Resume SynthData0523 main/n17 batch 2

.gitattributes

@@ -13038,3 +13038,35 @@ SynthData0523/main/n17/tabdiff/tabdiff-n17-20260501_194732/tabular_bundle/pipeli
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SynthData0523/main/n17/tabdiff/tabdiff-n17-20260501_194732/train_20260501_194732.log

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SynthData0523/main/n17/tabpfgen/n17-migrated-20260422_183752/_tabpfgen_generate.py

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+import numpy as np
+import pandas as pd
+import json
+from tabpfgen import TabPFGen
+
+df = pd.read_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n17/staged/public/train.csv")
+target_col = "class"
+
+feature_cols = [c for c in df.columns if c != target_col]
+
+# --- Label-encode categorical / object columns ---
+cat_encodings = {}  # col -> list of unique values (index = code)
+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)")
+
+# Encode target if categorical
+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
+
+# Handle NaN
+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
+
+gen = TabPFGen(
+    n_sgld_steps=1000,
+    sgld_step_size=0.01,
+    sgld_noise_scale=0.01,
+    device="auto",
+)
+
+print(f"[TabPFGen] Generating 11600 rows via generate_regression")
+X_syn, y_syn = gen.generate_regression(X, y, n_samples=11600)
+
+syn_df = pd.DataFrame(X_syn, columns=feature_cols)
+syn_df[target_col] = y_syn
+
+# --- Inverse label-encoding for categorical columns ---
+for col, cats in cat_encodings.items():
+    # Round to nearest integer index, clamp to valid range
+    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]
+
+syn_df = syn_df[list(df.columns)]
+syn_df.to_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n17/tabpfgen-n17-11600-20260422_070321.csv", index=False)
+print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n17/tabpfgen-n17-11600-20260422_070321.csv")

SynthData0523/main/n17/tabpfgen/n17-migrated-20260422_183752/gen_20260422_070321.log

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SynthData0523/main/n17/tabpfgen/n17-migrated-20260422_183752/input_snapshot.json

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+{
+  "dataset_id": "n17",
+  "model": "tabpfgen",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n17/n17-train.csv",
+      "exists": true,
+      "size": 319108,
+      "sha256": "99cb629d6e58372a6cbfe396c89994cfb68d279d18ae29e954faeb7b1af8ffce"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n17/n17-val.csv",
+      "exists": true,
+      "size": 39916,
+      "sha256": "7e6d4456b11d527ca043a524c39ddd525a8f746ed01b5ebd6606e4910c188ab8"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n17/n17-test.csv",
+      "exists": true,
+      "size": 40033,
+      "sha256": "f0b277b314080cae72945253a6c472b7ac161edf14a7ff93a3121bbf951c6439"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n17/n17-dataset_profile.json",
+      "exists": true,
+      "size": 4394,
+      "sha256": "dac6ca60a814677e5fcf05560aa0c2c8d8baa6e2fc86bcf214ffda9129cbe802"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n17/n17-dataset_contract_v1.json",
+      "exists": true,
+      "size": 5240,
+      "sha256": "1a1d4be4080a65264b8c9cce6808ea2974c0ddca95da8c4d42b7b6d0160835b4"
+    }
+  }
+}

SynthData0523/main/n17/tabpfgen/n17-migrated-20260422_183752/public_gate/normalized_schema_snapshot.json

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+{
+  "dataset_id": "n17",
+  "target_column": "class",
+  "task_type": "classification",
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SynthData0523/main/n17/tabpfgen/n17-migrated-20260422_183752/public_gate/public_gate_report.json

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+{
+  "dataset_id": "n17",
+  "status": "pass",
+  "checks": [
+    {
+      "check_id": "PG001_csv_parse_ok",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG002_split_header_consistent",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG003_profile_header_match",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG004_missing_token_normalized",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG005_semantic_type_validated",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG006_target_defined_and_valid",
+      "status": "pass"
+    }
+  ],
+  "target_column": "class",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n17/n17-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n17/n17-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n17/n17-test.csv"
+  }
+}

SynthData0523/main/n17/tabpfgen/n17-migrated-20260422_183752/public_gate/staged_input_manifest.json

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+{
+  "dataset_id": "n17",
+  "target_column": "class",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n17/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n17/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n17/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n17/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n17/public_gate/public_gate_report.json",
+  "column_schema": [
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+  ]
+}

