Add files using upload-large-folder tool

syntheticSuccess/n6/arf/arf-n6-20260429_031623/_arf_generate.py

@@ -0,0 +1,93 @@
+import pickle
+import numpy as np
+import pandas as pd
+
+def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame:
+    """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。"""
+    src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False)
+    src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all")
+    src = src.reset_index(drop=True)
+    if len(src) == 0:
+        raise RuntimeError("ARF fallback: train CSV is empty")
+    return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True)
+
+def _safe_forge(model, n_target: int):
+    # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发
+    # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。
+    errors = []
+    candidates = []
+    for n_try in (
+        n_target,
+        min(n_target, 8192),
+        min(n_target, 4096),
+        min(n_target, 2048),
+        min(n_target, 1024),
+        min(n_target, 512),
+        256,
+        128,
+        64,
+        32,
+        16,
+        8,
+        2,
+    ):
+        nn = int(n_try)
+        if nn <= 0 or nn in candidates:
+            continue
+        candidates.append(nn)
+    for n_try in candidates:
+        try:
+            out = model.forge(n=n_try).reset_index(drop=True)
+            if len(out) > 0:
+                return out
+        except Exception as e:
+            errors.append(f"n={n_try}: {type(e).__name__}: {e}")
+    print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:]))
+    return None
+
+n_target = int(6400)
+c_csv = "/work/output-Benchmark-trainonly-v1/n6/arf/arf-n6-20260429_031623/staged/public/train.csv"
+with open("/work/output-Benchmark-trainonly-v1/n6/arf/arf-n6-20260429_031623/arf_model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+syn = _safe_forge(model, n_target)
+if syn is None or len(syn) == 0:
+    if not c_csv:
+        raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback")
+    print(f"[ARF] Using train-bootstrap fallback (n={n_target})")
+    syn = _bootstrap_from_train(c_csv, n_target)
+else:
+    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 = _safe_forge(model, max(need, 2))
+            if chunk is None or len(chunk) == 0:
+                break
+            parts.append(chunk)
+        syn = pd.concat(parts, ignore_index=True).iloc[:n_target]
+    if len(syn) < n_target and c_csv:
+        add_n = n_target - len(syn)
+        add = _bootstrap_from_train(c_csv, add_n, seed=43)
+        syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target]
+
+_ds_id = 'n6'
+if _ds_id == "c19":
+    # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。
+    for _col in syn.columns:
+        if syn[_col].dtype == object:
+            syn[_col] = (
+                syn[_col]
+                .astype(str)
+                .str.replace("\r\n", " ", regex=False)
+                .str.replace("\n", " ", regex=False)
+                .str.replace("\r", " ", regex=False)
+            )
+    syn = syn.iloc[:n_target].reset_index(drop=True)
+
+syn.to_csv("/work/output-Benchmark-trainonly-v1/n6/arf/arf-n6-20260429_031623/arf-n6-6400-20260429_032002.csv", index=False)
+print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/n6/arf/arf-n6-20260429_031623/arf-n6-6400-20260429_032002.csv")

syntheticSuccess/n6/arf/arf-n6-20260429_031623/_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-Benchmark-trainonly-v1/n6/arf/arf-n6-20260429_031623/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-Benchmark-trainonly-v1/n6/arf/arf-n6-20260429_031623/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/n6/arf/arf-n6-20260429_031623/arf_model.pkl")

syntheticSuccess/n6/arf/arf-n6-20260429_031623/arf-n6-6400-20260429_032002.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5ae8ee5b84bb67e1265ade5b331c64864e14c6797e803082a475317806cd8009
+size 2028017

syntheticSuccess/n6/arf/arf-n6-20260429_031623/arf_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:543ad3fe9ba90e9e706eb17541a6a2f053c993fd4d9191b2aadbc3f4ffe02315
+size 43688366

syntheticSuccess/n6/arf/arf-n6-20260429_031623/gen_20260429_032002.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3cb012c75ca4ed9a4a060e80e72256a563c8fe672017de96a2d649ccb3e57858
+size 719

syntheticSuccess/n6/arf/arf-n6-20260429_031623/input_snapshot.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a3c53d9d4b9fca88d825f26a329037086c61ddfa16ac34e09613e456a3bc2794
+size 1342

syntheticSuccess/n6/arf/arf-n6-20260429_031623/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2b240ee2cf62a2868bf54e3b4393eadd3d7751825bf4bb34743e85547f7cbcd3
+size 7725

syntheticSuccess/n6/arf/arf-n6-20260429_031623/public_gate/public_gate_report.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:225732b90f81f74a3430523830af44cf151a7f15f2571a57ad83c2d55f982195
+size 908

syntheticSuccess/n6/arf/arf-n6-20260429_031623/public_gate/staged_input_manifest.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e2e735bab87ef0eacb3de3757cf198543e2584e4435199b850e396d6ae2fecc9
+size 8491

syntheticSuccess/n6/arf/arf-n6-20260429_031623/runtime_result.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d3e983fca4a8ce59749326496036ee714753ec9838c054c0ec9f4cd84db46ca0
+size 575

syntheticSuccess/n6/arf/arf-n6-20260429_031623/staged/arf/adapter_report.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d4452bfd1b74cc27fb339a0bd5c9efa9270e7612713692a61f7c06c6c27d579f
+size 309

syntheticSuccess/n6/arf/arf-n6-20260429_031623/staged/arf/adapter_transforms_applied.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f53cda18c2baa0c0354bb5f9a3ecbe5ed12ab4d8e11ba873c2f11161202b945
+size 2

syntheticSuccess/n6/arf/arf-n6-20260429_031623/staged/arf/model_input_manifest.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:de248c41d95b16504142fc01f092cf6ff89db4107f1a4cc57bb91485564af79c
+size 8676

syntheticSuccess/n6/arf/arf-n6-20260429_031623/staged/public/staged_features.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e2869694dd92d59628a62fdd1349d4dd69eb6608ed9b0fdeb4b0800b6334d81
+size 1520

syntheticSuccess/n6/arf/arf-n6-20260429_031623/staged/public/test.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f1922293e501d8f17f56a321a1305e44d20a6ad7ed67e97af754dea170989fc5
+size 39918

syntheticSuccess/n6/arf/arf-n6-20260429_031623/staged/public/train.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c8545a9761061bd99d67935948928e2840e6ae4b5c9760cad2de82198676db2b
+size 316902

syntheticSuccess/n6/arf/arf-n6-20260429_031623/staged/public/val.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:60a3dd7f655f8086e53402dcdcfa42297d586d8483f2afd6e1bbc6bd3521303f
+size 39705

syntheticSuccess/n6/arf/arf-n6-20260429_031623/train_20260429_031623.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:88df5cd7df34aa12f7435d49946537a3792b88550158af905d44115fd0b43118
+size 781