diff --git a/syntheticSuccess/n16/arf/arf-n16-20260326_090706/_arf_generate.py b/syntheticSuccess/n16/arf/arf-n16-20260326_090706/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..446837f02bd861c06019ecf076c94213198106e4 --- /dev/null +++ b/syntheticSuccess/n16/arf/arf-n16-20260326_090706/_arf_generate.py @@ -0,0 +1,6 @@ +import pickle +with open("/work/output-SpecializedModels/n16/arf/arf-n16-20260326_090706/arf_model.pkl", "rb") as f: + model = pickle.load(f) +syn = model.forge(n=227845) +syn.to_csv("/work/output-SpecializedModels/n16/arf/arf-n16-20260326_090706/arf-n16-227845-20260330_070853.csv", index=False) +print(f"[ARF] Generated 227845 rows -> /work/output-SpecializedModels/n16/arf/arf-n16-20260326_090706/arf-n16-227845-20260330_070853.csv") diff --git a/syntheticSuccess/n16/arf/arf-n16-20260326_090706/_arf_train.py b/syntheticSuccess/n16/arf/arf-n16-20260326_090706/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..7a8266ac0080a88b6a661014e9ce69ac83dbbdb9 --- /dev/null +++ b/syntheticSuccess/n16/arf/arf-n16-20260326_090706/_arf_train.py @@ -0,0 +1,19 @@ +import pickle +import pandas as pd +from arfpy import arf + +df = pd.read_csv("/work/output-SpecializedModels/n16/arf/arf-n16-20260326_090706/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/n16/arf/arf-n16-20260326_090706/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-SpecializedModels/n16/arf/arf-n16-20260326_090706/arf_model.pkl") diff --git a/syntheticSuccess/n16/arf/arf-n16-20260326_090706/arf-n16-1000-20260326_191806.csv 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open("/work/output-SpecializedModels/n16/bayesnet/bayesnet-n16-20260422_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(227845) +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/n16/bayesnet/bayesnet-n16-20260422_060152/bayesnet-n16-227845-20260422_060347.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-SpecializedModels/n16/bayesnet/bayesnet-n16-20260422_060152/bayesnet-n16-227845-20260422_060347.csv") diff --git a/syntheticSuccess/n16/bayesnet/bayesnet-n16-20260422_060152/_bayesnet_train.py b/syntheticSuccess/n16/bayesnet/bayesnet-n16-20260422_060152/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..7d6d43b16a7fca81628895f4e15826d454e7b63d --- /dev/null +++ b/syntheticSuccess/n16/bayesnet/bayesnet-n16-20260422_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/n16/bayesnet/bayesnet-n16-20260422_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/n16/bayesnet/bayesnet-n16-20260422_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/n16/bayesnet/bayesnet-n16-20260422_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/n16/bayesnet/bayesnet-n16-20260422_060152/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/n16/bayesnet/bayesnet-n16-20260422_060152/bayesnet_model.pkl") diff --git a/syntheticSuccess/n16/bayesnet/bayesnet-n16-20260422_060152/bayesnet-n16-227845-20260422_060347.csv b/syntheticSuccess/n16/bayesnet/bayesnet-n16-20260422_060152/bayesnet-n16-227845-20260422_060347.csv new file mode 100644 index 0000000000000000000000000000000000000000..badc14ca3a159a0209f8e11947ef55f79607421c --- /dev/null +++ b/syntheticSuccess/n16/bayesnet/bayesnet-n16-20260422_060152/bayesnet-n16-227845-20260422_060347.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ef573fc616417fe5233e1632dc670c69b7a57d964381eb5fc59d551d25cf7bdb +size 134088022 diff --git a/syntheticSuccess/n16/bayesnet/bayesnet-n16-20260422_060152/bayesnet_coltypes.json b/syntheticSuccess/n16/bayesnet/bayesnet-n16-20260422_060152/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..138683c25b9b5411854cc1c6d27c499a4e740a99 --- /dev/null +++ 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@@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3abf3e2b22202b610a02bb43c40fc2751b5ff791d7c12fbc78ec217fdd5bebb2 +size 15025064 diff --git a/syntheticSuccess/n16/tabddpm/tabddpm-n16-20260321_163935/_tabddpm_sample.py b/syntheticSuccess/n16/tabddpm/tabddpm-n16-20260321_163935/_tabddpm_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..1dcc7a4116cb41f5191a50ded1fa56448b70c861 --- /dev/null +++ b/syntheticSuccess/n16/tabddpm/tabddpm-n16-20260321_163935/_tabddpm_sample.py @@ -0,0 +1,66 @@ +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 227845 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/n16/tabddpm/tabddpm-n16-20260321_163935/config_sample_20260425_080506.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-SpecializedModels/n16/tabddpm/tabddpm-n16-20260321_163935/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-SpecializedModels/n16/tabddpm/tabddpm-n16-20260321_163935/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/n16/tabddpm/tabddpm-n16-20260321_163935/tabddpm-n16-227845-20260425_080506.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/n16/tabddpm/tabddpm-n16-20260321_163935/tabddpm-n16-227845-20260425_080506.