diff --git a/syntheticSuccess/m5/arf/arf-m5-20260422_055912/_arf_generate.py b/syntheticSuccess/m5/arf/arf-m5-20260422_055912/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..971bdabba7ec2743cdc20093f6a3c39b2b50c917 --- /dev/null +++ b/syntheticSuccess/m5/arf/arf-m5-20260422_055912/_arf_generate.py @@ -0,0 +1,23 @@ +import pickle +import pandas as pd + +n_target = int(3539) +with open("/work/output-SpecializedModels/m5/arf/arf-m5-20260422_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/m5/arf/arf-m5-20260422_055912/arf-m5-3539-20260422_060829.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-SpecializedModels/m5/arf/arf-m5-20260422_055912/arf-m5-3539-20260422_060829.csv") diff --git a/syntheticSuccess/m5/arf/arf-m5-20260422_055912/_arf_train.py b/syntheticSuccess/m5/arf/arf-m5-20260422_055912/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..74600c4cdbbbbeb2eec983bcbe08e13d011f5f6a --- /dev/null +++ b/syntheticSuccess/m5/arf/arf-m5-20260422_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/m5/arf/arf-m5-20260422_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/m5/arf/arf-m5-20260422_055912/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-SpecializedModels/m5/arf/arf-m5-20260422_055912/arf_model.pkl") diff --git a/syntheticSuccess/m5/arf/arf-m5-20260422_055912/arf-m5-3539-20260422_060829.csv b/syntheticSuccess/m5/arf/arf-m5-20260422_055912/arf-m5-3539-20260422_060829.csv new file mode 100644 index 0000000000000000000000000000000000000000..38d4c3a404df27b37cbdb37058b94203207d7b75 --- /dev/null +++ b/syntheticSuccess/m5/arf/arf-m5-20260422_055912/arf-m5-3539-20260422_060829.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:12bd49daeec11a0c2f5635a2615285bcc458c6b27f7b13f21ba3399dc5b03c08 +size 1912738 diff --git a/syntheticSuccess/m5/arf/arf-m5-20260422_055912/arf_model.pkl b/syntheticSuccess/m5/arf/arf-m5-20260422_055912/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..17c4e257d185ddccee39bbcff141db2b1c2b6816 --- /dev/null +++ b/syntheticSuccess/m5/arf/arf-m5-20260422_055912/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:48297851fbf4b5cb9749048d291da9bcc117578ebf36ce6ec610ee5a15389a2b +size 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sha256:012f009ed84b309df0bf0da0669101c48652c390666cb59f9a07341a16b7056f +size 422717 diff --git a/syntheticSuccess/m5/arf/arf-m5-20260422_055912/staged/public/val.csv b/syntheticSuccess/m5/arf/arf-m5-20260422_055912/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..79d7c32196ec171999137f0f7e4e6752e6bc3e4d --- /dev/null +++ b/syntheticSuccess/m5/arf/arf-m5-20260422_055912/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b9b623a7cea9350fc17384754b26aba373ab6c1914b7c0efb7a8a21ad5ac1557 +size 53889 diff --git a/syntheticSuccess/m5/arf/arf-m5-20260422_055912/train_20260422_055912.log b/syntheticSuccess/m5/arf/arf-m5-20260422_055912/train_20260422_055912.log new file mode 100644 index 0000000000000000000000000000000000000000..4972d45eaadb0d078a9d44ca77b9a427e30c5aa2 --- /dev/null +++ b/syntheticSuccess/m5/arf/arf-m5-20260422_055912/train_20260422_055912.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6083e73e5532ece05305d1fe94141798440d70d9770d2b6d037c0d04f76efdd1 +size 467 diff --git a/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/_bayesnet_generate.py b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..c0f5f65b156d94896a43bd24639158f6d33f54e9 --- /dev/null +++ b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_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/m5/bayesnet/bayesnet-m5-20260422_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(3539) +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/m5/bayesnet/bayesnet-m5-20260422_060152/bayesnet-m5-3539-20260422_060305.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-SpecializedModels/m5/bayesnet/bayesnet-m5-20260422_060152/bayesnet-m5-3539-20260422_060305.csv") diff --git a/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/_bayesnet_train.py b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..06a11cdef725e8aabd7f54439f8ee728fa9f10b0 --- /dev/null +++ b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_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/m5/bayesnet/bayesnet-m5-20260422_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/m5/bayesnet/bayesnet-m5-20260422_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/m5/bayesnet/bayesnet-m5-20260422_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/m5/bayesnet/bayesnet-m5-20260422_060152/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/m5/bayesnet/bayesnet-m5-20260422_060152/bayesnet_model.pkl") diff --git a/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/bayesnet-m5-3539-20260422_060305.csv b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/bayesnet-m5-3539-20260422_060305.csv new file mode 100644 index 0000000000000000000000000000000000000000..3fe65da3bb1816cc80278628093c2580b3d2663c --- /dev/null +++ b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/bayesnet-m5-3539-20260422_060305.