Protein Engineering

pab1 · Linear

reference_pab1_lr_plm_modulof1_20261104-181412

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
train0.60110.91470.91280.83271937.00000.05487.00000.60000.28191.52040.10000.00000.73890.62000.09531.52040.04000.00000.73450.67000.09491.52040.06000.00000.76770.63090.04681.52040.05370.00000.7678
validation1.64510.73640.74720.5417116.0000-0.1615277.00000.5000-0.06850.30300.00000.00000.76090.3200-0.29540.45460.12000.00000.73460.3000-0.23670.47190.25000.00000.80050.4286-0.21440.30300.00000.00000.7139
test2.03220.71660.72560.4909132.0000-0.2588520.00000.3000-0.25240.03640.00000.00000.69410.1600-0.30550.12810.00000.00000.76200.1500-0.30660.29770.09000.00000.79590.2353-0.26700.03640.00000.00000.7190

Loss per epoch

training validation

Config

name: reference_pab1_lr_plm_modulof1
task: dms
data:
  dataset: pab1
  encoding: one_hot,aa_index,plm,plm_sum
  split_dir: null
  split_scheme: modulo
  split_fold: 1
model:
  name: lr
train:
  epochs: 300
  batch_size: 128
  learning_rate: 0.001
  patience: 15
  min_delta: 1.0e-05
  seed: 7