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b/SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/train_20260429_042701.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4335aee0b3501b5df97d1dc245f7249e7c2f16190805842a0891e45c31c1fe5d +size 450169 diff --git a/SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/_tabpfgen_generate.py b/SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..0374b598a6c34c805496e343becc569367aff821 --- /dev/null +++ b/SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/_tabpfgen_generate.py @@ -0,0 +1,68 @@ +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/staged/public/train.csv") +target_col = "VisitorType" + +feature_cols = [c for c in df.columns if c != target_col] + +# --- Label-encode categorical / object columns --- +cat_encodings = {} # col -> list of unique values (index = code) +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +# Encode target if categorical +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values + +# Handle NaN +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device="auto", +) + +print(f"[TabPFGen] Generating 9864 rows via generate_classification") +X_syn, y_syn = gen.generate_classification(X, y, n_samples=9864) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +# --- Inverse label-encoding for categorical columns --- +for col, cats in cat_encodings.items(): + # Round to nearest integer index, clamp to valid range + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +syn_df = syn_df[list(df.columns)] +syn_df.to_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/tabpfgen-m6-9864-20260422_070321.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/tabpfgen-m6-9864-20260422_070321.csv") diff --git a/SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/gen_20260422_070321.log b/SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/gen_20260422_070321.log new file mode 100644 index 0000000000000000000000000000000000000000..b0000ec5bfa2cf3aaf46fd21e9c293e0770476f3 --- /dev/null +++ b/SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/gen_20260422_070321.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a3bfbf2de3d5f6fe8bccae8484ad3fabd4df4baba9f0a3b189425b5b3a8a9eec +size 588 diff --git a/SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/input_snapshot.json b/SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..8508a20b6499aa7c3d1c2d992165b962275eb5f8 --- /dev/null +++ b/SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m6", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-train.csv", + "exists": true, + "size": 856785, + "sha256": "a5d1c487a8f2611385915fcc5a52bad546680ddbc8d23fc695f442cdd6dafa0c" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-val.csv", + "exists": true, + "size": 107758, + "sha256": "598196cecc227cfba95c9796b80bc1baf684a0117e6673b8662b89482cdcb78f" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-test.csv", + "exists": true, + "size": 107996, + "sha256": "ec939ad96a3b14dd960886359fb6c5d45591adc8a734661ade3dee1417a015de" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m6/m6-dataset_profile.json", + "exists": true, + "size": 7622, + "sha256": "859f1fe93806c8ecdea9c9db9db34fb6cf94bc112b5c0a66b2436e8ef71c2e98" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m6/m6-dataset_contract_v1.json", + "exists": true, + "size": 8990, + "sha256": "01142eeb121af615a644c3e312f5f3e79d805396339f40d5a300ba3560cf8e90" + } + } +} \ No newline at end of file diff --git a/SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/public_gate/normalized_schema_snapshot.json b/SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0d48abfe61f58323fd4a425d175b354d51045ae1 --- /dev/null +++ b/SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,377 @@ +{ + "dataset_id": "m6", + "target_column": "VisitorType", + "task_type": "classification", + "columns": [ + { + "name": "Administrative", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 26, + "unique_ratio": 0.002636, + "example_values": [ + "0", + "3", + "2", + "6", + "1" + ] + } + }, + { + "name": "Administrative_Duration", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2789, + "unique_ratio": 