diff --git a/DATASET_CARD.md b/DATASET_CARD.md new file mode 100644 index 0000000000000000000000000000000000000000..b26ecea73a1e802b29810249e6bfb4951adb0e6c --- /dev/null +++ b/DATASET_CARD.md @@ -0,0 +1,458 @@ +# Temporal Twins Dataset Card + +## 1. Dataset Summary + +Temporal Twins is a synthetic UPI-style transaction benchmark for temporal fraud detection. It is designed to evaluate whether a model can distinguish fraud from benign behavior using order-sensitive temporal structure rather than static aggregates such as total transaction count, account age, or prefix length. + +The benchmark simulates users sending transactions over time and then assigns fraud labels through delayed temporal mechanisms. Its core design is a matched fraud/benign temporal-twin construction: + +- each positive example is a fraud twin evaluated at a local event index `k` +- each negative example is a benign twin evaluated at the same local event index `k` +- both twins are matched on static and prefix-level summaries +- the benign twin contains the same unordered ingredients but violates the fraud-relevant temporal order + +Temporal Twins exposes four benchmark modes: + +- `oracle_calib` +- `easy` +- `medium` +- `hard` + +The frozen paper-suite configuration used in this repository is: + +- `num_users = 350` +- `simulation_days = 45` +- `seeds = [0, 1, 2, 3, 4]` +- `fast_mode = false` +- `n_checkpoints = 8` + +## 2. Dataset Motivation + +Many fraud datasets can be solved by static shortcuts: longer histories, later evaluation times, higher transaction counts, or other aggregate correlates can make a benchmark look temporally rich while actually rewarding non-temporal models. Temporal Twins was built to remove those shortcuts and isolate order-sensitive temporal reasoning. + +The benchmark therefore aims to answer a narrower research question: + +- when static summaries are matched between positives and negatives, can a model still recover delayed fraud signals from temporal order alone? + +It is intended for benchmarking temporal representation learning, causal order sensitivity, and delayed-label detection under controlled synthetic conditions. + +## 3. Dataset Composition + +Temporal Twins is generated programmatically from synthetic user and transaction processes. There is no fixed real-world corpus. Each generated artifact is an event table in which each row is a synthetic transaction. + +At a high level, each run contains: + +- a synthetic user population +- a synthetic stream of UPI-style transactions +- risk-engine outputs such as transaction risk scores and failures +- benchmark-specific fraud and audit annotations +- matched fraud/benign evaluation pairs extracted from the event stream + +The paper-scale suite in this repository contains 20 deterministic runs: + +- `oracle_calib` with seeds `0..4` +- `easy` with seeds `0..4` +- `medium` with seeds `0..4` +- `hard` with seeds `0..4` + +Mean matched evaluation-pair counts in the frozen paper suite are: + +| Mode | Matched evaluation pairs (mean +- std) | +|---|---:| +| `oracle_calib` | `2606.6 +- 454.3` | +| `easy` | `2222.2 +- 128.4` | +| `medium` | `2356.6 +- 18.0` | +| `hard` | `2317.6 +- 22.0` | + +Each paper-suite run is class-balanced at evaluation time: + +- positives = negatives +- positive rate = `0.5000` + +## 4. Dataset Generation Process + +The generation pipeline has four stages: + +1. Synthetic user generation +2. Synthetic transaction generation +3. Synthetic risk and retry generation +4. Fraud-mechanism and matched-twin generation + +More concretely: + +1. A synthetic user set is created with user-level behavioral parameters. +2. A synthetic transaction stream is sampled with sender IDs, receiver IDs, timestamps, transaction amounts, and transaction types. +3. A risk engine adds synthetic risk-related fields such as `risk_score`, `fail_prob`, `failed`, and retry-like events. +4. The fraud engine applies benchmark-mode-specific temporal mechanisms and constructs matched temporal twins. + +For the `temporal_twins` benchmark family, the generator then: + +- constructs fraud twins and benign twins from matched carrier users and templates +- preserves matched static and prefix-level summaries +- injects delayed fraud labels into fraud twins +- forces benign twins to avoid the fraud-relevant temporal motif while retaining similar unordered ingredients + +The benchmark is deterministic under fixed configuration, seed, and runtime settings. + +## 5. Fraud Mechanisms + +Temporal Twins uses delayed, order-sensitive fraud mechanisms rather than directly labeling static outliers. Important mechanisms include: + +- velocity-like activity acceleration +- retry-like behavior +- delayed receiver revisits +- burst-release-burst motifs +- adversarial timing perturbations +- delayed fraud assignment +- hidden latent fraud-state dynamics + +These mechanisms are combined with difficulty-dependent noise and camouflage. In the standard `easy`, `medium`, and `hard` modes, the fraud signal is intentionally imperfect and partially obscured. In `oracle_calib`, the construction is designed to validate motif and evaluation alignment under matched-prefix conditions. + +## 6. Matched-Control Construction + +The central benchmark control is the fraud/benign temporal twin. + +For every fraud twin positive label at local event index `k`: + +- the benign twin is evaluated at the same local event index `k` +- both examples use the same local prefix length +- both examples are truncated at prefix index `k` +- no future events are visible to the model + +Within each matched pair, the protocol additionally matches: + +- total transaction count +- local prefix length +- evaluation timestamp +- account age +- active age +- receiver histograms +- static aggregate summaries + +In words: + +- the fraud twin contains a temporally meaningful order pattern that triggers a delayed positive label +- the benign twin contains comparable ingredients and prefix statistics but violates the fraud-relevant temporal order + +This design is meant to prevent performance from arising from: + +- longer histories +- older accounts +- later prefix positions +- different transaction totals +- unmatched prefix ages +- benign negatives evaluated at arbitrary or easier positions + +## 7. Dataset Modes and Difficulty Ladder + +Temporal Twins provides four modes. + +### `oracle_calib` + +This is the calibration mode used to validate that the matched-prefix protocol is working as intended. + +- Oracle metrics remain near-perfect. +- Static shortcut baselines remain at chance. +- Benign motif hit rate remains zero. +- This mode is primarily for protocol validation rather than realistic difficulty. + +### `easy` + +- strong motif signal +- low noise +- shorter delay +- expected SeqGRU performance near `0.90-1.00` + +### `medium` + +- moderate motif signal +- moderate noise +- longer delay +- expected SeqGRU performance near `0.80-0.90` + +### `hard` + +- weaker motif signal +- longer delay +- adversarial perturbations and decoys +- expected SeqGRU performance near `0.70-0.85` + +Naming convention: + +- in `oracle_calib`, `AuditOracle` and `RawMotifOracle` are true oracle-style references +- in standard `easy`, `medium`, and `hard`, the corresponding scores are reported as `MotifProbe` and `RawMotifProbe` because realism and noise make them probes rather than perfect oracles + +## 8. Data Schema + +The event table contains model-facing fields, supervision labels, and audit/oracle-only fields. The table below lists the most important columns used in this repository. + +| Column name | Type | Description | Exposed to ordinary models? | Notes | +|---|---|---|---|---| +| `txn_id` | `int32` | Synthetic transaction identifier | Yes | Identifier only; not a benchmark target | +| `sender_id` | `int32` / `int64` | Synthetic sender account ID | Yes | Node identity available to temporal models | +| `receiver_id` | `int32` / `int64` | Synthetic receiver account ID | Yes | Used for graph and sequence structure | +| `timestamp` | `float32` | Synthetic event time in seconds from simulation start | Yes | Prefix truncation is based on timestamp and local index | +| `amount` | `float32` | Synthetic transaction amount | Yes | Not tied to real currency records | +| `txn_type` | `int8` | Synthetic transaction-type code | Yes | UPI-style categorical event attribute | +| `risk_score` | `float32` | Synthetic risk score from the risk engine | Yes | No real production risk model is used | +| `fail_prob` | `float32` | Synthetic failure probability | Yes | Risk-engine output | +| `failed` | `int8` | Binary failure indicator | Yes | Used as a normal model-facing field | +| `is_retry` | `int8` / derived | Retry-like event indicator | Yes | Available to ordinary models when present | +| `pair_freq` | `float32` / derived | Sender-receiver interaction-frequency feature | Yes | Derived from visible event history | +| `risk_noisy` | `float32` | Noisy synthetic risk feature | Yes | Benchmark feature, not an audit signal | +| `txn_count_10` | `float32` / derived | Recent-count feature over a short window | Yes | Derived from visible history | +| `amount_sum_10` | `float32` / derived | Recent amount-sum feature | Yes | Derived from visible history | +| `is_fraud` | `int8` | Binary fraud label | No | Supervision target only, not a model input | +| `twin_pair_id` | `int64` | Matched fraud/benign pair identifier | No | Audit/oracle-only; not exposed to learned baselines | +| `twin_role` | `string` | Twin role such as `fraud`, `benign`, or `background` | No | Audit/oracle-only | +| `twin_label` | `int8` | Pairwise matched label for audit utilities | No | Audit/oracle-only | +| `template_id` | `int64` | Source template identifier used during twin construction | No | Audit/oracle-only | +| `dynamic_fraud_state` | `float32` | Latent synthetic fraud-state variable | No | Hidden mechanism for analysis only | +| `motif_source` | `int8` | Indicator for motif-source events in a sequence | No | Audit/oracle-only | +| `motif_hit_count` | `int32` | Count of motif hits in the sequence | No | Audit/oracle-only | +| `trigger_event_idx` | `int32` | Local event index of the trigger event | No | Audit/oracle-only | +| `label_event_idx` | `int32` | Local event index at which the fraud label becomes active | No | Audit/oracle-only | +| `label_delay` | `int32` | Delay between trigger and labeled event index | No | Audit/oracle-only | +| `fraud_source` | `string` | Cause of fraud label, e.g. motif or fallback chain | No | Audit/oracle-only | +| `is_fallback_label` | `int8` | Indicator that a label came from fallback logic | No | Audit/oracle-only | +| `motif_chain_state` | `float32` | Internal motif-chain analysis field | No | Audit/oracle-only | +| `motif_strength` | `float32` | Internal motif-strength analysis field | No | Audit/oracle-only | + +Not every baseline uses every model-facing column. The important guarantee is that learned baselines do not receive the audit/oracle-only fields listed above. + +## 9. Model-Facing vs Audit/Oracle-Only Columns + +Ordinary learned baselines are restricted to model-facing transaction attributes and histories. In this repository, audit/oracle-only columns are explicitly stripped before learned baselines are trained or evaluated. + +Ordinary models may use fields such as: + +- `sender_id` +- `receiver_id` +- `timestamp` +- `amount` +- `risk_score` +- `fail_prob` +- `failed` +- `txn_type` +- other derived non-oracle features built from visible prefix history + +Ordinary models must not use: + +- `motif_hit_count` +- `motif_source` +- `trigger_event_idx` +- `label_event_idx` +- `label_delay` +- `fraud_source` +- `twin_role` +- `twin_label` +- `twin_pair_id` +- `template_id` +- `dynamic_fraud_state` +- other oracle-only diagnostics + +This separation is necessary for the benchmark claim that performance should come from temporal reasoning rather than privileged audit information. + +## 10. Benchmark Tasks + +Temporal Twins supports the following benchmark task: + +- binary fraud detection on matched prefix examples + +The standard evaluation protocol is: + +- build matched fraud/benign examples +- truncate each sender history at the matched prefix index `k` +- train or score on the visible prefix only +- evaluate binary discrimination at the matched example level + +Primary reported metrics include: + +- ROC-AUC +- PR-AUC +- shuffled-order ROC-AUC +- shuffle delta = shuffled ROC-AUC minus clean ROC-AUC + +The shuffled-order test is important: it measures how much performance depends on event order rather than unordered ingredients. + +## 11. Baselines and Reference Results + +The frozen 5-seed paper suite uses: + +- `num_users = 350` +- `simulation_days = 45` +- `seeds = [0, 1, 2, 3, 4]` +- `fast_mode = false` +- `n_checkpoints = 8` + +Compact reference results: + +| Mode | Primary reference | Secondary reference | XGBoost ROC-AUC | StaticGNN ROC-AUC | SeqGRU ROC-AUC | SeqGRU shuffled delta | +|---|---:|---:|---:|---:|---:|---:| +| `oracle_calib` | `AuditOracle 1.0000 +- 0.0000` | `RawMotifOracle 1.0000 +- 0.0000` | `0.5000 +- 0.0000` | `0.5222 +- 0.0235` | `1.0000 +- 0.0000` | `-0.5032 +- 0.0043` | +| `easy` | `MotifProbe 1.0000 +- 0.0000` | `RawMotifProbe 0.9983 +- 0.0011` | `0.5000 +- 0.0000` | `0.4946 +- 0.0128` | `1.0000 +- 0.0000` | `-0.5003 +- 0.0096` | +| `medium` | `MotifProbe 0.6374 +- 0.0069` | `RawMotifProbe 0.6482 +- 0.0058` | `0.5000 +- 0.0000` | `0.4922 +- 0.0203` | `0.8391 +- 0.0174` | `-0.3337 +- 0.0191` | +| `hard` | `MotifProbe 0.5790 +- 0.0045` | `RawMotifProbe 0.5910 +- 0.0105` | `0.5000 +- 0.0000` | `0.5026 +- 0.0198` | `0.6876 +- 0.0128` | `-0.1883 +- 0.0111` | + +Static shortcut audit across all 20 paper-suite runs: + +- `static_agg_auc = 0.5000 +- 0.0000` +- `total_txn_count AUC = 0.5000 +- 0.0000` +- `local_event_idx AUC = 0.5000 +- 0.0000` +- `prefix_txn_count AUC = 0.5000 +- 0.0000` +- `timestamp AUC = 0.5000 +- 0.0000` +- `account_age AUC = 0.5000 +- 0.0000` +- `active_age AUC = 0.5000 +- 0.0000` +- `benign_motif_hit_rate = 0.0000 +- 0.0000` + +These results support the intended interpretation: + +- static shortcuts are neutralized +- `oracle_calib` validates matched-prefix correctness +- `easy` is readily learnable by order-sensitive sequence models +- `medium` remains learnable but meaningfully harder +- `hard` remains above static baselines but is substantially more challenging + +Full paper-suite artifacts, including temporal GNN results and per-seed CSVs, are stored under: + +- `results/paper_suite_20260503_202810/` + +## 12. Intended Use + +This dataset is intended for: + +- research on temporal fraud detection +- benchmarking order-sensitive sequence and temporal-graph models +- evaluating whether performance survives matched static controls +- studying delayed labels and prefix-only evaluation +- comparing clean-order and shuffled-order performance + +It is appropriate for methodology papers, controlled ablation studies, and robustness checks on temporal inductive bias. + +## 13. Out-of-Scope Use + +Temporal Twins is out of scope for: + +- direct training of production fraud systems +- making real financial, banking, or payment decisions +- approving or denying transactions for real users +- risk-scoring real individuals or organizations +- regulatory, legal, or operational decisions in production financial systems + +The dataset must not be used to train production fraud systems directly or to make real financial decisions. + +## 14. Limitations + +Important limitations include: + +- the benchmark is fully synthetic and reflects designer assumptions +- user behavior, fraud behavior, and benign behavior are simplified relative to real financial ecosystems +- the only ground truth is the generator's own labeling logic +- real-world fraud often depends on richer institutional, device, merchant, and social context not present here +- difficulty levels are benchmark design choices, not calibrated measures of real operational difficulty +- temporal GNN underperformance on this benchmark should not be generalized to all real fraud settings + +## 15. Biases and Risks + +As a synthetic benchmark, Temporal Twins inherits the modeling biases of its generator: + +- it emphasizes order-sensitive motifs chosen by the benchmark designers +- it encodes a particular notion of delayed fraud and camouflage +- it may reward models that are well aligned to these synthetic mechanisms +- it may underrepresent other real fraud styles not captured by the