diff --git a/syntheticSuccess/m12/arf/arf-m12-20260422_055912/_arf_generate.py b/syntheticSuccess/m12/arf/arf-m12-20260422_055912/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..46cc6d6ace0a5e9f980a1707f16352fa089ed8a8 --- /dev/null +++ b/syntheticSuccess/m12/arf/arf-m12-20260422_055912/_arf_generate.py @@ -0,0 +1,23 @@ +import pickle +import pandas as pd + +n_target = int(95512) +with open("/work/output-SpecializedModels/m12/arf/arf-m12-20260422_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/m12/arf/arf-m12-20260422_055912/arf-m12-95512-20260422_084037.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-SpecializedModels/m12/arf/arf-m12-20260422_055912/arf-m12-95512-20260422_084037.csv") diff --git a/syntheticSuccess/m12/arf/arf-m12-20260422_055912/_arf_train.py b/syntheticSuccess/m12/arf/arf-m12-20260422_055912/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..00049e96ca31a6b27c9b6df1641aecd560dd3ff7 --- /dev/null +++ b/syntheticSuccess/m12/arf/arf-m12-20260422_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/m12/arf/arf-m12-20260422_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/m12/arf/arf-m12-20260422_055912/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-SpecializedModels/m12/arf/arf-m12-20260422_055912/arf_model.pkl") diff --git a/syntheticSuccess/m12/arf/arf-m12-20260422_055912/arf-m12-95512-20260422_084037.csv b/syntheticSuccess/m12/arf/arf-m12-20260422_055912/arf-m12-95512-20260422_084037.csv new file mode 100644 index 0000000000000000000000000000000000000000..13121ee795727aade2aca8f0c77b3457b0f1d67d --- /dev/null +++ b/syntheticSuccess/m12/arf/arf-m12-20260422_055912/arf-m12-95512-20260422_084037.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cee010aea59c87ea325c6eb0247c7912ac6d8d559a2720e8816d9fecf5039368 +size 29705829 diff --git a/syntheticSuccess/m12/arf/arf-m12-20260422_055912/arf_model.pkl b/syntheticSuccess/m12/arf/arf-m12-20260422_055912/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..e8af503e16e37042583bd63340f387c5f1365876 --- /dev/null +++ b/syntheticSuccess/m12/arf/arf-m12-20260422_055912/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:12c4324079b9e8fb2fa9e4a617c11040e4304d4c9fa84e80fe77798e0d726e03 +size 1102812550 diff --git a/syntheticSuccess/m12/arf/arf-m12-20260422_055912/gen_20260422_084037.log b/syntheticSuccess/m12/arf/arf-m12-20260422_055912/gen_20260422_084037.log new file mode 100644 index 0000000000000000000000000000000000000000..29eec91b04a92fc33191831b08df4197c4d45e54 --- /dev/null +++ b/syntheticSuccess/m12/arf/arf-m12-20260422_055912/gen_20260422_084037.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:44060c1484159698e4363ec7762499c8717d9e2a8b223842ac4711fcf9eeae3b +size 3937 diff --git a/syntheticSuccess/m12/arf/arf-m12-20260422_055912/input_snapshot.json b/syntheticSuccess/m12/arf/arf-m12-20260422_055912/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..07fb8ddd79a6dadddef05169159c3e23dc27744d --- /dev/null +++ b/syntheticSuccess/m12/arf/arf-m12-20260422_055912/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m12", + "model": "arf", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m12/m12-train.csv", + "exists": true, + "size": 13165359, + "sha256": "38507849456d473e77f70bde2e03dd69c9694d3640debeb0e1dd3a99b251862e" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m12/m12-val.csv", + "exists": true, + "size": 1646795, + "sha256": "7efe1eb3cfa68fbb779e8cb8490109c774b8241e5896a77d083544f04ccf6c66" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m12/m12-test.csv", + "exists": true, + "size": 1646190, + "sha256": "77c539bda6d6db356a6f1eb5d8344449f25d36b5309fa660bec41d855e49cefc" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m12/m12-dataset_profile.json", + "exists": true, + "size": 12659, + "sha256": "d8c17ef1f421dc55e8669ecc08ba8b3d6cbb007f953b59ef6014af322c187cc3" + }, + "contract_json": { + "path": 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"keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 920, + "unique_ratio": 0.009632, + "example_values": [ + "2016-10-15", + "2016-11-09", + "2016-12-02", + "2016-06-21", + "2016-10-23" + ] + } + } + ] +} \ No newline at end of file diff --git a/syntheticSuccess/m12/arf/arf-m12-20260422_055912/public_gate/public_gate_report.json b/syntheticSuccess/m12/arf/arf-m12-20260422_055912/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..bf3bf58f493bcfc411bffebeec97fba57674499b --- /dev/null +++ b/syntheticSuccess/m12/arf/arf-m12-20260422_055912/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m12", + "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", + 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b/syntheticSuccess/m12/arf/arf-m12-20260422_055912/staged/public/staged_features.json @@ -0,0 +1,162 @@ +[ + { + "feature_name": "hotel", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "is_canceled", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "lead_time", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "arrival_date_year", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "arrival_date_month", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "arrival_date_week_number", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "arrival_date_day_of_month", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "stays_in_weekend_nights", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "stays_in_week_nights", + "data_type": "continuous", + "is_target": false + }, + { + 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0000000000000000000000000000000000000000..4c6391252375304252e6705a5d9593e9b8706f8f --- /dev/null +++ b/syntheticSuccess/m12/arf/arf-m12-20260422_055912/train_20260422_055918.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c51e2ba0fce94300ef16ab0e0506900d7c354ba30438ad4e43902c291a321c9 +size 411 diff --git a/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/_bayesnet_generate.py b/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..afaba4f6753523dd17ffa108342b15e5f05600bc --- /dev/null +++ b/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/_bayesnet_generate.py @@ -0,0 +1,75 @@ + +import pickle +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +with open("/work/output-SpecializedModels/m12/bayesnet/bayesnet-m12-20260420_035322/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 {} + +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=95512, show_progress=False) + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +final.to_csv("/work/output-SpecializedModels/m12/bayesnet/bayesnet-m12-20260420_035322/bayesnet-m12-95512-20260420_052116.csv", index=False) +print(f"[BayesNet] Generated 95512 rows -> /work/output-SpecializedModels/m12/bayesnet/bayesnet-m12-20260420_035322/bayesnet-m12-95512-20260420_052116.csv") diff --git a/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/_bayesnet_train.py b/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..ca1566f6c57b5686b865c723edfdc3ad1dfae765 --- /dev/null +++ b/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/_bayesnet_train.py @@ -0,0 +1,93 @@ + +import json +import pickle +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +with open("/work/output-SpecializedModels/m12/bayesnet/bayesnet-m12-20260420_035322/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/m12/bayesnet/bayesnet-m12-20260420_035322/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/m12/bayesnet/bayesnet-m12-20260420_035322/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +max_bins = 10 + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + uniques = sorted(s.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +dag = TreeSearch(enc).estimate(show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +network.fit(enc) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-SpecializedModels/m12/bayesnet/bayesnet-m12-20260420_035322/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/m12/bayesnet/bayesnet-m12-20260420_035322/bayesnet_model.pkl") diff --git a/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/bayesnet-m12-95512-20260420_052116.csv b/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/bayesnet-m12-95512-20260420_052116.csv new file mode 100644 index 0000000000000000000000000000000000000000..3497a282a3bb01f132f70febeed171b2976a533a --- /dev/null +++ b/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/bayesnet-m12-95512-20260420_052116.