diff --git a/syntheticSuccess/c12/arf/arf-c12-20260321_133736/_arf_generate.py b/syntheticSuccess/c12/arf/arf-c12-20260321_133736/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..123147f357a706d11b44196388e99924898312fa --- /dev/null +++ b/syntheticSuccess/c12/arf/arf-c12-20260321_133736/_arf_generate.py @@ -0,0 +1,6 @@ +import pickle +with open("/work/output-SpecializedModels/c12/arf/arf-c12-20260321_133736/arf_model.pkl", "rb") as f: + model = pickle.load(f) +syn = model.forge(n=2623) +syn.to_csv("/work/output-SpecializedModels/c12/arf/arf-c12-20260321_133736/arf-c12-2623-20260330_065419.csv", index=False) +print(f"[ARF] Generated 2623 rows -> /work/output-SpecializedModels/c12/arf/arf-c12-20260321_133736/arf-c12-2623-20260330_065419.csv") diff --git a/syntheticSuccess/c12/arf/arf-c12-20260321_133736/_arf_train.py b/syntheticSuccess/c12/arf/arf-c12-20260321_133736/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..f336174f66ac6b7cd92490b6f7eeaf10799fa651 --- /dev/null +++ b/syntheticSuccess/c12/arf/arf-c12-20260321_133736/_arf_train.py @@ -0,0 +1,19 @@ +import pickle +import pandas as pd +from arfpy import arf + +df = pd.read_csv("/work/output-SpecializedModels/c12/arf/arf-c12-20260321_133736/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") + +model = arf.arf(x=df) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-SpecializedModels/c12/arf/arf-c12-20260321_133736/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-SpecializedModels/c12/arf/arf-c12-20260321_133736/arf_model.pkl") diff --git a/syntheticSuccess/c12/arf/arf-c12-20260321_133736/arf-c12-1000-20260321_160614.csv b/syntheticSuccess/c12/arf/arf-c12-20260321_133736/arf-c12-1000-20260321_160614.csv new file mode 100644 index 0000000000000000000000000000000000000000..e03c384b838cef5c6b50105a6ba64f706797d0be --- /dev/null +++ b/syntheticSuccess/c12/arf/arf-c12-20260321_133736/arf-c12-1000-20260321_160614.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:14dad6f0c66864575b2ccdb01f702fe60e4cd4124ba46ac40f5a9b81e82df8e4 +size 12525154 diff --git a/syntheticSuccess/c12/arf/arf-c12-20260321_133736/arf-c12-2623-20260330_065419.csv b/syntheticSuccess/c12/arf/arf-c12-20260321_133736/arf-c12-2623-20260330_065419.csv new file mode 100644 index 0000000000000000000000000000000000000000..c05ff10f96f4faa0d81cdae69d4b175b1f10e009 --- /dev/null +++ b/syntheticSuccess/c12/arf/arf-c12-20260321_133736/arf-c12-2623-20260330_065419.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:684ec0d098cea414a3972a4f3d46784904d4864784c6511b20b4742688ee357a +size 32812469 diff --git a/syntheticSuccess/c12/arf/arf-c12-20260321_133736/arf_model.pkl b/syntheticSuccess/c12/arf/arf-c12-20260321_133736/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..b48d7c163fe7e546ee1b82c069820433223d822a --- /dev/null +++ b/syntheticSuccess/c12/arf/arf-c12-20260321_133736/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b112d47e2dc8c2fc7073874b07f5a0937e23325218e889c07d1eb9e5ce830c6c +size 745588565 diff --git a/syntheticSuccess/c12/arf/arf-c12-20260321_133736/gen_20260321_160614.log b/syntheticSuccess/c12/arf/arf-c12-20260321_133736/gen_20260321_160614.log new file mode 100644 index 0000000000000000000000000000000000000000..532b67d7570848bd0b12671f12b1e97251213e43 --- /dev/null +++ b/syntheticSuccess/c12/arf/arf-c12-20260321_133736/gen_20260321_160614.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:347a808aedcc69a0d6bf8bed4fd624e8d988b492154556c70d61970eb718778a +size 491821 diff 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b/syntheticSuccess/c12/bayesnet/bayesnet-c12-20260321_062527/_bayesnet_generate.py @@ -0,0 +1,43 @@ +import subprocess, sys, os + +pip_libs = "/pip_libs" +sys.path.insert(0, pip_libs) +os.environ["PYTHONPATH"] = pip_libs + os.pathsep + os.environ.get("PYTHONPATH", "") + +def _ensure_deps(): + try: + import synthcity + except ModuleNotFoundError: + print("[BayesNet] synthcity not found - installing to cache...") + subprocess.run( + [sys.executable, "-m", "pip", "install", + "--target", pip_libs, "synthcity==0.2.12", "numpy<2", "-q"], + check=True + ) + import shutil, glob + for pat in ["torch", "torch-*", "torchvision", "torchvision-*", + "torchvision.libs", "torchgen", "nvidia*", "triton*"]: + for p in glob.glob(os.path.join(pip_libs, pat)): + if os.path.isdir(p): shutil.rmtree(p) + else: os.remove(p) + if pip_libs not in sys.path: + sys.path.insert(0, pip_libs) + +_ensure_deps() + +import pickle, json as _json +with open("/work/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260321_062527/bayesnet_model.pkl", "rb") as f: + plugin = pickle.load(f) +syn = plugin.generate(count=2623).dataframe() + +# Restore zero-variance columns that were dropped during training +const_path = "/work/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260321_062527/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +if os.path.exists(const_path): + with open(const_path) as _f: + const_cols = _json.load(_f) + for col, val in const_cols.items(): + syn[col] = val + print(f"[BayesNet] Restored constant column '{col}' = {val}") + +syn.to_csv("/work/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260321_062527/bayesnet-c12-2623-20260330_065421.csv", index=False) +print(f"[BayesNet] Generated 2623 rows -> /work/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260321_062527/bayesnet-c12-2623-20260330_065421.csv") diff --git a/syntheticSuccess/c12/bayesnet/bayesnet-c12-20260321_062527/_bayesnet_train.py b/syntheticSuccess/c12/bayesnet/bayesnet-c12-20260321_062527/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..05be48d57939fdfab45faa89f4da786d0da9d9c8 --- /dev/null +++ b/syntheticSuccess/c12/bayesnet/bayesnet-c12-20260321_062527/_bayesnet_train.py @@ -0,0 +1,62 @@ +import subprocess, sys, os + +pip_libs = "/pip_libs" +sys.path.insert(0, pip_libs) +os.environ["PYTHONPATH"] = pip_libs + os.pathsep + os.environ.get("PYTHONPATH", "") + +def _ensure_deps(): + try: + import synthcity + except ModuleNotFoundError: + print("[BayesNet] synthcity not found - installing to cache (first run, may take minutes)...") + # Install synthcity with numpy<2 to avoid conflicts + subprocess.run( + [sys.executable, "-m", "pip", "install", + "--target", pip_libs, "synthcity==0.2.12", "numpy<2", "-q"], + check=True + ) + # Remove torch/torchvision from pip_libs to avoid shadowing system versions + import shutil, glob + for pat in ["torch", "torch-*", "torchvision", "torchvision-*", + "torchvision.libs", "torchgen", "nvidia*", "triton*"]: + for p in glob.glob(os.path.join(pip_libs, pat)): + if os.path.isdir(p): shutil.rmtree(p) + else: os.remove(p) + if pip_libs not in sys.path: + sys.path.insert(0, pip_libs) + +_ensure_deps() + +from synthcity.plugins import Plugins +import pickle +import pandas as pd +from synthcity.plugins.core.dataloader import GenericDataLoader + +df = pd.read_csv("/work/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260321_062527/staged/public/train.csv") +df = df.dropna(axis=1, how="all") + +# Drop zero-variance columns (only 1 unique value) to avoid +# synthcity encoder KeyError during generation +import json as _json +const_cols = {} +for col in list(df.columns): + nuniq = df[col].nunique() + if nuniq <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}' (value={const_cols[col]})") + +# Save constant columns info so generate can restore them +const_path = "/work/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260321_062527/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w") as _f: + _json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +print(f"[BayesNet] Training on {len(df)} rows, {len(df.columns)} cols") + +loader = GenericDataLoader(df) +plugin = Plugins().get("bayesian_network") +plugin.fit(loader) + +with open("/work/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260321_062527/bayesnet_model.pkl", "wb") as f: + pickle.dump(plugin, f) +print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260321_062527/bayesnet_model.pkl") diff --git a/syntheticSuccess/c12/bayesnet/bayesnet-c12-20260321_062527/bayesnet-c12-1000-20260321_071907.csv 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b/syntheticSuccess/c12/realtabformer/rtf-c12-20260329_190210/train_20260329_190225.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a503956f8c93864dbeae5683e4d8569ad0ff85136971e5af93ae41d989473f67 +size 625580 diff --git a/syntheticSuccess/c12/tabpfgen/c12-migrated-20260422_193053/_tabpfgen_generate.py b/syntheticSuccess/c12/tabpfgen/c12-migrated-20260422_193053/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..670498fc98571ec9daadc3d7b86b3f83881e951b --- /dev/null +++ b/syntheticSuccess/c12/tabpfgen/c12-migrated-20260422_193053/_tabpfgen_generate.py @@ -0,0 +1,87 @@ +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_191739/c12/staged/public/train.csv") +target_col = "class" + +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 +target_n = int(2623) + +# 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 {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 + +# --- 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] + +# Ensure output row count is strictly aligned with target_n. +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: + # Defensive fallback: if generator returns empty, bootstrap from training rows. + 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/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c12/tabpfgen-c12-2623-20260422_191757.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c12/tabpfgen-c12-2623-20260422_191757.csv") diff --git a/syntheticSuccess/c12/tabpfgen/c12-migrated-20260422_193053/gen_20260422_191757.log b/syntheticSuccess/c12/tabpfgen/c12-migrated-20260422_193053/gen_20260422_191757.log new file mode 100644 index 0000000000000000000000000000000000000000..a7f608047ef34eea00aaa07deca5dfc92663ed07 --- /dev/null +++ b/syntheticSuccess/c12/tabpfgen/c12-migrated-20260422_193053/gen_20260422_191757.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a11f8cc8116bf134e2295942d9a13e4fa9111a1c97efb164e8fc759bcf3de5b5 +size 91846 diff --git a/syntheticSuccess/c12/tabpfgen/c12-migrated-20260422_193053/input_snapshot.json b/syntheticSuccess/c12/tabpfgen/c12-migrated-20260422_193053/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..b3c79675abe556a7b2658e2dc360a97cd0b87bbe --- /dev/null +++ b/syntheticSuccess/c12/tabpfgen/c12-migrated-20260422_193053/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c12", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c12/c12-train.csv", + "exists": true, + "size": 32714405, + "sha256": "33bb117347198c71163d5f3264264432732b63d98589da2700f184e3a81f4456" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c12/c12-val.csv", + "exists": true, + "size": 4101144, + "sha256": "378b56871d42f5b6a36b250994ed5784943d07f1e3d5b07ec1e3703bcb712c82" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c12/c12-test.csv", + "exists": true, + "size": 4125941, + "sha256": "f92d9e4c6743fbcff792d530af9fc93e6107201cc0e1fabf4c41efa8b51a609e" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c12/c12-dataset_profile.json", + "exists": true, + "size": 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a/syntheticSuccess/c12/tabpfgen/c12-migrated-20260422_193053/tabpfgen-c12-2623-20260422_191757.csv b/syntheticSuccess/c12/tabpfgen/c12-migrated-20260422_193053/tabpfgen-c12-2623-20260422_191757.csv new file mode 100644 index 0000000000000000000000000000000000000000..a4c9dc8e55e36c704b804860a23f8160d4932b8d --- /dev/null +++ b/syntheticSuccess/c12/tabpfgen/c12-migrated-20260422_193053/tabpfgen-c12-2623-20260422_191757.