generator + +There is also a scientific risk: + +- because the benchmark intentionally removes common static shortcuts, performance on Temporal Twins may differ from performance on operational datasets where those shortcuts exist, for better or worse + +## 16. Privacy and Sensitive Data + +Temporal Twins contains no real financial or personal data. + +Specifically: + +- no real UPI data +- no real users +- no real bank accounts +- no real transactions +- no personal financial records +- no protected demographic attributes + +All user IDs, receiver IDs, timestamps, amounts, and risk signals are synthetic artifacts produced by the generator. + +## 17. Ethical Considerations + +Temporal Twins is safer to share than real financial logs because it does not contain real persons or institutions. However, ethical care is still needed. + +Users of the dataset should not: + +- present synthetic results as direct evidence of production readiness +- claim fairness or social validity that has not been tested on real populations +- use the dataset as justification for automated decisions about real people + +The intended ethical use is research benchmarking, not operational deployment. + +## 18. Reproducibility + +The repository includes deterministic generation and evaluation settings for the frozen paper suite. + +Paper-suite configuration: + +- `num_users = 350` +- `simulation_days = 45` +- `seeds = [0, 1, 2, 3, 4]` +- `fast_mode = false` +- `n_checkpoints = 8` + +Reproducibility properties: + +- stable deterministic seed derivation is used for benchmark modes and profiles +- Python, NumPy, and PyTorch seeds are fixed per run +- deterministic runtime flags are enabled where safe +- matched-prefix datasets are reproducible under fixed config and seed +- the final paper suite in this repository is stored as deterministic CSV artifacts + +Reference artifacts: + +- `results/paper_suite_20260503_202810/paper_suite_runs.csv` +- `results/paper_suite_20260503_202810/paper_suite_summary.csv` +- `results/paper_suite_20260503_202810/paper_suite_runtime.csv` +- `results/paper_suite_20260503_202810/paper_suite_failed_checks.csv` + +## 19. Hosting, License, and Citation + +### Hosting + +The benchmark is currently generated from code in this repository rather than distributed as a fixed external archive. + +Current status: + +- dataset hosting location: [https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins) +- canonical pre-generated release archive: [https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins) +- Croissant metadata URL: [https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/raw/main/metadata/temporal_twins_croissant.json](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/raw/main/metadata/temporal_twins_croissant.json) +- Croissant metadata browser page: [https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/blob/main/metadata/temporal_twins_croissant.json](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/blob/main/metadata/temporal_twins_croissant.json) +- data files: [https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/data](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/data) +- results: [https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/results](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/results) +- configs: [https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/configs](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/configs) +- metadata directory: [https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/metadata](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/metadata) +- reference paper-suite results: `results/paper_suite_20260503_202810/` + +### License + +- Dataset license: `CC BY 4.0` (`CC-BY-4.0`) +- Code license: `Apache License 2.0` (`Apache-2.0`) + +### Citation + +`TODO` placeholder BibTeX: + +```bibtex +@dataset{temporal_twins_todo, + title = {Temporal Twins: A Synthetic UPI-Style Benchmark for Temporal Fraud Detection}, + author = {TODO}, + year = {TODO}, + howpublished = {TODO}, + note = {Synthetic matched-prefix temporal fraud benchmark}, + url = {TODO} +} +``` diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..db4ef7bf1147da34108f17f792da258fb7f08278 --- /dev/null +++ b/LICENSE @@ -0,0 +1,203 @@ +SPDX-License-Identifier: Apache-2.0 + +Apache License +Version 2.0, January 2004 +https://www.apache.org/licenses/LICENSE-2.0 + +TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + +1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + +Copyright [yyyy] [name of copyright owner] + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + https://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. diff --git a/LICENSE-DATA b/LICENSE-DATA new file mode 100644 index 0000000000000000000000000000000000000000..4590c031d49c93145962b552898532fd594d6760 --- /dev/null +++ b/LICENSE-DATA @@ -0,0 +1,18 @@ +SPDX-License-Identifier: CC-BY-4.0 + +Temporal Twins dataset artifacts, generated synthetic data, metadata, dataset card, +and released benchmark files are licensed under the Creative Commons Attribution +4.0 International license (CC BY 4.0). + +Canonical license URL: +https://creativecommons.org/licenses/by/4.0/ + +This applies to released synthetic benchmark artifacts, including generated data +exports, metadata files, release bundle contents, and benchmark documentation that +describes the dataset. + +Attribution requirement: +"If you use Temporal Twins, please cite the associated paper and dataset release." + +Temporal Twins contains synthetic benchmark data only. It does not include real UPI +transactions, real users, real bank accounts, or personal financial records. diff --git a/MANIFEST.sha256 b/MANIFEST.sha256 new file mode 100644 index 0000000000000000000000000000000000000000..da47a0a3af1144dbb15003198b9a146acb31a087 --- /dev/null +++ b/MANIFEST.sha256 @@ -0,0 +1,126 @@ +b880f7dc777e417cd1a526b141b6e15866e89e62f8d4bfe627609aa909d40a65 .DS_Store +6bebd0daf847a3885b372df91ca9bbb5548e7b84d162db9e1f3c6178af6a9465 DATASET_CARD.md +15b001b0571fac1a30ea353be175df2724e7368d9c7ac9b433b9ac7afe2eb698 LICENSE +6da8eaaf7b897c14b497468e485beae1b5c3d0514f1b1461e30133b890be996b LICENSE-DATA +108e920e2e46828bed2239f8c204da0275454e18a2106904296813668a35d7bd README.md +7d90d4c3ea8a567a4b264a46b28549d4d45a4350ee81493f1e7579f4e5aa5e8e README_REPO.md +47125bd1c86a1c1d471fbfaef505a1a1af660374f537de6dac57aee622e3522f RELEASE_CHECKLIST.md +7417423f35a909ebf8ed2e26b9124d00d677a0f10830b75db556c58dd3610b50 configs/default.yaml 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+aabe56ba6dfcb585903b4df74c53fcbcdb82a0b48e75b1214232f2fa2daaa6e4 results/paper_suite_summary.csv +839a448c5e8ab2e2c41647d2af607afd858a48f5ca8f213719bb0e480167c110 results/paper_suite_summary.md diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..5c7fd672a55ef7f636659ab73223404a7eea373b --- /dev/null +++ b/README.md @@ -0,0 +1,96 @@ +# Temporal Twins Release Bundle + +This `release/` directory is a manual-hosting bundle for the Temporal Twins benchmark. It is intended to be uploaded later to a repository such as Hugging Face, Kaggle, Dataverse, OpenML, or another archival host. Hosting has **not** been performed by this preparation step. + +## What This Release Contains + +- `DATASET_CARD.md`: NeurIPS-style dataset card for the benchmark +- `README_REPO.md`: copied repository README from the project root +- `LICENSE`: copied project license file +- `results/`: final deterministic paper-suite outputs +- `configs/`: benchmark configuration files and a paper-suite reference config +- `metadata/`: Croissant metadata and validation notes +- `data/`: empty per-mode/per-seed directory scaffold plus generation instructions +- `MANIFEST.sha256`: SHA256 manifest for all files in this bundle + +## How To Use The Data + +1. Read `DATASET_CARD.md` for benchmark scope, schema, intended use, and limitations. +2. Read `metadata/temporal_twins_croissant.json` for machine-readable release metadata. +3. Use `results/paper_suite_summary.csv` and `results/paper_suite_summary.md` for the paper-ready reference results. +4. Populate `data/` with the generated per-seed transaction and matched-prefix files before public hosting. + +## How To Regenerate The Data + +The repository does not currently store per-seed `transactions.parquet` and `matched_pairs.parquet` release exports. To generate them without changing benchmark logic, follow the instructions in `data/README_GENERATION.md`. + +## How To Reproduce Paper Results + +The final deterministic paper suite used: + +- benchmark groups: `oracle_calib`, `easy`, `medium`, `hard` +- benchmark modes: + - `oracle_calib` -> `temporal_twins_oracle_calib` + - `easy`, `medium`, `hard` -> `temporal_twins` +- seeds: `0 1 2 3 4` +- `num_users = 350` +- `simulation_days = 45` +- `fast_mode = false` +- `n_checkpoints = 8` +- device: `cpu` + +Reference artifacts: + +- `results/paper_suite_runs.csv` +- `results/paper_suite_summary.csv` +- `results/paper_suite_runtime.csv` +- `results/paper_suite_failed_checks.csv` +- `results/paper_suite_summary.md` +- `results/paper_suite_meta.json` + +## File Structure + +```text +release/ +├── README.md +├── README_REPO.md +├── DATASET_CARD.md +├── LICENSE +├── MANIFEST.sha256 +├── RELEASE_CHECKLIST.md +├── data/ +│ ├── README_GENERATION.md +│ ├── oracle_calib/seed_{0..4}/ +│ ├── easy/seed_{0..4}/ +│ ├── medium/seed_{0..4}/ +│ └── hard/seed_{0..4}/ +├── configs/ +├── results/ +└── metadata/ +``` + +## Hosted URLs + +- Dataset URL: [https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins) +- Croissant metadata URL: [https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/raw/main/metadata/temporal_twins_croissant.json](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/raw/main/metadata/temporal_twins_croissant.json) +- Croissant metadata browser page: [https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/blob/main/metadata/temporal_twins_croissant.json](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/blob/main/metadata/temporal_twins_croissant.json) +- Data URL: [https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/data](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/data) +- Results URL: [https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/results](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/results) +- Configs URL: [https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/configs](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/configs) +- Metadata URL: [https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/metadata](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/metadata) +- Full release archive: [https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins) +- Code repository: `TODO_CODE_REPOSITORY_URL` +- Paper or preprint: `TODO_PAPER_URL` + +## Licenses + +- Code: Apache License 2.0 (`Apache-2.0`) +- Dataset and generated benchmark artifacts: Creative Commons Attribution 4.0 International (`CC-BY-4.0`) +- Code SPDX-License-Identifier: `Apache-2.0` +- Dataset SPDX-License-Identifier: `CC-BY-4.0` +- No real UPI data or personal financial records are included in this release bundle. + +## Notes + +- This bundle contains no real UPI data, no real users, no real bank accounts, and no personal financial records. +- The benchmark code, generator logic, labels, matched-prefix protocol, and model logic were not modified while preparing this release directory. diff --git a/README_REPO.md b/README_REPO.md new file mode 100644 index 0000000000000000000000000000000000000000..3ca9a01cb124d7b7a66c7ed12c7ae00641bbf37d --- /dev/null +++ b/README_REPO.md @@ -0,0 +1,437 @@ +# Temporal Twins: A Matched-Control Benchmark for Temporal Fraud Detection + +Temporal Twins is a synthetic UPI-style temporal transaction benchmark where fraud and benign trajectories are statically matched but differ in delayed event-order structure. The benchmark is designed to test whether models can exploit temporal ordering under matched-prefix controls rather than relying on static transaction summaries or prefix-length shortcuts. + +## Installation + +Recommended Python version: `3.11+` (`3.13` also works in the checked environment). + +### pip + +```bash +pip install -r requirements.txt +``` + +### conda + +If [environment.yml](environment.yml) is present: + +```bash +conda env create -f environment.yml +conda activate temporal-twins +``` + +## Repository Structure + +- `src/`: synthetic user, transaction, risk, fraud, graph, and core config code +- `models/`: learned baselines and probe/oracle wrappers, including SeqGRU and temporal GNNs +- `experiments/`: benchmark runner and matched-prefix evaluation code +- `config/`: checked-in YAML configs used as base configs for experiments +- `results/`: frozen experiment artifacts, including the final deterministic paper suite +- `metadata/`: Croissant metadata and release-side validation notes +- `release/`: manual-hosting bundle prepared for later upload + +## Quick Smoke Test + +The public CLI supports a fast audit-mode smoke test: + +```bash +PYTHONPATH=. python3 experiments/run_all.py \ + --fast \ + --seed 0 \ + --benchmark-mode temporal_twins_oracle_calib \ + --experiments audit \ + --device cpu +``` + +## Exact Paper-Style Group Runner + +The checked-in CLI does **not** expose `--difficulty`, `--num-users`, or `--simulation-days` flags. The exact grouped reproductions below therefore use the existing helper functions in [experiments/run_all.py](experiments/run_all.py) through an inline Python wrapper. + +Define this shell helper once in your session: + +```bash +run_group() { + local group="$1" + local seed="$2" + local out_json="$3" + + PYTHONPATH=. python3 - "$group" "$seed" "$out_json" <<'PY' +import json +import math +import sys +import time +from pathlib import Path + +from src.core.config_loader import load_config +from experiments.run_all import ( + build_gate_pool_from_frames, + gate_volume_is_sufficient, + generate_single_difficulty, + offset_gate_namespace, + prepare_gate_subset, + run_motif_validity_check, + set_global_determinism, +) + + +def normalize(value): + if isinstance(value, dict): + return {k: normalize(v) for k, v in value.items()} + if isinstance(value, (list, tuple)): + return [normalize(v) for v in value] + if hasattr(value, "item"): + try: + value = value.item() + except Exception: + pass + if isinstance(value, float) and not math.isfinite(value): + return None + return value + + +group = sys.argv[1] +seed = int(sys.argv[2]) +out_json = Path(sys.argv[3]) + +if group == "oracle_calib": + benchmark_mode = "temporal_twins_oracle_calib" + difficulty = "easy" + hard_abort = True + force_temporal_models = True +else: + benchmark_mode = "temporal_twins" + difficulty = group + hard_abort = False + force_temporal_models = True + +cfg = load_config("config/default.yaml") +cfg = cfg.model_copy( + update={ + "num_users": 350, + "simulation_days": 45, + "benchmark_mode": benchmark_mode, + "random_seed": seed, + } +) + +set_global_determinism(seed) +pool = generate_single_difficulty( + cfg, + difficulty=difficulty, + seed=seed, + benchmark_mode=benchmark_mode, +) +gate = prepare_gate_subset(pool, seed=seed, fast_mode=False) +pack_count = 1 + +while (not gate_volume_is_sufficient(gate["volume"], False)) and pack_count <= 6: + extra_seed = seed + pack_count * 10007 + extra_pack = generate_single_difficulty( + cfg, + difficulty=difficulty, + seed=extra_seed, + benchmark_mode=benchmark_mode, + ) + extra_pack = offset_gate_namespace(extra_pack, pack_count) + pool = build_gate_pool_from_frames([pool, extra_pack]) + gate = prepare_gate_subset(pool, seed=seed, fast_mode=False) + pack_count += 1 + +gate["source_pool_events"] = int(len(pool)) +gate["source_pool_pairs"] = int(pool.loc[pool["twin_pair_id"] >= 0, "twin_pair_id"].nunique()) if "twin_pair_id" in pool.columns else 0 +gate["source_pool_packs"] = int(pack_count) + +start = time.time() +gate_pass, report = run_motif_validity_check( + df=pool, + config=cfg, + seed=seed, + device="cpu", + num_epochs=3, + node_epochs=150, + n_checkpoints=8, + hard_abort=hard_abort, + benchmark_mode=benchmark_mode, + fast_mode=False, + force_temporal_models=force_temporal_models, + prebuilt_gate=gate, +) +elapsed = time.time() - start + +result = { + "benchmark_group": group, + "benchmark_mode": benchmark_mode, + "seed": seed, + "primary_metric_label": report["audit_metric_label"], + "secondary_metric_label": report["raw_metric_label"], + "gate_pass": bool(gate_pass), + "run_wall_time_sec": float(elapsed), + **report, +} + +out_json.parent.mkdir(parents=True, exist_ok=True) +out_json.write_text(json.dumps(normalize(result), indent=2) + "\n") +print(f"Wrote {out_json}") +PY +} +``` + +## Reproduce Oracle Calibration + +Non-fast, reliable-volume `temporal_twins_oracle_calib`, seed `0`, `num_users=350`, `simulation_days=45`: + +```bash +run_group oracle_calib 0 results/paper_suite_repro/jobs/oracle_calib_0.json +``` + +## Reproduce Easy / Medium / Hard + +Each command below reproduces the matched-prefix grouped benchmark for seed `0` with the paper-scale non-fast settings (`num_users=350`, `simulation_days=45`, `n_checkpoints=8`, deterministic CPU runtime): + +```bash +run_group easy 0 results/paper_suite_repro/jobs/easy_0.json +run_group medium 0 results/paper_suite_repro/jobs/medium_0.json +run_group hard 0 results/paper_suite_repro/jobs/hard_0.json +``` + +## Reproduce Full Paper Suite + +There is no single checked-in `paper_suite` driver script. The exact grouped reproduction can be run as a shell loop over benchmark groups and seeds, followed by a small aggregation step that writes the artifact files: + +### 1. Generate per-run JSON files + +```bash +mkdir -p results/paper_suite_repro/jobs + +for group in oracle_calib easy medium hard; do + for seed in 0 1 2 3 4; do + run_group "$group" "$seed" "results/paper_suite_repro/jobs/${group}_${seed}.json" + done +done +``` + +### 2. Aggregate into paper-suite CSV and Markdown files + +```bash +PYTHONPATH=. python3 - <<'PY' +import json +from pathlib import Path + +import pandas as pd + + +def summarize_mean_std(df, group_col): + numeric_cols = [c for c in df.columns if c != group_col and pd.api.types.is_numeric_dtype(df[c])] + grouped = df.groupby(group_col, dropna=False)[numeric_cols].agg(["mean", "std"]).reset_index() + grouped.columns = [ + group_col if col == group_col else f"{col}_{stat}" + for col, stat in grouped.columns + ] + return grouped + + +def volume_failures(row): + fails = [] + if row["matched_eval_pairs"] < 2000: + fails.append(f"matched_eval_pairs={row['matched_eval_pairs']} (<2000)") + if row["positives"] < 500: + fails.append(f"positives={row['positives']} (<500)") + if row["negatives"] < 500: + fails.append(f"negatives={row['negatives']} (<500)") + if row["unique_fraud_users"] < 50: + fails.append(f"unique_fraud_users={row['unique_fraud_users']} (<50)") + if row["unique_benign_users"] < 50: + fails.append(f"unique_benign_users={row['unique_benign_users']} (<50)") + if not (0.35 <= row["positive_rate"] <= 0.65): + fails.append(f"positive_rate={row['positive_rate']:.4f} (outside [0.35,0.65])") + return " | ".join(fails) + + +def hard_gate_failures(row): + checks = [ + (row["primary_metric_label"], row["audit_roc_auc"], ">=", 0.99), + (f"{row['primary_metric_label']} pair-sep", row["audit_pair_sep"], ">=", 0.99), + (row["secondary_metric_label"], row["raw_roc_auc"], ">=", 0.95), + (f"{row['secondary_metric_label']} pair-sep", row["raw_pair_sep"], ">=", 0.90), + ("static_agg_auc", row["static_agg_auc"], "<=", 0.60), + ("XGBoost ROC-AUC", row["xgb_roc_auc"], "<=", 0.65), + ("StaticGNN ROC-AUC", row["static_gnn_roc"], "<=", 0.70), + ("SeqGRU ROC-AUC", row["seqgru_roc_auc"], ">=", 0.80), + ("SeqGRU shuffle delta", row["seqgru_shuffle_delta"], "<=", -0.10), + ] + fails = [] + for label, value, op, threshold in checks: + ok = value >= threshold if op == ">=" else value <= threshold + if not ok: + fails.append(f"{label}: {value:.4f} ({op}{threshold})") + return " | ".join(fails) + + +def advisory_failures(row): + checks = [ + ("TGN ROC-AUC", row["tgn_roc_auc"], ">=", 0.75), + ("TGN shuffle delta", row["tgn_shuffle_delta"], "<=", -0.10), + ("TGAT ROC-AUC", row["tgat_roc_auc"], ">=", 0.75), + ("TGAT shuffle delta", row["tgat_shuffle_delta"], "<=", -0.10), + ("DyRep ROC-AUC", row["dyrep_roc_auc"], ">=", 0.75), + ("DyRep shuffle delta", row["dyrep_shuffle_delta"], "<=", -0.10), + ("JODIE ROC-AUC", row["jodie_roc_auc"], ">=", 0.75), + ("JODIE shuffle delta", row["jodie_shuffle_delta"], "<=", -0.10), + ] + fails = [] + for label, value, op, threshold in checks: + ok = value >= threshold if op == ">=" else value <= threshold + if not ok: + fails.append(f"{label}: {value:.4f} ({op}{threshold})") + return " | ".join(fails) + + +jobs_dir = Path("results/paper_suite_repro/jobs") +out_dir = jobs_dir.parent +rows = [json.loads(path.read_text()) for path in sorted(jobs_dir.glob("*.json"))] +df = pd.DataFrame(rows).sort_values(["benchmark_group", "seed"]).reset_index(drop=True) + +runs_path = out_dir / "paper_suite_runs.csv" +summary_path = out_dir / "paper_suite_summary.csv" +runtime_path = out_dir / "paper_suite_runtime.csv" +failed_path = out_dir / "paper_suite_failed_checks.csv" +summary_md_path = out_dir / "paper_suite_summary.md" +meta_path = out_dir / "paper_suite_meta.json" + +df.to_csv(runs_path, index=False) + +summary = summarize_mean_std(df, "benchmark_group") +summary.to_csv(summary_path, index=False) + +runtime_cols = [ + "benchmark_group", + "seed", + "run_wall_time_sec", + "static_gnn_eval_time_sec", + "static_gnn_unique_prefix_cutoffs", + "static_gnn_graph_builds", + "static_gnn_cache_hit_rate", +] +df[runtime_cols].to_csv(runtime_path, index=False) + +failed = df[["benchmark_group", "seed", "gate_pass"]].copy() +failed["volume_failures"] = df.apply(volume_failures, axis=1) +failed["hard_gate_failures"] = df.apply(hard_gate_failures, axis=1) +failed["advisory_failures"] = df.apply(advisory_failures, axis=1) +failed.to_csv(failed_path, index=False) + +meta = { + "device": "cpu", + "num_users": 350, + "simulation_days": 45, + "num_epochs": 3, + "node_epochs": 150, + "n_checkpoints": 8, + "fast_mode": False, + "seeds": [0, 1, 2, 3, 4], +} +meta_path.write_text(json.dumps(meta, indent=2) + "\n") + +headline = summary[ + [ + "benchmark_group", + "xgb_roc_auc_mean", + "static_gnn_roc_mean", + "seqgru_roc_auc_mean", + "seqgru_shuffle_delta_mean", + ] +].copy() + +lines = [ + "# Paper Suite Summary", + "", + "| benchmark_group | xgb_roc_auc_mean | static_gnn_roc_mean | seqgru_roc_auc_mean | seqgru_shuffle_delta_mean |", + "|---|---:|---:|---:|---:|", +] +for row in headline.itertuples(index=False): + lines.append( + f"| {row.benchmark_group} | {row.xgb_roc_auc_mean:.4f} | {row.static_gnn_roc_mean:.4f} | {row.seqgru_roc_auc_mean:.4f} | {row.seqgru_shuffle_delta_mean:.4f} |" + ) +summary_md_path.write_text("\n".join(lines) + "\n") + +print(f"Wrote {runs_path}") +print(f"Wrote {summary_path}") +print(f"Wrote {runtime_path}") +print(f"Wrote {failed_path}") +print(f"Wrote {summary_md_path}") +print(f"Wrote {meta_path}") +PY +``` + +This aggregation step writes: + +- `results/paper_suite_repro/paper_suite_runs.csv` +- `results/paper_suite_repro/paper_suite_summary.csv` +- `results/paper_suite_repro/paper_suite_runtime.csv` +- `results/paper_suite_repro/paper_suite_failed_checks.csv` +- `results/paper_suite_repro/paper_suite_summary.md` +- `results/paper_suite_repro/paper_suite_meta.json` + +The frozen reference artifacts checked into this repository live in [results/paper_suite_20260503_202810](results/paper_suite_20260503_202810). + +## Expected Headline Results + +| benchmark_group | XGBoost ROC-AUC | StaticGNN ROC-AUC | SeqGRU ROC-AUC | SeqGRU shuffle delta | +|---|---:|---:|---:|---:| +| `oracle_calib` | 0.5000 | 0.5222 | 1.0000 | -0.5032 | +| `easy` | 0.5000 | 0.4946 | 1.0000 | -0.5003 | +| `medium` | 0.5000 | 0.4922 | 0.8391 | -0.3337 | +| `hard` | 0.5000 | 0.5026 | 0.6876 | -0.1883 | + +## Determinism + +- Deterministic CPU runtime is enabled in [experiments/run_all.py](experiments/run_all.py). +- The same seed should produce identical matched-prefix data and identical metrics under the same deterministic environment. +- Deterministic settings intentionally trade speed for repeatability and will slow larger runs. + +For more detail, see [docs/DETERMINISM.md](docs/DETERMINISM.md). + +## Runtime Note + +Mean wall-clock runtime per benchmark group in the final deterministic paper suite: + +- `oracle_calib`: `1136.6s` +- `easy`: `1345.9s` +- `medium`: `2181.9s` +- `hard`: `2613.7s` +- cumulative summed runtime across all 20 runs: about `10.11` hours + +## Data and Metadata + +- Dataset card: [DATASET_CARD.md](DATASET_CARD.md) +- Croissant metadata: [metadata/temporal_twins_croissant.json](metadata/temporal_twins_croissant.json) +- Manual-hosting release bundle: [release/](release/) + +Hosted URLs: + +- Dataset URL: [temporal-twins on Hugging Face](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins) +- Croissant metadata URL: [raw JSON-LD](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/raw/main/metadata/temporal_twins_croissant.json) +- Croissant metadata browser page: [blob view](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/blob/main/metadata/temporal_twins_croissant.json) +- Data URL: [data tree](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/data) +- Results URL: [results tree](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/results) +- Configs URL: [configs tree](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/configs) +- Metadata URL: [metadata tree](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/metadata) + +## License + +- Code: Apache License 2.0 (`Apache-2.0`) +- Dataset and generated benchmark artifacts: Creative Commons Attribution 4.0 International (`CC-BY-4.0`) +- Code SPDX-License-Identifier: `Apache-2.0` +- Dataset SPDX-License-Identifier: `CC-BY-4.0` +- No real UPI data or personal financial records are included. + +## Citation + +`TODO_REVEAL_AFTER_REVIEW` + +## Warning + +- Synthetic data only +- No real UPI transactions +- Not for production fraud deployment diff --git a/RELEASE_CHECKLIST.md b/RELEASE_CHECKLIST.md new file mode 100644 index 0000000000000000000000000000000000000000..f6ace0341b895c8ea24c93a50f2f4e82628c0c4b --- /dev/null +++ b/RELEASE_CHECKLIST.md @@ -0,0 +1,22 @@ +# Release Checklist + +- Dataset card included. +- Croissant metadata included. +- Croissant dataset/data/results/configs/metadata URLs replaced after hosting. +- Code license selected: `Apache-2.0`. +- Dataset license selected: `CC-BY-4.0`. +- Code URL added. +- Dataset URL added. +- Manifest generated. +- No real user data. +- No secrets. +- Anonymized paths. + +Hosted URLs: + +- Dataset URL: `https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins` +- Croissant metadata URL: `https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/raw/main/metadata/temporal_twins_croissant.json` +- Data URL: `https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/data` +- Results URL: `https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/results` +- Configs URL: `https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/configs` +- Metadata URL: `https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/metadata` diff --git a/configs/default.yaml b/configs/default.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d22b862b50859b4854ae4dd1d9dfc7630b80ce34 --- /dev/null +++ b/configs/default.yaml @@ -0,0 +1,29 @@ +num_users: 1000 +simulation_days: 365 +fraud_ratio: 0.05 +benchmark_mode: temporal_twins + +user_params: + lambda_mean: 5.0 + lambda_std: 1.0 + mu_mean: 7.5 + mu_std: 1.0 + sigma_mean: 0.5 + sigma_std: 0.2 + +upi_limits: + max_txn_amount: 100000 + daily_limit: 100000 + +risk_model: + weights: + amount_ratio: 1.0 + daily_ratio: 0.8 + velocity: 1.2 + time_anomaly: 0.6 + graph_anomaly: 1.0 + retry: 0.8 + kyc: 0.5 + user_risk: 0.8 + +random_seed: 42 diff --git a/configs/paper_suite_reference.yaml b/configs/paper_suite_reference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b7ca5d4c0f5a619c243d16490bb745df75b50b01 --- /dev/null +++ b/configs/paper_suite_reference.yaml @@ -0,0 +1,25 @@ +paper_suite: + benchmark_groups: + - oracle_calib + - easy + - medium + - hard + benchmark_modes: + oracle_calib: temporal_twins_oracle_calib + easy: temporal_twins + medium: temporal_twins + hard: temporal_twins + seeds: + - 0 + - 1 + - 2 + - 3 + - 4 + num_users: 350 + simulation_days: 45 + fast_mode: false + n_checkpoints: 8 + device: cpu + num_epochs: 3 + node_epochs: 150 + source_results_dir: results/paper_suite_20260503_202810 diff --git a/configs/temporal_twins_calib.yaml b/configs/temporal_twins_calib.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c16100795171bf50d1e92c8153f508ff2ba617f5 --- /dev/null +++ b/configs/temporal_twins_calib.yaml @@ -0,0 +1,29 @@ +num_users: 120 +simulation_days: 30 +fraud_ratio: 0.05 +benchmark_mode: temporal_twins + +user_params: + lambda_mean: 5.0 + lambda_std: 1.0 + mu_mean: 7.5 + mu_std: 1.0 + sigma_mean: 0.5 + sigma_std: 0.2 + +upi_limits: + max_txn_amount: 100000 + daily_limit: 100000 + +risk_model: + weights: + amount_ratio: 1.0 + daily_ratio: 0.8 + velocity: 1.2 + time_anomaly: 0.6 + graph_anomaly: 1.0 + retry: 0.8 + kyc: 0.5 + user_risk: 0.8 + +random_seed: 42 diff --git a/croissant.json b/croissant.json new file mode 100644 index 0000000000000000000000000000000000000000..259dbf4c88f9eb2d71b749e83893d0862533863f --- /dev/null +++ b/croissant.json @@ -0,0 +1,796 @@ +{ + "@context": { + "@vocab": "https://schema.org/", + "sc": "https://schema.org/", + "cr": "http://mlcommons.org/croissant/", + "dct": "http://purl.org/dc/terms/", + "prov": "http://www.w3.org/ns/prov#", + "rai": "http://mlcommons.org/croissant/RAI/", + "field": "cr:field", + "recordSet": "cr:recordSet", + "source": "cr:source", + "fileObject": "cr:fileObject", + "fileSet": "cr:fileSet", + "extract": "cr:extract", + "containedIn": "cr:containedIn", + "includes": "cr:includes", + "conformsTo": "dct:conformsTo", + "citeAs": "cr:citeAs" + }, + "@type": "sc:Dataset", + "name": "Temporal Twins Benchmark", + "description": "Temporal Twins is a synthetic UPI-style transaction benchmark for temporal fraud detection. The collection contains oracle_calib, easy, medium, and hard matched-prefix benchmark slices across deterministic seeds 0, 1, 2, 3, and 4. Fraud labels are assigned through delayed temporal mechanisms rather than static per-transaction attributes, and matched fraud/benign twin examples are aligned at the same local prefix index to suppress static shortcuts while preserving order-sensitive temporal structure.", + "url": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins", + "license": "https://creativecommons.org/licenses/by/4.0/", + "isBasedOn": { + "@type": "sc:SoftwareSourceCode", + "name": "Temporal Twins benchmark code", + "url": "TODO_CODE_REPOSITORY_URL", + "license": "https://www.apache.org/licenses/LICENSE-2.0", + "identifier": "Apache-2.0" + }, + "conformsTo": "http://mlcommons.org/croissant/1.1", + "citation": "TODO: Replace with the final Temporal Twins paper citation. Paper URL: TODO_PAPER_URL", + "citeAs": "Temporal Twins Benchmark (synthetic UPI-style temporal fraud benchmark), paper URL TODO_PAPER_URL, code repository TODO_CODE_REPOSITORY_URL.", + "creator": [ + { + "@type": "sc:Organization", + "name": "Temporal Twins Benchmark Contributors" + } + ], + "dateCreated": "2026-05-04", + "version": "1.0.0", + "keywords": [ + "synthetic financial transactions", + "UPI-style benchmark", + "temporal fraud detection", + "matched temporal twins", + "matched-prefix evaluation", + "sequence modeling", + "dynamic graph learning", + "reproducible benchmark" + ], + "distribution": [ + { + "@id": "transactions-archive", + "@type": "cr:FileObject", + "name": "Transactions archive", + "description": "Hosted archive containing synthetic transaction files for oracle_calib, easy, medium, and hard across seeds 0 through 4.", + "contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/data", + "encodingFormat": "application/zip" + }, + { + "@id": "matched-prefix-archive", + "@type": "cr:FileObject", + "name": "Matched-prefix examples archive", + "description": "Hosted release archive containing matched-prefix fraud/benign evaluation examples under release/data/*/seed_*/matched_pairs.parquet.", + "contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins", + "encodingFormat": "application/zip" + }, + { + "@id": "configs-archive", + "@type": "cr:FileObject", + "name": "Configs archive", + "description": "Hosted release archive containing benchmark configuration files under release/configs/*.yaml.", + "contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins", + "encodingFormat": "application/zip" + }, + { + "@id": "results-archive", + "@type": "cr:FileObject", + "name": "Results archive", + "description": "Hosted release archive containing the deterministic 5-seed paper-suite outputs under