Add fields and parallelize with nothing

Project.toml

@@ -20,6 +20,7 @@ ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
+TaijaBase = "10284c91-9f28-4c9a-abbf-ee43576dfff6"
 
 [compat]
 Aqua = "0.8"

src/ConformalPrediction.jl

@@ -1,5 +1,7 @@
 module ConformalPrediction
 
+using TaijaBase
+
 # Conformal Models:
 include("conformal_models/conformal_models.jl")
 export ConformalModel

src/conformal_models/inductive_classification.jl

@@ -29,16 +29,18 @@ mutable struct SimpleInductiveClassifier{Model<:Supervised} <: ConformalProbabil
     coverage::AbstractFloat
     scores::Union{Nothing,Dict{Any,Any}}
     heuristic::Function
+    parallelizer::Union{Nothing,AbstractParallelizer}
     train_ratio::AbstractFloat
 end
 
 function SimpleInductiveClassifier(
     model::Supervised;
     coverage::AbstractFloat=0.95,
     heuristic::Function=minus_softmax,
+    parallelizer::Union{Nothing,AbstractParallelizer}=nothing,
     train_ratio::AbstractFloat=0.5,
 )
-    return SimpleInductiveClassifier(model, coverage, nothing, heuristic, train_ratio)
+    return SimpleInductiveClassifier(model, coverage, nothing, heuristic, parallelizer, train_ratio)
 end
 
 """
@@ -127,16 +129,18 @@ mutable struct AdaptiveInductiveClassifier{Model<:Supervised} <: ConformalProbab
     coverage::AbstractFloat
     scores::Union{Nothing,Dict{Any,Any}}
     heuristic::Function
+    parallelizer::Union{Nothing,AbstractParallelizer}
     train_ratio::AbstractFloat
 end
 
 function AdaptiveInductiveClassifier(
     model::Supervised;
     coverage::AbstractFloat=0.95,
     heuristic::Function=minus_softmax,
+    parallelizer::Union{Nothing,AbstractParallelizer}=nothing,
     train_ratio::AbstractFloat=0.5,
 )
-    return AdaptiveInductiveClassifier(model, coverage, nothing, heuristic, train_ratio)
+    return AdaptiveInductiveClassifier(model, coverage, nothing, heuristic, parallelizer, train_ratio)
 end
 
 @doc raw"""

src/conformal_models/inductive_regression.jl

@@ -6,16 +6,18 @@ mutable struct SimpleInductiveRegressor{Model<:Supervised} <: ConformalInterval
     coverage::AbstractFloat
     scores::Union{Nothing,AbstractArray}
     heuristic::Function
+    parallelizer::Union{Nothing,AbstractParallelizer}
     train_ratio::AbstractFloat
 end
 
 function SimpleInductiveRegressor(
     model::Supervised;
     coverage::AbstractFloat=0.95,
     heuristic::Function=absolute_error,
+    parallelizer::Union{Nothing,AbstractParallelizer}=nothing,
     train_ratio::AbstractFloat=0.5,
 )
-    return SimpleInductiveRegressor(model, coverage, nothing, heuristic, train_ratio)
+    return SimpleInductiveRegressor(model, coverage, nothing, heuristic, parallelizer, train_ratio)
 end
 
 @doc raw"""
@@ -84,6 +86,7 @@ mutable struct ConformalQuantileRegressor{Model<:QuantileModel} <: ConformalInte
     coverage::AbstractFloat
     scores::Union{Nothing,AbstractArray}
     heuristic::Function
+    parallelizer::Union{Nothing,AbstractParallelizer}
     train_ratio::AbstractFloat
 end
 
@@ -93,9 +96,10 @@ function ConformalQuantileRegressor(
     heuristic::Function=function f(y, ŷ_lb, ŷ_ub)
         return reduce((x, y) -> max.(x, y), [ŷ_lb - y, y - ŷ_ub])
     end,
+    parallelizer::Union{Nothing,AbstractParallelizer}=nothing,
     train_ratio::AbstractFloat=0.5,
 )
-    return ConformalQuantileRegressor(model, coverage, nothing, heuristic, train_ratio)
+    return ConformalQuantileRegressor(model, coverage, nothing, heuristic, parallelizer, train_ratio)
 end
 
