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sha256:ce94face8378a92275ddc373d0661a1e7dcdc8224fe79a8c63183fc5e1eb8baf +size 595 diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030507/_tabpfgen_generate.py b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030507/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..5d95448517ea1b87bb3e6cb50f664cad861a28ec --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030507/_tabpfgen_generate.py @@ -0,0 +1,122 @@ +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/m4/tabpfgen/tabpfgen-m4-20260505_030507/staged/public/train.csv") +target_col = "charges" + +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 +fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000"))) +if len(X) > fit_rows_cap: + rng = np.random.default_rng(42) + idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False)) + X = X[idx] + y = y[idx] + print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})") +target_n = int(2217) + +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 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +# 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=device, +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_regression (chunk_rows={chunk_rows}, device={device})") +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_regression(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +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/m4/tabpfgen/tabpfgen-m4-20260505_030507/tabpfgen-m4-2217-20260505_030507.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030507/tabpfgen-m4-2217-20260505_030507.csv") diff --git 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0000000000000000000000000000000000000000..172e70f44599e4f4fc9a3bb8a0479bb23ff33237 --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030613/_tabpfgen_generate.py @@ -0,0 +1,122 @@ +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/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/train.csv") +target_col = "charges" + +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 +fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000"))) +if len(X) > fit_rows_cap: + rng = np.random.default_rng(42) + idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False)) + X = X[idx] + y = y[idx] + print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})") +target_n = int(2217) + +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 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +# 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=device, +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_regression (chunk_rows={chunk_rows}, device={device})") +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_regression(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +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/m4/tabpfgen/tabpfgen-m4-20260505_030613/tabpfgen-m4-2217-20260505_030613.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/tabpfgen-m4-2217-20260505_030613.csv") diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030613/gen_20260505_030613.log b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030613/gen_20260505_030613.log new file mode 100644 index 0000000000000000000000000000000000000000..0c66486eeff0b894593c8f5850e837b6d8bb807e 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diff --git a/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030613/train_20260505_030613.log b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030613/train_20260505_030613.log new file mode 100644 index 0000000000000000000000000000000000000000..a8ad9eebf29c42b01eed819dd01524cf420336ec --- /dev/null +++ b/SynthData0523/main/m4/tabpfgen/tabpfgen-m4-20260505_030613/train_20260505_030613.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:14aea4672cf7c13dc2a5a28db1da48549b8cfba2404a3e71c4f829e2884de765 +size 595 diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/_tabsyn_sample.py b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..3446b13345a06b8e796ba47cccc3dae0abca43c8 --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118" +dataname = "tabsyn_m4" +output_csv = "/work/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118/tabsyn-m4-2217-20260501_004653.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 2217 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}") diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/_tabsyn_train.py b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..4b8ffe2256df09defa4e72d758ca779366cd74d1 --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/_tabsyn_train.py @@ -0,0 +1,65 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118" +dataname = "tabsyn_m4" +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") +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_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)") diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/X_cat_test.npy b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..35977ccea6d36736d37a5000108e57633f2bb227 --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d8893cf5626d7469e90c946b0f59fca148a445e109a27fa5a2acdd425b6bb7d2 +size 53336 diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/X_cat_train.npy b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..35977ccea6d36736d37a5000108e57633f2bb227 --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d8893cf5626d7469e90c946b0f59fca148a445e109a27fa5a2acdd425b6bb7d2 +size 53336 diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/X_num_test.npy b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/X_num_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..dc149960a879e21fd1aeed7de2419851175627e7 --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/X_num_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4a21bd2fd5a226c07d543ddf78616a8edb2d52b51097fc7a5df5b05612267af8 +size 26732 diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/X_num_train.npy b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..dc149960a879e21fd1aeed7de2419851175627e7 --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4a21bd2fd5a226c07d543ddf78616a8edb2d52b51097fc7a5df5b05612267af8 +size 26732 diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/info.json b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/info.json new file mode 100644 index 