step_w=1.5,
num_layers_A=1,
step_A=1.5,
num_layers_y=2,
step_y=1.5),
models_H.FAIR_Bernoulli_H_class(input_size=inp,
num_layers_w=2,
step_w=1.5,
num_layers_A=1,
step_A=1.5,
num_layers_y=2,
step_y=1.5),
models_H.FAIR_betaREP_H_class(input_size=inp,
num_layers_w=2,
step_w=1.5,
num_layers_A=1,
step_A=1.5,
num_layers_y=2,
step_y=1.5),
models_H.FAD_prob_H_class(flow_length=2,
no_sample=32,
input_size=inp,
num_layers_y=2,
step_y=2,
step_z=2)
]
x_train, x_test = atribute.iloc[train_index, :], atribute.iloc[
test_index, :]
y_train, y_test = output.iloc[train_index], output.iloc[test_index]
A_train, A_test = sensitive.iloc[train_index], sensitive.iloc[
step_w=2,
num_layers_A=2,
step_A=2,
num_layers_y=3,
step_y=2),
models_H.FAIR_Bernoulli_H_class(input_size=inp,
num_layers_w=2,
step_w=2,
num_layers_A=2,
step_A=2,
num_layers_y=3,
step_y=2),
models_H.FAIR_betaREP_H_class(input_size=inp,
num_layers_w=2,
step_w=2,
num_layers_A=2,
step_A=2,
num_layers_y=3,
step_y=2),
models_H.FAD_prob_H_class(flow_length=2,
no_sample=1,
input_size=inp,
num_layers_y=3,
step_y=2,
step_z=2)
]
x_train, x_test, y_train, y_test, A_train, A_test, data_train, data_test = train_test_split(
atribute, output, sensitive, data, test_size=0.2, random_state=42)
if std_scl == 1: