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b/SynthData0523/main/m9/tabdiff/tabdiff-m9-20260501_162649/train_20260501_162650.log new file mode 100644 index 0000000000000000000000000000000000000000..186890908ab1fda1057ef82cbef4d1be6ae4f787 --- /dev/null +++ b/SynthData0523/main/m9/tabdiff/tabdiff-m9-20260501_162649/train_20260501_162650.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2085f424d659d1a9151c4384a0f1bd16eaab75ca06ea33eaa805071b6b3247f2 +size 597760 diff --git a/SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/_tabpfgen_generate.py b/SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..7f14ac9ae542c6b6f66527c6b1b2f540e3c6555d --- /dev/null +++ b/SynthData0523/main/m9/tabpfgen/m9-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/m9/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(15326) + +# 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/m9/tabpfgen-m9-15326-20260422_191741.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/tabpfgen-m9-15326-20260422_191741.csv") diff --git a/SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/gen_20260422_191741.log b/SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/gen_20260422_191741.log new file mode 100644 index 0000000000000000000000000000000000000000..796313214c94e59f7ffbb5264e34f64b6a0aea3f --- /dev/null +++ b/SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/gen_20260422_191741.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7687d662119a29c59f79c8a9cad8610a6bfd6d4210c5ce5662afbbaf3601618c +size 1087 diff --git a/SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/input_snapshot.json b/SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..a7976e3a03b008f727800a00db937403d4b350d5 --- /dev/null +++ b/SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m9", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-train.csv", + "exists": true, + "size": 1582666, + "sha256": "a3f8afdc773b440d51abd0d3490ff6dd140f091235334aa58569b075b44a200a" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-val.csv", + "exists": true, + "size": 199049, + "sha256": "e502e558e93bf38ce6eb32cd839eebaf5a0e428bd8949a13d6f6bbc6c94b56b6" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-test.csv", + "exists": true, + "size": 198975, + "sha256": "ea619037db7eed6d1be2023c1b315852b421cf64e17dcb85bd3068d306a093a0" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m9/m9-dataset_profile.json", + "exists": true, + "size": 6232, + "sha256": "4c610aaaca65a4d30b558d1400dd96c2bf90091fcf91257fbcb164d0072d5f20" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m9/m9-dataset_contract_v1.json", + "exists": true, + "size": 7292, + "sha256": "260b8491ad4298f588ea417cbe5685f50711c85e61ee38789fb8bf3f7269178c" + } + } +} \ No newline at end of file diff --git a/SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/public_gate/normalized_schema_snapshot.json b/SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..ddd5199aed66b1b6fb226e8f963ac35bd3c271c2 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+ "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.00013, + "example_values": [ + "0.0", + "1.0" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/tabpfgen-m9-15326-20260422_191741.csv b/SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/tabpfgen-m9-15326-20260422_191741.csv new file mode 100644 index 0000000000000000000000000000000000000000..b6af154c0eac98f86b367d82ef64f43053a0d594 --- /dev/null +++ b/SynthData0523/main/m9/tabpfgen/m9-migrated-20260422_193053/tabpfgen-m9-15326-20260422_191741.