Resume SynthData0523 main/c5 batch 3

.gitattributes

@@ -4536,3 +4536,40 @@ SynthData0523/main/c5/realtabformer/rtf-c5-20260328_094434/rtf-c5-1000-20260328_
 SynthData0523/main/c5/realtabformer/rtf-c5-20260328_094434/rtf-c5-6732-20260418_074509.csv filter=lfs diff=lfs merge=lfs -text
 SynthData0523/main/c5/realtabformer/rtf-c5-20260328_094434/rtf-c5-6732-20260418_082736.csv filter=lfs diff=lfs merge=lfs -text
 SynthData0523/main/c5/realtabformer/rtf-c5-20260328_094434/staged/public/test.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/realtabformer/rtf-c5-20260328_094434/staged/public/train.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/realtabformer/rtf-c5-20260328_094434/staged/public/val.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/realtabformer/rtf-c5-20260328_094434/train_20260328_094434.log filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/input_snapshot.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/models_tabbyflow/trained.pt filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/public_gate/normalized_schema_snapshot.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/public_gate/public_gate_report.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/public_gate/staged_input_manifest.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/runtime_result.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/staged/public/staged_features.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/staged/public/test.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/staged/public/train.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/staged/public/val.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/staged/tabbyflow/adapter_report.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/staged/tabbyflow/adapter_transforms_applied.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/staged/tabbyflow/model_input_manifest.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabbyflow-c5-6732-20260420_061354.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabbyflow_train_meta.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/X_cat_test.npy filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/X_cat_train.npy filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/X_cat_val.npy filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/X_num_test.npy filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/X_num_train.npy filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/X_num_val.npy filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/info.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/real.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/test.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/val.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/y_test.npy filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/y_train.npy filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/y_val.npy filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/train_20260420_060659.log filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabddpm/tabddpm-c5-20260422_211246/data/X_cat_train.npy filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabddpm/tabddpm-c5-20260422_211246/data/y_train.npy filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabddpm/tabddpm-c5-20260422_211246/gen_20260422_211947.log filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabddpm/tabddpm-c5-20260422_211246/output/X_cat_train.npy filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c5/tabddpm/tabddpm-c5-20260422_211246/output/X_cat_unnorm.npy filter=lfs diff=lfs merge=lfs -text

SynthData0523/main/c5/realtabformer/rtf-c5-20260328_094434/staged/public/train.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2a23d9f1558e4268759d44a5a58662ddff4e0b757a65c49e7019e9db25203034
+size 997613

SynthData0523/main/c5/realtabformer/rtf-c5-20260328_094434/staged/public/val.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fa96f3f053a3ae913c7cd723e390bf19f86afc22192eea09094e136ce2a6eb6c
+size 124824

SynthData0523/main/c5/realtabformer/rtf-c5-20260328_094434/staged/realtabformer/adapter_report.json

@@ -0,0 +1,7 @@
+{
+  "adapter_ready_status": "pass",
+  "adapter_fail_reason_code": null,
+  "adapter_fail_detail": null,
+  "adapter_transforms_applied": [],
+  "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/realtabformer/rtf-c5-20260328_094434/staged/realtabformer/model_input_manifest.json"
+}

SynthData0523/main/c5/realtabformer/rtf-c5-20260328_094434/staged/realtabformer/adapter_transforms_applied.json

@@ -0,0 +1 @@
+[]

SynthData0523/main/c5/realtabformer/rtf-c5-20260328_094434/staged/realtabformer/model_input_manifest.json

