Add syntheticSuccess n8

syntheticSuccess/n8/arf/arf-n8-20260422_055912/_arf_generate.py

@@ -0,0 +1,23 @@
+import pickle
+import pandas as pd
+
+n_target = int(1253)
+with open("/work/output-SpecializedModels/n8/arf/arf-n8-20260422_055912/arf_model.pkl", "rb") as f:
+    model = pickle.load(f)
+syn = model.forge(n=n_target)
+syn = syn.reset_index(drop=True)
+if len(syn) > n_target:
+    syn = syn.iloc[:n_target]
+elif len(syn) < n_target:
+    parts = [syn]
+    tries = 0
+    while sum(len(p) for p in parts) < n_target and tries < 64:
+        tries += 1
+        need = n_target - sum(len(p) for p in parts)
+        chunk = model.forge(n=max(need, 1)).reset_index(drop=True)
+        if len(chunk) == 0:
+            break
+        parts.append(chunk)
+    syn = pd.concat(parts, ignore_index=True).iloc[:n_target]
+syn.to_csv("/work/output-SpecializedModels/n8/arf/arf-n8-20260422_055912/arf-n8-1253-20260422_062609.csv", index=False)
+print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-SpecializedModels/n8/arf/arf-n8-20260422_055912/arf-n8-1253-20260422_062609.csv")

syntheticSuccess/n8/arf/arf-n8-20260422_055912/_arf_train.py

@@ -0,0 +1,37 @@
+import pickle
+import numpy as np
+import pandas as pd
+from arfpy import arf
+
+def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame:
+    """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。"""
+    df = df.replace([np.inf, -np.inf], np.nan)
+    df = df.dropna(axis=1, how="all")
+    for col in df.select_dtypes(include=[np.number]).columns:
+        med = df[col].median()
+        if pd.isna(med):
+            med = 0.0
+        df[col] = df[col].fillna(med)
+        nu = int(df[col].nunique(dropna=True))
+        if nu <= 1:
+            continue
+        lo, hi = df[col].quantile(0.001), df[col].quantile(0.999)
+        if pd.notna(lo) and pd.notna(hi) and lo < hi:
+            df[col] = df[col].clip(lo, hi)
+    return df
+
+df = pd.read_csv("/work/output-SpecializedModels/n8/arf/arf-n8-20260422_055912/staged/public/train.csv")
+df = _sanitize_for_arf(df)
+print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols")
+
+model = arf.arf(x=df)
+if hasattr(model, "fit"):
+    model.fit()
+elif hasattr(model, "forde"):
+    model.forde()
+else:
+    raise RuntimeError("arfpy API: no fit() / forde()")
+
+with open("/work/output-SpecializedModels/n8/arf/arf-n8-20260422_055912/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/n8/arf/arf-n8-20260422_055912/arf_model.pkl")

syntheticSuccess/n8/arf/arf-n8-20260422_055912/arf-n8-1253-20260422_062609.csv

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+size 11858497

syntheticSuccess/n8/arf/arf-n8-20260422_055912/arf_model.pkl

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+size 204001241

syntheticSuccess/n8/arf/arf-n8-20260422_055912/gen_20260422_062609.log

@@ -0,0 +1,3 @@
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+size 771

syntheticSuccess/n8/arf/arf-n8-20260422_055912/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "n8",
+  "model": "arf",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n8/n8-train.csv",
+      "exists": true,
+      "size": 4256459,
+      "sha256": "a00236527a87c63c83bc9d36fe05043df85428bbdd53cc27631b13609588ac59"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n8/n8-val.csv",
+      "exists": true,
+      "size": 535895,
+      "sha256": "25695dc1bee6afbb6fa06472a98b7ed2aee813f9a7c191e940464f5f37a013d7"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n8/n8-test.csv",
+      "exists": true,
+      "size": 541240,
+      "sha256": "f3cd2f5eb6640892bafab0783599d65bd9dcdd46e84610b59857bb16516cf7f3"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n8/n8-dataset_profile.json",
+      "exists": true,
+      "size": 225887,
+      "sha256": "81bac56b4e45143f0af94e18ae89ce2715b1827c57c95c10589fde0862160d93"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n8/n8-dataset_contract_v1.json",
+      "exists": true,
+      "size": 286297,
+      "sha256": "164b6d0d56f1bac36374a254cc2488f411eeaa9e37248ccf2b3fb04ae59791c4"
+    }
+  }
+}