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2dfacf8926d94c75ec9a9a12bc7ed2c1afcb522fc337e28672f47911a63b92e3 +size 1919273 diff --git a/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/bayesnet_coltypes.json b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..f2aafdca33ce2427ea01e88be49a7410e750b6fa --- /dev/null +++ b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/bayesnet_coltypes.json @@ -0,0 +1,153 @@ +{ + "columns": [ + { + "name": "Marital status", + "type": "continuous" + }, + { + "name": "Application mode", + "type": "continuous" + }, + { + "name": "Application order", + "type": "continuous" + }, + { + "name": "Course", + "type": "continuous" + }, + { + "name": "Daytime/evening attendance", + "type": "categorical" + }, + { + "name": "Previous qualification", + "type": "continuous" + }, + { + "name": "Previous qualification (grade)", + "type": "continuous" + }, + { + "name": "Nacionality", + "type": "continuous" + }, + { + "name": "Mother's qualification", + "type": "continuous" + }, + { + "name": "Father's qualification", + "type": "continuous" + }, + { + "name": "Mother's occupation", + "type": "continuous" + }, + { + "name": "Father's occupation", + "type": "continuous" + }, + { + "name": "Admission grade", + "type": "continuous" + }, + { + "name": "Displaced", + "type": "categorical" + }, + { + "name": "Educational special needs", + "type": "categorical" + }, + { + "name": "Debtor", + "type": "categorical" + }, + { + "name": "Tuition fees up to date", + "type": "categorical" + }, + { + "name": "Gender", + "type": "categorical" + }, + { + "name": "Scholarship holder", + "type": "categorical" + }, + { + "name": "Age at enrollment", + "type": "continuous" + }, + { + "name": "International", + "type": "categorical" + }, + { + "name": "Curricular units 1st sem (credited)", + "type": "continuous" + }, + { + "name": "Curricular units 1st sem (enrolled)", + "type": "continuous" + }, + { + "name": "Curricular units 1st sem (evaluations)", + "type": "continuous" + }, + { + "name": "Curricular units 1st sem (approved)", + "type": "continuous" + }, + { + "name": "Curricular units 1st sem (grade)", + "type": "continuous" + }, + { + "name": "Curricular units 1st sem (without evaluations)", + "type": "continuous" + }, + { + "name": "Curricular units 2nd sem (credited)", + "type": "continuous" + }, + { + "name": "Curricular units 2nd sem (enrolled)", + "type": "continuous" + }, + { + "name": "Curricular units 2nd sem (evaluations)", + "type": "continuous" + }, + { + "name": "Curricular units 2nd sem (approved)", + "type": "continuous" + }, + { + "name": "Curricular units 2nd sem (grade)", + "type": "continuous" + }, + { + "name": "Curricular units 2nd sem (without evaluations)", + "type": "continuous" + }, + { + "name": "Unemployment rate", + "type": "continuous" + }, + { + "name": "Inflation rate", + "type": "continuous" + }, + { + "name": "GDP", + "type": "continuous" + }, + { + "name": "Target", + "type": "categorical" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/bayesnet_model.pkl b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..bbc82bfd10d7e6bae343ead4a2ada1df88c24d33 --- /dev/null +++ b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1b14884707956f60bc2b120e7ad887d5ababd59542201c79e2632563d19b6f4e +size 23577 diff --git a/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/const_cols.json b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/gen_20260422_060305.log b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/gen_20260422_060305.log new file mode 100644 index 0000000000000000000000000000000000000000..2c4db16460f5d923167ac82e79ada1e72a40a62b --- /dev/null +++ b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/gen_20260422_060305.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ab03b1da312114c594d84f250952034574fd80ad4c7d7e809a5f4f537edbd0bb +size 3387 diff --git a/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/input_snapshot.json b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..e6e31e70cde4c79c092d747472a17376f91780f7 --- /dev/null +++ b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m5", + "model": "bayesnet", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m5/m5-train.csv", + "exists": true, + "size": 422717, + "sha256": "012f009ed84b309df0bf0da0669101c48652c390666cb59f9a07341a16b7056f" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m5/m5-val.csv", + "exists": true, + "size": 53889, + "sha256": "b9b623a7cea9350fc17384754b26aba373ab6c1914b7c0efb7a8a21ad5ac1557" + }, + "test_csv": { + "path": 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0000000000000000000000000000000000000000..76dce49947cb777ec46920f782a866e4be7080a8 --- /dev/null +++ b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,758 @@ +{ + "dataset_id": "m5", + "target_column": "Target", + "task_type": "classification", + "columns": [ + { + "name": "Marital status", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.001695, + "example_values": [ + "1", + "2", + "4", + "5", + "3" + ] + } + }, + { + "name": "Application