@@ -0,0 +1,474 @@
+{
+  "dataset_id": "c5",
+  "model": "realtabformer",
+  "target_column": "class",
+  "task_type": "classification",
+  "column_schema": [
+    {
+      "name": "class",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000297,
+        "example_values": [
+          "EDIBLE",
+          "POISONOUS"
+        ]
+      }
+    },
+    {
+      "name": "cap-shape",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 6,
+        "unique_ratio": 0.000891,
+        "example_values": [
+          "CONVEX",
+          "BELL",
+          "FLAT",
+          "KNOBBED",
+          "CONICAL"
+        ]
+      }
+    },
+    {
+      "name": "cap-surface",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.000594,
+        "example_values": [
+          "SCALY",
+          "SMOOTH",
+          "FIBROUS",
+          "GROOVES"
+        ]
+      }
+    },
+    {
+      "name": "cap-color",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 10,
+        "unique_ratio": 0.001485,
+        "example_values": [
+          "YELLOW",
+          "GRAY",
+          "BUFF",
+          "WHITE",
+          "BROWN"
+        ]
+      }
+    },
+    {
+      "name": "bruises?",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000297,
+        "example_values": [
+          "BRUISES",
+          "NO"
+        ]
+      }
+    },
+    {
+      "name": "odor",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [
+        "NONE"
+      ],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.453209,
+        "unique_count": 8,
+        "unique_ratio": 0.002173,
+        "example_values": [
+          "ALMOND",
+          "FOUL",
+          "FISHY",
+          "SPICY",
+          "ANISE"
+        ]
+      }
+    },
+    {
+      "name": "gill-attachment",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000297,
+        "example_values": [
+          "FREE",
+          "ATTACHED"
+        ]
+      }
+    },
+    {
+      "name": "gill-spacing",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000297,
+        "example_values": [
+          "CLOSE",
+          "CROWDED"
+        ]
+      }
+    },
+    {
+      "name": "gill-size",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000297,
+        "example_values": [
+          "BROAD",
+          "NARROW"
+        ]
+      }
+    },
+    {
+      "name": "gill-color",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 12,
+        "unique_ratio": 0.001783,
+        "example_values": [
+          "BROWN",
+          "BLACK",
+          "GRAY",
+          "PINK",
+          "CHOCOLATE"
+        ]
+      }
+    },
+    {
+      "name": "stalk-shape",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000297,
+        "example_values": [
+          "ENLARGING",
+          "TAPERING"
+        ]
+      }
+    },
+    {
+      "name": "stalk-root",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [
+        "?"
+      ],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.295009,
+        "unique_count": 4,
+        "unique_ratio": 0.000843,
+        "example_values": [
+          "CLUB",
+          "BULBOUS",
+          "EQUAL",
+          "ROOTED"
+        ]
+      }
+    },
+    {
+      "name": "stalk-surface-above-ring",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.000594,
+        "example_values": [
+          "SMOOTH",
+          "SILKY",
+          "FIBROUS",
+          "SCALY"
+        ]
+      }
+    },
+    {
+      "name": "stalk-surface-below-ring",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.000594,
+        "example_values": [
+          "SMOOTH",
+          "SILKY",
+          "FIBROUS",
+          "SCALY"
+        ]
+      }
+    },
+    {
+      "name": "stalk-color-above-ring",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 9,
+        "unique_ratio": 0.001337,
+        "example_values": [
+          "WHITE",
+          "BROWN",
+          "PINK",
+          "BUFF",
+          "GRAY"
+        ]
+      }
+    },
+    {
+      "name": "stalk-color-below-ring",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 9,
+        "unique_ratio": 0.001337,
+        "example_values": [
+          "WHITE",
+          "BUFF",
+          "PINK",
+          "BROWN",
+          "GRAY"
+        ]
+      }
+    },
+    {
+      "name": "veil-type",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 1,
+        "unique_ratio": 0.000149,
+        "example_values": [
+          "PARTIAL"
+        ]
+      }
+    },
+    {
+      "name": "veil-color",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.000594,
+        "example_values": [
+          "WHITE",
+          "BROWN",
+          "ORANGE",
+          "YELLOW"
+        ]
+      }
+    },
+    {
+      "name": "ring-number",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [
+        "NONE"
+      ],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.005496,
+        "unique_count": 2,
+        "unique_ratio": 0.000299,
+        "example_values": [
+          "ONE",
+          "TWO"
+        ]
+      }
+    },
+    {
+      "name": "ring-type",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [
+        "NONE"
+      ],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.005496,
+        "unique_count": 4,
+        "unique_ratio": 0.000597,
+        "example_values": [
+          "PENDANT",
+          "LARGE",
+          "EVANESCENT",
+          "FLARING"
+        ]
+      }
+    },
+    {
+      "name": "spore-print-color",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 9,
+        "unique_ratio": 0.001337,
+        "example_values": [
+          "BLACK",
+          "CHOCOLATE",
+          "BROWN",
+          "WHITE",
+          "YELLOW"
+        ]
+      }
+    },
+    {
+      "name": "population",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 6,
+        "unique_ratio": 0.000891,
+        "example_values": [
+          "NUMEROUS",
+          "SEVERAL",
+          "SOLITARY",
+          "SCATTERED",
+          "ABUNDANT"
+        ]
+      }
+    },
+    {
+      "name": "habitat",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7,
+        "unique_ratio": 0.00104,
+        "example_values": [
+          "MEADOWS",
+          "GRASSES",
+          "WOODS",
+          "URBAN",
+          "LEAVES"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/realtabformer/rtf-c5-20260328_094434/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/realtabformer/rtf-c5-20260328_094434/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/realtabformer/rtf-c5-20260328_094434/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/realtabformer/rtf-c5-20260328_094434/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/realtabformer/rtf-c5-20260328_094434/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/realtabformer/rtf-c5-20260328_094434/public_gate/public_gate_report.json"
+}