release/results/.", + "contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins", + "encodingFormat": "application/zip" + }, + { + "@id": "metadata-files", + "@type": "cr:FileSet", + "name": "Metadata files", + "description": "Metadata payload for the public release, including this Croissant file and companion notes.", + "containedIn": { + "@id": "results-archive" + }, + "includes": "release/metadata/*" + }, + { + "@id": "transactions-files", + "@type": "cr:FileSet", + "name": "Synthetic transactions parquet files", + "description": "Expected synthetic transaction files for benchmark modes oracle_calib, easy, medium, and hard across seeds 0 through 4.", + "containedIn": { + "@id": "transactions-archive" + }, + "includes": "release/data/*/seed_*/transactions.parquet", + "encodingFormat": "application/x-parquet" + }, + { + "@id": "matched-prefix-files", + "@type": "cr:FileSet", + "name": "Matched-prefix example parquet files", + "description": "Expected matched-prefix benchmark examples for the release. Each file contains fraud and benign twin examples evaluated at the same local prefix index.", + "containedIn": { + "@id": "matched-prefix-archive" + }, + "includes": "release/data/*/seed_*/matched_pairs.parquet", + "encodingFormat": "application/x-parquet" + }, + { + "@id": "config-files", + "@type": "cr:FileSet", + "name": "Benchmark config files", + "description": "YAML configuration files for the public release.", + "containedIn": { + "@id": "configs-archive" + }, + "includes": "release/configs/*.yaml" + }, + { + "@id": "paper-suite-runs-csv", + "@type": "cr:FileObject", + "name": "Per-run paper-suite results", + "description": "Per-run deterministic results for the final 5-seed paper-scale suite.", + "contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/raw/main/results/paper_suite_runs.csv", + "containedIn": { + "@id": "results-archive" + }, + "encodingFormat": "text/csv" + }, + { + "@id": "paper-suite-summary-csv", + "@type": "cr:FileObject", + "name": "Paper-suite summary results", + "description": "Mean and standard deviation summary of the deterministic 5-seed paper suite.", + "contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/raw/main/results/paper_suite_summary.csv", + "containedIn": { + "@id": "results-archive" + }, + "encodingFormat": "text/csv" + }, + { + "@id": "paper-suite-runtime-csv", + "@type": "cr:FileObject", + "name": "Paper-suite runtime summary", + "description": "Runtime and StaticGNN evaluation diagnostics for the final paper suite.", + "contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/raw/main/results/paper_suite_runtime.csv", + "containedIn": { + "@id": "results-archive" + }, + "encodingFormat": "text/csv" + }, + { + "@id": "paper-suite-failed-checks-csv", + "@type": "cr:FileObject", + "name": "Paper-suite failed gate checks", + "description": "Gate-check and advisory-check outcomes for each run in the final paper suite.", + "contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/raw/main/results/paper_suite_failed_checks.csv", + "containedIn": { + "@id": "results-archive" + }, + "encodingFormat": "text/csv" + }, + { + "@id": "croissant-file", + "@type": "cr:FileObject", + "name": "Temporal Twins Croissant metadata", + "description": "MLCommons Croissant 1.1 metadata for the full Temporal Twins benchmark collection.", + "contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/raw/main/metadata/temporal_twins_croissant.json", + "containedIn": { + "@id": "metadata-files" + }, + "encodingFormat": "application/ld+json" + } + ], + "recordSet": [ + { + "@id": "transactions", + "@type": "cr:RecordSet", + "name": "transactions", + "description": "Synthetic UPI-style transactions spanning oracle_calib, easy, medium, and hard, with deterministic seeds 0 through 4.", + "field": [ + { + "@id": "transactions/sender_id", + "@type": "cr:Field", + "name": "sender_id", + "description": "Synthetic sender account identifier.", + "dataType": "sc:Text", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "sender_id" + } + } + }, + { + "@id": "transactions/receiver_id", + "@type": "cr:Field", + "name": "receiver_id", + "description": "Synthetic receiver account identifier.", + "dataType": "sc:Text", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "receiver_id" + } + } + }, + { + "@id": "transactions/timestamp", + "@type": "cr:Field", + "name": "timestamp", + "description": "Synthetic event timestamp used to order transactions within each sender history.", + "dataType": "sc:Number", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "timestamp" + } + } + }, + { + "@id": "transactions/amount", + "@type": "cr:Field", + "name": "amount", + "description": "Synthetic transaction amount.", + "dataType": "sc:Number", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "amount" + } + } + }, + { + "@id": "transactions/risk_score", + "@type": "cr:Field", + "name": "risk_score", + "description": "Synthetic noisy risk score emitted by the simulator's risk engine.", + "dataType": "sc:Number", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "risk_score" + } + } + }, + { + "@id": "transactions/failed", + "@type": "cr:Field", + "name": "failed", + "description": "Indicator for whether the synthetic transaction attempt failed.", + "dataType": "sc:Boolean", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "failed" + } + } + }, + { + "@id": "transactions/is_fraud", + "@type": "cr:Field", + "name": "is_fraud", + "description": "Delayed synthetic fraud label attached to specific transactions.", + "dataType": "sc:Boolean", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "is_fraud" + } + } + } + ] + }, + { + "@id": "matched_prefix_examples", + "@type": "cr:RecordSet", + "name": "matched_prefix_examples", + "description": "Matched fraud and benign evaluation examples. Each benign twin is evaluated at the same local prefix index as the paired fraud twin, with matched static and prefix-level summaries. The release-facing field matched_local_event_idx is the matched prefix index and may correspond to the internal eval_local_event_idx column if files are exported directly from the current pipeline.", + "field": [ + { + "@id": "matched_prefix_examples/twin_pair_id", + "@type": "cr:Field", + "name": "twin_pair_id", + "description": "Matched fraud/benign twin pair identifier.", + "dataType": "sc:Integer", + "source": { + "fileSet": { + "@id": "matched-prefix-files" + }, + "extract": { + "column": "twin_pair_id" + } + } + }, + { + "@id": "matched_prefix_examples/sender_id", + "@type": "cr:Field", + "name": "sender_id", + "description": "Sender evaluated at the matched prefix.", + "dataType": "sc:Text", + "source": { + "fileSet": { + "@id": "matched-prefix-files" + }, + "extract": { + "column": "sender_id" + } + } + }, + { + "@id": "matched_prefix_examples/matched_local_event_idx", + "@type": "cr:Field", + "name": "matched_local_event_idx", + "description": "Release-facing matched-prefix event index k used for both the fraud twin and its benign control.", + "dataType": "sc:Integer", + "source": { + "fileSet": { + "@id": "matched-prefix-files" + }, + "extract": { + "column": "matched_local_event_idx" + } + } + }, + { + "@id": "matched_prefix_examples/label", + "@type": "cr:Field", + "name": "label", + "description": "Binary matched-prefix label where 1 denotes the fraud twin example and 0 denotes the benign matched control.", + "dataType": "sc:Boolean", + "source": { + "fileSet": { + "@id": "matched-prefix-files" + }, + "extract": { + "column": "label" + } + } + }, + { + "@id": "matched_prefix_examples/benchmark_mode", + "@type": "cr:Field", + "name": "benchmark_mode", + "description": "Benchmark mode identifier, e.g. temporal_twins_oracle_calib or temporal_twins.", + "dataType": "sc:Text", + "source": { + "fileSet": { + "@id": "matched-prefix-files" + }, + "extract": { + "column": "benchmark_mode" + } + } + }, + { + "@id": "matched_prefix_examples/difficulty", + "@type": "cr:Field", + "name": "difficulty", + "description": "Difficulty slice within the release: oracle_calib, easy, medium, or hard.", + "dataType": "sc:Text", + "source": { + "fileSet": { + "@id": "matched-prefix-files" + }, + "extract": { + "column": "difficulty" + } + } + }, + { + "@id": "matched_prefix_examples/seed", + "@type": "cr:Field", + "name": "seed", + "description": "Deterministic benchmark seed in the final paper-scale suite.", + "dataType": "sc:Integer", + "source": { + "fileSet": { + "@id": "matched-prefix-files" + }, + "extract": { + "column": "seed" + } + } + } + ] + }, + { + "@id": "audit_columns", + "@type": "cr:RecordSet", + "name": "audit_columns", + "description": "Audit and probe support columns carried by the synthetic generator for analysis, oracle-style scoring, and benchmark validation. These columns are not intended for ordinary model training and should be excluded from learned baseline inputs in benchmark evaluations.", + "field": [ + { + "@id": "audit_columns/twin_role", + "@type": "cr:Field", + "name": "twin_role", + "description": "Twin role label such as fraud, benign, or background; excluded from ordinary model features.", + "dataType": "sc:Text", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "twin_role" + } + } + }, + { + "@id": "audit_columns/template_id", + "@type": "cr:Field", + "name": "template_id", + "description": "Identifier for the matched temporal template used to construct a twin pair; excluded from ordinary model features.", + "dataType": "sc:Integer", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "template_id" + } + } + }, + { + "@id": "audit_columns/motif_hit_count", + "@type": "cr:Field", + "name": "motif_hit_count", + "description": "Count of motif hits in the generator trace; exposed only for audit or probe logic, not learned baselines.", + "dataType": "sc:Integer", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "motif_hit_count" + } + } + }, + { + "@id": "audit_columns/motif_source", + "@type": "cr:Field", + "name": "motif_source", + "description": "Generator-side motif provenance label; excluded from ordinary model features.", + "dataType": "sc:Text", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "motif_source" + } + } + }, + { + "@id": "audit_columns/trigger_event_idx", + "@type": "cr:Field", + "name": "trigger_event_idx", + "description": "Internal trigger event index for delayed fraud assignment; excluded from ordinary model features.", + "dataType": "sc:Integer", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "trigger_event_idx" + } + } + }, + { + "@id": "audit_columns/label_event_idx", + "@type": "cr:Field", + "name": "label_event_idx", + "description": "Internal event index at which the delayed fraud label is attached; excluded from ordinary model features.", + "dataType": "sc:Integer", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "label_event_idx" + } + } + }, + { + "@id": "audit_columns/label_delay", + "@type": "cr:Field", + "name": "label_delay", + "description": "Internal delay between trigger and labeled event; excluded from ordinary model features.", + "dataType": "sc:Integer", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "label_delay" + } + } + }, + { + "@id": "audit_columns/fraud_source", + "@type": "cr:Field", + "name": "fraud_source", + "description": "Internal fraud-source annotation such as motif or fallback; excluded from ordinary model features.", + "dataType": "sc:Text", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "fraud_source" + } + } + }, + { + "@id": "audit_columns/dynamic_fraud_state", + "@type": "cr:Field", + "name": "dynamic_fraud_state", + "description": "Latent generator-side fraud-state variable used for mechanistic analysis; excluded from ordinary model features.", + "dataType": "sc:Number", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "dynamic_fraud_state" + } + } + } + ] + }, + { + "@id": "paper_suite_summary_results", + "@type": "cr:RecordSet", + "name": "paper_suite_summary_results", + "description": "Deterministic 5-seed summary results for the final paper-scale Temporal Twins suite.", + "field": [ + { + "@id": "paper_suite_summary_results/benchmark_group", + "@type": "cr:Field", + "name": "benchmark_group", + "description": "Benchmark slice summarized in the row, e.g. oracle_calib, easy, medium, or hard.", + "dataType": "sc:Text", + "source": { + "fileObject": { + "@id": "paper-suite-summary-csv" + }, + "extract": { + "column": "benchmark_group" + } + } + }, + { + "@id": "paper_suite_summary_results/matched_eval_pairs_mean", + "@type": "cr:Field", + "name": "matched_eval_pairs_mean", + "description": "Mean number of matched-prefix evaluation pairs across seeds.", + "dataType": "sc:Number", + "source": { + "fileObject": { + "@id": "paper-suite-summary-csv" + }, + "extract": { + "column": "matched_eval_pairs_mean" + } + } + }, + { + "@id": "paper_suite_summary_results/static_agg_auc_mean", + "@type": "cr:Field", + "name": "static_agg_auc_mean", + "description": "Mean ROC-AUC of the static aggregate shortcut audit.", + "dataType": "sc:Number", + "source": { + "fileObject": { + "@id": "paper-suite-summary-csv" + }, + "extract": { + "column": "static_agg_auc_mean" + } + } + }, + { + "@id": "paper_suite_summary_results/audit_roc_auc_mean", + "@type": "cr:Field", + "name": "audit_roc_auc_mean", + "description": "Mean oracle or probe ROC-AUC depending on benchmark mode.", + "dataType": "sc:Number", + "source": { + "fileObject": { + "@id": "paper-suite-summary-csv" + }, + "extract": { + "column": "audit_roc_auc_mean" + } + } + }, + { + "@id": "paper_suite_summary_results/raw_roc_auc_mean", + "@type": "cr:Field", + "name": "raw_roc_auc_mean", + "description": "Mean raw motif oracle or probe ROC-AUC depending on benchmark mode.", + "dataType": "sc:Number", + "source": { + "fileObject": { + "@id": "paper-suite-summary-csv" + }, + "extract": { + "column": "raw_roc_auc_mean" + } + } + }, + { + "@id": "paper_suite_summary_results/xgb_roc_auc_mean", + "@type": "cr:Field", + "name": "xgb_roc_auc_mean", + "description": "Mean XGBoost ROC-AUC across seeds.", + "dataType": "sc:Number", + "source": { + "fileObject": { + "@id": "paper-suite-summary-csv" + }, + "extract": { + "column": "xgb_roc_auc_mean" + } + } + }, + { + "@id": "paper_suite_summary_results/static_gnn_roc_auc_mean", + "@type": "cr:Field", + "name": "static_gnn_roc_auc_mean", + "description": "Mean StaticGNN ROC-AUC across seeds.", + "dataType": "sc:Number", + "source": { + "fileObject": { + "@id": "paper-suite-summary-csv" + }, + "extract": { + "column": "static_gnn_roc_auc_mean" + } + } + }, + { + "@id": "paper_suite_summary_results/seqgru_clean_roc_auc_mean", + "@type": "cr:Field", + "name": "seqgru_clean_roc_auc_mean", + "description": "Mean clean SeqGRU ROC-AUC across seeds.", + "dataType": "sc:Number", + "source": { + "fileObject": { + "@id": "paper-suite-summary-csv" + }, + "extract": { + "column": "seqgru_clean_roc_auc_mean" + } + } + }, + { + "@id": "paper_suite_summary_results/seqgru_shuffle_delta_mean", + "@type": "cr:Field", + "name": "seqgru_shuffle_delta_mean", + "description": "Mean change in SeqGRU ROC-AUC under shuffled event order.", + "dataType": "sc:Number", + "source": { + "fileObject": { + "@id": "paper-suite-summary-csv" + }, + "extract": { + "column": "seqgru_shuffle_delta_mean" + } + } + }, + { + "@id": "paper_suite_summary_results/tgn_clean_roc_auc_mean", + "@type": "cr:Field", + "name": "tgn_clean_roc_auc_mean", + "description": "Mean TGN ROC-AUC across seeds.", + "dataType": "sc:Number", + "source": { + "fileObject": { + "@id": "paper-suite-summary-csv" + }, + "extract": { + "column": "tgn_clean_roc_auc_mean" + } + } + }, + { + "@id": "paper_suite_summary_results/tgat_clean_roc_auc_mean", + "@type": "cr:Field", + "name": "tgat_clean_roc_auc_mean", + "description": "Mean TGAT ROC-AUC across seeds.", + "dataType": "sc:Number", + "source": { + "fileObject": { + "@id": "paper-suite-summary-csv" + }, + "extract": { + "column": "tgat_clean_roc_auc_mean" + } + } + }, + { + "@id": "paper_suite_summary_results/dyrep_clean_roc_auc_mean", + "@type": "cr:Field", + "name": "dyrep_clean_roc_auc_mean", + "description": "Mean DyRep ROC-AUC across seeds.", + "dataType": "sc:Number", + "source": { + "fileObject": { + "@id": "paper-suite-summary-csv" + }, + "extract": { + "column": "dyrep_clean_roc_auc_mean" + } + } + }, + { + "@id": "paper_suite_summary_results/jodie_clean_roc_auc_mean", + "@type": "cr:Field", + "name": "jodie_clean_roc_auc_mean", + "description": "Mean JODIE ROC-AUC across