Protein Engineering

avgfp · Linear

reference_avgfp_lr_s5_20261104-024238

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
test0.36320.82250.85520.6764490.00000.2449267.00000.50003.68093.83260.00000.00000.75910.54003.72913.96480.12000.00000.84220.51003.70393.96480.19000.00000.84990.53703.73273.96480.14810.00000.8479
train0.32650.84240.86090.70964088.00000.24830.00000.75330.04000.80930.08000.82000.13010.8556
validation0.36380.82270.85710.6765466.00000.2546107.00000.60003.71493.97050.10000.00000.77980.60003.71853.97050.08000.00000.83870.53003.67384.00880.18000.00000.83180.61223.72033.97050.08160.00000.8394

Loss per epoch

training validation

Config

name: reference_avgfp_lr_s5
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: 5