SynthData0523/main/c5/realtabformer/rtf-c5-20260328_094434/train_20260328_094434.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ee88e260121894a7cc137c2dae1d2e0dc8e7158e6cbf234d452a34b3d30dfb75
+size 1507409

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/_tabbyflow_gen.py

@@ -0,0 +1,33 @@
+
+import os, shutil, subprocess, sys
+root = r"/workspace/ef-vfm"
+name = r"pipeline_ds"
+src = r"/work/output-SpecializedModels/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds"
+dst_data = os.path.join(root, "data", name)
+shutil.rmtree(dst_data, ignore_errors=True)
+shutil.copytree(src, dst_data)
+dst_syn = os.path.join(root, "synthetic", name)
+os.makedirs(dst_syn, exist_ok=True)
+for fn in ("real.csv", "test.csv", "val.csv"):
+    shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn))
+os.chdir(root)
+os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "")
+subprocess.check_call([
+    sys.executable, "main.py",
+    "--dataname", name, "--mode", "test", "--gpu", "0",
+    "--no_wandb", "--exp_name", r"adapter_efvfm",
+    "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_ds/adapter_efvfm/model_500.pt",
+    "--num_samples_to_generate", str(int(6732)),
+])
+base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm")
+best = None
+best_t = -1.0
+for r, _, files in os.walk(base):
+    if "samples.csv" in files:
+        p = os.path.join(r, "samples.csv")
+        t = os.path.getmtime(p)
+        if t > best_t:
+            best_t, best = t, p
+if not best:
+    raise SystemExit("tabbyflow: no samples.csv in " + base)
+shutil.copy(best, r"/work/output-SpecializedModels/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabbyflow-c5-6732-20260420_061354.csv")

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/_tabbyflow_train.py

@@ -0,0 +1,22 @@
+
+import os, shutil, subprocess, sys
+root = r"/workspace/ef-vfm"
+name = r"pipeline_ds"
+src = r"/work/output-SpecializedModels/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds"
+os.makedirs(os.path.join(root, "data", name), exist_ok=True)
+dst_data = os.path.join(root, "data", name)
+dst_syn = os.path.join(root, "synthetic", name)
+shutil.rmtree(dst_data, ignore_errors=True)
+shutil.copytree(src, dst_data)
+os.makedirs(dst_syn, exist_ok=True)
+for fn in ("real.csv", "test.csv", "val.csv"):
+    shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn))
+os.chdir(root)
+os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "")
+os.environ["EFVFM_SMOKE_STEPS"] = "500"
+os.environ["EFVFM_ADAPTER_TRAIN"] = "1"
+subprocess.check_call([
+    sys.executable, "main.py",
+    "--dataname", name, "--mode", "train", "--gpu", "0",
+    "--no_wandb", "--exp_name", r"adapter_efvfm",
+])