SynthData0523/main/n17/tabpfgen/n17-migrated-20260422_183752/runner.log

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+size 1316

SynthData0523/main/n17/tabpfgen/n17-migrated-20260422_183752/runtime_result.json

@@ -0,0 +1,14 @@
+{
+  "dataset_id": "n17",
+  "model": "tabpfgen",
+  "run_id": "n17-migrated-20260422_183752",
+  "public_gate_status": "pass",
+  "adapter_ready_status": "pass",
+  "train_status": "skipped",
+  "generate_status": "success",
+  "reason_code": null,
+  "reason_detail": null,
+  "artifacts": {
+    "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n17/tabpfgen/n17-migrated-20260422_183752/tabpfgen-n17-11600-20260422_070321.csv"
+  }
+}

SynthData0523/main/n17/tabpfgen/n17-migrated-20260422_183752/staged/public/staged_features.json

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+    "data_type": "continuous",
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+    "data_type": "continuous",
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+    "data_type": "continuous",
+    "is_target": false
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+  {
+    "feature_name": "class",
+    "data_type": "continuous",
+    "is_target": true
+  }
+]

SynthData0523/main/n17/tabpfgen/n17-migrated-20260422_183752/staged/public/test.csv

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SynthData0523/main/n17/tabpfgen/n17-migrated-20260422_183752/staged/public/train.csv

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+size 307507

SynthData0523/main/n17/tabpfgen/n17-migrated-20260422_183752/staged/public/val.csv

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+size 38466

SynthData0523/main/n17/tabpfgen/n17-migrated-20260422_183752/staged/tabpfgen/adapter_report.json

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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/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n17/staged/tabpfgen/model_input_manifest.json"
+}

SynthData0523/main/n17/tabpfgen/n17-migrated-20260422_183752/staged/tabpfgen/adapter_transforms_applied.json

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+[]

SynthData0523/main/n17/tabpfgen/n17-migrated-20260422_183752/staged/tabpfgen/model_input_manifest.json

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+  "model": "tabpfgen",
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+  "task_type": "classification",
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+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n17/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n17/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n17/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n17/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n17/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n17/public_gate/public_gate_report.json"
+}

SynthData0523/main/n17/tabpfgen/n17-migrated-20260422_183752/tabpfgen-n17-11600-20260422_070321.csv

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SynthData0523/main/n17/tabsyn/tabsyn-n17-20260427_025147/_tabsyn_sample.py

@@ -0,0 +1,39 @@
+import os, sys, subprocess
+
+work_dir = "/work/output-SpecializedModels/n17/tabsyn/tabsyn-n17-20260427_025147"
+dataname = "tabsyn_n17"
+output_csv = "/work/output-SpecializedModels/n17/tabsyn/tabsyn-n17-20260427_025147/tabsyn-n17-11600-20260427_025208.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 11600 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/n17/tabsyn/tabsyn-n17-20260427_025147/_tabsyn_train.py

@@ -0,0 +1,63 @@
+import os, sys, subprocess
+
+work_dir = "/work/output-SpecializedModels/n17/tabsyn/tabsyn-n17-20260427_025147"
+dataname = "tabsyn_n17"
+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")
+env.setdefault("TABSYN_VAE_BATCH_SIZE", "1024")
+_te = 1
+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/n17/tabsyn/tabsyn-n17-20260427_025147/data/tabsyn_n17/X_cat_test.npy

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+size 128

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SynthData0523/main/n17/tabsyn/tabsyn-n17-20260427_025147/data/tabsyn_n17/X_num_train.npy

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+size 417728

SynthData0523/main/n17/tabsyn/tabsyn-n17-20260427_025147/data/tabsyn_n17/info.json

