config_rgcn_pytorch.yml

runDescription: "example run for pytorch rgcn"

# Input files
obs_file: "../river_dl/tests/test_data/obs_temp_flow_seg_id_nat"
sntemp_file: "../river_dl/tests/test_data/test_data_seg_id_nat"
dist_matrix_file: "../river_dl/tests/test_data/test_dist_matrix.npz"
code_dir: ".."


#### for flow modeling, seg_width should be removed from x_vars
x_vars: ['seg_ccov', 'seg_rain', 'seg_slope', 'seg_tave_air']

out_dir: "output_test_pytorch"

seed: False #random seed for training False==No seed, otherwise specify the seed

y_vars_pretrain: ['seg_tave_water']
y_vars_finetune: ['temp_c']
#y_vars_pretrain: ['seg_tave_water', 'seg_outflow']
#y_vars_finetune: ['temp_c', 'discharge_cms']

lambdas: [1]
#lambdas: [1,0]

trn_offset: 1.0
tst_val_offset: 1.0

#Define early stopping criteria, setting this to False will turn off early stopping rounds
early_stopping: 1



train_start_date:
  - '2003-09-15'
train_end_date:
  - '2005-09-14'
val_start_date:
  - '2005-09-14'
val_end_date:
  - '2006-09-14'
test_start_date:
  - '1980-10-01'
test_end_date:
  - '1985-09-30'


pt_epochs: 5
ft_epochs: 5
hidden_size: 20

dropout: 0.2
recurrent_dropout: 0.2
pretrain_learning_rate: 0.005
finetune_learning_rate: 0.01