Add syntheticSuccess m10

syntheticSuccess/m10/arf/arf-m10-20260325_034752/_arf_generate.py

@@ -0,0 +1,6 @@
+import pickle
+with open("/work/output-SpecializedModels/m10/arf/arf-m10-20260325_034752/arf_model.pkl", "rb") as f:
+    model = pickle.load(f)
+syn = model.forge(n=1600)
+syn.to_csv("/work/output-SpecializedModels/m10/arf/arf-m10-20260325_034752/arf-m10-1600-20260330_065838.csv", index=False)
+print(f"[ARF] Generated 1600 rows -> /work/output-SpecializedModels/m10/arf/arf-m10-20260325_034752/arf-m10-1600-20260330_065838.csv")

syntheticSuccess/m10/arf/arf-m10-20260325_034752/_arf_train.py

@@ -0,0 +1,19 @@
+import pickle
+import pandas as pd
+from arfpy import arf
+
+df = pd.read_csv("/work/output-SpecializedModels/m10/arf/arf-m10-20260325_034752/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/m10/arf/arf-m10-20260325_034752/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/m10/arf/arf-m10-20260325_034752/arf_model.pkl")

syntheticSuccess/m10/arf/arf-m10-20260325_034752/arf-m10-1000-20260325_034828.csv

@@ -0,0 +1,3 @@
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+size 377298

syntheticSuccess/m10/arf/arf-m10-20260325_034752/arf-m10-1600-20260330_065838.csv

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+size 601838

syntheticSuccess/m10/arf/arf-m10-20260325_034752/arf_model.pkl

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+size 13233960

syntheticSuccess/m10/arf/arf-m10-20260325_034752/gen_20260325_034828.log

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syntheticSuccess/m10/arf/arf-m10-20260325_034752/gen_20260330_065838.log

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syntheticSuccess/m10/arf/arf-m10-20260325_034752/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "m10",
+  "model": "arf",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m10/m10-train.csv",
+      "exists": true,
+      "size": 99527,
+      "sha256": "f5d9190ff0362e5a8802578b8df8ee74b0cf6218cd44191ce0f72c5183967590"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m10/m10-val.csv",
+      "exists": true,
+      "size": 12624,
+      "sha256": "5dfea77c318e1b33832c3e2be026e8e3f58dbaf20957b89550096fce0c27b565"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m10/m10-test.csv",
+      "exists": true,
+      "size": 12593,
+      "sha256": "3a0adff58fccbf7f6793d268163bf0cab70337abbba68e60c9a738a6d3fd5fbb"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m10/m10-dataset_profile.json",
+      "exists": true,
+      "size": 8149,
+      "sha256": "7b7c69aaac6285cc80c08a1b9086918b870737a17ea9b696a408903ca42b6a90"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m10/m10-dataset_contract_v1.json",
+      "exists": true,
+      "size": 9905,
+      "sha256": "fa72d3540ca4a1d3748e15e8a714ed9bc9dc9dc015a313c363a03c773b26006f"
+    }
+  }
+}

syntheticSuccess/m10/arf/arf-m10-20260325_034752/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,429 @@
+{
+  "dataset_id": "m10",
+  "target_column": "int_memory",
+  "task_type": "regression",
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+}

syntheticSuccess/m10/arf/arf-m10-20260325_034752/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "m10",
+  "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": "int_memory",
+  "task_type": "regression",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m10/m10-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m10/m10-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m10/m10-test.csv"
+  }
+}

syntheticSuccess/m10/arf/arf-m10-20260325_034752/public_gate/staged_input_manifest.json

@@ -0,0 +1,434 @@
+{
+  "dataset_id": "m10",
+  "target_column": "int_memory",
+  "task_type": "regression",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m10/arf/arf-m10-20260325_034752/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m10/arf/arf-m10-20260325_034752/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m10/arf/arf-m10-20260325_034752/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m10/arf/arf-m10-20260325_034752/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m10/arf/arf-m10-20260325_034752/public_gate/public_gate_report.json",
+  "column_schema": [
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syntheticSuccess/m10/arf/arf-m10-20260325_034752/runtime_result.json

@@ -0,0 +1,14 @@
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+}

syntheticSuccess/m10/arf/arf-m10-20260325_034752/staged/arf/adapter_report.json

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+}

syntheticSuccess/m10/arf/arf-m10-20260325_034752/staged/arf/adapter_transforms_applied.json

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syntheticSuccess/m10/arf/arf-m10-20260325_034752/staged/arf/model_input_manifest.json

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+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m10/arf/arf-m10-20260325_034752/staged/public/val.csv",
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+}

syntheticSuccess/m10/arf/arf-m10-20260325_034752/staged/public/staged_features.json

@@ -0,0 +1,107 @@
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syntheticSuccess/m10/bayesnet/bayesnet-m10-20260321_080304/_bayesnet_generate.py