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d20e4382f7380aebfb7352b74489fa127282eaedbc5fa11c85833cf1f6f8a995 +size 1894465 diff --git a/SynthData0523/main/m9/tabsyn/tabsyn-m9-20260421_005114/_tabsyn_sample.py b/SynthData0523/main/m9/tabsyn/tabsyn-m9-20260421_005114/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..1b934b82f3d0e3d73617ea408a62f62e7dcdafc6 --- /dev/null +++ b/SynthData0523/main/m9/tabsyn/tabsyn-m9-20260421_005114/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/m9/tabsyn/tabsyn-m9-20260421_005114" +dataname = "tabsyn_m9" +output_csv = "/work/output-SpecializedModels/m9/tabsyn/tabsyn-m9-20260421_005114/tabsyn-m9-15326-20260421_021410.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 15326 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/m9/tabsyn/tabsyn-m9-20260421_005114/_tabsyn_train.py b/SynthData0523/main/m9/tabsyn/tabsyn-m9-20260421_005114/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..b3492cd736de3e883f229f002cc3dffd80f65dd7 --- /dev/null +++ b/SynthData0523/main/m9/tabsyn/tabsyn-m9-20260421_005114/_tabsyn_train.py @@ -0,0 +1,62 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/m9/tabsyn/tabsyn-m9-20260421_005114" +dataname = "tabsyn_m9" +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/m9/tabsyn/tabsyn-m9-20260421_005114/data/tabsyn_m9/X_cat_test.npy 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"/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/tabsyn/tabsyn-m9-20260421_005114/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/tabsyn/tabsyn-m9-20260421_005114/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/tabsyn/tabsyn-m9-20260421_005114/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/SynthData0523/main/m9/tabsyn/tabsyn-m9-20260421_005114/synthetic/tabsyn_m9/real.csv b/SynthData0523/main/m9/tabsyn/tabsyn-m9-20260421_005114/synthetic/tabsyn_m9/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..a6b7f45a44b7a8ff0f8dd73b1e672bb8dabd59c9 --- /dev/null +++ b/SynthData0523/main/m9/tabsyn/tabsyn-m9-20260421_005114/synthetic/tabsyn_m9/real.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:63528ad6a7339d36f3513325bd781b5219fd2fc0bb3e4ab28d347ff05297048a +size 685623 diff --git a/SynthData0523/main/m9/tabsyn/tabsyn-m9-20260421_005114/synthetic/tabsyn_m9/test.csv b/SynthData0523/main/m9/tabsyn/tabsyn-m9-20260421_005114/synthetic/tabsyn_m9/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..84a2628aaeb072f6060e1de20e0ee9b6d58bbd5b --- /dev/null +++ b/SynthData0523/main/m9/tabsyn/tabsyn-m9-20260421_005114/synthetic/tabsyn_m9/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c725a1357bb8b8b0baf466ae991f0c451b0d8d671612dc535a52d4a8bc19b2fc +size 76144 diff --git a/SynthData0523/main/m9/tabsyn/tabsyn-m9-20260421_005114/tabsyn-m9-15326-20260421_021410.csv b/SynthData0523/main/m9/tabsyn/tabsyn-m9-20260421_005114/tabsyn-m9-15326-20260421_021410.csv new file mode 100644 index 0000000000000000000000000000000000000000..b12f0ea42aac6204d8af6a77f0847bc3f6d64bed --- /dev/null +++ b/SynthData0523/main/m9/tabsyn/tabsyn-m9-20260421_005114/tabsyn-m9-15326-20260421_021410.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4b520012edd4fa88003462f2530f2d489c8f45af60956c606f2ee04b3e47fa02 +size 1671326 diff --git a/SynthData0523/main/m9/tabsyn/tabsyn-m9-20260421_005114/train_20260421_005116.log b/SynthData0523/main/m9/tabsyn/tabsyn-m9-20260421_005114/train_20260421_005116.log new file mode 100644 index 0000000000000000000000000000000000000000..11029746a6f9859aa3e061cce3de4f0eeaa03107 --- /dev/null +++ b/SynthData0523/main/m9/tabsyn/tabsyn-m9-20260421_005114/train_20260421_005116.