diff --git a/syntheticSuccess/n8/arf/arf-n8-20260422_055912/_arf_generate.py b/syntheticSuccess/n8/arf/arf-n8-20260422_055912/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..8bbfb1c0b359b80ca3ad31b59f16eb7e80011747 --- /dev/null +++ b/syntheticSuccess/n8/arf/arf-n8-20260422_055912/_arf_generate.py @@ -0,0 +1,23 @@ +import pickle +import pandas as pd + +n_target = int(1253) +with open("/work/output-SpecializedModels/n8/arf/arf-n8-20260422_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/n8/arf/arf-n8-20260422_055912/arf-n8-1253-20260422_062609.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-SpecializedModels/n8/arf/arf-n8-20260422_055912/arf-n8-1253-20260422_062609.csv") diff --git a/syntheticSuccess/n8/arf/arf-n8-20260422_055912/_arf_train.py b/syntheticSuccess/n8/arf/arf-n8-20260422_055912/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..9bbe4cfe6b02ee2f921629188603dafcce58c884 --- /dev/null +++ b/syntheticSuccess/n8/arf/arf-n8-20260422_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/n8/arf/arf-n8-20260422_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/n8/arf/arf-n8-20260422_055912/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-SpecializedModels/n8/arf/arf-n8-20260422_055912/arf_model.pkl") diff --git a/syntheticSuccess/n8/arf/arf-n8-20260422_055912/arf-n8-1253-20260422_062609.csv b/syntheticSuccess/n8/arf/arf-n8-20260422_055912/arf-n8-1253-20260422_062609.csv new file mode 100644 index 0000000000000000000000000000000000000000..862f723b05fcd9a510a4cc661d64caf0a085891e --- /dev/null +++ b/syntheticSuccess/n8/arf/arf-n8-20260422_055912/arf-n8-1253-20260422_062609.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:151787f8a33881d28247a04954e385fcc66c74cf06e297d1b439da202689881e +size 11858497 diff --git a/syntheticSuccess/n8/arf/arf-n8-20260422_055912/arf_model.pkl b/syntheticSuccess/n8/arf/arf-n8-20260422_055912/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..b3818deb9f1c8bf5782e99fcb508ac319a7facfc --- /dev/null +++ b/syntheticSuccess/n8/arf/arf-n8-20260422_055912/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:57eef4a47e44066523c328391d892f9d042203655f670c21acde4819677e2b9b +size 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+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 + +final.to_csv("/work/output-SpecializedModels/n8/bayesnet/bayesnet-n8-20260420_052724/bayesnet-n8-1253-20260420_063732.csv", index=False) +print(f"[BayesNet] Generated 1253 rows -> /work/output-SpecializedModels/n8/bayesnet/bayesnet-n8-20260420_052724/bayesnet-n8-1253-20260420_063732.csv") diff --git a/syntheticSuccess/n8/bayesnet/bayesnet-n8-20260420_052724/_bayesnet_train.py b/syntheticSuccess/n8/bayesnet/bayesnet-n8-20260420_052724/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..ad43c4ecc7df69b5504358275a716abfd508ab73 --- /dev/null +++ b/syntheticSuccess/n8/bayesnet/bayesnet-n8-20260420_052724/_bayesnet_train.py @@ -0,0 +1,93 @@ + +import json +import pickle +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) + +with open("/work/output-SpecializedModels/n8/bayesnet/bayesnet-n8-20260420_052724/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/n8/bayesnet/bayesnet-n8-20260420_052724/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/n8/bayesnet/bayesnet-n8-20260420_052724/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) +max_bins = 10 + +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)") + +dag = TreeSearch(enc).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/n8/bayesnet/bayesnet-n8-20260420_052724/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/n8/bayesnet/bayesnet-n8-20260420_052724/bayesnet_model.pkl") diff --git a/syntheticSuccess/n8/bayesnet/bayesnet-n8-20260420_052724/bayesnet-n8-1253-20260420_063732.csv b/syntheticSuccess/n8/bayesnet/bayesnet-n8-20260420_052724/bayesnet-n8-1253-20260420_063732.csv new file mode 100644 index 0000000000000000000000000000000000000000..2cc71b42a2b2e45027e875f28cdcbd87aa8b35e5 --- /dev/null +++ b/syntheticSuccess/n8/bayesnet/bayesnet-n8-20260420_052724/bayesnet-n8-1253-20260420_063732.