diff --git a/syntheticSuccess/n1/arf/arf-n1-20260422_055912/_arf_generate.py b/syntheticSuccess/n1/arf/arf-n1-20260422_055912/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..95b0b2689a3a51322be032fe024b7fb7805d448c --- /dev/null +++ b/syntheticSuccess/n1/arf/arf-n1-20260422_055912/_arf_generate.py @@ -0,0 +1,23 @@ +import pickle +import pandas as pd + +n_target = int(3680) +with open("/work/output-SpecializedModels/n1/arf/arf-n1-20260422_055912/arf_model.pkl", "rb") as f: + model = pickle.load(f) +syn = model.forge(n=n_target) +syn = syn.reset_index(drop=True) +if len(syn) > n_target: + syn = syn.iloc[:n_target] +elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = model.forge(n=max(need, 1)).reset_index(drop=True) + if len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] +syn.to_csv("/work/output-SpecializedModels/n1/arf/arf-n1-20260422_055912/arf-n1-3680-20260422_061320.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-SpecializedModels/n1/arf/arf-n1-20260422_055912/arf-n1-3680-20260422_061320.csv") diff --git a/syntheticSuccess/n1/arf/arf-n1-20260422_055912/_arf_train.py b/syntheticSuccess/n1/arf/arf-n1-20260422_055912/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..a497e310ccf5006a3db92f64572681e0eb304190 --- /dev/null +++ b/syntheticSuccess/n1/arf/arf-n1-20260422_055912/_arf_train.py @@ -0,0 +1,37 @@ +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + lo, hi = df[col].quantile(0.001), df[col].quantile(0.999) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-SpecializedModels/n1/arf/arf-n1-20260422_055912/staged/public/train.csv") +df = _sanitize_for_arf(df) +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/n1/arf/arf-n1-20260422_055912/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-SpecializedModels/n1/arf/arf-n1-20260422_055912/arf_model.pkl") diff --git a/syntheticSuccess/n1/arf/arf-n1-20260422_055912/arf-n1-3680-20260422_061320.csv b/syntheticSuccess/n1/arf/arf-n1-20260422_055912/arf-n1-3680-20260422_061320.csv new file mode 100644 index 0000000000000000000000000000000000000000..08425b4fa3b9d321385fa0846c3019e72b074dd3 --- /dev/null +++ b/syntheticSuccess/n1/arf/arf-n1-20260422_055912/arf-n1-3680-20260422_061320.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:912e4f20ac476ce74eab18fb0a4da53ed19e5876b74df5483385b6bdde862ff0 +size 2692127 diff --git a/syntheticSuccess/n1/arf/arf-n1-20260422_055912/arf_model.pkl b/syntheticSuccess/n1/arf/arf-n1-20260422_055912/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..7fbe2f84cdba823d5f253675dfa9df7de2c73d39 --- /dev/null +++ b/syntheticSuccess/n1/arf/arf-n1-20260422_055912/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f02f7a2a0691c9c1374e859eb1279110c3714119df2baef75683d3cb41029b82 +size 70017137 diff --git a/syntheticSuccess/n1/arf/arf-n1-20260422_055912/gen_20260422_061320.log b/syntheticSuccess/n1/arf/arf-n1-20260422_055912/gen_20260422_061320.log new file mode 100644 index 0000000000000000000000000000000000000000..d5d7be5baf94baa313188c3908df699c6571877a --- /dev/null +++ b/syntheticSuccess/n1/arf/arf-n1-20260422_055912/gen_20260422_061320.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e9bbc194d6449ff602a1300843158a3d5162f44a8f5c9250dda06cdde5f9daab +size 455 diff --git a/syntheticSuccess/n1/arf/arf-n1-20260422_055912/input_snapshot.json b/syntheticSuccess/n1/arf/arf-n1-20260422_055912/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..e9c59e67dfd69c05bc345a4cfed9c4e7c1d220d0 --- /dev/null +++ b/syntheticSuccess/n1/arf/arf-n1-20260422_055912/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n1", + "model": "arf", + "inputs": { + "train_csv": { + "path": 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"data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_over", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_remove", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_internet", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_order", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_mail", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_receive", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_will", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_people", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_report", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_addresses", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_free", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_business", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_email", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_you", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_credit", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_your", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