Resume SynthData0523 main/c7 batch 8

.gitattributes

@@ -5665,3 +5665,38 @@ SynthData0523/main/c7/tabdiff/tabdiff-c7-20260429_033922/tabular_bundle/pipeline
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 SynthData0523/main/c7/tabdiff/tabdiff-c7-20260429_033922/tabular_bundle/pipeline_ds/val.csv filter=lfs diff=lfs merge=lfs -text
 SynthData0523/main/c7/tabdiff/tabdiff-c7-20260429_033922/tabular_bundle/pipeline_ds/y_test.npy filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/tabdiff/tabdiff-c7-20260429_033922/tabular_bundle/pipeline_ds/y_train.npy filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/tabdiff/tabdiff-c7-20260429_033922/tabular_bundle/pipeline_ds/y_val.npy filter=lfs diff=lfs merge=lfs -text
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SynthData0523/main/c7/tabdiff/tabdiff-c7-20260429_033922/tabular_bundle/pipeline_ds/y_train.npy

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SynthData0523/main/c7/tabpfgen/tabpfgen-c7-20260429_061314/_tabpfgen_generate.py

@@ -0,0 +1,100 @@
+import os
+import numpy as np
+import pandas as pd
+import json
+from tabpfgen import TabPFGen
+
+df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/public/train.csv")
+target_col = "class"
+
+target_missing = df[target_col].isna()
+if target_missing.any():
+    dropped = int(target_missing.sum())
+    df = df.loc[~target_missing].copy()
+    print(
+        f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'"
+    )
+if df.empty:
+    raise ValueError(
+        f"[TabPFGen] No rows remain after dropping missing target '{target_col}'"
+    )
+
+feature_cols = [c for c in df.columns if c != target_col]
+
+cat_encodings = {}
+for col in feature_cols:
+    if df[col].dtype == object or str(df[col].dtype) == 'category':
+        cats = sorted(df[col].dropna().unique().tolist(), key=str)
+        cat_map = {v: i for i, v in enumerate(cats)}
+        df[col] = df[col].map(cat_map).astype(float)
+        cat_encodings[col] = cats
+        print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)")
+
+target_cats = None
+if df[target_col].dtype == object or str(df[target_col].dtype) == 'category':
+    cats = sorted(df[target_col].dropna().unique().tolist(), key=str)
+    t_map = {v: i for i, v in enumerate(cats)}
+    df[target_col] = df[target_col].map(t_map).astype(float)
+    target_cats = cats
+    print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)")
+
+X = df[feature_cols].values.astype(np.float32)
+y = df[target_col].values
+target_n = int(10368)
+
+for i in range(X.shape[1]):
+    col_vals = X[:, i]
+    mask = np.isnan(col_vals)
+    if mask.any():
+        mean_val = np.nanmean(col_vals)
+        X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0
+
+# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。
+# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。）
+gen = TabPFGen(
+    n_sgld_steps=1000,
+    sgld_step_size=0.01,
+    sgld_noise_scale=0.01,
+    device="auto",
+)
+
+print(f"[TabPFGen] Generating {target_n} rows via generate_classification")
+X_syn, y_syn = gen.generate_classification(X, y, n_samples=target_n)
+
+syn_df = pd.DataFrame(X_syn, columns=feature_cols)
+syn_df[target_col] = y_syn
+
+for col, cats in cat_encodings.items():
+    codes = np.round(syn_df[col].values).astype(int)
+    codes = np.clip(codes, 0, len(cats) - 1)
+    syn_df[col] = [cats[c] for c in codes]
+
+if target_cats is not None:
+    codes = np.round(syn_df[target_col].values).astype(int)
+    codes = np.clip(codes, 0, len(target_cats) - 1)
+    syn_df[target_col] = [target_cats[c] for c in codes]
+
+if len(syn_df) > target_n:
+    print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}")
+    syn_df = syn_df.iloc[:target_n].copy()
+elif len(syn_df) < target_n:
+    deficit = target_n - len(syn_df)
+    print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})")
+    if len(syn_df) > 0:
+        extra = syn_df.sample(n=deficit, replace=True, random_state=42)
+        syn_df = pd.concat(
+            [syn_df.reset_index(drop=True), extra.reset_index(drop=True)],
+            ignore_index=True,
+        )
+    else:
+        syn_df = df[feature_cols + [target_col]].sample(
+            n=target_n, replace=True, random_state=42
+        ).reset_index(drop=True)
+
+syn_df = syn_df[list(df.columns)]
+if len(syn_df) != target_n:
+    raise RuntimeError(
+        f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}"
+    )
+syn_df.to_csv("/work/output-Benchmark-trainonly-v1/c7/tabpfgen/tabpfgen-c7-20260429_061314/tabpfgen-c7-10368-20260429_061314.csv", index=False)
+print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/c7/tabpfgen/tabpfgen-c7-20260429_061314/tabpfgen-c7-10368-20260429_061314.csv")

