Resume SynthData0523 main/c5 batch 5

.gitattributes

@@ -4612,3 +4612,38 @@ SynthData0523/main/c5/tabdiff/tabdiff-c5-20260420_055925/tabular_bundle/pipeline
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 SynthData0523/main/c5/tabdiff/tabdiff-c5-20260420_055925/train_20260420_055925.log filter=lfs diff=lfs merge=lfs -text
 SynthData0523/main/c5/tabpfgen/tabpfgen-c5-20260511_061054/gen_20260511_061054.log filter=lfs diff=lfs merge=lfs -text
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SynthData0523/main/c5/tabsyn/tabsyn-c5-20260420_233446/_tabsyn_sample.py

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+import os, sys, subprocess
+
+work_dir = "/work/output-SpecializedModels/c5/tabsyn/tabsyn-c5-20260420_233446"
+dataname = "tabsyn_c5"
+output_csv = "/work/output-SpecializedModels/c5/tabsyn/tabsyn-c5-20260420_233446/tabsyn-c5-6732-20260421_004948.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 6732 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/c5/tabsyn/tabsyn-c5-20260420_233446/_tabsyn_train.py

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+import os, sys, subprocess
+
+work_dir = "/work/output-SpecializedModels/c5/tabsyn/tabsyn-c5-20260420_233446"
+dataname = "tabsyn_c5"
+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)")

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+{
+  "name": "tabsyn_c5",
+  "task_type": "multiclass",
+  "n_num_features": 1,
+  "n_cat_features": 21,
+  "train_size": 7573,
+  "num_col_idx": [
+    1
+  ],
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+    22
+  ],
+  "target_col_idx": [
+    0
+  ],
+  "column_names": [
+    "class",
+    "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"
+  ],
+  "train_num": 7573,
+  "test_num": 843,
+  "header": 0,
+  "file_type": "csv",
+  "data_path": "data/tabsyn_c5/train.csv",
+  "test_path": null,
+  "idx_mapping": {
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+    "4": "bruises?",
+    "5": "odor",
+    "6": "gill-attachment",
+    "7": "gill-spacing",
+    "8": "gill-size",
+    "9": "gill-color",
+    "10": "stalk-shape",
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+    "13": "stalk-surface-below-ring",
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+          "ORANGE",
+          "YELLOW"
+        ]
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+    {
+      "name": "ring-number",
+      "role": "feature",
+      "semantic_type": "categorical",
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+        "NONE"
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+      "role": "feature",
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+          "BROWN",
+          "WHITE",
+          "YELLOW"
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+      "role": "feature",
+      "semantic_type": "categorical",
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+          "NUMEROUS",
+          "SEVERAL",
+          "SOLITARY",
+          "SCATTERED",
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+        ]
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+    },
+    {
+      "name": "habitat",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
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+          "MEADOWS",
+          "GRASSES",
+          "WOODS",
+          "URBAN",
+          "LEAVES"
+        ]
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+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/tabsyn/tabsyn-c5-20260420_233446/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/tabsyn/tabsyn-c5-20260420_233446/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/tabsyn/tabsyn-c5-20260420_233446/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/tabsyn/tabsyn-c5-20260420_233446/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/tabsyn/tabsyn-c5-20260420_233446/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/tabsyn/tabsyn-c5-20260420_233446/public_gate/public_gate_report.json"
+}

SynthData0523/main/c5/tabsyn/tabsyn-c5-20260420_233446/synthetic/tabsyn_c5/real.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:be645b323a5e46b76d5b8b86978c4e3f826963c34a5e1825173a7c8a38c6632d
+size 348766

SynthData0523/main/c5/tabsyn/tabsyn-c5-20260420_233446/synthetic/tabsyn_c5/test.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:de784e42be81816df34db9430e38d02fe9fceb971dfd34d75451e27e75266d9e
+size 39088

SynthData0523/main/c5/tabsyn/tabsyn-c5-20260420_233446/tabsyn-c5-6732-20260421_004948.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cbdc34c50f7ecae7e1d48923fcf263117421690751e3bff5fc7d393939b37f92
+size 1029231

SynthData0523/main/c5/tabsyn/tabsyn-c5-20260420_233446/train_20260420_233447.log

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+version https://git-lfs.github.com/spec/v1
+oid sha256:29591a3952c3960ec21d63ae98f352d444e72708529a421764e04f8b428bbca3
+size 2626439

SynthData0523/main/c5/tvae/tvae-c5-20260321_070511/_tvae_generate.py

@@ -0,0 +1,5 @@
+from ctgan.synthesizers.tvae import TVAE
+model = TVAE.load("/work/output-SpecializedModels/c5/tvae/tvae-c5-20260321_070511/models_300epochs/tvae_300epochs.pt")
+samples = model.sample(6732)
+samples.to_csv("/work/output-SpecializedModels/c5/tvae/tvae-c5-20260321_070511/tvae-c5-6732-20260330_065259.csv", index=False)
+print(f"[TVAE] Generated 6732 rows -> /work/output-SpecializedModels/c5/tvae/tvae-c5-20260321_070511/tvae-c5-6732-20260330_065259.csv")

SynthData0523/main/c5/tvae/tvae-c5-20260321_070511/_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/c5/tvae/tvae-c5-20260321_070511/staged/public/train.csv"
+meta_path = "/work/output-SpecializedModels/c5/tvae/tvae-c5-20260321_070511/tvae_metadata.json"
+save_path = "/work/output-SpecializedModels/c5/tvae/tvae-c5-20260321_070511/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/c5/tvae/tvae-c5-20260321_070511/gen_20260321_070708.log

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+size 126

SynthData0523/main/c5/tvae/tvae-c5-20260321_070511/gen_20260330_065259.log

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SynthData0523/main/c5/tvae/tvae-c5-20260321_070511/input_snapshot.json

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+{
+  "dataset_id": "c5",
+  "model": "tvae",
+  "inputs": {
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+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c5/c5-train.csv",
+      "exists": true,
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+      "sha256": "47ca8fcb0dce8411cee7c20652d6bf10a48a4c284cef58267e775b807c625180"
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+      "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": {
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+      "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"
+    }
+  }
+}