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/input_snapshot.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:af122b30f604b076718db4b7e3465ecf97d69402416ffd34e150f83e67faf707
+size 1352

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/models_tabbyflow/trained.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:adb734fec12f2251befd371dca69e481b19464aded2a6823105a01b4be6ddbe5
+size 40

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6a82dde0a0b02bb1e4531b9f5e04984f7873c1c4981a2b343015d3a380445ebf
+size 10604

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/public_gate/public_gate_report.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cb44bf24e6a7f07ae132440102f3b87d7e9090ba9ab8c151c25a589b4221c34b
+size 912

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/public_gate/staged_input_manifest.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:86149a354a7e95b54d3d5bbce7ac2a468948006d84a431221a64d18bd73d59d3
+size 11405

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/runtime_result.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:77fbf78dc1160bd3211fdff7e44127ee4cf01430c7626eddfc3ecb6c7c70fe64
+size 621

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/staged/public/staged_features.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0ace7d0e8b3fdcd849ddbae15387f2fbaeb5a1756991805f39eca5eae193e7f7
+size 2303

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/staged/public/test.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:22374ee05e54a92c07546639c8485d89e02a7c7b38db99ef9ac5dfb259bd032d
+size 125218

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/staged/public/train.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2a23d9f1558e4268759d44a5a58662ddff4e0b757a65c49e7019e9db25203034
+size 997613

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/staged/public/val.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fa96f3f053a3ae913c7cd723e390bf19f86afc22192eea09094e136ce2a6eb6c
+size 124824

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/staged/tabbyflow/adapter_report.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b6b5314ffebedcfbe2a52cc4dca1bb7c6d497c84de2209839e58e57bde2516b5
+size 322

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/staged/tabbyflow/adapter_transforms_applied.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f53cda18c2baa0c0354bb5f9a3ecbe5ed12ab4d8e11ba873c2f11161202b945
+size 2

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/staged/tabbyflow/model_input_manifest.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4557325b0c4f05fd9be145ebbe6252571ccfbf9d8127d9c289abf118042ebb83
+size 11603

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabbyflow-c5-6732-20260420_061354.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:71f25f6f67cd5cf6a5b3c3c89ae3be44b0f501829a2a36c131873969eb7bfc02
+size 1034546

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabbyflow_train_meta.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1bbe507388dc5629e9155e6ecf504bc121a9c227c10ff53532d15220fc65ee13
+size 78

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/X_cat_test.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:389c37893fd830ac4e262a3a53b9568956ddd4ea74820ee54d96d671bd95abdb
+size 148496

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/X_cat_train.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d2e5a8320a843e2485682b858182bafd262c91a7389b4627e7c2a43d15ca001a
+size 1184960

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/X_cat_val.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:743a2862405bfe4585130f7767735125a7f412a5c65faddf2296bac9dd42e3b4
+size 148144

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/X_num_test.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b850f5917e1d237ec5f6c9e3bd9ca42f11d8cf5304f6096d40a004aa32893f5d
+size 128

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/X_num_train.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:80f86d92d70d812c8834cd1824906c437baac6d0df9446c8f92f56aa2c762d54
+size 128

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/X_num_val.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:96a17508af6c00fffd3bc9af50ce9d133ea4c7e037fa9c3068cbdf376981ddd7
+size 128

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/info.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c394824c93bf4cad58137bf3ba16a91facb70095f8ff87465f1e5ce45814f6bd
+size 3574

