# 温和正则 + 略提高 V_pi 权重（在 default 基线上小幅改动，便于对比）
# 使用: python -m src.main train --config configs/mild_reg.yaml

data_path: data/dataset.txt

split_ratios: [0.7, 0.15, 0.15]
random_seed: 42
split_mode: grouped_stratified
split_stratify_target: V_pi
split_stratify_bins: 10

remove_duplicate_rows: true

outlier_strategy: none
outlier_apply_to: targets
outlier_config:
  iqr_k: 1.5
  zscore_threshold: 4.0
  quantile_lower: 0.001
  quantile_upper: 0.999

filter_v_pi_range: true
v_pi_min: 0.0
v_pi_max: 500.0

remove_nonpositive_vpi: false

model:
  input_dim: 8
  hidden_dims: [200, 300, 350, 300, 200]
  output_dim: 3
  batchnorm: false
  dropout: 0.05
  residual: false

optimizer:
  name: adamw
  lr: 0.001
  weight_decay: 0.0001

scheduler:
  type: cosine
  plateau_factor: 0.5
  plateau_patience: 10
  plateau_min_lr: 1.0e-6

training:
  batch_size: 128
  epochs: 300
  early_stopping_patience: 30
  num_workers: 0

loss:
  type: huber
  huber_delta: 1.0
  target_weights: [1.0, 1.0, 1.2]

output_dir: results
last_run_dir: null