mode", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 18, + "unique_ratio": 0.005086, + "example_values": [ + "43", + "17", + "1", + "39", + "44" + ] + } + }, + { + "name": "Application order", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 8, + "unique_ratio": 0.002261, + "example_values": [ + "1", + "2", + "6", + "3", + "5" + ] + } + }, + { + "name": "Course", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 17, + "unique_ratio": 0.004804, + "example_values": [ + "9773", + "9147", + "9853", + "9500", + "9085" + ] + } + }, + { + "name": "Daytime/evening attendance", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000565, + "example_values": [ + "1", + "0" + ] + } + }, + { + "name": "Previous qualification", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 16, + "unique_ratio": 0.004521, + "example_values": [ + "1", + "39", + "3", + "2", + "19" + ] + } + }, + { + "name": "Previous qualification (grade)", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 93, + "unique_ratio": 0.026279, + "example_values": [ + "127.0", + "122.0", + "121.0", + "158.0", + "141.0" + ] + } + }, + { + "name": "Nacionality", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + 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"Target", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/staged/public/test.csv b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..f03d3058f21523994e0429f6919fa3b04d35580c --- /dev/null +++ b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:696cfc46d2e611ee56a5419f4496b758f643f49f3386d4181296783760117c8c +size 53943 diff --git a/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/staged/public/train.csv b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..f804ebbebaecb3232ce4ae4749287bea90b09003 --- /dev/null +++ b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:012f009ed84b309df0bf0da0669101c48652c390666cb59f9a07341a16b7056f +size 422717 diff --git a/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/staged/public/val.csv b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..79d7c32196ec171999137f0f7e4e6752e6bc3e4d --- /dev/null +++ b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b9b623a7cea9350fc17384754b26aba373ab6c1914b7c0efb7a8a21ad5ac1557 +size 53889 diff --git a/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/train_20260422_060152.log b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/train_20260422_060152.log new file mode 100644 index 0000000000000000000000000000000000000000..0c78d2994a12b49b148f37eac80b0d3854480f40 --- /dev/null +++ b/syntheticSuccess/m5/bayesnet/bayesnet-m5-20260422_060152/train_20260422_060152.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:53b04263f1043980be5de1a117a1d6bac7bdf9407e8cf70daa9786dc955db745 +size 3934 diff --git a/syntheticSuccess/m5/ctgan/ctgan-m5-20260422_025941/_ctgan_generate.py b/syntheticSuccess/m5/ctgan/ctgan-m5-20260422_025941/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..f3001f89d81c27ebcc04083ddf9363aef41f5379 --- /dev/null +++ b/syntheticSuccess/m5/ctgan/ctgan-m5-20260422_025941/_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/m5/ctgan/ctgan-m5-20260422_025941/models_300epochs/ctgan_300epochs.pt") +total = 3539 +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/m5/ctgan/ctgan-m5-20260422_025941/ctgan-m5-3539-20260422_030436.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-SpecializedModels/m5/ctgan/ctgan-m5-20260422_025941/ctgan-m5-3539-20260422_030436.csv") \ No newline at end of file diff --git a/syntheticSuccess/m5/ctgan/ctgan-m5-20260422_025941/ctgan-m5-3539-20260422_030436.csv b/syntheticSuccess/m5/ctgan/ctgan-m5-20260422_025941/ctgan-m5-3539-20260422_030436.csv new file mode 100644 index 0000000000000000000000000000000000000000..dfc2ed8baad7c50b088409a1a4746c06fd536e1d --- /dev/null +++ b/syntheticSuccess/m5/ctgan/ctgan-m5-20260422_025941/ctgan-m5-3539-20260422_030436.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2a46cb75fbe6817954b29f51bf7fb765aad55ff8144065c639707d474f0a2584 +size 732426 diff --git a/syntheticSuccess/m5/ctgan/ctgan-m5-20260422_025941/ctgan_metadata.json b/syntheticSuccess/m5/ctgan/ctgan-m5-20260422_025941/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..78428a5013d9449b0de0dcdca59aeab32ecc52d3 --- /dev/null +++ b/syntheticSuccess/m5/ctgan/ctgan-m5-20260422_025941/ctgan_metadata.json @@ -0,0 +1,152 @@ +{ + "columns": [ + { + "name": "Marital status", + "type": "continuous" + }, + { + "name": "Application mode", + "type": "continuous" + }, + { + "name": "Application order", + "type": "continuous" + }, + { + "name": "Course", + "type": "continuous" + }, + { + "name": "Daytime/evening attendance", + "type": "categorical" + }, + { + "name": "Previous qualification", + "type": "continuous" + }, + { + "name": "Previous qualification (grade)", + "type": "continuous" + }, + { + "name": "Nacionality", + "type": "continuous" + }, + { + "name": "Mother's qualification", + "type": "continuous" + }, + { + "name": "Father's qualification", + "type": "continuous" + }, + { + "name": "Mother's