diff --git a/syntheticSuccess/c8/arf/arf-c8-20260321_064711/_arf_generate.py b/syntheticSuccess/c8/arf/arf-c8-20260321_064711/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..65ab6e350d4aaa8bcbfea8fd3413aadc13028e4d --- /dev/null +++ b/syntheticSuccess/c8/arf/arf-c8-20260321_064711/_arf_generate.py @@ -0,0 +1,6 @@ +import pickle +with open("/work/output-SpecializedModels/c8/arf/arf-c8-20260321_064711/arf_model.pkl", "rb") as f: + model = pickle.load(f) +syn = model.forge(n=8844) +syn.to_csv("/work/output-SpecializedModels/c8/arf/arf-c8-20260321_064711/arf-c8-8844-20260330_065322.csv", index=False) +print(f"[ARF] Generated 8844 rows -> /work/output-SpecializedModels/c8/arf/arf-c8-20260321_064711/arf-c8-8844-20260330_065322.csv") diff --git a/syntheticSuccess/c8/arf/arf-c8-20260321_064711/_arf_train.py b/syntheticSuccess/c8/arf/arf-c8-20260321_064711/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..158fcac35389f6c73d10f7ddc68953641c2892ff --- /dev/null +++ b/syntheticSuccess/c8/arf/arf-c8-20260321_064711/_arf_train.py @@ -0,0 +1,19 @@ +import pickle +import pandas as pd +from arfpy import arf + +df = pd.read_csv("/work/output-SpecializedModels/c8/arf/arf-c8-20260321_064711/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/c8/arf/arf-c8-20260321_064711/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-SpecializedModels/c8/arf/arf-c8-20260321_064711/arf_model.pkl") diff --git a/syntheticSuccess/c8/arf/arf-c8-20260321_064711/arf-c8-1000-20260321_065504.csv b/syntheticSuccess/c8/arf/arf-c8-20260321_064711/arf-c8-1000-20260321_065504.csv new file mode 100644 index 0000000000000000000000000000000000000000..6521b40d9ad1e9d97445da365aca661e0f537a01 --- /dev/null +++ b/syntheticSuccess/c8/arf/arf-c8-20260321_064711/arf-c8-1000-20260321_065504.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f5a4495f80333e74642bf880fc4d9bcc2bc374d67231a435d84f9c80046de974 +size 293128 diff --git a/syntheticSuccess/c8/arf/arf-c8-20260321_064711/arf-c8-8844-20260330_065322.csv b/syntheticSuccess/c8/arf/arf-c8-20260321_064711/arf-c8-8844-20260330_065322.csv new file mode 100644 index 0000000000000000000000000000000000000000..018a554b1cae3212c7f7d09403f71767094b71a6 --- /dev/null +++ b/syntheticSuccess/c8/arf/arf-c8-20260321_064711/arf-c8-8844-20260330_065322.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6c6486f0ffe8e9b829a0bc005d1aebf555163795e14368c0f7e3c0d0a77ac6b7 +size 2613474 diff --git a/syntheticSuccess/c8/arf/arf-c8-20260321_064711/arf_model.pkl b/syntheticSuccess/c8/arf/arf-c8-20260321_064711/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..6e3c1c9ca953f5ff7dd96186acda2923d1a81b2f --- /dev/null +++ b/syntheticSuccess/c8/arf/arf-c8-20260321_064711/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:effa83e3fbc89afd2023fa25189b775cdb037e85e4bbf296f83a535c9a513803 +size 79043840 diff --git a/syntheticSuccess/c8/arf/arf-c8-20260321_064711/gen_20260321_065504.log b/syntheticSuccess/c8/arf/arf-c8-20260321_064711/gen_20260321_065504.log new file mode 100644 index 0000000000000000000000000000000000000000..08b97e4a8b74bc23706d963ec5cc0452891c4ec7 --- /dev/null +++ b/syntheticSuccess/c8/arf/arf-c8-20260321_064711/gen_20260321_065504.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:40d4c54536918b53a641a2d7972bfdb14362fcee901348ca21c043529b71e6eb +size 438 diff --git 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a/syntheticSuccess/c8/arf/arf-c8-20260321_064711/public_gate/public_gate_report.json b/syntheticSuccess/c8/arf/arf-c8-20260321_064711/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..83cfe39b0f0de2f423da5f2d1826ec0f830ae0b7 --- /dev/null +++ b/syntheticSuccess/c8/arf/arf-c8-20260321_064711/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c8", + "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": "having_At_Symbol", + "task_type": "classification", + "input_splits": { + 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"semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000226, + "example_values": [ + "-1", + "1" + ] + } + }, + { + "name": "Google_Index", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000226, + "example_values": [ + "1", + "-1" + ] + } + }, + { + "name": "Links_pointing_to_page", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000339, + "example_values": [ + "0", + "1", + "-1" + ] + } + }, + { + "name": "Statistical_report", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000226, + "example_values": [ + "1", + "-1" + ] + } + }, + { + "name": "Result", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000226, + "example_values": [ + "-1", + "1" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c8/arf/arf-c8-20260321_064711/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c8/arf/arf-c8-20260321_064711/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c8/arf/arf-c8-20260321_064711/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c8/arf/arf-c8-20260321_064711/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c8/arf/arf-c8-20260321_064711/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c8/arf/arf-c8-20260321_064711/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/c8/arf/arf-c8-20260321_064711/staged/public/staged_features.json b/syntheticSuccess/c8/arf/arf-c8-20260321_064711/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..6e070cd9cc9dc9bf44f5e96b48aa03b0af05735a --- /dev/null +++ b/syntheticSuccess/c8/arf/arf-c8-20260321_064711/staged/public/staged_features.json @@ -0,0 +1,157 @@ +[ + { + "feature_name": "having_IP_Address", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "URL_Length", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Shortining_Service", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "having_At_Symbol", + "data_type": "continuous", + "is_target": true + }, + { + "feature_name": "double_slash_redirecting", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Prefix_Suffix", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "having_Sub_Domain", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "SSLfinal_State", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Domain_registeration_length", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Favicon", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "port", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "HTTPS_token", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Request_URL", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "URL_of_Anchor", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Links_in_tags", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "SFH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Submitting_to_email", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Abnormal_URL", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Redirect", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "on_mouseover", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "RightClick", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "popUpWidnow", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Iframe", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "age_of_domain", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "DNSRecord", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "web_traffic", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Page_Rank", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Google_Index", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Links_pointing_to_page", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Statistical_report", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Result", + "data_type": "continuous", + "is_target": false + } +] \ No newline at end of file diff --git a/syntheticSuccess/c8/arf/arf-c8-20260321_064711/staged/public/test.csv b/syntheticSuccess/c8/arf/arf-c8-20260321_064711/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..17380f6a9b9a9e8ac335908874f6bc5b26ec25ad --- /dev/null +++ b/syntheticSuccess/c8/arf/arf-c8-20260321_064711/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:75e0809428463e710fffe7443c63202bb0df096f71365262a3e32e43e365fa35 +size 79280 diff --git a/syntheticSuccess/c8/arf/arf-c8-20260321_064711/staged/public/train.csv b/syntheticSuccess/c8/arf/arf-c8-20260321_064711/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..8cfd1f6fb29a5bdf13202de8d9026d5d4a6c1ab2 --- /dev/null +++ b/syntheticSuccess/c8/arf/arf-c8-20260321_064711/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:88a639183f8fa6acc2a2465b4d787607df6b931c2b681b0e9d0e1be007fea0da +size 631235 diff --git a/syntheticSuccess/c8/arf/arf-c8-20260321_064711/staged/public/val.csv b/syntheticSuccess/c8/arf/arf-c8-20260321_064711/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..739e1e854fc6485ff594dcf0a66b76423871eeb8 --- /dev/null +++ b/syntheticSuccess/c8/arf/arf-c8-20260321_064711/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:272551def8b1e81583201f55065453d772f25b8dd17958d4f25478c033769f9e +size 79486 diff --git a/syntheticSuccess/c8/arf/arf-c8-20260321_064711/train_20260321_064712.log b/syntheticSuccess/c8/arf/arf-c8-20260321_064711/train_20260321_064712.log new file mode 100644 index 0000000000000000000000000000000000000000..10439a65c078393d077cf920fae93788a2a1aeb4 --- /dev/null +++ b/syntheticSuccess/c8/arf/arf-c8-20260321_064711/train_20260321_064712.