SynthData0523/main/c7/tabpfgen/tabpfgen-c7-20260429_061314/gen_20260429_061314.log

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SynthData0523/main/c7/tabpfgen/tabpfgen-c7-20260429_061314/train_20260429_061314.log

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SynthData0523/main/c7/tabsyn/tabsyn-c7-20260420_233446/_tabsyn_sample.py

@@ -0,0 +1,39 @@
+import os, sys, subprocess
+
+work_dir = "/work/output-SpecializedModels/c7/tabsyn/tabsyn-c7-20260420_233446"
+dataname = "tabsyn_c7"
+output_csv = "/work/output-SpecializedModels/c7/tabsyn/tabsyn-c7-20260420_233446/tabsyn-c7-10368-20260421_004501.csv"
+tabsyn_root = "/workspace/tabsyn"
+
+assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}"
+
+old = os.environ.get("PYTHONPATH", "")
+os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "")
+sys.path.insert(0, tabsyn_root)
+
+os.chdir(tabsyn_root)
+
+# Ensure data symlink exists
+data_link = os.path.join(tabsyn_root, "data", dataname)
+data_src = os.path.join(work_dir, "data", dataname)
+os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True)
+if os.path.exists(data_link):
+    os.remove(data_link)
+os.symlink(data_src, data_link)
+
+print(f"[TabSyn] Sampling 10368 rows")
+env = os.environ.copy()
+env.setdefault("TABSYN_RESUME", "1")
+ret = subprocess.run(
+    [sys.executable, "main.py",
+     "--dataname", dataname,
+     "--mode", "sample",
+     "--method", "tabsyn",
+     "--gpu", "0",
+     "--save_path", output_csv],
+    cwd=tabsyn_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+print(f"[TabSyn] Saved -> {output_csv}")

SynthData0523/main/c7/tabsyn/tabsyn-c7-20260420_233446/_tabsyn_train.py

@@ -0,0 +1,62 @@
+import os, sys, subprocess
+
+work_dir = "/work/output-SpecializedModels/c7/tabsyn/tabsyn-c7-20260420_233446"
+dataname = "tabsyn_c7"
+tabsyn_root = "/workspace/tabsyn"
+
+assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}"
+
+old = os.environ.get("PYTHONPATH", "")
+os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "")
+sys.path.insert(0, tabsyn_root)
+
+os.chdir(tabsyn_root)
+
+# Symlink data dir into TabSyn data/
+data_link = os.path.join(tabsyn_root, "data", dataname)
+data_src = os.path.join(work_dir, "data", dataname)
+os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True)
+if os.path.exists(data_link):
+    os.remove(data_link)
+os.symlink(data_src, data_link)
+
+env = os.environ.copy()
+env.setdefault("TABSYN_RESUME", "1")
+_te = None
+if _te is not None:
+    env["TABSYN_VAE_EPOCHS"] = str(_te)
+    env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2))
+
+# Data preprocessing is done on the host side (_prepare_data_dir)
+# which creates .npy files, train/test CSVs, and info.json
+
+# Step 1: Train VAE (produces latent embeddings)
+print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}")
+ret = subprocess.run(
+    [sys.executable, "main.py",
+     "--dataname", dataname,
+     "--mode", "train",
+     "--method", "vae",
+     "--gpu", "0"],
+    cwd=tabsyn_root,
+    env=env
+)
+if ret.returncode != 0:
+    print("[TabSyn] VAE training failed")
+    sys.exit(ret.returncode)
+
+# Step 2: Train diffusion model on latent space
+print(f"[TabSyn] Step 2/2: Training diffusion model")
+ret = subprocess.run(
+    [sys.executable, "main.py",
+     "--dataname", dataname,
+     "--mode", "train",
+     "--method", "tabsyn",
+     "--gpu", "0"],
+    cwd=tabsyn_root,
+    env=env
+)
+if ret.returncode != 0:
+    print("[TabSyn] Diffusion training failed")
+    sys.exit(ret.returncode)
+print("[TabSyn] Training complete (VAE + Diffusion)")

SynthData0523/main/c7/tabsyn/tabsyn-c7-20260420_233446/data/tabsyn_c7/X_cat_test.npy

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+  "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/c7/tabsyn/tabsyn-c7-20260420_233446/staged/tabsyn/model_input_manifest.json"
+}

SynthData0523/main/c7/tabsyn/tabsyn-c7-20260420_233446/staged/tabsyn/adapter_transforms_applied.json

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+[]

