diff --git a/.gitattributes b/.gitattributes index e5f6e93545e9e6d67d6f915f9ef223129597d2aa..a9d17db1a38b2417f8821fead62b9fe43126ce34 100644 --- a/.gitattributes +++ b/.gitattributes @@ -4653,3 +4653,21 @@ SynthData0523/main/c5/tvae/tvae-c5-20260321_070511/staged/public/train.csv filte SynthData0523/main/c5/tvae/tvae-c5-20260321_070511/staged/public/val.csv filter=lfs diff=lfs merge=lfs -text SynthData0523/main/c5/tvae/tvae-c5-20260321_070511/tvae-c5-1000-20260321_070708.csv filter=lfs diff=lfs merge=lfs -text SynthData0523/main/c5/tvae/tvae-c5-20260321_070511/tvae-c5-6732-20260330_065259.csv filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/c6/arf/arf-c6-20260422_055912/arf-c6-7636-20260422_060120.csv filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/c6/arf/arf-c6-20260422_055912/arf_model.pkl filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/c6/arf/arf-c6-20260422_055912/gen_20260422_060120.log filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/c6/arf/arf-c6-20260422_055912/staged/public/test.csv filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/c6/arf/arf-c6-20260422_055912/staged/public/train.csv filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/c6/arf/arf-c6-20260422_055912/staged/public/val.csv filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/c6/arf/arf-c6-20260422_055912/train_20260422_055912.log filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/bayesnet-c6-7636-20260422_060304.csv filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/bayesnet_model.pkl filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/gen_20260422_060304.log filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/staged/public/test.csv filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/staged/public/train.csv filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/staged/public/val.csv filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/train_20260422_060152.log filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/ctgan-c6-7636-20260422_030517.csv filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/gen_20260422_030517.log filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/models_300epochs/ctgan_300epochs.pt filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/models_300epochs/train_20260422_025941.log filter=lfs diff=lfs merge=lfs -text diff --git a/SynthData0523/main/c6/arf/arf-c6-20260422_055912/_arf_generate.py b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..9bbd5682615880cafb58547a8b2cce5c4389baf4 --- /dev/null +++ b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/_arf_generate.py @@ -0,0 +1,23 @@ +import pickle +import pandas as pd + +n_target = int(7636) +with open("/work/output-SpecializedModels/c6/arf/arf-c6-20260422_055912/arf_model.pkl", "rb") as f: + model = pickle.load(f) +syn = model.forge(n=n_target) +syn = syn.reset_index(drop=True) +if len(syn) > n_target: + syn = syn.iloc[:n_target] +elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = model.forge(n=max(need, 1)).reset_index(drop=True) + if len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] +syn.to_csv("/work/output-SpecializedModels/c6/arf/arf-c6-20260422_055912/arf-c6-7636-20260422_060120.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-SpecializedModels/c6/arf/arf-c6-20260422_055912/arf-c6-7636-20260422_060120.csv") diff --git a/SynthData0523/main/c6/arf/arf-c6-20260422_055912/_arf_train.py b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..790fc9c830a5eb9ed79bba3713678483381ce874 --- /dev/null +++ b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/_arf_train.py @@ -0,0 +1,37 @@ +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + lo, hi = df[col].quantile(0.001), df[col].quantile(0.999) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-SpecializedModels/c6/arf/arf-c6-20260422_055912/staged/public/train.csv") +df = _sanitize_for_arf(df) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") + +model = arf.arf(x=df) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-SpecializedModels/c6/arf/arf-c6-20260422_055912/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-SpecializedModels/c6/arf/arf-c6-20260422_055912/arf_model.pkl") diff --git a/SynthData0523/main/c6/arf/arf-c6-20260422_055912/arf-c6-7636-20260422_060120.csv b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/arf-c6-7636-20260422_060120.csv new file mode 100644 index 0000000000000000000000000000000000000000..65d70843c73f6891ab6fcc9587b8b9352eeb703a --- /dev/null +++ b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/arf-c6-7636-20260422_060120.