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:08b161bff7a9e0dbb257fd713ae645fac17afef074a1549859b3ca16da62949f +size 11832549 diff --git a/syntheticSuccess/n8/bayesnet/bayesnet-n8-20260420_052724/bayesnet_coltypes.json 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a/syntheticSuccess/n8/ctgan/ctgan-n8-20260422_031259/_ctgan_generate.py b/syntheticSuccess/n8/ctgan/ctgan-n8-20260422_031259/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..02d44fe7b44a48c2bbed997a2a36344a4f65e4cd --- /dev/null +++ b/syntheticSuccess/n8/ctgan/ctgan-n8-20260422_031259/_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/n8/ctgan/ctgan-n8-20260422_031259/models_300epochs/ctgan_300epochs.pt") +total = 1253 +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/n8/ctgan/ctgan-n8-20260422_031259/ctgan-n8-1253-20260422_034656.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-SpecializedModels/n8/ctgan/ctgan-n8-20260422_031259/ctgan-n8-1253-20260422_034656.csv") \ No newline at end of file diff --git a/syntheticSuccess/n8/ctgan/ctgan-n8-20260422_031259/ctgan-n8-1253-20260422_034656.csv b/syntheticSuccess/n8/ctgan/ctgan-n8-20260422_031259/ctgan-n8-1253-20260422_034656.csv new file mode 100644 index 0000000000000000000000000000000000000000..a0f7ecc3a8755ceb9ff5efc64d80507024d50aef --- /dev/null +++ b/syntheticSuccess/n8/ctgan/ctgan-n8-20260422_031259/ctgan-n8-1253-20260422_034656.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:37dff8b2bc9635246ed242623f84dab521437696015e251f38de28e8cb6a0adb +size 11795933 diff --git a/syntheticSuccess/n8/ctgan/ctgan-n8-20260422_031259/ctgan_metadata.json 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sha256:069fc1c4a1a9a53be0de2722d20b3013bfe5b2b7c5d8a2ae4bc79a33b9cafd68 +size 347773 diff --git a/syntheticSuccess/n8/tvae/tvae-n8-20260420_210002/_tvae_generate.py b/syntheticSuccess/n8/tvae/tvae-n8-20260420_210002/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..dedf7a7bcc161cfbc4422050eef921367baf7f90 --- /dev/null +++ b/syntheticSuccess/n8/tvae/tvae-n8-20260420_210002/_tvae_generate.py @@ -0,0 +1,21 @@ +import os, 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.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-SpecializedModels/n8/tvae/tvae-n8-20260420_210002/models_300epochs/tvae_300epochs.pt") +total = 1253 +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-SpecializedModels/n8/tvae/tvae-n8-20260420_210002/tvae-n8-1253-20260420_212833.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-SpecializedModels/n8/tvae/tvae-n8-20260420_210002/tvae-n8-1253-20260420_212833.csv") diff --git a/syntheticSuccess/n8/tvae/tvae-n8-20260420_210002/_tvae_train.py b/syntheticSuccess/n8/tvae/tvae-n8-20260420_210002/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..7b1f8e24a6a7dd9f8dac428eef5243e6bf0ef776 --- /dev/null +++ b/syntheticSuccess/n8/tvae/tvae-n8-20260420_210002/_tvae_train.py @@ -0,0 +1,21 @@ +import json, os, sys +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") + +csv_path = "/work/output-SpecializedModels/n8/tvae/tvae-n8-20260420_210002/staged/public/train.csv" +meta_path = "/work/output-SpecializedModels/n8/tvae/tvae-n8-20260420_210002/tvae_metadata.json" +save_path = "/work/output-SpecializedModels/n8/tvae/tvae-n8-20260420_210002/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/n8/tvae/tvae-n8-20260420_210002/gen_20260420_212833.log b/syntheticSuccess/n8/tvae/tvae-n8-20260420_210002/gen_20260420_212833.log 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