Add syntheticFail c17

syntheticFail/c17/bayesnet/bayesnet-c17-20260321_075106/_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/c17/bayesnet/bayesnet-c17-20260321_075106/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=7045, 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/c17/bayesnet/bayesnet-c17-20260321_075106/bayesnet-c17-7045-20260419_072420.csv", index=False)
+print(f"[BayesNet] Generated 7045 rows -> /work/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260321_075106/bayesnet-c17-7045-20260419_072420.csv")

syntheticFail/c17/bayesnet/bayesnet-c17-20260321_075106/_bayesnet_train.py

@@ -0,0 +1,62 @@
+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/c17/bayesnet/bayesnet-c17-20260321_075106/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/c17/bayesnet/bayesnet-c17-20260321_075106/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/c17/bayesnet/bayesnet-c17-20260321_075106/bayesnet_model.pkl", "wb") as f:
+    pickle.dump(plugin, f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260321_075106/bayesnet_model.pkl")

syntheticFail/c17/bayesnet/bayesnet-c17-20260321_075106/bayesnet-c17-1000-20260321_075232.csv

@@ -0,0 +1,3 @@
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+oid sha256:0c5147a55da67297d730a0c65be71b021bd4192382fc33c837f4075f89d394e6
+size 417774

syntheticFail/c17/bayesnet/bayesnet-c17-20260321_075106/bayesnet-c17-7045-20260330_065449.csv

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+size 2945279

syntheticFail/c17/bayesnet/bayesnet-c17-20260321_075106/bayesnet_model.pkl

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+size 3055518558

syntheticFail/c17/bayesnet/bayesnet-c17-20260321_075106/const_cols.json

@@ -0,0 +1 @@
+{}

syntheticFail/c17/bayesnet/bayesnet-c17-20260321_075106/gen_20260321_075232.log

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syntheticFail/c17/bayesnet/bayesnet-c17-20260321_075106/gen_20260330_065449.log

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syntheticFail/c17/bayesnet/bayesnet-c17-20260321_075106/gen_20260419_072420.log

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+size 2186

syntheticFail/c17/bayesnet/bayesnet-c17-20260321_075106/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c17",
+  "model": "bayesnet",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c17/c17-train.csv",
+      "exists": true,
+      "size": 2726614,
+      "sha256": "b77d66258f90989c221df405c960fb64e4e947a5369ced2b884002e17e47e1e9"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c17/c17-val.csv",
+      "exists": true,
+      "size": 342007,
+      "sha256": "d98c48176aedfd33341199220483be09f753ac63f2a63e829d0835286ab577f3"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c17/c17-test.csv",
+      "exists": true,
+      "size": 339976,
+      "sha256": "e067ef64b2334774f8cc291445c6723301cd374cde1a3db26a51af8da46bda0a"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c17/c17-dataset_profile.json",
+      "exists": true,
+      "size": 6842,
+      "sha256": "75a4478c7d058e9e4753c49ecfa5e7e7764263a853380d2bacbf48401854370e"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c17/c17-dataset_contract_v1.json",
+      "exists": true,
+      "size": 7632,
+      "sha256": "26a27c28d1bb9de6b75ff00efa045708e5a23ea264abb037a6ba47d7e55027fd"
+    }
+  }
+}

