Populate SynthData0523 main/c10 batch 1

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@@ -30,3 +30,328 @@ evaluation/tail/tail_threshold_runs/20260505_latest48_keyset_tail_threshold/summ
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SynthData0523/main/c10/arf/arf-c10-20260321_070559/_arf_generate.py

@@ -0,0 +1,6 @@
+import pickle
+with open("/work/output-SpecializedModels/c10/arf/arf-c10-20260321_070559/arf_model.pkl", "rb") as f:
+    model = pickle.load(f)
+syn = model.forge(n=820008)
+syn.to_csv("/work/output-SpecializedModels/c10/arf/arf-c10-20260321_070559/arf-c10-820008-20260330_065346.csv", index=False)
+print(f"[ARF] Generated 820008 rows -> /work/output-SpecializedModels/c10/arf/arf-c10-20260321_070559/arf-c10-820008-20260330_065346.csv")

SynthData0523/main/c10/arf/arf-c10-20260321_070559/_arf_train.py

@@ -0,0 +1,19 @@
+import pickle
+import pandas as pd
+from arfpy import arf
+
+df = pd.read_csv("/work/output-SpecializedModels/c10/arf/arf-c10-20260321_070559/staged/public/train.csv")
+df = df.dropna(axis=1, how="all")
+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/c10/arf/arf-c10-20260321_070559/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/c10/arf/arf-c10-20260321_070559/arf_model.pkl")

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SynthData0523/main/c10/arf/arf-c10-20260321_070559/input_snapshot.json

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+{
+  "dataset_id": "c10",
+  "model": "arf",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c10/c10-train.csv",
+      "exists": true,
+      "size": 20120947,
+      "sha256": "3f0896c550f560b85109ac3068970247496d270dffbe4e3a65f1ae8c802efb69"
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+    },
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+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c10/c10-dataset_contract_v1.json",
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+}

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SynthData0523/main/c10/arf/arf-c10-20260321_070559/public_gate/public_gate_report.json

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SynthData0523/main/c10/arf/arf-c10-20260321_070559/public_gate/staged_input_manifest.json

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SynthData0523/main/c10/arf/arf-c10-20260321_070559/runtime_result.json

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SynthData0523/main/c10/arf/arf-c10-20260321_070559/staged/arf/adapter_report.json

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SynthData0523/main/c10/arf/arf-c10-20260321_070559/staged/arf/adapter_transforms_applied.json

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SynthData0523/main/c10/bayesnet/bayesnet-c10-20260422_060152/_bayesnet_generate.py

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+import subprocess
+import sys
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+
+import numpy as np
+import pandas as pd
+from pgmpy.sampling import BayesianModelSampling
+
+warnings.filterwarnings("ignore", category=FutureWarning)
+
+def _ensure_cloudpickle():
+    try:
+        import cloudpickle  # noqa: F401
+    except ModuleNotFoundError:
+        subprocess.check_call(
+            [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"],
+        )
+
+_ensure_cloudpickle()
+
+with open("/work/output-SpecializedModels/c10/bayesnet/bayesnet-c10-20260422_060152/bayesnet_model.pkl", "rb") as f:
+    bundle = pickle.load(f)
+
+network = bundle["network"]
+inverse = bundle["inverse"]
+cols = bundle["column_order"]
+integer_columns = set(bundle.get("integer_columns") or [])
+full_order = bundle.get("full_column_order") or cols
+const_cols = bundle.get("const_cols") or {}
+
+num_rows = int(820008)
+sampler = BayesianModelSampling(network)
+raw = sampler.forward_sample(size=num_rows, show_progress=False)
+raw = raw.reset_index(drop=True)
+if len(raw) > num_rows:
+    raw = raw.iloc[:num_rows]
+_tries = 0
+while len(raw) < num_rows and _tries < 64:
+    _tries += 1
+    nextra = min(10000, num_rows - len(raw))
+    more = sampler.forward_sample(size=max(nextra, 1), show_progress=False)
+    more = more.reset_index(drop=True)
+    if len(more) == 0:
+        break
+    raw = pd.concat([raw, more], ignore_index=True)
+    if len(raw) > num_rows:
+        raw = raw.iloc[:num_rows]
+
+out = pd.DataFrame(index=raw.index)
+rng = np.random.default_rng()
+
+for c in cols:
+    if c in inverse["categorical"]:
+        levels = inverse["categorical"][c]
+        idx = raw[c].astype(int).to_numpy()
+        idx = np.clip(idx, 0, max(0, len(levels) - 1))
+        out[c] = [levels[i] for i in idx]
+    else:
+        edges = np.asarray(inverse["continuous"][c], dtype=float)
+        if edges.size < 2:
+            out[c] = 0.0
+        else:
+            nbin = edges.size - 1
+            res = []
+            for k in raw[c].astype(int).to_numpy():
+                k = int(k)
+                if k < 0:
+                    k = 0
+                if k >= nbin:
+                    k = nbin - 1
+                lo, hi = float(edges[k]), float(edges[k + 1])
+                if hi < lo:
+                    lo, hi = hi, lo
+                v = rng.uniform(lo, hi)
+                if c in integer_columns:
+                    v = int(round(v))
+                res.append(v)
+            out[c] = res
+
+final = pd.DataFrame(index=out.index)
+for c in full_order:
+    if c in const_cols:
+        final[c] = const_cols[c]
+    elif c in out.columns:
+        final[c] = out[c]
+
+dtypes = bundle.get("original_dtypes") or {}
+for c, dts in dtypes.items():
+    if c not in final.columns:
+        continue
+    try:
+        if "int" in dts:
+            final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64")
+        elif "float" in dts:
+            final[c] = pd.to_numeric(final[c], errors="coerce")
+    except Exception:
+        pass
+
+if len(final) != num_rows:
+    final = final.iloc[:num_rows].copy()
+final.to_csv("/work/output-SpecializedModels/c10/bayesnet/bayesnet-c10-20260422_060152/bayesnet-c10-820008-20260422_060308.csv", index=False)
+print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-SpecializedModels/c10/bayesnet/bayesnet-c10-20260422_060152/bayesnet-c10-820008-20260422_060308.csv")