occupation", + "type": "continuous" + }, + { + "name": "Father's occupation", + "type": "continuous" + }, + { + "name": "Admission grade", + "type": "continuous" + }, + { + "name": "Displaced", + "type": "categorical" + }, + { + "name": "Educational special needs", + "type": "categorical" + }, + { + "name": "Debtor", + "type": "categorical" + }, + { + "name": "Tuition fees up to date", + "type": "categorical" + }, + { + "name": "Gender", + "type": "categorical" + }, + { + "name": "Scholarship holder", + "type": "categorical" + }, + { + "name": "Age at enrollment", + "type": "continuous" + }, + { + "name": "International", + "type": "categorical" + }, + { + "name": "Curricular units 1st sem (credited)", + "type": "continuous" + }, + { + "name": "Curricular units 1st sem (enrolled)", + "type": "continuous" + }, + { + "name": "Curricular units 1st sem (evaluations)", + "type": "continuous" + }, + { + "name": "Curricular units 1st sem (approved)", + "type": "continuous" + }, + { + "name": "Curricular units 1st sem (grade)", + "type": "continuous" + }, + { + "name": "Curricular units 1st sem (without evaluations)", + "type": "continuous" + }, + { + "name": "Curricular units 2nd sem (credited)", + "type": "continuous" + }, + { + "name": "Curricular units 2nd sem (enrolled)", + "type": "continuous" + }, + { + "name": "Curricular units 2nd sem (evaluations)", + "type": "continuous" + }, + { + "name": "Curricular units 2nd sem (approved)", + "type": "continuous" + }, + { + "name": "Curricular units 2nd sem (grade)", + "type": "continuous" + }, + { + "name": "Curricular units 2nd sem (without evaluations)", + "type": "continuous" + }, + { + "name": "Unemployment rate", + "type": "continuous" + }, + { + "name": "Inflation rate", + "type": "continuous" + }, + { + "name": "GDP", + "type": "continuous" + }, + { + "name": "Target", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/syntheticSuccess/m5/ctgan/ctgan-m5-20260422_025941/gen_20260422_030436.log b/syntheticSuccess/m5/ctgan/ctgan-m5-20260422_025941/gen_20260422_030436.log new file mode 100644 index 0000000000000000000000000000000000000000..e548ee05f08c8543b43cd64842d53e147bc996d5 --- /dev/null +++ b/syntheticSuccess/m5/ctgan/ctgan-m5-20260422_025941/gen_20260422_030436.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:83da843b93523372d62d770c2a6b490e858e5484f03a288b994fe0b79a163c91 +size 292 diff --git a/syntheticSuccess/m5/ctgan/ctgan-m5-20260422_025941/input_snapshot.json b/syntheticSuccess/m5/ctgan/ctgan-m5-20260422_025941/input_snapshot.json new file 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b/syntheticSuccess/m5/ctgan/ctgan-m5-20260422_025941/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,758 @@ +{ + "dataset_id": "m5", + "target_column": "Target", + "task_type": "classification", + "columns": [ + { + "name": "Marital status", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.001695, + "example_values": [ + "1", + "2", + "4", + "5", + "3" + ] + } + }, + { + "name": "Application mode", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 18, + "unique_ratio": 0.005086, + "example_values": [ + "43", + "17", + "1", + "39", + "44" + ] + } + }, + { + "name": "Application order", + "role": "feature", + "semantic_type": "numeric", 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"/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m5/realtabformer/rtf-m5-20260330_005221/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m5/realtabformer/rtf-m5-20260330_005221/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m5/realtabformer/rtf-m5-20260330_005221/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m5/realtabformer/rtf-m5-20260330_005221/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m5/realtabformer/rtf-m5-20260330_005221/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/m5/realtabformer/rtf-m5-20260330_005221/train_20260330_005222.log b/syntheticSuccess/m5/realtabformer/rtf-m5-20260330_005221/train_20260330_005222.log new file mode 100644 index 0000000000000000000000000000000000000000..987d9c8fdd4f62c19bfb3cc34b94491a07779c8f --- /dev/null +++ b/syntheticSuccess/m5/realtabformer/rtf-m5-20260330_005221/train_20260330_005222.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0446d6f95323eb5e868906c8c3400cf9e1d732b1c3521e9e4dc62d803805b099 +size 789632 diff --git a/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/_tabddpm_sample.py b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/_tabddpm_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..6e8cc58deac3c7d4a7ea2ad808ff5e28fc16cd23 --- /dev/null +++ b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_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 3539 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/m5/tabddpm/tabddpm-m5-20260424_033725/config_sample_20260424_034106.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/m5/tabddpm/tabddpm-m5-20260424_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/m5/tabddpm/tabddpm-m5-20260424_033725/tabddpm-m5-3539-20260424_034106.