_font", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_000", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_money", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_hp", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_hpl", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_george", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_650", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_lab", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_labs", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_telnet", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_857", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_data", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_415", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_85", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_technology", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_1999", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_parts", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "word_freq_pm", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_direct", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_cs", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_meeting", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_original", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_project", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_re", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_edu", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "word_freq_table", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "word_freq_conference", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "char_freq_%3B", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "char_freq_%28", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "char_freq_%5B", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "char_freq_%21", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "char_freq_%24", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "char_freq_%23", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "capital_run_length_average", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "capital_run_length_longest", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "capital_run_length_total", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "binary", + "is_target": true + } +] \ No newline at end of file diff --git a/syntheticSuccess/n1/arf/arf-n1-20260422_055912/staged/public/test.csv b/syntheticSuccess/n1/arf/arf-n1-20260422_055912/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..628770353833f1dc63d727f5bbc3c5bf7d8180e7 --- /dev/null +++ b/syntheticSuccess/n1/arf/arf-n1-20260422_055912/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:165b9f8df9afdbccf1a6362155a785860134438138af92d7288cbe88b4965b03 +size 111841 diff --git a/syntheticSuccess/n1/arf/arf-n1-20260422_055912/staged/public/train.csv b/syntheticSuccess/n1/arf/arf-n1-20260422_055912/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..dabe62a5dca077c9f19f338b30e0ea316947620d --- /dev/null +++ b/syntheticSuccess/n1/arf/arf-n1-20260422_055912/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7f723c8c6f475f4dee37b81af1fc803cca3362ca22bea09c90cc39404b034b23 +size 885832 diff --git a/syntheticSuccess/n1/arf/arf-n1-20260422_055912/staged/public/val.csv b/syntheticSuccess/n1/arf/arf-n1-20260422_055912/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d03fc614be3e0e5f3e8e96f7c7f84a14889d16ec --- /dev/null +++ b/syntheticSuccess/n1/arf/arf-n1-20260422_055912/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ff8bfb6a9fa9b786c8e9a34d4619140e43ad25ca275d2459cc179352865f670c +size 111513 diff --git a/syntheticSuccess/n1/arf/arf-n1-20260422_055912/train_20260422_055912.log b/syntheticSuccess/n1/arf/arf-n1-20260422_055912/train_20260422_055912.log new file mode 100644 index 0000000000000000000000000000000000000000..a323c54b1c888a7b2d9267ba1e3c2f1274b501b4 --- /dev/null +++ b/syntheticSuccess/n1/arf/arf-n1-20260422_055912/train_20260422_055912.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d43c48a31852c2a5814b08b1ec564972482a92d310f14b7afd00f581b0f210e6 +size 738 diff --git a/syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/_bayesnet_generate.py b/syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..c277a3b24245c31ba96ba49f59f27b8339559db8 --- /dev/null +++ b/syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/_bayesnet_generate.py @@ -0,0 +1,104 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-SpecializedModels/n1/bayesnet/bayesnet-n1-20260422_060152/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(3680) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final.to_csv("/work/output-SpecializedModels/n1/bayesnet/bayesnet-n1-20260422_060152/bayesnet-n1-3680-20260422_060304.