Protein Engineering

bgl3 · Linear

reference_bgl3_lr_plm_randomf4_20261105-041332

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
train1.18010.64140.60640.40556204.00000.22364175.00000.90001.36912.45670.00000.00000.50040.96001.44882.45670.02000.00000.59440.93001.30052.45670.03000.00000.57560.91251.26392.85480.07500.00000.5864
validation1.43520.51030.46420.25762046.00000.09003040.00000.80000.76101.86900.00000.00000.34330.70000.71932.20540.02000.00000.38230.75000.69182.20540.01000.00000.39990.69810.71892.20540.01890.00000.3840
test1.40540.51880.47350.26652018.00000.124450.00000.90001.02832.36370.00000.00000.43150.70000.75003.11600.08000.00000.38740.74000.73843.11600.06000.00000.41230.69810.72753.11600.09430.00000.3812

Loss per epoch

training validation

Config

name: reference_bgl3_lr_plm_randomf4
task: dms
data:
  dataset: bgl3
  encoding: one_hot,aa_index,plm,plm_sum
  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