syntheticFail/c17/bayesnet/bayesnet-c17-20260321_075106/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,256 @@
+{
+  "dataset_id": "c17",
+  "target_column": "type",
+  "task_type": "classification",
+  "columns": [
+    {
+      "name": "show_id",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7045,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "s4961",
+          "s5783",
+          "s4235",
+          "s8539",
+          "s2374"
+        ]
+      }
+    },
+    {
+      "name": "type",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000284,
+        "example_values": [
+          "Movie",
+          "TV Show"
+        ]
+      }
+    },
+    {
+      "name": "title",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7044,
+        "unique_ratio": 0.999858,
+        "example_values": [
+          "Happy Anniversary",
+          "Amanda Knox",
+          "Gina Yashere: Laughing to America",
+          "The Truth About Alcohol",
+          "Saladin"
+        ]
+      }
+    },
+    {
+      "name": "director",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.299787,
+        "unique_count": 3784,
+        "unique_ratio": 0.767079,
+        "example_values": [
+          "Jared Stern",
+          "Rod Blackhurst, Brian McGinn",
+          "Paul M. Green",
+          "David Briggs",
+          "Youssef Chahine"
+        ]
+      }
+    },
+    {
+      "name": "cast",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.095387,
+        "unique_count": 6179,
+        "unique_ratio": 0.969559,
+        "example_values": [
+          "Noël Wells, Ben Schwartz, Joe Pantoliano, Annie Potts, Rahul Kohli, Kristin Bauer van Straten, David Walton, Leonardo Nam, Kate Berlant",
+          "Gina Yashere",
+          "Javid Abdelmoneim",
+          "Ahmad Mazhar, Salah Zo El Faqqar, Nadia Lotfi, Hamdy Gheith, Laila Fawzy, Omar El-Hariri, Laila Taher, Hussein Riad, Mahmoud El Meleigy, Zaki Tolaimat",
+          "Vikas Vasistha, Sandeep Varanasi, Rag Mayur, Trishara, Munivenkatapa, Uma Yg, Sirivennela Yanamandhala, Sindhu Sreenivasa Murthy"
+        ]
+      }
+    },
+    {
+      "name": "country",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.095529,
+        "unique_count": 621,
+        "unique_ratio": 0.097458,
+        "example_values": [
+          "United States",
+          "Denmark, United States",
+          "United Kingdom",
+          "Egypt",
+          "India"
+        ]
+      }
+    },
+    {
+      "name": "date_added",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.001136,
+        "unique_count": 1593,
+        "unique_ratio": 0.226375,
+        "example_values": [
+          "March 30, 2018",
+          "September 30, 2016",
+          "December 31, 2018",
+          "August 1, 2017",
+          "June 18, 2020"
+        ]
+      }
+    },
+    {
+      "name": "release_year",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 74,
+        "unique_ratio": 0.010504,
+        "example_values": [
+          "2018",
+          "2016",
+          "2013",
+          "1963",
+          "2021"
+        ]
+      }
+    },
+    {
+      "name": "rating",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.000568,
+        "unique_count": 15,
+        "unique_ratio": 0.00213,
+        "example_values": [
+          "TV-MA",
+          "TV-14",
+          "R",
+          "PG",
+          "TV-PG"
+        ]
+      }
+    },
+    {
+      "name": "duration",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.000142,
+        "unique_count": 211,
+        "unique_ratio": 0.029955,
+        "example_values": [
+          "78 min",
+          "92 min",
+          "68 min",
+          "58 min",
+          "194 min"
+        ]
+      }
+    },
+    {
+      "name": "listed_in",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 484,
+        "unique_ratio": 0.068701,
+        "example_values": [
+          "Comedies, Romantic Movies",
+          "Documentaries",
+          "Stand-Up Comedy",
+          "Documentaries, International Movies",
+          "Action & Adventure, Classic Movies, Dramas"
+        ]
+      }
+    },
+    {
+      "name": "description",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7026,
+        "unique_ratio": 0.997303,
+        "example_values": [
+          "A quirky couple spends their three-year dating anniversary looking back at their relationship and contemplating whether they should break up.",
+          "She was twice convicted and acquitted of murder. Amanda Knox and the people closest to her case speak out in this illuminating documentary.",
+          "British comic Gina Yashere takes the stage in San Francisco, where she shares her thoughts on everything from toilet ninjas to her troublesome name.",
+          "Emergency room doctor Javid Abdelmoneim endeavors to learn the truth about alcohol, both its benefits and risks, by exploring the science of drinking.",
+          "The Sultan of Egypt and Syria launches a campaign to retake Jerusalem amid the Crusades."
+        ]
+      }
+    }
+  ]
+}

syntheticFail/c17/bayesnet/bayesnet-c17-20260321_075106/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c17",
+  "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": "type",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c17/c17-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c17/c17-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c17/c17-test.csv"
+  }
+}

syntheticFail/c17/bayesnet/bayesnet-c17-20260321_075106/public_gate/staged_input_manifest.json