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-SpecializedModels/n1/bayesnet/bayesnet-n1-20260422_060152/bayesnet-n1-3680-20260422_060304.csv") diff --git a/syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/_bayesnet_train.py b/syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..6fb2f748c22ee3c45f3577b4b3de124b9137c5c7 --- /dev/null +++ b/syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/_bayesnet_train.py @@ -0,0 +1,118 @@ + +import json +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-SpecializedModels/n1/bayesnet/bayesnet-n1-20260422_060152/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-SpecializedModels/n1/bayesnet/bayesnet-n1-20260422_060152/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 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}'") + +const_path = "/work/output-SpecializedModels/n1/bayesnet/bayesnet-n1-20260422_060152/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = 10 +if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 +if _n_plan > 55: + max_bins = 4 +print(f"[BayesNet] max_bins={max_bins} (cols_in_df={_n_plan}, rows={_n_samples})") + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + uniques = sorted(s.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > 25000: + enc_struct = enc.sample(n=25000, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct).estimate(show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +network.fit(enc) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-SpecializedModels/n1/bayesnet/bayesnet-n1-20260422_060152/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/n1/bayesnet/bayesnet-n1-20260422_060152/bayesnet_model.pkl") diff --git a/syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/bayesnet-n1-3680-20260422_060304.csv b/syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/bayesnet-n1-3680-20260422_060304.csv new file mode 100644 index 0000000000000000000000000000000000000000..1708dae795b74c9d1f235fbdfdf0cbe862ae7194 --- /dev/null +++ b/syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/bayesnet-n1-3680-20260422_060304.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:beb0dd8853c0472ab1854588b60728fb099b431aba7ba3526b480a0297651f7b +size 3770782 diff --git a/syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/bayesnet_coltypes.json b/syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..b671a7da140bc6753b66fe1e458671fe390a4c90 --- /dev/null +++ b/syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/bayesnet_coltypes.json @@ -0,0 +1,237 @@ +{ + "columns": [ + { + "name": "word_freq_make", + "type": "continuous" + }, + { + "name": "word_freq_address", + "type": "continuous" + }, + { + "name": "word_freq_all", + "type": "continuous" + }, + { + "name": "word_freq_3d", + "type": "categorical" + }, + { + "name": "word_freq_our", + "type": "continuous" + }, + { + "name": "word_freq_over", + "type": "continuous" + }, + { + "name": "word_freq_remove", + "type": "continuous" + }, + { + "name": "word_freq_internet", + "type": "continuous" + }, + { + "name": "word_freq_order", + "type": "continuous" + }, + { + "name": "word_freq_mail", + "type": "continuous" + }, + { + "name": "word_freq_receive", + "type": "continuous" + }, + { + "name": "word_freq_will", + "type": "continuous" + }, + { + "name": "word_freq_people", + "type": "continuous" + }, + { + "name": "word_freq_report", + "type": "continuous" + }, + { + "name": "word_freq_addresses", + "type": "continuous" + }, + { + "name": "word_freq_free", + "type": "continuous" + }, + { + "name": "word_freq_business", + "type": "continuous" + }, + { + "name": "word_freq_email", + "type": "continuous" + }, + { + "name": "word_freq_you", + "type": "continuous" + }, + { + "name": "word_freq_credit", + "type": "continuous" + }, + { + "name": "word_freq_your", + "type": "continuous" + }, + { + "name": "word_freq_font", + "type": "continuous" + }, + { + "name": "word_freq_000", + "type": "continuous" + }, + { + "name": "word_freq_money", + "type": "continuous" + }, + { + "name": "word_freq_hp", + "type": "continuous" + }, + { + "name": "word_freq_hpl", + "type": "continuous" + }, + { + "name": "word_freq_george", + "type": "continuous" + }, + { + "name": "word_freq_650", + "type": "continuous" + }, + { + "name": "word_freq_lab", + "type": "continuous" + }, + { + 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"type": "continuous" + }, + { + "name": "word_freq_table", + "type": "categorical" + }, + { + "name": "word_freq_conference", + "type": "continuous" + }, + { + "name": "char_freq_%3B", + "type": "continuous" + }, + { + "name": "char_freq_%28", + "type": "continuous" + }, + { + "name": "char_freq_%5B", + "type": "continuous" + }, + { + "name": "char_freq_%21", + "type": "continuous" + }, + { + "name": "char_freq_%24", + "type": "continuous" + }, + { + "name": "char_freq_%23", + "type": "continuous" + }, + { + "name": "capital_run_length_average", + "type": "continuous" + }, + { + "name": "capital_run_length_longest", + "type": "continuous" + }, + { + "name": "capital_run_length_total", + "type": "continuous" + }, + { + "name": "class", + "type": "categorical" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/bayesnet_model.pkl b/syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/bayesnet_model.pkl 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a/syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/staged/public/train.csv b/syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..dabe62a5dca077c9f19f338b30e0ea316947620d --- /dev/null +++ b/syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7f723c8c6f475f4dee37b81af1fc803cca3362ca22bea09c90cc39404b034b23 +size 885832 diff --git a/syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/staged/public/val.csv b/syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d03fc614be3e0e5f3e8e96f7c7f84a14889d16ec --- /dev/null +++ b/syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ff8bfb6a9fa9b786c8e9a34d4619140e43ad25ca275d2459cc179352865f670c +size 111513 diff --git a/syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/train_20260422_060153.log b/syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/train_20260422_060153.log new file mode 100644 index 0000000000000000000000000000000000000000..b02ea6ef7934317ec108a845d061d7582650ee5e --- /dev/null +++ b/syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/train_20260422_060153.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7de58556c923ed61a24bcc7ab335a3a4bac038e8b0259262cea01583af7f3c0b +size 5645 diff --git a/syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/_ctgan_generate.py b/syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..1470af39e6d82af17d923d8d3eccf4864b8be803 --- /dev/null +++ b/syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-SpecializedModels/n1/ctgan/ctgan-n1-20260413_043449/models_1epochs/ctgan_1epochs.pt") +total = 3680 +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 +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-SpecializedModels/n1/ctgan/ctgan-n1-20260413_043449/ctgan-n1-3680-20260420_030338.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-SpecializedModels/n1/ctgan/ctgan-n1-20260413_043449/ctgan-n1-3680-20260420_030338.csv") \ No newline at end of file diff --git a/syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/ctgan-n1-100-20260413_043659.csv b/syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/ctgan-n1-100-20260413_043659.csv new file mode 100644 index 0000000000000000000000000000000000000000..169d665521d9a07799d86832c25d1fab8d06f7ba --- /dev/null +++ b/syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/ctgan-n1-100-20260413_043659.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:57f8bc12d9929194881b36ed4e227ad4a41d0a25aca3970481a9d9b49728b54b +size 106092 diff --git a/syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/ctgan-n1-3680-20260420_030338.csv b/syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/ctgan-n1-3680-20260420_030338.csv new file mode 100644 index 0000000000000000000000000000000000000000..14697a33175a58f08a93b87553722447bf50aee8 --- /dev/null +++ b/syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/ctgan-n1-3680-20260420_030338.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e5677322338a73cc622e29587268b8ed65f3606c55d17924a4f5cd462c3b38e3 +size 4142786 diff --git a/syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/ctgan_metadata.json b/syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..8be15bd2f23278eb3593c251cae9e22965bd5f77 --- /dev/null +++ b/syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/ctgan_metadata.json @@ -0,0 +1,236 @@ +{ + "columns": [ + { + "name": "word_freq_make", + "type": "continuous" + }, + { + "name": "word_freq_address", + "type": "continuous" + }, + { + "name": "word_freq_all", + "type": "continuous" + }, + { + "name": "word_freq_3d", + "type": "categorical" + }, + { + "name": "word_freq_our", + "type": "continuous" + }, + { + "name": "word_freq_over", + "type": "continuous" + }, + { + "name": "word_freq_remove", + "type": "continuous" + }, + { + "name": "word_freq_internet", + "type": "continuous" + }, + { + "name": "word_freq_order", + "type": "continuous" + }, + { + "name": "word_freq_mail", + "type": "continuous" + }, + { + "name": "word_freq_receive", + "type": "continuous" + }, + { + "name": "word_freq_will", + "type": "continuous" + }, + { + "name": "word_freq_people", + "type": "continuous" + }, + { + "name": "word_freq_report", + "type": "continuous" + }, + { + "name": "word_freq_addresses", + "type": "continuous" + }, + { + "name": "word_freq_free", + "type": "continuous" + }, + { + "name": "word_freq_business", + "type": "continuous" + }, + { + "name": "word_freq_email", + "type": "continuous" + }, + { + "name": "word_freq_you", + "type": "continuous" + }, + { + "name": "word_freq_credit", + "type": "continuous" + }, + { + "name": "word_freq_your", + "type": "continuous" + }, + { + "name": "word_freq_font", + "type": "continuous" + }, + { + "name": "word_freq_000", + "type": "continuous" + }, + { + "name": "word_freq_money", + "type": "continuous" + }, + { + "name": "word_freq_hp", + "type": "continuous" + }, + { + "name": "word_freq_hpl", + "type": "continuous" + }, + { + "name": "word_freq_george", + "type": "continuous" + }, + { + "name": "word_freq_650", + "type": "continuous" + }, + { + "name": "word_freq_lab", + "type": "continuous" + }, + { + "name": "word_freq_labs", + "type": "continuous" + }, + { + "name": "word_freq_telnet", + "type": "continuous" + }, + { + "name": "word_freq_857", + "type": "continuous" + }, + { + "name": "word_freq_data", + "type": "continuous" + }, + { + "name": "word_freq_415", + "type": "continuous" + }, + { + "name": "word_freq_85", + "type": "continuous" + }, + { + "name": "word_freq_technology", + "type": "continuous" + }, + { + "name": "word_freq_1999", + "type": "continuous" + }, + { + "name": "word_freq_parts", + "type": "categorical" + }, + { + "name": "word_freq_pm", + "type": "continuous" + }, + { + "name": "word_freq_direct", + "type": "continuous" + }, + { + "name": "word_freq_cs", + "type": "continuous" + }, + { + "name": "word_freq_meeting", + "type": "continuous" + }, + { + "name": "word_freq_original", + "type": "continuous" + }, + { + "name": "word_freq_project", + "type": "continuous" + }, + { + "name": "word_freq_re", + "type": "continuous" + }, + { + "name": "word_freq_edu", + "type": "continuous" + }, + { + "name": "word_freq_table", + "type": "categorical" + }, + { + "name": "word_freq_conference", + "type": "continuous" + }, + { + "name": "char_freq_%3B", + "type": "continuous" + }, + { + "name": "char_freq_%28", + "type": "continuous" + }, + { + "name": "char_freq_%5B", + "type": "continuous" + }, + { + "name": "char_freq_%21", + "type": "continuous" + }, + { + "name": "char_freq_%24", + "type": "continuous" + }, + { + "name": "char_freq_%23", + "type": "continuous" + }, + { + "name": "capital_run_length_average", + "type": "continuous" + }, + { + "name": "capital_run_length_longest", + "type": "continuous" + }, + { + "name": "capital_run_length_total", + "type": "continuous" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/gen_20260413_043659.log b/syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/gen_20260413_043659.log new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/gen_20260420_030338.log b/syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/gen_20260420_030338.log new file mode 100644 index 0000000000000000000000000000000000000000..628b1b721143ff11752642d69c6518c77632cce0 --- /dev/null +++ b/syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/gen_20260420_030338.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e70c53d76aa34e7f77eb13b72d319db7982620f34a2417cf1e7bdd8d6054b70c +size 142 diff --git a/syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/input_snapshot.json b/syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..f7daa19d27bb5bff59ff589fcf53912f8e0803d5 --- /dev/null +++ b/syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n1", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n1/n1-train.csv", + "exists": true, + "size": 563021, + "sha256": "e603fe5195edac0e926f63b398fd1af936766c1b502b300341a478e70aef4df5" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n1/n1-val.csv", + "exists": true, + "size": 71100, + "sha256": "29ec664376ba652ee157545c9afe81d1afa054e30a554bc50ab2ebf1c52cef83" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n1/n1-test.csv", + "exists": true, + "size": 71605, + "sha256": "2696873d18a2f693dc4b08f1c30f34a30cf96f1195790479ab8947fee8bc35ef" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n1/n1-dataset_profile.json", + "exists": true, + "size": 21967, + "sha256": "62128c1a7c75e9b7c23136d477b5795a9477bc710734776bd4a1378d3d81dc33" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n1/n1-dataset_contract_v1.json", + "exists": true, + "size": 27866, + "sha256": "cf9ef940957593e898ef55899808bb02267775fdad024ec6bf75793ef28b7ba1" + } + } +} \ No newline at end of file diff --git a/syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/models_1epochs/ctgan_1epochs.pt