From b674b4a1ea7e26205af59119e0e1c2e9a156a211 Mon Sep 17 00:00:00 2001
From: pat-alt <altmeyerpat@gmail.com>
Date: Wed, 27 Sep 2023 13:03:14 +0200
Subject: [PATCH] rejigged grid search a little
---
experiments/grid_search.jl | 27 +++++++++++++++++++--------
1 file changed, 19 insertions(+), 8 deletions(-)
diff --git a/experiments/grid_search.jl b/experiments/grid_search.jl
index fc0efebb..d06214f5 100644
--- a/experiments/grid_search.jl
+++ b/experiments/grid_search.jl
@@ -1,3 +1,5 @@
+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
--
GitLab