fix(training): patch lightgbm sklearn compatibility

backend/core/generate_dataset.py

@@ -264,6 +264,28 @@ def sample_event(rng, employee):
     return event
 
 
+def attach_event_timeline(df):
+    df = df.copy()
+    rng = np.random.default_rng(config.RANDOM_STATE)
+    base_date = np.datetime64('2025-01-01')
+    timelines = []
+
+    for employee_id, group in df.groupby('员工编号', sort=False):
+        group = group.copy().reset_index(drop=True)
+        event_count = len(group)
+        offsets = np.sort(rng.integers(0, 365, size=event_count))
+        group['事件日期'] = [
+            str(pd.Timestamp(base_date + np.timedelta64(int(offset), 'D')).date())
+            for offset in offsets
+        ]
+        group['事件日期索引'] = offsets.astype(int)
+        group['事件序号'] = np.arange(1, event_count + 1)
+        group['员工历史事件数'] = event_count
+        timelines.append(group)
+
+    return pd.concat(timelines, ignore_index=True)
+
+
 def validate_dataset(df):
     required_columns = [
         '员工编号',
@@ -273,6 +295,9 @@ def validate_dataset(df):
         '通勤时长分钟',
         '是否慢性病史',
         '请假类型',
+        '事件序号',
+        '事件日期索引',
+        '员工历史事件数',
         '缺勤时长（小时）',
     ]
     for column in required_columns:
@@ -309,7 +334,7 @@ def generate_dataset(output_path=None, sample_count=12000, random_state=None):
     for idx in employee_idx:
         events.append(sample_event(rng, employees[int(idx)]))
 
-    df = pd.DataFrame(events)
+    df = attach_event_timeline(pd.DataFrame(events))
     validate_dataset(df)
 
     if output_path: