improved post-processing funcitons

experiments/post_processing/results.jl

@@ -3,48 +3,36 @@
 
 Helper function to quickly filter a benchmark table for the distance from targets: the smaller this distance, the higher the plausibility.
 """
-function plausibility(outcome::ExperimentOutcome)
+function summarise_outcome(outcome::ExperimentOutcome; measure="distance_from_targets", model::Union{Nothing,String}=nothing)
     bmk = outcome.bmk
-    df = @chain bmk() begin
-        @group_by(dataname, generator, model, variable)
-        @summarize(mean=mean(value),sd=std(value))
-        @filter(variable == "distance_from_targets")
-        @ungroup
-        @arrange(mean)
+    df = groupby(bmk(), [:dataname, :generator, :model, :variable]) |>
+        x -> combine(x, :value => mean => :mean, :value => std => :std) |>
+        x -> subset(x, :variable => ByRow(x -> x ==measure))
+    if !isnothing(model)
+        df = subset(df, :model => ByRow(x -> x == model))
     end
+    sort!(df, [:model, :mean])
     return df
 end
 
+"""
+    plausibility(outcome::ExperimentOutcome)
+
+Helper function to quickly filter a benchmark table for the distance from targets: the smaller this distance, the higher the plausibility.
+"""
+plausibility(outcome::ExperimentOutcome; kwrgs...) = summarise_outcome(outcome, measure="distance_from_targets", kwrgs...)
+
+
 """
     faithfulness(outcome::ExperimentOutcome)
 
 Helper function to quickly filter a benchmark table for the distance from energy: the smaller this distance, the higher the faithfulness.
 """
-function faithfulness(outcome::ExperimentOutcome)
-    bmk = outcome.bmk
-    df = @chain bmk() begin
-        @group_by(dataname, generator, model, variable)
-        @summarize(mean=mean(value),sd=std(value))
-        @filter(variable == "distance_from_energy")
-        @ungroup
-        @arrange(mean)
-    end
-    return df
-end
+faithfulness(outcome::ExperimentOutcome; kwrgs...) = summarise_outcome(outcome, measure="distance_from_energy", kwrgs...)
 
 """
     closeness(outcome::ExperimentOutcome)
 
 Helper function to quickly filter a benchmark table for the distance from the factual: the smaller this distance, the higher the closeness desideratum.
 """
-function closeness(outcome::ExperimentOutcome)
-    bmk = outcome.bmk
-    df = @chain bmk() begin
-        @group_by(dataname, generator, model, variable)
-        @summarize(mean=mean(value),sd=std(value))
-        @filter(variable == "distance")
-        @ungroup
-        @arrange(mean)
-    end
-    return df
-end
\ No newline at end of file
+closeness(outcome::ExperimentOutcome; kwrgs...) = summarise_outcome(outcome, measure="distance", kwrgs...)
\ No newline at end of file

experiments/setup_env.jl

@@ -19,7 +19,7 @@ using JointEnergyModels
 using LazyArtifacts
 using Logging
 using Metalhead
-using MLJBase: multiclass_f1score, accuracy, multiclass_precision, table, machine, fit!
+using MLJBase: multiclass_f1score, accuracy, multiclass_precision, table, machine, fit!, Supervised
 using MLJEnsembles
 using MLJFlux
 using Random
@@ -37,6 +37,7 @@ include("experiment.jl")
 include("grid_search.jl")
 include("data/data.jl")
 include("models/models.jl")
+include("model_tuning.jl")
 include("benchmarking/benchmarking.jl")
 include("post_processing/post_processing.jl")
 include("utils.jl")