syntheticSuccess/n8/arf/arf-n8-20260422_055912/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "n8",
+  "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": "label_2",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n8/n8-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n8/n8-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n8/n8-test.csv"
+  }
+}

syntheticSuccess/n8/arf/arf-n8-20260422_055912/runtime_result.json

@@ -0,0 +1,15 @@
+{
+  "dataset_id": "n8",
+  "model": "arf",
+  "run_id": "arf-n8-20260422_055912",
+  "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-SpecializedModels/n8/arf/arf-n8-20260422_055912/arf-n8-1253-20260422_062609.csv",
+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n8/arf/arf-n8-20260422_055912/arf_model.pkl"
+  }
+}

syntheticSuccess/n8/arf/arf-n8-20260422_055912/staged/arf/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-SpecializedModels/n8/arf/arf-n8-20260422_055912/staged/arf/model_input_manifest.json"
+}

syntheticSuccess/n8/arf/arf-n8-20260422_055912/staged/arf/adapter_transforms_applied.json

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+[]

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@@ -0,0 +1,2967 @@
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syntheticSuccess/n8/arf/arf-n8-20260422_055912/staged/public/test.csv

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+size 574665

syntheticSuccess/n8/arf/arf-n8-20260422_055912/staged/public/train.csv

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syntheticSuccess/n8/bayesnet/bayesnet-n8-20260420_052724/_bayesnet_generate.py

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+
+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/n8/bayesnet/bayesnet-n8-20260420_052724/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=1253, 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/n8/bayesnet/bayesnet-n8-20260420_052724/bayesnet-n8-1253-20260420_063732.csv", index=False)
+print(f"[BayesNet] Generated 1253 rows -> /work/output-SpecializedModels/n8/bayesnet/bayesnet-n8-20260420_052724/bayesnet-n8-1253-20260420_063732.csv")

