Add Temporal Twins benchmark release v0.1
Browse filesThis view is limited to 50 files because it contains too many changes. See raw diff
- DATASET_CARD.md +458 -0
- LICENSE +203 -0
- LICENSE-DATA +18 -0
- MANIFEST.sha256 +126 -0
- README.md +96 -0
- README_REPO.md +437 -0
- RELEASE_CHECKLIST.md +22 -0
- configs/default.yaml +29 -0
- configs/paper_suite_reference.yaml +25 -0
- configs/temporal_twins_calib.yaml +29 -0
- croissant.json +796 -0
- data/.DS_Store +0 -0
- data/README_GENERATION.md +120 -0
- data/_export_summary.csv +21 -0
- data/easy/.DS_Store +0 -0
- data/easy/seed_0/audit_summary.csv +2 -0
- data/easy/seed_0/config.yaml +30 -0
- data/easy/seed_0/matched_pairs.parquet +3 -0
- data/easy/seed_0/schema.json +84 -0
- data/easy/seed_0/transactions.parquet +3 -0
- data/easy/seed_1/audit_summary.csv +2 -0
- data/easy/seed_1/config.yaml +30 -0
- data/easy/seed_1/matched_pairs.parquet +3 -0
- data/easy/seed_1/schema.json +84 -0
- data/easy/seed_1/transactions.parquet +3 -0
- data/easy/seed_2/audit_summary.csv +2 -0
- data/easy/seed_2/config.yaml +30 -0
- data/easy/seed_2/matched_pairs.parquet +3 -0
- data/easy/seed_2/schema.json +84 -0
- data/easy/seed_2/transactions.parquet +3 -0
- data/easy/seed_3/audit_summary.csv +2 -0
- data/easy/seed_3/config.yaml +30 -0
- data/easy/seed_3/matched_pairs.parquet +3 -0
- data/easy/seed_3/schema.json +84 -0
- data/easy/seed_3/transactions.parquet +3 -0
- data/easy/seed_4/audit_summary.csv +2 -0
- data/easy/seed_4/config.yaml +30 -0
- data/easy/seed_4/matched_pairs.parquet +3 -0
- data/easy/seed_4/schema.json +84 -0
- data/easy/seed_4/transactions.parquet +3 -0
- data/hard/.DS_Store +0 -0
- data/hard/seed_0/audit_summary.csv +2 -0
- data/hard/seed_0/config.yaml +30 -0
- data/hard/seed_0/matched_pairs.parquet +3 -0
- data/hard/seed_0/schema.json +84 -0
- data/hard/seed_0/transactions.parquet +3 -0
- data/hard/seed_1/audit_summary.csv +2 -0
- data/hard/seed_1/config.yaml +30 -0
- data/hard/seed_1/matched_pairs.parquet +3 -0
- data/hard/seed_1/schema.json +84 -0
DATASET_CARD.md
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| 1 |
+
# Temporal Twins Dataset Card
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| 2 |
+
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| 3 |
+
## 1. Dataset Summary
|
| 4 |
+
|
| 5 |
+
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.
|
| 6 |
+
|
| 7 |
+
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:
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| 8 |
+
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| 9 |
+
- each positive example is a fraud twin evaluated at a local event index `k`
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| 10 |
+
- each negative example is a benign twin evaluated at the same local event index `k`
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| 11 |
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- both twins are matched on static and prefix-level summaries
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| 12 |
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- the benign twin contains the same unordered ingredients but violates the fraud-relevant temporal order
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| 13 |
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| 14 |
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Temporal Twins exposes four benchmark modes:
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| 15 |
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| 16 |
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- `oracle_calib`
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| 17 |
+
- `easy`
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| 18 |
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- `medium`
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| 19 |
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- `hard`
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| 20 |
+
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| 21 |
+
The frozen paper-suite configuration used in this repository is:
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| 22 |
+
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| 23 |
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- `num_users = 350`
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| 24 |
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- `simulation_days = 45`
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| 25 |
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- `seeds = [0, 1, 2, 3, 4]`
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| 26 |
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- `fast_mode = false`
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| 27 |
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- `n_checkpoints = 8`
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| 28 |
+
|
| 29 |
+
## 2. Dataset Motivation
|
| 30 |
+
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| 31 |
+
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.
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| 32 |
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| 33 |
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The benchmark therefore aims to answer a narrower research question:
|
| 34 |
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| 35 |
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- when static summaries are matched between positives and negatives, can a model still recover delayed fraud signals from temporal order alone?
|
| 36 |
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|
| 37 |
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It is intended for benchmarking temporal representation learning, causal order sensitivity, and delayed-label detection under controlled synthetic conditions.
|
| 38 |
+
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| 39 |
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## 3. Dataset Composition
|
| 40 |
+
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| 41 |
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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.
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| 42 |
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| 43 |
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At a high level, each run contains:
|
| 44 |
+
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| 45 |
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- a synthetic user population
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| 46 |
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- a synthetic stream of UPI-style transactions
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| 47 |
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- risk-engine outputs such as transaction risk scores and failures
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| 48 |
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- benchmark-specific fraud and audit annotations
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| 49 |
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- matched fraud/benign evaluation pairs extracted from the event stream
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| 50 |
+
|
| 51 |
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The paper-scale suite in this repository contains 20 deterministic runs:
|
| 52 |
+
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| 53 |
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- `oracle_calib` with seeds `0..4`
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| 54 |
+
- `easy` with seeds `0..4`
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| 55 |
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- `medium` with seeds `0..4`
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| 56 |
+
- `hard` with seeds `0..4`
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| 57 |
+
|
| 58 |
+
Mean matched evaluation-pair counts in the frozen paper suite are:
|
| 59 |
+
|
| 60 |
+
| Mode | Matched evaluation pairs (mean +- std) |
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| 61 |
+
|---|---:|
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| 62 |
+
| `oracle_calib` | `2606.6 +- 454.3` |
|
| 63 |
+
| `easy` | `2222.2 +- 128.4` |
|
| 64 |
+
| `medium` | `2356.6 +- 18.0` |
|
| 65 |
+
| `hard` | `2317.6 +- 22.0` |
|
| 66 |
+
|
| 67 |
+
Each paper-suite run is class-balanced at evaluation time:
|
| 68 |
+
|
| 69 |
+
- positives = negatives
|
| 70 |
+
- positive rate = `0.5000`
|
| 71 |
+
|
| 72 |
+
## 4. Dataset Generation Process
|
| 73 |
+
|
| 74 |
+
The generation pipeline has four stages:
|
| 75 |
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| 76 |
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1. Synthetic user generation
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| 77 |
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2. Synthetic transaction generation
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| 78 |
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3. Synthetic risk and retry generation
|
| 79 |
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4. Fraud-mechanism and matched-twin generation
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| 80 |
+
|
| 81 |
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More concretely:
|
| 82 |
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|
| 83 |
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1. A synthetic user set is created with user-level behavioral parameters.
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| 84 |
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2. A synthetic transaction stream is sampled with sender IDs, receiver IDs, timestamps, transaction amounts, and transaction types.
|
| 85 |
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3. A risk engine adds synthetic risk-related fields such as `risk_score`, `fail_prob`, `failed`, and retry-like events.
|
| 86 |
+
4. The fraud engine applies benchmark-mode-specific temporal mechanisms and constructs matched temporal twins.
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| 87 |
+
|
| 88 |
+
For the `temporal_twins` benchmark family, the generator then:
|
| 89 |
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|
| 90 |
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- constructs fraud twins and benign twins from matched carrier users and templates
|
| 91 |
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- preserves matched static and prefix-level summaries
|
| 92 |
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- injects delayed fraud labels into fraud twins
|
| 93 |
+
- forces benign twins to avoid the fraud-relevant temporal motif while retaining similar unordered ingredients
|
| 94 |
+
|
| 95 |
+
The benchmark is deterministic under fixed configuration, seed, and runtime settings.
|
| 96 |
+
|
| 97 |
+
## 5. Fraud Mechanisms
|
| 98 |
+
|
| 99 |
+
Temporal Twins uses delayed, order-sensitive fraud mechanisms rather than directly labeling static outliers. Important mechanisms include:
|
| 100 |
+
|
| 101 |
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- velocity-like activity acceleration
|
| 102 |
+
- retry-like behavior
|
| 103 |
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- delayed receiver revisits
|
| 104 |
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- burst-release-burst motifs
|
| 105 |
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- adversarial timing perturbations
|
| 106 |
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- delayed fraud assignment
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| 107 |
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- hidden latent fraud-state dynamics
|
| 108 |
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|
| 109 |
+
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.
