Protein Engineering

gb1 · Linear

reference_gb1_lr_randomf4_20261104-164427

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
train0.71500.92870.96280.862556646.00000.8002100.00001.00001.66231.92400.00000.00000.87941.00001.63242.09420.06000.00000.91931.00001.63872.52950.21000.00000.94200.99911.28192.52950.76160.00000.9819
validation0.72210.92800.96220.861218587.00000.7931478.00001.00001.70691.99510.10000.00000.92421.00001.64041.99510.32000.00000.95201.00001.61751.99510.48000.00000.96700.99911.27072.45390.75280.00000.9804
test0.71160.92890.96290.862918803.00000.8092160.00001.00001.54701.93620.10000.00000.89051.00001.57502.01090.26000.00000.93791.00001.56162.10590.36000.00000.95531.00001.28622.14550.75750.00000.9806

Loss per epoch

training validation

Config

name: reference_gb1_lr_randomf4
task: dms
data:
  dataset: gb1
  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