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8327bd9a68f225365c89d5c865250ab6d4c8ead2e6d181dc1b9c9c6ad60ac417 +size 34560100 diff --git a/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/bayesnet_coltypes.json b/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..c80d8903743c12b46cf44111dd13eb5a41cb11b9 --- /dev/null +++ b/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/bayesnet_coltypes.json @@ -0,0 +1,133 @@ +{ + "columns": [ + { + "name": "hotel", + "type": "categorical" + }, + { + "name": "is_canceled", + "type": "categorical" + }, + { + "name": "lead_time", + "type": "continuous" + }, + { + "name": "arrival_date_year", + "type": "continuous" + }, + { + "name": "arrival_date_month", + "type": "categorical" + }, + { + "name": "arrival_date_week_number", + "type": "continuous" + }, + { + "name": "arrival_date_day_of_month", + "type": "continuous" + }, + { + "name": "stays_in_weekend_nights", + "type": "continuous" + }, + { + "name": "stays_in_week_nights", + "type": "continuous" + }, + { + "name": "adults", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "babies", + "type": "categorical" + }, + { + "name": "meal", + "type": "categorical" + }, + { + "name": "country", + "type": "categorical" + }, + { + "name": "market_segment", + "type": "categorical" + }, + { + "name": "distribution_channel", + "type": "categorical" + }, + { + "name": "is_repeated_guest", + "type": "categorical" + }, + { + "name": "previous_cancellations", + "type": "categorical" + }, + { + "name": "previous_bookings_not_canceled", + "type": "categorical" + }, + { + "name": "reserved_room_type", + "type": "categorical" + }, + { + "name": "assigned_room_type", + "type": "categorical" + }, + { + "name": "booking_changes", + "type": "continuous" + }, + { + "name": "deposit_type", + "type": "categorical" + }, + { + "name": "agent", + "type": "continuous" + }, + { + "name": "company", + "type": "continuous" + }, + { + "name": "days_in_waiting_list", + "type": "continuous" + }, + { + "name": "customer_type", + "type": "categorical" + }, + { + "name": "adr", + "type": "continuous" + }, + { + "name": "required_car_parking_spaces", + "type": "categorical" + }, + { + "name": "total_of_special_requests", + "type": "continuous" + }, + { + "name": "reservation_status", + "type": "categorical" + }, + { + "name": "reservation_status_date", + "type": "categorical" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/bayesnet_model.pkl b/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/bayesnet_model.pkl 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b/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..a8a6df78b1e7df37d82c9f69413e26d4ae848a3c --- /dev/null +++ b/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/staged/public/staged_features.json @@ -0,0 +1,162 @@ +[ + { + "feature_name": "hotel", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "is_canceled", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "lead_time", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "arrival_date_year", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "arrival_date_month", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "arrival_date_week_number", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "arrival_date_day_of_month", + "data_type": "continuous", + 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+ "is_target": false + }, + { + "feature_name": "required_car_parking_spaces", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "total_of_special_requests", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "reservation_status", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "reservation_status_date", + "data_type": "timestamp", + "is_target": false + } +] \ No newline at end of file diff --git a/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/staged/public/test.csv b/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..30c1b4b016bf3e7de99246eb0c12090a548eb9ff --- /dev/null +++ b/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a5f493029a41815df91c3b28f521a2264951567318150110e5c27fa757ebc734 +size 1694120 diff --git a/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/staged/public/train.csv b/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..eb7c1b15cfae80b8f1256e5ab12043201bba9e7c --- /dev/null +++ b/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e2f868c246063628371ded60d767d155528ace18d424271c7271617a8ef4643 +size 13548268 diff --git a/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/staged/public/val.csv b/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..72af4a34a232e0bcfaf78244335bd139c3d0aa2a --- /dev/null +++ b/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ce173da72624b2b531e2d913ada1d29b77c0926be15b8d83a75911a1f5e36679 +size 1694777 diff --git a/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/train_20260420_051859.log b/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/train_20260420_051859.log new file mode 100644 index 0000000000000000000000000000000000000000..2e925a9412a361c3a6f8a4d11ed9b15bc6870976 --- /dev/null +++ b/syntheticSuccess/m12/bayesnet/bayesnet-m12-20260420_035322/train_20260420_051859.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:aa11b18dea436b5f10c982ff02c9729fd0254aa7bf4cb3483aab51f00b3cc9b7 +size 1271 diff --git a/syntheticSuccess/m12/ctgan/ctgan-m12-20260422_031259/_ctgan_generate.py b/syntheticSuccess/m12/ctgan/ctgan-m12-20260422_031259/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..182abd863db74781eaeaa143ba8c148dbba9f643 --- /dev/null +++ b/syntheticSuccess/m12/ctgan/ctgan-m12-20260422_031259/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-SpecializedModels/m12/ctgan/ctgan-m12-20260422_031259/models_300epochs/ctgan_300epochs.pt") +total = 95512 +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/m12/ctgan/ctgan-m12-20260422_031259/ctgan-m12-95512-20260422_063641.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-SpecializedModels/m12/ctgan/ctgan-m12-20260422_031259/ctgan-m12-95512-20260422_063641.csv") \ No newline at end of file diff --git a/syntheticSuccess/m12/ctgan/ctgan-m12-20260422_031259/ctgan-m12-95512-20260422_063641.csv b/syntheticSuccess/m12/ctgan/ctgan-m12-20260422_031259/ctgan-m12-95512-20260422_063641.csv new file mode 100644 index 0000000000000000000000000000000000000000..906a2c29ab3934c43be0c3580dd06460c3cc90f1 --- /dev/null +++ b/syntheticSuccess/m12/ctgan/ctgan-m12-20260422_031259/ctgan-m12-95512-20260422_063641.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:629ffbcf3135f06b0c08662302cb37026a19293bd624e10ea5ae4cf2f6c9ce0e +size 19339101 diff --git a/syntheticSuccess/m12/ctgan/ctgan-m12-20260422_031259/ctgan_metadata.json b/syntheticSuccess/m12/ctgan/ctgan-m12-20260422_031259/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..15abefec626fa7cf1da9be5c202fb549aeb78a4f --- /dev/null +++ b/syntheticSuccess/m12/ctgan/ctgan-m12-20260422_031259/ctgan_metadata.json @@ -0,0 +1,132 @@ +{ + "columns": [ + { + "name": "hotel", + "type": "categorical" + }, + { + "name": "is_canceled", + "type": "categorical" + }, + { + "name": "lead_time", + "type": "continuous" + }, + { + "name": "arrival_date_year", + "type": "continuous" + }, + { + "name": "arrival_date_month", + "type": "categorical" + }, + { + "name": "arrival_date_week_number", + "type": "continuous" + }, + { + "name": "arrival_date_day_of_month", + "type": "continuous" + }, + { + "name": "stays_in_weekend_nights", + "type": "continuous" + }, + { + "name": "stays_in_week_nights", + "type": "continuous" + }, + { + "name": "adults", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "babies", + "type": "categorical" + }, + { + "name": "meal", + "type": "categorical" + }, + { + "name": "country", + "type": "categorical" + }, + { + "name": "market_segment", + "type": "categorical" + }, + { + "name": "distribution_channel", + "type": "categorical" + }, + { + "name": "is_repeated_guest", + "type": "categorical" + }, + { + "name": "previous_cancellations", + "type": "categorical" + }, + { + "name": "previous_bookings_not_canceled", + "type": "categorical