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| 110 |
+
|
| 111 |
+
## 6. Matched-Control Construction
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| 112 |
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| 113 |
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The central benchmark control is the fraud/benign temporal twin.
|
| 114 |
+
|
| 115 |
+
For every fraud twin positive label at local event index `k`:
|
| 116 |
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|
| 117 |
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- the benign twin is evaluated at the same local event index `k`
|
| 118 |
+
- both examples use the same local prefix length
|
| 119 |
+
- both examples are truncated at prefix index `k`
|
| 120 |
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- no future events are visible to the model
|
| 121 |
+
|
| 122 |
+
Within each matched pair, the protocol additionally matches:
|
| 123 |
+
|
| 124 |
+
- total transaction count
|
| 125 |
+
- local prefix length
|
| 126 |
+
- evaluation timestamp
|
| 127 |
+
- account age
|
| 128 |
+
- active age
|
| 129 |
+
- receiver histograms
|
| 130 |
+
- static aggregate summaries
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| 131 |
+
|
| 132 |
+
In words:
|
| 133 |
+
|
| 134 |
+
- the fraud twin contains a temporally meaningful order pattern that triggers a delayed positive label
|
| 135 |
+
- the benign twin contains comparable ingredients and prefix statistics but violates the fraud-relevant temporal order
|
| 136 |
+
|
| 137 |
+
This design is meant to prevent performance from arising from:
|
| 138 |
+
|
| 139 |
+
- longer histories
|
| 140 |
+
- older accounts
|
| 141 |
+
- later prefix positions
|
| 142 |
+
- different transaction totals
|
| 143 |
+
- unmatched prefix ages
|
| 144 |
+
- benign negatives evaluated at arbitrary or easier positions
|
| 145 |
+
|
| 146 |
+
## 7. Dataset Modes and Difficulty Ladder
|
| 147 |
+
|
| 148 |
+
Temporal Twins provides four modes.
|
| 149 |
+
|
| 150 |
+
### `oracle_calib`
|
| 151 |
+
|
| 152 |
+
This is the calibration mode used to validate that the matched-prefix protocol is working as intended.
|
| 153 |
+
|
| 154 |
+
- Oracle metrics remain near-perfect.
|
| 155 |
+
- Static shortcut baselines remain at chance.
|
| 156 |
+
- Benign motif hit rate remains zero.
|
| 157 |
+
- This mode is primarily for protocol validation rather than realistic difficulty.
|
| 158 |
+
|
| 159 |
+
### `easy`
|
| 160 |
+
|
| 161 |
+
- strong motif signal
|
| 162 |
+
- low noise
|
| 163 |
+
- shorter delay
|
| 164 |
+
- expected SeqGRU performance near `0.90-1.00`
|
| 165 |
+
|
| 166 |
+
### `medium`
|
| 167 |
+
|
| 168 |
+
- moderate motif signal
|
| 169 |
+
- moderate noise
|
| 170 |
+
- longer delay
|
| 171 |
+
- expected SeqGRU performance near `0.80-0.90`
|
| 172 |
+
|
| 173 |
+
### `hard`
|
| 174 |
+
|
| 175 |
+
- weaker motif signal
|
| 176 |
+
- longer delay
|
| 177 |
+
- adversarial perturbations and decoys
|
| 178 |
+
- expected SeqGRU performance near `0.70-0.85`
|
| 179 |
+
|
| 180 |
+
Naming convention:
|
| 181 |
+
|
| 182 |
+
- in `oracle_calib`, `AuditOracle` and `RawMotifOracle` are true oracle-style references
|
| 183 |
+
- 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
|
| 184 |
+
|
| 185 |
+
## 8. Data Schema
|
| 186 |
+
|
| 187 |
+
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.
|
| 188 |
+
|
| 189 |
+
| Column name | Type | Description | Exposed to ordinary models? | Notes |
|
| 190 |
+
|---|---|---|---|---|
|
| 191 |
+
| `txn_id` | `int32` | Synthetic transaction identifier | Yes | Identifier only; not a benchmark target |
|
| 192 |
+
| `sender_id` | `int32` / `int64` | Synthetic sender account ID | Yes | Node identity available to temporal models |
|
| 193 |
+
| `receiver_id` | `int32` / `int64` | Synthetic receiver account ID | Yes | Used for graph and sequence structure |
|
| 194 |
+
| `timestamp` | `float32` | Synthetic event time in seconds from simulation start | Yes | Prefix truncation is based on timestamp and local index |
|
| 195 |
+
| `amount` | `float32` | Synthetic transaction amount | Yes | Not tied to real currency records |
|
| 196 |
+
| `txn_type` | `int8` | Synthetic transaction-type code | Yes | UPI-style categorical event attribute |
|
| 197 |
+
| `risk_score` | `float32` | Synthetic risk score from the risk engine | Yes | No real production risk model is used |
|
| 198 |
+
| `fail_prob` | `float32` | Synthetic failure probability | Yes | Risk-engine output |
|
| 199 |
+
| `failed` | `int8` | Binary failure indicator | Yes | Used as a normal model-facing field |
|
| 200 |
+
| `is_retry` | `int8` / derived | Retry-like event indicator | Yes | Available to ordinary models when present |
|
| 201 |
+
| `pair_freq` | `float32` / derived | Sender-receiver interaction-frequency feature | Yes | Derived from visible event history |
|
| 202 |
+
| `risk_noisy` | `float32` | Noisy synthetic risk feature | Yes | Benchmark feature, not an audit signal |
|
| 203 |
+
| `txn_count_10` | `float32` / derived | Recent-count feature over a short window | Yes | Derived from visible history |
|
| 204 |
+
| `amount_sum_10` | `float32` / derived | Recent amount-sum feature | Yes | Derived from visible history |
|
| 205 |
+
| `is_fraud` | `int8` | Binary fraud label | No | Supervision target only, not a model input |
|
| 206 |
+
| `twin_pair_id` | `int64` | Matched fraud/benign pair identifier | No | Audit/oracle-only; not exposed to learned baselines |
|
| 207 |
+
| `twin_role` | `string` | Twin role such as `fraud`, `benign`, or `background` | No | Audit/oracle-only |
|
| 208 |
+
| `twin_label` | `int8` | Pairwise matched label for audit utilities | No | Audit/oracle-only |
|
| 209 |
+
| `template_id` | `int64` | Source template identifier used during twin construction | No | Audit/oracle-only |
|
| 210 |
+
| `dynamic_fraud_state` | `float32` | Latent synthetic fraud-state variable | No | Hidden mechanism for analysis only |
|
| 211 |
+
| `motif_source` | `int8` | Indicator for motif-source events in a sequence | No | Audit/oracle-only |
|
| 212 |
+
| `motif_hit_count` | `int32` | Count of motif hits in the sequence | No | Audit/oracle-only |
|
| 213 |
+
| `trigger_event_idx` | `int32` | Local event index of the trigger event | No | Audit/oracle-only |
|
| 214 |
+
| `label_event_idx` | `int32` | Local event index at which the fraud label becomes active | No | Audit/oracle-only |
|
| 215 |
+
| `label_delay` | `int32` | Delay between trigger and labeled event index | No | Audit/oracle-only |
|
| 216 |
+
| `fraud_source` | `string` | Cause of fraud label, e.g. motif or fallback chain | No | Audit/oracle-only |
|
| 217 |
+
| `is_fallback_label` | `int8` | Indicator that a label came from fallback logic | No | Audit/oracle-only |
|
| 218 |
+
| `motif_chain_state` | `float32` | Internal motif-chain analysis field | No | Audit/oracle-only |
|
| 219 |
+
| `motif_strength` | `float32` | Internal motif-strength analysis field | No | Audit/oracle-only |
|
| 220 |
+
|
| 221 |
+
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.
|
| 222 |
+
|
| 223 |
+
## 9. Model-Facing vs Audit/Oracle-Only Columns
|
| 224 |
+
|
| 225 |
+
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.
