## data set specific
dataset: GraphMutationDataset
data_file_train: ./data.files/ICC.seed.0/P15056/training.csv
data_file_train_ddp_prefix: ./data.files/pretrain/training
data_file_test: ./archive/data.files/ICC.seed.0/P15056/testing.csv
data_type: GLOF
loop: true # add self loop or not
node_embedding_type: esm # esm, one-hot, one-hot-idx, or aa-5dim
graph_type: af2 # af2 or 1d-neighbor
add_plddt: true # add plddt or not
add_conservation: true # add conservation or not
add_position: true # add positional embeddings or not
add_sidechain: true # add side chain or not
use_cb: true
loaded_msa: true
add_msa: true # add msa or not
add_dssp: true # add dssp or not
alt_type: concat # concat or alt
computed_graph: true
max_len: 251
radius: 50 # radius for KNN graph, larger than curoff_upper

## model specific
load_model: ./PreMode.results/PreMode/model.step.46000.pt
model_class: PreMode_Star_CON
model: equivariant-transformer-star2-softmax
neighbor_embedding: true
cutoff_lower: 0.0 # graph related
cutoff_upper: 36.0 # graph related
max_num_neighbors: 36 # graph related
x_in_channels: 1313 # x input size, only used if different from x_channels, 1280 + 1 + 20 + 12
alt_projector: 2593 # alt input size, 1280 + 1 + 20 + 12 + 1280
x_in_embedding_type: Linear_gelu # x input embedding type, only used if x_in_channels is not None
x_channels: 512 # x embedding size
x_hidden_channels: 512 # x hidden size
vec_in_channels: 35 # vector embedding size
vec_channels: 32 # vector hidden size
vec_hidden_channels: 512 # vector hidden size, must be equal to x_channels (why? go to model page)
distance_influence: both
share_kv: false
num_heads: 16 # number of attention heads
num_layers: 2
num_edge_attr: 444 # 1, from msa_contacts
num_nodes: 1
num_rbf: 32 # number of radial basis functions, use a small size for quicker training
rbf_type: expnormunlim
trainable_rbf: true
num_workers: 0
output_model: EquivariantBinaryClassificationStarPoolScalar
reduce_op: mean
output_dim: 1
activation: silu
attn_activation: silu
# aggr: mean # has to be mean because different protein sizes, removed and set to default (note previous default was add)
drop_out: 0.1

## training specific
trainer_fn: PreMode_trainer
seed: 0
lr: 1.0e-04 # important
lr_factor: 0.8 # important
weight_decay: 0.0
lr_min: 1.0e-07 # important
lr_patience: 2 # important
num_steps_update: 1 # important, how many steps before updating the model, use large number for large batch size
lr_warmup_steps: 200 # important
batch_size: 8
ngpus: 1
use_lora: 
num_epochs: 20
loss_fn: weighted_loss
data_split_fn: ""
y_weight: 1.0
contrastive_loss_fn: null
reset_train_dataloader_each_epoch: true
test_size: null
train_size: 0.75
val_size: 0.25


## log specific
num_save_epochs: 1
num_save_batches: 5 # save every 1000 batches, this also control the validation frequency
log_dir: ./PreMode.results/TEST/

loaded_esm: true