gmsc.jl

# Data:
dataname = "GMSC"
counterfactual_data, test_data = train_test_split(load_gmsc(nothing); test_size=TEST_SIZE)
nobs = size(counterfactual_data.X, 2)

# VAE:
using CounterfactualExplanations.GenerativeModels: VAE, train!
X = counterfactual_data.X
y = counterfactual_data.output_encoder.y
vae = VAE(size(X, 1); nll=Flux.Losses.mse, epochs=100, λ=0.01, latent_dim=5)
train!(vae, X, y)
counterfactual_data.generative_model = vae

# Dimensionality reduction:
maxout_dim = vae.params.latent_dim
counterfactual_data.dt = MultivariateStats.fit(MultivariateStats.PCA, counterfactual_data.X; maxoutdim=maxout_dim);

# Model tuning:
model_tuning_params = DEFAULT_MODEL_TUNING_LARGE

# Tuning parameters:
tuning_params = DEFAULT_GENERATOR_TUNING_LARGE

# Parameter choices:
params = (
    n_hidden=32,
    activation=Flux.relu,
    builder=default_builder(n_hidden=32, n_layers=3, activation=Flux.relu),
    α = [1.0, 1.0, 1e-1],
    sampling_batch_size = 10,
    sampling_steps = 30,
    use_ensembling = true,
    opt=Flux.Optimise.Descent(0.05),
    dim_reduction=true,
)

# Best grid search params:
append_best_params!(params, dataname)

if GRID_SEARCH
    grid_search(
        counterfactual_data, test_data;
        dataname=dataname,
        tuning_params=tuning_params,
        params...
    )
elseif FROM_GRID_SEARCH
    outcomes_file_path = joinpath(
        DEFAULT_OUTPUT_PATH,
        "grid_search",
        "$(replace(lowercase(dataname), " " => "_")).jls",
    )
    save_best(outcomes_file_path)
    bmk2csv(dataname)
else
    run_experiment(
        counterfactual_data, test_data;
        dataname=dataname,
        model_tuning_params=model_tuning_params,
        params...
    )
end