@@ -0,0 +1,261 @@
+{
+  "dataset_id": "c17",
+  "target_column": "type",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260321_075106/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260321_075106/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260321_075106/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260321_075106/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260321_075106/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "show_id",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7045,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "s4961",
+          "s5783",
+          "s4235",
+          "s8539",
+          "s2374"
+        ]
+      }
+    },
+    {
+      "name": "type",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000284,
+        "example_values": [
+          "Movie",
+          "TV Show"
+        ]
+      }
+    },
+    {
+      "name": "title",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7044,
+        "unique_ratio": 0.999858,
+        "example_values": [
+          "Happy Anniversary",
+          "Amanda Knox",
+          "Gina Yashere: Laughing to America",
+          "The Truth About Alcohol",
+          "Saladin"
+        ]
+      }
+    },
+    {
+      "name": "director",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.299787,
+        "unique_count": 3784,
+        "unique_ratio": 0.767079,
+        "example_values": [
+          "Jared Stern",
+          "Rod Blackhurst, Brian McGinn",
+          "Paul M. Green",
+          "David Briggs",
+          "Youssef Chahine"
+        ]
+      }
+    },
+    {
+      "name": "cast",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
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+        ]
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+          "United Kingdom",
+          "Egypt",
+          "India"
+        ]
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+          "August 1, 2017",
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+        ]
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+          "PG",
+          "TV-PG"
+        ]
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+    {
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+      "role": "feature",
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+      "impute_strategy": "keep_raw",
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+      "role": "feature",
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+        "example_values": [
+          "Comedies, Romantic Movies",
+          "Documentaries",
+          "Stand-Up Comedy",
+          "Documentaries, International Movies",
+          "Action & Adventure, Classic Movies, Dramas"
+        ]
+      }
+    },
+    {
+      "name": "description",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
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+      "profile_stats": {
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+        "unique_ratio": 0.997303,
+        "example_values": [
+          "A quirky couple spends their three-year dating anniversary looking back at their relationship and contemplating whether they should break up.",
+          "She was twice convicted and acquitted of murder. Amanda Knox and the people closest to her case speak out in this illuminating documentary.",
+          "British comic Gina Yashere takes the stage in San Francisco, where she shares her thoughts on everything from toilet ninjas to her troublesome name.",
+          "Emergency room doctor Javid Abdelmoneim endeavors to learn the truth about alcohol, both its benefits and risks, by exploring the science of drinking.",
+          "The Sultan of Egypt and Syria launches a campaign to retake Jerusalem amid the Crusades."
+        ]
+      }
+    }
+  ]
+}

syntheticFail/c17/bayesnet/bayesnet-c17-20260321_075106/runtime_result.json

@@ -0,0 +1,12 @@
+{
+  "dataset_id": "c17",
+  "model": "bayesnet",
+  "run_id": "bayesnet-c17-20260321_075106",
+  "public_gate_status": "pass",
+  "adapter_ready_status": "pass",
+  "train_status": "skipped",
+  "generate_status": "fail",
+  "reason_code": "adapter_runtime_error",
+  "reason_detail": "Command '['docker', 'run', '--rm', '--init', '--cidfile', '/tmp/bench_docker_bayesnet_5n3n2r4v/container.cid', '-v', '/data/jialinzhang/SynthesizePipeline-server:/work', '-w', '/work', 'benchmark:bayesnet-zjl', 'python', '/work/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260321_075106/_bayesnet_generate.py']' returned non-zero exit status 1.",
+  "artifacts": {}
+}

syntheticFail/c17/bayesnet/bayesnet-c17-20260321_075106/staged/bayesnet/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/c17/bayesnet/bayesnet-c17-20260321_075106/staged/bayesnet/model_input_manifest.json"
+}

syntheticFail/c17/bayesnet/bayesnet-c17-20260321_075106/staged/bayesnet/adapter_transforms_applied.json

@@ -0,0 +1 @@
+[]

syntheticFail/c17/bayesnet/bayesnet-c17-20260321_075106/staged/bayesnet/model_input_manifest.json

