Merge pull request #761 from JuliaAI/dev

For a 0.20.1 release

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJBase"
 uuid = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 authors = ["Anthony D. Blaom <[email protected]>"]
-version = "0.20.0"
+version = "0.20.1"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"

src/machines.jl

@@ -769,14 +769,15 @@ Returns a shallow copy of the machine to make it serializable. In particular,
 all training data is removed and, if necessary, learned parameters are replaced
 with persistent representations.
 
-Any general purpose Julia serialization may be applied to the output of
+Any general purpose Julia serializer may be applied to the output of
 `serializable` (eg, JLSO, BSON, JLD) but you must call `restore!(mach)` on
 the deserialised object `mach` before using it. See the example below.
 
 If using Julia's standard Serialization library, a shorter workflow is
 available using the [`save`](@ref) method.
 
-A machine returned by serializable is characterized by the property `mach.state == -1`.
+A machine returned by `serializable` is characterized by the property
+`mach.state == -1`.
 
 ### Example using [JLSO](https://invenia.github.io/JLSO.jl/stable/)
 
@@ -860,8 +861,7 @@ supported) using the Serialization module.
 To serialise using a different format, see [`serializable`](@ref).
 
 Machines are deserialized using the `machine` constructor as shown in
-the example below. Data (or nodes) may be optionally passed to the
-constructor for retraining on new data using the saved model.
+the example below.
 
 > The implementation of `save` for machines changed in MLJ 0.18
 >  (MLJBase 0.20). You can only restore a machine saved using older

src/resampling.jl

@@ -503,6 +503,8 @@ struct.
   machine `mach` training in resampling - one machine per train/test
   pair.
 
+- `train_test_rows`: a vector of tuples, each of the form `(train, test)`, where `train` and `test` 
+   are vectors of row (observation) indices for training and evaluation respectively. 
 """
 struct PerformanceEvaluation{M,
                              Measurement,
@@ -538,7 +540,7 @@ function Base.show(io::IO, ::MIME"text/plain", e::PerformanceEvaluation)
             "with these fields:")
     println(io, "  measure, measurement, operation, per_fold,\n"*
             "  per_observation, fitted_params_per_fold,\n"*
-            "  report_per_fold, train_test_pairs")
+            "  report_per_fold, train_test_rows")
     println(io, "Extract:")
     show_color = MLJBase.SHOW_COLOR
     color_off()
@@ -617,7 +619,7 @@ function _check_measure(measure, operation, model, y)
 
 end
 
-_check_measures(measures, operations, model, y) = begin
+function _check_measures(measures, operations, model, y)
     all(eachindex(measures)) do j
         _check_measure(measures[j], operations[j], model, y)
     end

test/resampling.jl

@@ -766,5 +766,23 @@ end
              measures=[LogLoss(), BrierScore()], verbosity=0)
 end
 
+@testset "reported fields in documentation" begin
+    # Using `evaluate` to obtain a `PerformanceEvaluation` object.
+    clf = ConstantClassifier()
+    X, y = make_moons(100)
+    evaluations = evaluate(clf, X, y, resampling=CV())
+    T = typeof(evaluations)
+    @test T <: PerformanceEvaluation
+
+    show_text = sprint(show, MIME"text/plain"(), evaluations)
+    docstring_text = string(@doc(PerformanceEvaluation))
+    for fieldname in fieldnames(PerformanceEvaluation)
+        @test contains(show_text, string(fieldname))
+        # string(text::Markdown.MD) converts `-` list items to `*`.
+        @test contains(docstring_text, " * `$fieldname`")
+    end
+end
+
 #end
 true
+