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Reduce optimization example complexity to reduce CI execution time. #80

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Jul 9, 2024
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32 changes: 8 additions & 24 deletions examples/Opt-ACE-aHfO2/fit-opt-ace-ahfo2.jl
Original file line number Diff line number Diff line change
Expand Up @@ -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. Alternatively, use **random sampling** (sampler = RandomSampler()).
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.
Expand All @@ -74,21 +76,3 @@ 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);

# 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)
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