@@ -0,0 +1,263 @@
+{
+  "dataset_id": "c17",
+  "model": "bayesnet",
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+  "task_type": "classification",
+  "column_schema": [
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+      "role": "id",
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+          "s8539",
+          "s2374"
+        ]
+      }
+    },
+    {
+      "name": "type",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000284,
+        "example_values": [
+          "Movie",
+          "TV Show"
+        ]
+      }
+    },
+    {
+      "name": "title",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7044,
+        "unique_ratio": 0.999858,
+        "example_values": [
+          "Happy Anniversary",
+          "Amanda Knox",
+          "Gina Yashere: Laughing to America",
+          "The Truth About Alcohol",
+          "Saladin"
+        ]
+      }
+    },
+    {
+      "name": "director",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
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+      "impute_strategy": "keep_raw",
+      "profile_stats": {
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+        "unique_ratio": 0.767079,
+        "example_values": [
+          "Jared Stern",
+          "Rod Blackhurst, Brian McGinn",
+          "Paul M. Green",
+          "David Briggs",
+          "Youssef Chahine"
+        ]
+      }
+    },
+    {
+      "name": "cast",
+      "role": "id",
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+        "example_values": [
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+          "Gina Yashere",
+          "Javid Abdelmoneim",
+          "Ahmad Mazhar, Salah Zo El Faqqar, Nadia Lotfi, Hamdy Gheith, Laila Fawzy, Omar El-Hariri, Laila Taher, Hussein Riad, Mahmoud El Meleigy, Zaki Tolaimat",
+          "Vikas Vasistha, Sandeep Varanasi, Rag Mayur, Trishara, Munivenkatapa, Uma Yg, Sirivennela Yanamandhala, Sindhu Sreenivasa Murthy"
+        ]
+      }
+    },
+    {
+      "name": "country",
+      "role": "feature",
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+      "nullable": true,
+      "missing_tokens": [],
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+      "impute_strategy": "keep_raw",
+      "profile_stats": {
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+        "unique_ratio": 0.097458,
+        "example_values": [
+          "United States",
+          "Denmark, United States",
+          "United Kingdom",
+          "Egypt",
+          "India"
+        ]
+      }
+    },
+    {
+      "name": "date_added",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
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+      "profile_stats": {
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+        "unique_ratio": 0.226375,
+        "example_values": [
+          "March 30, 2018",
+          "September 30, 2016",
+          "December 31, 2018",
+          "August 1, 2017",
+          "June 18, 2020"
+        ]
+      }
+    },
+    {
+      "name": "release_year",
+      "role": "feature",
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+      "nullable": false,
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+      "role": "feature",
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+        ]
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+    },
+    {
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+      "role": "id",
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+          "She was twice convicted and acquitted of murder. Amanda Knox and the people closest to her case speak out in this illuminating documentary.",
+          "British comic Gina Yashere takes the stage in San Francisco, where she shares her thoughts on everything from toilet ninjas to her troublesome name.",
+          "Emergency room doctor Javid Abdelmoneim endeavors to learn the truth about alcohol, both its benefits and risks, by exploring the science of drinking.",
+          "The Sultan of Egypt and Syria launches a campaign to retake Jerusalem amid the Crusades."
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260321_075106/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260321_075106/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260321_075106/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260321_075106/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260321_075106/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260321_075106/public_gate/public_gate_report.json"
+}

syntheticFail/c17/bayesnet/bayesnet-c17-20260321_075106/staged/public/staged_features.json

@@ -0,0 +1,62 @@
+[
+  {
+    "feature_name": "show_id",
+    "data_type": "ID",
+    "is_target": false
+  },
+  {
+    "feature_name": "type",
+    "data_type": "categorical",
+    "is_target": true
+  },
+  {
+    "feature_name": "title",
+    "data_type": "ID",
+    "is_target": false
+  },
+  {
+    "feature_name": "director",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "cast",
+    "data_type": "ID",
+    "is_target": false
+  },
+  {
+    "feature_name": "country",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "date_added",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "release_year",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "rating",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "duration",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "listed_in",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "description",
+    "data_type": "ID",
+    "is_target": false
+  }
+]

syntheticFail/c17/bayesnet/bayesnet-c17-20260321_075106/staged/public/test.csv

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+version https://git-lfs.github.com/spec/v1
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+size 339093

syntheticFail/c17/bayesnet/bayesnet-c17-20260321_075106/staged/public/train.csv

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+version https://git-lfs.github.com/spec/v1
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+size 2719568

syntheticFail/c17/bayesnet/bayesnet-c17-20260321_075106/staged/public/val.csv

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+version https://git-lfs.github.com/spec/v1
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+size 341126

syntheticFail/c17/bayesnet/bayesnet-c17-20260321_075106/train_20260321_075106.log

