rejigged grid search a little

experiments/grid_search.jl

+using DataFrames
+
 """
     grid_search(
         couterfactual_data::CounterfactualData,
@@ -24,7 +26,8 @@ function grid_search(
     # Grid setup:
     tuning_params = [Pair.(k, vals) for (k, vals) in pairs(tuning_params)]
     grid = Iterators.product(tuning_params...)
-    outcomes = Dict{Any,Any}()
+    df_params = []
+    df_outcomes = []
 
     # Search:
     counter = 1
@@ -40,10 +43,18 @@ function grid_search(
             tuning_params...,
             kwargs...,
         )
-        outcomes[tuning_params] = outcome
+        _df_params = DataFrame(Dict(:id => counter, pairs(tuning_params)))
+        _df_outcomes = DataFrame(Dict(:id => counter, :params => tuning_params, :outcome => outcome))
+        push!(df_params, _df_params)
+        push!(df_outcomes, _df_outcomes)
         counter += 1
     end
 
+    outcomes = Dict(
+        :df_params => vcat(df_params...),
+        :df_outcomes => vcat(df_outcomes...),
+    )
+
     # Save:
     if !(is_multi_processed(PLZ) && MPI.Comm_rank(PLZ.comm) != 0)
         Serialization.serialize(
@@ -104,7 +115,7 @@ function best_rank_outcome(
     df_weights = DataFrame(variable = measure, weight = weights)
 
     ranks = []
-    for (params, outcome) in outcomes
+    for outcome in outcomes.df_outcomes.outcome
         _ranks =
             generator_rank(
                 outcome;
@@ -119,8 +130,8 @@ function best_rank_outcome(
     end
     best_index = argmin(ranks)
     best_outcome = (
-        params = collect(keys(outcomes))[best_index],
-        outcome = collect(values(outcomes))[best_index],
+        params=outcomes.df_outcomes.params[best_index],
+        outcome=outcomes.df_outcomes.outcomes[best_index],
     )
     return best_outcome
 end
@@ -148,7 +159,7 @@ function best_absolute_outcome(
     df_weights = DataFrame(variable = measure, weight = weights)
 
     avg_values = []
-    for (params, outcome) in outcomes
+    for (params, outcome) in (outcomes.df_outcomes.params, outcomes.df_outcomes.outcome)
 
         # Setup
         evaluation = deepcopy(outcome.bmk.evaluation)
@@ -191,8 +202,8 @@ function best_absolute_outcome(
     end
     best_index = argmin(avg_values)
     best_outcome = (
-        params = collect(keys(outcomes))[best_index],
-        outcome = collect(values(outcomes))[best_index],
+        params = outcomes.df_outcomes.params[best_index],
+        outcome = outcomes.df_outcomes.outcomes[best_index],
     )
 end