avgfp · Graph CNN

reference_avgfp_gcn_plm_randomf4_20261105-041016

Metrics

Set	mse	pearsonr	spearmanr	r2	above_wt_n	above_wt_spearmanr	budget_to_max	hit_rate_at_10	mean_at_10	max_at_10	precision_at_10	captured_at_10	hit_rate_at_50	mean_at_50	max_at_50	precision_at_50	captured_at_50	hit_rate_at_100	mean_at_100	max_at_100	precision_at_100	captured_at_100	hit_rate_at_1pct	mean_at_1pct	max_at_1pct	precision_at_1pct	captured_at_1pct
train	0.0622	0.9722	0.8859	0.9447	2996.0000	0.2920	963.0000	0.9000	3.8272	3.9540	0.0000	0.8141	0.7600	3.8059	4.1079	0.1000	0.8456	0.7800	3.8036	4.1079	0.1200	0.8657	0.6852	3.7725	4.1145	0.1975	0.8855
validation	0.0842	0.9622	0.8669	0.9254	1035.0000	0.2365	14.0000	0.6000	3.7836	4.0307	0.1000	0.8134	0.6200	3.7724	4.1068	0.1000	0.8479	0.6500	3.7732	4.1068	0.1800	0.8773	0.6389	3.7671	4.1068	0.1667	0.8757
test	0.0824	0.9628	0.8712	0.9264	1013.0000	0.2689	184.0000	0.6000	3.7772	3.9328	0.0000	0.8230	0.6600	3.7741	4.0292	0.1200	0.8568	0.6600	3.7659	4.0292	0.2000	0.8738	0.6667	3.7667	4.0292	0.2037	0.8772

Loss per epoch

training validation

Config

name: reference_avgfp_gcn_plm_randomf4
task: dms
data:
  dataset: avgfp
  encoding: one_hot,aa_index,plm,plm_sum
  split_dir: null
  graph_threshold: 7
  split_scheme: random
  split_fold: 4
model:
  name: gcn
  filters:
  - 128
  - 128
  hidden:
  - 100
  dropout: 0.2
train:
  epochs: 300
  batch_size: 32
  learning_rate: 0.0001
  patience: 15
  min_delta: 1.0e-05
  seed: 7