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b/SynthData0523/main/m5/tabpfgen/tabpfgen-m5-20260422_200335/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..b0b4750975b183345b1ef759525ec7c1fbe4bf91 --- /dev/null +++ b/SynthData0523/main/m5/tabpfgen/tabpfgen-m5-20260422_200335/_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/output-SpecializedModels/m5/tabpfgen/tabpfgen-m5-20260422_200335/staged/public/train.csv") +target_col = "Target" + +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(3539) + +# 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_classification") +X_syn, y_syn = gen.generate_classification(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/output-SpecializedModels/m5/tabpfgen/tabpfgen-m5-20260422_200335/tabpfgen-m5-3539-20260422_200336.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-SpecializedModels/m5/tabpfgen/tabpfgen-m5-20260422_200335/tabpfgen-m5-3539-20260422_200336.csv") diff --git a/SynthData0523/main/m5/tabpfgen/tabpfgen-m5-20260422_200335/gen_20260422_200336.log b/SynthData0523/main/m5/tabpfgen/tabpfgen-m5-20260422_200335/gen_20260422_200336.log new file mode 100644 index 0000000000000000000000000000000000000000..fae09de7950860a250ad9061382e3322f8ac3b5c --- /dev/null +++ b/SynthData0523/main/m5/tabpfgen/tabpfgen-m5-20260422_200335/gen_20260422_200336.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:23fa57b6d1c42b7a1d99443ef2aa04ada11a213b6f360cb5e38c7ef1463c78c2 +size 592 diff --git a/SynthData0523/main/m5/tabpfgen/tabpfgen-m5-20260422_200335/input_snapshot.json b/SynthData0523/main/m5/tabpfgen/tabpfgen-m5-20260422_200335/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..3113f538dabc9cfd3ea70cc3b6426de07065106b --- /dev/null +++ b/SynthData0523/main/m5/tabpfgen/tabpfgen-m5-20260422_200335/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m5", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": 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a/SynthData0523/main/m5/tabpfgen/tabpfgen-m5-20260422_200335/tabpfgen_meta.json b/SynthData0523/main/m5/tabpfgen/tabpfgen-m5-20260422_200335/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..6151fc5241047c8d93afe128c45d3e82a9c640ba --- /dev/null +++ b/SynthData0523/main/m5/tabpfgen/tabpfgen-m5-20260422_200335/tabpfgen_meta.json @@ -0,0 +1,8 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m5/tabpfgen/tabpfgen-m5-20260422_200335/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m5/tabpfgen/tabpfgen-m5-20260422_200335/staged/public/staged_features.json", + "target_col": "Target", + "is_classification": true, + "n_rows": 3539, + "n_cols": 37 +} \ No newline at end of file diff --git a/SynthData0523/main/m5/tabpfgen/tabpfgen-m5-20260422_200335/train_20260422_200336.log b/SynthData0523/main/m5/tabpfgen/tabpfgen-m5-20260422_200335/train_20260422_200336.log new file mode 100644 index 0000000000000000000000000000000000000000..f72540c52ab5ff38b7491ea29a0b2c6f5fae6284 --- /dev/null +++ b/SynthData0523/main/m5/tabpfgen/tabpfgen-m5-20260422_200335/train_20260422_200336.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a1f3a133d48c190c52620cfea2ad6e85f890c3d304d020ea20e15a052dcb1c82 +size 186 diff --git a/SynthData0523/main/m5/tabsyn/tabsyn-m5-20260421_023648/_tabsyn_sample.py b/SynthData0523/main/m5/tabsyn/tabsyn-m5-20260421_023648/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..3c3aed174f72ec9b097a36e255abd77a09223bc4 --- /dev/null +++ b/SynthData0523/main/m5/tabsyn/tabsyn-m5-20260421_023648/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/m5/tabsyn/tabsyn-m5-20260421_023648" +dataname = "tabsyn_m5" +output_csv = "/work/output-SpecializedModels/m5/tabsyn/tabsyn-m5-20260421_023648/tabsyn-m5-3539-20260421_034347.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 3539 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/SynthData0523/main/m5/tabsyn/tabsyn-m5-20260421_023648/_tabsyn_train.py b/SynthData0523/main/m5/tabsyn/tabsyn-m5-20260421_023648/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..9d31a3720f8251ee8c73183f1f11370eddb68b3f --- /dev/null +++ b/SynthData0523/main/m5/tabsyn/tabsyn-m5-20260421_023648/_tabsyn_train.py @@ -0,0 +1,62 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/m5/tabsyn/tabsyn-m5-20260421_023648" +dataname = "tabsyn_m5" +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/SynthData0523/main/m5/tabsyn/tabsyn-m5-20260421_023648/data/tabsyn_m5/X_cat_test.npy b/SynthData0523/main/m5/tabsyn/tabsyn-m5-20260421_023648/data/tabsyn_m5/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..204df8677255fef985382f85724084c6e428043a --- /dev/null +++ b/SynthData0523/main/m5/tabsyn/tabsyn-m5-20260421_023648/data/tabsyn_m5/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:448b01d0a3428efb90d0c23119abd5f09c47276d378c10e27e0359caed1c960a +size 28480 diff --git 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diff --git a/SynthData0523/main/m5/tvae/tvae-m5-20260419_191713/_tvae_generate.py b/SynthData0523/main/m5/tvae/tvae-m5-20260419_191713/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..9b708bea5ef110187644e3ca8e52093d82931965 --- /dev/null +++ b/SynthData0523/main/m5/tvae/tvae-m5-20260419_191713/_tvae_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.tvae import TVAE +model = TVAE.load("/work/output-SpecializedModels/m5/tvae/tvae-m5-20260419_191713/models_300epochs/tvae_300epochs.pt") +total = 3539 +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 +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-SpecializedModels/m5/tvae/tvae-m5-20260419_191713/tvae-m5-3539-20260419_192224.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-SpecializedModels/m5/tvae/tvae-m5-20260419_191713/tvae-m5-3539-20260419_192224.csv") diff --git a/SynthData0523/main/m5/tvae/tvae-m5-20260419_191713/_tvae_train.py b/SynthData0523/main/m5/tvae/tvae-m5-20260419_191713/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c247bd237098fddcb93cdc1171b8ca0b4b54a9bd --- /dev/null +++ b/SynthData0523/main/m5/tvae/tvae-m5-20260419_191713/_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/m5/tvae/tvae-m5-20260419_191713/staged/public/train.csv" +meta_path = "/work/output-SpecializedModels/m5/tvae/tvae-m5-20260419_191713/tvae_metadata.json" +save_path = "/work/output-SpecializedModels/m5/tvae/tvae-m5-20260419_191713/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 a/SynthData0523/main/m5/tvae/tvae-m5-20260419_191713/gen_20260419_192224.log b/SynthData0523/main/m5/tvae/tvae-m5-20260419_191713/gen_20260419_192224.log new file mode 100644 index 0000000000000000000000000000000000000000..4db969efbd116295bf57ff5865294772ecc91f3e --- /dev/null +++ b/SynthData0523/main/m5/tvae/tvae-m5-20260419_191713/gen_20260419_192224.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2200544984d1174adc4ed2bc0d52d8ccd3f5529d0e237a5305997e744c8efc86 +size 137 diff --git 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