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticSuccess/n16/tabddpm/tabddpm-n16-20260321_163935/_tabddpm_train.py b/syntheticSuccess/n16/tabddpm/tabddpm-n16-20260321_163935/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c74b289d4d61db77d43d38b0e990881186f34e56 --- /dev/null +++ b/syntheticSuccess/n16/tabddpm/tabddpm-n16-20260321_163935/_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/n16/tabddpm/tabddpm-n16-20260321_163935/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/n16/tabddpm/tabddpm-n16-20260321_163935/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/syntheticSuccess/n16/tabddpm/tabddpm-n16-20260321_163935/config.toml 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b/syntheticSuccess/n16/tabddpm/tabddpm-n16-20260321_163935/train_20260321_164024.log new file mode 100644 index 0000000000000000000000000000000000000000..491068e265cf2145c073ca63e05d7411347534d1 --- /dev/null +++ b/syntheticSuccess/n16/tabddpm/tabddpm-n16-20260321_163935/train_20260321_164024.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9d97f19f5263cc42094902e2af95a80c4690e0fd6154dc21bf0fcfeb7199dec3 +size 1124 diff --git a/syntheticSuccess/n16/tabsyn/tabsyn-n16-20260426_220916/_tabsyn_sample.py b/syntheticSuccess/n16/tabsyn/tabsyn-n16-20260426_220916/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..510c4ac08b77792525813eefe3feafea28ffc728 --- /dev/null +++ b/syntheticSuccess/n16/tabsyn/tabsyn-n16-20260426_220916/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/n16/tabsyn/tabsyn-n16-20260426_220916" +dataname = "tabsyn_n16" +output_csv = "/work/output-SpecializedModels/n16/tabsyn/tabsyn-n16-20260426_220916/tabsyn-n16-227845-20260427_002515.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 227845 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/n16/tabsyn/tabsyn-n16-20260426_220916/_tabsyn_train.py b/syntheticSuccess/n16/tabsyn/tabsyn-n16-20260426_220916/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..1c8bdb43456d42a6bf742f39c9b7629f826f811c --- /dev/null +++ b/syntheticSuccess/n16/tabsyn/tabsyn-n16-20260426_220916/_tabsyn_train.py @@ -0,0 +1,63 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/n16/tabsyn/tabsyn-n16-20260426_220916" +dataname = "tabsyn_n16" +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") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "1024") +_te = 1000 +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/n16/tabsyn/tabsyn-n16-20260426_220916/data/tabsyn_n16/X_cat_test.npy b/syntheticSuccess/n16/tabsyn/tabsyn-n16-20260426_220916/data/tabsyn_n16/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..010b9b9a432ea415d8a994e8bfd064d4a83d555c --- /dev/null +++ b/syntheticSuccess/n16/tabsyn/tabsyn-n16-20260426_220916/data/tabsyn_n16/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ad912bdeee70b2f0dcc962ecfee2edb74ccdc3743624cf91fe758fdf219a1068 +size 128 diff --git 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file mode 100644 index 0000000000000000000000000000000000000000..97fc14eab160326066fc8bb68ae49a90dc7317a0 --- /dev/null +++ b/syntheticSuccess/n16/tabsyn/tabsyn-n16-20260426_220916/tabsyn-n16-227845-20260427_002515.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:468e1df6fd87c21b4962f794d9355d6eba657bbaf9412107fa848196fce558d9 +size 75938879 diff --git a/syntheticSuccess/n16/tabsyn/tabsyn-n16-20260426_220916/train_20260426_221025.log b/syntheticSuccess/n16/tabsyn/tabsyn-n16-20260426_220916/train_20260426_221025.log new file mode 100644 index 0000000000000000000000000000000000000000..212746726db9baf6f914c4e29d9ddfdfbf41bfdd --- /dev/null +++ b/syntheticSuccess/n16/tabsyn/tabsyn-n16-20260426_220916/train_20260426_221025.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:27a0e6a472152db0f33fbc2388e3682c8aa7aa74254a7c95da3c0940940d1a5a +size 9203438 diff --git a/syntheticSuccess/n16/tvae/tvae-n16-20260328_053742/_tvae_generate.py b/syntheticSuccess/n16/tvae/tvae-n16-20260328_053742/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..399063a5190ff548aea28ec8a078013c09cfbb34 --- /dev/null +++ b/syntheticSuccess/n16/tvae/tvae-n16-20260328_053742/_tvae_generate.py @@ -0,0 +1,5 @@ +from ctgan.synthesizers.tvae import TVAE +model = TVAE.load("/work/output-SpecializedModels/n16/tvae/tvae-n16-20260328_053742/models_300epochs/tvae_300epochs.pt") +samples = model.sample(227845) +samples.to_csv("/work/output-SpecializedModels/n16/tvae/tvae-n16-20260328_053742/tvae-n16-227845-20260330_070842.csv", index=False) +print(f"[TVAE] Generated 227845 rows -> /work/output-SpecializedModels/n16/tvae/tvae-n16-20260328_053742/tvae-n16-227845-20260330_070842.csv") diff --git a/syntheticSuccess/n16/tvae/tvae-n16-20260328_053742/_tvae_train.py b/syntheticSuccess/n16/tvae/tvae-n16-20260328_053742/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..923572adbf702c9b2ebcaa2d768042e171ae4aae --- /dev/null +++ b/syntheticSuccess/n16/tvae/tvae-n16-20260328_053742/_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/n16/tvae/tvae-n16-20260328_053742/staged/public/train.csv" +meta_path = "/work/output-SpecializedModels/n16/tvae/tvae-n16-20260328_053742/tvae_metadata.json" +save_path = "/work/output-SpecializedModels/n16/tvae/tvae-n16-20260328_053742/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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