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+version https://git-lfs.github.com/spec/v1
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+size 11711

syntheticFail/c17/tabddpm/tabddpm-c17-20260510_215506/_tabddpm_train.py

@@ -0,0 +1,32 @@
+import os, sys, subprocess
+
+tabddpm_root = "/workspace/tabddpm/code"
+assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}"
+env = os.environ.copy()
+env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", ""))
+
+# Write a wrapper that patches collections.Sequence (removed in Python 3.10+)
+# before running pipeline.py - needed because skorch uses old API
+wrapper = os.path.join(tabddpm_root, "_compat_run.py")
+with open(wrapper, "w") as f:
+    f.write(
+        "import collections, collections.abc\n"
+        "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping',"
+        "'MutableSet','Set','Callable','Iterable','Iterator'):\n"
+        "    if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n"
+        "import sys, runpy\n"
+        "sys.argv = sys.argv[1:]\n"
+        "runpy.run_path(sys.argv[0], run_name='__main__')\n"
+    )
+
+print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/c17/tabddpm/tabddpm-c17-20260510_215506/config.toml")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-Benchmark-trainonly-v1/c17/tabddpm/tabddpm-c17-20260510_215506/config.toml",
+     "--train"],
+    cwd=tabddpm_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+print("[TabDDPM] Training complete")

syntheticFail/c17/tabddpm/tabddpm-c17-20260510_215506/config.toml

@@ -0,0 +1,39 @@
+seed = 0
+parent_dir = "/work/output-Benchmark-trainonly-v1/c17/tabddpm/tabddpm-c17-20260510_215506/output"
+real_data_path = "/work/output-Benchmark-trainonly-v1/c17/tabddpm/tabddpm-c17-20260510_215506/data"
+model_type = "mlp"
+num_numerical_features = 1
+device = "cuda:0"
+
+[model_params]
+d_in = 11
+num_classes = 2
+is_y_cond = true
+
+[model_params.rtdl_params]
+d_layers = [256, 256]
+dropout = 0.0
+
+[diffusion_params]
+num_timesteps = 200
+gaussian_loss_type = "mse"
+
+[train.main]
+steps = 2000
+lr = 0.001
+weight_decay = 0.0
+batch_size = 256
+
+[train.T]
+seed = 0
+normalization = "quantile"
+num_nan_policy = "__none__"
+cat_nan_policy = "__none__"
+cat_min_frequency = "__none__"
+cat_encoding = "__none__"
+y_policy = "default"
+
+[sample]
+num_samples = 1000
+batch_size = 256
+seed = 0

syntheticFail/c17/tabddpm/tabddpm-c17-20260510_215506/data/X_cat_train.npy

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+size 563728

syntheticFail/c17/tabddpm/tabddpm-c17-20260510_215506/data/X_num_train.npy

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+version https://git-lfs.github.com/spec/v1
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+size 28308

syntheticFail/c17/tabddpm/tabddpm-c17-20260510_215506/data/info.json

@@ -0,0 +1,40 @@
+{
+  "name": "benchmark_dataset",
+  "task_type": "multiclass",
+  "n_num_features": 1,
+  "n_cat_features": 10,
+  "train_size": 7045,
+  "num_col_idx": [
+    0
+  ],
+  "cat_col_idx": [
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10
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+  "env_overrides": {
+    "BENCHMARK_TABDDPM_GPUS": "device=3",
+    "TABDDPM_NUM_TIMESTEPS": "200",
+    "TABDDPM_SAMPLE_BATCH_SIZE": "256",
+    "TABDDPM_STEPS_PER_EPOCH": "40",
+    "TABDDPM_TRAIN_BATCH_SIZE": "256",
+    "TABDDPM_TRAIN_LR": "0.001"
+  }
+}

syntheticFail/c17/tabddpm/tabddpm-c17-20260510_215506/runtime_result.json

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+{
+  "dataset_id": "c17",
+  "model": "tabddpm",
+  "run_id": "tabddpm-c17-20260510_215506",
+  "public_gate_status": "pass",
+  "adapter_ready_status": "pass",
+  "train_status": "fail",
+  "generate_status": "skipped",
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+  "artifacts": {},
+  "timings": {
+    "train": {
+      "started_at": "2026-05-10T21:55:06",
+      "ended_at": "2026-05-10T21:55:07",
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+      "ended_at": null,
+      "duration_sec": null
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+          "Gina Yashere",
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+          "2013",
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+          "2021"
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+          "TV-14",
+          "R",
+          "PG",
+          "TV-PG"
+        ]
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+    {
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+        ]
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+          "British comic Gina Yashere takes the stage in San Francisco, where she shares her thoughts on everything from toilet ninjas to her troublesome name.",
+          "Emergency room doctor Javid Abdelmoneim endeavors to learn the truth about alcohol, both its benefits and risks, by exploring the science of drinking.",
+          "The Sultan of Egypt and Syria launches a campaign to retake Jerusalem amid the Crusades."
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+    }
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+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c17/tabddpm/tabddpm-c17-20260510_215506/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c17/tabddpm/tabddpm-c17-20260510_215506/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c17/tabddpm/tabddpm-c17-20260510_215506/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c17/tabddpm/tabddpm-c17-20260510_215506/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c17/tabddpm/tabddpm-c17-20260510_215506/public_gate/public_gate_report.json"
+}