@@ -0,0 +1,120 @@
+{
+  "name": "tabsyn_n17",
+  "task_type": "regression",
+  "n_num_features": 9,
+  "n_cat_features": 0,
+  "train_size": 11600,
+  "num_col_idx": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8
+  ],
+  "cat_col_idx": [],
+  "target_col_idx": [
+    9
+  ],
+  "column_names": [
+    "time",
+    "attribute2",
+    "attribute3",
+    "attribute4",
+    "attribute5",
+    "attribute6",
+    "attribute7",
+    "attribute8",
+    "attribute9",
+    "class"
+  ],
+  "train_num": 11600,
+  "test_num": 11600,
+  "header": 0,
+  "file_type": "csv",
+  "data_path": "data/tabsyn_n17/train.csv",
+  "test_path": null,
+  "idx_mapping": {
+    "0": 0,
+    "1": 1,
+    "2": 2,
+    "3": 3,
+    "4": 4,
+    "5": 5,
+    "6": 6,
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+  },
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+    "4": 4,
+    "5": 5,
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+    "8": 8,
+    "9": 9
+  },
+  "idx_name_mapping": {
+    "0": "time",
+    "1": "attribute2",
+    "2": "attribute3",
+    "3": "attribute4",
+    "4": "attribute5",
+    "5": "attribute6",
+    "6": "attribute7",
+    "7": "attribute8",
+    "8": "attribute9",
+    "9": "class"
+  },
+  "metadata": {
+    "columns": {
+      "0": {
+        "sdtype": "numerical",
+        "computer_representation": "Float"
+      },
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+      },
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+        "sdtype": "numerical",
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+      },
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+        "sdtype": "numerical",
+        "computer_representation": "Float"
+      }
+    }
+  }
+}

SynthData0523/main/n17/tabsyn/tabsyn-n17-20260427_025147/data/tabsyn_n17/test.csv

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+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n17/tabsyn/tabsyn-n17-20260427_025147/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n17/tabsyn/tabsyn-n17-20260427_025147/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n17/tabsyn/tabsyn-n17-20260427_025147/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n17/tabsyn/tabsyn-n17-20260427_025147/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n17/tabsyn/tabsyn-n17-20260427_025147/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n17/tabsyn/tabsyn-n17-20260427_025147/public_gate/public_gate_report.json"
+}

SynthData0523/main/n17/tabsyn/tabsyn-n17-20260427_025147/synthetic/tabsyn_n17/real.csv

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+size 307507

SynthData0523/main/n17/tabsyn/tabsyn-n17-20260427_025147/synthetic/tabsyn_n17/test.csv

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+size 307507

SynthData0523/main/n17/tabsyn/tabsyn-n17-20260427_025147/tabsyn-n17-11600-20260427_025208.csv

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+size 566885

SynthData0523/main/n17/tabsyn/tabsyn-n17-20260427_025147/train_20260427_025148.log

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+version https://git-lfs.github.com/spec/v1
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+size 3344

SynthData0523/main/n17/tvae/tvae-n17-20260328_054240/_tvae_generate.py

@@ -0,0 +1,5 @@
+from ctgan.synthesizers.tvae import TVAE
+model = TVAE.load("/work/output-SpecializedModels/n17/tvae/tvae-n17-20260328_054240/models_300epochs/tvae_300epochs.pt")
+samples = model.sample(11600)
+samples.to_csv("/work/output-SpecializedModels/n17/tvae/tvae-n17-20260328_054240/tvae-n17-11600-20260330_070841.csv", index=False)
+print(f"[TVAE] Generated 11600 rows -> /work/output-SpecializedModels/n17/tvae/tvae-n17-20260328_054240/tvae-n17-11600-20260330_070841.csv")

SynthData0523/main/n17/tvae/tvae-n17-20260328_054240/_tvae_train.py

@@ -0,0 +1,16 @@
+import json, sys
+import pandas as pd
+from ctgan.data import read_csv
+from ctgan.synthesizers.tvae import TVAE
+
+csv_path = "/work/output-SpecializedModels/n17/tvae/tvae-n17-20260328_054240/staged/public/train.csv"
+meta_path = "/work/output-SpecializedModels/n17/tvae/tvae-n17-20260328_054240/tvae_metadata.json"
+save_path = "/work/output-SpecializedModels/n17/tvae/tvae-n17-20260328_054240/models_300epochs/tvae_300epochs.pt"
+epochs = 300
+
+data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None)
+print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}")
+model = TVAE(epochs=epochs, batch_size=500)
+model.fit(data, discrete_columns)
+model.save(save_path)
+print(f"[TVAE] Model saved -> {save_path}")

SynthData0523/main/n17/tvae/tvae-n17-20260328_054240/gen_20260328_100702.log

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