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fix(hyperopt): TRIAL_SUMMARY best_epoch was epochs_completed, not actual best

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best_epoch always showed 20 (total epochs) instead of the epoch where
the best val_loss checkpoint was saved. Misleading — looked like the
model never peaked. Now correctly reports actual_best_epoch from the
trainer's checkpoint tracker.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
jgrusewski
2026-03-29 19:29:42 +02:00
parent 8a42af0bc6
commit b59e2f7847
1 changed files with 2 additions and 1 deletions
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3
crates/ml/src/hyperopt/adapters/dqn.rs
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@@ -3149,6 +3149,7 @@ impl HyperparameterOptimizable for DQNTrainer {
None
};
let actual_best_epoch = internal_trainer.get_best_epoch();
let metrics = DQNMetrics {
train_loss: training_metrics.loss,
val_loss: internal_trainer.get_best_val_loss(), // Use best validation loss for hyperopt
@@ -3302,7 +3303,7 @@ impl HyperparameterOptimizable for DQNTrainer {
let best_sharpe = metrics.backtest_metrics.as_ref().map(|b| b.sharpe_ratio).unwrap_or(0.0);
let best_win_rate = metrics.backtest_metrics.as_ref().map(|b| b.win_rate * 100.0).unwrap_or(0.0);
let best_max_dd = metrics.backtest_metrics.as_ref().map(|b| b.max_drawdown_pct).unwrap_or(0.0);
let best_epoch = metrics.epochs_completed;
let best_epoch = actual_best_epoch;
info!(
"TRIAL_SUMMARY: trial={}, objective={:.4}, sharpe={:.4}, win_rate={:.2}%, \