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/real.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dd43460071d86dce017276becc12b0541051db9121551ce47da0d554f5741927
+size 354085

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/test.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:32a28cbc85fdf1341dfb202b0034268effa08b15aed1faa9f291a20db1f5d3ba
+size 44605

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/val.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fdda52a19a0be6c4e399676ece5caa2355189af5b4571993592b404b8ad0a5f3
+size 44537

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/y_test.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:207fa1b324f3cf0aa593a2ba26063ea41c2cde1ba9ab003e5c1a59ac2a7a4d5c
+size 6872

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/y_train.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1b36003f6d55aaa9706a2d1248efd2c4736d8f8d8a8fdac7857df93028d682ec
+size 53984

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/tabular_bundle/pipeline_ds/y_val.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d4ffb3741355db4f834b4237cf3021045df9f7343821ec96e53b2bb3019be77b
+size 6856

SynthData0523/main/c5/tabbyflow/tabbyflow-c5-20260420_060659/train_20260420_060659.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3580b26b5ad937bbc0c20212b73241f6ff662e55d244763bf2a59e7836851bbe
+size 356789

SynthData0523/main/c5/tabddpm/tabddpm-c5-20260422_211246/_tabddpm_sample.py

@@ -0,0 +1,67 @@
+import os, sys, subprocess, json
+import numpy as np
+import pandas as pd
+
+tabddpm_root = "/workspace/tabddpm/code"
+assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}"
+env = os.environ.copy()
+env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", ""))
+
+# Reuse the compat wrapper (patches collections.Sequence for skorch)
+wrapper = os.path.join(tabddpm_root, "_compat_run.py")
+if not os.path.exists(wrapper):
+    with open(wrapper, "w") as f:
+        f.write(
+            "import collections, collections.abc\n"
+            "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping',"
+            "'MutableSet','Set','Callable','Iterable','Iterator'):\n"
+            "    if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n"
+            "import sys, runpy\n"
+            "sys.argv = sys.argv[1:]\n"
+            "runpy.run_path(sys.argv[0], run_name='__main__')\n"
+        )
+
+print(f"[TabDDPM] Sampling 6732 rows")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260422_211246/config_sample_20260422_211947.toml",
+     "--sample"],
+    cwd=tabddpm_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+
+# 将 .npy 输出转为 CSV
+work_dir = "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260422_211246"
+info_path = os.path.join(work_dir, "data", "info.json")
+with open(info_path) as f:
+    info = json.load(f)
+
+output_dir = os.path.join(work_dir, "output")
+col_names = info.get("column_names", [])
+
+parts = []
+x_num_path = os.path.join(output_dir, "X_num_train.npy")
+x_cat_path = os.path.join(output_dir, "X_cat_train.npy")
+y_path = os.path.join(output_dir, "y_train.npy")
+
+if os.path.exists(x_num_path):
+    parts.append(np.load(x_num_path, allow_pickle=True))
+if os.path.exists(x_cat_path):
+    parts.append(np.load(x_cat_path, allow_pickle=True).astype(float))
+if os.path.exists(y_path):
+    y = np.load(y_path, allow_pickle=True)
+    parts.append(y.reshape(-1, 1) if y.ndim == 1 else y)
+
+if parts:
+    combined = np.concatenate(parts, axis=1)
+    if col_names and len(col_names) == combined.shape[1]:
+        df = pd.DataFrame(combined, columns=col_names)
+    else:
+        df = pd.DataFrame(combined)
+    df.to_csv("/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260422_211246/tabddpm-c5-6732-20260422_211947.csv", index=False)
+    print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260422_211246/tabddpm-c5-6732-20260422_211947.csv")
+else:
+    print("[TabDDPM] WARNING: No output .npy files found")
+    sys.exit(1)

