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a24cb8f 3c400c8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 | # Generating Release Data Files
The repository currently includes the **results** of the final paper suite, but it does **not** include pre-exported per-seed release files under `release/data/`. This document explains how to generate them using the existing Temporal Twins benchmark code without changing generator logic, labels, matched-prefix construction, or model logic.
## Expected Outputs Per Seed
Each directory `release/data/<mode>/seed_<seed>/` is expected to contain:
- `transactions.parquet`
- `matched_pairs.parquet`
- `audit_summary.csv`
- `schema.json`
- `config.yaml`
Where:
- `<mode>` is one of `oracle_calib`, `easy`, `medium`, `hard`
- `<seed>` is one of `0`, `1`, `2`, `3`, `4`
## Benchmark Mapping
- `oracle_calib` uses `benchmark_mode = "temporal_twins_oracle_calib"` and `difficulty = "easy"`
- `easy` uses `benchmark_mode = "temporal_twins"` and `difficulty = "easy"`
- `medium` uses `benchmark_mode = "temporal_twins"` and `difficulty = "medium"`
- `hard` uses `benchmark_mode = "temporal_twins"` and `difficulty = "hard"`
## Exact Export Command
Run this command from the repository root:
```bash
PYTHONPATH=. python3 - <<'PY'
from pathlib import Path
import json
import pandas as pd
import yaml
from src.core.config_loader import load_config
from experiments.run_all import (
build_matched_control_tables,
generate_single_difficulty,
report_matched_control_audits,
set_global_determinism,
)
release_root = Path("release/data")
seeds = [0, 1, 2, 3, 4]
mode_specs = [
("oracle_calib", "temporal_twins_oracle_calib", "easy"),
("easy", "temporal_twins", "easy"),
("medium", "temporal_twins", "medium"),
("hard", "temporal_twins", "hard"),
]
base_cfg = load_config("config/default.yaml")
base_cfg.num_users = 350
base_cfg.simulation_days = 45
for release_mode, benchmark_mode, difficulty in mode_specs:
for seed in seeds:
cfg = base_cfg.model_copy(deep=True)
cfg.benchmark_mode = benchmark_mode
cfg.random_seed = seed
set_global_determinism(seed)
df = generate_single_difficulty(
cfg,
difficulty=difficulty,
seed=seed,
benchmark_mode=benchmark_mode,
)
matched_examples, pair_rows, pair_counts = build_matched_control_tables(df)
audit = report_matched_control_audits(matched_examples, pair_rows, pair_counts)
out_dir = release_root / release_mode / f"seed_{seed}"
out_dir.mkdir(parents=True, exist_ok=True)
matched_export = matched_examples.rename(
columns={"eval_local_event_idx": "matched_local_event_idx"}
).copy()
matched_export["benchmark_mode"] = benchmark_mode
matched_export["difficulty"] = release_mode
matched_export["seed"] = seed
df.to_parquet(out_dir / "transactions.parquet", index=False)
matched_export.to_parquet(out_dir / "matched_pairs.parquet", index=False)
pd.DataFrame([audit]).to_csv(out_dir / "audit_summary.csv", index=False)
schema = {
"transactions_columns": {k: str(v) for k, v in df.dtypes.items()},
"matched_pairs_columns": {k: str(v) for k, v in matched_export.dtypes.items()},
"files": [
"transactions.parquet",
"matched_pairs.parquet",
"audit_summary.csv",
"schema.json",
"config.yaml",
],
}
(out_dir / "schema.json").write_text(json.dumps(schema, indent=2) + "\\n")
(out_dir / "config.yaml").write_text(
yaml.safe_dump(
{
**cfg.model_dump(),
"benchmark_mode": benchmark_mode,
"difficulty": difficulty,
"release_mode": release_mode,
"seed": seed,
"fast_mode": False,
"n_checkpoints": 8,
},
sort_keys=False,
)
)
PY
```
## Paper Result Reproduction
After generating the release data files, the final paper-suite metrics can be reproduced from the benchmark runner with the frozen deterministic settings and the same `num_users`, `simulation_days`, `seeds`, and `n_checkpoints` recorded in `release/results/paper_suite_meta.json`.
## Loading the Hosted Data Archive
Download `temporal_twins_data.zip` from:
`https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/resolve/main/temporal_twins_data.zip`
It contains `20` `transactions.parquet` files and `20` `matched_pairs.parquet` files. You can read files directly from the zip archive with pandas/pyarrow, or unzip the archive first.
```python
import zipfile
import pandas as pd
zip_path = "temporal_twins_data.zip"
with zipfile.ZipFile(zip_path) as zf:
with zf.open("data/medium/seed_0/transactions.parquet") as f:
transactions = pd.read_parquet(f)
with zf.open("data/medium/seed_0/matched_pairs.parquet") as f:
matched_pairs = pd.read_parquet(f)
print(transactions.columns.tolist())
print(matched_pairs.columns.tolist())
print(transactions.head())
print(matched_pairs.head())
```
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