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experimental_setup.yaml
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recourse_methods:
ar:
hyperparams:
fs_size: 150
cchvae:
hyperparams:
n_search_samples: 100
p_norm: 1
step: 0.1
max_iter: 1000
binary_cat_features: True
vae_params:
layers: [ 512, 256, 8 ]
train: True
lambda_reg: 0.000001
epochs: 5
lr: 0.001
batch_size: 32
cem:
hyperparams:
batch_size: 1
kappa: 0.1
init_learning_rate: 0.01
binary_search_steps: 9
max_iterations: 100
initial_const: 10
beta: 0.9
gamma: 0.0
mode: "PN"
num_classes: 2
ae_params:
hidden_layer: [20, 10, 7]
train_ae: True
epochs: 5
cem-vae:
hyperparams:
batch_size: 1
kappa: 0.1
init_learning_rate: 0.01
binary_search_steps: 9
max_iterations: 100
initial_const: 10
beta: 0.9
gamma: 1.0
mode: "PN"
num_classes: 2
ae_params:
hidden_layer: [20, 10, 7]
train_ae: True
epochs: 5
clue:
hyperparams:
train_vae: True
width: 10
depth: 3
latent_dim: 12
batch_size: 64
epochs: 1
lr: 0.001
early_stop: 10
cruds:
hyperparams:
lambda_param: 0.001
optimizer: "RMSprop"
lr: 0.008
max_iter: 2000
vae_params:
layers: [ 16, 8 ]
train: True
epochs: 5
lr: 0.001
batch_size: 32
dice:
hyperparams:
num: 1
desired_class: 1
posthoc_sparsity_param: 0
face_knn:
hyperparams:
mode: "knn"
fraction: 0.15
face_epsilon:
hyperparams:
mode: "epsilon"
fraction: 0.15
gs:
hyperparams:
revise:
hyperparams:
lambda: 0.5
optimizer: "adam"
lr: 0.1
max_iter: 1500
target_class: [ 0, 1 ]
binary_cat_features: True
vae_params:
layers: [ 512, 256, 8 ]
activFun:
train: True
lambda_reg: 0.000001
epochs: 5
lr: 0.001
batch_size: 32
wachter:
hyperparams:
loss_type: "BCE"
binary_cat_features: True