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:599df750830e11cf227b39b152ad292827a3b5671b67ece0a788ab082d1730cc +size 1049122 diff --git a/SynthData0523/main/c6/arf/arf-c6-20260422_055912/arf_model.pkl b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..e3eb5e888cebbfca77a59232ace875cd5b268030 --- /dev/null +++ b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:305c25ca33e81129d5fa7eb09eb315f78cb0f71835f6dd49930ad5b0c0e4b90e +size 24861828 diff --git a/SynthData0523/main/c6/arf/arf-c6-20260422_055912/gen_20260422_060120.log b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/gen_20260422_060120.log new file mode 100644 index 0000000000000000000000000000000000000000..aba23b9c0741b3b8c3d456aeda8f822691c23582 --- /dev/null +++ b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/gen_20260422_060120.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:baafbab42422c39eb0b2bdeea60a2a5705952a6945683a21985c3e822c0e1754 +size 1719 diff --git a/SynthData0523/main/c6/arf/arf-c6-20260422_055912/input_snapshot.json b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..165b543a77e5b9a8f79f31fdf96a244bad6f72bf --- /dev/null +++ b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c6", + "model": "arf", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c6/c6-train.csv", + "exists": true, + "size": 849500, + "sha256": "7d8f85a52de0e63e292778c26cb06223383b366c589d4226c3de68b111ba5272" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c6/c6-val.csv", + "exists": true, + "size": 108137, + "sha256": "9ede9f1e2036e743d822e8ed8d7b5e1050159e8fc7b402b758a294f7a14528fe" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c6/c6-test.csv", + "exists": true, + "size": 107696, + "sha256": "d28b60b361526450f0c203ddf50498854cb66ad5c1978516a99c265f529f8e4f" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c6/c6-dataset_profile.json", + "exists": true, + "size": 4145, + "sha256": "70c4d3f4f544b9bff7543f502136d9b1403d8589ad5ef0a9695842d8ef9d5185" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c6/c6-dataset_contract_v1.json", + "exists": true, + "size": 4740, + "sha256": "602750e8159221cf97836d44d530098411b5f2cd6fc47c06776171da79d06593" + } + } +} \ No newline at end of file diff --git a/SynthData0523/main/c6/arf/arf-c6-20260422_055912/public_gate/normalized_schema_snapshot.json b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..62de672c6242a8fe33594746bc19ea183e203ce7 --- /dev/null +++ b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,169 @@ +{ + "dataset_id": "c6", + "target_column": "Type of Answer", + "task_type": "classification", + "columns": [ + { + "name": "Student ID", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 367, + "unique_ratio": 0.048062, + "example_values": [ + "473", + "351", + "967", + "1557", + "394" + ] + } + }, + { + "name": "Student Country", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 8, + "unique_ratio": 0.001048, + "example_values": [ + "Portugal", + "Italy", + "Lithuania", + "Slovenia", + "Ireland" + ] + } + }, + { + "name": "Question ID", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 796, + "unique_ratio": 0.104243, + "example_values": [ + "346", + "796", + "453", + "87", + "325" + ] + } + }, + { + "name": "Type of Answer", + "role": "target", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000262, + "example_values": [ + "0", + "1" + ] + } + }, + { + "name": "Question Level", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000262, + "example_values": [ + "Advanced", + "Basic" + ] + } + }, + { + "name": "Topic", + "role": "feature", + "semantic_type": "text", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 14, + "unique_ratio": 0.001833, + "example_values": [ + "Complex Numbers", + "Fundamental Mathematics", + "Linear Algebra", + "Real Functions of a single variable", + "Analytic Geometry" + ] + } + }, + { + "name": "Subtopic", + "role": "feature", + "semantic_type": "text", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 24, + "unique_ratio": 0.003143, + "example_values": [ + "Complex Numbers", + "Algebraic expressions, Equations, and Inequalities", + "Vector Spaces", + "Limits and Continuity", + "Linear Transformations" + ] + } + }, + { + "name": "Keywords", + "role": "feature", + "semantic_type": "text", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 360, + "unique_ratio": 0.047145, + "example_values": [ + "Imaginary part,Modulus of a complex number,Operations with complex numbers,Conjugate number,Real part", + "Logarithmic function,Exponential function,Simplify expressions", + "Linear independence,Span,Linear dependence", + "Indeterminate forms,Limits", + "Range,Kernel" + ] + } + } + ] +} \ No newline at end of file diff --git a/SynthData0523/main/c6/arf/arf-c6-20260422_055912/public_gate/public_gate_report.json b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..68d5e4a7e5533d3434a96c463bb1367c9d25256e --- /dev/null +++ b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c6", + "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 of Answer", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c6/c6-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c6/c6-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c6/c6-test.csv" + } +} \ No newline at end of file diff --git a/SynthData0523/main/c6/arf/arf-c6-20260422_055912/public_gate/staged_input_manifest.json b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..2d91e2ad8567b11f0b5e9f678080f8c309c4d414 --- /dev/null +++ b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/public_gate/staged_input_manifest.json @@ -0,0 +1,174 @@ +{ + "dataset_id": "c6", + "target_column": "Type of Answer", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/arf/arf-c6-20260422_055912/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/arf/arf-c6-20260422_055912/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/arf/arf-c6-20260422_055912/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/arf/arf-c6-20260422_055912/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/arf/arf-c6-20260422_055912/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "Student ID", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 367, + "unique_ratio": 0.048062, + "example_values": [ + "473", + "351", + "967", + "1557", + "394" + ] + } + }, + { + "name": "Student Country", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 8, + "unique_ratio": 0.001048, + "example_values": [ + "Portugal", + "Italy", + "Lithuania", + "Slovenia", + "Ireland" + ] + } + }, + { + "name": "Question ID", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 796, + "unique_ratio": 0.104243, + "example_values": [ + "346", + "796", + "453", + "87", + "325" + ] + } + }, + { + "name": "Type of Answer", + "role": "target", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000262, + "example_values": [ + "0", + "1" + ] + } + }, + { + "name": "Question Level", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000262, + "example_values": [ + "Advanced", + "Basic" + ] + } + }, + { + "name": "Topic", + "role": "feature", + "semantic_type": "text", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 14, + "unique_ratio": 0.001833, + "example_values": [ + "Complex Numbers", + "Fundamental Mathematics", + "Linear Algebra", + "Real Functions of a single variable", + "Analytic Geometry" + ] + } + }, + { + "name": "Subtopic", + "role": "feature", + "semantic_type": "text", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 24, + "unique_ratio": 0.003143, + "example_values": [ + "Complex Numbers", + "Algebraic expressions, Equations, and Inequalities", + "Vector Spaces", + "Limits and Continuity", + "Linear Transformations" + ] + } + }, + { + "name": "Keywords", + "role": "feature", + "semantic_type": "text", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 360, + "unique_ratio": 0.047145, + "example_values": [ + "Imaginary part,Modulus of a complex number,Operations with complex numbers,Conjugate number,Real part", + "Logarithmic function,Exponential function,Simplify expressions", + "Linear independence,Span,Linear dependence", + "Indeterminate forms,Limits", + "Range,Kernel" + ] + } + } + ] +} \ No newline at end of file diff --git a/SynthData0523/main/c6/arf/arf-c6-20260422_055912/runtime_result.json b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..af87f21066af8eca2e62d6ca02a50b76aa9d9cc6 --- /dev/null +++ b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/runtime_result.json @@ -0,0 +1,15 @@ +{ + "dataset_id": "c6", + "model": "arf", + "run_id": "arf-c6-20260422_055912", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/arf/arf-c6-20260422_055912/arf-c6-7636-20260422_060120.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/arf/arf-c6-20260422_055912/arf_model.pkl" + } +} \ No newline at end of file diff --git a/SynthData0523/main/c6/arf/arf-c6-20260422_055912/staged/arf/adapter_report.json b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/staged/arf/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..26428e5de6d3f2de0d734632d70ffc0c1de67887 --- /dev/null +++ b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/staged/arf/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/c6/arf/arf-c6-20260422_055912/staged/arf/model_input_manifest.json" +} \ No newline at end of file diff --git a/SynthData0523/main/c6/arf/arf-c6-20260422_055912/staged/arf/adapter_transforms_applied.json b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/staged/arf/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/staged/arf/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/SynthData0523/main/c6/arf/arf-c6-20260422_055912/staged/arf/model_input_manifest.json b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/staged/arf/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..98cecf6af6fc941deee9c3ebcd2ab2fdbd542cbc --- /dev/null +++ b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/staged/arf/model_input_manifest.json @@ -0,0 +1,176 @@ +{ + "dataset_id": "c6", + "model": "arf", + "target_column": "Type of Answer", + "task_type": "classification", + "column_schema": [ + { + "name": "Student ID", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 367, + "unique_ratio": 0.048062, + "example_values": [ + "473", + "351", + "967", + "1557", + "394" + ] + } + }, + { + "name": "Student Country", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 8, + "unique_ratio": 0.001048, + "example_values": [ + "Portugal", + "Italy", + "Lithuania", + "Slovenia", + "Ireland" + ] + } + }, + { + "name": "Question ID", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 796, + "unique_ratio": 0.104243, + "example_values": [ + "346", + "796", + "453", + "87", + "325" + ] + } + }, + { + "name": "Type of Answer", + "role": "target", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000262, + "example_values": [ + "0", + "1" + ] + } + }, + { + "name": "Question Level", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000262, + "example_values": [ + "Advanced", + "Basic" + ] + } + }, + { + "name": "Topic", + "role": "feature", + "semantic_type": "text", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 14, + "unique_ratio": 0.001833, + "example_values": [ + "Complex Numbers", + "Fundamental Mathematics", + "Linear Algebra", + "Real Functions of a single variable", + "Analytic Geometry" + ] + } + }, + { + "name": "Subtopic", + "role": "feature", + "semantic_type": "text", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 24, + "unique_ratio": 0.003143, + "example_values": [ + "Complex Numbers", + "Algebraic expressions, Equations, and Inequalities", + "Vector Spaces", + "Limits and Continuity", + "Linear Transformations" + ] + } + }, + { + "name": "Keywords", + "role": "feature", + "semantic_type": "text", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 360, + "unique_ratio": 0.047145, + "example_values": [ + "Imaginary part,Modulus of a complex number,Operations with complex numbers,Conjugate number,Real part", + "Logarithmic function,Exponential function,Simplify expressions", + "Linear independence,Span,Linear dependence", + "Indeterminate forms,Limits", + "Range,Kernel" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/arf/arf-c6-20260422_055912/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/arf/arf-c6-20260422_055912/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/arf/arf-c6-20260422_055912/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/arf/arf-c6-20260422_055912/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/arf/arf-c6-20260422_055912/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/arf/arf-c6-20260422_055912/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/SynthData0523/main/c6/arf/arf-c6-20260422_055912/staged/public/staged_features.json b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..5523fb7a16718db5c4122f8381d3cff51e337c45 --- /dev/null +++ b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/staged/public/staged_features.json @@ -0,0 +1,42 @@ +[ + { + "feature_name": "Student ID", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Student Country", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Question ID", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Type of Answer", + "data_type": "binary", + "is_target": true + }, + { + "feature_name": "Question Level", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Topic", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Subtopic", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Keywords", + "data_type": "categorical", + "is_target": false + } +] \ No newline at end of file diff --git a/SynthData0523/main/c6/arf/arf-c6-20260422_055912/staged/public/test.csv b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..221052bae020346f46e1c71d36fce7eb347ed62d --- /dev/null +++ b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d28b60b361526450f0c203ddf50498854cb66ad5c1978516a99c265f529f8e4f +size 107696 diff --git a/SynthData0523/main/c6/arf/arf-c6-20260422_055912/staged/public/train.csv b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..28f63ae8128dbf03647f9cda7458db51d98581f5 --- /dev/null +++ b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7d8f85a52de0e63e292778c26cb06223383b366c589d4226c3de68b111ba5272 +size 849500 diff --git a/SynthData0523/main/c6/arf/arf-c6-20260422_055912/staged/public/val.csv b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..400c35bc93bd5a660ae20c7574c7bea7bc24035f --- /dev/null +++ b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9ede9f1e2036e743d822e8ed8d7b5e1050159e8fc7b402b758a294f7a14528fe +size 108137 diff --git a/SynthData0523/main/c6/arf/arf-c6-20260422_055912/train_20260422_055912.log b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/train_20260422_055912.log new file mode 100644 index 0000000000000000000000000000000000000000..b2681ba4d7278696ce72617e9259bb09bf7f6332 --- /dev/null +++ b/SynthData0523/main/c6/arf/arf-c6-20260422_055912/train_20260422_055912.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3ad4fe1b3113c746d8adc3d872ee211266d85a3dd6688227da9569c0ad8f3f93 +size 289 diff --git a/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/_bayesnet_generate.py b/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..f8423a54fd6d6e70b3da2f898801502cfc68134e --- /dev/null +++ b/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/_bayesnet_generate.py @@ -0,0 +1,104 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-SpecializedModels/c6/bayesnet/bayesnet-c6-20260422_060152/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(7636) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final.to_csv("/work/output-SpecializedModels/c6/bayesnet/bayesnet-c6-20260422_060152/bayesnet-c6-7636-20260422_060304.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-SpecializedModels/c6/bayesnet/bayesnet-c6-20260422_060152/bayesnet-c6-7636-20260422_060304.csv") diff --git a/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/_bayesnet_train.py b/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..d6c6895bc60aac0a0bdd26965b6d955ae772e3f7 --- /dev/null +++ b/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/_bayesnet_train.py @@ -0,0 +1,118 @@ + +import json +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-SpecializedModels/c6/bayesnet/bayesnet-c6-20260422_060152/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-SpecializedModels/c6/bayesnet/bayesnet-c6-20260422_060152/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-SpecializedModels/c6/bayesnet/bayesnet-c6-20260422_060152/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = 10 +if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 +if _n_plan > 55: + max_bins = 4 +print(f"[BayesNet] max_bins={max_bins} (cols_in_df={_n_plan}, rows={_n_samples})") + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + uniques = sorted(s.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > 25000: + enc_struct = enc.sample(n=25000, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct).estimate(show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +network.fit(enc) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-SpecializedModels/c6/bayesnet/bayesnet-c6-20260422_060152/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c6/bayesnet/bayesnet-c6-20260422_060152/bayesnet_model.pkl") diff --git a/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/bayesnet-c6-7636-20260422_060304.csv b/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/bayesnet-c6-7636-20260422_060304.csv new file mode 100644 index 0000000000000000000000000000000000000000..153c2d194324b90aa58b47e714840e04cbc7c79b --- /dev/null +++ b/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/bayesnet-c6-7636-20260422_060304.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9dc9bfad4d7f78d8f802bd200dad9a0a88c3665230a565b1d194f4d89839d2ee +size 1058662 diff --git a/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/bayesnet_coltypes.json b/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..1b5e9b2571099fabb160b233af3b2c15f6b9fd97 --- /dev/null +++ b/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/bayesnet_coltypes.json @@ -0,0 +1,37 @@ +{ + "columns": [ + { + "name": "Student ID", + "type": "continuous" + }, + { + "name": "Student Country", + "type": "categorical" + }, + { + "name": "Question ID", + "type": "continuous" + }, + { + "name": "Type of Answer", + "type": "categorical" + }, + { + "name": "Question Level", + "type": "categorical" + }, + { + "name": "Topic", + "type": "categorical" + }, + { + "name": "Subtopic", + "type": "categorical" + }, + { + "name": "Keywords", + "type": "categorical" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/bayesnet_model.pkl 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"feature_name": "Question ID", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Type of Answer", + "data_type": "binary", + "is_target": true + }, + { + "feature_name": "Question Level", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Topic", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Subtopic", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Keywords", + "data_type": "categorical", + "is_target": false + } +] \ No newline at end of file diff --git a/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/staged/public/test.csv b/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..221052bae020346f46e1c71d36fce7eb347ed62d --- /dev/null +++ b/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d28b60b361526450f0c203ddf50498854cb66ad5c1978516a99c265f529f8e4f +size 107696 diff --git a/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/staged/public/train.csv b/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..28f63ae8128dbf03647f9cda7458db51d98581f5 --- /dev/null +++ b/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7d8f85a52de0e63e292778c26cb06223383b366c589d4226c3de68b111ba5272 +size 849500 diff --git a/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/staged/public/val.csv b/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..400c35bc93bd5a660ae20c7574c7bea7bc24035f --- /dev/null +++ b/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9ede9f1e2036e743d822e8ed8d7b5e1050159e8fc7b402b758a294f7a14528fe +size 108137 diff --git a/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/train_20260422_060152.log b/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/train_20260422_060152.log new file mode 100644 index 0000000000000000000000000000000000000000..2443106230311bc831c9105a9c63d363dd3540b4 --- /dev/null +++ b/SynthData0523/main/c6/bayesnet/bayesnet-c6-20260422_060152/train_20260422_060152.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dc2aa7b0c2aa4d0c44a3c3173b854e55e1e6900bc02e85df242bbf2e60c0e961 +size 3444 diff --git a/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/_ctgan_generate.py b/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..079de08754d4cefd9661c4b86244f5000fdf4ad6 --- /dev/null +++ b/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-SpecializedModels/c6/ctgan/ctgan-c6-20260422_025941/models_300epochs/ctgan_300epochs.pt") +total = 7636 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-SpecializedModels/c6/ctgan/ctgan-c6-20260422_025941/ctgan-c6-7636-20260422_030517.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-SpecializedModels/c6/ctgan/ctgan-c6-20260422_025941/ctgan-c6-7636-20260422_030517.csv") \ No newline at end of file diff --git a/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/ctgan-c6-7636-20260422_030517.csv b/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/ctgan-c6-7636-20260422_030517.csv new file mode 100644 index 0000000000000000000000000000000000000000..abeab461eb48c83f788f2e06239779c53b9940f7 --- /dev/null +++ b/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/ctgan-c6-7636-20260422_030517.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c8574499d27c66e25c1b1448d26ed51aeb9d65ee11b173509931b6819b98514 +size 850526 diff --git a/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/ctgan_metadata.json b/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..fd44f1730c28205b8349695531f19bedb2470087 --- /dev/null +++ b/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/ctgan_metadata.json @@ -0,0 +1,36 @@ +{ + "columns": [ + { + "name": "Student ID", + "type": "continuous" + }, + { + "name": "Student Country", + "type": "categorical" + }, + { + "name": "Question ID", + "type": "continuous" + }, + { + "name": "Type of Answer", + "type": "categorical" + }, + { + "name": "Question Level", + "type": "categorical" + }, + { + "name": "Topic", + "type": "categorical" + }, + { + "name": "Subtopic", + "type": "categorical" + }, + { + "name": "Keywords", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/gen_20260422_030517.log b/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/gen_20260422_030517.log new file mode 100644 index 0000000000000000000000000000000000000000..216e52308d8116af2be685c0c3a1fa49ec074b2a --- /dev/null +++ b/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/gen_20260422_030517.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c6dd494ebb6158864cd3a7114813febb0efc2fc6b624a0c97ea35cc53e634ef9 +size 292 diff --git a/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/input_snapshot.json b/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..6168765e31eaf2f2f4406b02a88b7b33fb522782 --- /dev/null +++ b/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c6", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c6/c6-train.csv", + "exists": true, + "size": 849500, + "sha256": "7d8f85a52de0e63e292778c26cb06223383b366c589d4226c3de68b111ba5272" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c6/c6-val.csv", + "exists": true, + "size": 108137, + "sha256": "9ede9f1e2036e743d822e8ed8d7b5e1050159e8fc7b402b758a294f7a14528fe" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c6/c6-test.csv", + "exists": true, + "size": 107696, + "sha256": "d28b60b361526450f0c203ddf50498854cb66ad5c1978516a99c265f529f8e4f" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c6/c6-dataset_profile.json", + "exists": true, + "size": 4145, + "sha256": "70c4d3f4f544b9bff7543f502136d9b1403d8589ad5ef0a9695842d8ef9d5185" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c6/c6-dataset_contract_v1.json", + "exists": true, + "size": 4740, + "sha256": "602750e8159221cf97836d44d530098411b5f2cd6fc47c06776171da79d06593" + } + } +} \ No newline at end of file diff --git a/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/models_300epochs/ctgan_300epochs.pt b/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/models_300epochs/ctgan_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..eb18aa2f8b25e88a3b10f2371146e2efd13164ba --- /dev/null +++ b/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/models_300epochs/ctgan_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bd0eabb3aac5a61c6a2aebd14017b03a67781edf7d756d16605680ca30bedbc2 +size 3909987 diff --git a/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/models_300epochs/train_20260422_025941.log b/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/models_300epochs/train_20260422_025941.log new file mode 100644 index 0000000000000000000000000000000000000000..1cf211d293ea30684d7f5d4c0cf09b3eb2c039e1 --- /dev/null +++ b/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/models_300epochs/train_20260422_025941.