连续型特征在时许相关性上的不足

example/export_samples.py

@@ -111,6 +111,7 @@ def main():
     vmax = stats.get("max", {})
     int_like = stats.get("int_like", {})
     max_decimals = stats.get("max_decimals", {})
+    transforms = stats.get("transform", {})
 
     vocab_json = json.load(open(args.vocab_path, "r", encoding="utf-8"))
     vocab = vocab_json["vocab"]
@@ -141,6 +142,13 @@ def main():
     model = HybridDiffusionModel(
         cont_dim=len(cont_cols),
         disc_vocab_sizes=vocab_sizes,
+        time_dim=int(cfg.get("model_time_dim", 64)),
+        hidden_dim=int(cfg.get("model_hidden_dim", 256)),
+        num_layers=int(cfg.get("model_num_layers", 1)),
+        dropout=float(cfg.get("model_dropout", 0.0)),
+        ff_mult=int(cfg.get("model_ff_mult", 2)),
+        pos_dim=int(cfg.get("model_pos_dim", 64)),
+        use_pos_embed=bool(cfg.get("model_use_pos_embed", True)),
         cond_vocab_size=cond_vocab_size if use_condition else 0,
         cond_dim=int(cfg.get("cond_dim", 32)),
         use_tanh_eps=bool(cfg.get("use_tanh_eps", False)),
@@ -220,6 +228,9 @@ def main():
     mean_vec = torch.tensor([mean[c] for c in cont_cols], dtype=x_cont.dtype)
     std_vec = torch.tensor([std[c] for c in cont_cols], dtype=x_cont.dtype)
     x_cont = x_cont * std_vec + mean_vec
+    for i, c in enumerate(cont_cols):
+        if transforms.get(c) == "log1p":
+            x_cont[:, :, i] = torch.expm1(x_cont[:, :, i])
     # clamp to observed min/max per feature
     if vmin and vmax:
         for i, c in enumerate(cont_cols):
@@ -246,8 +257,8 @@ def main():
                     row["__cond_file_id"] = str(int(cond[b].item())) if cond is not None else "-1"
                 if args.include_time and time_col in header:
                     row[time_col] = str(row_index)
-                for i, c in enumerate(cont_cols):
-                    val = float(x_cont[b, t, i])
+        for i, c in enumerate(cont_cols):
+            val = float(x_cont[b, t, i])
                     if int_like.get(c, False):
                         row[c] = str(int(round(val)))
                     else: