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b/SynthData0523/main/m11/tabdiff/tabdiff-m11-20260501_164336/train_20260501_164344.log new file mode 100644 index 0000000000000000000000000000000000000000..2cad73e8a7ed499fcf164d739b5c6d55eea764bd --- /dev/null +++ b/SynthData0523/main/m11/tabdiff/tabdiff-m11-20260501_164336/train_20260501_164344.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dbf98a386759f4c41a96c47dd27c8770f829ffe91dc4a07755c6d94aac297ac1 +size 7828071 diff --git a/SynthData0523/main/m11/tabpfgen/tabpfgen-m11-20260512_035731/_tabpfgen_generate.py b/SynthData0523/main/m11/tabpfgen/tabpfgen-m11-20260512_035731/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..c899ccbdd4c62029205ab7ca5cffc4b314437d7b --- /dev/null +++ b/SynthData0523/main/m11/tabpfgen/tabpfgen-m11-20260512_035731/_tabpfgen_generate.py @@ -0,0 +1,131 @@ +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/m11/tabpfgen/tabpfgen-m11-20260512_035731/staged/public/train.csv") +target_col = "Previously_Insured" + +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(304887) + +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" + +n_sgld_steps = max(1, int(os.environ.get("TABPFGEN_N_SGLD_STEPS", "1000"))) +sgld_step_size = float(os.environ.get("TABPFGEN_SGLD_STEP_SIZE", "0.01")) +sgld_noise_scale = float(os.environ.get("TABPFGEN_SGLD_NOISE_SCALE", "0.01")) + +# 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=n_sgld_steps, + sgld_step_size=sgld_step_size, + sgld_noise_scale=sgld_noise_scale, + device=device, +) + +print( + f"[TabPFGen] Generating {target_n} rows via generate_classification " + f"(chunk_rows={chunk_rows}, device={device}, " + f"n_sgld_steps={n_sgld_steps}, sgld_step_size={sgld_step_size}, " + f"sgld_noise_scale={sgld_noise_scale})" +) +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_classification(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/m11/tabpfgen/tabpfgen-m11-20260512_035731/tabpfgen-m11-304887-20260512_035732.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/m11/tabpfgen/tabpfgen-m11-20260512_035731/tabpfgen-m11-304887-20260512_035732.csv") diff --git a/SynthData0523/main/m11/tabpfgen/tabpfgen-m11-20260512_035731/gen_20260512_035732.log b/SynthData0523/main/m11/tabpfgen/tabpfgen-m11-20260512_035731/gen_20260512_035732.log new 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b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..f96caa319ef62c3e8546a24619c3e29eb6dd7828 --- /dev/null +++ b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/m11/tabsyn/tabsyn-m11-20260426_220916" +dataname = "tabsyn_m11" +output_csv = "/work/output-SpecializedModels/m11/tabsyn/tabsyn-m11-20260426_220916/tabsyn-m11-304887-20260427_005302.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 304887 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/m11/tabsyn/tabsyn-m11-20260426_220916/_tabsyn_train.py b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..3b9be84840f218365a0b5fcda7b160bb99dc879c --- /dev/null +++ b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/_tabsyn_train.py @@ -0,0 +1,63 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/m11/tabsyn/tabsyn-m11-20260426_220916" +dataname = "tabsyn_m11" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "1024") +_te = 1000 +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/m11/tabsyn/tabsyn-m11-20260426_220916/data/tabsyn_m11/X_cat_test.npy b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/data/tabsyn_m11/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..e5e4a16e567f2dcf0bb3c7fdcc48978c3bd19e22 --- /dev/null +++ b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/data/tabsyn_m11/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:27480c329674397c8ad4fe21092011c17aed9b26a6da2d39f3d2ecf94ae7f66a +size 12195608 diff --git a/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/data/tabsyn_m11/X_cat_train.npy b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/data/tabsyn_m11/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..e5e4a16e567f2dcf0bb3c7fdcc48978c3bd19e22 --- /dev/null +++ b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/data/tabsyn_m11/X_cat_train.npy @@ -0,0 +1,3 @@ +version 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"Vehicle_Damage", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "Annual_Premium", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Policy_Sales_Channel", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Vintage", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Response", + "data_type": "binary", + "is_target": false + } +] \ No newline at end of file diff --git a/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/staged/public/test.csv b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..60f06794190d5ae30c52339de8d35314cbbcd55b --- /dev/null +++ b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:45a031a23f36e6dec485a2c50a0314a1e7d92b0f084fe2fe226b068b2781783d +size 2143487 diff --git a/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/staged/public/train.csv b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..0b2402fec31a5a265e98680aef6ea40a037f14b5 --- /dev/null +++ b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:929bf5af6a31a08475878fb8f509e60d9b9aa0a48344ec40053e242f33eb15aa +size 17145673 diff --git a/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/staged/public/val.csv b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..e576899dddbc20c2142f10c7293c6465a6255543 --- /dev/null +++ b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/staged/tabsyn/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/staged/tabsyn/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/staged/tabsyn/model_input_manifest.json b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/staged/tabsyn/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..30deb4a3edcfe66509fbd8cba3e54bb351b22d1e --- /dev/null +++ b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/staged/tabsyn/model_input_manifest.json @@ -0,0 +1,249 @@ +{ + "dataset_id": "m11", + "model": "tabsyn", + "target_column": "Previously_Insured", + "task_type": "classification", + "column_schema": [ + { + "name": "id", 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"unique_count": 2, + "unique_ratio": 7e-06, + "example_values": [ + "0", + "1" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/tabsyn/tabsyn-m11-20260426_220916/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/tabsyn/tabsyn-m11-20260426_220916/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/tabsyn/tabsyn-m11-20260426_220916/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/tabsyn/tabsyn-m11-20260426_220916/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/tabsyn/tabsyn-m11-20260426_220916/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/tabsyn/tabsyn-m11-20260426_220916/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/synthetic/tabsyn_m11/real.csv b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/synthetic/tabsyn_m11/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..348df9e604521bc5198952727ce4bf681c714521 --- /dev/null +++ b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/synthetic/tabsyn_m11/real.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:beb736ac056307756ee2182709007b114cc1911ea976b608e4e002c4c7f782b4 +size 13345416 diff --git a/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/synthetic/tabsyn_m11/test.csv b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/synthetic/tabsyn_m11/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..348df9e604521bc5198952727ce4bf681c714521 --- /dev/null +++ b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/synthetic/tabsyn_m11/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:beb736ac056307756ee2182709007b114cc1911ea976b608e4e002c4c7f782b4 +size 13345416 diff --git a/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/tabsyn-m11-304887-20260427_005302.csv b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/tabsyn-m11-304887-20260427_005302.csv new file mode 100644 index 0000000000000000000000000000000000000000..4095d4b0626fe733a4d5ed677d7dc97f3446a926 --- /dev/null +++ b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/tabsyn-m11-304887-20260427_005302.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4f6da9a79ce72220fa29c06d04d59bd36ad80248e111a3032a48c2dd13e9bf04 +size 20229890 diff --git a/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/train_20260426_220925.log b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/train_20260426_220925.log new file mode 100644 index 0000000000000000000000000000000000000000..0ab3830f39824b734a9bea157fa63d73a9b7fdcc --- /dev/null +++ b/SynthData0523/main/m11/tabsyn/tabsyn-m11-20260426_220916/train_20260426_220925.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:97076516c9a29b5f48347ca4e76a1dc0f714a9fa83ead2eaa1368b1fc17b125d +size 9228025 diff --git a/SynthData0523/main/m11/tvae/tvae-m11-20260328_052639/_tvae_generate.py b/SynthData0523/main/m11/tvae/tvae-m11-20260328_052639/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..b0dbbfd516cb9bcbd9e9a515b8de7e1275a01085 --- /dev/null +++ b/SynthData0523/main/m11/tvae/tvae-m11-20260328_052639/_tvae_generate.py @@ -0,0 +1,5 @@ +from ctgan.synthesizers.tvae import TVAE +model = TVAE.load("/work/output-SpecializedModels/m11/tvae/tvae-m11-20260328_052639/models_300epochs/tvae_300epochs.pt") +samples = model.sample(304887) +samples.to_csv("/work/output-SpecializedModels/m11/tvae/tvae-m11-20260328_052639/tvae-m11-304887-20260330_065910.csv", index=False) +print(f"[TVAE] Generated 304887 rows -> /work/output-SpecializedModels/m11/tvae/tvae-m11-20260328_052639/tvae-m11-304887-20260330_065910.csv") diff --git a/SynthData0523/main/m11/tvae/tvae-m11-20260328_052639/_tvae_train.py b/SynthData0523/main/m11/tvae/tvae-m11-20260328_052639/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..cc85589492a9cb0e0e802406b68fc9c0ef8820f3 --- /dev/null +++ b/SynthData0523/main/m11/tvae/tvae-m11-20260328_052639/_tvae_train.py @@ -0,0 +1,16 @@ +import json, sys +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +csv_path = "/work/output-SpecializedModels/m11/tvae/tvae-m11-20260328_052639/staged/public/train.csv" +meta_path = "/work/output-SpecializedModels/m11/tvae/tvae-m11-20260328_052639/tvae_metadata.json" +save_path = "/work/output-SpecializedModels/m11/tvae/tvae-m11-20260328_052639/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/m11/tvae/tvae-m11-20260328_052639/gen_20260328_133409.log b/SynthData0523/main/m11/tvae/tvae-m11-20260328_052639/gen_20260328_133409.log new file mode 100644 index 0000000000000000000000000000000000000000..332e93e5526fa9d46298b7189bf3090aad35e9f1 --- /dev/null +++ b/SynthData0523/main/m11/tvae/tvae-m11-20260328_052639/gen_20260328_133409.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:49973cba0b17e4ce2bfb6e29a1055e4bf58315c8fc28036f2c800f1f10c61732 +size 129 diff --git a/SynthData0523/main/m11/tvae/tvae-m11-20260328_052639/gen_20260330_065910.log b/SynthData0523/main/m11/tvae/tvae-m11-20260328_052639/gen_20260330_065910.log new file mode 100644 index 0000000000000000000000000000000000000000..9c180583e2f41ef733fb7e09b06d945138b76ad3 --- /dev/null +++ b/SynthData0523/main/m11/tvae/tvae-m11-20260328_052639/gen_20260330_065910.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:439c7ebca3b3f6d79b51d867e4d9f5c97cdba6fe368086890e79d7ff9dba08b0 +size 133 diff --git a/SynthData0523/main/m11/tvae/tvae-m11-20260328_052639/input_snapshot.json b/SynthData0523/main/m11/tvae/tvae-m11-20260328_052639/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0e6d9a93a000380d89a467b817d4b3859efbcba7 --- /dev/null +++ b/SynthData0523/main/m11/tvae/tvae-m11-20260328_052639/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m11", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m11/m11-train.csv", + "exists": true, + "size": 17450561, + "sha256": "01325179667586bada6e9b69ae7d1fe3f59df8ff4ddc3f41822af5e99f6b6e32" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m11/m11-val.csv", + "exists": true, + "size": 2181590, + "sha256": "1066848f54a0895112eb7fa66933159bc43ebd8ec01b60d70aa6b1a9b87d1db1" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m11/m11-test.csv", + "exists": true, + "size": 2181600, + "sha256": "bf23f0509554f8ee3a54a40d5fb242b0191a63d0d36c2a0f4c066ee89939b168" + }, + "profile_json": { + "path": 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