优化6个类，现在ks降低到0.28，史称3.0版本

example/evaluate_generated.py

@@ -75,15 +75,16 @@ def ks_statistic(x: List[float], y: List[float]) -> float:
     n = len(x_sorted)
     m = len(y_sorted)
     i = j = 0
-    cdf_x = cdf_y = 0.0
     d = 0.0
-    while i < n and j < m:
-        if x_sorted[i] <= y_sorted[j]:
+    # Iterate over merged unique values to handle ties correctly
+    merged = sorted(set(x_sorted) | set(y_sorted))
+    for v in merged:
+        while i < n and x_sorted[i] <= v:
             i += 1
-            cdf_x = i / n
-        else:
+        while j < m and y_sorted[j] <= v:
             j += 1
-            cdf_y = j / m
+        cdf_x = i / n
+        cdf_y = j / m
         d = max(d, abs(cdf_x - cdf_y))
     return d
 
@@ -103,31 +104,43 @@ def lag1_corr(values: List[float]) -> float:
     return num / math.sqrt(den_x * den_y)
 
 
-def resolve_reference_path(path: str) -> Optional[str]:
+def resolve_reference_paths(path: str) -> List[str]:
     if not path:
-        return None
+        return []
     if any(ch in path for ch in ["*", "?", "["]):
-        base = Path(path).parent
+        base = Path(path).parent.resolve()
         pat = Path(path).name
         matches = sorted(base.glob(pat))
-        return str(matches[0]) if matches else None
-    return str(path)
+        return [str(p) for p in matches]
+    return [str(path)]
 
 
 def main():
     args = parse_args()
     base_dir = Path(__file__).resolve().parent
-    args.generated = str((base_dir / args.generated).resolve()) if not Path(args.generated).is_absolute() else args.generated
-    args.split = str((base_dir / args.split).resolve()) if not Path(args.split).is_absolute() else args.split
-    args.stats = str((base_dir / args.stats).resolve()) if not Path(args.stats).is_absolute() else args.stats
-    args.vocab = str((base_dir / args.vocab).resolve()) if not Path(args.vocab).is_absolute() else args.vocab
-    args.out = str((base_dir / args.out).resolve()) if not Path(args.out).is_absolute() else args.out
+
+    def resolve_file(p: str) -> str:
+        path = Path(p)
+        if path.is_absolute():
+            return str(path)
+        if path.exists():
+            return str(path.resolve())
+        candidate = base_dir / path
+        if candidate.exists():
+            return str(candidate.resolve())
+        return str((base_dir / path).resolve())
+
+    args.generated = resolve_file(args.generated)
+    args.split = resolve_file(args.split)
+    args.stats = resolve_file(args.stats)
+    args.vocab = resolve_file(args.vocab)
+    args.out = resolve_file(args.out)
     if args.reference and not Path(args.reference).is_absolute():
         if any(ch in args.reference for ch in ["*", "?", "["]):
             args.reference = str(base_dir / args.reference)
         else:
             args.reference = str((base_dir / args.reference).resolve())
-    ref_path = resolve_reference_path(args.reference)
+    ref_paths = resolve_reference_paths(args.reference)
     split = load_json(args.split)
     time_col = split.get("time_column", "time")
     cont_cols = [c for c in split["continuous"] if c != time_col]
@@ -156,7 +169,7 @@ def main():
                 except Exception:
                     v = 0.0
                 update_stats(cont_stats, c, v)
-                if ref_path:
+                if ref_paths:
                     pass
             for c in disc_cols:
                 if row[c] not in vocab_sets[c]:
@@ -182,7 +195,7 @@ def main():
     }
 
     # Optional richer metrics using reference data
-    if ref_path:
+    if ref_paths:
         ref_cont = {c: [] for c in cont_cols}
         ref_disc = {c: {} for c in disc_cols}
         gen_cont = {c: [] for c in cont_cols}
@@ -202,21 +215,26 @@ def main():
                     tok = row[c]
                     gen_disc[c][tok] = gen_disc[c].get(tok, 0) + 1
 
-        with open_csv(ref_path) as f:
-            reader = csv.DictReader(f)
-            for i, row in enumerate(reader):
-                if time_col in row:
-                    row.pop(time_col, None)
-                for c in cont_cols:
-                    try:
-                        ref_cont[c].append(float(row[c]))
-                    except Exception:
-                        ref_cont[c].append(0.0)
-                for c in disc_cols:
-                    tok = row[c]
-                    ref_disc[c][tok] = ref_disc[c].get(tok, 0) + 1
-                if args.max_rows and i + 1 >= args.max_rows:
-                    break
+        loaded = 0
+        for ref_path in ref_paths:
+            with open_csv(ref_path) as f:
+                reader = csv.DictReader(f)
+                for row in reader:
+                    if time_col in row:
+                        row.pop(time_col, None)
+                    for c in cont_cols:
+                        try:
+                            ref_cont[c].append(float(row[c]))
+                        except Exception:
+                            ref_cont[c].append(0.0)
+                    for c in disc_cols:
+                        tok = row[c]
+                        ref_disc[c][tok] = ref_disc[c].get(tok, 0) + 1
+                    loaded += 1
+                    if args.max_rows and loaded >= args.max_rows:
+                        break
+            if args.max_rows and loaded >= args.max_rows:
+                break
 
         # Continuous metrics: KS + quantiles + lag1 correlation
         cont_ks = {}