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6a8b219a24e4957bea5078b3265e10ffec4d0d4907f58db84ec7ef3fc278d24c +size 32758372 diff --git a/syntheticSuccess/c12/tvae/tvae-c12-20260321_113634/_tvae_generate.py b/syntheticSuccess/c12/tvae/tvae-c12-20260321_113634/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..dfa1c62fdb66d7defc24cbfe3a1c2bc612b4aaf8 --- /dev/null +++ b/syntheticSuccess/c12/tvae/tvae-c12-20260321_113634/_tvae_generate.py @@ -0,0 +1,5 @@ +from ctgan.synthesizers.tvae import TVAE +model = TVAE.load("/work/output-SpecializedModels/c12/tvae/tvae-c12-20260321_113634/models_300epochs/tvae_300epochs.pt") +samples = model.sample(2623) +samples.to_csv("/work/output-SpecializedModels/c12/tvae/tvae-c12-20260321_113634/tvae-c12-2623-20260330_065417.csv", index=False) +print(f"[TVAE] Generated 2623 rows -> /work/output-SpecializedModels/c12/tvae/tvae-c12-20260321_113634/tvae-c12-2623-20260330_065417.csv") diff --git a/syntheticSuccess/c12/tvae/tvae-c12-20260321_113634/_tvae_train.py b/syntheticSuccess/c12/tvae/tvae-c12-20260321_113634/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..5f361b16590883c92de5232d20c6682ac9759fec --- /dev/null +++ b/syntheticSuccess/c12/tvae/tvae-c12-20260321_113634/_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/c12/tvae/tvae-c12-20260321_113634/staged/public/train.csv" +meta_path = "/work/output-SpecializedModels/c12/tvae/tvae-c12-20260321_113634/tvae_metadata.json" +save_path = "/work/output-SpecializedModels/c12/tvae/tvae-c12-20260321_113634/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/syntheticSuccess/c12/tvae/tvae-c12-20260321_113634/gen_20260321_125605.log b/syntheticSuccess/c12/tvae/tvae-c12-20260321_113634/gen_20260321_125605.log new file mode 100644 index 0000000000000000000000000000000000000000..c7f20049eab2c51bd11c12972d226638e65da906 --- /dev/null +++ b/syntheticSuccess/c12/tvae/tvae-c12-20260321_113634/gen_20260321_125605.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7b2d54505a0e737bfb2083ce187f6351247a33366196668b91e3184c7cb377d2 +size 129 diff --git a/syntheticSuccess/c12/tvae/tvae-c12-20260321_113634/gen_20260330_065417.log b/syntheticSuccess/c12/tvae/tvae-c12-20260321_113634/gen_20260330_065417.log new file mode 100644 index 0000000000000000000000000000000000000000..774efa4f7c6d26117676b520cb23feb7b9db84ef --- /dev/null +++ b/syntheticSuccess/c12/tvae/tvae-c12-20260321_113634/gen_20260330_065417.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e92f5a4b0fcc7e60b1268451e0ae93f0e4645f44415adc9e42a1d51babbb3a97 +size 129 diff --git a/syntheticSuccess/c12/tvae/tvae-c12-20260321_113634/input_snapshot.json b/syntheticSuccess/c12/tvae/tvae-c12-20260321_113634/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..3c35c7591003daf407d402efc6e2600048d43258 --- /dev/null +++ b/syntheticSuccess/c12/tvae/tvae-c12-20260321_113634/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c12", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c12/c12-train.csv", + "exists": true, + "size": 32714405, + "sha256": "33bb117347198c71163d5f3264264432732b63d98589da2700f184e3a81f4456" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c12/c12-val.csv", + "exists": true, + "size": 4101144, + "sha256": "378b56871d42f5b6a36b250994ed5784943d07f1e3d5b07ec1e3703bcb712c82" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c12/c12-test.csv", + "exists": true, + "size": 4125941, + "sha256": "f92d9e4c6743fbcff792d530af9fc93e6107201cc0e1fabf4c41efa8b51a609e" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c12/c12-dataset_profile.json", + "exists": true, + "size": 503575, + "sha256": "e655d13d30e13553c67ee13ddee5a9bdf512fbeb7d7b9d213f59d67b44e4d5d9" + }, + "contract_json": { + "path": 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