0000000000000000000000000000000000000000..a6affaecfbfc8f45e63d3ab86bbffb378c04caa5 --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/info.json @@ -0,0 +1,91 @@ +{ + "name": "tabsyn_m4", + "task_type": "regression", + "n_num_features": 3, + "n_cat_features": 3, + "train_size": 2217, + "num_col_idx": [ + 0, + 2, + 3 + ], + "cat_col_idx": [ + 1, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "age", + "sex", + "bmi", + "children", + "smoker", + "region", + "charges" + ], + "train_num": 2217, + "test_num": 2217, + "header": 0, + "file_type": "csv", + "data_path": "data/tabsyn_m4/train.csv", + "test_path": null, + "idx_mapping": { + "0": 0, + "1": 3, + "2": 1, + "3": 2, + "4": 4, + "5": 5, + "6": 6 + }, + "inverse_idx_mapping": { + "0": 0, + "3": 1, + "1": 2, + "2": 3, + "4": 4, + "5": 5, + "6": 6 + }, + "idx_name_mapping": { + "0": "age", + "1": "sex", + "2": "bmi", + "3": "children", + "4": "smoker", + "5": "region", + "6": "charges" + }, + "metadata": { + "columns": { + "0": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "2": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "3": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "1": { + "sdtype": "categorical" + }, + "4": { + "sdtype": "categorical" + }, + "5": { + "sdtype": "categorical" + }, + "6": { + "sdtype": "numerical", + "computer_representation": "Float" + } + } + } +} \ No newline at end of file diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/test.csv b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..152c9a200d3e5ce590ed1621c80749a36fc97e13 --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:67f64f75063e2f8d838d7f7040ebc12f8c46df4dc54450874b851786224ba875 +size 60802 diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/train.csv b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..152c9a200d3e5ce590ed1621c80749a36fc97e13 --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:67f64f75063e2f8d838d7f7040ebc12f8c46df4dc54450874b851786224ba875 +size 60802 diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/y_test.npy b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..c1d23d8e35b403e2d772e5544af9d68ff32a8ea0 --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4d6e4ad93beee28740cc25ee808a180c01fa94a053f79ff383504888f2d8f39e +size 17864 diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/y_train.npy b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..c1d23d8e35b403e2d772e5544af9d68ff32a8ea0 --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4d6e4ad93beee28740cc25ee808a180c01fa94a053f79ff383504888f2d8f39e +size 17864 diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/gen_20260501_004653.log b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/gen_20260501_004653.log new file mode 100644 index 0000000000000000000000000000000000000000..6d052f7d521e744b02159470dee052bdb17086df --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/gen_20260501_004653.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:af7610447f698cf17d603bce882093da4e5c45e69d1cf8b99faad5f1e536496d +size 930 diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/input_snapshot.json b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..21fe15b6e6863f7441ca4017d82218a03e3a90b4 --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tabsyn", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/public_gate/normalized_schema_snapshot.json b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "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": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "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": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/public_gate/public_gate_report.json b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "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": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/public_gate/staged_input_manifest.json b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..b7c70717cfee2c8bde2c692c55e2e2627ed5f184 --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "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": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "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": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/runtime_result.json b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..c59eed7391f5f5388a42fa2f69ad176aabab9692 --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tabsyn", + "run_id": "tabsyn-m4-20260501_002118", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118/tabsyn-m4-2217-20260501_004653.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118" + }, + "timings": { + "train": { + "started_at": "2026-05-01T00:21:18", + "ended_at": "2026-05-01T00:46:53", + "duration_sec": 1534.909 + }, + "generate": { + "started_at": "2026-05-01T00:46:53", + "ended_at": "2026-05-01T00:46:58", + "duration_sec": 5.22 + } + } +} \ No newline at end of file diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/staged/public/staged_features.json b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/staged/public/test.csv b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/staged/public/train.csv b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/staged/public/val.csv b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/staged/tabsyn/adapter_report.json b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/staged/tabsyn/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..abb6db567735b329c272a2186a8c72ae991e057f --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/staged/tabsyn/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-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118/staged/tabsyn/model_input_manifest.json" +} \ No newline at end of file diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/staged/tabsyn/adapter_transforms_applied.json b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/staged/tabsyn/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/staged/tabsyn/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/staged/tabsyn/model_input_manifest.json b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/staged/tabsyn/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..1234a03c0f902a27d8b3f09a352a58b6abdb20fc --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/staged/tabsyn/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tabsyn", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "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": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "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": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/synthetic/tabsyn_m4/real.csv b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/synthetic/tabsyn_m4/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..152c9a200d3e5ce590ed1621c80749a36fc97e13 --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/synthetic/tabsyn_m4/real.