Protein Engineering

pab1 · Linear

reference_pab1_lr_plm_randomf3_20261105-035548

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
train0.63290.91290.92400.83212684.00000.12341.00000.70000.22801.52040.10000.00000.70780.64000.14261.52040.04000.00000.74000.62000.07811.52040.06000.00000.74910.63670.08141.52040.10200.00000.7966
validation0.69610.90260.91340.8135915.0000-0.0093897.00000.50000.12350.64430.00000.00000.71690.56000.02830.64430.04000.00000.74840.65000.06050.70400.10000.00000.79710.63410.06800.70400.07320.00000.7895
test0.70240.90120.91550.8106917.00000.0681357.00000.4000-0.36960.51660.00000.00000.54250.4800-0.07550.51660.04000.00000.71400.4800-0.02430.76580.11000.00000.76860.4878-0.02660.76580.10980.00000.7572

Loss per epoch

training validation

Config

name: reference_pab1_lr_plm_randomf3
task: dms
data:
  dataset: pab1
  encoding: one_hot,aa_index,plm,plm_sum
  split_dir: null
  split_scheme: random
  split_fold: 3
model:
  name: lr
train:
  epochs: 300
  batch_size: 128
  learning_rate: 0.001
  patience: 15
  min_delta: 1.0e-05
  seed: 7