### use # to comment
### code for parsing this file is in [utils/utils.py]

### general
device                  cuda            # gpu device name
score_function          Er              # choose from Er, others not implemented
eval_metric             bestF1          # choose from bestF1, bestF1pa

### default dataset config (can be overwritten if defined in dataset)
default_dataset_config                  # do not delete
tst_stride              no_rep          # choose a number or no_rep, which means tst_stride = dl
clamp_max               4
clamp_min               -4              # test data is clamped between [clamp_min, clamp_max]

### default model/training config (can be overwritten if defined in the individual settings)
default_model_trn_config                # do not delete
model                   NPSR
z_dim                   10              # latent dim (= D_lat) 
learn_rate              1e-4
batch_size              64
ff_mult                 4
enc_depth               4               # (= N_perf)
pred_depth              8               # (= N_enc)
epochs                  100

### individual settings for dataset and model/training
dset_model_trn_config                   # do not delete
dataset                 SWaT
downsample              10
dl                      100             # window size for M_pt (= W)
stride                  10              # stride for M_pt
model                   NPSR            # should match one model in [default model config] above
pred_dl                 100             # window size for M_seq (= W_0)
delta                   20              # M_seq output window size
heads                   9
theta_N_ratio           0.9985          # cf. sec 3.4
 
dataset                 WADI
downsample              10
dl                      100
stride                  10
model                   NPSR
pred_dl                 100
delta                   20
heads                   14
theta_N_ratio           0.9985

dataset                 PSM
downsample              10
dl                      100
stride                  10
model                   NPSR
pred_dl                 100
delta                   20
heads                   5
batch_size              32
theta_N_ratio           0.9985

dataset                 MSL
downsample              1
dl                      100
stride                  10
entities                all             # can be a list of nums (ent IDs) separated by , w/o spaces
train_method            train_together  # combined method for multi-entity datasets
model                   NPSR
pred_dl                 50
delta                   6
heads                   12
theta_N_ratio           0.975

dataset                 SMAP
downsample              1
dl                      50
stride                  10
entities                all
train_method            train_together
model                   NPSR
pred_dl                 50
delta                   6
heads                   10
theta_N_ratio           0.9985

dataset                 SMD
downsample              2
dl                      50
stride                  10
entities                all
train_method            train_together
model                   NPSR
pred_dl                 50
delta                   6
heads                   11
theta_N_ratio           0.9985

dataset                 MSL
downsample              1
dl                      100
stride                  10
entities                all
train_method            train_per_entity    # standard method for multi-entity datasets
model                   NPSR
pred_dl                 50
delta                   6
heads                   11
theta_N_ratio           0.9985

dataset                 SMAP
downsample              1
dl                      50
stride                  10
entities                all
train_method            train_per_entity
model                   NPSR
pred_dl                 50
delta                   6
heads                   5
theta_N_ratio           0.9985

dataset                 SMD
downsample              2
dl                      50
stride                  10
entities                all
train_method            train_per_entity
model                   NPSR
pred_dl                 50
delta                   6
heads                   8
theta_N_ratio           0.9985