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:67f64f75063e2f8d838d7f7040ebc12f8c46df4dc54450874b851786224ba875 +size 60802 diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/synthetic/tabsyn_m4/test.csv b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/synthetic/tabsyn_m4/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..152c9a200d3e5ce590ed1621c80749a36fc97e13 --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/synthetic/tabsyn_m4/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:67f64f75063e2f8d838d7f7040ebc12f8c46df4dc54450874b851786224ba875 +size 60802 diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/tabsyn-m4-2217-20260501_004653.csv b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/tabsyn-m4-2217-20260501_004653.csv new file mode 100644 index 0000000000000000000000000000000000000000..f129d96892e35c7d00a81028bc10aa5e46962c0d --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/tabsyn-m4-2217-20260501_004653.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e08e67b8796e858f470e956b447484f1ed7b456e05ab70c366452ee831eadc2 +size 107601 diff --git a/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/train_20260501_002118.log b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/train_20260501_002118.log new file mode 100644 index 0000000000000000000000000000000000000000..1c27436cf4bfaa4e9830827e0101afdfb87664a5 --- /dev/null +++ b/SynthData0523/main/m4/tabsyn/tabsyn-m4-20260501_002118/train_20260501_002118.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3c6391abc9ca72c238e7717562f1ed0ee8afb97fbb34f6e20088940e2044c724 +size 2153039 diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260501_055812/_tvae_generate.py b/SynthData0523/main/m4/tvae/tvae-m4-20260501_055812/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..fd4d7affa32f0d281c334aa3ed7cf8baff921c00 --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260501_055812/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/tvae-m4-2217-20260501_055834.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/tvae-m4-2217-20260501_055834.csv") diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260501_055812/_tvae_train.py b/SynthData0523/main/m4/tvae/tvae-m4-20260501_055812/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..ef5925ca28ee1f4772e7c03eee8d4a0207656339 --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260501_055812/_tvae_train.py @@ -0,0 +1,30 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/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}") diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260501_055812/gen_20260501_055834.log b/SynthData0523/main/m4/tvae/tvae-m4-20260501_055812/gen_20260501_055834.log new file mode 100644 index 0000000000000000000000000000000000000000..f33c02adfb39603116028ea6d8a455ac9ca79881 --- /dev/null +++ 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0000000000000000000000000000000000000000..5a04b28e3bdce4d1c68ce1066d178dda6f0a7475 --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260501_055812/models_300epochs/train_20260501_055812.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cea974684b89ac9abfdc4feedad02c71b9abaf15de17fa78960b6447fc1157d8 +size 531 diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260501_055812/models_300epochs/tvae_300epochs.pt b/SynthData0523/main/m4/tvae/tvae-m4-20260501_055812/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..843058a63404d636cb94e88b60dbef9dc99523e0 --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260501_055812/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6abd8eec6a8d217c00b45f3f0a99a036041e4856f65e0e0c9e960159f0af57dd +size 399276 diff --git 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https://git-lfs.github.com/spec/v1 +oid sha256:2255efd8294fcee0c03b0289f55f0063a189220b13f331820a3f0b5b97a2bfb7 +size 4198 diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260501_055812/tvae-m4-2217-20260501_055834.csv b/SynthData0523/main/m4/tvae/tvae-m4-20260501_055812/tvae-m4-2217-20260501_055834.csv new file mode 100644 index 0000000000000000000000000000000000000000..4b6d8c9c71a48a66edcac9603890345aa2a286eb --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260501_055812/tvae-m4-2217-20260501_055834.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b9348dc4652c821a0d9d0063497456c91ee369c89f18f6b1a4c8104120077d71 +size 135727 diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260501_055812/tvae_metadata.json b/SynthData0523/main/m4/tvae/tvae-m4-20260501_055812/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..25a9f15e988a5b8fb08ca7cf844cba17995d739f --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260501_055812/tvae_metadata.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:453bde8642429b67f8a59bfbefca62573d46fb1571b05f9b3d4a44579562544e +size 466 diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_172924/_tvae_generate.py b/SynthData0523/main/m4/tvae/tvae-m4-20260504_172924/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..64d07e128ef6dbb294fc5e3083f11bfa36692685 --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_172924/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/tvae-m4-2217-20260504_172948.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/tvae-m4-2217-20260504_172948.csv") diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_172924/_tvae_train.py b/SynthData0523/main/m4/tvae/tvae-m4-20260504_172924/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..d28215e962c43113cd7268680d82572ec243c015 --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_172924/_tvae_train.py @@ -0,0 +1,30 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/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}") diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_172924/gen_20260504_172948.log b/SynthData0523/main/m4/tvae/tvae-m4-20260504_172924/gen_20260504_172948.log new file mode 100644 index 0000000000000000000000000000000000000000..9cb264d80125eb9865bcf1ff211a5f47e5408296 --- /dev/null +++ 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--- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174302/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/tvae-m4-2217-20260504_174327.