0.282745, + "example_values": [ + "0", + "45.8", + "77.7", + "52", + "46.33333333" + ] + } + }, + { + "name": "Informational", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + 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"example_values": [ + "6", + "3", + "4", + "1", + "9" + ] + } + }, + { + "name": "TrafficType", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 20, + "unique_ratio": 0.002028, + "example_values": [ + "2", + "8", + "3", + "11", + "1" + ] + } + }, + { + "name": "VisitorType", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000304, + "example_values": [ + "Returning_Visitor", + "New_Visitor", + "Other" + ] + } + }, + { + "name": "Weekend", + "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": 0.000203, + "example_values": [ + "FALSE", + "TRUE" + ] + } + }, + { + "name": "Revenue", + "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": 0.000203, + "example_values": [ + "FALSE", + "TRUE" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/tabpfgen-m6-9864-20260422_070321.csv b/SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/tabpfgen-m6-9864-20260422_070321.csv new file mode 100644 index 0000000000000000000000000000000000000000..b3935c91220d3c4560126ec861fda64c0017ccfe --- /dev/null +++ b/SynthData0523/main/m6/tabpfgen/m6-migrated-20260422_183752/tabpfgen-m6-9864-20260422_070321.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2e051e11efa0bbca0495b445e9ee238b6804b24b3de4d39549a023d3d14ef7d5 +size 1312741 diff --git a/SynthData0523/main/m6/tabpfgen/tabpfgen-m6-20260429_061036/_tabpfgen_generate.py b/SynthData0523/main/m6/tabpfgen/tabpfgen-m6-20260429_061036/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..6959482ccde3d2e68d476cdc72babe8915d7b6f8 --- /dev/null +++ b/SynthData0523/main/m6/tabpfgen/tabpfgen-m6-20260429_061036/_tabpfgen_generate.py @@ -0,0 +1,100 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m6/tabpfgen/tabpfgen-m6-20260429_061036/staged/public/train.csv") +target_col = "VisitorType" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +target_n = int(9864) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device="auto", +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification") +X_syn, y_syn = gen.generate_classification(X, y, n_samples=target_n) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/m6/tabpfgen/tabpfgen-m6-20260429_061036/tabpfgen-m6-9864-20260429_061037.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/m6/tabpfgen/tabpfgen-m6-20260429_061036/tabpfgen-m6-9864-20260429_061037.csv") diff --git a/SynthData0523/main/m6/tabpfgen/tabpfgen-m6-20260429_061036/gen_20260429_061037.log b/SynthData0523/main/m6/tabpfgen/tabpfgen-m6-20260429_061036/gen_20260429_061037.log new file mode 100644 index 0000000000000000000000000000000000000000..6099a453eeb443247b283676095a122ba7abaa18 --- /dev/null +++ b/SynthData0523/main/m6/tabpfgen/tabpfgen-m6-20260429_061036/gen_20260429_061037.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c1298acab3bbb43c47659b717ab2fc82eefbb9b5e3f3fe5b0bdefee94ea6ce77 +size 1009 diff --git a/SynthData0523/main/m6/tabpfgen/tabpfgen-m6-20260429_061036/input_snapshot.json b/SynthData0523/main/m6/tabpfgen/tabpfgen-m6-20260429_061036/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..b34be967b88866f587e429cdffbc84b73e4b0f79 --- /dev/null +++ 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0000000000000000000000000000000000000000..c24d9e48eb43c865da36ad40085790e47b2b23e9 --- /dev/null +++ b/SynthData0523/main/m6/tabpfgen/tabpfgen-m6-20260429_061036/train_20260429_061037.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cb1ebbf34920b287311be084a383babac0d04e9f6379859eeb08115d9c1ee42e +size 595 diff --git a/SynthData0523/main/m6/tabsyn/tabsyn-m6-20260429_052236/_tabsyn_sample.py b/SynthData0523/main/m6/tabsyn/tabsyn-m6-20260429_052236/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..3aa741b6000357c1afaa1b62f5387887e827589f --- /dev/null +++ b/SynthData0523/main/m6/tabsyn/tabsyn-m6-20260429_052236/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/m6/tabsyn/tabsyn-m6-20260429_052236" +dataname = "tabsyn_m6" +output_csv = "/work/output-Benchmark-trainonly-v1/m6/tabsyn/tabsyn-m6-20260429_052236/tabsyn-m6-9864-20260429_061024.