syntheticSuccess/n8/bayesnet/bayesnet-n8-20260420_052724/_bayesnet_train.py

@@ -0,0 +1,93 @@
+
+import json
+import pickle
+import warnings
+
+import numpy as np
+import pandas as pd
+from pgmpy.estimators import TreeSearch
+from pgmpy.models import DiscreteBayesianNetwork
+warnings.filterwarnings("ignore", category=FutureWarning)
+
+with open("/work/output-SpecializedModels/n8/bayesnet/bayesnet-n8-20260420_052724/bayesnet_coltypes.json", "r", encoding="utf-8") as _f:
+    colmeta = json.load(_f)
+integer_columns = set(colmeta.get("integer_columns") or [])
+
+df = pd.read_csv("/work/output-SpecializedModels/n8/bayesnet/bayesnet-n8-20260420_052724/staged/public/train.csv")
+df = df.dropna(axis=1, how="all")
+full_column_order = list(df.columns)
+
+const_cols = {}
+for col in list(df.columns):
+    if df[col].nunique(dropna=True) <= 1:
+        const_cols[col] = df[col].iloc[0] if len(df) > 0 else None
+        df = df.drop(columns=[col])
+        print(f"[BayesNet] Dropped zero-variance column '{col}'")
+
+const_path = "/work/output-SpecializedModels/n8/bayesnet/bayesnet-n8-20260420_052724/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json")
+with open(const_path, "w", encoding="utf-8") as _f:
+    json.dump({k: str(v) for k, v in const_cols.items()}, _f)
+
+inverse = {"categorical": {}, "continuous": {}}
+enc = pd.DataFrame(index=df.index)
+max_bins = 10
+
+for entry in colmeta["columns"]:
+    name = entry["name"]
+    if name not in df.columns:
+        continue
+    kind = entry["type"]
+    s = df[name]
+    if kind == "categorical":
+        uniques = sorted(s.dropna().unique(), key=lambda x: str(x))
+        mapping = {str(v): i for i, v in enumerate(uniques)}
+        inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))]
+        enc[name] = s.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int)
+    else:
+        s_num = pd.to_numeric(s, errors="coerce")
+        nu = int(s_num.nunique(dropna=True))
+        q = min(max_bins, max(2, nu))
+        if nu < 2:
+            enc[name] = np.zeros(len(s_num), dtype=int)
+            lo, hi = float(s_num.min()), float(s_num.max())
+            inverse["continuous"][name] = [lo, hi]
+        else:
+            try:
+                _, bins = pd.qcut(
+                    s_num, q=q, retbins=True, duplicates="drop"
+                )
+            except Exception:
+                med = float(s_num.median())
+                s2 = s_num.fillna(med)
+                _, bins = pd.qcut(
+                    s2, q=min(q, 3), retbins=True, duplicates="drop"
+                )
+            bins = np.asarray(bins, dtype=float)
+            lab = pd.cut(
+                s_num, bins=bins, labels=False, include_lowest=True
+            )
+            enc[name] = lab.fillna(0).astype(int)
+            inverse["continuous"][name] = bins.tolist()
+
+print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)")
+
+dag = TreeSearch(enc).estimate(show_progress=False)
+for col in enc.columns:
+    if col not in dag.nodes():
+        dag.add_node(col)
+        print(f"[BayesNet] Added isolated node to DAG: {col}")
+network = DiscreteBayesianNetwork(dag)
+network.fit(enc)
+
+bundle = {
+    "network": network,
+    "inverse": inverse,
+    "column_order": list(enc.columns),
+    "full_column_order": full_column_order,
+    "integer_columns": list(integer_columns),
+    "original_dtypes": {c: str(df[c].dtype) for c in enc.columns},
+    "const_cols": const_cols,
+}
+with open("/work/output-SpecializedModels/n8/bayesnet/bayesnet-n8-20260420_052724/bayesnet_model.pkl", "wb") as _f:
+    pickle.dump(bundle, _f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/n8/bayesnet/bayesnet-n8-20260420_052724/bayesnet_model.pkl")

syntheticSuccess/n8/bayesnet/bayesnet-n8-20260420_052724/bayesnet-n8-1253-20260420_063732.csv

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+oid sha256:08b161bff7a9e0dbb257fd713ae645fac17afef074a1549859b3ca16da62949f
+size 11832549

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syntheticSuccess/n8/ctgan/ctgan-n8-20260422_031259/_ctgan_generate.py

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+import sys
+sys.path.insert(0, "/work")
+from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix
+apply_ctgan_inverse_fix()
+import pandas as pd
+from ctgan.synthesizers.ctgan import CTGAN
+model = CTGAN.load("/work/output-SpecializedModels/n8/ctgan/ctgan-n8-20260422_031259/models_300epochs/ctgan_300epochs.pt")
+total = 1253
+chunk = min(50000, total) if total > 50000 else total
+parts = []
+left = total
+while left > 0:
+    take = min(chunk, left)
+    parts.append(model.sample(take))
+    left -= take
+sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0]
+sampled.to_csv("/work/output-SpecializedModels/n8/ctgan/ctgan-n8-20260422_031259/ctgan-n8-1253-20260422_034656.csv", index=False)
+print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-SpecializedModels/n8/ctgan/ctgan-n8-20260422_031259/ctgan-n8-1253-20260422_034656.csv")

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