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photonAI/configs/reg_vpi_try1.yaml
huangfu 745868a456 Improve split strategy for more reliable training evaluation
Group samples by identical inputs before splitting, add target-aware stratification options, and cover the behavior with tests so repeated-input rows no longer leak across train, validation, and test sets.

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2026-04-19 16:43:00 +08:00

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# 实验:较强正则 + 提高 V_pi 损失权重2026-04-19 试跑)
# 结果早停偏早test 整体差于 default 基线;仅作记录,日常训练请用 default.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: 0.15
residual: false
optimizer:
name: adamw
lr: 0.001
weight_decay: 0.0002
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, 1.75]
output_dir: results
last_run_dir: null