" + }, + { + "name": "reserved_room_type", + "type": "categorical" + }, + { + "name": "assigned_room_type", + "type": "categorical" + }, + { + "name": "booking_changes", + "type": "continuous" + }, + { + "name": "deposit_type", + "type": "categorical" + }, + { + "name": "agent", + "type": "continuous" + }, + { + "name": "company", + "type": "continuous" + }, + { + "name": "days_in_waiting_list", + "type": "continuous" + }, + { + "name": "customer_type", + "type": "categorical" + }, + { + "name": "adr", + "type": "continuous" + }, + { + "name": "required_car_parking_spaces", + "type": "categorical" + }, + { + "name": "total_of_special_requests", + "type": "continuous" + }, + { + "name": "reservation_status", + "type": "categorical" + }, + { + "name": "reservation_status_date", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/syntheticSuccess/m12/ctgan/ctgan-m12-20260422_031259/ctgan_train_continuous_imputed.csv b/syntheticSuccess/m12/ctgan/ctgan-m12-20260422_031259/ctgan_train_continuous_imputed.csv new file mode 100644 index 0000000000000000000000000000000000000000..4a6bcb2c49355ace5a58acbde759aa826723446b --- /dev/null +++ b/syntheticSuccess/m12/ctgan/ctgan-m12-20260422_031259/ctgan_train_continuous_imputed.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c6b43b13837fa9a5fb3cf7a1df4e80c2325e80660d808c62b41f1ecfc1d282e2 +size 14051058 diff --git a/syntheticSuccess/m12/ctgan/ctgan-m12-20260422_031259/gen_20260422_063641.log b/syntheticSuccess/m12/ctgan/ctgan-m12-20260422_031259/gen_20260422_063641.log new file mode 100644 index 0000000000000000000000000000000000000000..1b832c79dff609eaaa512ee8511f67fcd5a76f38 --- /dev/null +++ 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a/syntheticSuccess/m12/ctgan/ctgan-m12-20260422_031259/models_300epochs/ctgan_300epochs.pt b/syntheticSuccess/m12/ctgan/ctgan-m12-20260422_031259/models_300epochs/ctgan_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..b394a82fb2a36b67e8d3cfa34189856a3849289a --- /dev/null +++ b/syntheticSuccess/m12/ctgan/ctgan-m12-20260422_031259/models_300epochs/ctgan_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a5a1f4b91c94c401c16dfc60766bb97cd0f5f0c9e41b71dab02e2c85fdaa3aeb +size 29824995 diff --git a/syntheticSuccess/m12/ctgan/ctgan-m12-20260422_031259/models_300epochs/train_20260422_031308.log b/syntheticSuccess/m12/ctgan/ctgan-m12-20260422_031259/models_300epochs/train_20260422_031308.log new file mode 100644 index 0000000000000000000000000000000000000000..8ca65fe011b6534a96e298b0eaacb07ff4f4ac03 --- /dev/null +++ b/syntheticSuccess/m12/ctgan/ctgan-m12-20260422_031259/models_300epochs/train_20260422_031308.log @@ -0,0 +1,3 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b/syntheticSuccess/m12/realtabformer/rtf-m12-20260330_074847/train_20260330_074852.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f15577b5526d660b4908cab8cd29fd7956f3a3d04fd29fad1cf62415a0258d8e +size 22788552 diff --git a/syntheticSuccess/m12/tabsyn/tabsyn-m12-20260420_221746/_tabsyn_sample.py b/syntheticSuccess/m12/tabsyn/tabsyn-m12-20260420_221746/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..b610c1c68620ef474e1d30276acfbf948c5cedae --- /dev/null +++ b/syntheticSuccess/m12/tabsyn/tabsyn-m12-20260420_221746/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/m12/tabsyn/tabsyn-m12-20260420_221746" +dataname = "tabsyn_m12" +output_csv = "/work/output-SpecializedModels/m12/tabsyn/tabsyn-m12-20260420_221746/tabsyn-m12-95512-20260502_045948.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 95512 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/m12/tabsyn/tabsyn-m12-20260420_221746/_tabsyn_train.py b/syntheticSuccess/m12/tabsyn/tabsyn-m12-20260420_221746/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..62ccf8f36c68e2527cc0339ba516c3a245a55937 --- /dev/null +++ b/syntheticSuccess/m12/tabsyn/tabsyn-m12-20260420_221746/_tabsyn_train.py @@ -0,0 +1,62 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/m12/tabsyn/tabsyn-m12-20260420_221746" +dataname = "tabsyn_m12" +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/m12/tabsyn/tabsyn-m12-20260420_221746/data/tabsyn_m12/X_cat_test.npy