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:670e17d84cada8394a0f90855ec9a012bcb85293d01b76e983f2573546a1e658 +size 348 diff --git a/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/_bayesnet_generate.py b/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..3df52ffe08857b7d9911836313976975930e5c91 --- /dev/null +++ b/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/_bayesnet_generate.py @@ -0,0 +1,43 @@ +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/c8/bayesnet/bayesnet-c8-20260321_061937/bayesnet_model.pkl", "rb") as f: + plugin = pickle.load(f) +syn = plugin.generate(count=8844).dataframe() + +# Restore zero-variance columns that were dropped during training +const_path = "/work/output-SpecializedModels/c8/bayesnet/bayesnet-c8-20260321_061937/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/c8/bayesnet/bayesnet-c8-20260321_061937/bayesnet-c8-8844-20260330_065323.csv", index=False) +print(f"[BayesNet] Generated 8844 rows -> /work/output-SpecializedModels/c8/bayesnet/bayesnet-c8-20260321_061937/bayesnet-c8-8844-20260330_065323.csv") diff --git a/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/_bayesnet_train.py b/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..92306823866b151413cffcbe1e8be3134db09a03 --- /dev/null +++ b/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/_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/c8/bayesnet/bayesnet-c8-20260321_061937/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/c8/bayesnet/bayesnet-c8-20260321_061937/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/c8/bayesnet/bayesnet-c8-20260321_061937/bayesnet_model.pkl", "wb") as f: + pickle.dump(plugin, f) +print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c8/bayesnet/bayesnet-c8-20260321_061937/bayesnet_model.pkl") diff --git a/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/bayesnet-c8-1000-20260321_062026.csv b/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/bayesnet-c8-1000-20260321_062026.csv new file mode 100644 index 0000000000000000000000000000000000000000..2acc4925cb100211c52bd5b5e779f12aa29534e1 --- /dev/null +++ b/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/bayesnet-c8-1000-20260321_062026.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:093fdfcb674298ece354a51158ee1e65fa9f754f8c30d8c3d134c4f3dfa487f8 +size 71773 diff --git a/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/bayesnet-c8-8844-20260330_065323.csv b/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/bayesnet-c8-8844-20260330_065323.csv new file mode 100644 index 0000000000000000000000000000000000000000..d6e09d0fe0917b7385e357bad2668f678ba03af1 --- /dev/null +++ b/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/bayesnet-c8-8844-20260330_065323.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7a599f0103d279ccc84fd66e7d0e0dbf6323177db7094754bd81cf357e6149ea +size 632535 diff --git a/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/bayesnet_model.pkl b/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..a98deb43a795c24874f3b19d9173cdc88a65d062 --- /dev/null +++ b/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6a5e8cc2f33b835874bff37c0a95a2caa3cebdf48009819dd9eb3f9d09160f83 +size 4467884 diff --git a/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/const_cols.json b/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/gen_20260321_062026.log b/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/gen_20260321_062026.log new file mode 100644 index 0000000000000000000000000000000000000000..49597f6b8a1b3a5a9c5cf882a2e460e30156dd8e --- /dev/null +++ b/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/gen_20260321_062026.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d57368ab6251d8ea92df3ab89243cabc53f431af5aa04cf9dfaa33f8a6474c21 +size 232 diff --git a/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/gen_20260330_065323.log b/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/gen_20260330_065323.log new file mode 100644 index 0000000000000000000000000000000000000000..82a81c09f24f404ec65b4490e20671de7b7b56c3 --- /dev/null +++ b/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/gen_20260330_065323.