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syntheticFail/c17/tabddpm/tabddpm-c17-20260510_224142/_tabddpm_runtime/.gitignore

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+.DS_Store
+__pycache__/
+catboost_info/
+**/**.pt
+**/**.ipynb
+!agg_results.ipynb
+**/**.npy
+**/**.gz
+**/**.sh
+**/**.obj
+**/**.png
+**/**.tar
+**/**.code-workspace
+**/**.csv
+exp/**/**/results_catboost.json
+exp/**/**/results_mlp.json
+
+configs/
+data/
+junk/
+RF/
+exps/

syntheticFail/c17/tabddpm/tabddpm-c17-20260510_224142/_tabddpm_runtime/.gitmodules

@@ -0,0 +1,9 @@
+[submodule "ctgan"]
+	# path = CTGAN/CTGAN
+	url = https://github.com/sdv-dev/CTGAN
+[submodule "ctabgan"]
+	# path = CTAB-GAN
+	url = https://github.com/Team-TUD/CTAB-GAN
+[submodule "ctabgan+"]
+	# path = CTAB-GAN-Plus
+	url = https://github.com/Team-TUD/CTAB-GAN-Plus

syntheticFail/c17/tabddpm/tabddpm-c17-20260510_224142/_tabddpm_runtime/CONFIG_DESCRIPTION.md

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+# Description of .toml config for TabDDPM
+First of all, `train.T` and `eval.T` denote preprocessing for training and for evaluation, respectively.  
+
+Here we list non-obvious parameters.  
+
+Main part:
+- `seed = 0` -- evaluation seed (and training, but for training it is fixed to 0)
+- `parent_dir = "exp/abalone/check"` -- exp folder
+- `real_data_path = "data/abalone/"`
+- `model_type = "mlp"` -- model type that approximates the reverse process
+- `num_numerical_features ` -- a number of numerical features in dataset
+- `device = "cuda:0"`
+
+Model params:
+- `is_y_cond` -- false for regression, true for classification
+- `d_in` -- input dimension (not necessary, since scripts calculate it automatically)
+- `num_calsses` -- zero for regression, a number of classes for classification
+- `rtdl_params` -- MLP parameters
+
+```toml
+seed = 0
+parent_dir = "exp/abalone/check"
+real_data_path = "data/abalone/"
+model_type = "mlp"
+num_numerical_features = 7
+device = "cuda:0"
+
+[model_params]
+is_y_cond = false
+d_in = 11
+num_classes = 0
+
+[model_params.rtdl_params]
+d_layers = [
+    256,
+    256,
+]
+dropout = 0.0
+
+[diffusion_params]
+num_timesteps = 1000
+gaussian_loss_type = "mse"
+scheduler = "cosine"
+
+[train.main]
+steps = 1000
+lr = 0.001
+weight_decay = 1e-05
+batch_size = 4096
+
+[train.T]
+seed = 0
+normalization = "quantile"
+num_nan_policy = "__none__"
+cat_nan_policy = "__none__"
+cat_min_frequency = "__none__"
+cat_encoding = "__none__"
+y_policy = "default"
+
+[sample]
+num_samples = 20800
+batch_size = 10000
+seed = 0
+
+[eval.type]
+eval_model = "catboost"
+eval_type = "synthetic"
+
+[eval.T]
+seed = 0
+normalization = "__none__"
+num_nan_policy = "__none__"
+cat_nan_policy = "__none__"
+cat_min_frequency = "__none__"
+cat_encoding = "__none__"
+y_policy = "default"
+
+```

syntheticFail/c17/tabddpm/tabddpm-c17-20260510_224142/_tabddpm_runtime/CTAB-GAN-Plus/.gitignore