b/syntheticSuccess/m12/tabsyn/tabsyn-m12-20260420_221746/data/tabsyn_m12/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..798dee3e5e16a2073d95a52d8b9bf4ce0cece016 --- /dev/null +++ b/syntheticSuccess/m12/tabsyn/tabsyn-m12-20260420_221746/data/tabsyn_m12/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1240f7a7798581566a4be9ba36fde3f86bf0bc330426e89820fab6f9d4654062 +size 1528320 diff --git a/syntheticSuccess/m12/tabsyn/tabsyn-m12-20260420_221746/data/tabsyn_m12/X_cat_train.npy b/syntheticSuccess/m12/tabsyn/tabsyn-m12-20260420_221746/data/tabsyn_m12/X_cat_train.npy new file mode 100644 index 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"val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m12/tabsyn/tabsyn-m12-20260420_221746/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m12/tabsyn/tabsyn-m12-20260420_221746/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m12/tabsyn/tabsyn-m12-20260420_221746/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m12/tabsyn/tabsyn-m12-20260420_221746/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/m12/tabsyn/tabsyn-m12-20260420_221746/synthetic/tabsyn_m12/real.csv b/syntheticSuccess/m12/tabsyn/tabsyn-m12-20260420_221746/synthetic/tabsyn_m12/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..1f0366435687875efbce5202fb86f378f9c55c47 --- /dev/null +++ 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100644 index 0000000000000000000000000000000000000000..23bbde6c8ea3b5f5f6b1e967d66026c339a3b9d1 --- /dev/null +++ b/syntheticSuccess/m12/tabsyn/tabsyn-m12-20260420_221746/tabsyn-m12-95512-20260421_022103.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:63768e17492d451932a1df11a172d62e861a8ea3d526b2d630927475a8b1bcba +size 12708334 diff --git a/syntheticSuccess/m12/tabsyn/tabsyn-m12-20260420_221746/tabsyn-m12-95512-20260502_045948.csv b/syntheticSuccess/m12/tabsyn/tabsyn-m12-20260420_221746/tabsyn-m12-95512-20260502_045948.csv new file mode 100644 index 0000000000000000000000000000000000000000..9e39e6d1a71e3c9e52fd6ffd621430d7874cb1b9 --- /dev/null +++ b/syntheticSuccess/m12/tabsyn/tabsyn-m12-20260420_221746/tabsyn-m12-95512-20260502_045948.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:903fbcaead8b5a467b85820c5abbfc26dc55bfc0a56cb3e0137d7ea198cd8b0b +size 12707635 diff --git a/syntheticSuccess/m12/tabsyn/tabsyn-m12-20260420_221746/train_20260420_221757.log b/syntheticSuccess/m12/tabsyn/tabsyn-m12-20260420_221746/train_20260420_221757.log new file mode 100644 index 0000000000000000000000000000000000000000..0947f6d0e6879b37ef60c93d6be9bc49a07fc2a5 --- /dev/null +++ b/syntheticSuccess/m12/tabsyn/tabsyn-m12-20260420_221746/train_20260420_221757.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5393783a93aa2f0279d63ee45018c160aaebd3cf6a9aa2938c136770d0d3acfb +size 14163298 diff --git a/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/_tvae_generate.py b/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..ce4d10617cfc3429d442a56e864ade17b5c574c0 --- /dev/null +++ b/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/_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/m12/tvae/tvae-m12-20260328_052851/models_300epochs/tvae_300epochs.pt") +total = 95512 +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/m12/tvae/tvae-m12-20260328_052851/tvae-m12-95512-20260420_024729.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-SpecializedModels/m12/tvae/tvae-m12-20260328_052851/tvae-m12-95512-20260420_024729.csv") diff --git a/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/_tvae_train.py b/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..80c0eb1952bb9b178f0486d026796e671e61fd23 --- /dev/null +++ b/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/_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/m12/tvae/tvae-m12-20260328_052851/staged/public/train.csv" +meta_path = "/work/output-SpecializedModels/m12/tvae/tvae-m12-20260328_052851/tvae_metadata.json" +save_path = "/work/output-SpecializedModels/m12/tvae/tvae-m12-20260328_052851/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/m12/tvae/tvae-m12-20260328_052851/gen_20260328_105322.log b/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/gen_20260328_105322.log new file mode 100644 index 0000000000000000000000000000000000000000..3e2ad3e290f398bfb29294b5ea2da02f2e45071a --- /dev/null +++ b/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/gen_20260328_105322.