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/tvae-m4-2217-20260504_174327.csv") diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_174302/_tvae_train.py b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174302/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..1aa090f428d3524915512adaba16283538755528 --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174302/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +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}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_174302/gen_20260504_174327.log b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174302/gen_20260504_174327.log new 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100644 index 0000000000000000000000000000000000000000..cc89d367bbc77fcaa87035697f57afb63c3dd0f5 --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174302/tvae-m4-2217-20260504_174327.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:18426a62b60277585bdaec0b71059b5102ff55a45beba0836da8fbf8759b3a39 +size 135340 diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_174302/tvae_metadata.json b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174302/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..25a9f15e988a5b8fb08ca7cf844cba17995d739f --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174302/tvae_metadata.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:453bde8642429b67f8a59bfbefca62573d46fb1571b05f9b3d4a44579562544e +size 466 diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_174345/_tvae_generate.py b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174345/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..5752f6a3066cdfc5f9df8ba23e3090b04584f4d8 --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174345/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/tvae-m4-2217-20260504_174408.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/tvae-m4-2217-20260504_174408.csv") diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_174345/_tvae_train.py b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174345/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..aca9f4cee652b8d957c0618750c8f79706afa308 --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174345/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +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}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git 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b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..986470909887e64074ae7b5468923539ce59c61e --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/tvae-m4-2217-20260504_174448.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/tvae-m4-2217-20260504_174448.csv") diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/_tvae_train.py b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..51849963b3e68038221e8e94145da119f0101e40 --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +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}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/gen_20260504_174448.log b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/gen_20260504_174448.log new file mode 100644 index 0000000000000000000000000000000000000000..bf3bec0ffb0b16650e96487ab6aef4b913e7cf21 --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/gen_20260504_174448.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a129533a2ad4f0771f09c7c1113903202b4304c36ffa3260f021cba649543cbe +size 404 diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/input_snapshot.json b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..f40f36994954d7819c145fb129632bb506a3b8ae --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/input_snapshot.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1215498d276ee726fcc0782b0cf0d9893493e31f4a5cccebbea1ae20d8fe0196 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https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/staged/tvae/adapter_report.json b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..58d87b9c03e20977526194c33776cdb112903979 --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/staged/tvae/adapter_report.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f9d574fe55cfc62f5d0b27f824b6fd873d24c166ea0f34301ac7876868a4200b +size 312 diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/staged/tvae/adapter_transforms_applied.json b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..6f3aaa01a627a955d2022165805d2786c0da78ba --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4f53cda18c2baa0c0354bb5f9a3ecbe5ed12ab4d8e11ba873c2f11161202b945 +size 2 diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/staged/tvae/model_input_manifest.json b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..2df648952b24b18e8d8107dadd81443cce3bd5c2 --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/staged/tvae/model_input_manifest.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2cb12e8944eed1d94a8e75b87996b14c2ad3d6b1f13b97f66128832465616eb5 +size 4198 diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/tvae-m4-2217-20260504_174448.csv b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/tvae-m4-2217-20260504_174448.csv new file mode 100644 index 0000000000000000000000000000000000000000..224d4c92d849978c5d816faa4fda791406550bce --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/tvae-m4-2217-20260504_174448.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bcf9234a7900b6ed86dbc6178935c16a0f4ba186b337d1c84cff04c15a40e1f4 +size 135512 diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/tvae_metadata.json b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..25a9f15e988a5b8fb08ca7cf844cba17995d739f --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_174426/tvae_metadata.