Merge pull request #229 from alan-turing-institute/dev

extend [compat] StatsBase = "^0.32,^0.33"

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJBase"
 uuid = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 authors = ["Anthony D. Blaom <[email protected]>"]
-version = "0.12.4"
+version = "0.12.5"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
@@ -46,7 +46,7 @@ Parameters = "^0.12"
 PrettyTables = "^0.8"
 ProgressMeter = "^1.1"
 ScientificTypes = "^0.7"
-StatsBase = "^0.32"
+StatsBase = "^0.32,^0.33"
 Tables = "^0.2,^1.0"
 julia = "1"
 

src/MLJBase.jl

@@ -14,7 +14,7 @@ import MLJModelInterface: fit, update, update_data, transform,
                           inverse_transform, fitted_params, predict,
                           predict_mode, predict_mean, predict_median,
                           evaluate, clean!, is_same_except,
-                          save, restore
+                          save, restore, is_same_except
 
 # Containers & data manipulation
 using Tables
@@ -262,8 +262,9 @@ include("composition/networks.jl")
 include("composition/composites.jl")
 include("composition/pipelines.jl")
 include("composition/pipeline_static.jl")
-VERSION ≥ v"1.3.0-" && include("composition/arrows.jl")
-
+@static if VERSION ≥ v"1.3.0-"
+    include("composition/arrows.jl")
+end
 include("operations.jl")
 
 include("resampling.jl")

src/hyperparam/one_dimensional_range_methods.jl

@@ -173,6 +173,7 @@ end
 
 ### Fallback and docstring
 
+
 """
     Distributions.fit(D, r::MLJBase.NumericRange)
 
@@ -292,9 +293,14 @@ The argument `probs` can be any probability vector with the same
 length as `r.values`. The second `sampler` method above calls the
 first with a uniform `probs` vector.
 
-The argument `d`, can be either an arbitrary instance of
+The argument `d` can be either an arbitrary instance of
 `UnivariateDistribution` from the Distributions.jl package, or one of
-the Distributions.jl types specified in the table below. 
+a Distributions.jl *types* for which `fit(d, ::NumericRange)` is
+defined. These include: `Arcsine`, `Uniform`, `Biweight`, `Cosine`,
+`Epanechnikov`, `SymTriangularDist`, `Triweight`, `Normal`, `Gamma`,
+`InverseGaussian`, `Logistic`, `LogNormal`, `Cauchy`, `Gumbel`,
+`Laplace`, and `Poisson`; but see the doc-string for
+[`Distributions.fit`](@ref) for an up-to-date list.
 
 If `d` is an *instance*, then sampling is from a truncated form of the
 supplied distribution `d`, the truncation bounds being `r.lower` and