SynthData0523/main/c5/tabddpm/tabddpm-c5-20260422_211246/_tabddpm_train.py

@@ -0,0 +1,32 @@
+import os, sys, subprocess
+
+tabddpm_root = "/workspace/tabddpm/code"
+assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}"
+env = os.environ.copy()
+env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", ""))
+
+# Write a wrapper that patches collections.Sequence (removed in Python 3.10+)
+# before running pipeline.py - needed because skorch uses old API
+wrapper = os.path.join(tabddpm_root, "_compat_run.py")
+with open(wrapper, "w") as f:
+    f.write(
+        "import collections, collections.abc\n"
+        "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping',"
+        "'MutableSet','Set','Callable','Iterable','Iterator'):\n"
+        "    if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n"
+        "import sys, runpy\n"
+        "sys.argv = sys.argv[1:]\n"
+        "runpy.run_path(sys.argv[0], run_name='__main__')\n"
+    )
+
+print(f"[TabDDPM] Training, config=/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260422_211246/config.toml")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260422_211246/config.toml",
+     "--train"],
+    cwd=tabddpm_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+print("[TabDDPM] Training complete")

SynthData0523/main/c5/tabddpm/tabddpm-c5-20260422_211246/config.toml

@@ -0,0 +1,39 @@
+seed = 0
+parent_dir = "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260422_211246/output"
+real_data_path = "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260422_211246/data"
+model_type = "mlp"
+num_numerical_features = 0
+device = "cuda:0"
+
+[model_params]
+d_in = 22
+num_classes = 2
+is_y_cond = true
+
+[model_params.rtdl_params]
+d_layers = [256, 256]
+dropout = 0.0
+
+[diffusion_params]
+num_timesteps = 1000
+gaussian_loss_type = "mse"
+
+[train.main]
+steps = 5000
+lr = 0.001
+weight_decay = 0.0
+batch_size = 256
+
+[train.T]
+seed = 0
+normalization = "quantile"
+num_nan_policy = "__none__"
+cat_nan_policy = "__none__"
+cat_min_frequency = "__none__"
+cat_encoding = "__none__"
+y_policy = "default"
+
+[sample]
+num_samples = 1000
+batch_size = 1000
+seed = 0

SynthData0523/main/c5/tabddpm/tabddpm-c5-20260422_211246/config_sample_20260422_211947.toml

@@ -0,0 +1,39 @@
+seed = 0
+parent_dir = "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260422_211246/output"
+real_data_path = "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260422_211246/data"
+model_type = "mlp"
+num_numerical_features = 0
+device = "cuda:0"
+
+[model_params]
+d_in = 22
+num_classes = 2
+is_y_cond = true
+
+[model_params.rtdl_params]
+d_layers = [256, 256]
+dropout = 0.0
+
+[diffusion_params]
+num_timesteps = 1000
+gaussian_loss_type = "mse"
+
+[train.main]
+steps = 5000
+lr = 0.001
+weight_decay = 0.0
+batch_size = 256
+
+[train.T]
+seed = 0
+normalization = "quantile"
+num_nan_policy = "__none__"
+cat_nan_policy = "__none__"
+cat_min_frequency = "__none__"
+cat_encoding = "__none__"
+y_policy = "default"
+
+[sample]
+num_samples = 6732
+batch_size = 1000
+seed = 0

SynthData0523/main/c5/tabddpm/tabddpm-c5-20260422_211246/data/X_cat_train.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d2e5a8320a843e2485682b858182bafd262c91a7389b4627e7c2a43d15ca001a
+size 1184960