seeds.", + "dataType": "sc:Number", + "source": { + "fileObject": { + "@id": "paper-suite-summary-csv" + }, + "extract": { + "column": "jodie_clean_roc_auc_mean" + } + } + } + ] + } + ], + "rai:dataLimitations": [ + "Temporal Twins is fully synthetic and is not representative of real UPI fraud prevalence, transaction mix, or institutional controls.", + "The benchmark is designed to isolate temporal-order reasoning under matched static controls rather than to reproduce a production fraud environment.", + "Standard-mode probe scores are informative benchmark probes, not upper bounds on real-world fraud detectability." + ], + "rai:dataBiases": [ + "Behavioral patterns are simulator-defined and reflect the assumptions of the Temporal Twins generator rather than observed user behavior.", + "Difficulty slices intentionally reshape motif strength, noise, delay, and adversarial perturbations, so conclusions should be interpreted as benchmark-relative rather than population-representative." + ], + "rai:personalSensitiveInformation": "None. The dataset contains no real UPI data, no real users, no real bank accounts, no real transactions, no personal financial records, and no protected demographic attributes.", + "rai:dataUseCases": [ + "Intended for temporal machine learning benchmark research, including sequence models, dynamic graph models, matched-control evaluation, and shortcut auditing.", + "Suitable for studying whether a model uses causal temporal order rather than static transaction summaries." + ], + "rai:dataSocialImpact": [ + "Positive use may include more rigorous evaluation of temporal fraud-detection methods under matched static controls.", + "Potential misuse includes treating synthetic behavior as if it were real user behavior or using the dataset to justify deployment decisions without external validation on real, appropriately governed data." + ], + "rai:hasSyntheticData": true, + "prov:wasGeneratedBy": { + "@type": "prov:Activity", + "name": "Temporal Twins synthetic UPI transaction generator", + "description": "Synthetic benchmark generation for oracle_calib, easy, medium, and hard using deterministic seeds [0, 1, 2, 3, 4], num_users=350, simulation_days=45, fast_mode=false, and n_checkpoints=8. The generator emits matched fraud/benign twins evaluated at matched local prefix indices and preserves paper-suite shortcut audits and summary results.", + "prov:used": [ + { + "@type": "prov:Entity", + "name": "Temporal Twins benchmark code repository", + "url": "TODO_CODE_REPOSITORY_URL", + "license": "https://www.apache.org/licenses/LICENSE-2.0", + "identifier": "Apache-2.0" + }, + { + "@type": "prov:Entity", + "name": "Temporal Twins paper", + "url": "TODO_PAPER_URL" + } + ] + } +} diff --git a/data/.DS_Store b/data/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5a160b1053fed6e1048e573b4a7f8d87cd7a7368 Binary files /dev/null and b/data/.DS_Store differ diff --git a/data/README_GENERATION.md b/data/README_GENERATION.md new file mode 100644 index 0000000000000000000000000000000000000000..8bfdbb40640036640611d47e563514d58c31d48f --- /dev/null +++ b/data/README_GENERATION.md @@ -0,0 +1,120 @@ +# 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//seed_/` is expected to contain: + +- `transactions.parquet` +- `matched_pairs.parquet` +- `audit_summary.csv` +- `schema.json` +- `config.yaml` + +Where: + +- `` is one of `oracle_calib`, `easy`, `medium`, `hard` +- `` 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`. diff --git a/data/_export_summary.csv b/data/_export_summary.csv new file mode 100644 index 0000000000000000000000000000000000000000..fd3d02d43bbfb977c87ec4ea4af0bd4ff89cef69 --- /dev/null +++ b/data/_export_summary.csv @@ -0,0 +1,21 @@ +release_mode,seed,transactions,matched_examples,matched_pairs,audit_examples +oracle_calib,0,35408,2240,1120,2240 +oracle_calib,1,31994,2106,1053,2106 +oracle_calib,2,35922,2390,1195,2390 +oracle_calib,3,32228,2222,1111,2222 +oracle_calib,4,32108,2276,1138,2276 +easy,0,46386,3398,1699,3398 +easy,1,40462,3132,1566,3132 +easy,2,44958,3558,1779,3558 +easy,3,46312,3374,1687,3374 +easy,4,41482,3296,1648,3296 +medium,0,77692,3184,1592,3184 +medium,1,76870,3168,1584,3168 +medium,2,80150,3174,1587,3174 +medium,3,80172,3148,1574,3148 +medium,4,78456,3144,1572,3144 +hard,0,81978,2600,1300,2600 +hard,1,97936,2656,1328,2656 +hard,2,86358,2624,1312,2624 +hard,3,76070,2614,1307,2614 +hard,4,76406,2618,1309,2618 diff --git a/data/easy/.DS_Store b/data/easy/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..f75a70905112f061fb71bb58218fe2c19c7ba61b Binary files /dev/null and b/data/easy/.DS_Store differ diff --git a/data/easy/seed_0/audit_summary.csv b/data/easy/seed_0/audit_summary.csv new file mode 100644 index 0000000000000000000000000000000000000000..10221eebd2edddd61c3624332a89815c01445d7d --- /dev/null +++ b/data/easy/seed_0/audit_summary.csv @@ -0,0 +1,2 @@ +pair_total_txn_count_diff_mean,pair_total_txn_count_diff_max,auc_total_txn_count,auc_local_event_idx,auc_prefix_txn_count,auc_timestamp,auc_account_age,auc_active_age,fraud_label_event_idx_mean,fraud_label_event_idx_max,benign_eval_event_idx_mean,benign_eval_event_idx_max,pair_event_idx_diff_mean,pair_event_idx_diff_max,pair_active_age_diff_mean,pair_active_age_diff_max,pair_timestamp_diff_mean,pair_timestamp_diff_max,benign_motif_hit_rate,benign_motif_hit_pairs,matched_control_examples,matched_control_pair_events +0.0,0.0,0.5,0.49999999999999994,0.49999999999999994,0.5,0.5,0.5,40.72630959387875,104.0,40.72630959387875,104.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,3398,1699 diff --git a/data/easy/seed_0/config.yaml b/data/easy/seed_0/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..83d64590f62a1b9d0ae5a5be8c726c6d8389bd72 --- /dev/null +++ b/data/easy/seed_0/config.yaml @@ -0,0 +1,30 @@ +num_users: 350 +simulation_days: 45 +fraud_ratio: 0.05 +benchmark_mode: temporal_twins +user_params: + lambda_mean: 5.0 + lambda_std: 1.0 + mu_mean: 7.5 + mu_std: 1.0 + sigma_mean: 0.5 + sigma_std: 0.2 +upi_limits: + max_txn_amount: 100000.0 + daily_limit: 100000.0 +risk_model: + weights: + amount_ratio: 1.0 + daily_ratio: 0.8 + velocity: 1.2 + time_anomaly: 0.6 + graph_anomaly: 1.0 + retry: 0.8 + kyc: 0.5 + user_risk: 0.8 +random_seed: 0 +difficulty: easy +release_mode: easy +seed: 0 +fast_mode: false +n_checkpoints: 8 diff --git a/data/easy/seed_0/matched_pairs.parquet b/data/easy/seed_0/matched_pairs.parquet new file mode 100644 index 0000000000000000000000000000000000000000..8bdfefb4504731f8e4b3c17c641788db421be371 --- /dev/null +++ b/data/easy/seed_0/matched_pairs.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d08d99bb61bfc154f2020f20458f4ba34084737ca12c191784d00e4c5f9a968b +size 81815 diff --git a/data/easy/seed_0/schema.json b/data/easy/seed_0/schema.json new file mode 100644 index 0000000000000000000000000000000000000000..f729733d6123ccca38cd0faa01a5be9da8f01f04 --- /dev/null +++ b/data/easy/seed_0/schema.json @@ -0,0 +1,84 @@ +{ + "transactions_columns": { + "txn_id": "int32", + "sender_id": "int64", + "receiver_id": "int64", + "amount": "float32", + "timestamp": "float32", + "txn_type": "int8", + "is_fraud": "int8", + "fraud_type": "str", + "is_retry": "int8", + "risk_score": "float32", + "fail_prob": "float32", + "failed": "int8", + "twin_pair_id": "int64", + "template_id": "int64", + "twin_role": "str", + "twin_label": "int8", + "motif_source": "int8", + "motif_chain_state": "float32", + "motif_strength": "float32", + "dynamic_fraud_state": "float32", + "fraud_source": "str", + "motif_hit_count": "int32", + "trigger_event_idx": "int32", + "label_event_idx": "int32", + 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+0.0,0.0,0.5,0.49999999999999994,0.49999999999999994,0.5000000000000001,0.5,0.5,36.748403575989784,100.0,36.748403575989784,100.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,3132,1566 diff --git a/data/easy/seed_1/config.yaml b/data/easy/seed_1/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ecd6dfd5cdf9555ac1c434def27e8f9adbae6d05 --- /dev/null +++ b/data/easy/seed_1/config.yaml @@ -0,0 +1,30 @@ +num_users: 350 +simulation_days: 45 +fraud_ratio: 0.05 +benchmark_mode: temporal_twins +user_params: + lambda_mean: 5.0 + lambda_std: 1.0 + mu_mean: 7.5 + mu_std: 1.0 + sigma_mean: 0.5 + sigma_std: 0.2 +upi_limits: + max_txn_amount: 100000.0 + daily_limit: 100000.0 +risk_model: + weights: + amount_ratio: 1.0 + daily_ratio: 0.8 + velocity: 1.2 + time_anomaly: 0.6 + graph_anomaly: 1.0 + retry: 0.8 + kyc: 0.5 + user_risk: 0.8 +random_seed: 1 +difficulty: easy +release_mode: easy +seed: 1 +fast_mode: false +n_checkpoints: 8 diff --git a/data/easy/seed_1/matched_pairs.parquet b/data/easy/seed_1/matched_pairs.parquet new file mode 100644 index 0000000000000000000000000000000000000000..e5927ec67327f7d56415e54291031ca08bc7f305 --- /dev/null +++ b/data/easy/seed_1/matched_pairs.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7503b30b1b597adc9507fb10361d8ef0c0ca6e5add89c502843caba3a0aa9e74 +size 77643 diff --git a/data/easy/seed_1/schema.json b/data/easy/seed_1/schema.json new file mode 100644 index 0000000000000000000000000000000000000000..f729733d6123ccca38cd0faa01a5be9da8f01f04 --- /dev/null +++ b/data/easy/seed_1/schema.json @@ -0,0 +1,84 @@ +{ + "transactions_columns": { + "txn_id": "int32", + "sender_id": "int64", + "receiver_id": "int64", + "amount": "float32", + "timestamp": "float32", + "txn_type": "int8", + "is_fraud": "int8", + "fraud_type": "str", + "is_retry": "int8", + "risk_score": "float32", + "fail_prob": "float32", + "failed": "int8", + "twin_pair_id": "int64", + "template_id": "int64", + "twin_role": "str", + 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"float64", + "pair_total_txn_count_diff_max": "float64", + "auc_total_txn_count": "float64", + "auc_local_event_idx": "float64", + "auc_prefix_txn_count": "float64", + "auc_timestamp": "float64", + "auc_account_age": "float64", + "auc_active_age": "float64", + "fraud_label_event_idx_mean": "float64", + "fraud_label_event_idx_max": "float64", + "benign_eval_event_idx_mean": "float64", + "benign_eval_event_idx_max": "float64", + "pair_event_idx_diff_mean": "float64", + "pair_event_idx_diff_max": "float64", + "pair_active_age_diff_mean": "float64", + "pair_active_age_diff_max": "float64", + "pair_timestamp_diff_mean": "float64", + "pair_timestamp_diff_max": "float64", + "benign_motif_hit_rate": "float64", + "benign_motif_hit_pairs": "int64", + "matched_control_examples": "int64", + "matched_control_pair_events": "int64" + }, + "files": [ + "transactions.parquet", + "matched_pairs.parquet", + "audit_summary.csv", + "schema.json", + "config.yaml" + ] +} diff --git a/data/easy/seed_1/transactions.parquet b/data/easy/seed_1/transactions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..0aac780b562cd7d00e0bcb109a33a0a25ee9605f --- /dev/null +++ b/data/easy/seed_1/transactions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:22f5283274a62e56a22458610709081d8b1bbf6e4793b6363c3ca9e518dc7f3e +size 1229511 diff --git a/data/easy/seed_2/audit_summary.csv b/data/easy/seed_2/audit_summary.csv new file mode 100644 index 0000000000000000000000000000000000000000..7785f1639642f6867c1ccd18ca54749ee1fe1e02 --- /dev/null +++ b/data/easy/seed_2/audit_summary.csv @@ -0,0 +1,2 @@ +pair_total_txn_count_diff_mean,pair_total_txn_count_diff_max,auc_total_txn_count,auc_local_event_idx,auc_prefix_txn_count,auc_timestamp,auc_account_age,auc_active_age,fraud_label_event_idx_mean,fraud_label_event_idx_max,benign_eval_event_idx_mean,benign_eval_event_idx_max,pair_event_idx_diff_mean,pair_event_idx_diff_max,pair_active_age_diff_mean,pair_active_age_diff_max,pair_timestamp_diff_mean,pair_timestamp_diff_max,benign_motif_hit_rate,benign_motif_hit_pairs,matched_control_examples,matched_control_pair_events +0.0,0.0,0.5,0.5,0.5,0.5,0.5,0.5,41.19336706014615,107.0,41.19336706014615,107.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,3558,1779 diff --git a/data/easy/seed_2/config.yaml b/data/easy/seed_2/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3345d587baca024787396a9490d622a350f12fe9 --- /dev/null +++ b/data/easy/seed_2/config.yaml @@ -0,0 +1,30 @@ +num_users: 350 +simulation_days: 45 +fraud_ratio: 0.05 +benchmark_mode: temporal_twins +user_params: + lambda_mean: 5.0 + lambda_std: 1.0 + mu_mean: 7.5 + mu_std: 1.0 + sigma_mean: 0.5 + sigma_std: 0.2 +upi_limits: + max_txn_amount: 100000.0 + daily_limit: 100000.0 +risk_model: + weights: + amount_ratio: 1.0 + daily_ratio: 0.8 + velocity: 1.2 + time_anomaly: 0.6 + graph_anomaly: 1.0 + retry: 0.8 + kyc: 0.5 + user_risk: 0.8 +random_seed: 2 +difficulty: easy +release_mode: easy +seed: 2 +fast_mode: false +n_checkpoints: 8 diff --git a/data/easy/seed_2/matched_pairs.parquet b/data/easy/seed_2/matched_pairs.parquet new file mode 100644 index 0000000000000000000000000000000000000000..87108e2febead663bde41e48eedf463f8fc3eb8a --- /dev/null +++ b/data/easy/seed_2/matched_pairs.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:583ee815999e1deade36980ead3d9f8cc93a23303e607b925728e153e2e250bd +size 84709 diff --git a/data/easy/seed_2/schema.json b/data/easy/seed_2/schema.json new file mode 100644 index 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file mode 100644 index 0000000000000000000000000000000000000000..f729733d6123ccca38cd0faa01a5be9da8f01f04 --- /dev/null +++ b/data/hard/seed_0/schema.json @@ -0,0 +1,84 @@ +{ + "transactions_columns": { + "txn_id": "int32", + "sender_id": "int64", + "receiver_id": "int64", + "amount": "float32", + "timestamp": "float32", + "txn_type": "int8", + "is_fraud": "int8", + "fraud_type": "str", + "is_retry": "int8", + "risk_score": "float32", + "fail_prob": "float32", + "failed": "int8", + "twin_pair_id": "int64", + "template_id": "int64", + "twin_role": "str", + "twin_label": "int8", + "motif_source": "int8", + "motif_chain_state": "float32", + "motif_strength": "float32", + "dynamic_fraud_state": "float32", + "fraud_source": "str", + "motif_hit_count": "int32", + "trigger_event_idx": "int32", + "label_event_idx": "int32", + "label_delay": "int32", + "is_fallback_label": "int8", + "risk_noisy": "float32", + "neighbor_score": "float32", + "pair_freq": "float32", + "txn_count_10": "float32", + 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index 0000000000000000000000000000000000000000..2b51d7fe32e986f9e157ba9f429c1fdb9a6f9f51 --- /dev/null +++ b/data/hard/seed_4/config.yaml @@ -0,0 +1,30 @@ +num_users: 350 +simulation_days: 45 +fraud_ratio: 0.05 +benchmark_mode: temporal_twins +user_params: + lambda_mean: 5.0 + lambda_std: 1.0 + mu_mean: 7.5 + mu_std: 1.0 + sigma_mean: 0.5 + sigma_std: 0.2 +upi_limits: + max_txn_amount: 100000.0 + daily_limit: 100000.0 +risk_model: + weights: + amount_ratio: 1.0 + daily_ratio: 0.8 + velocity: 1.2 + time_anomaly: 0.6 + graph_anomaly: 1.0 + retry: 0.8 + kyc: 0.5 + user_risk: 0.8 +random_seed: 4 +difficulty: hard +release_mode: hard +seed: 4 +fast_mode: false +n_checkpoints: 8 diff --git a/data/hard/seed_4/matched_pairs.parquet b/data/hard/seed_4/matched_pairs.parquet new file mode 100644 index 0000000000000000000000000000000000000000..5466eb96b9dbdc96abb6f84934f7111bf654b9eb --- /dev/null +++ b/data/hard/seed_4/matched_pairs.parquet @@ -0,0 +1,3 @@ +version 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b/data/medium/seed_2/matched_pairs.