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+**/**.csv

syntheticFail/c17/tabddpm/tabddpm-c17-20260510_224142/_tabddpm_runtime/CTAB-GAN-Plus/README.md

@@ -0,0 +1,49 @@
+# CTAB-GAN+
+This is the official git paper [CTAB-GAN+: Enhancing Tabular Data Synthesis](https://arxiv.org/abs/2204.00401). Current code is without differential privacy part.
+If you have any question, please contact `z.zhao-8@tudelft.nl` for more information.
+
+
+## Prerequisite
+
+The required package version
+```
+numpy==1.21.0
+torch==1.9.1
+pandas==1.2.4
+sklearn==0.24.1
+dython==0.6.4.post1
+scipy==1.4.1
+```
+The sklean package in newer version has updated its function for `sklearn.mixture.BayesianGaussianMixture`. Therefore, user should use this proposed sklearn version to successfully run the code!
+
+## Example
+`Experiment_Script_Adult.ipynb`  `Experiment_Script_king.ipynb` are two example notebooks for training CTAB-GAN+ with Adult (classification) and king (regression) datasets. The datasets are alread under `Real_Datasets` folder.
+The evaluation code is also provided.
+
+## Problem type
+
+You can either indicate your dataset problem type as Classification, Regression. If there is no problem type, you can leave the problem type as None as follows:
+```
+problem_type= {None: None}
+```
+
+## For large dataset
+
+If your dataset has large number of column, you may encounter the problem that our currnet code cannot encode all of your data since CTAB-GAN+ will wrap the encoded data into an image-like format. What you can do is changing the line 378 and 385 in `model/synthesizer/ctabgan_synthesizer.py`. The number in the `slide` list
+```
+sides = [4, 8, 16, 24, 32]
+```
+is the side size of image. You can enlarge the list to [4, 8, 16, 24, 32, 64] or [4, 8, 16, 24, 32, 64, 128] for accepting larger dataset.
+
+## Bibtex
+
+To cite this paper, you could use this bibtex
+
+```
+@article{zhao2022ctab,
+  title={CTAB-GAN+: Enhancing Tabular Data Synthesis},
+  author={Zhao, Zilong and Kunar, Aditya and Birke, Robert and Chen, Lydia Y},
+  journal={arXiv preprint arXiv:2204.00401},
+  year={2022}
+}
+```

syntheticFail/c17/tabddpm/tabddpm-c17-20260510_224142/_tabddpm_runtime/CTAB-GAN-Plus/columns.json

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+{
+    "churn2": {
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+        "problem_type": {"Classification": "y"}
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+        "integer_columns": ["0", "1", "2", "5"],
+        "general_columns": ["0", "1", "7"],
+        "problem_type": {"Classification": "y"}
+    },
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+        "problem_type": {"Regression": "y"}
+    },
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+        "problem_type": {"Classification": "y"}
+    },
+    "buddy": {
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+        "mixed_columns": {},
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+        "problem_type": {"Classification": "y"}
+    },
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+        "problem_type": {"Classification": "y"}
+    },
+    "wilt": {
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+        "general_columns": ["0", "3"],
+        "problem_type": {"Classification": "y"}
+    },
+    "satellite": {
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+        "mixed_columns": {},
+        "integer_columns": [],
+        "problem_type": {"Classification": "y"}
+    },
+    "higgs-small": {
+        "categorical_columns": ["y"],
+        "mixed_columns": {},
+        "integer_columns": [],
+        "general_columns": ["1", "2", "4", "6", "10", "11", "14", "15", "18","19"],
+        "problem_type": {"Classification": "y"}
+    },
+    "diabetes": {
+        "categorical_columns": ["y"],
+        "mixed_columns": {"3": [0.0], "4": [0.0]},
+        "general_columns": [],
+        "integer_columns": ["0", "1", "2", "5", "7"],
+        "problem_type": {"Classification": "y"}
+    },
+    "abalone": {
+        "categorical_columns": ["7"],
+        "mixed_columns": {},
+        "integer_columns": ["y"],
+        "general_columns": [],
+        "problem_type": {"Regression": "y"}
+    },
+    "insurance": {
+        "categorical_columns": ["3", "4", "5"],
+        "mixed_columns": {},
+        "general_columns": [],
+        "integer_columns": ["0", "2"],
+        "problem_type": {"Regression": "y"}
+    },
+    "king": {
+        "categorical_columns": ["17", "18", "19"],
+        "mixed_columns": {"9": [0.0], "11":[0.0]},
+        "general_columns": ["2", "6", "7"],
+        "integer_columns": ["0", "2", "5", "7", "8", "9", "12"],
+        "problem_type": {"Regression": "y"}
+    },
+    "cardio": {
+        "categorical_columns": ["5", "6", "7", "8", "9", "10", "y"],
+        "mixed_columns": {},
+        "integer_columns": ["0", "1", "3", "4"],
+        "problem_type": {"Classification": "y"}
+    },
+    "house": {
+        "categorical_columns": [],
+        "mixed_columns": {"2": [0.0], "6": [0.0], "8": [0.0], "11": [0.0], "12": [1.0], "14": [0.0]},
+        "general_columns": ["1", "7"],
+        "integer_columns": ["0"],
+        "problem_type": {"Regression": "y"}
+    },
+    "miniboone": {
+        "categorical_columns": ["y"],
+        "mixed_columns": {},
+        "integer_columns": [],
+        "general_columns": ["8", "9", "10", "19", "20", "21", "28", "29", "35", "39", "45", "49"],
+        "problem_type": {"Classification": "y"}
+    },
+    "fb-comments": {
+        "categorical_columns": ["36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50"],
+        "mixed_columns": {"1": [0.0], "8": [0.0], "10": [0.0]},
+        "general_columns": ["26", "36"],
+        "integer_columns": [],
+        "problem_type": {"Regression": "y"}
+    }
+}