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a5f33a28ed6b87584e589d98920ec074d2036d479906169e8f786e70c043656d +size 232 diff --git a/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/input_snapshot.json b/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..d714fda9c2a669ed6a3c26e041de3e8c3afc60f2 --- /dev/null +++ b/syntheticSuccess/c8/bayesnet/bayesnet-c8-20260321_061937/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c8", + "model": "bayesnet", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c8/c8-train.csv", + "exists": true, + "size": 640080, + "sha256": 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"/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c8/realtabformer/rtf-c8-20260328_150848/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c8/realtabformer/rtf-c8-20260328_150848/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c8/realtabformer/rtf-c8-20260328_150848/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c8/realtabformer/rtf-c8-20260328_150848/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c8/realtabformer/rtf-c8-20260328_150848/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c8/realtabformer/rtf-c8-20260328_150848/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/c8/realtabformer/rtf-c8-20260328_150848/train_20260328_150848.log b/syntheticSuccess/c8/realtabformer/rtf-c8-20260328_150848/train_20260328_150848.log new file mode 100644 index 0000000000000000000000000000000000000000..009c6ab158806b20120ddfbbe08617156cddb730 --- /dev/null +++ b/syntheticSuccess/c8/realtabformer/rtf-c8-20260328_150848/train_20260328_150848.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0bc8571bb7ace49b37c2300a920aded06d7cdd621c3843f67d9e577cea48625b +size 1988132 diff --git a/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/_tabddpm_sample.py b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/_tabddpm_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..f2f8c91bcf6d9fb2a3321cdd4fc5abe516ecd910 --- /dev/null +++ b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/_tabddpm_sample.py @@ -0,0 +1,67 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +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", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + 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] Sampling 8844 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/c8/tabddpm/tabddpm-c8-20260422_212506/config_sample_20260422_212734.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV +work_dir = "/work/output-SpecializedModels/c8/tabddpm/tabddpm-c8-20260422_212506" +info_path = os.path.join(work_dir, "data", "info.json") +with open(info_path) as f: + info = json.load(f) + +output_dir = os.path.join(work_dir, "output") +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-SpecializedModels/c8/tabddpm/tabddpm-c8-20260422_212506/tabddpm-c8-8844-20260422_212734.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/c8/tabddpm/tabddpm-c8-20260422_212506/tabddpm-c8-8844-20260422_212734.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/_tabddpm_train.py b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..88262eb7975da8a761922407a6203dd5a6bbcdf2 --- /dev/null +++ b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/_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-SpecializedModels/c8/tabddpm/tabddpm-c8-20260422_212506/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/c8/tabddpm/tabddpm-c8-20260422_212506/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/config.toml b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..66101225440856dfbb3ff24a0e05941479677f58 --- /dev/null +++ b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/c8/tabddpm/tabddpm-c8-20260422_212506/output" +real_data_path = "/work/output-SpecializedModels/c8/tabddpm/tabddpm-c8-20260422_212506/data" +model_type = "mlp" +num_numerical_features = 29 +device = "cuda:0" + +[model_params] +d_in = 30 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +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 = 1000 +seed = 0 diff --git a/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/config_sample_20260422_212734.toml b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/config_sample_20260422_212734.toml new file mode 100644 index 0000000000000000000000000000000000000000..1dc15534379fee6102729e51b5d37d6bab3027c8 --- /dev/null +++ b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/config_sample_20260422_212734.