SynthData0523/main/c10/bayesnet/bayesnet-c10-20260422_060152/_bayesnet_train.py

@@ -0,0 +1,118 @@
+
+import json
+import pickle
+import subprocess
+import sys
+import warnings
+
+import numpy as np
+import pandas as pd
+from pgmpy.estimators import TreeSearch
+from pgmpy.models import DiscreteBayesianNetwork
+warnings.filterwarnings("ignore", category=FutureWarning)
+
+def _ensure_cloudpickle():
+    try:
+        import cloudpickle  # noqa: F401
+    except ModuleNotFoundError:
+        subprocess.check_call(
+            [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"],
+        )
+
+_ensure_cloudpickle()
+
+with open("/work/output-SpecializedModels/c10/bayesnet/bayesnet-c10-20260422_060152/bayesnet_coltypes.json", "r", encoding="utf-8") as _f:
+    colmeta = json.load(_f)
+integer_columns = set(colmeta.get("integer_columns") or [])
+
+df = pd.read_csv("/work/output-SpecializedModels/c10/bayesnet/bayesnet-c10-20260422_060152/staged/public/train.csv")
+df = df.dropna(axis=1, how="all")
+full_column_order = list(df.columns)
+
+const_cols = {}
+for col in list(df.columns):
+    if df[col].nunique(dropna=True) <= 1:
+        const_cols[col] = df[col].iloc[0] if len(df) > 0 else None
+        df = df.drop(columns=[col])
+        print(f"[BayesNet] Dropped zero-variance column '{col}'")
+
+const_path = "/work/output-SpecializedModels/c10/bayesnet/bayesnet-c10-20260422_060152/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json")
+with open(const_path, "w", encoding="utf-8") as _f:
+    json.dump({k: str(v) for k, v in const_cols.items()}, _f)
+
+inverse = {"categorical": {}, "continuous": {}}
+enc = pd.DataFrame(index=df.index)
+_n_samples = len(df)
+_n_plan = sum(
+    1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns
+)
+max_bins = 10
+if _n_plan > 35 or _n_samples > 200000:
+    max_bins = 5
+if _n_plan > 55:
+    max_bins = 4
+print(f"[BayesNet] max_bins={max_bins} (cols_in_df={_n_plan}, rows={_n_samples})")
+
+for entry in colmeta["columns"]:
+    name = entry["name"]
+    if name not in df.columns:
+        continue
+    kind = entry["type"]
+    s = df[name]
+    if kind == "categorical":
+        uniques = sorted(s.dropna().unique(), key=lambda x: str(x))
+        mapping = {str(v): i for i, v in enumerate(uniques)}
+        inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))]
+        enc[name] = s.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int)
+    else:
+        s_num = pd.to_numeric(s, errors="coerce")
+        nu = int(s_num.nunique(dropna=True))
+        q = min(max_bins, max(2, nu))
+        if nu < 2:
+            enc[name] = np.zeros(len(s_num), dtype=int)
+            lo, hi = float(s_num.min()), float(s_num.max())
+            inverse["continuous"][name] = [lo, hi]
+        else:
+            try:
+                _, bins = pd.qcut(
+                    s_num, q=q, retbins=True, duplicates="drop"
+                )
+            except Exception:
+                med = float(s_num.median())
+                s2 = s_num.fillna(med)
+                _, bins = pd.qcut(
+                    s2, q=min(q, 3), retbins=True, duplicates="drop"
+                )
+            bins = np.asarray(bins, dtype=float)
+            lab = pd.cut(
+                s_num, bins=bins, labels=False, include_lowest=True
+            )
+            enc[name] = lab.fillna(0).astype(int)
+            inverse["continuous"][name] = bins.tolist()
+
+print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)")
+
+enc_struct = enc
+if len(enc) > 25000:
+    enc_struct = enc.sample(n=25000, random_state=0, replace=False)
+    print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})")
+dag = TreeSearch(enc_struct).estimate(show_progress=False)
+for col in enc.columns:
+    if col not in dag.nodes():
+        dag.add_node(col)
+        print(f"[BayesNet] Added isolated node to DAG: {col}")
+network = DiscreteBayesianNetwork(dag)
+network.fit(enc)
+
+bundle = {
+    "network": network,
+    "inverse": inverse,
+    "column_order": list(enc.columns),
+    "full_column_order": full_column_order,
+    "integer_columns": list(integer_columns),
+    "original_dtypes": {c: str(df[c].dtype) for c in enc.columns},
+    "const_cols": const_cols,
+}
+with open("/work/output-SpecializedModels/c10/bayesnet/bayesnet-c10-20260422_060152/bayesnet_model.pkl", "wb") as _f:
+    pickle.dump(bundle, _f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c10/bayesnet/bayesnet-c10-20260422_060152/bayesnet_model.pkl")

