diff --git a/.gitattributes b/.gitattributes index 0b9fce10beb6c597f5e88bc0519f66277c33ef5a..ae819984d34ede59fdedc19366c22274883fc389 100644 --- a/.gitattributes +++ b/.gitattributes @@ -5420,3 +5420,34 @@ SynthData0523/main/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet-c7-10368-202 SynthData0523/main/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet_model.pkl filter=lfs diff=lfs merge=lfs -text SynthData0523/main/c7/bayesnet/bayesnet-c7-20260321_061816/gen_20260321_061903.log filter=lfs diff=lfs merge=lfs -text SynthData0523/main/c7/bayesnet/bayesnet-c7-20260321_061816/gen_20260330_065316.log filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/test.csv filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/train.csv filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/val.csv filter=lfs diff=lfs merge=lfs -text 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b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260321_061816/train_20260321_061816.log new file mode 100644 index 0000000000000000000000000000000000000000..06c1d73f326de078e49cf2ea2fea75a87e4c5577 --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260321_061816/train_20260321_061816.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ac5d75bda1da96923ed90d790fc4743707eff8968b962898056f8d855f54505 +size 465 diff --git a/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/_bayesnet_generate.py b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..78641417cc9d3360992d592f1855feaa882ce648 --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +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-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031038/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(10368) +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 = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet-c7-10368-20260429_031053.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet-c7-10368-20260429_031053.csv") diff --git a/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/_bayesnet_train.py b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..11a124de915aefe335a4cace17f610c3d2b370c2 --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/_bayesnet_train.py @@ -0,0 +1,133 @@ + +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-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031038/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-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031038/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-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031038/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 +max_cat_levels = 256 +if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 +if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels} " + f"(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": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.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] = s2.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) +enc_fit = enc +if len(enc) > 120000: + enc_fit = enc.sample(n=120000, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +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-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet_model.pkl") diff --git 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0000000000000000000000000000000000000000..4c8e5e0c5031fc7e8e25213132c1231950ec29da --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b37f6b2ef5257f40bd826ac956749881f0f474362bdb56e8c5728ad629242e3a +size 847349 diff --git a/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/staged/public/val.csv b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..55792963756b1ae35b4d79dee11881372962b8d5 --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eff6dec27c3740661a1ae84dea391d690dfb60342bfd5d7527b903fdd6009780 +size 106192 diff --git a/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/train_20260429_031038.log b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/train_20260429_031038.log new file mode 100644 index 0000000000000000000000000000000000000000..9104255c0a1b829752d5d6a5b2aff7a04598814b --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/train_20260429_031038.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4111c669d5532e874b7f7b68b102dfe387746d6756cffa5d3ad8d7cb0ea7f147 +size 3741 diff --git a/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/_bayesnet_generate.py b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..2f68468ec9c3fb4110422420ec5c9dc1886f8c18 --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +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-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/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(10368) +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 = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet-c7-10368-20260429_031608.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet-c7-10368-20260429_031608.csv") diff --git a/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/_bayesnet_train.py b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..4b564a2f8e858b3932ee00b96d13a5fd46926031 --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/_bayesnet_train.py @@ -0,0 +1,133 @@ + +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-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/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-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/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-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/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 +max_cat_levels = 256 +if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 +if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels} " + f"(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": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.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] = s2.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) +enc_fit = enc +if len(enc) > 120000: + enc_fit = enc.sample(n=120000, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +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-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_model.pkl") diff --git a/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet-c7-10368-20260429_031608.csv b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet-c7-10368-20260429_031608.csv new file mode 100644 index 0000000000000000000000000000000000000000..524cf9d802c20f757784f989c1f23f22ff5f9f48 --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet-c7-10368-20260429_031608.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:39ab8a39e164ff03b6176d8e2ecb2cd4bcaad07a36ece74636a690a5b5fc0128 +size 848576 diff --git a/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_coltypes.json b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..6408be4c6bd3187fdebffa9c5b618f974690b33d --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_coltypes.json @@ -0,0 +1,41 @@ +{ + "columns": [ + { + "name": "parents", + "type": "categorical" + }, + { + "name": "has_nurs", + "type": "categorical" + }, + { + "name": "form", + "type": "categorical" + }, + { + "name": "children", + "type": "categorical" + }, + { + "name": "housing", + "type": "categorical" + }, + { + "name": "finance", + "type": "categorical" + }, + { + "name": "social", + "type": "categorical" + }, + { + "name": "health", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_model.pkl b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..291862b34113b529e8bf02f930bf59f1fe805dee --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:342563385d822291efea03ef34858a3fa7a9c37695d1d9aa761ad616bb14bd4f +size 5680 diff --git a/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/const_cols.json b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/gen_20260429_031608.log b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/gen_20260429_031608.log new file mode 100644 index 0000000000000000000000000000000000000000..769d74dae5c7714ab4945767de4b0ca18029f370 --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/gen_20260429_031608.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:29ad479e9d4fb60e922c5012efa4a0a98dbd7d317e09898bbbf71679ac4fc146 +size 3664 diff --git a/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/input_snapshot.json b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..b61f053445c3620fe2db11628eda0f82572c1916 --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c7", + "model": "bayesnet", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv", + "exists": true, + "size": 857718, + "sha256": "0ec97b49cecfd452f07551a63db7b812b5998a1e37101eae82255d00aa6a6243" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-val.csv", + "exists": true, + "size": 107489, + "sha256": "4501bb2be19f7e13b7ff5e9dedd74e3dd42f2cafc8cefd5435bda61fc974a769" + }, + "test_csv": { + 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100644 index 0000000000000000000000000000000000000000..3ea4859ec2aa245a19531b254f45d87237c23069 --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,183 @@ +{ + "dataset_id": "c7", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "less_conv", + "convenient", + "critical" + ] + } + }, + { + "name": "finance", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000193, + "example_values": [ + "convenient", + "inconv" + ] + } + }, + { + "name": "social", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + 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b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c7", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-test.csv" + } +} \ No newline at end of file diff --git a/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/public_gate/staged_input_manifest.json b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..80c958d28ddeaeeb4e6d6d3e3a2bbaa0e488979d --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/public_gate/staged_input_manifest.json @@ -0,0 +1,188 @@ +{ + "dataset_id": "c7", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "less_conv", + "convenient", + "critical" + ] + } + }, + { + "name": "finance", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000193, + "example_values": [ + "convenient", + "inconv" + ] + } + }, + { + "name": "social", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ] +} \ No newline at end of file diff --git a/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/runtime_result.json b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..8dff74eaa82d23969060956c35c4b590c3123652 --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/runtime_result.json @@ -0,0 +1,15 @@ +{ + "dataset_id": "c7", + "model": "bayesnet", + "run_id": "bayesnet-c7-20260429_031537", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet-c7-10368-20260429_031608.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_model.pkl" + } +} \ No newline at end of file diff --git a/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/bayesnet/adapter_report.json b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/bayesnet/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..bb99555631b53b5961bafee3429625d6bcf8d4e4 --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/bayesnet/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/staged/bayesnet/model_input_manifest.json" +} \ No newline at end of file diff --git a/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/bayesnet/adapter_transforms_applied.json b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/bayesnet/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/bayesnet/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/bayesnet/model_input_manifest.json b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/bayesnet/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..cc9e5b8d709ad50c0dd67d7fd5bccb38aff83173 --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/bayesnet/model_input_manifest.json @@ -0,0 +1,190 @@ +{ + "dataset_id": "c7", + "model": "bayesnet", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "less_conv", + "convenient", + "critical" + ] + } + }, + { + "name": "finance", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000193, + "example_values": [ + "convenient", + "inconv" + ] + } + }, + { + "name": "social", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/staged_features.json b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..0e23df6bcfb7ecfd44dfefc3d0ca0bf6b6aebc60 --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/staged_features.json @@ -0,0 +1,47 @@ +[ + { + "feature_name": "parents", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "has_nurs", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "form", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "housing", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "finance", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "social", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "health", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/test.csv b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..cb9d7081ca745e9eed28a35168872d450c4a2a44 --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2042076337d5c37c6476e6bca2bd33cb5a171450c27894534ef50ac223256058 +size 106030 diff --git a/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/train.csv b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..4c8e5e0c5031fc7e8e25213132c1231950ec29da --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b37f6b2ef5257f40bd826ac956749881f0f474362bdb56e8c5728ad629242e3a +size 847349 diff --git a/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/val.csv b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..55792963756b1ae35b4d79dee11881372962b8d5 --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eff6dec27c3740661a1ae84dea391d690dfb60342bfd5d7527b903fdd6009780 +size 106192 diff --git a/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/train_20260429_031537.log b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/train_20260429_031537.log new file mode 100644 index 0000000000000000000000000000000000000000..5bdc045b3f9046a909b43dabaf0f8a3f80061ee2 --- /dev/null +++ b/SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/train_20260429_031537.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1788e94eba6e542f6afdf6c0054a1d521deae3fba20e72212282c5bd06f20d84 +size 3741 diff --git a/SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/_ctgan_generate.py b/SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..07dcc9743b472953e91f3e07ecf5bba82a9e4e69 --- /dev/null +++ b/SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/_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-Benchmark-trainonly-v1/c7/ctgan/ctgan-c7-20260429_032054/models_300epochs/ctgan_300epochs.pt") +total = 10368 +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-Benchmark-trainonly-v1/c7/ctgan/ctgan-c7-20260429_032054/ctgan-c7-10368-20260429_032437.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/c7/ctgan/ctgan-c7-20260429_032054/ctgan-c7-10368-20260429_032437.csv") \ No newline at end of file diff --git a/SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/ctgan-c7-10368-20260429_032437.csv b/SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/ctgan-c7-10368-20260429_032437.csv new file mode 100644 index 0000000000000000000000000000000000000000..cf988bee2b95867429d9dbc730c1922a9bf65abc --- /dev/null +++ b/SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/ctgan-c7-10368-20260429_032437.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6292ae1576f8682712eb05a628de61384dab5cf211e620852f27aaea02e87599 +size 840588 diff --git a/SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/ctgan_metadata.json b/SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..0bf809d7a48c0bcb44098f15f0d72afe908535e3 --- /dev/null +++ b/SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/ctgan_metadata.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2f309bc7a17cfc24e527dcb0e3b9c4d21775cc90e61487e45e5d2f723844e57e +size 610