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 9864 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/m6/tabsyn/tabsyn-m6-20260429_052236/_tabsyn_train.py b/SynthData0523/main/m6/tabsyn/tabsyn-m6-20260429_052236/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..5046457476de92de33f824cb443cd014d5641228 --- /dev/null +++ b/SynthData0523/main/m6/tabsyn/tabsyn-m6-20260429_052236/_tabsyn_train.py @@ -0,0 +1,65 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/m6/tabsyn/tabsyn-m6-20260429_052236" +dataname = "tabsyn_m6" +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/m6/tabsyn/tabsyn-m6-20260429_052236/data/tabsyn_m6/X_cat_test.npy b/SynthData0523/main/m6/tabsyn/tabsyn-m6-20260429_052236/data/tabsyn_m6/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..8884f48287fb9f926c3463bfbdd06705e40c69d6 --- /dev/null +++ b/SynthData0523/main/m6/tabsyn/tabsyn-m6-20260429_052236/data/tabsyn_m6/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:00c2416031b4d6f8d108691a3b9c7b3009c7e0fdc44ddca005fbecf399ae2435 +size 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diff --git a/SynthData0523/main/m6/tvae/tvae-m6-20260320_071000/_tvae_generate.py b/SynthData0523/main/m6/tvae/tvae-m6-20260320_071000/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..7e8b58d9af49fab0fdf159550d723e307a5209fd --- /dev/null +++ b/SynthData0523/main/m6/tvae/tvae-m6-20260320_071000/_tvae_generate.py @@ -0,0 +1,5 @@ +from ctgan.synthesizers.tvae import TVAE +model = TVAE.load("/work/output-SpecializedModels/m6/tvae/tvae-m6-20260320_071000/models_300epochs/tvae_300epochs.pt") +samples = model.sample(9864) +samples.to_csv("/work/output-SpecializedModels/m6/tvae/tvae-m6-20260320_071000/tvae-m6-9864-20260330_065647.csv", index=False) +print(f"[TVAE] Generated 9864 rows -> /work/output-SpecializedModels/m6/tvae/tvae-m6-20260320_071000/tvae-m6-9864-20260330_065647.csv") diff --git a/SynthData0523/main/m6/tvae/tvae-m6-20260320_071000/_tvae_train.py b/SynthData0523/main/m6/tvae/tvae-m6-20260320_071000/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..2fb285af8196f6b9457d75a6f00a7ee86e1da067 --- /dev/null +++ b/SynthData0523/main/m6/tvae/tvae-m6-20260320_071000/_tvae_train.py @@ -0,0 +1,16 @@ +import json, sys +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +csv_path = "/work/DatasetNew/m6/m6-train.csv" +meta_path = "/work/output-SpecializedModels/m6/tvae/tvae-m6-20260320_071000/tvae_metadata.json" +save_path = "/work/output-SpecializedModels/m6/tvae/tvae-m6-20260320_071000/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/m6/tvae/tvae-m6-20260320_071000/gen_20260320_071402.log 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"sha256": "ec939ad96a3b14dd960886359fb6c5d45591adc8a734661ade3dee1417a015de" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m6/m6-dataset_profile.json", + "exists": true, + "size": 7622, + "sha256": "859f1fe93806c8ecdea9c9db9db34fb6cf94bc112b5c0a66b2436e8ef71c2e98" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m6/m6-dataset_contract_v1.json", + "exists": true, + "size": 8990, + "sha256": "01142eeb121af615a644c3e312f5f3e79d805396339f40d5a300ba3560cf8e90" + } + } +} \ No newline at end of file diff --git a/SynthData0523/main/m6/tvae/tvae-m6-20260320_071000/models_300epochs/train_20260320_071000.log b/SynthData0523/main/m6/tvae/tvae-m6-20260320_071000/models_300epochs/train_20260320_071000.log new file mode 100644 index 0000000000000000000000000000000000000000..055657df6559cadea907db28783d115401f874d7 --- /dev/null +++ 