Merge pull request #415 from alan-turing-institute/dev

For 0.6.1 release

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJ"
 uuid = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
 authors = ["Anthony D. Blaom <anthony.blaom@gmail.com>"]
-version = "0.6"
+version = "0.6.1"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
@@ -37,7 +37,7 @@ OrderedCollections = "^1.1"
 PrettyTables = "^0.6"
 ProgressMeter = "^1.1"
 RecipesBase = "^0.7"
-ScientificTypes = "^0.3"
+ScientificTypes = "^0.3.2"
 StatsBase = "^0.32"
 Tables = "^0.2"
 julia = "1"

src/resampling.jl

@@ -47,7 +47,7 @@ Holdout resampling strategy, for use in `evaluate!`, `evaluate` and in tuning.
 
 Returns the pair `[(train, test)]`, where `train` and `test` are
 vectors such that `rows=vcat(train, test)` and
-`length(train)/length(test)` is approximatey equal to fraction_train`.
+`length(train)/length(rows)` is approximatey equal to fraction_train`.
 
 Pre-shuffling of `rows` is controlled by `rng` and `shuffle`. If `rng`
 is an integer, then the `Holdout` keyword constructor resets it to

src/scitypes.jl

@@ -2,21 +2,21 @@
 
 struct SupervisedScitype{input_scitype, target_scitype, prediction_type} end
 
-ScientificTypes.scitype(model::Deterministic, ::Val{:mlj}) =
+ScientificTypes.scitype(model::Deterministic, ::ScientificTypes.MLJ) =
     SupervisedScitype{input_scitype(model),
                     target_scitype(model),
                     :deterministic}
-                    
-ScientificTypes.scitype(model::Probabilistic, ::Val{:mlj}) =
+
+ScientificTypes.scitype(model::Probabilistic, ::ScientificTypes.MLJ) =
     SupervisedScitype{input_scitype(model),
                     target_scitype(model),
                     :probabilistic}
-                    
-ScientificTypes.scitype(model::Interval, ::Val{:mlj}) =
+
+ScientificTypes.scitype(model::Interval, ::ScientificTypes.MLJ) =
     SupervisedScitype{input_scitype(model),
                     target_scitype(model),
                     :interval}
-                    
+
 function Base.getproperty(::SupervisedScitype{input_scitype, target_scitype, prediction_type},
                           field::Symbol) where {input_scitype, target_scitype, prediction_type}
     if field === :input_scitype
@@ -48,7 +48,7 @@ end
 
 struct UnsupervisedScitype{input_scitype, output_scitype} end
 
-ScientificTypes.scitype(model::Unsupervised, ::Val{:mlj}) =
+ScientificTypes.scitype(model::Unsupervised, ::ScientificTypes.MLJ) =
     UnsupervisedScitype{input_scitype(model),
                       MLJBase.output_scitype(model)}
 
@@ -77,7 +77,7 @@ function Base.show(io::IO, ::MIME"text/plain", S::UnsupervisedScitype)
     show(io, MIME("text/plain"), _as_named_tuple(S))
 end
 
-              
+
 ## MEASURES
 
 struct MeasureScitype{target_scitype,
@@ -87,7 +87,7 @@ struct MeasureScitype{target_scitype,
                is_feature_dependent,
                supports_weights} end
 
-ScientificTypes.scitype(measure, ::Val{:mlj}, ::Val{:measure}) =
+ScientificTypes.scitype(measure, ::ScientificTypes.MLJ, ::Val{:measure}) =
     MeasureScitype{target_scitype(measure),
                prediction_type(measure),
                orientation(measure),
@@ -140,5 +140,3 @@ end
 function Base.show(io::IO, ::MIME"text/plain", M::MeasureScitype)
       show(io, MIME("text/plain"), _as_named_tuple(M))
 end
-
-