feat(hyperopt): implement Successive Halving early stopping strategy

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

ml/src/hyperopt/early_stopping.rs

@@ -1056,10 +1056,22 @@ impl EarlyStoppingObserver {
                 let strategy = PercentilePrunerStrategy::new(*percentile, *warmup_steps);
                 strategy.should_stop(epoch, val_loss, &self.trial_best_losses)
             },
-            EarlyStoppingStrategy::SuccessiveHalving { .. } => {
-                // TODO: Implement successive halving
-                warn!("SuccessiveHalving not yet implemented, defaulting to Continue");
-                false
+            EarlyStoppingStrategy::SuccessiveHalving { reduction_factor } => {
+                // Need at least reduction_factor completed trials for meaningful comparison
+                if self.trial_best_losses.len() < *reduction_factor {
+                    return false;
+                }
+
+                // Sort completed trial losses ascending (NaN-safe)
+                let mut sorted = self.trial_best_losses.clone();
+                sorted.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
+
+                // Keep top 1/eta fraction, prune the rest
+                let keep_count = (sorted.len() / reduction_factor).max(1);
+                let threshold = sorted[keep_count - 1];
+
+                // Prune if current trial is worse than the threshold
+                val_loss > threshold
             },
             EarlyStoppingStrategy::Hyperband { .. } => {
                 // TODO: Implement hyperband
@@ -1216,4 +1228,86 @@ mod tests {
         // Above 25th percentile (0.3 < 0.4, so not pruned)
         assert!(!strategy.should_stop(5, 0.3, &history));
     }
+
+    #[test]
+    fn test_successive_halving_prunes_bottom_trials() {
+        // 9 completed trials with losses 0.1..=0.9, reduction_factor=3
+        // keep_count = max(1, 9/3) = 3
+        // sorted: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
+        // threshold = sorted[2] = 0.3
+        // New trial with val_loss=0.5 > 0.3 => should be pruned (StopTrial)
+        let config = EarlyStoppingConfig {
+            patience_epochs: 10,
+            min_delta: 1e-4,
+            min_epochs: 0,
+            strategy: EarlyStoppingStrategy::SuccessiveHalving {
+                reduction_factor: 3,
+            },
+            ..Default::default()
+        };
+        let mut observer = EarlyStoppingObserver::new(config);
+
+        // Register 9 completed trials
+        for i in 0..9 {
+            observer.on_trial_start(i, &format!("trial_{}", i));
+            let loss = (i as f64 + 1.0) / 10.0; // 0.1, 0.2, ..., 0.9
+            observer.on_trial_complete(i, loss);
+        }
+
+        // Start a new trial (trial 9) with a bad loss
+        observer.on_trial_start(9, "trial_9");
+        let metrics = EpochMetrics {
+            epoch: 1,
+            train_loss: 0.5,
+            val_loss: 0.5, // worse than threshold 0.3
+            timestamp: 1.0,
+        };
+        let decision = observer.on_epoch_complete(9, 1, &metrics);
+        assert_eq!(
+            decision,
+            ObserverDecision::StopTrial,
+            "Trial with val_loss=0.5 should be pruned (threshold=0.3)"
+        );
+    }
+
+    #[test]
+    fn test_successive_halving_keeps_top_trials() {
+        // 9 completed trials with losses 0.1..=0.9, reduction_factor=3
+        // keep_count = max(1, 9/3) = 3
+        // sorted: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
+        // threshold = sorted[2] = 0.3
+        // New trial with val_loss=0.2 <= 0.3 => should continue
+        let config = EarlyStoppingConfig {
+            patience_epochs: 10,
+            min_delta: 1e-4,
+            min_epochs: 0,
+            strategy: EarlyStoppingStrategy::SuccessiveHalving {
+                reduction_factor: 3,
+            },
+            ..Default::default()
+        };
+        let mut observer = EarlyStoppingObserver::new(config);
+
+        // Register 9 completed trials
+        for i in 0..9 {
+            observer.on_trial_start(i, &format!("trial_{}", i));
+            let loss = (i as f64 + 1.0) / 10.0; // 0.1, 0.2, ..., 0.9
+            observer.on_trial_complete(i, loss);
+        }
+
+        // Start a new trial (trial 9) with a good loss
+        observer.on_trial_start(9, "trial_9");
+        let metrics = EpochMetrics {
+            epoch: 1,
+            train_loss: 0.2,
+            val_loss: 0.2, // within top 1/3 (threshold=0.3)
+            timestamp: 1.0,
+        };
+        let decision = observer.on_epoch_complete(9, 1, &metrics);
+        assert_eq!(
+            decision,
+            ObserverDecision::Continue,
+            "Trial with val_loss=0.2 should continue (threshold=0.3)"
+        );
+    }
 }