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9a2b75dcb094d8b27ab26d99a9a68d4d55d392c1de2bd5024bc47620ab60bb2f +size 80599 diff --git a/data/medium/seed_2/schema.json b/data/medium/seed_2/schema.json new file mode 100644 index 0000000000000000000000000000000000000000..f729733d6123ccca38cd0faa01a5be9da8f01f04 --- /dev/null +++ b/data/medium/seed_2/schema.json @@ -0,0 +1,84 @@ +{ + "transactions_columns": { + "txn_id": "int32", + "sender_id": "int64", + "receiver_id": "int64", + "amount": "float32", + "timestamp": "float32", + "txn_type": "int8", + "is_fraud": "int8", + "fraud_type": "str", + "is_retry": "int8", + "risk_score": "float32", + "fail_prob": "float32", + "failed": "int8", + "twin_pair_id": "int64", + "template_id": "int64", + "twin_role": "str", + "twin_label": "int8", + "motif_source": "int8", + "motif_chain_state": "float32", + "motif_strength": "float32", + "dynamic_fraud_state": "float32", + 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"float32", + "amount_sum_10": "float32" + }, + "matched_pairs_columns": { + "pair_event_id": "int64", + "twin_pair_id": "int64", + "template_id": "int64", + "matched_local_event_idx": "int64", + "prefix_txn_count": "int64", + "sender_id": "int64", + "label": "int64", + "twin_role": "str", + "matched_sender_id": "int64", + "total_txn_count": "int64", + "eval_timestamp": "float64", + "account_age": "float64", + "active_age": "float64", + "benchmark_mode": "str", + "difficulty": "str", + "seed": "int64" + }, + "audit_summary_columns": { + "pair_total_txn_count_diff_mean": "float64", + "pair_total_txn_count_diff_max": "float64", + "auc_total_txn_count": "float64", + "auc_local_event_idx": "float64", + "auc_prefix_txn_count": "float64", + "auc_timestamp": "float64", + "auc_account_age": "float64", + "auc_active_age": "float64", + "fraud_label_event_idx_mean": "float64", + "fraud_label_event_idx_max": "float64", + "benign_eval_event_idx_mean": "float64", + "benign_eval_event_idx_max": "float64", + "pair_event_idx_diff_mean": "float64", + "pair_event_idx_diff_max": "float64", + "pair_active_age_diff_mean": "float64", + "pair_active_age_diff_max": "float64", + "pair_timestamp_diff_mean": "float64", + "pair_timestamp_diff_max": "float64", + "benign_motif_hit_rate": "float64", + "benign_motif_hit_pairs": "int64", + "matched_control_examples": "int64", + "matched_control_pair_events": "int64" + }, + "files": [ + "transactions.parquet", + "matched_pairs.parquet", + "audit_summary.csv", + "schema.json", + "config.yaml" + ] +} diff --git a/data/oracle_calib/seed_4/transactions.parquet b/data/oracle_calib/seed_4/transactions.parquet new file mode 100644 index 0000000000000000000000000000000000000000..4ca416ee7db5256f40469860f3454f7d89713775 --- /dev/null +++ b/data/oracle_calib/seed_4/transactions.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6974ebe8b33b719486fd44ccf39a08bf08dfda366ceaa34b65de2bc50ab7b01c +size 1006003 diff --git a/metadata/CROISSANT_VALIDATION_NOTES.md b/metadata/CROISSANT_VALIDATION_NOTES.md new file mode 100644 index 0000000000000000000000000000000000000000..cda4c7cde6a632bc00e94f8f48a14b85bb723627 --- /dev/null +++ b/metadata/CROISSANT_VALIDATION_NOTES.md @@ -0,0 +1,58 @@ +# Temporal Twins Croissant Validation Notes + +## 1. How to Validate + +Use the official MLCommons Croissant tooling after the final release files are hosted. + +1. Replace the placeholder URLs in `metadata/temporal_twins_croissant.json` with the final hosted dataset, archive, repository, and paper URLs. +2. Validate the file with the official Croissant validator from the MLCommons Croissant project. If you use the web validator, upload the final JSON-LD file or point it at the hosted Croissant URL. +3. As a local smoke check, you can also load the JSON-LD with a JSON parser before running the full validator: + +```bash +python3 - <<'PY' +import json +from pathlib import Path +path = Path("metadata/temporal_twins_croissant.json") +with path.open() as f: + json.load(f) +print("JSON parse OK") +PY +``` + +4. After JSON parsing succeeds, run the official Croissant validation step and confirm the record sets, fields, and distribution references resolve correctly. + +## 2. Hosted URLs and Remaining Placeholders + +Dataset-side URLs now resolve to: + +- Dataset URL: `https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins` +- Croissant metadata URL: `https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/raw/main/metadata/temporal_twins_croissant.json` +- Croissant metadata browser page: `https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/blob/main/metadata/temporal_twins_croissant.json` +- Data URL: `https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/data` +- Results URL: `https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/results` +- Configs URL: `https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/configs` +- Metadata URL: `https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/metadata` +- Release landing URL: `https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins` + +Remaining URL placeholders intentionally left unresolved: + +- `TODO_CODE_REPOSITORY_URL`: public code repository URL +- `TODO_PAPER_URL`: paper or preprint URL + +## 3. Release Checklist + +- Dataset URL is accessible to reviewers. +- Croissant file validates with the official MLCommons Croissant validator. +- Distribution URLs resolve to the intended hosted artifacts. +- Record-set columns match the actual hosted files. +- RAI fields are present. +- Dataset license is present (`CC-BY-4.0`). +- Code repository license is present (`Apache-2.0`). + +## 4. Packaging Notes + +- The Croissant file describes four dataset slices: `oracle_calib`, `easy`, `medium`, and `hard`. +- It assumes deterministic release seeds `0, 1, 2, 3, 4`. +- It assumes paper-suite configuration `num_users=350`, `simulation_days=45`, `fast_mode=false`, and `n_checkpoints=8`. +- The `matched_prefix_examples` record set uses the release-facing column name `matched_local_event_idx`. +- If the final hosted matched-pairs files keep the internal pipeline column name `eval_local_event_idx` instead, either rename that column in the export or update the Croissant metadata so the record-set field names match the hosted files exactly. diff --git a/metadata/temporal_twins_croissant.json b/metadata/temporal_twins_croissant.json new file mode 100644 index 0000000000000000000000000000000000000000..259dbf4c88f9eb2d71b749e83893d0862533863f --- /dev/null +++ b/metadata/temporal_twins_croissant.json @@ -0,0 +1,796 @@ +{ + "@context": { + "@vocab": "https://schema.org/", + "sc": "https://schema.org/", + "cr": "http://mlcommons.org/croissant/", + "dct": "http://purl.org/dc/terms/", + "prov": "http://www.w3.org/ns/prov#", + "rai": "http://mlcommons.org/croissant/RAI/", + "field": "cr:field", + "recordSet": "cr:recordSet", + "source": "cr:source", + "fileObject": "cr:fileObject", + "fileSet": "cr:fileSet", + "extract": "cr:extract", + "containedIn": "cr:containedIn", + "includes": "cr:includes", + "conformsTo": "dct:conformsTo", + "citeAs": "cr:citeAs" + }, + "@type": "sc:Dataset", + "name": "Temporal Twins Benchmark", + "description": "Temporal Twins is a synthetic UPI-style transaction benchmark for temporal fraud detection. The collection contains oracle_calib, easy, medium, and hard matched-prefix benchmark slices across deterministic seeds 0, 1, 2, 3, and 4. Fraud labels are assigned through delayed temporal mechanisms rather than static per-transaction attributes, and matched fraud/benign twin examples are aligned at the same local prefix index to suppress static shortcuts while preserving order-sensitive temporal structure.", + "url": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins", + "license": "https://creativecommons.org/licenses/by/4.0/", + "isBasedOn": { + "@type": "sc:SoftwareSourceCode", + "name": "Temporal Twins benchmark code", + "url": "TODO_CODE_REPOSITORY_URL", + "license": "https://www.apache.org/licenses/LICENSE-2.0", + "identifier": "Apache-2.0" + }, + "conformsTo": "http://mlcommons.org/croissant/1.1", + "citation": "TODO: Replace with the final Temporal Twins paper citation. Paper URL: TODO_PAPER_URL", + "citeAs": "Temporal Twins Benchmark (synthetic UPI-style temporal fraud benchmark), paper URL TODO_PAPER_URL, code repository TODO_CODE_REPOSITORY_URL.", + "creator": [ + { + "@type": "sc:Organization", + "name": "Temporal Twins Benchmark Contributors" + } + ], + "dateCreated": "2026-05-04", + "version": "1.0.0", + "keywords": [ + "synthetic financial transactions", + "UPI-style benchmark", + "temporal fraud detection", + "matched temporal twins", + "matched-prefix evaluation", + "sequence modeling", + "dynamic graph learning", + "reproducible benchmark" + ], + "distribution": [ + { + "@id": "transactions-archive", + "@type": "cr:FileObject", + "name": "Transactions archive", + "description": "Hosted archive containing synthetic transaction files for oracle_calib, easy, medium, and hard across seeds 0 through 4.", + "contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/data", + "encodingFormat": "application/zip" + }, + { + "@id": "matched-prefix-archive", + "@type": "cr:FileObject", + "name": "Matched-prefix examples archive", + "description": "Hosted release archive containing matched-prefix fraud/benign evaluation examples under release/data/*/seed_*/matched_pairs.parquet.", + "contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins", + "encodingFormat": "application/zip" + }, + { + "@id": "configs-archive", + "@type": "cr:FileObject", + "name": "Configs archive", + "description": "Hosted release archive containing benchmark configuration files under release/configs/*.yaml.", + "contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins", + "encodingFormat": "application/zip" + }, + { + "@id": "results-archive", + "@type": "cr:FileObject", + "name": "Results archive", + "description": "Hosted release archive containing the deterministic 5-seed paper-suite outputs under release/results/.", + "contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins", + "encodingFormat": "application/zip" + }, + { + "@id": "metadata-files", + "@type": "cr:FileSet", + "name": "Metadata files", + "description": "Metadata payload for the public release, including this Croissant file and companion notes.", + "containedIn": { + "@id": "results-archive" + }, + "includes": "release/metadata/*" + }, + { + "@id": "transactions-files", + "@type": "cr:FileSet", + "name": "Synthetic transactions parquet files", + "description": "Expected synthetic transaction files for benchmark modes oracle_calib, easy, medium, and hard across seeds 0 through 4.", + "containedIn": { + "@id": "transactions-archive" + }, + "includes": "release/data/*/seed_*/transactions.parquet", + "encodingFormat": "application/x-parquet" + }, + { + "@id": "matched-prefix-files", + "@type": "cr:FileSet", + "name": "Matched-prefix example parquet files", + "description": "Expected matched-prefix benchmark examples for the release. Each file contains fraud and benign twin examples evaluated at the same local prefix index.", + "containedIn": { + "@id": "matched-prefix-archive" + }, + "includes": "release/data/*/seed_*/matched_pairs.parquet", + "encodingFormat": "application/x-parquet" + }, + { + "@id": "config-files", + "@type": "cr:FileSet", + "name": "Benchmark config files", + "description": "YAML configuration files for the public release.", + "containedIn": { + "@id": "configs-archive" + }, + "includes": "release/configs/*.yaml" + }, + { + "@id": "paper-suite-runs-csv", + "@type": "cr:FileObject", + "name": "Per-run paper-suite results", + "description": "Per-run deterministic results for the final 5-seed paper-scale suite.", + "contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/raw/main/results/paper_suite_runs.csv", + "containedIn": { + "@id": "results-archive" + }, + "encodingFormat": "text/csv" + }, + { + "@id": "paper-suite-summary-csv", + "@type": "cr:FileObject", + "name": "Paper-suite summary results", + "description": "Mean and standard deviation summary of the deterministic 5-seed paper suite.", + "contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/raw/main/results/paper_suite_summary.csv", + "containedIn": { + "@id": "results-archive" + }, + "encodingFormat": "text/csv" + }, + { + "@id": "paper-suite-runtime-csv", + "@type": "cr:FileObject", + "name": "Paper-suite runtime summary", + "description": "Runtime and StaticGNN evaluation diagnostics for the final paper suite.", + "contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/raw/main/results/paper_suite_runtime.csv", + "containedIn": { + "@id": "results-archive" + }, + "encodingFormat": "text/csv" + }, + { + "@id": "paper-suite-failed-checks-csv", + "@type": "cr:FileObject", + "name": "Paper-suite failed gate checks", + "description": "Gate-check and advisory-check outcomes for each run in the final paper suite.", + "contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/raw/main/results/paper_suite_failed_checks.csv", + "containedIn": { + "@id": "results-archive" + }, + "encodingFormat": "text/csv" + }, + { + "@id": "croissant-file", + "@type": "cr:FileObject", + "name": "Temporal Twins Croissant metadata", + "description": "MLCommons Croissant 1.1 metadata for the full Temporal Twins benchmark collection.", + "contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/raw/main/metadata/temporal_twins_croissant.json", + "containedIn": { + "@id": "metadata-files" + }, + "encodingFormat": "application/ld+json" + } + ], + "recordSet": [ + { + "@id": "transactions", + "@type": "cr:RecordSet", + "name": "transactions", + "description": "Synthetic UPI-style transactions spanning oracle_calib, easy, medium, and hard, with deterministic seeds 0 through 4.", + "field": [ + { + "@id": "transactions/sender_id", + "@type": "cr:Field", + "name": "sender_id", + "description": "Synthetic sender account identifier.", + "dataType": "sc:Text", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "sender_id" + } + } + }, + { + "@id": "transactions/receiver_id", + "@type": "cr:Field", + "name": "receiver_id", + "description": "Synthetic receiver account identifier.", + "dataType": "sc:Text", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "receiver_id" + } + } + }, + { + "@id": "transactions/timestamp", + "@type": "cr:Field", + "name": "timestamp", + "description": "Synthetic event timestamp used to order transactions within each sender history.", + "dataType": "sc:Number", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "timestamp" + } + } + }, + { + "@id": "transactions/amount", + "@type": "cr:Field", + "name": "amount", + "description": "Synthetic transaction amount.", + "dataType": "sc:Number", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "amount" + } + } + }, + { + "@id": "transactions/risk_score", + "@type": "cr:Field", + "name": "risk_score", + "description": "Synthetic noisy risk score emitted by the simulator's risk engine.", + "dataType": "sc:Number", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "risk_score" + } + } + }, + { + "@id": "transactions/failed", + "@type": "cr:Field", + "name": "failed", + "description": "Indicator for whether the synthetic transaction attempt failed.", + "dataType": "sc:Boolean", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "failed" + } + } + }, + { + "@id": "transactions/is_fraud", + "@type": "cr:Field", + "name": "is_fraud", + "description": "Delayed synthetic fraud label attached to specific transactions.", + "dataType": "sc:Boolean", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "is_fraud" + } + } + } + ] + }, + { + "@id": "matched_prefix_examples", + "@type": "cr:RecordSet", + "name": "matched_prefix_examples", + "description": "Matched fraud and benign evaluation examples. Each benign twin is evaluated at the same local prefix index as the paired fraud twin, with matched static and prefix-level summaries. The release-facing field matched_local_event_idx is the matched prefix index and may correspond to the internal eval_local_event_idx column if files are exported directly from the current pipeline.", + "field": [ + { + "@id": "matched_prefix_examples/twin_pair_id", + "@type": "cr:Field", + "name": "twin_pair_id", + "description": "Matched fraud/benign twin pair identifier.", + "dataType": "sc:Integer", + "source": { + "fileSet": { + "@id": "matched-prefix-files" + }, + "extract": { + "column": "twin_pair_id" + } + } + }, + { + "@id": "matched_prefix_examples/sender_id", + "@type": "cr:Field", + "name": "sender_id", + "description": "Sender evaluated at the matched prefix.", + "dataType": "sc:Text", + "source": { + "fileSet": { + "@id": "matched-prefix-files" + }, + "extract": { + "column": "sender_id" + } + } + }, + { + "@id": "matched_prefix_examples/matched_local_event_idx", + "@type": "cr:Field", + "name": "matched_local_event_idx", + "description": "Release-facing matched-prefix event index k used for both the fraud twin and its benign control.", + "dataType": "sc:Integer", + "source": { + "fileSet": { + "@id": "matched-prefix-files" + }, + "extract": { + "column": "matched_local_event_idx" + } + } + }, + { + "@id": "matched_prefix_examples/label", + "@type": "cr:Field", + "name": "label", + "description": "Binary matched-prefix label where 1 denotes the fraud twin example and 0 denotes the benign matched control.", + "dataType": "sc:Boolean", + "source": { + "fileSet": { + "@id": "matched-prefix-files" + }, + "extract": { + "column": "label" + } + } + }, + { + "@id": "matched_prefix_examples/benchmark_mode", + "@type": "cr:Field", + "name": "benchmark_mode", + "description": "Benchmark mode identifier, e.g. temporal_twins_oracle_calib or