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/c8/tabddpm/tabddpm-c8-20260422_212506/output" +real_data_path = "/work/output-SpecializedModels/c8/tabddpm/tabddpm-c8-20260422_212506/data" +model_type = "mlp" +num_numerical_features = 29 +device = "cuda:0" + +[model_params] +d_in = 30 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +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 = 8844 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/data/X_cat_train.npy b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..8a277b12126796ea2dbd676ad44ea373580c3e43 --- /dev/null +++ b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:69c080f550f12c0813332ad1406d251d5a9cbbba6c83d4b2f87dcc0d05e52d3f +size 70880 diff --git a/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/data/X_num_train.npy b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/data/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..2e661cb976ef9b8467c9c574f362a8501ef7b947 --- /dev/null +++ b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/data/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:75dae443e666996315960652398f63cf5f6b43b5807f9203e0c699f3acccc87e +size 1026032 diff --git a/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/data/info.json b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..85ea9a604efb27eeb50e0ed6c61825e44f02edc9 --- /dev/null +++ b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/data/info.json @@ -0,0 +1,78 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 29, + "n_cat_features": 1, + "train_size": 8844, + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28 + ], + "cat_col_idx": [ + 29 + ], + "target_col_idx": [ + 30 + ], + "column_names": [ + "having_IP_Address", + "URL_Length", + "Shortining_Service", + "double_slash_redirecting", + "Prefix_Suffix", + "having_Sub_Domain", + "SSLfinal_State", + "Domain_registeration_length", + "Favicon", + "port", + "HTTPS_token", + "Request_URL", + "URL_of_Anchor", + "Links_in_tags", + "SFH", + "Submitting_to_email", + "Abnormal_URL", + "on_mouseover", + "RightClick", + "popUpWidnow", + "Iframe", + "age_of_domain", + "DNSRecord", + "web_traffic", + "Page_Rank", + "Google_Index", + "Links_pointing_to_page", + "Statistical_report", + "Result", + "Redirect", + "having_At_Symbol" + ], + "num_classes": 2 +} \ No newline at end of file diff --git a/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/data/y_train.npy b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/data/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..c33a2812d34d5af016bc01c619c1fb3df03de5ad --- /dev/null +++ b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/data/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d8ac3cb9a9b23e0c3e08da4c4d46fe060013b300acb48580e9c603d7a332fdae +size 70880 diff --git a/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/gen_20260422_212734.log 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0000000000000000000000000000000000000000..6b68f2438e0b1714c4c995469da92f5255ec3d9b --- /dev/null +++ b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/output/X_num_unnorm.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1382b842e03dbcfb8c670cd96001f80d0bcbe1a3dddf8fe519c35a345b4eb9b1 +size 2051936 diff --git a/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/output/config.toml b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/output/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..1dc15534379fee6102729e51b5d37d6bab3027c8 --- /dev/null +++ b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/output/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/c8/tabddpm/tabddpm-c8-20260422_212506/output" +real_data_path = "/work/output-SpecializedModels/c8/tabddpm/tabddpm-c8-20260422_212506/data" +model_type = "mlp" +num_numerical_features = 29 +device = "cuda:0" + +[model_params] +d_in = 30 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +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 = 8844 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/output/info.json b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/output/info.json new file mode 100644 index 0000000000000000000000000000000000000000..85ea9a604efb27eeb50e0ed6c61825e44f02edc9 --- /dev/null +++ b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/output/info.json @@ -0,0 +1,78 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 29, + "n_cat_features": 1, + "train_size": 8844, + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28 + ], + "cat_col_idx": [ + 29 + ], + "target_col_idx": [ + 30 + ], + "column_names": [ + "having_IP_Address", + "URL_Length", + "Shortining_Service", + "double_slash_redirecting", + "Prefix_Suffix", + "having_Sub_Domain", + "SSLfinal_State", + "Domain_registeration_length", + "Favicon", + "port", + "HTTPS_token", + "Request_URL", + "URL_of_Anchor", + "Links_in_tags", + "SFH", + "Submitting_to_email", + "Abnormal_URL", + "on_mouseover", + "RightClick", + "popUpWidnow", + "Iframe", + "age_of_domain", + "DNSRecord", + "web_traffic", + "Page_Rank", + "Google_Index", + "Links_pointing_to_page", + "Statistical_report", + "Result", + "Redirect", + "having_At_Symbol" + ], + "num_classes": 2 +} \ No newline at end of file diff --git a/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/output/loss.csv b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/output/loss.csv new file mode 100644 index 0000000000000000000000000000000000000000..8917f819bf566813f22f24b9664272524dcdbbe5 --- /dev/null +++ b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/output/loss.