Protein Engineering

bgl3 · Linear

reference_bgl3_lr_randomf4_20261104-163050

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
train1.12530.66150.63370.43316204.00000.24313793.00001.00001.59152.62040.00000.00000.56610.98001.49192.62040.04000.00000.60880.97001.42762.62040.03000.00000.62070.96251.38802.85480.08750.00000.6322
validation1.40310.52740.48590.27422046.00000.11253014.00000.90000.88151.86900.00000.00000.38130.88000.98222.20540.02000.00000.47810.83000.88802.54190.03000.00000.47660.88680.98242.20540.01890.00000.4804
test1.37640.53500.49240.28162018.00000.13977.00000.90001.10623.11600.10000.00000.45630.86001.00043.11600.06000.00000.47740.82000.94263.11600.05000.00000.49130.84910.97053.11600.05660.00000.4690

Loss per epoch

training validation

Config

name: reference_bgl3_lr_randomf4
task: dms
data:
  dataset: bgl3
  encoding: one_hot,aa_index
  split_dir: null
  split_scheme: random
  split_fold: 4
model:
  name: lr
train:
  epochs: 300
  batch_size: 128
  learning_rate: 0.0001
  patience: 15
  min_delta: 1.0e-05
  seed: 7