Protein Engineering

pab1 · Linear

reference_pab1_lr_plm_modulof4_20261105-043023

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
train1.08800.91160.92930.71801414.00000.12359.00000.70000.31811.52040.10000.00000.76700.76000.19061.52040.08000.00000.80450.73000.17191.52040.13000.00000.83010.72730.15551.52040.14690.00000.8407
validation1.79820.70510.71050.4403171.00000.0826204.00000.1000-0.41430.10460.00000.00000.55940.2200-0.30700.38430.04000.00000.70050.2400-0.28170.90330.17000.00000.75140.2353-0.33700.15670.00000.00000.6239
test2.05540.74390.71210.4471234.00000.02911015.00000.3000-0.18390.36570.00000.00000.65520.2800-0.19810.38070.10000.00000.71280.2700-0.21910.53070.16000.00000.73780.3333-0.17860.36570.00000.00000.6724

Loss per epoch

training validation

Config

name: reference_pab1_lr_plm_modulof4
task: dms
data:
  dataset: pab1
  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: 0.001
  patience: 15
  min_delta: 1.0e-05
  seed: 7