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f4c837c3f01fdfbb02ea5774bfd47bd6944ecc00e4597f5d3187e680c445f48c +size 1552 diff --git a/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/output/model.pt b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/output/model.pt new file mode 100644 index 0000000000000000000000000000000000000000..5a0cbeed35198f77ec18917ffccc2b9889140ff6 --- /dev/null +++ b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/output/model.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:434c489fb411d073e9d382d5f4367c1cb5ff796df05d8e627de00891482f66a0 +size 581270 diff --git a/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/output/model_ema.pt b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/output/model_ema.pt new file mode 100644 index 0000000000000000000000000000000000000000..7aaed92216e894e6e0eb2934bbd400bf7d23bc3b --- /dev/null +++ b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/output/model_ema.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:de99047e52e91e5cb5b51d6b03d1e232344fa93b01ca93ea095859fbe8ccdde8 +size 582114 diff --git a/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/output/y_train.npy b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/output/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..c937429c5578e6a75ab37b82ecef2712944e497f --- /dev/null +++ b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/output/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ff0ab81196cc9437f63f6dd52dcb08c1efe8a9746c360819a58c202da7213522 +size 70880 diff --git a/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/public_gate/normalized_schema_snapshot.json b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..4960e55b9c70bbd4faa7f9fb0d30142240a3173c --- /dev/null +++ b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,573 @@ +{ + "dataset_id": "c8", + "target_column": "having_At_Symbol", + "task_type": "classification", + "columns": [ + { + "name": "having_IP_Address", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000226, + "example_values": [ + "-1", + "1" + ] + } + }, + { + "name": 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a/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/tabddpm-c8-8844-20260422_212734.csv b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/tabddpm-c8-8844-20260422_212734.csv new file mode 100644 index 0000000000000000000000000000000000000000..6cee45048fc750c767ef63189f0236cad74cc50d --- /dev/null +++ b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/tabddpm-c8-8844-20260422_212734.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:140250234bc394f83e50020a1e1a95328a9872dc3e70aaba1337a858806c4010 +size 1240305 diff --git a/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/train_20260422_212506.log b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/train_20260422_212506.log new file mode 100644 index 0000000000000000000000000000000000000000..8cd5f2dd22772261baa373391d3db709b43bbe0f --- /dev/null +++ b/syntheticSuccess/c8/tabddpm/tabddpm-c8-20260422_212506/train_20260422_212506.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1a20e5d9b590f5af263799b598fb811c0bcbfa9a9b8db24b85728ab11028d4aa +size 844 diff --git a/syntheticSuccess/c8/tabpfgen/c8-migrated-20260422_193053/_tabpfgen_generate.py b/syntheticSuccess/c8/tabpfgen/c8-migrated-20260422_193053/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..0285c9c71da366bb2ee20eb57968e1d468af7197 --- /dev/null +++ b/syntheticSuccess/c8/tabpfgen/c8-migrated-20260422_193053/_tabpfgen_generate.py @@ -0,0 +1,87 @@ +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c8/staged/public/train.csv") +target_col = "having_At_Symbol" + +feature_cols = [c for c in df.columns if c != target_col] + +# --- Label-encode categorical / object columns --- +cat_encodings = {} # col -> list of unique values (index = code) +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +# Encode