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+import subprocess, sys, os
+
+pip_libs = "/pip_libs"
+sys.path.insert(0, pip_libs)
+os.environ["PYTHONPATH"] = pip_libs + os.pathsep + os.environ.get("PYTHONPATH", "")
+
+def _ensure_deps():
+    try:
+        import synthcity
+    except ModuleNotFoundError:
+        print("[BayesNet] synthcity not found - installing to cache...")
+        subprocess.run(
+            [sys.executable, "-m", "pip", "install",
+             "--target", pip_libs, "synthcity==0.2.12", "numpy<2", "-q"],
+            check=True
+        )
+        import shutil, glob
+        for pat in ["torch", "torch-*", "torchvision", "torchvision-*",
+                    "torchvision.libs", "torchgen", "nvidia*", "triton*"]:
+            for p in glob.glob(os.path.join(pip_libs, pat)):
+                if os.path.isdir(p): shutil.rmtree(p)
+                else: os.remove(p)
+        if pip_libs not in sys.path:
+            sys.path.insert(0, pip_libs)
+
+_ensure_deps()
+
+import pickle, json as _json
+with open("/work/output-SpecializedModels/m10/bayesnet/bayesnet-m10-20260321_080304/bayesnet_model.pkl", "rb") as f:
+    plugin = pickle.load(f)
+syn = plugin.generate(count=1600).dataframe()
+
+# Restore zero-variance columns that were dropped during training
+const_path = "/work/output-SpecializedModels/m10/bayesnet/bayesnet-m10-20260321_080304/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json")
+if os.path.exists(const_path):
+    with open(const_path) as _f:
+        const_cols = _json.load(_f)
+    for col, val in const_cols.items():
+        syn[col] = val
+        print(f"[BayesNet] Restored constant column '{col}' = {val}")
+
+syn.to_csv("/work/output-SpecializedModels/m10/bayesnet/bayesnet-m10-20260321_080304/bayesnet-m10-1600-20260330_065844.csv", index=False)
+print(f"[BayesNet] Generated 1600 rows -> /work/output-SpecializedModels/m10/bayesnet/bayesnet-m10-20260321_080304/bayesnet-m10-1600-20260330_065844.csv")

syntheticSuccess/m10/bayesnet/bayesnet-m10-20260321_080304/_bayesnet_train.py

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+import subprocess, sys, os
+
+pip_libs = "/pip_libs"
+sys.path.insert(0, pip_libs)
+os.environ["PYTHONPATH"] = pip_libs + os.pathsep + os.environ.get("PYTHONPATH", "")
+
+def _ensure_deps():
+    try:
+        import synthcity
+    except ModuleNotFoundError:
+        print("[BayesNet] synthcity not found - installing to cache (first run, may take minutes)...")
+        # Install synthcity with numpy<2 to avoid conflicts
+        subprocess.run(
+            [sys.executable, "-m", "pip", "install",
+             "--target", pip_libs, "synthcity==0.2.12", "numpy<2", "-q"],
+            check=True
+        )
+        # Remove torch/torchvision from pip_libs to avoid shadowing system versions
+        import shutil, glob
+        for pat in ["torch", "torch-*", "torchvision", "torchvision-*",
+                    "torchvision.libs", "torchgen", "nvidia*", "triton*"]:
+            for p in glob.glob(os.path.join(pip_libs, pat)):
+                if os.path.isdir(p): shutil.rmtree(p)
+                else: os.remove(p)
+        if pip_libs not in sys.path:
+            sys.path.insert(0, pip_libs)
+
+_ensure_deps()
+
+from synthcity.plugins import Plugins
+import pickle
+import pandas as pd
+from synthcity.plugins.core.dataloader import GenericDataLoader
+
+df = pd.read_csv("/work/output-SpecializedModels/m10/bayesnet/bayesnet-m10-20260321_080304/staged/public/train.csv")
+df = df.dropna(axis=1, how="all")
+
+# Drop zero-variance columns (only 1 unique value) to avoid
+# synthcity encoder KeyError during generation
+import json as _json
+const_cols = {}
+for col in list(df.columns):
+    nuniq = df[col].nunique()
+    if nuniq <= 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}' (value={const_cols[col]})")
+
+# Save constant columns info so generate can restore them
+const_path = "/work/output-SpecializedModels/m10/bayesnet/bayesnet-m10-20260321_080304/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json")
+with open(const_path, "w") as _f:
+    _json.dump({k: str(v) for k, v in const_cols.items()}, _f)
+
+print(f"[BayesNet] Training on {len(df)} rows, {len(df.columns)} cols")
+
+loader = GenericDataLoader(df)
+plugin = Plugins().get("bayesian_network")
+plugin.fit(loader)
+
+with open("/work/output-SpecializedModels/m10/bayesnet/bayesnet-m10-20260321_080304/bayesnet_model.pkl", "wb") as f:
+    pickle.dump(plugin, f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/m10/bayesnet/bayesnet-m10-20260321_080304/bayesnet_model.pkl")

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+{
+  "dataset_id": "m10",
+  "model": "bayesnet",
+  "inputs": {
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+      "exists": true,
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+      "exists": true,
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+      "sha256": "5dfea77c318e1b33832c3e2be026e8e3f58dbaf20957b89550096fce0c27b565"
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+      "exists": true,
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+      "sha256": "3a0adff58fccbf7f6793d268163bf0cab70337abbba68e60c9a738a6d3fd5fbb"
+    },
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+    }
+  }
+}

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syntheticSuccess/m10/bayesnet/bayesnet-m10-20260321_080304/public_gate/public_gate_report.json

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+{
+  "dataset_id": "m10",
+  "status": "pass",
+  "checks": [
+    {
+      "check_id": "PG001_csv_parse_ok",
+      "status": "pass"
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syntheticSuccess/m10/bayesnet/bayesnet-m10-20260321_080304/public_gate/staged_input_manifest.json

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syntheticSuccess/m10/bayesnet/bayesnet-m10-20260321_080304/runtime_result.json

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syntheticSuccess/m10/bayesnet/bayesnet-m10-20260321_080304/staged/bayesnet/adapter_report.json

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syntheticSuccess/m10/bayesnet/bayesnet-m10-20260321_080304/staged/bayesnet/adapter_transforms_applied.json

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+}