SynthData0523/main/c10/bayesnet/bayesnet-c10-20260422_060152/bayesnet-c10-820008-20260422_060308.csv

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+oid sha256:a97f61460e5c9911b4517c758bce2375c467d1f81acf8f3da0981da3d4d711bc
+size 19142970

SynthData0523/main/c10/bayesnet/bayesnet-c10-20260422_060152/bayesnet_coltypes.json

@@ -0,0 +1,49 @@
+{
+  "columns": [
+    {
+      "name": "s1",
+      "type": "continuous"
+    },
+    {
+      "name": "c1",
+      "type": "continuous"
+    },
+    {
+      "name": "s2",
+      "type": "continuous"
+    },
+    {
+      "name": "c2",
+      "type": "continuous"
+    },
+    {
+      "name": "s3",
+      "type": "continuous"
+    },
+    {
+      "name": "c3",
+      "type": "continuous"
+    },
+    {
+      "name": "s4",
+      "type": "continuous"
+    },
+    {
+      "name": "c4",
+      "type": "continuous"
+    },
+    {
+      "name": "s5",
+      "type": "continuous"
+    },
+    {
+      "name": "c5",
+      "type": "continuous"
+    },
+    {
+      "name": "class",
+      "type": "continuous"
+    }
+  ],
+  "integer_columns": []
+}

SynthData0523/main/c10/bayesnet/bayesnet-c10-20260422_060152/bayesnet_model.pkl

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+oid sha256:cae3493050dcda7012e685b3478c6c033aed068daeda5eadf97b23f92a5392e6
+size 6598

SynthData0523/main/c10/bayesnet/bayesnet-c10-20260422_060152/const_cols.json

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+{}

SynthData0523/main/c10/bayesnet/bayesnet-c10-20260422_060152/gen_20260422_060308.log

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+oid sha256:1fa1a96f62ec4273d2b83605fb6b603b22ec0e921933add7f4bb630e2ddaa896
+size 3396

