, 8, 9],

test/models/rfe.jl

@@ -106,8 +106,10 @@ const DTM = DummyTestModels
     # Traits
     @test MLJBase.package_name(selector) == "FeatureSelection"
     @test MLJBase.load_path(selector) == "FeatureSelection.RecursiveFeatureElimination"
-    @test MLJBase.iteration_parameter(selector) == FeatureSelection.prepend(:model, MLJBase.iteration_parameter(selector.model))
-    @test MLJBase.training_losses(selector, rpt) == MLJBase.training_losses(selector.model, rpt.model_report)
+    @test MLJBase.iteration_parameter(selector) ==
+        FeatureSelection.prepend(:model, MLJBase.iteration_parameter(selector.model))
+    @test MLJBase.training_losses(selector, rpt) ==
+        MLJBase.training_losses(selector.model, rpt.model_report)
 end
 
 @testset "Compare results for RFE with scikit-learn" begin