Protein Engineering

pab1 · Linear

reference_pab1_lr_randomf4_20261104-165011

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
train0.66680.90700.91530.82212750.00000.19931107.00000.70000.14920.56400.00000.00000.67050.56000.06790.56400.00000.00000.70360.60000.08350.98160.04000.00000.74450.64490.09030.99450.14690.00000.7956
validation0.75330.89470.90110.8002851.00000.165281.00000.2000-0.92740.32920.00000.00000.37090.4400-0.34160.73310.04000.00000.62630.4400-0.17570.86980.09000.00000.71980.4512-0.22080.86980.07320.00000.6932
test0.74040.89560.90160.8016915.00000.1366332.00000.70000.23920.64430.00000.00000.75800.60000.02270.85320.08000.00000.74620.59000.02720.85320.13000.00000.78350.62200.04930.85320.13410.00000.7820

Loss per epoch

training validation

Config

name: reference_pab1_lr_randomf4
task: dms
data:
  dataset: pab1
  encoding: one_hot,aa_index
  split_dir: null
  split_scheme: random
  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