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syntheticSuccess/c11/tabsyn/tabsyn-c11-20260329_134004/_tabsyn_sample.py

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+import os, sys, subprocess
+
+work_dir = "/work/output-SpecializedModels/c11/tabsyn/tabsyn-c11-20260329_134004"
+dataname = "benchmark_ds"
+output_csv = "/work/output-SpecializedModels/c11/tabsyn/tabsyn-c11-20260329_134004/tabsyn-c11-54045-20260329_161537.csv"
+tabsyn_root = "/workspace/tabsyn"
+
+assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}"
+
+pip_libs = "/pip_libs"
+ppath = tabsyn_root + os.pathsep + pip_libs
+old = os.environ.get("PYTHONPATH", "")
+if old:
+    ppath += os.pathsep + old
+os.environ["PYTHONPATH"] = ppath
+sys.path.insert(0, pip_libs)
+sys.path.insert(0, tabsyn_root)
+
+try:
+    import icecream
+except ModuleNotFoundError:
+    print("[TabSyn] Installing dependencies (first run, cached for later)...")
+    subprocess.run(
+        [sys.executable, "-m", "pip", "install", "--target", pip_libs,
+         "icecream", "category_encoders", "tomli", "tomli-w",
+         "imbalanced-learn", "ml_collections", "rdt",
+         "xgboost", "xlrd", "openpyxl", "numpy<2", "-q"],
+        check=True
+    )
+    subprocess.run(
+        [sys.executable, "-m", "pip", "install", "--target", pip_libs,
+         "--no-deps", "libzero", "-q"],
+        check=True
+    )
+
+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 54045 rows")
+env = os.environ.copy()
+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}")

syntheticSuccess/c11/tabsyn/tabsyn-c11-20260329_134004/_tabsyn_train.py

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+import os, sys, subprocess
+
+work_dir = "/work/output-SpecializedModels/c11/tabsyn/tabsyn-c11-20260329_134004"
+dataname = "benchmark_ds"
+tabsyn_root = "/workspace/tabsyn"
+
+assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}"
+
+pip_libs = "/pip_libs"
+# Add pip_libs to PYTHONPATH so cached packages are found
+ppath = tabsyn_root + os.pathsep + pip_libs
+old = os.environ.get("PYTHONPATH", "")
+if old:
+    ppath += os.pathsep + old
+os.environ["PYTHONPATH"] = ppath
+sys.path.insert(0, pip_libs)
+sys.path.insert(0, tabsyn_root)
+
+# Install missing deps into persistent cache (first run only)
+try:
+    import icecream
+except ModuleNotFoundError:
+    print("[TabSyn] Installing dependencies (first run, cached for later)...")
+    # Step 1: main packages (with numpy<2 to avoid torch conflict)
+    subprocess.run(
+        [sys.executable, "-m", "pip", "install", "--target", pip_libs,
+         "icecream", "category_encoders", "tomli", "tomli-w",
+         "imbalanced-learn", "ml_collections", "rdt",
+         "xgboost", "xlrd", "openpyxl", "numpy<2", "-q"],
+        check=True
+    )
+    # Step 2: libzero with --no-deps (avoids dependency conflict)
+    subprocess.run(
+        [sys.executable, "-m", "pip", "install", "--target", pip_libs,
+         "--no-deps", "libzero", "-q"],
+        check=True
+    )
+
+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()
+
+# 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)")

syntheticSuccess/c11/tabsyn/tabsyn-c11-20260329_134004/data/benchmark_ds/X_cat_test.npy

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