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/m5/tabddpm/tabddpm-m5-20260424_033725/tabddpm-m5-3539-20260424_034106.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/_tabddpm_train.py b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..6bd0dfcd1cc4c8847c3e177c85731f0aee7d6d30 --- /dev/null +++ b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_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/m5/tabddpm/tabddpm-m5-20260424_033725/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/m5/tabddpm/tabddpm-m5-20260424_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/m5/tabddpm/tabddpm-m5-20260424_033725/config.toml b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..53eda911241faac2511f571b2a4bf000902a1e40 --- /dev/null +++ b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/m5/tabddpm/tabddpm-m5-20260424_033725/output" +real_data_path = "/work/output-SpecializedModels/m5/tabddpm/tabddpm-m5-20260424_033725/data" +model_type = "mlp" +num_numerical_features = 28 +device = "cuda:0" + +[model_params] +d_in = 36 +num_classes = 3 +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/m5/tabddpm/tabddpm-m5-20260424_033725/config_sample_20260424_034106.toml b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/config_sample_20260424_034106.toml new file mode 100644 index 0000000000000000000000000000000000000000..00ca8ba41e6309dd857986fdc9852ce4cfc34af3 --- /dev/null +++ b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/config_sample_20260424_034106.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/m5/tabddpm/tabddpm-m5-20260424_033725/output" +real_data_path = "/work/output-SpecializedModels/m5/tabddpm/tabddpm-m5-20260424_033725/data" +model_type = "mlp" +num_numerical_features = 28 +device = "cuda:0" + +[model_params] +d_in = 36 +num_classes = 3 +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 = 3539 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/data/X_cat_train.npy b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..528ff12a2de8c7f3105c034c67931809936ef661 --- /dev/null +++ b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19a03fbf6df4a4eac29023c548b18d1662eebbb5b7eb090384d305990fa27da7 +size 226624 diff --git a/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/data/X_num_train.npy b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/data/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..4a685e0fa3dc253b1154f19a4314f24095c2351f --- /dev/null +++ b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/data/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e5692fb1a4675971a5c761d985237b9a5846dd3fbd274cfe101f7f949505ee7d +size 396496 diff --git a/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/data/info.json b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..ab57d51fb5905131daac354f3079fe8418d0d479 --- /dev/null +++ b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/data/info.json @@ -0,0 +1,90 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 28, + "n_cat_features": 8, + "train_size": 3539, + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27 + ], + "cat_col_idx": [ + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35 + ], + "target_col_idx": [ + 36 + ], + "column_names": [ + "Marital status", + "Application mode", + "Application order", + "Course", + "Previous qualification", + "Previous qualification (grade)", + "Nacionality", + "Mother's qualification", + "Father's qualification", + "Mother's occupation", + "Father's occupation", + "Admission grade", + "Age at enrollment", + "Curricular units 1st sem (credited)", + "Curricular units 1st sem (enrolled)", + "Curricular units 1st sem (evaluations)", + "Curricular units 1st sem (approved)", + "Curricular units 1st sem (grade)", + "Curricular units 1st sem (without evaluations)", + "Curricular units 2nd sem (credited)", + "Curricular units 2nd sem (enrolled)", + "Curricular units 2nd sem (evaluations)", + "Curricular units 2nd sem (approved)", + "Curricular units 2nd sem (grade)", + "Curricular units 2nd sem (without evaluations)", + "Unemployment rate", + "Inflation rate", + "GDP", + "Daytime/evening attendance", + "Displaced", + "Educational special needs", + "Debtor", + "Tuition fees up to date", + "Gender", + "Scholarship holder", + "International", + "Target" + ], + "num_classes": 3 +} \ No newline at end of file diff --git a/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/data/y_train.npy b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/data/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..9ce696bbabf38f3885ae44f1f9c4ed08b6da442d --- /dev/null +++ b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/data/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3ab031f3326755e790d2443975f7f66ce7f45ca0de47ccdcf9f6d8bfd8a6b15f +size 28440 diff --git a/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/gen_20260424_034106.log b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/gen_20260424_034106.log new file mode 100644 index 0000000000000000000000000000000000000000..184bf6909439e0df2795add455afddda6ddcaec8 --- /dev/null +++ b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/gen_20260424_034106.