temporal_twins.", + "dataType": "sc:Text", + "source": { + "fileSet": { + "@id": "matched-prefix-files" + }, + "extract": { + "column": "benchmark_mode" + } + } + }, + { + "@id": "matched_prefix_examples/difficulty", + "@type": "cr:Field", + "name": "difficulty", + "description": "Difficulty slice within the release: oracle_calib, easy, medium, or hard.", + "dataType": "sc:Text", + "source": { + "fileSet": { + "@id": "matched-prefix-files" + }, + "extract": { + "column": "difficulty" + } + } + }, + { + "@id": "matched_prefix_examples/seed", + "@type": "cr:Field", + "name": "seed", + "description": "Deterministic benchmark seed in the final paper-scale suite.", + "dataType": "sc:Integer", + "source": { + "fileSet": { + "@id": "matched-prefix-files" + }, + "extract": { + "column": "seed" + } + } + } + ] + }, + { + "@id": "audit_columns", + "@type": "cr:RecordSet", + "name": "audit_columns", + "description": "Audit and probe support columns carried by the synthetic generator for analysis, oracle-style scoring, and benchmark validation. These columns are not intended for ordinary model training and should be excluded from learned baseline inputs in benchmark evaluations.", + "field": [ + { + "@id": "audit_columns/twin_role", + "@type": "cr:Field", + "name": "twin_role", + "description": "Twin role label such as fraud, benign, or background; excluded from ordinary model features.", + "dataType": "sc:Text", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "twin_role" + } + } + }, + { + "@id": "audit_columns/template_id", + "@type": "cr:Field", + "name": "template_id", + "description": "Identifier for the matched temporal template used to construct a twin pair; excluded from ordinary model features.", + "dataType": "sc:Integer", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "template_id" + } + } + }, + { + "@id": "audit_columns/motif_hit_count", + "@type": "cr:Field", + "name": "motif_hit_count", + "description": "Count of motif hits in the generator trace; exposed only for audit or probe logic, not learned baselines.", + "dataType": "sc:Integer", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "motif_hit_count" + } + } + }, + { + "@id": "audit_columns/motif_source", + "@type": "cr:Field", + "name": "motif_source", + "description": "Generator-side motif provenance label; excluded from ordinary model features.", + "dataType": "sc:Text", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "motif_source" + } + } + }, + { + "@id": "audit_columns/trigger_event_idx", + "@type": "cr:Field", + "name": "trigger_event_idx", + "description": "Internal trigger event index for delayed fraud assignment; excluded from ordinary model features.", + "dataType": "sc:Integer", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "trigger_event_idx" + } + } + }, + { + "@id": "audit_columns/label_event_idx", + "@type": "cr:Field", + "name": "label_event_idx", + "description": "Internal event index at which the delayed fraud label is attached; excluded from ordinary model features.", + "dataType": "sc:Integer", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "label_event_idx" + } + } + }, + { + "@id": "audit_columns/label_delay", + "@type": "cr:Field", + "name": "label_delay", + "description": "Internal delay between trigger and labeled event; excluded from ordinary model features.", + "dataType": "sc:Integer", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "label_delay" + } + } + }, + { + "@id": "audit_columns/fraud_source", + "@type": "cr:Field", + "name": "fraud_source", + "description": "Internal fraud-source annotation such as motif or fallback; excluded from ordinary model features.", + "dataType": "sc:Text", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "fraud_source" + } + } + }, + { + "@id": "audit_columns/dynamic_fraud_state", + "@type": "cr:Field", + "name": "dynamic_fraud_state", + "description": "Latent generator-side fraud-state variable used for mechanistic analysis; excluded from ordinary model features.", + "dataType": "sc:Number", + "source": { + "fileSet": { + "@id": "transactions-files" + }, + "extract": { + "column": "dynamic_fraud_state" + } + } + } + ] + }, + { + "@id": "paper_suite_summary_results", + "@type": "cr:RecordSet", + "name": "paper_suite_summary_results", + "description": "Deterministic 5-seed summary results for the final paper-scale Temporal Twins suite.", + "field": [ + { + "@id": "paper_suite_summary_results/benchmark_group", + "@type": "cr:Field", + "name": "benchmark_group", + "description": "Benchmark slice summarized in the row, e.g. oracle_calib, easy, medium, or hard.", + "dataType": "sc:Text", + "source": { + "fileObject": { + "@id": "paper-suite-summary-csv" + }, + "extract": { + "column": "benchmark_group" + } + } + }, + { + "@id": "paper_suite_summary_results/matched_eval_pairs_mean", + "@type": "cr:Field", + "name": "matched_eval_pairs_mean", + "description": "Mean number of matched-prefix evaluation pairs across seeds.", + "dataType": "sc:Number", + "source": { + "fileObject": { + "@id": "paper-suite-summary-csv" + }, + "extract": { + "column": "matched_eval_pairs_mean" + } + } + }, + { + "@id": "paper_suite_summary_results/static_agg_auc_mean", + "@type": "cr:Field", + "name": "static_agg_auc_mean", + "description": "Mean ROC-AUC of the static aggregate shortcut audit.", + "dataType": "sc:Number", + "source": { + "fileObject": { + "@id": "paper-suite-summary-csv" + }, + "extract": { + "column": "static_agg_auc_mean" + } + } + }, + { + "@id": "paper_suite_summary_results/audit_roc_auc_mean", + "@type": "cr:Field", + "name": "audit_roc_auc_mean", + "description": "Mean oracle or probe ROC-AUC depending on benchmark mode.", + "dataType": "sc:Number", + "source": { + "fileObject": { + "@id": "paper-suite-summary-csv" + }, + "extract": { + "column": "audit_roc_auc_mean" + } + } + }, + { + "@id": "paper_suite_summary_results/raw_roc_auc_mean", + "@type": "cr:Field", + "name": "raw_roc_auc_mean", + "description": "Mean raw motif oracle or probe ROC-AUC depending on benchmark mode.", + "dataType": "sc:Number", + "source": { + "fileObject": { + "@id": "paper-suite-summary-csv" + }, + "extract": { + "column": "raw_roc_auc_mean" + } + } + }, + { + "@id": "paper_suite_summary_results/xgb_roc_auc_mean", + "@type": "cr:Field", + "name": "xgb_roc_auc_mean", + "description": "Mean XGBoost ROC-AUC across seeds.", + "dataType": "sc:Number", + "source": { + "fileObject": { + "@id": "paper-suite-summary-csv" + }, + "extract": { + "column": "xgb_roc_auc_mean" + } + } + }, + { + "@id": "paper_suite_summary_results/static_gnn_roc_auc_mean", + "@type": "cr:Field", + "name": "static_gnn_roc_auc_mean", + "description": "Mean StaticGNN ROC-AUC across seeds.", + "dataType": "sc:Number", + "source": { + "fileObject": { + "@id": "paper-suite-summary-csv" + }, + "extract": { + "column": "static_gnn_roc_auc_mean" + } + } + }, + { + "@id": "paper_suite_summary_results/seqgru_clean_roc_auc_mean", + "@type": "cr:Field", + "name": "seqgru_clean_roc_auc_mean", + "description": "Mean clean SeqGRU ROC-AUC across seeds.", + "dataType": "sc:Number", + "source": { + "fileObject": { + "@id": "paper-suite-summary-csv" + }, + "extract": { + "column": "seqgru_clean_roc_auc_mean" + } + } + }, + { + "@id": "paper_suite_summary_results/seqgru_shuffle_delta_mean", + "@type": "cr:Field", + "name": "seqgru_shuffle_delta_mean", + "description": "Mean change in SeqGRU ROC-AUC under shuffled event order.", + "dataType": "sc:Number", + "source": { + "fileObject": { + "@id": "paper-suite-summary-csv" + }, + "extract": { + "column": "seqgru_shuffle_delta_mean" + } + } + }, + { + "@id": "paper_suite_summary_results/tgn_clean_roc_auc_mean", + "@type": "cr:Field", + "name": 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"paper_suite_summary_results/jodie_clean_roc_auc_mean", + "@type": "cr:Field", + "name": "jodie_clean_roc_auc_mean", + "description": "Mean JODIE ROC-AUC across seeds.", + "dataType": "sc:Number", + "source": { + "fileObject": { + "@id": "paper-suite-summary-csv" + }, + "extract": { + "column": "jodie_clean_roc_auc_mean" + } + } + } + ] + } + ], + "rai:dataLimitations": [ + "Temporal Twins is fully synthetic and is not representative of real UPI fraud prevalence, transaction mix, or institutional controls.", + "The benchmark is designed to isolate temporal-order reasoning under matched static controls rather than to reproduce a production fraud environment.", + "Standard-mode probe scores are informative benchmark probes, not upper bounds on real-world fraud detectability." + ], + "rai:dataBiases": [ + "Behavioral patterns are simulator-defined and reflect the assumptions of the Temporal Twins generator rather than observed user behavior.", + "Difficulty slices intentionally reshape motif strength, noise, delay, and adversarial perturbations, so conclusions should be interpreted as benchmark-relative rather than population-representative." + ], + "rai:personalSensitiveInformation": "None. The dataset contains no real UPI data, no real users, no real bank accounts, no real transactions, no personal financial records, and no protected demographic attributes.", + "rai:dataUseCases": [ + "Intended for temporal machine learning benchmark research, including sequence models, dynamic graph models, matched-control evaluation, and shortcut auditing.", + "Suitable for studying whether a model uses causal temporal order rather than static transaction summaries." + ], + "rai:dataSocialImpact": [ + "Positive use may include more rigorous evaluation of temporal fraud-detection methods under matched static controls.", + "Potential misuse includes treating synthetic behavior as if it were real user behavior or using the dataset to justify deployment decisions without external validation on real, appropriately governed data." + ], + "rai:hasSyntheticData": true, + "prov:wasGeneratedBy": { + "@type": "prov:Activity", + "name": "Temporal Twins synthetic UPI transaction generator", + "description": "Synthetic benchmark generation for oracle_calib, easy, medium, and hard using deterministic seeds [0, 1, 2, 3, 4], num_users=350, simulation_days=45, fast_mode=false, and n_checkpoints=8. The generator emits matched fraud/benign twins evaluated at matched local prefix indices and preserves paper-suite shortcut audits and summary results.", + "prov:used": [ + { + "@type": "prov:Entity", + "name": "Temporal Twins benchmark code repository", + "url": "TODO_CODE_REPOSITORY_URL", + "license": "https://www.apache.org/licenses/LICENSE-2.0", + "identifier": "Apache-2.0" + }, + { + "@type": "prov:Entity", + "name": "Temporal Twins paper", + "url": "TODO_PAPER_URL" + } + ] + } +} diff --git a/results/PAPER_GATE_INTERPRETATION.md b/results/PAPER_GATE_INTERPRETATION.md new file mode 100644 index 0000000000000000000000000000000000000000..c6d9f580a7e0a37e742748cb974d20f89ad929fa --- /dev/null +++ b/results/PAPER_GATE_INTERPRETATION.md @@ -0,0 +1,113 @@ +# Paper Gate Interpretation for Temporal Twins + +This document is the paper-facing interpretation layer for the final Temporal Twins suite. It does **not** alter the raw diagnostic output in `results/paper_suite_20260503_202810/paper_suite_failed_checks.csv`. Instead, it reclassifies which checks are true hard gates versus descriptive or advisory findings for the paper. + +## Gate Categories + +### A. Hard Gates for `oracle_calib` + +These are true benchmark validity gates for `temporal_twins_oracle_calib`: + +- `matched_eval_pairs >=` required threshold +- `positive_rate = 0.5` +- `benign_motif_hit_rate = 0` +- `static_agg_auc` near `0.5` +- shortcut AUCs near `0.5` +- `XGBoost` near `0.5` +- `StaticGNN` near chance +- `AuditOracle` near `1.0` +- `RawMotifOracle` near `1.0` +- `SeqGRU` high +- `SeqGRU` shuffle delta strongly negative + +### B. Hard Gates for Standard `easy` / `medium` / `hard` + +For the standard `temporal_twins` difficulty ladder, the hard gates are the matched static-control checks: + +- `matched_eval_pairs >=` required threshold +- `positive_rate = 0.5` +- `benign_motif_hit_rate = 0` +- `static_agg_auc` near `0.5` +- shortcut AUCs near `0.5` +- `XGBoost` near `0.5` +- `StaticGNN` near chance + +These conditions verify that the benchmark remains shortcut-resistant and that fraud and benign twins are properly matched at evaluation. + +### C. Advisory / Descriptive Checks for Standard `easy` / `medium` / `hard` + +The following are **not** hard validity gates for the standard difficulty ladder: + +- `MotifProbe` +- `RawMotifProbe` +- `SeqGRU` difficulty trend +- `SeqGRU` shuffle delta +- temporal-GNN performance +- temporal-GNN shuffle delta + +These measurements are descriptive benchmark outcomes. They characterize difficulty and inductive bias; they do not determine whether the dataset itself is valid. + +## Reclassified Final Paper-Suite Status + +### `oracle_calib` + +- hard gate passes: `5/5` +- `AuditOracle = 1.0000 ± 0.0000` +- `RawMotifOracle = 1.0000 ± 0.0000` +- `XGBoost = 0.5000 ± 0.0000` +- `StaticGNN = 0.5222 ± 0.0235` +- `SeqGRU = 1.0000 ± 0.0000` +- `SeqGRU delta = -0.5032 ± 0.0043` + +Interpretation: `oracle_calib` passes the intended hard benchmark validation. The oracle/probe alignment is correct, static shortcuts are eliminated, and a causal sequence model recovers the signal with a large negative shuffle delta. + +### `easy` + +- static-control hard gates pass: `5/5` +- `XGBoost = 0.5000 ± 0.0000` +- `StaticGNN = 0.4946 ± 0.0128` +- `SeqGRU = 1.0000 ± 0.0000` +- `SeqGRU delta = -0.5003 ± 0.0096` + +Interpretation: `easy` is a valid standard benchmark slice. Static shortcuts remain suppressed, and the temporal sequence signal is strong. + +### `medium` + +- static-control hard gates pass: `5/5` +- `XGBoost = 0.5000 ± 0.0000` +- `StaticGNN = 0.4922 ± 0.0203` +- `SeqGRU = 0.8391 ± 0.0174` +- `SeqGRU delta = -0.3337 ± 0.0191` +- `MotifProbe` and `RawMotifProbe` are lower by design and should **not** be treated as hard-gate failures + +Interpretation: `medium` is **not** a failed dataset. It passes the static-control hard gates and shows the intended increase in temporal difficulty. + +### `hard` + +- static-control hard gates pass: `5/5` +- `XGBoost = 0.5000 ± 0.0000` +- `StaticGNN = 0.5026 ± 0.0198` +- `SeqGRU = 0.6876 ± 0.0128` +- `SeqGRU delta = -0.1883 ± 0.0111` +- lower probe and SeqGRU scores reflect intended difficulty + +Interpretation: `hard` is **not** a failed dataset. It passes the static-control hard gates and intentionally weakens temporal recoverability relative to `easy` and `medium`. + +## Reclassified Status Table + +| Benchmark | Static-control hard gates | Probe/oracle status | SeqGRU status | Temporal-GNN status | Paper interpretation | +|---|---|---|---|---|---| +| `oracle_calib` | Pass `5/5` | `AuditOracle` and `RawMotifOracle` both near `1.0`; oracle behavior validated | `1.0000 ± 0.0000`, delta `-0.5032 ± 0.0043` | Underperformance is advisory only | Valid calibration benchmark with correct motif-label alignment and dead static shortcuts | +| `easy` | Pass `5/5` | `MotifProbe` / `RawMotifProbe` high, descriptive | `1.0000 ± 0.0000`, delta `-0.5003 ± 0.0096` | Advisory underperformance only | Valid standard benchmark with strong temporal signal | +| `medium` | Pass `5/5` | Lower probes are expected and descriptive, not failures | `0.8391 ± 0.0174`, delta `-0.3337 ± 0.0191` | Advisory underperformance only | Valid medium-difficulty benchmark with increased temporal challenge | +| `hard` | Pass `5/5` | Lower probes are expected and descriptive, not failures | `0.6876 ± 0.0128`, delta `-0.1883 ± 0.0111` | Advisory underperformance only | Valid hard benchmark with intentionally reduced temporal recoverability | + +Temporal-GNN advisory failures are **not** benchmark failures. They support the paper finding that current temporal GNNs may not exploit order-sensitive temporal structure as effectively as a causal sequence model under matched static controls. + +`medium` and `hard` are **not** failed datasets. They are intended difficulty levels in the Temporal Twins ladder. Their lower `MotifProbe`, `RawMotifProbe`, and `SeqGRU` values show increasing temporal difficulty rather than invalid benchmark construction. + +## Notes on the Raw Diagnostic File + +- `results/paper_suite_20260503_202810/paper_suite_failed_checks.csv` is retained unchanged as the raw diagnostic output. +- The raw file still reflects older gate semantics in which standard-mode probe thresholds and temporal-GNN thresholds appeared in failure columns. +- This document is the corrected paper-facing interpretation layer and should be cited when describing benchmark validity in the manuscript. diff --git a/results/paper_suite_failed_checks.csv b/results/paper_suite_failed_checks.csv new file mode 100644 index 0000000000000000000000000000000000000000..930448709878a70be91668d879990836b909f055 --- /dev/null +++ b/results/paper_suite_failed_checks.csv @@ -0,0 +1,21 @@ +benchmark_group,seed,gate_pass,volume_failures,hard_gate_failures,advisory_failures +easy,0,True,,,TGN ROC-AUC: 0.5835 (>=0.75) | TGN shuffle delta: -0.0859 (<=-0.1) | TGAT ROC-AUC: 0.5060 (>=0.75) | TGAT shuffle delta: 0.0037 (<=-0.1) | DyRep ROC-AUC: 0.5403 (>=0.75) | DyRep shuffle delta: -0.0398 (<=-0.1) | JODIE ROC-AUC: 0.5382 (>=0.75) | JODIE shuffle delta: -0.0824 (<=-0.1) +easy,1,True,,,TGN ROC-AUC: 0.5495 (>=0.75) | TGN shuffle delta: -0.0536 (<=-0.1) | TGAT ROC-AUC: 0.5254 (>=0.75) | TGAT shuffle delta: -0.0422 (<=-0.1) | DyRep ROC-AUC: 0.5251 (>=0.75) | DyRep shuffle delta: -0.0090 (<=-0.1) | JODIE ROC-AUC: 0.5186 (>=0.75) | JODIE shuffle delta: -0.0257 (<=-0.1) +easy,2,True,,,TGN ROC-AUC: 0.5638 (>=0.75) | TGN shuffle delta: -0.0725 (<=-0.1) | TGAT ROC-AUC: 0.5103 (>=0.75) | TGAT shuffle delta: -0.0200 (<=-0.1) | DyRep ROC-AUC: 0.5372 (>=0.75) | DyRep shuffle delta: -0.0433 (<=-0.1) | JODIE ROC-AUC: 0.5357 (>=0.75) | JODIE shuffle delta: -0.0324 (<=-0.1) +easy,3,True,,,TGN ROC-AUC: 0.5693 (>=0.75) | TGN shuffle delta: -0.0604 (<=-0.1) | TGAT ROC-AUC: 0.5229 (>=0.75) | TGAT shuffle delta: -0.0271 (<=-0.1) | DyRep ROC-AUC: 0.5375 (>=0.75) | DyRep shuffle delta: -0.0401 (<=-0.1) | JODIE ROC-AUC: 0.5259 (>=0.75) | JODIE shuffle delta: -0.0310 (<=-0.1) +easy,4,True,,,TGN ROC-AUC: 0.5639 (>=0.75) | TGN shuffle delta: -0.0617 (<=-0.1) | TGAT ROC-AUC: 0.5353 (>=0.75) | TGAT shuffle delta: -0.0616 (<=-0.1) | DyRep ROC-AUC: 0.5198 (>=0.75) | DyRep shuffle delta: -0.0208 (<=-0.1) | JODIE ROC-AUC: 0.5225 (>=0.75) | JODIE shuffle delta: -0.0386 (<=-0.1) +hard,0,False,,MotifProbe ROC-AUC: 0.5838 (>=0.99) | MotifProbe pair-sep: 0.1676 (>=0.99) | RawMotifProbe ROC-AUC: 0.5946 (>=0.95) | RawMotifProbe pair-sep: 0.2200 (>=0.9) | SeqGRU ROC-AUC: 0.7069 (>=0.8),TGN ROC-AUC: 0.5072 (>=0.75) | TGN shuffle delta: 0.0107 (<=-0.1) | TGAT ROC-AUC: 0.5192 (>=0.75) | TGAT shuffle delta: -0.0276 (<=-0.1) | DyRep ROC-AUC: 0.5277 (>=0.75) | DyRep shuffle delta: -0.0311 (<=-0.1) | JODIE ROC-AUC: 0.5097 (>=0.75) | JODIE shuffle delta: -0.0098 (<=-0.1) +hard,1,False,,MotifProbe ROC-AUC: 0.5771 (>=0.99) | MotifProbe pair-sep: 0.1543 (>=0.99) | RawMotifProbe ROC-AUC: 0.5873 (>=0.95) | RawMotifProbe pair-sep: 0.2181 (>=0.9) | SeqGRU ROC-AUC: 0.6795 (>=0.8),TGN ROC-AUC: 0.4995 (>=0.75) | TGN shuffle delta: 0.0021 (<=-0.1) | TGAT ROC-AUC: 0.5159 (>=0.75) | TGAT shuffle delta: -0.0116 (<=-0.1) | DyRep ROC-AUC: 0.5243 (>=0.75) | DyRep shuffle delta: -0.0118 (<=-0.1) | JODIE ROC-AUC: 0.5218 (>=0.75) | JODIE shuffle delta: -0.0348 (<=-0.1) +hard,2,False,,MotifProbe ROC-AUC: 0.5824 (>=0.99) | MotifProbe pair-sep: 0.1648 (>=0.99) | RawMotifProbe ROC-AUC: 0.6057 (>=0.95) | RawMotifProbe pair-sep: 0.2333 (>=0.9) | SeqGRU ROC-AUC: 0.6946 (>=0.8),TGN ROC-AUC: 0.5187 (>=0.75) | TGN shuffle delta: -0.0215 (<=-0.1) | TGAT ROC-AUC: 0.5278 (>=0.75) | TGAT shuffle delta: -0.0359 (<=-0.1) | DyRep ROC-AUC: 0.5257 (>=0.75) | DyRep shuffle delta: -0.0312 (<=-0.1) | JODIE ROC-AUC: 0.5231 (>=0.75) | JODIE shuffle delta: -0.0260 (<=-0.1) +hard,3,False,,MotifProbe ROC-AUC: 0.5724 (>=0.99) | MotifProbe pair-sep: 0.1448 (>=0.99) | RawMotifProbe ROC-AUC: 0.5768 (>=0.95) | RawMotifProbe pair-sep: 0.2038 (>=0.9) | SeqGRU ROC-AUC: 0.6799 (>=0.8),TGN ROC-AUC: 0.4986 (>=0.75) | TGN shuffle delta: 0.0097 (<=-0.1) | TGAT ROC-AUC: 0.4929 (>=0.75) | TGAT shuffle delta: 0.0066 (<=-0.1) | DyRep ROC-AUC: 0.5146 (>=0.75) | DyRep shuffle delta: -0.0155 (<=-0.1) | JODIE ROC-AUC: 0.5109 (>=0.75) | JODIE shuffle delta: -0.0075 (<=-0.1) +hard,4,False,,MotifProbe ROC-AUC: 0.5790 (>=0.99) | MotifProbe pair-sep: 0.1581 (>=0.99) | RawMotifProbe ROC-AUC: 0.5904 (>=0.95) | RawMotifProbe pair-sep: 0.2238 (>=0.9) | SeqGRU ROC-AUC: 0.6773 (>=0.8),TGN ROC-AUC: 0.5059 (>=0.75) | TGN shuffle delta: -0.0038 (<=-0.1) | TGAT ROC-AUC: 0.5225 (>=0.75) | TGAT shuffle delta: -0.0040 (<=-0.1) | DyRep ROC-AUC: 0.5225 (>=0.75) | DyRep shuffle delta: 0.0039 (<=-0.1) | JODIE ROC-AUC: 0.5032 (>=0.75) | JODIE shuffle delta: -0.0375 (<=-0.1) +medium,0,False,,MotifProbe ROC-AUC: 0.6463 (>=0.99) | MotifProbe pair-sep: 0.2926 (>=0.99) | RawMotifProbe ROC-AUC: 0.6473 (>=0.95) | RawMotifProbe pair-sep: 0.4194 (>=0.9),TGN ROC-AUC: 0.5194 (>=0.75) | TGN shuffle delta: -0.0238 (<=-0.1) | TGAT ROC-AUC: 0.5019 (>=0.75) | TGAT shuffle delta: -0.0047 (<=-0.1) | DyRep ROC-AUC: 0.4964 (>=0.75) | DyRep shuffle delta: -0.0249 (<=-0.1) | JODIE ROC-AUC: 0.5229 (>=0.75) | JODIE shuffle delta: -0.0293 (<=-0.1) +medium,1,False,,MotifProbe ROC-AUC: 0.6303 (>=0.99) | MotifProbe pair-sep: 0.2606 (>=0.99) | RawMotifProbe ROC-AUC: 0.6470 (>=0.95) | RawMotifProbe pair-sep: 0.3909 (>=0.9),TGN ROC-AUC: 0.5228 (>=0.75) | TGN shuffle delta: -0.0197 (<=-0.1) | TGAT ROC-AUC: 0.5341 (>=0.75) | TGAT shuffle delta: -0.0414 (<=-0.1) | DyRep ROC-AUC: 0.5343 (>=0.75) | DyRep shuffle delta: -0.0539 (<=-0.1) | JODIE ROC-AUC: 0.5375 (>=0.75) | JODIE shuffle delta: -0.0232 (<=-0.1) +medium,2,False,,MotifProbe ROC-AUC: 0.6423 (>=0.99) | MotifProbe pair-sep: 0.2846 (>=0.99) | RawMotifProbe ROC-AUC: 0.6578 (>=0.95) | RawMotifProbe pair-sep: 0.4114 (>=0.9),TGN ROC-AUC: 0.5101 (>=0.75) | TGN shuffle delta: -0.0055 (<=-0.1) | TGAT ROC-AUC: 0.5268 (>=0.75) | TGAT shuffle delta: -0.0349 (<=-0.1) | DyRep ROC-AUC: 0.5555 (>=0.75) | DyRep shuffle delta: -0.0263 (<=-0.1) | JODIE ROC-AUC: 0.5293 (>=0.75) | JODIE shuffle delta: -0.0392 (<=-0.1) +medium,3,False,,MotifProbe ROC-AUC: 0.6314 (>=0.99) | MotifProbe pair-sep: 0.2629 (>=0.99) | RawMotifProbe ROC-AUC: 0.6422 (>=0.95) | RawMotifProbe pair-sep: 0.3806 (>=0.9),TGN ROC-AUC: 0.5241 (>=0.75) | TGN shuffle delta: -0.0116 (<=-0.1) | TGAT ROC-AUC: 0.5299 (>=0.75) | TGAT shuffle delta: -0.0618 (<=-0.1) | DyRep ROC-AUC: 0.5460 (>=0.75) | DyRep shuffle delta: -0.0360 (<=-0.1) | JODIE ROC-AUC: 0.5261 (>=0.75) | JODIE shuffle delta: -0.0314 (<=-0.1) +medium,4,False,,MotifProbe ROC-AUC: 0.6366 (>=0.99) | MotifProbe pair-sep: 0.2731 (>=0.99) | RawMotifProbe ROC-AUC: 0.6467 (>=0.95) | RawMotifProbe pair-sep: 0.4023 (>=0.9),TGN ROC-AUC: 0.5121 (>=0.75) | TGN shuffle delta: -0.0013 (<=-0.1) | TGAT ROC-AUC: 0.5067 (>=0.75) | TGAT shuffle delta: -0.0239 (<=-0.1) | DyRep ROC-AUC: 0.5105 (>=0.75) | DyRep shuffle delta: -0.0041 (<=-0.1) | JODIE ROC-AUC: 0.5331 (>=0.75) | JODIE shuffle delta: -0.0263 (<=-0.1) +oracle_calib,0,True,,,TGN ROC-AUC: 0.6135 (>=0.75) | TGAT ROC-AUC: 0.6152 (>=0.75) | TGAT shuffle delta: -0.0937 (<=-0.1) | DyRep ROC-AUC: 0.5936 (>=0.75) | DyRep shuffle delta: -0.0774 (<=-0.1) | JODIE ROC-AUC: 0.5632 (>=0.75) | JODIE shuffle delta: -0.0554 (<=-0.1) +oracle_calib,1,True,,,TGN ROC-AUC: 0.6250 (>=0.75) | TGAT ROC-AUC: 0.5882 (>=0.75) | TGAT shuffle delta: -0.0837 (<=-0.1) | DyRep ROC-AUC: 0.6369 (>=0.75) | JODIE ROC-AUC: 0.6069 (>=0.75) +oracle_calib,2,True,,,TGN ROC-AUC: 0.6353 (>=0.75) | TGAT ROC-AUC: 0.7061 (>=0.75) | DyRep ROC-AUC: 0.6658 (>=0.75) | JODIE ROC-AUC: 0.6025 (>=0.75) +oracle_calib,3,True,,,TGN ROC-AUC: 0.6223 (>=0.75) | TGAT ROC-AUC: 0.6055 (>=0.75) | DyRep ROC-AUC: 0.5408 (>=0.75) | DyRep shuffle delta: -0.0406 (<=-0.1) | JODIE ROC-AUC: 0.5901 (>=0.75) | 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diff --git a/results/paper_suite_summary.md b/results/paper_suite_summary.md new file mode 100644 index 0000000000000000000000000000000000000000..f655040bd852bcd46a7f14174add97483edeaae6 --- /dev/null +++ b/results/paper_suite_summary.md @@ -0,0 +1,59 @@ +# Final Paper Suite Summary + +Rows: 20 + +## Dataset and Audit +| Benchmark | Matched Pairs | Positives | Negatives | Fraud Users | Benign Users | Templates | Positive Rate | Benign Motif Hit | static_agg_auc | Txn AUC | Idx AUC | Prefix AUC | Time AUC | Account AUC | Active AUC | +|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| easy | 2222.2000 ± 128.4434 | 2222.2000 ± 128.4434 | 2222.2000 ± 128.4434 | 304.6000 ± 23.2551 | 304.6000 ± 23.2551 | 300.2000 ± 24.2425 | 0.5000 ± 0.0000 | 0.0000 ± 0.0000 | 0.5000 ± 0.0000 | 0.5000 ± 0.0000 | 0.5000 ± 0.0000 | 0.5000 ± 0.0000 | 0.5000 ± 0.0000 | 0.5000 ± 0.0000 | 0.5000 ± 0.0000 | +| hard | 2317.6000 ± 21.9727 | 2317.6000 ± 21.9727 | 2317.6000 ± 21.9727 | 315.0000 ± 0.0000 | 315.0000 ± 0.0000 | 315.0000 ± 0.0000 | 0.5000 ± 0.0000 | 0.0000 ± 0.0000 | 0.5000 ± 0.0000 | 0.5000 ± 0.0000 | 0.5000 ± 0.0000 | 0.5000 ± 0.0000 | 0.5000 ± 0.0000 | 0.5000 ± 0.0000 | 0.5000 ± 0.0000 | +| medium | 2356.6000 ± 18.0361 | 2356.6000 ± 18.0361 | 2356.6000 ± 18.0361 | 263.0000 ± 0.0000 | 263.0000 ± 0.0000 | 263.0000 ± 0.0000 | 0.5000 ± 0.0000 | 0.0000 ± 0.0000 | 0.5000 ± 0.0000 | 0.5000 ± 0.0000 | 0.5000 ± 0.0000 | 0.5000 ± 0.0000 | 0.5000 ± 0.0000 | 0.5000 ± 0.0000 | 0.5000 ± 0.0000 | +| oracle_calib | 2606.6000 ± 454.3449 | 2606.6000 ± 454.3449 | 2606.6000 ± 454.3449 | 409.8000 ± 86.5402 | 409.8000 ± 86.5402 | 409.8000 ± 86.5402 | 0.5000 ± 0.0000 | 0.0000 ± 0.0000 | 0.5000 ± 0.0000 | 0.5000 ± 0.0000 | 0.5000 ± 0.0000 | 0.5000 ± 0.0000 | 0.5000 ± 0.0000 | 0.5000 ± 0.0000 | 0.5000 ± 0.0000 | + +## Probes and Models +| Benchmark | Primary Probe | Secondary Probe | XGB ROC/PR | StaticGNN ROC/PR | SeqGRU Clean ROC/PR | SeqGRU Shuf ROC/PR | SeqGRU Delta | +|---|---:|---:|---:|---:|---:|---:|---:| +| easy | MotifProbe 1.0000 ± 0.0000 | RawMotifProbe 0.9983 ± 0.0011 | 0.5000 ± 0.0000 / 0.5000 ± 0.0000 | 0.4946 ± 0.0128 / 0.5001 ± 0.0099 | 1.0000 ± 0.0000 / 1.0000 ± 0.0000 | 0.4997 ± 0.0096 / 0.5008 ± 0.0077 | -0.5003 ± 0.0096 | +| hard | MotifProbe 0.5790 ± 0.0045 | RawMotifProbe 0.5910 ± 0.0105 | 0.5000 ± 0.0000 / 0.5000 ± 0.0000 | 0.5026 ± 0.0198 / 0.5081 ± 0.0128 | 0.6876 ± 0.0128 / 0.7058 ± 0.0250 | 0.4994 ± 0.0033 / 0.4981 ± 0.0031 | -0.1883 ± 0.0111 | +| medium | MotifProbe 0.6374 ± 0.0069 | RawMotifProbe 0.6482 ± 0.0058 | 0.5000 ± 0.0000 / 0.5000 ± 0.0000 | 0.4922 ± 0.0203 / 0.4956 ± 0.0231 | 0.8391 ± 0.0174 / 0.8520 ± 0.0110 | 0.5053 ± 0.0071 / 0.5054 ± 0.0078 | -0.3337 ± 0.0191 | +| oracle_calib | AuditOracle 1.0000 ± 0.0000 | RawMotifOracle 1.0000 ± 0.0000 | 0.5000 ± 0.0000 / 0.5000 ± 0.0000 | 0.5222 ± 0.0235 / 0.5141 ± 0.0202 | 1.0000 ± 0.0000 / 1.0000 ± 0.0000 | 0.4968 ± 0.0043 / 0.5008 ± 0.0057 | -0.5032 ± 0.0043 | + +## Temporal GNNs +| Benchmark | TGN ROC/PR/Delta | TGAT ROC/PR/Delta | DyRep ROC/PR/Delta | JODIE ROC/PR/Delta | +|---|---:|---:|---:|---:| +| easy | 0.5660 ± 0.0123 / 0.5566 ± 0.0107 / -0.0668 ± 0.0126 | 0.5200 ± 0.0119 / 0.5177 ± 0.0086 / -0.0294 ± 0.0244 | 0.5320 ± 0.0090 / 0.5245 ± 0.0100 / -0.0306 ± 0.0150 | 0.5282 ± 0.0085 / 0.5210 ± 0.0079 / -0.0420 ± 0.0231 | +| hard | 0.5060 ± 0.0081 / 0.5069 ± 0.0062 / -0.0006 ± 0.0131 | 0.5157 ± 0.0135 / 0.5133 ± 0.0163 / -0.0145 ± 0.0173 | 0.5229 ± 0.0050 / 0.5172 ± 0.0072 / -0.0171 ± 0.0147 | 0.5137 ± 0.0085 / 0.5087 ± 0.0076 / -0.0231 ± 0.0139 | +| medium | 0.5177 ± 0.0063 / 0.5133 ± 0.0034 / -0.0124 ± 0.0094 | 0.5199 ± 0.0146 / 0.5121 ± 0.0105 / -0.0334 ± 0.0211 | 0.5285 ± 0.0246 / 0.5252 ± 0.0144 / -0.0290 ± 0.0181 | 0.5298 ± 0.0057 / 0.5235 ± 0.0103 / -0.0299 ± 0.0061 | +| oracle_calib | 0.6270 ± 0.0103 / 0.6107 ± 0.0144 / -0.1288 ± 0.0173 | 0.6327 ± 0.0465 / 0.6104 ± 0.0381 / -0.1403 ± 0.0654 | 0.6135 ± 0.0481 / 0.6010 ± 0.0400 / -0.1203 ± 0.0600 | 0.5903 ± 0.0170 / 0.5720 ± 0.0146 / -0.0947 ± 0.0227 | + +## Runtime +| Benchmark | Run Time (sec) | StaticGNN Eval Time (sec) | +|---|---:|---:| +| easy | 1345.9431 ± 135.9206 | 198.4129 ± 45.3445 | +| hard | 2613.7203 ± 231.4288 | 272.9988 ± 70.8384 | +| medium | 2181.9480 ± 228.0777 | 184.2332 ± 57.5306 | +| oracle_calib | 1136.5994 ± 283.1015 | 170.9515 ± 34.9402 | + +## Failed Gate Checks +| Benchmark | Seed | Gate Pass | Volume Failures | Hard Gate Failures | Advisory Failures | +|---|---:|---:|---|---|---| +| easy | 0 | 1 | - | - | TGN ROC-AUC: 0.5835 (>=0.75) | TGN shuffle delta: -0.0859 (<=-0.1) | TGAT ROC-AUC: 0.5060 (>=0.75) | TGAT shuffle delta: 0.0037 (<=-0.1) | DyRep ROC-AUC: 0.5403 (>=0.75) | DyRep shuffle delta: -0.0398 (<=-0.1) | JODIE ROC-AUC: 0.5382 (>=0.75) | JODIE shuffle delta: -0.0824 (<=-0.1) | +| easy | 1 | 1 | - | - | TGN ROC-AUC: 0.5495 (>=0.75) | TGN shuffle delta: -0.0536 (<=-0.1) | TGAT ROC-AUC: 0.5254 (>=0.75) | TGAT shuffle delta: -0.0422 (<=-0.1) | DyRep ROC-AUC: 0.5251 (>=0.75) | DyRep shuffle delta: -0.0090 (<=-0.1) | JODIE ROC-AUC: 0.5186 (>=0.75) | JODIE shuffle delta: -0.0257 (<=-0.1) | +| easy | 2 | 1 | - | - | TGN ROC-AUC: 0.5638 (>=0.75) | TGN shuffle delta: -0.0725 (<=-0.1) | TGAT ROC-AUC: 0.5103 (>=0.75) | TGAT shuffle delta: -0.0200 (<=-0.1) | DyRep ROC-AUC: 0.5372 (>=0.75) | DyRep shuffle delta: -0.0433 (<=-0.1) | JODIE ROC-AUC: 0.5357 (>=0.75) | JODIE shuffle delta: -0.0324 (<=-0.1) | +| easy | 3 | 1 | - | - | TGN ROC-AUC: 0.5693 (>=0.75) | TGN shuffle delta: -0.0604 (<=-0.1) | TGAT ROC-AUC: 0.5229 (>=0.75) | TGAT shuffle delta: -0.0271 (<=-0.1) | DyRep ROC-AUC: 0.5375 (>=0.75) | DyRep shuffle delta: -0.0401 (<=-0.1) | JODIE ROC-AUC: 0.5259 (>=0.75) | JODIE shuffle delta: -0.0310 (<=-0.1) | +| easy | 4 | 1 | - | - | TGN ROC-AUC: 0.5639 (>=0.75) | TGN shuffle delta: -0.0617 (<=-0.1) | TGAT ROC-AUC: 0.5353 (>=0.75) | TGAT shuffle delta: -0.0616 (<=-0.1) | DyRep ROC-AUC: 0.5198 (>=0.75) | DyRep shuffle delta: -0.0208 (<=-0.1) | JODIE ROC-AUC: 0.5225 (>=0.75) | JODIE shuffle delta: -0.0386 (<=-0.1) | +| hard | 0 | 0 | - | MotifProbe ROC-AUC: 0.5838 (>=0.99) | MotifProbe pair-sep: 0.1676 (>=0.99) | RawMotifProbe ROC-AUC: 0.5946 (>=0.95) | RawMotifProbe pair-sep: 0.2200 (>=0.9) | SeqGRU ROC-AUC: 0.7069 (>=0.8) | TGN ROC-AUC: 0.5072 (>=0.75) | TGN shuffle delta: 0.0107 (<=-0.1) | TGAT ROC-AUC: 0.5192 (>=0.75) | TGAT shuffle delta: -0.0276 (<=-0.1) | DyRep ROC-AUC: 0.5277 (>=0.75) | DyRep shuffle delta: -0.0311 (<=-0.1) | JODIE ROC-AUC: 0.5097 (>=0.75) | JODIE shuffle delta: -0.0098 (<=-0.1) | +| hard | 1 | 0 | - | MotifProbe ROC-AUC: 0.5771 (>=0.99) | MotifProbe pair-sep: 0.1543 (>=0.99) | RawMotifProbe ROC-AUC: 0.5873 (>=0.95) | RawMotifProbe pair-sep: 0.2181 (>=0.9) | SeqGRU ROC-AUC: 0.6795 (>=0.8) | TGN ROC-AUC: 0.4995 (>=0.75) | TGN shuffle delta: 0.0021 (<=-0.1) | TGAT ROC-AUC: 0.5159 (>=0.75) | TGAT shuffle delta: -0.0116 (<=-0.1) | DyRep ROC-AUC: 0.5243 (>=0.75) | DyRep shuffle delta: 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ROC-AUC: 0.5229 (>=0.75) | JODIE shuffle delta: -0.0293 (<=-0.1) | +| medium | 1 | 0 | - | MotifProbe ROC-AUC: 0.6303 (>=0.99) | MotifProbe pair-sep: 0.2606 (>=0.99) | RawMotifProbe ROC-AUC: 0.6470 (>=0.95) | RawMotifProbe pair-sep: 0.3909 (>=0.9) | TGN ROC-AUC: 0.5228 (>=0.75) | TGN shuffle delta: -0.0197 (<=-0.1) | TGAT ROC-AUC: 0.5341 (>=0.75) | TGAT shuffle delta: -0.0414 (<=-0.1) | DyRep ROC-AUC: 0.5343 (>=0.75) | DyRep shuffle delta: -0.0539 (<=-0.1) | JODIE ROC-AUC: 0.5375 (>=0.75) | JODIE shuffle delta: -0.0232 (<=-0.1) | +| medium | 2 | 0 | - | MotifProbe ROC-AUC: 0.6423 (>=0.99) | MotifProbe pair-sep: 0.2846 (>=0.99) | RawMotifProbe ROC-AUC: 0.6578 (>=0.95) | RawMotifProbe pair-sep: 0.4114 (>=0.9) | TGN ROC-AUC: 0.5101 (>=0.75) | TGN shuffle delta: -0.0055 (<=-0.1) | TGAT ROC-AUC: 0.5268 (>=0.75) | TGAT shuffle delta: -0.0349 (<=-0.1) | DyRep ROC-AUC: 0.5555 (>=0.75) | DyRep shuffle delta: -0.0263 (<=-0.1) | JODIE ROC-AUC: 0.5293 (>=0.75) | JODIE shuffle delta: -0.0392 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0.6135 (>=0.75) | TGAT ROC-AUC: 0.6152 (>=0.75) | TGAT shuffle delta: -0.0937 (<=-0.1) | DyRep ROC-AUC: 0.5936 (>=0.75) | DyRep shuffle delta: -0.0774 (<=-0.1) | JODIE ROC-AUC: 0.5632 (>=0.75) | JODIE shuffle delta: -0.0554 (<=-0.1) | +| oracle_calib | 1 | 1 | - | - | TGN ROC-AUC: 0.6250 (>=0.75) | TGAT ROC-AUC: 0.5882 (>=0.75) | TGAT shuffle delta: -0.0837 (<=-0.1) | DyRep ROC-AUC: 0.6369 (>=0.75) | JODIE ROC-AUC: 0.6069 (>=0.75) | +| oracle_calib | 2 | 1 | - | - | TGN ROC-AUC: 0.6353 (>=0.75) | TGAT ROC-AUC: 0.7061 (>=0.75) | DyRep ROC-AUC: 0.6658 (>=0.75) | JODIE ROC-AUC: 0.6025 (>=0.75) | +| oracle_calib | 3 | 1 | - | - | TGN ROC-AUC: 0.6223 (>=0.75) | TGAT ROC-AUC: 0.6055 (>=0.75) | DyRep ROC-AUC: 0.5408 (>=0.75) | DyRep shuffle delta: -0.0406 (<=-0.1) | JODIE ROC-AUC: 0.5901 (>=0.75) | JODIE shuffle delta: -0.0993 (<=-0.1) | +| oracle_calib | 4 | 1 | - | - | TGN ROC-AUC: 0.6391 (>=0.75) | TGAT ROC-AUC: 0.6486 (>=0.75) | DyRep ROC-AUC: 0.6303 (>=0.75) | JODIE ROC-AUC: 0.5890 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