Protein Engineering

avgfp · Linear

reference_avgfp_lr_randomf4_20261104-162712

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
train0.32550.84300.85860.71042996.00000.24781666.00000.50003.73753.88670.00000.00000.75350.54003.71603.98400.02000.00000.78130.56003.71554.08600.07000.00000.80110.55253.72774.08600.13270.00000.8510
validation0.36420.82350.85060.67761035.00000.204714.00000.50003.48403.83170.00000.00000.60490.60003.67664.10680.06000.00000.77770.57003.69694.10680.08000.00000.81940.54633.69274.10680.07410.00000.8191
test0.35670.82570.85320.68111013.00000.2421699.00000.40003.43473.90810.00000.00000.57310.50003.66033.96330.02000.00000.77020.50003.68984.01390.08000.00000.81430.46303.68494.01390.09260.00000.8130

Loss per epoch

training validation

Config

name: reference_avgfp_lr_randomf4
task: dms
data:
  dataset: avgfp
  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: 0.0001
  patience: 15
  min_delta: 1.0e-05
  seed: 7