b/syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/models_1epochs/ctgan_1epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..997f4c2fc8ea38e5bc5ebbc90edd4a5e5c423431 --- /dev/null +++ b/syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/models_1epochs/ctgan_1epochs.pt @@ -0,0 +1,3 @@ +version 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"/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n1/realtabformer/rtf-n1-20260413_060137/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n1/realtabformer/rtf-n1-20260413_060137/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n1/realtabformer/rtf-n1-20260413_060137/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n1/realtabformer/rtf-n1-20260413_060137/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n1/realtabformer/rtf-n1-20260413_060137/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/n1/realtabformer/rtf-n1-20260413_060137/train_20260413_060138.log b/syntheticSuccess/n1/realtabformer/rtf-n1-20260413_060137/train_20260413_060138.log new file mode 100644 index 0000000000000000000000000000000000000000..cdf09e2cb434a1d4cc00ae3d42ff5b8fbaec2f37 --- /dev/null +++ b/syntheticSuccess/n1/realtabformer/rtf-n1-20260413_060137/train_20260413_060138.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c1f9409f944995f889f6fd857651355aec3f48c4b27e993cad77b01eebb6fa66 +size 23747 diff --git a/syntheticSuccess/n1/tabddpm/tabddpm-n1-20260424_033725/_tabddpm_sample.py b/syntheticSuccess/n1/tabddpm/tabddpm-n1-20260424_033725/_tabddpm_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..c2cd2aac67ffc33ccf2a00adde62a0c8269cdf3f --- /dev/null +++ b/syntheticSuccess/n1/tabddpm/tabddpm-n1-20260424_033725/_tabddpm_sample.py @@ -0,0 +1,67 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 3680 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/n1/tabddpm/tabddpm-n1-20260424_033725/config_sample_20260424_034040.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV +work_dir = "/work/output-SpecializedModels/n1/tabddpm/tabddpm-n1-20260424_033725" +info_path = os.path.join(work_dir, "data", "info.json") +with open(info_path) as f: + info = json.load(f) + +output_dir = os.path.join(work_dir, "output") +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-SpecializedModels/n1/tabddpm/tabddpm-n1-20260424_033725/tabddpm-n1-3680-20260424_034040.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/n1/tabddpm/tabddpm-n1-20260424_033725/tabddpm-n1-3680-20260424_034040.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticSuccess/n1/tabddpm/tabddpm-n1-20260424_033725/_tabddpm_train.py b/syntheticSuccess/n1/tabddpm/tabddpm-n1-20260424_033725/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..a556af0e39409e05101083466777998c58776660 --- /dev/null +++ b/syntheticSuccess/n1/tabddpm/tabddpm-n1-20260424_033725/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-SpecializedModels/n1/tabddpm/tabddpm-n1-20260424_033725/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/n1/tabddpm/tabddpm-n1-20260424_033725/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/syntheticSuccess/n1/tabddpm/tabddpm-n1-20260424_033725/config.toml b/syntheticSuccess/n1/tabddpm/tabddpm-n1-20260424_033725/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..219790b84251a8d0952e1698c4eef3034b53ecfc --- /dev/null +++ b/syntheticSuccess/n1/tabddpm/tabddpm-n1-20260424_033725/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/n1/tabddpm/tabddpm-n1-20260424_033725/output" +real_data_path = "/work/output-SpecializedModels/n1/tabddpm/tabddpm-n1-20260424_033725/data" +model_type = "mlp" +num_numerical_features = 54 +device = "cuda:0" + +[model_params] +d_in = 57 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/n1/tabddpm/tabddpm-n1-20260424_033725/config_sample_20260424_034040.toml b/syntheticSuccess/n1/tabddpm/tabddpm-n1-20260424_033725/config_sample_20260424_034040.toml new file mode 100644 index 0000000000000000000000000000000000000000..75726adae15119890f24ff3bdfc344c2c7681d80 --- /dev/null +++ b/syntheticSuccess/n1/tabddpm/tabddpm-n1-20260424_033725/config_sample_20260424_034040.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/n1/tabddpm/tabddpm-n1-20260424_033725/output" +real_data_path = "/work/output-SpecializedModels/n1/tabddpm/tabddpm-n1-20260424_033725/data" +model_type = "mlp" +num_numerical_features = 54 +device = "cuda:0" + +[model_params] +d_in = 57 +num_classes = 2 +is_y_cond = true + 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No newline at end of file diff --git a/syntheticSuccess/n1/tabddpm/tabddpm-n1-20260424_033725/tabddpm-n1-3680-20260424_034040.csv b/syntheticSuccess/n1/tabddpm/tabddpm-n1-20260424_033725/tabddpm-n1-3680-20260424_034040.csv new file mode 100644 index 0000000000000000000000000000000000000000..f776c18877e86442ba3e3f2cb1baa0f4bcb89128 --- /dev/null +++ b/syntheticSuccess/n1/tabddpm/tabddpm-n1-20260424_033725/tabddpm-n1-3680-20260424_034040.