Protein Engineering

bgl3 · Linear

reference_bgl3_lr_s1_20261104-015527

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
test1.31830.55550.50000.30631036.00000.1676373.00000.80000.78561.69460.00000.00000.38540.80000.85032.20540.02000.00000.46880.74000.73142.45670.06000.00000.45500.77780.80891.69460.00000.00000.4262
train1.13650.65160.62640.42218299.00000.24390.10000.57980.04000.54770.07000.61240.05560.5958
validation1.37960.55160.50620.3021933.00000.05781395.00000.90001.04901.69460.00000.00000.47470.84000.92042.85480.04000.00000.49760.80000.82152.85480.09000.00000.49450.83330.90252.08410.00000.00000.4554

Loss per epoch

training validation

Config

name: reference_bgl3_lr_s1
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: 1