Protein Engineering

gb1 · Linear

reference_gb1_lr_plm_modulof4_20261105-042818

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
train1.09350.88930.92270.785635489.00000.4775203.00001.00001.53812.01760.00000.00000.85601.00001.45232.05310.06000.00000.87961.00001.36032.05310.15000.00000.87650.99320.99152.52950.32160.00000.8833
validation5.31430.25490.19900.04792689.00000.00819450.00000.0000-1.6141-0.92000.00000.00000.28760.0000-1.9606-0.92000.00000.00000.21210.0000-1.9902-0.92000.00000.00000.20950.0000-2.0195-0.62300.00000.00000.2068
test4.97210.04630.0354-0.07613230.0000-0.210018939.00000.0000-3.5023-3.03500.00000.0000-0.32230.0400-3.73350.36050.00000.0000-0.39770.0400-3.75130.39530.00000.0000-0.41150.0547-3.73500.39530.00000.0000-0.4181

Loss per epoch

training validation

Config

name: reference_gb1_lr_plm_modulof4
task: dms
data:
  dataset: gb1
  encoding: one_hot,aa_index,plm,plm_sum
  split_dir: null
  split_scheme: modulo
  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