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8084159720dca3ecae90dac850ce69d77b741680d61f6e0c681a6013e8bda450 +size 84291 diff --git a/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/input_snapshot.json b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0e63af4a8a9ca4623224dbfcc37c2ef120bc056a --- /dev/null +++ b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m5", + "model": "tabddpm", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m5/m5-train.csv", + "exists": true, + "size": 422717, + "sha256": "012f009ed84b309df0bf0da0669101c48652c390666cb59f9a07341a16b7056f" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m5/m5-val.csv", + "exists": true, + "size": 53889, + "sha256": "b9b623a7cea9350fc17384754b26aba373ab6c1914b7c0efb7a8a21ad5ac1557" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m5/m5-test.csv", + "exists": true, + "size": 53943, + "sha256": "696cfc46d2e611ee56a5419f4496b758f643f49f3386d4181296783760117c8c" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m5/m5-dataset_profile.json", + "exists": true, + "size": 14974, + "sha256": "6ca9a300883081c4197534dd44e5e37df852ef129b5c06666629d8dd8270af0d" + }, + "contract_json": { + "path": 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b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/output/X_num_unnorm.npy new file mode 100644 index 0000000000000000000000000000000000000000..cc35b7ca7a9ae92104adbb6eed7b75781ecde9e2 --- /dev/null +++ b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/output/X_num_unnorm.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9dba5e6028aa831d114c1a5922905f5eb511f3255242928ae2edbfdf6d025d5b +size 792864 diff --git a/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/output/config.toml b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/output/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..00ca8ba41e6309dd857986fdc9852ce4cfc34af3 --- /dev/null +++ b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/output/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/m5/tabddpm/tabddpm-m5-20260424_033725/output" +real_data_path = "/work/output-SpecializedModels/m5/tabddpm/tabddpm-m5-20260424_033725/data" +model_type = "mlp" +num_numerical_features = 28 +device = "cuda:0" + +[model_params] +d_in = 36 +num_classes = 3 +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 = 3539 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/output/info.json b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/output/info.json new file mode 100644 index 0000000000000000000000000000000000000000..ab57d51fb5905131daac354f3079fe8418d0d479 --- /dev/null +++ b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/output/info.json @@ -0,0 +1,90 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 28, + "n_cat_features": 8, + "train_size": 3539, + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27 + ], + "cat_col_idx": [ + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35 + ], + "target_col_idx": [ + 36 + ], + "column_names": [ + "Marital status", + "Application mode", + "Application order", + "Course", + "Previous qualification", + "Previous qualification (grade)", + "Nacionality", + "Mother's qualification", + "Father's qualification", + "Mother's occupation", + "Father's occupation", + "Admission grade", + "Age at enrollment", + "Curricular units 1st sem (credited)", + "Curricular units 1st sem (enrolled)", + "Curricular units 1st sem (evaluations)", + "Curricular units 1st sem (approved)", + "Curricular units 1st sem (grade)", + "Curricular units 1st sem (without evaluations)", + "Curricular units 2nd sem (credited)", + "Curricular units 2nd sem (enrolled)", + "Curricular units 2nd sem (evaluations)", + "Curricular units 2nd sem (approved)", + "Curricular units 2nd sem (grade)", + "Curricular units 2nd sem (without evaluations)", + "Unemployment rate", + "Inflation rate", + "GDP", + "Daytime/evening attendance", + "Displaced", + "Educational special needs", + "Debtor", + "Tuition fees up to date", + "Gender", + "Scholarship holder", + "International", + "Target" + ], + "num_classes": 3 +} \ No newline at end of file diff --git a/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/output/loss.csv b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/output/loss.csv new file mode 100644 index 0000000000000000000000000000000000000000..d819bb4a17511ac4085f4b20f4ffb2d5ef1d9d45 --- /dev/null +++ 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0000000000000000000000000000000000000000..1edd855af40e2982d20e7325db1e9f08284bcaf9 --- /dev/null +++ b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/output/model_ema.