One-way pAUC — pAUCLoss (1w) + SOPAs

Optimizes partial AUC restricted to a specific FPR range [0, max_fpr]. Useful when false positives are costly and sensitivity at low FPR is the primary concern.

Config

recipes/config_opauc.yaml

dataset:
  name: cifar10
  eval_splits: [val, test]
  kwargs:
    imratio: 0.02

model:
  name: resnet18
  pretrained: false
  num_classes: 1
  in_channels: 3

metrics:
  - AUROC
metric_kwargs:
  - max_fpr: 0.3

training:
  project_name: libauc
  experiment_name: resnet18_opauc_cifar10
  SEED: 2026
  epochs: 60
  batch_size: 128
  eval_batch_size: 256
  sampling_rate: 0.5
  num_workers: 0
  decay_epochs: [0.5, 0.75]
  loss: pAUCLoss
  loss_kwargs:
    mode: 1w
  optimizer: SOPAs
  output_path: ./output
  resume_from_checkpoint: false
  save_checkpoint_every: 5

automax:
  deterministic: true
  n_trials: 5
  SEED: 42
  name: resnet18_opauc_cifar10
  output_directory: ./automax_output
  overwrite: true

Run

python -m src.auto_trainer \
  --config_file recipes/config_opauc.yaml

Note

The max_fpr value in metric_kwargs controls which region of the ROC curve is optimized. A value of 0.3 means only the area under the curve for FPR ∈ [0, 0.3] is maximized.