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d83d33572d69d4e36f7568f5e6a39be49f52ef7576aa0ec8b3feec0fb4e7fa9d +size 3244267 diff --git a/SynthData0523/main/m9/tvae/tvae-m9-20260320_071415/_tvae_generate.py b/SynthData0523/main/m9/tvae/tvae-m9-20260320_071415/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..87e81d4367ea88ac666a1de88e32c3b7f13332e0 --- /dev/null +++ b/SynthData0523/main/m9/tvae/tvae-m9-20260320_071415/_tvae_generate.py @@ -0,0 +1,5 @@ +from ctgan.synthesizers.tvae import TVAE +model = TVAE.load("/work/output-SpecializedModels/m9/tvae/tvae-m9-20260320_071415/models_300epochs/tvae_300epochs.pt") +samples = model.sample(15326) +samples.to_csv("/work/output-SpecializedModels/m9/tvae/tvae-m9-20260320_071415/tvae-m9-15326-20260330_065724.csv", index=False) +print(f"[TVAE] Generated 15326 rows -> /work/output-SpecializedModels/m9/tvae/tvae-m9-20260320_071415/tvae-m9-15326-20260330_065724.csv") diff --git a/SynthData0523/main/m9/tvae/tvae-m9-20260320_071415/_tvae_train.py b/SynthData0523/main/m9/tvae/tvae-m9-20260320_071415/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..0ef6756bd89e32fa946968aeb0a50294db167968 --- /dev/null +++ b/SynthData0523/main/m9/tvae/tvae-m9-20260320_071415/_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/DatasetNew/m9/m9-train.csv" +meta_path = "/work/output-SpecializedModels/m9/tvae/tvae-m9-20260320_071415/tvae_metadata.json" +save_path = "/work/output-SpecializedModels/m9/tvae/tvae-m9-20260320_071415/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/m9/tvae/tvae-m9-20260320_071415/gen_20260320_071808.log b/SynthData0523/main/m9/tvae/tvae-m9-20260320_071415/gen_20260320_071808.log new file mode 100644 index 0000000000000000000000000000000000000000..7b54d1059007fad0ae88a33c648031b2ddc38f66 --- /dev/null +++ b/SynthData0523/main/m9/tvae/tvae-m9-20260320_071415/gen_20260320_071808.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6bff7a7807381233e8da476d42a0f25486aae5e26dbb97aa41873686f7dac00a +size 126 diff --git a/SynthData0523/main/m9/tvae/tvae-m9-20260320_071415/gen_20260330_065724.log b/SynthData0523/main/m9/tvae/tvae-m9-20260320_071415/gen_20260330_065724.log new file mode 100644 index 0000000000000000000000000000000000000000..e1061c7da73c2b9c5d41477d226bddd3cb83bc76 --- /dev/null +++ b/SynthData0523/main/m9/tvae/tvae-m9-20260320_071415/gen_20260330_065724.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3964c4f5b8fefcca855c03d7fe5435543af6c93710fdb3a2fe5ed09d18e7de32 +size 128 diff --git a/SynthData0523/main/m9/tvae/tvae-m9-20260320_071415/input_snapshot.json b/SynthData0523/main/m9/tvae/tvae-m9-20260320_071415/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0cc7041b5a4c895e32c5e7a60ed2d796c7c51313 --- /dev/null +++ b/SynthData0523/main/m9/tvae/tvae-m9-20260320_071415/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m9", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-train.csv", + "exists": true, + "size": 1582666, + "sha256": "a3f8afdc773b440d51abd0d3490ff6dd140f091235334aa58569b075b44a200a" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-val.csv", + "exists": true, + "size": 199049, + "sha256": "e502e558e93bf38ce6eb32cd839eebaf5a0e428bd8949a13d6f6bbc6c94b56b6" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-test.csv", + "exists": true, + "size": 198975, + "sha256": "ea619037db7eed6d1be2023c1b315852b421cf64e17dcb85bd3068d306a093a0" + }, + "profile_json": { + "path": 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/dev/null +++ b/SynthData0523/main/m9/tvae/tvae-m9-20260320_071415/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,291 @@ +{ + "dataset_id": "m9", + "target_column": "target", + "task_type": "classification", + "columns": [ + { + "name": "enrollee_id", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 15326, + "unique_ratio": 1.0, + "example_values": [ + "32408", + "5965", + "32011", + "32184", + "13892" + ] + } + }, + { + "name": "city", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 122, + "unique_ratio": 0.00796, + "example_values": [ + "city_103", + "city_13", + "city_136", + "city_114", + "city_21" + ] + } + }, + { + "name": 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