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:453bde8642429b67f8a59bfbefca62573d46fb1571b05f9b3d4a44579562544e +size 466 diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_175331/_tvae_generate.py b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175331/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..b70a0b4d228b99bd6d3e71c9b31701fd7efe7eea --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175331/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/tvae-m4-2217-20260504_175349.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/tvae-m4-2217-20260504_175349.csv") diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_175331/_tvae_train.py b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175331/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..72d435e2a4019187fa552c7ef6488a04c198cb74 --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175331/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +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}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git 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100644 index 0000000000000000000000000000000000000000..b4180a57b42d16ad5884478676d597804f2a2abc --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175331/tvae-m4-2217-20260504_175349.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b3901b669115b3a46120c956bc92e368c0b0ebe459743844f643091b7d2c6b1c +size 135479 diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_175331/tvae_metadata.json b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175331/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..25a9f15e988a5b8fb08ca7cf844cba17995d739f --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175331/tvae_metadata.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:453bde8642429b67f8a59bfbefca62573d46fb1571b05f9b3d4a44579562544e +size 466 diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_175407/_tvae_generate.py b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175407/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..ce22b690c0cf1d6a0c43dff49a21a99b4a9955a0 --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175407/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/tvae-m4-2217-20260504_175431.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/tvae-m4-2217-20260504_175431.csv") diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_175407/_tvae_train.py b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175407/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..d145109f933ae3ccfbc8e5969da96387a97fcd43 --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175407/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +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}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git 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b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175449/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..8ce529c5975d8135d8bc87e2d902ce1cd0a5fab7 --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175449/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/tvae-m4-2217-20260504_175513.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/tvae-m4-2217-20260504_175513.csv") diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_175449/_tvae_train.py b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175449/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..0ddb2b7321a12d3aff39be0c0f3c4c23e022d041 --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175449/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +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}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git 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b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175530/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..5630436cb207679ca36a2faa95aed68d8b90e70c --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175530/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/tvae-m4-2217-20260504_175610.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/tvae-m4-2217-20260504_175610.csv") diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_175530/_tvae_train.py b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175530/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..8bddc1e2fca3b1f61308cc45e156164c030dd8b2 --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175530/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +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}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git 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100644 index 0000000000000000000000000000000000000000..606a7d02f0adf19904c4d4ba33d3a4dfea5ca992 --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175530/tvae-m4-2217-20260504_175610.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2fb289413b48175dafe06bf13265b44045456603a5d208e7fe589e2889bdb54b +size 135518 diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_175530/tvae_metadata.json b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175530/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..25a9f15e988a5b8fb08ca7cf844cba17995d739f --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175530/tvae_metadata.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:453bde8642429b67f8a59bfbefca62573d46fb1571b05f9b3d4a44579562544e +size 466 diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_175629/_tvae_generate.py b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175629/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..39004fb75945aac1f990221a91985deb650fa5a0 --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175629/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/tvae-m4-2217-20260504_175710.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/tvae-m4-2217-20260504_175710.csv") diff --git a/SynthData0523/main/m4/tvae/tvae-m4-20260504_175629/_tvae_train.py b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175629/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..024f990670dcbc746aaa55122c04dee9d8ff3a9e --- /dev/null +++ b/SynthData0523/main/m4/tvae/tvae-m4-20260504_175629/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +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}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git 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