|
| 226 |
+
|
| 227 |
+
Ordinary models may use fields such as:
|
| 228 |
+
|
| 229 |
+
- `sender_id`
|
| 230 |
+
- `receiver_id`
|
| 231 |
+
- `timestamp`
|
| 232 |
+
- `amount`
|
| 233 |
+
- `risk_score`
|
| 234 |
+
- `fail_prob`
|
| 235 |
+
- `failed`
|
| 236 |
+
- `txn_type`
|
| 237 |
+
- other derived non-oracle features built from visible prefix history
|
| 238 |
+
|
| 239 |
+
Ordinary models must not use:
|
| 240 |
+
|
| 241 |
+
- `motif_hit_count`
|
| 242 |
+
- `motif_source`
|
| 243 |
+
- `trigger_event_idx`
|
| 244 |
+
- `label_event_idx`
|
| 245 |
+
- `label_delay`
|
| 246 |
+
- `fraud_source`
|
| 247 |
+
- `twin_role`
|
| 248 |
+
- `twin_label`
|
| 249 |
+
- `twin_pair_id`
|
| 250 |
+
- `template_id`
|
| 251 |
+
- `dynamic_fraud_state`
|
| 252 |
+
- other oracle-only diagnostics
|
| 253 |
+
|
| 254 |
+
This separation is necessary for the benchmark claim that performance should come from temporal reasoning rather than privileged audit information.
|
| 255 |
+
|
| 256 |
+
## 10. Benchmark Tasks
|
| 257 |
+
|
| 258 |
+
Temporal Twins supports the following benchmark task:
|
| 259 |
+
|
| 260 |
+
- binary fraud detection on matched prefix examples
|
| 261 |
+
|
| 262 |
+
The standard evaluation protocol is:
|
| 263 |
+
|
| 264 |
+
- build matched fraud/benign examples
|
| 265 |
+
- truncate each sender history at the matched prefix index `k`
|
| 266 |
+
- train or score on the visible prefix only
|
| 267 |
+
- evaluate binary discrimination at the matched example level
|
| 268 |
+
|
| 269 |
+
Primary reported metrics include:
|
| 270 |
+
|
| 271 |
+
- ROC-AUC
|
| 272 |
+
- PR-AUC
|
| 273 |
+
- shuffled-order ROC-AUC
|
| 274 |
+
- shuffle delta = shuffled ROC-AUC minus clean ROC-AUC
|
| 275 |
+
|
| 276 |
+
The shuffled-order test is important: it measures how much performance depends on event order rather than unordered ingredients.
|
| 277 |
+
|
| 278 |
+
## 11. Baselines and Reference Results
|
| 279 |
+
|
| 280 |
+
The frozen 5-seed paper suite uses:
|
| 281 |
+
|
| 282 |
+
- `num_users = 350`
|
| 283 |
+
- `simulation_days = 45`
|
| 284 |
+
- `seeds = [0, 1, 2, 3, 4]`
|
| 285 |
+
- `fast_mode = false`
|
| 286 |
+
- `n_checkpoints = 8`
|
| 287 |
+
|
| 288 |
+
Compact reference results:
|
| 289 |
+
|
| 290 |
+
| Mode | Primary reference | Secondary reference | XGBoost ROC-AUC | StaticGNN ROC-AUC | SeqGRU ROC-AUC | SeqGRU shuffled delta |
|
| 291 |
+
|---|---:|---:|---:|---:|---:|---:|
|
| 292 |
+
| `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` |
|
| 293 |
+
| `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` |
|
| 294 |
+
| `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` |
|
| 295 |
+
| `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` |
|
| 296 |
+
|
| 297 |
+
Static shortcut audit across all 20 paper-suite runs:
|
| 298 |
+
|
| 299 |
+
- `static_agg_auc = 0.5000 +- 0.0000`
|
| 300 |
+
- `total_txn_count AUC = 0.5000 +- 0.0000`
|
| 301 |
+
- `local_event_idx AUC = 0.5000 +- 0.0000`
|
| 302 |
+
- `prefix_txn_count AUC = 0.5000 +- 0.0000`
|
| 303 |
+
- `timestamp AUC = 0.5000 +- 0.0000`
|
| 304 |
+
- `account_age AUC = 0.5000 +- 0.0000`
|
| 305 |
+
- `active_age AUC = 0.5000 +- 0.0000`
|
| 306 |
+
- `benign_motif_hit_rate = 0.0000 +- 0.0000`
|
| 307 |
+
|
| 308 |
+
These results support the intended interpretation:
|
| 309 |
+
|
| 310 |
+
- static shortcuts are neutralized
|
| 311 |
+
- `oracle_calib` validates matched-prefix correctness
|
| 312 |
+
- `easy` is readily learnable by order-sensitive sequence models
|
| 313 |
+
- `medium` remains learnable but meaningfully harder
|
| 314 |
+
- `hard` remains above static baselines but is substantially more challenging
|
| 315 |
+
|
| 316 |
+
Full paper-suite artifacts, including temporal GNN results and per-seed CSVs, are stored under:
|
| 317 |
+
|
| 318 |
+
- `results/paper_suite_20260503_202810/`
|
| 319 |
+
|
| 320 |
+
## 12. Intended Use
|
| 321 |
+
|
| 322 |
+
This dataset is intended for:
|
| 323 |
+
|
| 324 |
+
- research on temporal fraud detection
|
| 325 |
+
- benchmarking order-sensitive sequence and temporal-graph models
|
| 326 |
+
- evaluating whether performance survives matched static controls
|
| 327 |
+
- studying delayed labels and prefix-only evaluation
|
| 328 |
+
- comparing clean-order and shuffled-order performance
|
| 329 |
+
|
| 330 |
+
It is appropriate for methodology papers, controlled ablation studies, and robustness checks on temporal inductive bias.
|
| 331 |
+
|
| 332 |
+
## 13. Out-of-Scope Use
|
| 333 |
+
|
| 334 |
+
Temporal Twins is out of scope for:
|
| 335 |
+
|
| 336 |
+
- direct training of production fraud systems
|
| 337 |
+
- making real financial, banking, or payment decisions
|
| 338 |
+
- approving or denying transactions for real users
|
| 339 |
+
- risk-scoring real individuals or organizations
|
| 340 |
+
- regulatory, legal, or operational decisions in production financial systems
|
| 341 |
+
|
| 342 |
+
The dataset must not be used to train production fraud systems directly or to make real financial decisions.
|
| 343 |
+
|
| 344 |
+
## 14. Limitations
|
| 345 |
+
|
| 346 |
+
Important limitations include:
|
| 347 |
+
|
| 348 |
+
- the benchmark is fully synthetic and reflects designer assumptions
|
| 349 |
+
- user behavior, fraud behavior, and benign behavior are simplified relative to real financial ecosystems
|
| 350 |
+
- the only ground truth is the generator's own labeling logic
|
| 351 |
+
- real-world fraud often depends on richer institutional, device, merchant, and social context not present here
|
| 352 |
+
- difficulty levels are benchmark design choices, not calibrated measures of real operational difficulty
|
| 353 |
+
- temporal GNN underperformance on this benchmark should not be generalized to all real fraud settings
|
| 354 |
+
|
| 355 |
+
## 15. Biases and Risks
|
| 356 |
+
|
| 357 |
+
As a synthetic benchmark, Temporal Twins inherits the modeling biases of its generator:
|
| 358 |
+
|
| 359 |
+
- it emphasizes order-sensitive motifs chosen by the benchmark designers
|
| 360 |
+
- it encodes a particular notion of delayed fraud and camouflage
|
| 361 |
+
- it may reward models that are well aligned to these synthetic mechanisms
|
| 362 |
+
- it may underrepresent other real fraud styles not captured by the generator
|
| 363 |
+
|
| 364 |
+
There is also a scientific risk:
|
| 365 |
+
|
| 366 |
+
- 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
|
| 367 |
+
|
| 368 |
+
## 16. Privacy and Sensitive Data
|
| 369 |
+
|
| 370 |
+
Temporal Twins contains no real financial or personal data.
|
| 371 |
+
|
| 372 |
+
Specifically:
|
| 373 |
+
|
| 374 |
+
- no real UPI data
|
| 375 |
+
- no real users
|
| 376 |
+
- no real bank accounts
|
| 377 |
+
- no real transactions
|
| 378 |
+
- no personal financial records
|
| 379 |
+
- no protected demographic attributes
|
| 380 |
+
|
| 381 |
+
All user IDs, receiver IDs, timestamps, amounts, and risk signals are synthetic artifacts produced by the generator.
