fix: train valid purged ML artifacts

ml/optimizer.py

@@ -156,6 +156,14 @@ def purged_time_series_splits(rows, n_splits=VALIDATION_SPLITS,
             yield np.array(purged_train, dtype=int), np.array(val_idx, dtype=int)
 
 
+def viable_classification_splits(y, splits):
+    """Yield only folds whose training window contains both target classes."""
+    for train_idx, val_idx in splits:
+        if len(np.unique(y[train_idx])) < 2:
+            continue
+        yield train_idx, val_idx
+
+
 def _build_model():
     return GradientBoostingClassifier(
         n_estimators=300,
@@ -377,12 +385,12 @@ def train_model(rows):
     cv_recall = []
     fold_results = []
 
-    splits = list(purged_time_series_splits(
+    splits = list(viable_classification_splits(y, purged_time_series_splits(
         labeled,
         n_splits=VALIDATION_SPLITS,
         label_horizon_days=LABEL_HORIZON_DAYS,
         embargo_days=0,
-    ))
+    )))
     if not splits:
         log.error("No viable purged validation splits. Need more history for %dd label horizon.",
                   LABEL_HORIZON_DAYS)