SynthData0523/main/c5/tabddpm/tabddpm-c5-20260422_211246/data/info.json

@@ -0,0 +1,61 @@
+{
+  "name": "benchmark_dataset",
+  "task_type": "multiclass",
+  "n_num_features": 0,
+  "n_cat_features": 22,
+  "train_size": 6732,
+  "num_col_idx": [],
+  "cat_col_idx": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10,
+    11,
+    12,
+    13,
+    14,
+    15,
+    16,
+    17,
+    18,
+    19,
+    20,
+    21
+  ],
+  "target_col_idx": [
+    22
+  ],
+  "column_names": [
+    "cap-shape",
+    "cap-surface",
+    "cap-color",
+    "bruises?",
+    "odor",
+    "gill-attachment",
+    "gill-spacing",
+    "gill-size",
+    "gill-color",
+    "stalk-shape",
+    "stalk-root",
+    "stalk-surface-above-ring",
+    "stalk-surface-below-ring",
+    "stalk-color-above-ring",
+    "stalk-color-below-ring",
+    "veil-type",
+    "veil-color",
+    "ring-number",
+    "ring-type",
+    "spore-print-color",
+    "population",
+    "habitat",
+    "class"
+  ],
+  "num_classes": 2
+}

SynthData0523/main/c5/tabddpm/tabddpm-c5-20260422_211246/data/y_train.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1b36003f6d55aaa9706a2d1248efd2c4736d8f8d8a8fdac7857df93028d682ec
+size 53984

SynthData0523/main/c5/tabddpm/tabddpm-c5-20260422_211246/gen_20260422_211947.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:28bcf3746a4bccab1205ef22bbbe3bbc0ecc628a81a5e912eec1c839e0fc54bb
+size 147189

SynthData0523/main/c5/tabddpm/tabddpm-c5-20260422_211246/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c5",
+  "model": "tabddpm",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c5/c5-train.csv",
+      "exists": true,
+      "size": 1004346,
+      "sha256": "47ca8fcb0dce8411cee7c20652d6bf10a48a4c284cef58267e775b807c625180"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c5/c5-val.csv",
+      "exists": true,
+      "size": 125666,
+      "sha256": "599dbe0d059984263e88e20a64ae75c0f9795a6ee662c7ae3d13fe4db35753c2"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c5/c5-test.csv",
+      "exists": true,
+      "size": 126062,
+      "sha256": "f4eab85438337cfa2fd60a783388c9bb9f2a67a8c077d09f929fe34ee2895c28"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c5/c5-dataset_profile.json",
+      "exists": true,
+      "size": 8949,
+      "sha256": "74d201cb6f2a25d865c87f0421b9f9c5969d2edfd8fbae898594ff626b33e393"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c5/c5-dataset_contract_v1.json",
+      "exists": true,
+      "size": 11183,
+      "sha256": "df30b6c2fb9044e5c99b3257357a58462928289a836a9cf591e2f754f49bf729"
+    }
+  }
+}

SynthData0523/main/c5/tabddpm/tabddpm-c5-20260422_211246/output/X_cat_train.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c72004b2165a0b03802bbf6a89fee6b12137ee6a5e2e1f0801bf1b25af6c6904
+size 296789

SynthData0523/main/c5/tabddpm/tabddpm-c5-20260422_211246/output/X_cat_unnorm.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:172d115c0fdee7f7bc2dfe21124538d3fc511a6b8558a3acfb9171e739cbc1ee
+size 1184960

SynthData0523/main/c5/tabddpm/tabddpm-c5-20260422_211246/output/config.toml

@@ -0,0 +1,39 @@
+seed = 0
+parent_dir = "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260422_211246/output"
+real_data_path = "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260422_211246/data"
+model_type = "mlp"
+num_numerical_features = 0
+device = "cuda:0"
+
+[model_params]
+d_in = 22
+num_classes = 2
+is_y_cond = true
+
+[model_params.rtdl_params]
+d_layers = [256, 256]
+dropout = 0.0
+
+[diffusion_params]
+num_timesteps = 1000
+gaussian_loss_type = "mse"
+
+[train.main]
+steps = 5000
+lr = 0.001
+weight_decay = 0.0
+batch_size = 256
+
+[train.T]
+seed = 0
+normalization = "quantile"
+num_nan_policy = "__none__"
+cat_nan_policy = "__none__"
+cat_min_frequency = "__none__"
+cat_encoding = "__none__"
+y_policy = "default"
+
+[sample]
+num_samples = 6732
+batch_size = 1000
+seed = 0