Protein Engineering

bgl3 · Linear

reference_bgl3_lr_s4_20261104-023213

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
test1.31420.55660.49990.30841036.00000.1638343.00000.90001.05461.69460.00000.00000.47920.80000.85462.20540.02000.00000.47060.70000.65362.20540.06000.00000.42130.81480.91381.69460.00000.00000.4658
train1.15110.64650.62080.41478299.00000.23750.10000.52370.04000.55710.06000.59960.05560.5881
validation1.38930.54660.50110.2972933.00000.05401544.00000.90001.04711.69460.00000.00000.47410.82000.92352.85480.04000.00000.49880.78000.80212.85480.11000.00000.48610.83330.89501.69460.00000.00000.4526

Loss per epoch

training validation

Config

name: reference_bgl3_lr_s4
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: 4