|
| 382 |
+
|
| 383 |
+
## 17. Ethical Considerations
|
| 384 |
+
|
| 385 |
+
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.
|
| 386 |
+
|
| 387 |
+
Users of the dataset should not:
|
| 388 |
+
|
| 389 |
+
- present synthetic results as direct evidence of production readiness
|
| 390 |
+
- claim fairness or social validity that has not been tested on real populations
|
| 391 |
+
- use the dataset as justification for automated decisions about real people
|
| 392 |
+
|
| 393 |
+
The intended ethical use is research benchmarking, not operational deployment.
|
| 394 |
+
|
| 395 |
+
## 18. Reproducibility
|
| 396 |
+
|
| 397 |
+
The repository includes deterministic generation and evaluation settings for the frozen paper suite.
|
| 398 |
+
|
| 399 |
+
Paper-suite configuration:
|
| 400 |
+
|
| 401 |
+
- `num_users = 350`
|
| 402 |
+
- `simulation_days = 45`
|
| 403 |
+
- `seeds = [0, 1, 2, 3, 4]`
|
| 404 |
+
- `fast_mode = false`
|
| 405 |
+
- `n_checkpoints = 8`
|
| 406 |
+
|
| 407 |
+
Reproducibility properties:
|
| 408 |
+
|
| 409 |
+
- stable deterministic seed derivation is used for benchmark modes and profiles
|
| 410 |
+
- Python, NumPy, and PyTorch seeds are fixed per run
|
| 411 |
+
- deterministic runtime flags are enabled where safe
|
| 412 |
+
- matched-prefix datasets are reproducible under fixed config and seed
|
| 413 |
+
- the final paper suite in this repository is stored as deterministic CSV artifacts
|
| 414 |
+
|
| 415 |
+
Reference artifacts:
|
| 416 |
+
|
| 417 |
+
- `results/paper_suite_20260503_202810/paper_suite_runs.csv`
|
| 418 |
+
- `results/paper_suite_20260503_202810/paper_suite_summary.csv`
|
| 419 |
+
- `results/paper_suite_20260503_202810/paper_suite_runtime.csv`
|
| 420 |
+
- `results/paper_suite_20260503_202810/paper_suite_failed_checks.csv`
|
| 421 |
+
|
| 422 |
+
## 19. Hosting, License, and Citation
|
| 423 |
+
|
| 424 |
+
### Hosting
|
| 425 |
+
|
| 426 |
+
The benchmark is currently generated from code in this repository rather than distributed as a fixed external archive.
|
| 427 |
+
|
| 428 |
+
Current status:
|
| 429 |
+
|
| 430 |
+
- dataset hosting location: [https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins)
|
| 431 |
+
- canonical pre-generated release archive: [https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins)
|
| 432 |
+
- 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)
|
| 433 |
+
- 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)
|
| 434 |
+
- 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)
|
| 435 |
+
- 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)
|
| 436 |
+
- 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)
|
| 437 |
+
- 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)
|
| 438 |
+
- reference paper-suite results: `results/paper_suite_20260503_202810/`
|
| 439 |
+
|
| 440 |
+
### License
|
| 441 |
+
|
| 442 |
+
- Dataset license: `CC BY 4.0` (`CC-BY-4.0`)
|
| 443 |
+
- Code license: `Apache License 2.0` (`Apache-2.0`)
|
| 444 |
+
|
| 445 |
+
### Citation
|
| 446 |
+
|
| 447 |
+
`TODO` placeholder BibTeX:
|
| 448 |
+
|
| 449 |
+
```bibtex
|
| 450 |
+
@dataset{temporal_twins_todo,
|
| 451 |
+
title = {Temporal Twins: A Synthetic UPI-Style Benchmark for Temporal Fraud Detection},
|
| 452 |
+
author = {TODO},
|
| 453 |
+
year = {TODO},
|
| 454 |
+
howpublished = {TODO},
|
| 455 |
+
note = {Synthetic matched-prefix temporal fraud benchmark},
|
| 456 |
+
url = {TODO}
|
| 457 |
+
}
|
| 458 |
+
```
|
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LICENSE-DATA
ADDED
|
@@ -0,0 +1,18 @@
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| 1 |
+
SPDX-License-Identifier: CC-BY-4.0
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Temporal Twins dataset artifacts, generated synthetic data, metadata, dataset card,
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and released benchmark files are licensed under the Creative Commons Attribution
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4.0 International license (CC BY 4.0).
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+
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Canonical license URL:
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https://creativecommons.org/licenses/by/4.0/
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This applies to released synthetic benchmark artifacts, including generated data
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describes the dataset.
|
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|
| 14 |
+
Attribution requirement:
|
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"If you use Temporal Twins, please cite the associated paper and dataset release."
|
| 16 |
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|
| 17 |
+
Temporal Twins contains synthetic benchmark data only. It does not include real UPI
|
| 18 |
+
transactions, real users, real bank accounts, or personal financial records.
|
MANIFEST.sha256
ADDED
|
@@ -0,0 +1,126 @@
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|
| 1 |
+
b880f7dc777e417cd1a526b141b6e15866e89e62f8d4bfe627609aa909d40a65 .DS_Store
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6bebd0daf847a3885b372df91ca9bbb5548e7b84d162db9e1f3c6178af6a9465 DATASET_CARD.md
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15b001b0571fac1a30ea353be175df2724e7368d9c7ac9b433b9ac7afe2eb698 LICENSE
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6da8eaaf7b897c14b497468e485beae1b5c3d0514f1b1461e30133b890be996b LICENSE-DATA
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|
|
|
| 1 |
+
# Temporal Twins Release Bundle
|
| 2 |
+
|
| 3 |
+
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.
|
| 4 |
+
|
| 5 |
+
## What This Release Contains
|
| 6 |
+
|
| 7 |
+
- `DATASET_CARD.md`: NeurIPS-style dataset card for the benchmark
|
| 8 |
+
- `README_REPO.md`: copied repository README from the project root
|
| 9 |
+
- `LICENSE`: copied project license file
|
| 10 |
+
- `results/`: final deterministic paper-suite outputs
|
| 11 |
+
- `configs/`: benchmark configuration files and a paper-suite reference config
|
| 12 |
+
- `metadata/`: Croissant metadata and validation notes
|
| 13 |
+
- `data/`: empty per-mode/per-seed directory scaffold plus generation instructions
|
| 14 |
+
- `MANIFEST.sha256`: SHA256 manifest for all files in this bundle
|
| 15 |
+
|
| 16 |
+
## How To Use The Data
|
| 17 |
+
|
| 18 |
+
1. Read `DATASET_CARD.md` for benchmark scope, schema, intended use, and limitations.
|
| 19 |
+
2. Read `metadata/temporal_twins_croissant.json` for machine-readable release metadata.
|
| 20 |
+
3. Use `results/paper_suite_summary.csv` and `results/paper_suite_summary.md` for the paper-ready reference results.
|
| 21 |
+
4. Populate `data/` with the generated per-seed transaction and matched-prefix files before public hosting.
|
| 22 |
+
|
| 23 |
+
## How To Regenerate The Data
|
| 24 |
+
|
| 25 |
+
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`.
