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example_primal.jl
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example_primal.jl
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include("types.jl")
include("shared.jl")
include("util.jl")
include("adv_cg.jl")
include("adv_bfgs.jl")
include("adv_sgd.jl")
## set
# alg = :bfgs
alg = :sgd
# sgd setting
step = 0.1
use_adagrad = true
verbose = false
### prepare data
dname = "glass"
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)
Cs = [2.0^i for i=0:3:12]
ncs = length(Cs)
# 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(ncs)
loss01_list = zeros(ncs)
# The first stage of CV
idx = randperm(n_train)
X_train = X_train[idx,:]
y_train = y_train[idx]
for i = 1:ncs
println(i, " | Adversarial | C = ", string(Cs[i]))
losses = zeros(n_train)
losses1 = 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 : ")
tol = Cs[i] * length(y_tr) * 1e-6
if alg == :bfgs
@time model = train_adv_bfgs(X_tr, y_tr, Cs[i], ftol = tol, grtol = tol, max_iter=1000, show_trace = verbose)
elseif alg == :sgd
@time model = train_adv_sgd(X_tr, y_tr, Cs[i], ftol = tol, step = step, use_adagrad = use_adagrad,
grtol = tol, max_iter=10000, show_trace = verbose)
end
_, ls, _, ls1, _, _ = test_adv(model, X_val, y_val)
losses[id_val] = ls
losses1[id_val] = ls1
end
loss_list[i] = mean(losses)
loss01_list[i] = mean(losses1)
# println("loss : ", string(mean(losses)))
println(" => loss01 : ", string(mean(losses1)))
println()
end
ind_max= indmin(loss01_list)
C0 = Cs[ind_max]
Cs = [C0*2.0^(i-3) for i=1:5]
ncs = length(Cs)
## Second stage
idx = randperm(n_train)
X_train = X_train[idx,:]
y_train = y_train[idx]
for i = 1:ncs
println(i, " | Adversarial | C = ", string(Cs[i]))
losses = zeros(n_train)
losses1 = 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 : ")
tol = Cs[i] * length(y_tr) * 1e-6
if alg == :bfgs
@time model = train_adv_bfgs(X_tr, y_tr, Cs[i], ftol = tol, grtol = tol, max_iter=1000, show_trace = verbose)
elseif alg == :sgd
@time model = train_adv_sgd(X_tr, y_tr, Cs[i], ftol = tol, step = step, use_adagrad = use_adagrad,
grtol = tol, max_iter=10000, show_trace = verbose)
end
_, ls, _, ls1, _, _ = test_adv(model, X_val, y_val)
losses[id_val] = ls
losses1[id_val] = ls1
#println("loss : ", string(ls))
#println("loss01 : ", string(ls))
end
loss_list[i] = mean(losses)
loss01_list[i] = mean(losses1)
# println("loss : ", string(mean(losses)))
println(" => loss01 : ", string(mean(losses1)))
println()
end
ind_max= indmin(loss01_list)
C_best = Cs[ind_max]
### Evaluation
n_split = size(id_train, 1)
v_model = Vector{ClassificationModel}()
v_result = Vector{Tuple}()
v_acc = 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
tol = C_best * length(y_train) * 1e-6
if alg == :bfgs
@time model = train_adv_bfgs(X_train, y_train, C_best, ftol = tol, grtol = tol, max_iter=1000, show_trace = verbose)
elseif alg == :sgd
@time model = train_adv_sgd(X_train, y_train, C_best, ftol = tol, step = step, use_adagrad = use_adagrad,
grtol = tol, max_iter=10000, show_trace = verbose)
end
result = test_adv(model, X_test, y_test)
loss01 = result[3]
acc = 1.0 - loss01
println("accuracy : ", acc)
push!(v_model, model)
push!(v_result, result)
v_acc[i] = acc
end
println(dname)
println("mean accuracy : ", mean(v_acc))
println("std accuracy : ", std(v_acc))