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:15da255f2bfbfe3fd4b7231fa63861329a6065239cbd64124e872e64c646de32 +size 602658 diff --git a/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/output/y_train.npy b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/output/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..2463693fa788b943bd0430cb6206c2b513beb656 --- /dev/null +++ b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/output/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e743773a10073afcf2f08dfbde17180f96ea9812524f231e49f7212d9c4f65f +size 28440 diff --git a/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/public_gate/normalized_schema_snapshot.json 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100644 index 0000000000000000000000000000000000000000..e8ab2f9099af3c9a54822344f8a4e314fa961187 --- /dev/null +++ b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/tabddpm-m5-3539-20260424_034106.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f6161df59302e969ac0e7d7dfa25cb65fe821fc4ba6233d67d28f6226224d673 +size 754080 diff --git a/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/train_20260424_033725.log b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/train_20260424_033725.log new file mode 100644 index 0000000000000000000000000000000000000000..abf9b2de993679060f57a36947dec2bce9a3f365 --- /dev/null +++ b/syntheticSuccess/m5/tabddpm/tabddpm-m5-20260424_033725/train_20260424_033725.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cd2c67dbf47108b24d1a90e9de0b63409a9b884e2906f462cf37e4711c98ed61 +size 873 diff --git a/syntheticSuccess/m5/tabpfgen/tabpfgen-m5-20260422_200335/_tabpfgen_generate.py b/syntheticSuccess/m5/tabpfgen/tabpfgen-m5-20260422_200335/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..b0b4750975b183345b1ef759525ec7c1fbe4bf91 --- /dev/null +++ b/syntheticSuccess/m5/tabpfgen/tabpfgen-m5-20260422_200335/_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/output-SpecializedModels/m5/tabpfgen/tabpfgen-m5-20260422_200335/staged/public/train.csv") +target_col = "Target" + +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(3539) + +# 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/output-SpecializedModels/m5/tabpfgen/tabpfgen-m5-20260422_200335/tabpfgen-m5-3539-20260422_200336.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-SpecializedModels/m5/tabpfgen/tabpfgen-m5-20260422_200335/tabpfgen-m5-3539-20260422_200336.csv") diff --git a/syntheticSuccess/m5/tabpfgen/tabpfgen-m5-20260422_200335/gen_20260422_200336.log b/syntheticSuccess/m5/tabpfgen/tabpfgen-m5-20260422_200335/gen_20260422_200336.log new file mode 100644 index 0000000000000000000000000000000000000000..fae09de7950860a250ad9061382e3322f8ac3b5c --- /dev/null +++ b/syntheticSuccess/m5/tabpfgen/tabpfgen-m5-20260422_200335/gen_20260422_200336.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:23fa57b6d1c42b7a1d99443ef2aa04ada11a213b6f360cb5e38c7ef1463c78c2 +size 592 diff --git a/syntheticSuccess/m5/tabpfgen/tabpfgen-m5-20260422_200335/input_snapshot.json b/syntheticSuccess/m5/tabpfgen/tabpfgen-m5-20260422_200335/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..3113f538dabc9cfd3ea70cc3b6426de07065106b --- /dev/null +++ b/syntheticSuccess/m5/tabpfgen/tabpfgen-m5-20260422_200335/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m5", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m5/m5-train.csv", + 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\ No newline at end of file diff --git a/syntheticSuccess/m5/tabpfgen/tabpfgen-m5-20260422_200335/tabpfgen-m5-3539-20260422_200336.csv b/syntheticSuccess/m5/tabpfgen/tabpfgen-m5-20260422_200335/tabpfgen-m5-3539-20260422_200336.csv new file mode 100644 index 0000000000000000000000000000000000000000..ff427e6802d69bee4ca9579f70793228dc325de9 --- /dev/null +++ b/syntheticSuccess/m5/tabpfgen/tabpfgen-m5-20260422_200335/tabpfgen-m5-3539-20260422_200336.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:36c13e90f96f15b42b02401fa7b5623c5b85b92554fe699a9843033a0b8ef905 +size 1408203 diff --git a/syntheticSuccess/m5/tabpfgen/tabpfgen-m5-20260422_200335/tabpfgen_meta.json b/syntheticSuccess/m5/tabpfgen/tabpfgen-m5-20260422_200335/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..6151fc5241047c8d93afe128c45d3e82a9c640ba --- /dev/null +++ b/syntheticSuccess/m5/tabpfgen/tabpfgen-m5-20260422_200335/tabpfgen_meta.json @@ -0,0 +1,8 @@ +{ + 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a/syntheticSuccess/m5/tabsyn/tabsyn-m5-20260421_023648/_tabsyn_sample.py b/syntheticSuccess/m5/tabsyn/tabsyn-m5-20260421_023648/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..3c3aed174f72ec9b097a36e255abd77a09223bc4 --- /dev/null +++ b/syntheticSuccess/m5/tabsyn/tabsyn-m5-20260421_023648/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/m5/tabsyn/tabsyn-m5-20260421_023648" +dataname = "tabsyn_m5" +output_csv = "/work/output-SpecializedModels/m5/tabsyn/tabsyn-m5-20260421_023648/tabsyn-m5-3539-20260421_034347.