 @doc raw"""

src/conformal_models/transductive_classification.jl

@@ -5,12 +5,16 @@ mutable struct NaiveClassifier{Model<:Supervised} <: ConformalProbabilisticSet
     coverage::AbstractFloat
     scores::Union{Nothing,AbstractArray}
     heuristic::Function
+    parallelizer::Union{Nothing,AbstractParallelizer}
 end
 
 function NaiveClassifier(
-    model::Supervised; coverage::AbstractFloat=0.95, heuristic::Function=minus_softmax
+    model::Supervised;
+    coverage::AbstractFloat=0.95,
+    heuristic::Function=minus_softmax,
+    parallelizer::Union{Nothing,AbstractParallelizer}=nothing,
 )
-    return NaiveClassifier(model, coverage, nothing, heuristic)
+    return NaiveClassifier(model, coverage, nothing, heuristic, parallelizer)
 end
 
 @doc raw"""

src/conformal_models/transductive_regression.jl

@@ -10,12 +10,16 @@ mutable struct NaiveRegressor{Model<:Supervised} <: ConformalInterval
     coverage::AbstractFloat
     scores::Union{Nothing,AbstractArray}
     heuristic::Function
+    parallelizer::Union{Nothing,AbstractParallelizer}
 end
 
 function NaiveRegressor(
-    model::Supervised; coverage::AbstractFloat=0.95, heuristic::Function=absolute_error
+    model::Supervised;
+    coverage::AbstractFloat=0.95,
+    heuristic::Function=absolute_error,
+    parallelizer::Union{Nothing,AbstractParallelizer}=nothing,
 )
-    return NaiveRegressor(model, coverage, nothing, heuristic)
+    return NaiveRegressor(model, coverage, nothing, heuristic, parallelizer)
 end
 
 @doc raw"""
@@ -79,12 +83,16 @@ mutable struct JackknifeRegressor{Model<:Supervised} <: ConformalInterval
     coverage::AbstractFloat
     scores::Union{Nothing,AbstractArray}
     heuristic::Function
+    parallelizer::Union{Nothing,AbstractParallelizer}
 end
 
 function JackknifeRegressor(
-    model::Supervised; coverage::AbstractFloat=0.95, heuristic::Function=absolute_error
+    model::Supervised;
+    coverage::AbstractFloat=0.95,
+    heuristic::Function=absolute_error,
+    parallelizer::Union{Nothing,AbstractParallelizer}=nothing,
 )
-    return JackknifeRegressor(model, coverage, nothing, heuristic)
+    return JackknifeRegressor(model, coverage, nothing, heuristic, parallelizer)
 end
 
 @doc raw"""
@@ -160,12 +168,16 @@ mutable struct JackknifePlusRegressor{Model<:Supervised} <: ConformalInterval
     coverage::AbstractFloat
     scores::Union{Nothing,AbstractArray}
     heuristic::Function
+    parallelizer::Union{Nothing,AbstractParallelizer}
 end
 
 function JackknifePlusRegressor(
-    model::Supervised; coverage::AbstractFloat=0.95, heuristic::Function=absolute_error
+    model::Supervised;
+    coverage::AbstractFloat=0.95,
+    heuristic::Function=absolute_error,
+    parallelizer::Union{Nothing,AbstractParallelizer}=nothing,
 )
-    return JackknifePlusRegressor(model, coverage, nothing, heuristic)
+    return JackknifePlusRegressor(model, coverage, nothing, heuristic, parallelizer)
 end
 
 @doc raw"""
@@ -249,12 +261,16 @@ mutable struct JackknifeMinMaxRegressor{Model<:Supervised} <: ConformalInterval
     coverage::AbstractFloat
     scores::Union{Nothing,AbstractArray}
     heuristic::Function
+    parallelizer::Union{Nothing,AbstractParallelizer}
 end
 
 function JackknifeMinMaxRegressor(
-    model::Supervised; coverage::AbstractFloat=0.95, heuristic::Function=absolute_error
+    model::Supervised;
+    coverage::AbstractFloat=0.95,
+    heuristic::Function=absolute_error,
+    parallelizer::Union{Nothing,AbstractParallelizer}=nothing,
 )
-    return JackknifeMinMaxRegressor(model, coverage, nothing, heuristic)
+    return JackknifeMinMaxRegressor(model, coverage, nothing, heuristic, parallelizer)
 end
 
 @doc raw"""
@@ -337,16 +353,18 @@ mutable struct CVPlusRegressor{Model<:Supervised} <: ConformalInterval
     coverage::AbstractFloat
     scores::Union{Nothing,AbstractArray}
     heuristic::Function
+    parallelizer::Union{Nothing,AbstractParallelizer}
     cv::MLJBase.CV
 end
 
 function CVPlusRegressor(
     model::Supervised;
     coverage::AbstractFloat=0.95,
     heuristic::Function=absolute_error,
+    parallelizer::Union{Nothing,AbstractParallelizer}=nothing,
     cv::MLJBase.CV=MLJBase.CV(),
 )
-    return CVPlusRegressor(model, coverage, nothing, heuristic, cv)
+    return CVPlusRegressor(model, coverage, nothing, heuristic, parallelizer, cv)
 end
 