target if categorical +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +target_n = int(8844) + +# Handle NaN +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device="auto", +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_regression") +X_syn, y_syn = gen.generate_regression(X, y, n_samples=target_n) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +# --- Inverse label-encoding for categorical columns --- +for col, cats in cat_encodings.items(): + # Round to nearest integer index, clamp to valid range + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +# Ensure output row count is strictly aligned with target_n. +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat([syn_df.reset_index(drop=True), extra.reset_index(drop=True)], ignore_index=True) + else: + # Defensive fallback: if generator returns empty, bootstrap from training rows. + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError(f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}") +syn_df.to_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c8/tabpfgen-c8-8844-20260422_191741.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c8/tabpfgen-c8-8844-20260422_191741.csv") diff --git a/syntheticSuccess/c8/tabpfgen/c8-migrated-20260422_193053/gen_20260422_191741.log b/syntheticSuccess/c8/tabpfgen/c8-migrated-20260422_193053/gen_20260422_191741.log new file mode 100644 index 0000000000000000000000000000000000000000..4610ba259138c3cbb3b5aa0a6e6058c368eebb47 --- /dev/null +++ b/syntheticSuccess/c8/tabpfgen/c8-migrated-20260422_193053/gen_20260422_191741.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c45e6758ca8e092d29139323359ef568b2b0e63ba2ce84d0a197b5a5f7d99cc3 +size 524 diff --git a/syntheticSuccess/c8/tabpfgen/c8-migrated-20260422_193053/input_snapshot.json b/syntheticSuccess/c8/tabpfgen/c8-migrated-20260422_193053/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..d8c7d8d008fc9b33ea9712d6bb74d9fe8a9933d3 --- /dev/null +++ b/syntheticSuccess/c8/tabpfgen/c8-migrated-20260422_193053/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c8", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c8/c8-train.csv", + "exists": true, + "size": 640080, + "sha256": "b79676a58294f2aad042d087ff1dcfc051164efcf602109c878dd1b722a610fc" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c8/c8-val.csv", + "exists": true, + "size": 80592, + "sha256": "47f84e81fc7ae28ac74afb86ed4a196ca5f4a5b6de2decf3a0fea52e9020c008" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c8/c8-test.csv", + "exists": true, + "size": 80387, + "sha256": "3f9f79d3671796c3b632f50938278cd9fe2a692c1849e80d5cd5680cd3d561ca" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c8/c8-dataset_profile.json", + "exists": true, + "size": 12087, + "sha256": "1dae703bc78790b4f9ab775d4aff0c39a375e8f7854b0d8783d1afd8e2e99e95" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c8/c8-dataset_contract_v1.json", + "exists": true, + "size": 13516, + "sha256": "6ae07dba7e830a4eea5110098aa6554dac7e956097b742e39f0cd27386c58789" + } + } +} \ No newline at end of file diff --git a/syntheticSuccess/c8/tabpfgen/c8-migrated-20260422_193053/public_gate/normalized_schema_snapshot.json b/syntheticSuccess/c8/tabpfgen/c8-migrated-20260422_193053/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..4960e55b9c70bbd4faa7f9fb0d30142240a3173c --- /dev/null +++ b/syntheticSuccess/c8/tabpfgen/c8-migrated-20260422_193053/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,573 @@ +{ + "dataset_id": "c8", + "target_column": "having_At_Symbol", + "task_type": "classification", + "columns": [ + { + "name": "having_IP_Address", + 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"/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c8/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c8/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c8/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/c8/tabpfgen/c8-migrated-20260422_193053/tabpfgen-c8-8844-20260422_191741.csv b/syntheticSuccess/c8/tabpfgen/c8-migrated-20260422_193053/tabpfgen-c8-8844-20260422_191741.csv new file mode 100644 index 0000000000000000000000000000000000000000..005e1488731975ac7dda9cf4b547f4ad51205bb8 --- /dev/null +++ b/syntheticSuccess/c8/tabpfgen/c8-migrated-20260422_193053/tabpfgen-c8-8844-20260422_191741.