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 3539 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/syntheticSuccess/m5/tabsyn/tabsyn-m5-20260421_023648/_tabsyn_train.py b/syntheticSuccess/m5/tabsyn/tabsyn-m5-20260421_023648/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..9d31a3720f8251ee8c73183f1f11370eddb68b3f --- /dev/null +++ b/syntheticSuccess/m5/tabsyn/tabsyn-m5-20260421_023648/_tabsyn_train.py @@ -0,0 +1,62 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/m5/tabsyn/tabsyn-m5-20260421_023648" +dataname = "tabsyn_m5" +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") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/syntheticSuccess/m5/tabsyn/tabsyn-m5-20260421_023648/data/tabsyn_m5/X_cat_test.npy 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a/syntheticSuccess/m5/tabsyn/tabsyn-m5-20260421_023648/synthetic/tabsyn_m5/real.csv b/syntheticSuccess/m5/tabsyn/tabsyn-m5-20260421_023648/synthetic/tabsyn_m5/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..463aee5471e4ff3a0f39735ae1aaa5bc44145c7d --- /dev/null +++ b/syntheticSuccess/m5/tabsyn/tabsyn-m5-20260421_023648/synthetic/tabsyn_m5/real.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0b5927126f444d8e1c1e3aaeabe01258d186730d1c9792d23025495f0493b09e +size 449149 diff --git a/syntheticSuccess/m5/tabsyn/tabsyn-m5-20260421_023648/synthetic/tabsyn_m5/test.csv b/syntheticSuccess/m5/tabsyn/tabsyn-m5-20260421_023648/synthetic/tabsyn_m5/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..9f0c16797a654b6b3275c5e3e02990252dc67043 --- /dev/null +++ b/syntheticSuccess/m5/tabsyn/tabsyn-m5-20260421_023648/synthetic/tabsyn_m5/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:45f46fa5e2369360610fabdf662241c35fe0f20b7fa8704f079a02382f08350f +size 50978 diff --git a/syntheticSuccess/m5/tabsyn/tabsyn-m5-20260421_023648/tabsyn-m5-3539-20260421_034347.csv b/syntheticSuccess/m5/tabsyn/tabsyn-m5-20260421_023648/tabsyn-m5-3539-20260421_034347.csv new file mode 100644 index 0000000000000000000000000000000000000000..1399e3de3dba758afcbe29c4a7abbce08fbbaa4a --- /dev/null +++ b/syntheticSuccess/m5/tabsyn/tabsyn-m5-20260421_023648/tabsyn-m5-3539-20260421_034347.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dacfc55296e10679e036541eda20b4a2bec37ec757d061412a6a53d3de9dab1c +size 657446 diff --git a/syntheticSuccess/m5/tabsyn/tabsyn-m5-20260421_023648/train_20260421_023649.log b/syntheticSuccess/m5/tabsyn/tabsyn-m5-20260421_023648/train_20260421_023649.log new file mode 100644 index 0000000000000000000000000000000000000000..bdd47874441e1201bd259db961f8ca9b61cac673 --- /dev/null +++ b/syntheticSuccess/m5/tabsyn/tabsyn-m5-20260421_023648/train_20260421_023649.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:82763f9738277fe496bcbbc37835d7f4e8b1b82b91352dfee1559c498c7ae74d +size 2417333 diff --git a/syntheticSuccess/m5/tvae/tvae-m5-20260419_191713/_tvae_generate.py b/syntheticSuccess/m5/tvae/tvae-m5-20260419_191713/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..9b708bea5ef110187644e3ca8e52093d82931965 --- /dev/null +++ b/syntheticSuccess/m5/tvae/tvae-m5-20260419_191713/_tvae_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.tvae import TVAE +model = TVAE.load("/work/output-SpecializedModels/m5/tvae/tvae-m5-20260419_191713/models_300epochs/tvae_300epochs.pt") +total = 3539 +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/m5/tvae/tvae-m5-20260419_191713/tvae-m5-3539-20260419_192224.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-SpecializedModels/m5/tvae/tvae-m5-20260419_191713/tvae-m5-3539-20260419_192224.csv") diff --git a/syntheticSuccess/m5/tvae/tvae-m5-20260419_191713/_tvae_train.py b/syntheticSuccess/m5/tvae/tvae-m5-20260419_191713/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c247bd237098fddcb93cdc1171b8ca0b4b54a9bd --- /dev/null +++ b/syntheticSuccess/m5/tvae/tvae-m5-20260419_191713/_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/m5/tvae/tvae-m5-20260419_191713/staged/public/train.csv" +meta_path = "/work/output-SpecializedModels/m5/tvae/tvae-m5-20260419_191713/tvae_metadata.json" +save_path = "/work/output-SpecializedModels/m5/tvae/tvae-m5-20260419_191713/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/m5/tvae/tvae-m5-20260419_191713/gen_20260419_192224.log b/syntheticSuccess/m5/tvae/tvae-m5-20260419_191713/gen_20260419_192224.log new file mode 100644 index 0000000000000000000000000000000000000000..4db969efbd116295bf57ff5865294772ecc91f3e --- /dev/null +++ b/syntheticSuccess/m5/tvae/tvae-m5-20260419_191713/gen_20260419_192224.log 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b/syntheticSuccess/m5/tvae/tvae-m5-20260419_191713/models_300epochs/train_20260419_191714.log new file mode 100644 index 0000000000000000000000000000000000000000..1f09a0338cf85c885180487a896bf557048dba13 --- /dev/null +++ b/syntheticSuccess/m5/tvae/tvae-m5-20260419_191713/models_300epochs/train_20260419_191714.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c688706fd1cb987b0b13d5b9c4871df93c01cde18a2c0bc667def189e47b6a36 +size 170 diff --git a/syntheticSuccess/m5/tvae/tvae-m5-20260419_191713/models_300epochs/tvae_300epochs.pt b/syntheticSuccess/m5/tvae/tvae-m5-20260419_191713/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..0da92b8a7654dca1c218fac385ea02e955e8accf --- /dev/null +++ b/syntheticSuccess/m5/tvae/tvae-m5-20260419_191713/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:86f4ffc1302df807c0393893fcff8b8276b165dffeb7a8e92208552f0442d976 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