Protein Engineering

bgl3 · Linear

reference_bgl3_lr_s5_20261104-024351

Metrics

Setmsepearsonrspearmanrr2above_wt_nabove_wt_spearmanrbudget_to_maxhit_rate_at_10mean_at_10max_at_10precision_at_10tie_share_at_10captured_at_10hit_rate_at_50mean_at_50max_at_50precision_at_50tie_share_at_50captured_at_50hit_rate_at_100mean_at_100max_at_100precision_at_100tie_share_at_100captured_at_100hit_rate_at_1pctmean_at_1pctmax_at_1pctprecision_at_1pcttie_share_at_1pctcaptured_at_1pct
test1.34040.54310.48280.29471036.00000.1486470.00000.70000.79471.69460.00000.00000.38860.76000.73792.31360.02000.00000.42380.71000.69432.31360.08000.00000.43890.70370.63001.69460.00000.00000.3586
train1.21390.62480.59760.38278299.00000.21510.10000.54750.02000.55140.04000.54500.05560.5765
validation1.41240.53460.48940.2855933.00000.02901517.00000.90001.04951.69460.00000.00000.47490.82000.77482.01740.02000.00000.43900.77000.77402.85480.10000.00000.47390.87500.86621.86900.00000.00000.4418

Loss per epoch

training validation

Config

name: reference_bgl3_lr_s5
task: dms
data:
  dataset: bgl3
  encoding: one_hot,aa_index
  split_dir: bgl3/splits/main
model:
  name: lr
train:
  epochs: 300
  batch_size: 128
  learning_rate: 0.0001
  patience: 15
  min_delta: 1.0e-05
  seed: 5