Handle sparse event buckets in entry screening

training/tests/test_training_contract.py

@@ -14,7 +14,7 @@ if str(TRAINING_ROOT) not in sys.path:
     sys.path.insert(0, str(TRAINING_ROOT))
 
 from trader_training.onnx_export import LinearHead, export_heads
-from trader_training.entry_feature_screen import _screen_edge_column
+from trader_training.entry_feature_screen import _bucket_edges, _screen_edge_column
 from trader_training.io_utils import read_json, write_json
 from trader_training.labels import ENTRY_LABEL_METHOD, _path_stats_for_group, build_entry_labels
 from trader_training.ofi_feature_experiment import l1_snapshot_diff_ofi_quote
@@ -48,6 +48,14 @@ class TrainingContractTest(unittest.TestCase):
         self.assertEqual("long_actual_plan_net_edge_bps", _screen_edge_column(dataset, "LONG"))
         self.assertEqual("short_actual_plan_net_edge_bps", _screen_edge_column(dataset, "SHORT"))
 
+    def test_entry_feature_screen_keeps_zero_inflated_event_features(self) -> None:
+        values = np.concatenate((np.zeros(5000), np.linspace(1.0, 100.0, 500)))
+        edges = _bucket_edges(values)
+
+        self.assertGreaterEqual(len(edges), 3)
+        self.assertEqual(-np.inf, edges[0])
+        self.assertEqual(np.inf, edges[-1])
+
     def test_split_builder_uses_locked_validation_contract(self) -> None:
         with tempfile.TemporaryDirectory() as tmp:
             data_root = Path(tmp)

training/trader_training/entry_feature_screen.py

@@ -146,7 +146,18 @@ def _bucket_edges(values: np.ndarray) -> np.ndarray:
     edges = np.quantile(clean, quantiles)
     edges = np.unique(edges)
     if edges.size < 3:
-        return np.array([], dtype="float64")
+        non_zero = clean[clean != 0.0]
+        if non_zero.size < 300:
+            return np.array([], dtype="float64")
+        # 突破/扫单类特征常常绝大多数为 0。普通十分位会全挤在 0，
+        # 这里单独保留“没有事件”和“有事件强弱”两类桶，避免漏掉稀有但可能有用的信号。
+        event_edges = np.unique(np.quantile(non_zero, np.linspace(0.0, 1.0, 6)))
+        if event_edges.size < 2:
+            return np.array([-np.inf, 0.0, np.inf], dtype="float64")
+        edges = np.unique(np.concatenate(([-np.inf, 0.0], event_edges[1:-1], [np.inf]))).astype("float64")
+        if edges.size < 3:
+            return np.array([], dtype="float64")
+        return edges
     edges[0] = -np.inf
     edges[-1] = np.inf
     return edges