|
| 26 |
+
|
| 27 |
+
## How To Reproduce Paper Results
|
| 28 |
+
|
| 29 |
+
The final deterministic paper suite used:
|
| 30 |
+
|
| 31 |
+
- benchmark groups: `oracle_calib`, `easy`, `medium`, `hard`
|
| 32 |
+
- benchmark modes:
|
| 33 |
+
- `oracle_calib` -> `temporal_twins_oracle_calib`
|
| 34 |
+
- `easy`, `medium`, `hard` -> `temporal_twins`
|
| 35 |
+
- seeds: `0 1 2 3 4`
|
| 36 |
+
- `num_users = 350`
|
| 37 |
+
- `simulation_days = 45`
|
| 38 |
+
- `fast_mode = false`
|
| 39 |
+
- `n_checkpoints = 8`
|
| 40 |
+
- device: `cpu`
|
| 41 |
+
|
| 42 |
+
Reference artifacts:
|
| 43 |
+
|
| 44 |
+
- `results/paper_suite_runs.csv`
|
| 45 |
+
- `results/paper_suite_summary.csv`
|
| 46 |
+
- `results/paper_suite_runtime.csv`
|
| 47 |
+
- `results/paper_suite_failed_checks.csv`
|
| 48 |
+
- `results/paper_suite_summary.md`
|
| 49 |
+
- `results/paper_suite_meta.json`
|
| 50 |
+
|
| 51 |
+
## File Structure
|
| 52 |
+
|
| 53 |
+
```text
|
| 54 |
+
release/
|
| 55 |
+
├── README.md
|
| 56 |
+
├── README_REPO.md
|
| 57 |
+
├── DATASET_CARD.md
|
| 58 |
+
├── LICENSE
|
| 59 |
+
├── MANIFEST.sha256
|
| 60 |
+
├── RELEASE_CHECKLIST.md
|
| 61 |
+
├── data/
|
| 62 |
+
│ ├── README_GENERATION.md
|
| 63 |
+
│ ├── oracle_calib/seed_{0..4}/
|
| 64 |
+
│ ├── easy/seed_{0..4}/
|
| 65 |
+
│ ├── medium/seed_{0..4}/
|
| 66 |
+
│ └── hard/seed_{0..4}/
|
| 67 |
+
├── configs/
|
| 68 |
+
├── results/
|
| 69 |
+
└── metadata/
|
| 70 |
+
```
|
| 71 |
+
|
| 72 |
+
## Hosted URLs
|
| 73 |
+
|
| 74 |
+
- Dataset URL: [https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins)
|
| 75 |
+
- 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)
|
| 76 |
+
- 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)
|
| 77 |
+
- 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)
|
| 78 |
+
- 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)
|
| 79 |
+
- 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)
|
| 80 |
+
- 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)
|
| 81 |
+
- Full release archive: [https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins)
|
| 82 |
+
- Code repository: `TODO_CODE_REPOSITORY_URL`
|
| 83 |
+
- Paper or preprint: `TODO_PAPER_URL`
|
| 84 |
+
|
| 85 |
+
## Licenses
|
| 86 |
+
|
| 87 |
+
- Code: Apache License 2.0 (`Apache-2.0`)
|
| 88 |
+
- Dataset and generated benchmark artifacts: Creative Commons Attribution 4.0 International (`CC-BY-4.0`)
|
| 89 |
+
- Code SPDX-License-Identifier: `Apache-2.0`
|
| 90 |
+
- Dataset SPDX-License-Identifier: `CC-BY-4.0`
|
| 91 |
+
- No real UPI data or personal financial records are included in this release bundle.
|
| 92 |
+
|
| 93 |
+
## Notes
|
| 94 |
+
|
| 95 |
+
- This bundle contains no real UPI data, no real users, no real bank accounts, and no personal financial records.
|
| 96 |
+
- The benchmark code, generator logic, labels, matched-prefix protocol, and model logic were not modified while preparing this release directory.
|
README_REPO.md
ADDED
|
@@ -0,0 +1,437 @@
|
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|
|
| 1 |
+
# Temporal Twins: A Matched-Control Benchmark for Temporal Fraud Detection
|
| 2 |
+
|
| 3 |
+
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.
|
| 4 |
+
|
| 5 |
+
## Installation
|
| 6 |
+
|
| 7 |
+
Recommended Python version: `3.11+` (`3.13` also works in the checked environment).
|
| 8 |
+
|
| 9 |
+
### pip
|
| 10 |
+
|
| 11 |
+
```bash
|
| 12 |
+
pip install -r requirements.txt
|
| 13 |
+
```
|
| 14 |
+
|
| 15 |
+
### conda
|
| 16 |
+
|
| 17 |
+
If [environment.yml](environment.yml) is present:
|
| 18 |
+
|
| 19 |
+
```bash
|
| 20 |
+
conda env create -f environment.yml
|
| 21 |
+
conda activate temporal-twins
|
| 22 |
+
```
|
| 23 |
+
|
| 24 |
+
## Repository Structure
|
| 25 |
+
|
| 26 |
+
- `src/`: synthetic user, transaction, risk, fraud, graph, and core config code
|
| 27 |
+
- `models/`: learned baselines and probe/oracle wrappers, including SeqGRU and temporal GNNs
|
| 28 |
+
- `experiments/`: benchmark runner and matched-prefix evaluation code
|
| 29 |
+
- `config/`: checked-in YAML configs used as base configs for experiments
|
| 30 |
+
- `results/`: frozen experiment artifacts, including the final deterministic paper suite
|
| 31 |
+
- `metadata/`: Croissant metadata and release-side validation notes
|
| 32 |
+
- `release/`: manual-hosting bundle prepared for later upload
|
| 33 |
+
|
| 34 |
+
## Quick Smoke Test
|
| 35 |
+
|
| 36 |
+
The public CLI supports a fast audit-mode smoke test:
|
| 37 |
+
|
| 38 |
+
```bash
|
| 39 |
+
PYTHONPATH=. python3 experiments/run_all.py \
|
| 40 |
+
--fast \
|
| 41 |
+
--seed 0 \
|
| 42 |
+
--benchmark-mode temporal_twins_oracle_calib \
|
| 43 |
+
--experiments audit \
|
| 44 |
+
--device cpu
|
| 45 |
+
```
|
| 46 |
+
|
| 47 |
+
## Exact Paper-Style Group Runner
|
| 48 |
+
|
| 49 |
+
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.
|
| 50 |
+
|
| 51 |
+
Define this shell helper once in your session:
|
| 52 |
+
|
| 53 |
+
```bash
|
| 54 |
+
run_group() {
|
| 55 |
+
local group="$1"
|
| 56 |
+
local seed="$2"
|
| 57 |
+
local out_json="$3"
|
| 58 |
+
|
| 59 |
+
PYTHONPATH=. python3 - "$group" "$seed" "$out_json" <<'PY'
|
| 60 |
+
import json
|
| 61 |
+
import math
|
| 62 |
+
import sys
|
| 63 |
+
import time
|
| 64 |
+
from pathlib import Path
|
| 65 |
+
|
| 66 |
+
from src.core.config_loader import load_config
|
| 67 |
+
from experiments.run_all import (
|
| 68 |
+
build_gate_pool_from_frames,
|
| 69 |
+
gate_volume_is_sufficient,
|
| 70 |
+
generate_single_difficulty,
|
| 71 |
+
offset_gate_namespace,
|
| 72 |
+
prepare_gate_subset,
|
| 73 |
+
run_motif_validity_check,
|
| 74 |
+
set_global_determinism,
|
| 75 |
+
)
|
| 76 |
+
|
| 77 |
+
|
| 78 |
+
def normalize(value):
|
| 79 |
+
if isinstance(value, dict):
|
| 80 |
+
return {k: normalize(v) for k, v in value.items()}
|
| 81 |
+
if isinstance(value, (list, tuple)):
|
| 82 |
+
return [normalize(v) for v in value]
|
| 83 |
+
if hasattr(value, "item"):
|
| 84 |
+
try:
|
| 85 |
+
value = value.item()
|
| 86 |
+
except Exception:
|
| 87 |
+
pass
|
| 88 |
+
if isinstance(value, float) and not math.isfinite(value):
|
| 89 |
+
return None
