Protein Engineering

avgfp · Linear

reference_avgfp_lr_s1_20261104-015427

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
test0.36260.82280.85480.6769490.00000.2495259.00000.50003.49573.83260.00000.00000.62320.52003.68783.96480.14000.00000.81000.50003.70313.96480.20000.00000.84920.48153.68483.96480.14810.00000.8105
train0.32690.84220.86060.70924088.00000.24840.00000.75770.04000.81560.09000.82940.12790.8571
validation0.36500.82220.85680.6755466.00000.2416132.00000.40003.31093.97050.10000.00000.49130.60003.64113.97050.08000.00000.77950.57003.68194.00880.19000.00000.83810.59183.63953.97050.08160.00000.7775

Loss per epoch

training validation

Config

name: reference_avgfp_lr_s1
task: dms
data:
  dataset: avgfp
  encoding: one_hot,aa_index
  split_dir: avgfp/splits/main
model:
  name: lr
train:
  epochs: 300
  batch_size: 128
  learning_rate: 0.0001
  patience: 15
  min_delta: 1.0e-05
  seed: 1