syntheticFail/c17/tabddpm/tabddpm-c17-20260510_224142/_tabddpm_runtime/CTAB-GAN-Plus/model copy/ctabgan.py

@@ -0,0 +1,70 @@
+"""
+Generative model training algorithm based on the CTABGANSynthesiser
+
+"""
+import pandas as pd
+import time
+from model.pipeline.data_preparation import DataPrep
+from model.synthesizer.ctabgan_synthesizer import CTABGANSynthesizer
+
+import warnings
+
+warnings.filterwarnings("ignore")
+
+class CTABGAN():
+
+    def __init__(self,
+                 df,
+                 test_ratio = 0.20,
+                 categorical_columns = [ 'workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'gender', 'native-country', 'income'], 
+                 log_columns = [],
+                 mixed_columns= {'capital-loss':[0.0],'capital-gain':[0.0]},
+                 general_columns = ["age"],
+                 non_categorical_columns = [],
+                 integer_columns = ['age', 'fnlwgt','capital-gain', 'capital-loss','hours-per-week'],
+                 problem_type= {"Classification": "income"},
+                 class_dim=(256, 256, 256, 256),
+                 random_dim=100,
+                 num_channels=64,
+                 l2scale=1e-5,
+                 batch_size=500,
+                 epochs=150,
+                 device="cpu"):
+
+        self.__name__ = 'CTABGAN'
+              
+        self.synthesizer = CTABGANSynthesizer(
+                class_dim=class_dim,
+                random_dim=random_dim,
+                num_channels=num_channels,
+                l2scale=l2scale,
+                batch_size=batch_size,
+                epochs=epochs,
+                device=device
+        )
+        self.raw_df = df
+        self.test_ratio = test_ratio
+        self.categorical_columns = categorical_columns
+        self.log_columns = log_columns
+        self.mixed_columns = mixed_columns
+        self.general_columns = general_columns
+        self.non_categorical_columns = non_categorical_columns
+        self.integer_columns = integer_columns
+        self.problem_type = problem_type
+                
+    def fit(self):
+        
+        start_time = time.time()
+        self.data_prep = DataPrep(self.raw_df,self.categorical_columns,self.log_columns,self.mixed_columns,self.general_columns,self.non_categorical_columns,self.integer_columns,self.problem_type,self.test_ratio)
+        self.synthesizer.fit(train_data=self.data_prep.df, categorical = self.data_prep.column_types["categorical"], mixed = self.data_prep.column_types["mixed"],
+        general = self.data_prep.column_types["general"], non_categorical = self.data_prep.column_types["non_categorical"], type=self.problem_type)
+        end_time = time.time()
+        print('Finished training in',end_time-start_time," seconds.")
+
+
+    def generate_samples(self, seed=0):
+        
+        sample = self.synthesizer.sample(len(self.raw_df), seed) 
+        sample_df = self.data_prep.inverse_prep(sample)
+        
+        return sample_df