|
| 90 |
+
return value
|
| 91 |
+
|
| 92 |
+
|
| 93 |
+
group = sys.argv[1]
|
| 94 |
+
seed = int(sys.argv[2])
|
| 95 |
+
out_json = Path(sys.argv[3])
|
| 96 |
+
|
| 97 |
+
if group == "oracle_calib":
|
| 98 |
+
benchmark_mode = "temporal_twins_oracle_calib"
|
| 99 |
+
difficulty = "easy"
|
| 100 |
+
hard_abort = True
|
| 101 |
+
force_temporal_models = True
|
| 102 |
+
else:
|
| 103 |
+
benchmark_mode = "temporal_twins"
|
| 104 |
+
difficulty = group
|
| 105 |
+
hard_abort = False
|
| 106 |
+
force_temporal_models = True
|
| 107 |
+
|
| 108 |
+
cfg = load_config("config/default.yaml")
|
| 109 |
+
cfg = cfg.model_copy(
|
| 110 |
+
update={
|
| 111 |
+
"num_users": 350,
|
| 112 |
+
"simulation_days": 45,
|
| 113 |
+
"benchmark_mode": benchmark_mode,
|
| 114 |
+
"random_seed": seed,
|
| 115 |
+
}
|
| 116 |
+
)
|
| 117 |
+
|
| 118 |
+
set_global_determinism(seed)
|
| 119 |
+
pool = generate_single_difficulty(
|
| 120 |
+
cfg,
|
| 121 |
+
difficulty=difficulty,
|
| 122 |
+
seed=seed,
|
| 123 |
+
benchmark_mode=benchmark_mode,
|
| 124 |
+
)
|
| 125 |
+
gate = prepare_gate_subset(pool, seed=seed, fast_mode=False)
|
| 126 |
+
pack_count = 1
|
| 127 |
+
|
| 128 |
+
while (not gate_volume_is_sufficient(gate["volume"], False)) and pack_count <= 6:
|
| 129 |
+
extra_seed = seed + pack_count * 10007
|
| 130 |
+
extra_pack = generate_single_difficulty(
|
| 131 |
+
cfg,
|
| 132 |
+
difficulty=difficulty,
|
| 133 |
+
seed=extra_seed,
|
| 134 |
+
benchmark_mode=benchmark_mode,
|
| 135 |
+
)
|
| 136 |
+
extra_pack = offset_gate_namespace(extra_pack, pack_count)
|
| 137 |
+
pool = build_gate_pool_from_frames([pool, extra_pack])
|
| 138 |
+
gate = prepare_gate_subset(pool, seed=seed, fast_mode=False)
|
| 139 |
+
pack_count += 1
|
| 140 |
+
|
| 141 |
+
gate["source_pool_events"] = int(len(pool))
|
| 142 |
+
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
|
| 143 |
+
gate["source_pool_packs"] = int(pack_count)
|
| 144 |
+
|
| 145 |
+
start = time.time()
|
| 146 |
+
gate_pass, report = run_motif_validity_check(
|
| 147 |
+
df=pool,
|
| 148 |
+
config=cfg,
|
| 149 |
+
seed=seed,
|
| 150 |
+
device="cpu",
|
| 151 |
+
num_epochs=3,
|
| 152 |
+
node_epochs=150,
|
| 153 |
+
n_checkpoints=8,
|
| 154 |
+
hard_abort=hard_abort,
|
| 155 |
+
benchmark_mode=benchmark_mode,
|
| 156 |
+
fast_mode=False,
|
| 157 |
+
force_temporal_models=force_temporal_models,
|
| 158 |
+
prebuilt_gate=gate,
|
| 159 |
+
)
|
| 160 |
+
elapsed = time.time() - start
|
| 161 |
+
|
| 162 |
+
result = {
|
| 163 |
+
"benchmark_group": group,
|
| 164 |
+
"benchmark_mode": benchmark_mode,
|
| 165 |
+
"seed": seed,
|
| 166 |
+
"primary_metric_label": report["audit_metric_label"],
|
| 167 |
+
"secondary_metric_label": report["raw_metric_label"],
|
| 168 |
+
"gate_pass": bool(gate_pass),
|
| 169 |
+
"run_wall_time_sec": float(elapsed),
|
| 170 |
+
**report,
|
| 171 |
+
}
|
| 172 |
+
|
| 173 |
+
out_json.parent.mkdir(parents=True, exist_ok=True)
|
| 174 |
+
out_json.write_text(json.dumps(normalize(result), indent=2) + "\n")
|
| 175 |
+
print(f"Wrote {out_json}")
|
| 176 |
+
PY
|
| 177 |
+
}
|
| 178 |
+
```
|
| 179 |
+
|
| 180 |
+
## Reproduce Oracle Calibration
|
| 181 |
+
|
| 182 |
+
Non-fast, reliable-volume `temporal_twins_oracle_calib`, seed `0`, `num_users=350`, `simulation_days=45`:
|
| 183 |
+
|
| 184 |
+
```bash
|
| 185 |
+
run_group oracle_calib 0 results/paper_suite_repro/jobs/oracle_calib_0.json
|
| 186 |
+
```
|
| 187 |
+
|
| 188 |
+
## Reproduce Easy / Medium / Hard
|
| 189 |
+
|
| 190 |
+
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):
|
| 191 |
+
|
| 192 |
+
```bash
|
| 193 |
+
run_group easy 0 results/paper_suite_repro/jobs/easy_0.json
|
| 194 |
+
run_group medium 0 results/paper_suite_repro/jobs/medium_0.json
|
| 195 |
+
run_group hard 0 results/paper_suite_repro/jobs/hard_0.json
|
| 196 |
+
```
|
| 197 |
+
|
| 198 |
+
## Reproduce Full Paper Suite
|
| 199 |
+
|
| 200 |
+
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:
|
| 201 |
+
|
| 202 |
+
### 1. Generate per-run JSON files
|
| 203 |
+
|
| 204 |
+
```bash
|
| 205 |
+
mkdir -p results/paper_suite_repro/jobs
|
| 206 |
+
|
| 207 |
+
for group in oracle_calib easy medium hard; do
|
| 208 |
+
for seed in 0 1 2 3 4; do
|
| 209 |
+
run_group "$group" "$seed" "results/paper_suite_repro/jobs/${group}_${seed}.json"
|
| 210 |
+
done
|
| 211 |
+
done
|
| 212 |
+
```
|
| 213 |
+
|
| 214 |
+
### 2. Aggregate into paper-suite CSV and Markdown files
|
| 215 |
+
|
| 216 |
+
```bash
|
| 217 |
+
PYTHONPATH=. python3 - <<'PY'
|
| 218 |
+
import json
|
| 219 |
+
from pathlib import Path
|
| 220 |
+
|
| 221 |
+
import pandas as pd
|
| 222 |
+
|
| 223 |
+
|
| 224 |
+
def summarize_mean_std(df, group_col):
|
| 225 |
+
numeric_cols = [c for c in df.columns if c != group_col and pd.api.types.is_numeric_dtype(df[c])]
|
| 226 |
+
grouped = df.groupby(group_col, dropna=False)[numeric_cols].agg(["mean", "std"]).reset_index()
|
| 227 |
+
grouped.columns = [
|
| 228 |
+
group_col if col == group_col else f"{col}_{stat}"
|
| 229 |
+
for col, stat in grouped.columns
|
| 230 |
+
]
|
| 231 |
+
return grouped
|
| 232 |
+
|
| 233 |
+
|
| 234 |
+
def volume_failures(row):
|
| 235 |
+
fails = []
|
| 236 |
+
if row["matched_eval_pairs"] < 2000:
|
| 237 |
+
fails.append(f"matched_eval_pairs={row['matched_eval_pairs']} (<2000)")
|
| 238 |
+
if row["positives"] < 500:
|
| 239 |
+
fails.append(f"positives={row['positives']} (<500)")
|
| 240 |
+
if row["negatives"] < 500:
|
| 241 |
+
fails.append(f"negatives={row['negatives']} (<500)")
|
| 242 |
+
if row["unique_fraud_users"] < 50:
|
| 243 |
+
fails.append(f"unique_fraud_users={row['unique_fraud_users']} (<50)")
|
| 244 |
+
if row["unique_benign_users"] < 50:
|
| 245 |
+
fails.append(f"unique_benign_users={row['unique_benign_users']} (<50)")
|
| 246 |
+
if not (0.35 <= row["positive_rate"] <= 0.65):
|
| 247 |
+
fails.append(f"positive_rate={row['positive_rate']:.4f} (outside [0.35,0.65])")
|
| 248 |
+
return " | ".join(fails)
|
| 249 |
+
|
| 250 |
+
|
| 251 |
+
def hard_gate_failures(row):
|
| 252 |
+
checks = [
|
| 253 |
+
(row["primary_metric_label"], row["audit_roc_auc"], ">=", 0.99),
|
| 254 |