SynthData0523/main/c7/tabsyn/tabsyn-c7-20260420_233446/staged/tabsyn/model_input_manifest.json

@@ -0,0 +1,190 @@
+{
+  "dataset_id": "c7",
+  "model": "tabsyn",
+  "target_column": "class",
+  "task_type": "classification",
+  "column_schema": [
+    {
+      "name": "parents",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "usual",
+          "pretentious",
+          "great_pret"
+        ]
+      }
+    },
+    {
+      "name": "has_nurs",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5,
+        "unique_ratio": 0.000482,
+        "example_values": [
+          "very_crit",
+          "critical",
+          "improper",
+          "less_proper",
+          "proper"
+        ]
+      }
+    },
+    {
+      "name": "form",
+      "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": 0.000386,
+        "example_values": [
+          "complete",
+          "completed",
+          "incomplete",
+          "foster"
+        ]
+      }
+    },
+    {
+      "name": "children",
+      "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": 0.000386,
+        "example_values": [
+          "1",
+          "3",
+          "2",
+          "more"
+        ]
+      }
+    },
+    {
+      "name": "housing",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "less_conv",
+          "convenient",
+          "critical"
+        ]
+      }
+    },
+    {
+      "name": "finance",
+      "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": 0.000193,
+        "example_values": [
+          "convenient",
+          "inconv"
+        ]
+      }
+    },
+    {
+      "name": "social",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "slightly_prob",
+          "nonprob",
+          "problematic"
+        ]
+      }
+    },
+    {
+      "name": "health",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "recommended",
+          "priority",
+          "not_recom"
+        ]
+      }
+    },
+    {
+      "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": 5,
+        "unique_ratio": 0.000482,
+        "example_values": [
+          "priority",
+          "spec_prior",
+          "not_recom",
+          "very_recom",
+          "recommend"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabsyn/tabsyn-c7-20260420_233446/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabsyn/tabsyn-c7-20260420_233446/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabsyn/tabsyn-c7-20260420_233446/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabsyn/tabsyn-c7-20260420_233446/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabsyn/tabsyn-c7-20260420_233446/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabsyn/tabsyn-c7-20260420_233446/public_gate/public_gate_report.json"
+}

SynthData0523/main/c7/tabsyn/tabsyn-c7-20260420_233446/synthetic/tabsyn_c7/real.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b982b7a55b4fc4e84901c92fd913136c195362e5f6933ec1a6bf0d0595fe1e76
+size 210019

SynthData0523/main/c7/tabsyn/tabsyn-c7-20260420_233446/synthetic/tabsyn_c7/test.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ef18313fe934718eeb4a24ef415da12acfedc7c21dae50bdf67f9c6e47ee8e66
+size 23395

SynthData0523/main/c7/tabsyn/tabsyn-c7-20260420_233446/tabsyn-c7-10368-20260421_004501.csv

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+oid sha256:fc66953b17a75efd8c07797a3ee6c82515d8cf9cc8fefc33fa264829e2829e68
+size 839451

SynthData0523/main/c7/tabsyn/tabsyn-c7-20260420_233446/train_20260420_233446.log

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d2458122d40859ae385d3e930ab90c49c0ba76234d7c4ffb447381faedc7c957
+size 2620627

SynthData0523/main/c7/tabsyn/tabsyn-c7-20260429_052312/_tabsyn_sample.py

@@ -0,0 +1,39 @@
+import os, sys, subprocess
+
+work_dir = "/work/output-Benchmark-trainonly-v1/c7/tabsyn/tabsyn-c7-20260429_052312"
+dataname = "tabsyn_c7"
+output_csv = "/work/output-Benchmark-trainonly-v1/c7/tabsyn/tabsyn-c7-20260429_052312/tabsyn-c7-10368-20260429_061302.csv"
+tabsyn_root = "/workspace/tabsyn"
+
+assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}"
+
+old = os.environ.get("PYTHONPATH", "")
+os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "")
+sys.path.insert(0, tabsyn_root)
+
+os.chdir(tabsyn_root)
+
+# Ensure data symlink exists
+data_link = os.path.join(tabsyn_root, "data", dataname)
+data_src = os.path.join(work_dir, "data", dataname)
+os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True)
+if os.path.exists(data_link):
+    os.remove(data_link)
+os.symlink(data_src, data_link)
+
+print(f"[TabSyn] Sampling 10368 rows")
+env = os.environ.copy()
+env.setdefault("TABSYN_RESUME", "1")
+ret = subprocess.run(
+    [sys.executable, "main.py",
+     "--dataname", dataname,
+     "--mode", "sample",
+     "--method", "tabsyn",
+     "--gpu", "0",
+     "--save_path", output_csv],
+    cwd=tabsyn_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+print(f"[TabSyn] Saved -> {output_csv}")