 @doc raw"""
@@ -442,16 +460,18 @@ mutable struct CVMinMaxRegressor{Model<:Supervised} <: ConformalInterval
     coverage::AbstractFloat
     scores::Union{Nothing,AbstractArray}
     heuristic::Function
+    parallelizer::Union{Nothing,AbstractParallelizer}
     cv::MLJBase.CV
 end
 
 function CVMinMaxRegressor(
     model::Supervised;
     coverage::AbstractFloat=0.95,
     heuristic::Function=absolute_error,
+    parallelizer::Union{Nothing,AbstractParallelizer}=nothing,
     cv::MLJBase.CV=MLJBase.CV(),
 )
-    return CVMinMaxRegressor(model, coverage, nothing, heuristic, cv)
+    return CVMinMaxRegressor(model, coverage, nothing, heuristic, parallelizer, cv)
 end
 
 @doc raw"""
@@ -567,6 +587,7 @@ mutable struct JackknifePlusAbRegressor{Model<:Supervised} <: ConformalInterval
     coverage::AbstractFloat
     scores::Union{Nothing,AbstractArray}
     heuristic::Function
+    parallelizer::Union{Nothing,AbstractParallelizer}
     nsampling::Int
     sample_size::AbstractFloat
     replacement::Bool
@@ -577,13 +598,14 @@ function JackknifePlusAbRegressor(
     model::Supervised;
     coverage::AbstractFloat=0.95,
     heuristic::Function=absolute_error,
+    parallelizer::Union{Nothing,AbstractParallelizer}=nothing,
     nsampling::Int=30,
     sample_size::AbstractFloat=0.5,
     replacement::Bool=true,
     aggregate::Union{Symbol,String}="mean",
 )
     return JackknifePlusAbRegressor(
-        model, coverage, nothing, heuristic, nsampling, sample_size, replacement, aggregate
+        model, coverage, nothing, heuristic, parallelizer, nsampling, sample_size, replacement, aggregate
     )
 end
 
@@ -673,6 +695,7 @@ mutable struct JackknifePlusAbMinMaxRegressor{Model<:Supervised} <: ConformalInt
     coverage::AbstractFloat
     scores::Union{Nothing,AbstractArray}
     heuristic::Function
+    parallelizer::Union{Nothing,AbstractParallelizer}
     nsampling::Int
     sample_size::AbstractFloat
     replacement::Bool
@@ -683,13 +706,14 @@ function JackknifePlusAbMinMaxRegressor(
     model::Supervised;
     coverage::AbstractFloat=0.95,
     heuristic::Function=absolute_error,
+    parallelizer::Union{Nothing,AbstractParallelizer}=nothing,
     nsampling::Int=30,
     sample_size::AbstractFloat=0.5,
     replacement::Bool=true,
     aggregate::Union{Symbol,String}="mean",
 )
     return JackknifePlusAbMinMaxRegressor(
-        model, coverage, nothing, heuristic, nsampling, sample_size, replacement, aggregate
+        model, coverage, nothing, heuristic, parallelizer, nsampling, sample_size, replacement, aggregate
     )
 end
 
@@ -777,6 +801,7 @@ mutable struct TimeSeriesRegressorEnsembleBatch{Model<:Supervised} <: ConformalI
     coverage::AbstractFloat
     scores::Union{Nothing,AbstractArray}
     heuristic::Function
+    parallelizer::Union{Nothing,AbstractParallelizer}
     nsampling::Int
     sample_size::AbstractFloat
     aggregate::Union{Symbol,String}
@@ -786,12 +811,13 @@ function TimeSeriesRegressorEnsembleBatch(
     model::Supervised;
     coverage::AbstractFloat=0.95,
     heuristic::Function=absolute_error,
+    parallelizer::Union{Nothing,AbstractParallelizer}=nothing,
     nsampling::Int=50,
     sample_size::AbstractFloat=0.3,
     aggregate::Union{Symbol,String}="mean",
 )
     return TimeSeriesRegressorEnsembleBatch(
-        model, coverage, nothing, heuristic, nsampling, sample_size, aggregate
+        model, coverage, nothing, heuristic, parallelizer, nsampling, sample_size, aggregate
     )
 end