+
(f"{row['primary_metric_label']} pair-sep", row["audit_pair_sep"], ">=", 0.99),
|
| 255 |
+
(row["secondary_metric_label"], row["raw_roc_auc"], ">=", 0.95),
|
| 256 |
+
(f"{row['secondary_metric_label']} pair-sep", row["raw_pair_sep"], ">=", 0.90),
|
| 257 |
+
("static_agg_auc", row["static_agg_auc"], "<=", 0.60),
|
| 258 |
+
("XGBoost ROC-AUC", row["xgb_roc_auc"], "<=", 0.65),
|
| 259 |
+
("StaticGNN ROC-AUC", row["static_gnn_roc"], "<=", 0.70),
|
| 260 |
+
("SeqGRU ROC-AUC", row["seqgru_roc_auc"], ">=", 0.80),
|
| 261 |
+
("SeqGRU shuffle delta", row["seqgru_shuffle_delta"], "<=", -0.10),
|
| 262 |
+
]
|
| 263 |
+
fails = []
|
| 264 |
+
for label, value, op, threshold in checks:
|
| 265 |
+
ok = value >= threshold if op == ">=" else value <= threshold
|
| 266 |
+
if not ok:
|
| 267 |
+
fails.append(f"{label}: {value:.4f} ({op}{threshold})")
|
| 268 |
+
return " | ".join(fails)
|
| 269 |
+
|
| 270 |
+
|
| 271 |
+
def advisory_failures(row):
|
| 272 |
+
checks = [
|
| 273 |
+
("TGN ROC-AUC", row["tgn_roc_auc"], ">=", 0.75),
|
| 274 |
+
("TGN shuffle delta", row["tgn_shuffle_delta"], "<=", -0.10),
|
| 275 |
+
("TGAT ROC-AUC", row["tgat_roc_auc"], ">=", 0.75),
|
| 276 |
+
("TGAT shuffle delta", row["tgat_shuffle_delta"], "<=", -0.10),
|
| 277 |
+
("DyRep ROC-AUC", row["dyrep_roc_auc"], ">=", 0.75),
|
| 278 |
+
("DyRep shuffle delta", row["dyrep_shuffle_delta"], "<=", -0.10),
|
| 279 |
+
("JODIE ROC-AUC", row["jodie_roc_auc"], ">=", 0.75),
|
| 280 |
+
("JODIE shuffle delta", row["jodie_shuffle_delta"], "<=", -0.10),
|
| 281 |
+
]
|
| 282 |
+
fails = []
|
| 283 |
+
for label, value, op, threshold in checks:
|
| 284 |
+
ok = value >= threshold if op == ">=" else value <= threshold
|
| 285 |
+
if not ok:
|
| 286 |
+
fails.append(f"{label}: {value:.4f} ({op}{threshold})")
|
| 287 |
+
return " | ".join(fails)
|
| 288 |
+
|
| 289 |
+
|
| 290 |
+
jobs_dir = Path("results/paper_suite_repro/jobs")
|
| 291 |
+
out_dir = jobs_dir.parent
|
| 292 |
+
rows = [json.loads(path.read_text()) for path in sorted(jobs_dir.glob("*.json"))]
|
| 293 |
+
df = pd.DataFrame(rows).sort_values(["benchmark_group", "seed"]).reset_index(drop=True)
|
| 294 |
+
|
| 295 |
+
runs_path = out_dir / "paper_suite_runs.csv"
|
| 296 |
+
summary_path = out_dir / "paper_suite_summary.csv"
|
| 297 |
+
runtime_path = out_dir / "paper_suite_runtime.csv"
|
| 298 |
+
failed_path = out_dir / "paper_suite_failed_checks.csv"
|
| 299 |
+
summary_md_path = out_dir / "paper_suite_summary.md"
|
| 300 |
+
meta_path = out_dir / "paper_suite_meta.json"
|
| 301 |
+
|
| 302 |
+
df.to_csv(runs_path, index=False)
|
| 303 |
+
|
| 304 |
+
summary = summarize_mean_std(df, "benchmark_group")
|
| 305 |
+
summary.to_csv(summary_path, index=False)
|
| 306 |
+
|
| 307 |
+
runtime_cols = [
|
| 308 |
+
"benchmark_group",
|
| 309 |
+
"seed",
|
| 310 |
+
"run_wall_time_sec",
|
| 311 |
+
"static_gnn_eval_time_sec",
|
| 312 |
+
"static_gnn_unique_prefix_cutoffs",
|
| 313 |
+
"static_gnn_graph_builds",
|
| 314 |
+
"static_gnn_cache_hit_rate",
|
| 315 |
+
]
|
| 316 |
+
df[runtime_cols].to_csv(runtime_path, index=False)
|
| 317 |
+
|
| 318 |
+
failed = df[["benchmark_group", "seed", "gate_pass"]].copy()
|
| 319 |
+
failed["volume_failures"] = df.apply(volume_failures, axis=1)
|
| 320 |
+
failed["hard_gate_failures"] = df.apply(hard_gate_failures, axis=1)
|
| 321 |
+
failed["advisory_failures"] = df.apply(advisory_failures, axis=1)
|
| 322 |
+
failed.to_csv(failed_path, index=False)
|
| 323 |
+
|
| 324 |
+
meta = {
|
| 325 |
+
"device": "cpu",
|
| 326 |
+
"num_users": 350,
|
| 327 |
+
"simulation_days": 45,
|
| 328 |
+
"num_epochs": 3,
|
| 329 |
+
"node_epochs": 150,
|
| 330 |
+
"n_checkpoints": 8,
|
| 331 |
+
"fast_mode": False,
|
| 332 |
+
"seeds": [0, 1, 2, 3, 4],
|
| 333 |
+
}
|
| 334 |
+
meta_path.write_text(json.dumps(meta, indent=2) + "\n")
|
| 335 |
+
|
| 336 |
+
headline = summary[
|
| 337 |
+
[
|
| 338 |
+
"benchmark_group",
|
| 339 |
+
"xgb_roc_auc_mean",
|
| 340 |
+
"static_gnn_roc_mean",
|
| 341 |
+
"seqgru_roc_auc_mean",
|
| 342 |
+
"seqgru_shuffle_delta_mean",
|
| 343 |
+
]
|
| 344 |
+
].copy()
|
| 345 |
+
|
| 346 |
+
lines = [
|
| 347 |
+
"# Paper Suite Summary",
|
| 348 |
+
"",
|
| 349 |
+
"| benchmark_group | xgb_roc_auc_mean | static_gnn_roc_mean | seqgru_roc_auc_mean | seqgru_shuffle_delta_mean |",
|
| 350 |
+
"|---|---:|---:|---:|---:|",
|
| 351 |
+
]
|
| 352 |
+
for row in headline.itertuples(index=False):
|
| 353 |
+
lines.append(
|
| 354 |
+
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} |"
|
| 355 |
+
)
|
| 356 |
+
summary_md_path.write_text("\n".join(lines) + "\n")
|
| 357 |
+
|
| 358 |
+
print(f"Wrote {runs_path}")
|
| 359 |
+
print(f"Wrote {summary_path}")
|
| 360 |
+
print(f"Wrote {runtime_path}")
|
| 361 |
+
print(f"Wrote {failed_path}")
|
| 362 |
+
print(f"Wrote {summary_md_path}")
|
| 363 |
+
print(f"Wrote {meta_path}")
|
| 364 |
+
PY
|
| 365 |
+
```
|
| 366 |
+
|
| 367 |
+
This aggregation step writes:
|
| 368 |
+
|
| 369 |
+
- `results/paper_suite_repro/paper_suite_runs.csv`
|
| 370 |
+
- `results/paper_suite_repro/paper_suite_summary.csv`
|
| 371 |
+
- `results/paper_suite_repro/paper_suite_runtime.csv`
|
| 372 |
+
- `results/paper_suite_repro/paper_suite_failed_checks.csv`
|
| 373 |
+
- `results/paper_suite_repro/paper_suite_summary.md`
|
| 374 |
+
- `results/paper_suite_repro/paper_suite_meta.json`
|
| 375 |
+
|
| 376 |
+
The frozen reference artifacts checked into this repository live in [results/paper_suite_20260503_202810](results/paper_suite_20260503_202810).
|
| 377 |
+
|
| 378 |
+
## Expected Headline Results
|
| 379 |
+
|
| 380 |
+
| benchmark_group | XGBoost ROC-AUC | StaticGNN ROC-AUC | SeqGRU ROC-AUC | SeqGRU shuffle delta |
|
| 381 |
+
|---|---:|---:|---:|---:|
|
| 382 |
+
| `oracle_calib` | 0.5000 | 0.5222 | 1.0000 | -0.5032 |
|
| 383 |
+
| `easy` | 0.5000 | 0.4946 | 1.0000 | -0.5003 |
|
| 384 |
+
| `medium` | 0.5000 | 0.4922 | 0.8391 | -0.3337 |
|
| 385 |
+
| `hard` | 0.5000 | 0.5026 | 0.6876 | -0.1883 |
|
| 386 |
+
|
| 387 |
+
## Determinism
|
| 388 |
+
|
| 389 |
+
- Deterministic CPU runtime is enabled in [experiments/run_all.py](experiments/run_all.py).