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9e5d467fe08474b48953cf4efce149be7f02618aac944d75fe163e20c7c1e566 +size 1956901 diff --git a/syntheticSuccess/n1/tabddpm/tabddpm-n1-20260424_033725/train_20260424_033725.log b/syntheticSuccess/n1/tabddpm/tabddpm-n1-20260424_033725/train_20260424_033725.log new file mode 100644 index 0000000000000000000000000000000000000000..d15987ba39357935fdcb525fc5cf534cca011638 --- /dev/null +++ b/syntheticSuccess/n1/tabddpm/tabddpm-n1-20260424_033725/train_20260424_033725.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:00da33bf41736c2603f506d7cf75e0f4076125b0a9b40482fb7482e4792d2ad9 +size 831 diff --git a/syntheticSuccess/n1/tabpfgen/n1-migrated-20260422_183752/_tabpfgen_generate.py b/syntheticSuccess/n1/tabpfgen/n1-migrated-20260422_183752/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..7e578c61fe757e08d47a70a52d26227f2fbcb3cc --- /dev/null +++ b/syntheticSuccess/n1/tabpfgen/n1-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/n1/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 + +# 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 3680 rows via generate_classification") +X_syn, y_syn = gen.generate_classification(X, y, n_samples=3680) + +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/n1/tabpfgen-n1-3680-20260422_070321.csv", index=False) +print(f"[TabPFGen] Saved 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"/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n1/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n1/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/n1/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/n1/tabpfgen/n1-migrated-20260422_183752/tabpfgen-n1-3680-20260422_070321.csv b/syntheticSuccess/n1/tabpfgen/n1-migrated-20260422_183752/tabpfgen-n1-3680-20260422_070321.csv new file mode 100644 index 0000000000000000000000000000000000000000..4174a27d3e33faac6ac5a31cb25c5d374d0bddf4 --- /dev/null +++ b/syntheticSuccess/n1/tabpfgen/n1-migrated-20260422_183752/tabpfgen-n1-3680-20260422_070321.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f2d35a16d60c7f27139a112247822f0eb777b4f63721c290d285e03bdc3794d7 +size 2726714 diff --git a/syntheticSuccess/n1/tvae/tvae-n1-20260321_065834/_tvae_generate.py b/syntheticSuccess/n1/tvae/tvae-n1-20260321_065834/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..237cb9bb7bd2cb048e142f92de1dc1a067262bd2 --- /dev/null +++ b/syntheticSuccess/n1/tvae/tvae-n1-20260321_065834/_tvae_generate.py @@ -0,0 +1,5 @@ +from ctgan.synthesizers.tvae import TVAE +model = TVAE.load("/work/output-SpecializedModels/n1/tvae/tvae-n1-20260321_065834/models_300epochs/tvae_300epochs.pt") +samples = model.sample(3680) +samples.to_csv("/work/output-SpecializedModels/n1/tvae/tvae-n1-20260321_065834/tvae-n1-3680-20260330_065936.csv", index=False) +print(f"[TVAE] Generated 3680 rows -> /work/output-SpecializedModels/n1/tvae/tvae-n1-20260321_065834/tvae-n1-3680-20260330_065936.csv") diff --git a/syntheticSuccess/n1/tvae/tvae-n1-20260321_065834/_tvae_train.py b/syntheticSuccess/n1/tvae/tvae-n1-20260321_065834/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..6f84ae0c4856a7f169bc345babdad1aaf2730b4d --- /dev/null +++ b/syntheticSuccess/n1/tvae/tvae-n1-20260321_065834/_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/n1/tvae/tvae-n1-20260321_065834/staged/public/train.csv" +meta_path = "/work/output-SpecializedModels/n1/tvae/tvae-n1-20260321_065834/tvae_metadata.json" +save_path = "/work/output-SpecializedModels/n1/tvae/tvae-n1-20260321_065834/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/n1/tvae/tvae-n1-20260321_065834/gen_20260321_070349.log b/syntheticSuccess/n1/tvae/tvae-n1-20260321_065834/gen_20260321_070349.log new file mode 100644 index 0000000000000000000000000000000000000000..f00478614529c2b8e0bf928ee77086138badae2c --- /dev/null +++ b/syntheticSuccess/n1/tvae/tvae-n1-20260321_065834/gen_20260321_070349.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7e5c26416b879b9f8c28c3bfee73676a57c6110cbcc25b1b15d696307c3544ca +size 126 diff --git a/syntheticSuccess/n1/tvae/tvae-n1-20260321_065834/gen_20260330_065936.log b/syntheticSuccess/n1/tvae/tvae-n1-20260321_065834/gen_20260330_065936.log new file mode 100644 index 0000000000000000000000000000000000000000..2f04af654d0a1a5f6508a77bbe7158344474c31c --- /dev/null +++ b/syntheticSuccess/n1/tvae/tvae-n1-20260321_065834/gen_20260330_065936.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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