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example_th.jl
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example_th.jl
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include("adv_or_th.jl")
include("util.jl")
verbose = false
### prepare data
dname = "diabetes"
D_all = readcsv("data-example/" * dname * ".csv")
id_train = readcsv("data-example/" * dname * ".train")
id_test = readcsv("data-example/" * dname * ".test")
id_train = round.(Int64, id_train)
id_test = round.(Int64, id_test)
println(dname)
### Cross Validation, using first split
## First stage
id_tr = vec(id_train[1,:])
id_ts = vec(id_test[1,:])
X_train = D_all[id_tr,1:end-1]
y_train = round.(Int, D_all[id_tr, end])
X_test = D_all[id_ts,1:end-1]
y_test = round.(Int, D_all[id_ts, end])
X_train, mean_vector, std_vector = standardize(X_train)
X_test = standardize(X_test, mean_vector, std_vector)
lambdas = [2.0^-i for i=1:2:13]
nls = length(lambdas)
# fold
n_train = size(X_train, 1)
n_test = size(X_test, 1)
kf = 5
# k folds
folds = k_fold(n_train, kf)
loss_list = zeros(nls)
# The first stage of CV
idx = randperm(n_train)
X_train = X_train[idx,:]
y_train = y_train[idx]
for i = 1:nls
println(i, " | Adversarial | lambda = ", string(lambdas[i]))
losses = zeros(n_train)
# k fold
for j = 1:kf
# prepare training and validation
id_tr = vcat(folds[[1:j-1; j+1:end]]...)
id_val = folds[j]
X_tr = X_train[id_tr, :]; y_tr = y_train[id_tr]
X_val = X_train[id_val, :]; y_val = y_train[id_val]
print(" ",j, "-th fold : ")
@time model = train_or_adv_th(X_tr, y_tr, lambdas[i], verbose=verbose)
ls = test_or_adv_th(model, X_val, y_val)
losses[id_val] = ls
end
loss_list[i] = mean(losses)
println("loss : ", string(mean(losses)))
println()
end
ind_max= indmin(loss_list)
L0 = lambdas[ind_max]
lambdas = [L0*2.0^((i-4)/2) for i=1:7]
nls = length(lambdas)
println("stage 1 lambda : ", L0)
## Second stage
idx = randperm(n_train)
X_train = X_train[idx,:]
y_train = y_train[idx]
for i = 1:nls
println(i, " | Adversarial | lambda = ", string(lambdas[i]))
losses = zeros(n_train)
# k fold
for j = 1:kf
# prepare training and validation
id_tr = vcat(folds[[1:j-1; j+1:end]]...)
id_val = folds[j]
X_tr = X_train[id_tr, :]; y_tr = y_train[id_tr]
X_val = X_train[id_val, :]; y_val = y_train[id_val]
print(" ",j, "-th fold : ")
@time model = train_or_adv_th(X_tr, y_tr, lambdas[i], verbose=verbose)
ls = test_or_adv_th(model, X_val, y_val)
losses[id_val] = ls
end
loss_list[i] = mean(losses)
println("loss : ", string(mean(losses)))
println()
end
ind_max= indmin(loss_list)
lambda_best = lambdas[ind_max]
# lambda_best = 1e-3
println("best lambda : ", lambda_best)
println("Start Evaluation")
### Evaluation
n_split = size(id_train, 1)
v_model = Vector{ClassificationModel}()
v_mae = zeros(n_split)
for i = 1:n_split
# standardize
id_tr = vec(id_train[i,:])
id_ts = vec(id_test[i,:])
X_train = D_all[id_tr,1:end-1]
y_train = round.(Int, D_all[id_tr, end])
X_test = D_all[id_ts,1:end-1]
y_test = round.(Int, D_all[id_ts, end])
X_train, mean_vector, std_vector = standardize(X_train)
X_test = standardize(X_test, mean_vector, std_vector)
#train and test
model = train_or_adv_th(X_train, y_train, lambda_best, verbose=verbose)
result = test_or_adv_th(model, X_test, y_test)
v_mae[i] = result
push!(v_model, model)
println(result)
end
println()
println(dname)
println("adversarial mean mae : ", mean(v_mae))
println("adversarial std mae : ", std(v_mae))