From 4c222954d8fbf4aba9d72908557acfae85c3a546 Mon Sep 17 00:00:00 2001
From: Emmanuel Lujan <lujan.emmanuel@gmail.com>
Date: Tue, 9 Jul 2024 12:50:06 -0400
Subject: [PATCH] Reduce optimization example by removing random sampler case.

---
 examples/Opt-ACE-aHfO2/fit-opt-ace-ahfo2.jl | 32 ++++++---------------
 1 file changed, 9 insertions(+), 23 deletions(-)

diff --git a/examples/Opt-ACE-aHfO2/fit-opt-ace-ahfo2.jl b/examples/Opt-ACE-aHfO2/fit-opt-ace-ahfo2.jl
index 3f0a186..7a4f2cb 100644
--- a/examples/Opt-ACE-aHfO2/fit-opt-ace-ahfo2.jl
+++ b/examples/Opt-ACE-aHfO2/fit-opt-ace-ahfo2.jl
@@ -52,14 +52,16 @@ end;
 model = ACE
 pars = OrderedDict( :body_order        => [2, 3, 4],
                     :polynomial_degree => [3, 4, 5],
-                    :rcutoff           => LinRange(3.5, 6.5, 10),
-                    :wL                => LinRange(0.3, 1.8, 10),
-                    :csp               => LinRange(0.3, 1.8, 10),
-                    :r0                => LinRange(0.3, 1.8, 10));
+                    :rcutoff           => LinRange(4, 6, 10),
+                    :wL                => LinRange(0.5, 1.5, 10),
+                    :csp               => LinRange(0.5, 1.5, 10),
+                    :r0                => LinRange(0.5, 1.5, 10));
 
-# Use **random sampling** to find the optimal hyper-parameters.
+# Use **latin hypercube sampling** to find the optimal hyper-parameters.
+sampler = CLHSampler(dims=[Categorical(3), Categorical(3), Continuous(),
+                           Continuous(), Continuous(), Continuous()])
 iap, res = hyperlearn!(model, pars, conf_train; 
-                       n_samples = 10, sampler = RandomSampler(),
+                       n_samples = 10, sampler = sampler,
                        loss = custom_loss, ws = [1.0, 1.0], int = true);
 
 # Save and show results.
@@ -74,21 +76,5 @@ err_time = plot_err_time(res)
 @save_fig res_path err_time
 DisplayAs.PNG(err_time)
 
-# Alternatively, use **latin hypercube sampling** to find the optimal hyper-parameters.
-sampler = CLHSampler(dims=[Categorical(3), Categorical(3), Continuous(),
-                           Continuous(), Continuous(), Continuous()])
-iap2, res2 = hyperlearn!(model, pars, conf_train; 
-                         n_samples = 10, sampler = sampler,
-                         loss = custom_loss, ws = [1.0, 1.0], int = true);
+# Alternatively, use **random sampling** using "sampler = RandomSampler()".
 
-# Save and show results.
-@save_var res_path iap2.β
-@save_var res_path iap2.β0
-@save_var res_path iap2.basis
-@save_dataframe res_path res2
-res2
-
-# Plot error vs time.
-err_time2 = plot_err_time(res2)
-@save_fig res_path err_time2
-DisplayAs.PNG(err_time2)