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:af52a3cefbdb4c943d5eaebe8364399190fd22914fe9fb76b4ac63ca5e1f15ca +size 1362 diff --git a/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/gen_20260419_065613.log b/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/gen_20260419_065613.log new file mode 100644 index 0000000000000000000000000000000000000000..0b589d9316e5b3d8d932a972ac3951042b356153 --- /dev/null +++ b/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/gen_20260419_065613.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:70d7007395086f40b5f28319234d349250aee098f7470d7c8adb4aa50aa69180 +size 2100 diff --git a/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/gen_20260420_024729.log b/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/gen_20260420_024729.log new file mode 100644 index 0000000000000000000000000000000000000000..c1047482775ba4d2ca7a5cc80814b4b0dc1b1826 --- /dev/null +++ b/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/gen_20260420_024729.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:21c9384ed7ea8501dbbe8fe2f2f132d4b75121e7d15aab9df2beebce9f15d540 +size 544 diff --git a/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/input_snapshot.json b/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..9ced2b4bb6cd4e3620de0851e311cb1dad2f2de0 --- /dev/null +++ b/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m12", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m12/m12-train.csv", + "exists": true, + "size": 13165359, + "sha256": "38507849456d473e77f70bde2e03dd69c9694d3640debeb0e1dd3a99b251862e" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m12/m12-val.csv", + "exists": true, + "size": 1646795, + "sha256": "7efe1eb3cfa68fbb779e8cb8490109c774b8241e5896a77d083544f04ccf6c66" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m12/m12-test.csv", + "exists": true, + "size": 1646190, + "sha256": "77c539bda6d6db356a6f1eb5d8344449f25d36b5309fa660bec41d855e49cefc" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m12/m12-dataset_profile.json", + "exists": true, + "size": 12659, + "sha256": "d8c17ef1f421dc55e8669ecc08ba8b3d6cbb007f953b59ef6014af322c187cc3" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m12/m12-dataset_contract_v1.json", + "exists": true, + "size": 15493, + "sha256": "439a5ec1e598498ffc934040b6975f626db2990104def7a6095d2100f5a974ff" + } + } +} \ No newline at end of file diff --git a/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/models_300epochs/train_20260328_052858.log b/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/models_300epochs/train_20260328_052858.log new file mode 100644 index 0000000000000000000000000000000000000000..ed3a5399f79c9d8226bf4c14d0e8638f60706b07 --- /dev/null +++ b/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/models_300epochs/train_20260328_052858.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6af71c3f5143d508bec890557b98f66afda1a699b7a4c2a34ad7188a99faab40 +size 173 diff --git a/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/models_300epochs/tvae_300epochs.pt b/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..c6166c2e9c407de5db03c816a7e76a252dc5aa3a --- /dev/null +++ b/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e6cba3162b80880c415c97b1308c153c91f1be6871cf7b6d4c549b35c1f84ca0 +size 3401900 diff --git a/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/public_gate/normalized_schema_snapshot.json b/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..13970fcc2a09d39cc1add97c9eb4654cee58f612 --- /dev/null +++ b/syntheticSuccess/m12/tvae/tvae-m12-20260328_052851/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,670 @@ +{ + "dataset_id": "m12", + "target_column": "customer_type", + "task_type": "classification", + 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