final minor fixes before PR

src/tuning.jl

@@ -1,29 +1,42 @@
 abstract type TuningStrategy <: MLJ.MLJType end
 
-mutable struct Grid <: TuningStrategy
-    resolution::Int
-    parallel::Bool
+"""
+$TYPEDEF
+
+Grid object for a grid search tuning strategy.
+"""
+@with_kw mutable struct Grid <: TuningStrategy
+    resolution::Int = 10
+    parallel::Bool = true
 end
 
-Grid(;resolution=10, parallel=true) = Grid(resolution, parallel)
 MLJBase.show_as_constructed(::Type{<:Grid}) = true
 
+"""
+$TYPEDEF
 
+Container for a deterministic tuning strategy.
+"""
 mutable struct DeterministicTunedModel{T,M<:Deterministic} <: MLJ.Deterministic
     model::M
-    tuning::T  # tuning strategy 
+    tuning::T  # tuning strategy
     resampling # resampling strategy
     measure
     operation
     ranges::Union{Vector,ParamRange}
     minimize::Bool
     full_report::Bool
-    train_best::Bool 
+    train_best::Bool
 end
 
+"""
+$TYPEDEF
+
+Container for a probabilistic tuning strategy.
+"""
 mutable struct ProbabilisticTunedModel{T,M<:Probabilistic} <: MLJ.Probabilistic
     model::M
-    tuning::T  # tuning strategy 
+    tuning::T  # tuning strategy
     resampling # resampling strategy
     measure
     operation
@@ -76,13 +89,13 @@ function TunedModel(;model=nothing,
                     minimize=true,
                     full_report=true,
                     train_best=true)
-    
+
     !isempty(ranges) || error("You need to specify ranges=... ")
     model !== nothing || error("You need to specify model=... ")
 
     message = clean!(model)
     isempty(message) || @info message
-    
+
     if model isa Deterministic
         return DeterministicTunedModel(model, tuning, resampling,
            measure, operation, ranges, minimize, full_report, train_best)
@@ -111,13 +124,13 @@ function MLJBase.fit(tuned_model::EitherTunedModel{Grid,M}, verbosity::Int, X, y
     end
 
     ranges isa Vector{<:ParamRange} ||
-        error("ranges must be a ParamRange object or a vector of "*
+        error("ranges must be a ParamRange object or a vector of " *
               "ParamRange objects. ")
-        
-    
+
+
     parameter_names = [string(r.field) for r in ranges]
     scales = [scale(r) for r in ranges]
-    
+
     # the mutating model:
     clone = deepcopy(tuned_model.model)
 
@@ -137,7 +150,7 @@ function MLJBase.fit(tuned_model::EitherTunedModel{Grid,M}, verbosity::Int, X, y
         elseif range isa MLJ.NumericRange
             MLJ.iterator(range, tuned_model.tuning.resolution)
         else
-            throw(TypeError(:iterator, "", MLJ.ParamRange, rrange))            
+            throw(TypeError(:iterator, "", MLJ.ParamRange, rrange))
         end
     end
 
@@ -154,24 +167,22 @@ function MLJBase.fit(tuned_model::EitherTunedModel{Grid,M}, verbosity::Int, X, y
 
     # initialize search for best model:
     best_model = deepcopy(tuned_model.model)
-    best_measurement =
-        tuned_model.minimize ? Inf : -Inf
-    s =
-        tuned_model.minimize ? 1 : -1
+    best_measurement = ifelse(tuned_model.minimize, Inf, -Inf)
+    s = ifelse(tuned_model.minimize, 1, -1)
 
     # evaluate all the models using specified resampling:
     # TODO: parallelize!
 
     meter = Progress(N+1, dt=0, desc="Iterating over a $N-point grid: ",
                      barglyphs=BarGlyphs("[=> ]"), barlen=25, color=:yellow)
     verbosity != 1 || next!(meter)
-    for i in 1:N
 
+    for i in 1:N
         verbosity != 1 || next!(meter)
 
         A_row = Tuple(A[i,:])
 
- #       new_params = copy(nested_iterators, A_row)   
+ #       new_params = copy(nested_iterators, A_row)
 
         # mutate `clone` (the model to which `resampler` points):
         for k in 1:n_iterators
@@ -181,19 +192,17 @@ function MLJBase.fit(tuned_model::EitherTunedModel{Grid,M}, verbosity::Int, X, y
 
         if verbosity == 2
             fit!(resampling_machine, verbosity=0)
-
         else
             fit!(resampling_machine, verbosity=verbosity-1)
         end
         e = mean(evaluate(resampling_machine))
 
         if verbosity > 1
-            text = reduce(*, ["$(parameter_names[j])=$(A_row[j]) \t"
-                              for j in 1:length(A_row)])
+            text = prod("$(parameter_names[j])=$(A_row[j]) \t" for j in 1:length(A_row))
             text *= "measurement=$e"
             println(text)
         end
-        
+
         if s*(best_measurement - e) > 0
             best_model = deepcopy(clone)
             best_measurement = e
@@ -203,7 +212,7 @@ function MLJBase.fit(tuned_model::EitherTunedModel{Grid,M}, verbosity::Int, X, y
  #           models[i] = deepcopy(clone)
             measurements[i] = e
         end
-        
+
     end
 
     if tuned_model.train_best
@@ -234,11 +243,11 @@ function MLJBase.fit(tuned_model::EitherTunedModel{Grid,M}, verbosity::Int, X, y
                   measurements=[best_measurement, ][1:0], # empty vector
                   best_measurement=best_measurement)
     end
-    
+
     cache = nothing
-    
+
     return fitresult, cache, report
-    
+
 end
 
 MLJBase.fitted_params(::EitherTunedModel, fitresult) = (best_model=fitresult.model,)
@@ -268,7 +277,7 @@ MLJBase.target_scitype_union(::Type{<:ProbabilisticTunedModel{T,M}}) where {T,M}
 MLJBase.input_is_multivariate(::Type{<:ProbabilisticTunedModel{T,M}}) where {T,M} = MLJBase.input_is_multivariate(M)
 
 
-## LEARNING CURVES 
+## LEARNING CURVES
 
 """
     curve = learning_curve!(mach; resolution=30, resampling=Holdout(), measure=rms, operation=predict, range=nothing, n=1)
@@ -310,9 +319,9 @@ plot(curves.parameter_values, curves.measurements, xlab=curves.parameter_name)
 
 """
 function learning_curve!(mach::Machine{<:Supervised};
-                        resolution=30,
-                        resampling=Holdout(),
-                         measure=rms, operation=predict, range=nothing, verbosity=1, n=1)
+                         resolution=30, resampling=Holdout(),
+                         measure=rms, operation=predict,
+                         range=nothing, verbosity=1, n=1)
 
     range !== nothing || error("No param range specified. Use range=... ")
 
@@ -328,14 +337,10 @@ function learning_curve!(mach::Machine{<:Supervised};
     parameter_name=report.parameter_names[1]
     parameter_scale=report.parameter_scales[1]
     parameter_values=[report.parameter_values[:, 1]...]
-    measurements_ =
-        n == 1 ? [measurements...] : measurements
-
+    measurements_ = (n == 1) ? [measurements...] : measurements
+
     return (parameter_name=parameter_name,
             parameter_scale=parameter_scale,
             parameter_values=parameter_values,
             measurements = measurements_)
 end
-
-
-