Protein Engineering

bgl3 · Linear

reference_bgl3_lr_s3_20261104-022019

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
test1.31630.55580.49960.30731036.00000.1710389.00000.90001.09251.69460.00000.00000.49250.78000.83631.86900.00000.00000.46330.70000.67562.20540.06000.00000.43080.77780.81171.69460.00000.00000.4272
train1.14460.64860.62340.41798299.00000.24000.10000.53370.06000.56140.06000.59780.06020.5948
validation1.38230.54970.50340.3007933.00000.05521486.00000.90001.04711.69460.00000.00000.47410.82000.89012.85480.04000.00000.48540.77000.79922.85480.09000.00000.48490.87500.99232.08410.00000.00000.4890

Loss per epoch

training validation

Config

name: reference_bgl3_lr_s3
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: 3