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9f5886c0b965ea1e4b86084f07e9b261910d4b11f6d04b9729553da214cf068d +size 3058159 diff --git a/syntheticSuccess/c8/tabsyn/tabsyn-c8-20260420_233446/_tabsyn_sample.py b/syntheticSuccess/c8/tabsyn/tabsyn-c8-20260420_233446/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..c73476069743d4b75e938d1b55e65dbfecaacc9f --- /dev/null +++ b/syntheticSuccess/c8/tabsyn/tabsyn-c8-20260420_233446/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/c8/tabsyn/tabsyn-c8-20260420_233446" +dataname = "tabsyn_c8" +output_csv = "/work/output-SpecializedModels/c8/tabsyn/tabsyn-c8-20260420_233446/tabsyn-c8-8844-20260421_005633.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 8844 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/syntheticSuccess/c8/tabsyn/tabsyn-c8-20260420_233446/_tabsyn_train.py b/syntheticSuccess/c8/tabsyn/tabsyn-c8-20260420_233446/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..ff34bc76b698905396459be9030c7f9390eb9a43 --- /dev/null +++ b/syntheticSuccess/c8/tabsyn/tabsyn-c8-20260420_233446/_tabsyn_train.py @@ -0,0 +1,62 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/c8/tabsyn/tabsyn-c8-20260420_233446" +dataname = "tabsyn_c8" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/syntheticSuccess/c8/tabsyn/tabsyn-c8-20260420_233446/data/tabsyn_c8/X_cat_test.npy b/syntheticSuccess/c8/tabsyn/tabsyn-c8-20260420_233446/data/tabsyn_c8/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..a0d0c45b055dba17e398c88de15bb6131481c671 --- /dev/null +++ b/syntheticSuccess/c8/tabsyn/tabsyn-c8-20260420_233446/data/tabsyn_c8/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:468d42b6ff814799b4ab69b4ae0dd508904f3c83e1024f87e04192e160d99640 +size 8976 diff --git a/syntheticSuccess/c8/tabsyn/tabsyn-c8-20260420_233446/data/tabsyn_c8/X_cat_train.npy b/syntheticSuccess/c8/tabsyn/tabsyn-c8-20260420_233446/data/tabsyn_c8/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..232a9214366ebf286aad01a698f6309f68a671cb --- /dev/null +++ b/syntheticSuccess/c8/tabsyn/tabsyn-c8-20260420_233446/data/tabsyn_c8/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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b/syntheticSuccess/c8/tabsyn/tabsyn-c8-20260420_233446/tabsyn-c8-8844-20260421_005633.csv new file mode 100644 index 0000000000000000000000000000000000000000..b6e86a755085cc00bb7bb9168883c129ce41411d --- /dev/null +++ b/syntheticSuccess/c8/tabsyn/tabsyn-c8-20260420_233446/tabsyn-c8-8844-20260421_005633.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9f756d9ca131c19d59c84e53769380a1708d787571cedfbcbb06a7a792a5b3a7 +size 1307446 diff --git a/syntheticSuccess/c8/tabsyn/tabsyn-c8-20260420_233446/train_20260420_233447.log b/syntheticSuccess/c8/tabsyn/tabsyn-c8-20260420_233446/train_20260420_233447.log new file mode 100644 index 0000000000000000000000000000000000000000..e9c6f73ba959191db1887b3bc85020b0a5c56296 --- /dev/null +++ b/syntheticSuccess/c8/tabsyn/tabsyn-c8-20260420_233446/train_20260420_233447.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:10e070eebbfb69ee6b4df59c383e4bc74577257ad0598ce6bf3bae8841297a50 +size 3119682 diff --git a/syntheticSuccess/c8/tvae/tvae-c8-20260321_070945/_tvae_generate.py b/syntheticSuccess/c8/tvae/tvae-c8-20260321_070945/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..a057c8319418ba8ca4b2060a98ed42d7fd3eff6b --- /dev/null +++ b/syntheticSuccess/c8/tvae/tvae-c8-20260321_070945/_tvae_generate.py @@ -0,0 +1,5 @@ +from ctgan.synthesizers.tvae import TVAE +model = TVAE.load("/work/output-SpecializedModels/c8/tvae/tvae-c8-20260321_070945/models_300epochs/tvae_300epochs.pt") +samples = model.sample(8844) +samples.to_csv("/work/output-SpecializedModels/c8/tvae/tvae-c8-20260321_070945/tvae-c8-8844-20260330_065316.csv", index=False) +print(f"[TVAE] Generated 8844 rows -> /work/output-SpecializedModels/c8/tvae/tvae-c8-20260321_070945/tvae-c8-8844-20260330_065316.csv") diff --git a/syntheticSuccess/c8/tvae/tvae-c8-20260321_070945/_tvae_train.py b/syntheticSuccess/c8/tvae/tvae-c8-20260321_070945/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..073d7e4b9a06534b861300311000d6ee57f6b53b --- /dev/null +++ b/syntheticSuccess/c8/tvae/tvae-c8-20260321_070945/_tvae_train.py @@ -0,0 +1,16 @@ +import json, sys +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +csv_path = "/work/output-SpecializedModels/c8/tvae/tvae-c8-20260321_070945/staged/public/train.csv" +meta_path = "/work/output-SpecializedModels/c8/tvae/tvae-c8-20260321_070945/tvae_metadata.json" +save_path = "/work/output-SpecializedModels/c8/tvae/tvae-c8-20260321_070945/models_300epochs/tvae_300epochs.pt" +epochs = 300 + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}") +model = TVAE(epochs=epochs, batch_size=500) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git 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