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"/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m6/tvae/tvae-m6-20260320_071000/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m6/tvae/tvae-m6-20260320_071000/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m6/tvae/tvae-m6-20260320_071000/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m6/tvae/tvae-m6-20260320_071000/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m6/tvae/tvae-m6-20260320_071000/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m6/tvae/tvae-m6-20260320_071000/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/SynthData0523/main/m6/tvae/tvae-m6-20260320_071000/tvae-m6-1000-20260320_071402.csv b/SynthData0523/main/m6/tvae/tvae-m6-20260320_071000/tvae-m6-1000-20260320_071402.csv new file mode 100644 index 0000000000000000000000000000000000000000..6489b924e0da1d31a1f91a9e015dd46a47f24168 --- /dev/null +++ b/SynthData0523/main/m6/tvae/tvae-m6-20260320_071000/tvae-m6-1000-20260320_071402.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6ceaf835763cb68fbe02121c63fdc8154037c376a58271e1680cba39b6d417a2 +size 191152 diff --git a/SynthData0523/main/m6/tvae/tvae-m6-20260320_071000/tvae-m6-9864-20260330_065647.csv b/SynthData0523/main/m6/tvae/tvae-m6-20260320_071000/tvae-m6-9864-20260330_065647.csv new file mode 100644 index 0000000000000000000000000000000000000000..badf01e329ddea3c5895f49b6a03bf33f2b633ac --- /dev/null +++ b/SynthData0523/main/m6/tvae/tvae-m6-20260320_071000/tvae-m6-9864-20260330_065647.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:afa09c562d44765ab63d71e7781b9a083c800b0ea31562c9fbb1a8cdc74359a9 +size 1628764 diff --git a/SynthData0523/main/m6/tvae/tvae-m6-20260320_071000/tvae_metadata.json b/SynthData0523/main/m6/tvae/tvae-m6-20260320_071000/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..bd02cd3078ddd1c4e6c63a87889218d62f25b7c8 --- /dev/null +++ b/SynthData0523/main/m6/tvae/tvae-m6-20260320_071000/tvae_metadata.json @@ -0,0 +1,76 @@ +{ + "columns": [ + { + "name": "Administrative", + "type": "continuous" + }, + { + "name": "Administrative_Duration", + "type": "continuous" + }, + { + "name": "Informational", + "type": "continuous" + }, + { + "name": "Informational_Duration", + "type": "continuous" + }, + { + "name": "ProductRelated", + "type": "continuous" + }, + { + "name": "ProductRelated_Duration", + "type": "continuous" + }, + { + "name": "BounceRates", + "type": "continuous" + }, + { + "name": "ExitRates", + "type": "continuous" + }, + { + "name": "PageValues", + "type": "continuous" + }, + { + "name": "SpecialDay", + "type": "continuous" + }, + { + "name": "Month", + "type": "categorical" + }, + { + "name": "OperatingSystems", + "type": "continuous" + }, + { + "name": "Browser", + "type": "continuous" + }, + { + "name": "Region", + "type": "continuous" + }, + { + "name": "TrafficType", + "type": "continuous" + }, + { + "name": "VisitorType", + "type": "categorical" + }, + { + "name": "Weekend", + "type": "categorical" + }, + { + "name": "Revenue", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/SynthData0523/main/m6/tvae/tvae-m6-20260429_031938/_tvae_generate.py b/SynthData0523/main/m6/tvae/tvae-m6-20260429_031938/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..37b58cd796975596ffd7f432f841924d50d476a2 --- /dev/null +++ b/SynthData0523/main/m6/tvae/tvae-m6-20260429_031938/_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/m6/tvae/tvae-m6-20260429_031938/models_300epochs/tvae_300epochs.pt") +total = 9864 +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/m6/tvae/tvae-m6-20260429_031938/tvae-m6-9864-20260429_032133.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m6/tvae/tvae-m6-20260429_031938/tvae-m6-9864-20260429_032133.csv") diff --git a/SynthData0523/main/m6/tvae/tvae-m6-20260429_031938/_tvae_train.py b/SynthData0523/main/m6/tvae/tvae-m6-20260429_031938/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..0c65c809cb6f1c9b97162d55cf1324379aff4516 --- /dev/null +++ b/SynthData0523/main/m6/tvae/tvae-m6-20260429_031938/_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/m6/tvae/tvae-m6-20260429_031938/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m6/tvae/tvae-m6-20260429_031938/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m6/tvae/tvae-m6-20260429_031938/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 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