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3bde6982a751134bac113ace027f4d8793e4b43b1707a1ac2da670f71e259c30 +size 1722 diff --git a/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/public_gate/normalized_schema_snapshot.json b/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..62de672c6242a8fe33594746bc19ea183e203ce7 --- /dev/null +++ b/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,169 @@ +{ + "dataset_id": "c6", + "target_column": "Type of Answer", + "task_type": "classification", + "columns": [ + { + "name": "Student ID", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 367, + "unique_ratio": 0.048062, + "example_values": [ + "473", + "351", + "967", + "1557", + "394" + ] + } + }, + { + "name": "Student Country", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 8, + "unique_ratio": 0.001048, + "example_values": [ + "Portugal", + "Italy", + "Lithuania", + "Slovenia", + "Ireland" + ] + } + }, + { + "name": "Question ID", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 796, + "unique_ratio": 0.104243, + "example_values": [ + "346", + "796", + "453", + "87", + "325" + ] + } + }, + { + "name": "Type of Answer", + "role": "target", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000262, + "example_values": [ + "0", + "1" + ] + } + }, + { + "name": "Question Level", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000262, + "example_values": [ + "Advanced", + "Basic" + ] + } + }, + { + "name": "Topic", + "role": "feature", + "semantic_type": "text", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 14, + "unique_ratio": 0.001833, + "example_values": [ + "Complex Numbers", + "Fundamental Mathematics", + "Linear Algebra", + "Real Functions of a single variable", + "Analytic Geometry" + ] + } + }, + { + "name": "Subtopic", + "role": "feature", + "semantic_type": "text", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 24, + "unique_ratio": 0.003143, + "example_values": [ + "Complex Numbers", + "Algebraic expressions, Equations, and Inequalities", + "Vector Spaces", + "Limits and Continuity", + "Linear Transformations" + ] + } + }, + { + "name": "Keywords", + "role": "feature", + "semantic_type": "text", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 360, + "unique_ratio": 0.047145, + "example_values": [ + "Imaginary part,Modulus of a complex number,Operations with complex numbers,Conjugate number,Real part", + "Logarithmic function,Exponential function,Simplify expressions", + "Linear independence,Span,Linear dependence", + "Indeterminate forms,Limits", + "Range,Kernel" + ] + } + } + ] +} \ No newline at end of file diff --git a/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/public_gate/public_gate_report.json b/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..68d5e4a7e5533d3434a96c463bb1367c9d25256e --- /dev/null +++ b/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c6", + "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 of Answer", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c6/c6-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c6/c6-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c6/c6-test.csv" + } +} \ No newline at end of file diff --git a/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/public_gate/staged_input_manifest.json b/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..236d7c4c0060068f5fa4641dba42b33c06a40151 --- /dev/null +++ b/SynthData0523/main/c6/ctgan/ctgan-c6-20260422_025941/public_gate/staged_input_manifest.json @@ -0,0 +1,174 @@ +{ + "dataset_id": "c6", + "target_column": "Type of Answer", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c6/ctgan/ctgan-c6-20260422_025941/staged/public/train.csv", + "val_csv": 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Country", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 8, + "unique_ratio": 0.001048, + "example_values": [ + "Portugal", + "Italy", + "Lithuania", + "Slovenia", + "Ireland" + ] + } + }, + { + "name": "Question ID", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 796, + "unique_ratio": 0.104243, + "example_values": [ + "346", + "796", + "453", + "87", + "325" + ] + } + }, + { + "name": "Type of Answer", + "role": "target", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000262, + 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