SynthData0523/main/c10/bayesnet/bayesnet-c10-20260422_060152/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c10",
+  "model": "bayesnet",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c10/c10-train.csv",
+      "exists": true,
+      "size": 20120947,
+      "sha256": "3f0896c550f560b85109ac3068970247496d270dffbe4e3a65f1ae8c802efb69"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c10/c10-val.csv",
+      "exists": true,
+      "size": 2515873,
+      "sha256": "f307196a54225193e989342002763a95db8b1f49f09fb016b4c5fde31ef17c2e"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c10/c10-test.csv",
+      "exists": true,
+      "size": 2515318,
+      "sha256": "50018d09041167df4319bfcdd8cdc537760bf82d392263957ffae4c91b148f5e"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c10/c10-dataset_profile.json",
+      "exists": true,
+      "size": 4980,
+      "sha256": "1383bc030a79a444c4c17990c67f5606f0d712d24b7d1a89283fcc74e1ffcf18"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c10/c10-dataset_contract_v1.json",
+      "exists": true,
+      "size": 5499,
+      "sha256": "e7584b87745a0a20b041c278948f6376c018b6f57a63ef6eff8bedec61b3c763"
+    }
+  }
+}

SynthData0523/main/c10/bayesnet/bayesnet-c10-20260422_060152/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,233 @@
+{
+  "dataset_id": "c10",
+  "target_column": "class",
+  "task_type": "classification",
+  "columns": [
+    {
+      "name": "s1",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 5e-06,
+        "example_values": [
+          "1",
+          "2",
+          "3",
+          "4"
+        ]
+      }
+    },
+    {
+      "name": "c1",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 13,
+        "unique_ratio": 1.6e-05,
+        "example_values": [
+          "1",
+          "9",
+          "2",
+          "5",
+          "10"
+        ]
+      }
+    },
+    {
+      "name": "s2",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 5e-06,
+        "example_values": [
+          "1",
+          "2",
+          "4",
+          "3"
+        ]
+      }
+    },
+    {
+      "name": "c2",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 13,
+        "unique_ratio": 1.6e-05,
+        "example_values": [
+          "13",
+          "5",
+          "12",
+          "6",
+          "11"
+        ]
+      }
+    },
+    {
+      "name": "s3",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 5e-06,
+        "example_values": [
+          "2",
+          "1",
+          "3",
+          "4"
+        ]
+      }
+    },
+    {
+      "name": "c3",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 13,
+        "unique_ratio": 1.6e-05,
+        "example_values": [
+          "4",
+          "10",
+          "13",
+          "5",
+          "7"
+        ]
+      }
+    },
+    {
+      "name": "s4",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 5e-06,
+        "example_values": [
+          "2",
+          "4",
+          "1",
+          "3"
+        ]
+      }
+    },
+    {
+      "name": "c4",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 13,
+        "unique_ratio": 1.6e-05,
+        "example_values": [
+          "3",
+          "11",
+          "4",
+          "10",
+          "9"
+        ]
+      }
+    },
+    {
+      "name": "s5",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 5e-06,
+        "example_values": [
+          "1",
+          "4",
+          "2",
+          "3"
+        ]
+      }
+    },
+    {
+      "name": "c5",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 13,
+        "unique_ratio": 1.6e-05,
+        "example_values": [
+          "12",
+          "8",
+          "3",
+          "11",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "class",
+      "role": "target",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 10,
+        "unique_ratio": 1.2e-05,
+        "example_values": [
+          "0",
+          "1",
+          "6",
+          "3",
+          "2"
+        ]
+      }
+    }
+  ]
+}

SynthData0523/main/c10/bayesnet/bayesnet-c10-20260422_060152/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c10",
+  "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/c10/c10-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c10/c10-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c10/c10-test.csv"
+  }
+}

SynthData0523/main/c10/bayesnet/bayesnet-c10-20260422_060152/public_gate/staged_input_manifest.json

@@ -0,0 +1,238 @@
+{
+  "dataset_id": "c10",
+  "target_column": "class",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c10/bayesnet/bayesnet-c10-20260422_060152/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c10/bayesnet/bayesnet-c10-20260422_060152/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c10/bayesnet/bayesnet-c10-20260422_060152/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c10/bayesnet/bayesnet-c10-20260422_060152/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c10/bayesnet/bayesnet-c10-20260422_060152/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "s1",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 5e-06,
+        "example_values": [
+          "1",
+          "2",
+          "3",
+          "4"
+        ]
+      }
+    },
+    {
+      "name": "c1",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 13,
+        "unique_ratio": 1.6e-05,
+        "example_values": [
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