|
| 390 |
+
- The same seed should produce identical matched-prefix data and identical metrics under the same deterministic environment.
|
| 391 |
+
- Deterministic settings intentionally trade speed for repeatability and will slow larger runs.
|
| 392 |
+
|
| 393 |
+
For more detail, see [docs/DETERMINISM.md](docs/DETERMINISM.md).
|
| 394 |
+
|
| 395 |
+
## Runtime Note
|
| 396 |
+
|
| 397 |
+
Mean wall-clock runtime per benchmark group in the final deterministic paper suite:
|
| 398 |
+
|
| 399 |
+
- `oracle_calib`: `1136.6s`
|
| 400 |
+
- `easy`: `1345.9s`
|
| 401 |
+
- `medium`: `2181.9s`
|
| 402 |
+
- `hard`: `2613.7s`
|
| 403 |
+
- cumulative summed runtime across all 20 runs: about `10.11` hours
|
| 404 |
+
|
| 405 |
+
## Data and Metadata
|
| 406 |
+
|
| 407 |
+
- Dataset card: [DATASET_CARD.md](DATASET_CARD.md)
|
| 408 |
+
- Croissant metadata: [metadata/temporal_twins_croissant.json](metadata/temporal_twins_croissant.json)
|
| 409 |
+
- Manual-hosting release bundle: [release/](release/)
|
| 410 |
+
|
| 411 |
+
Hosted URLs:
|
| 412 |
+
|
| 413 |
+
- Dataset URL: [temporal-twins on Hugging Face](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins)
|
| 414 |
+
- Croissant metadata URL: [raw JSON-LD](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/raw/main/metadata/temporal_twins_croissant.json)
|
| 415 |
+
- Croissant metadata browser page: [blob view](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/blob/main/metadata/temporal_twins_croissant.json)
|
| 416 |
+
- Data URL: [data tree](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/data)
|
| 417 |
+
- Results URL: [results tree](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/results)
|
| 418 |
+
- Configs URL: [configs tree](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/configs)
|
| 419 |
+
- Metadata URL: [metadata tree](https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/metadata)
|
| 420 |
+
|
| 421 |
+
## License
|
| 422 |
+
|
| 423 |
+
- Code: Apache License 2.0 (`Apache-2.0`)
|
| 424 |
+
- Dataset and generated benchmark artifacts: Creative Commons Attribution 4.0 International (`CC-BY-4.0`)
|
| 425 |
+
- Code SPDX-License-Identifier: `Apache-2.0`
|
| 426 |
+
- Dataset SPDX-License-Identifier: `CC-BY-4.0`
|
| 427 |
+
- No real UPI data or personal financial records are included.
|
| 428 |
+
|
| 429 |
+
## Citation
|
| 430 |
+
|
| 431 |
+
`TODO_REVEAL_AFTER_REVIEW`
|
| 432 |
+
|
| 433 |
+
## Warning
|
| 434 |
+
|
| 435 |
+
- Synthetic data only
|
| 436 |
+
- No real UPI transactions
|
| 437 |
+
- Not for production fraud deployment
|
RELEASE_CHECKLIST.md
ADDED
|
@@ -0,0 +1,22 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Release Checklist
|
| 2 |
+
|
| 3 |
+
- Dataset card included.
|
| 4 |
+
- Croissant metadata included.
|
| 5 |
+
- Croissant dataset/data/results/configs/metadata URLs replaced after hosting.
|
| 6 |
+
- Code license selected: `Apache-2.0`.
|
| 7 |
+
- Dataset license selected: `CC-BY-4.0`.
|
| 8 |
+
- Code URL added.
|
| 9 |
+
- Dataset URL added.
|
| 10 |
+
- Manifest generated.
|
| 11 |
+
- No real user data.
|
| 12 |
+
- No secrets.
|
| 13 |
+
- Anonymized paths.
|
| 14 |
+
|
| 15 |
+
Hosted URLs:
|
| 16 |
+
|
| 17 |
+
- Dataset URL: `https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins`
|
| 18 |
+
- Croissant metadata URL: `https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/raw/main/metadata/temporal_twins_croissant.json`
|
| 19 |
+
- Data URL: `https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/data`
|
| 20 |
+
- Results URL: `https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/results`
|
| 21 |
+
- Configs URL: `https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/configs`
|
| 22 |
+
- Metadata URL: `https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/metadata`
|
configs/default.yaml
ADDED
|
@@ -0,0 +1,29 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
num_users: 1000
|
| 2 |
+
simulation_days: 365
|
| 3 |
+
fraud_ratio: 0.05
|
| 4 |
+
benchmark_mode: temporal_twins
|
| 5 |
+
|
| 6 |
+
user_params:
|
| 7 |
+
lambda_mean: 5.0
|
| 8 |
+
lambda_std: 1.0
|
| 9 |
+
mu_mean: 7.5
|
| 10 |
+
mu_std: 1.0
|
| 11 |
+
sigma_mean: 0.5
|
| 12 |
+
sigma_std: 0.2
|
| 13 |
+
|
| 14 |
+
upi_limits:
|
| 15 |
+
max_txn_amount: 100000
|
| 16 |
+
daily_limit: 100000
|
| 17 |
+
|
| 18 |
+
risk_model:
|
| 19 |
+
weights:
|
| 20 |
+
amount_ratio: 1.0
|
| 21 |
+
daily_ratio: 0.8
|
| 22 |
+
velocity: 1.2
|
| 23 |
+
time_anomaly: 0.6
|
| 24 |
+
graph_anomaly: 1.0
|
| 25 |
+
retry: 0.8
|
| 26 |
+
kyc: 0.5
|
| 27 |
+
user_risk: 0.8
|
| 28 |
+
|
| 29 |
+
random_seed: 42
|
configs/paper_suite_reference.yaml
ADDED
|
@@ -0,0 +1,25 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
paper_suite:
|
| 2 |
+
benchmark_groups:
|
| 3 |
+
- oracle_calib
|
| 4 |
+
- easy
|
| 5 |
+
- medium
|
| 6 |
+
- hard
|
| 7 |
+
benchmark_modes:
|
| 8 |
+
oracle_calib: temporal_twins_oracle_calib
|
| 9 |
+
easy: temporal_twins
|
| 10 |
+
medium: temporal_twins
|
| 11 |
+
hard: temporal_twins
|
| 12 |
+
seeds:
|
| 13 |
+
- 0
|
| 14 |
+
- 1
|
| 15 |
+
- 2
|
| 16 |
+
- 3
|
| 17 |
+
- 4
|
| 18 |
+
num_users: 350
|
| 19 |
+
simulation_days: 45
|
| 20 |
+
fast_mode: false
|
| 21 |
+
n_checkpoints: 8
|
| 22 |
+
device: cpu
|
| 23 |
+
num_epochs: 3
|
| 24 |
+
node_epochs: 150
|
| 25 |
+
source_results_dir: results/paper_suite_20260503_202810
|
configs/temporal_twins_calib.yaml
ADDED
|
@@ -0,0 +1,29 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
num_users: 120
|
| 2 |
+
simulation_days: 30
|
| 3 |
+
fraud_ratio: 0.05
|
| 4 |
+
benchmark_mode: temporal_twins
|
| 5 |
+
|
| 6 |
+
user_params:
|
| 7 |
+
lambda_mean: 5.0
|
| 8 |
+
lambda_std: 1.0
|
| 9 |
+
mu_mean: 7.5
|
| 10 |
+
mu_std: 1.0
|
| 11 |
+
sigma_mean: 0.5
|
| 12 |
+
sigma_std: 0.2
|
| 13 |
+
|
| 14 |
+
upi_limits:
|
| 15 |
+
max_txn_amount: 100000
|
| 16 |
+
daily_limit: 100000
|
| 17 |
+
|
| 18 |
+
risk_model:
|
| 19 |
+
weights:
|
| 20 |
+
amount_ratio: 1.0
|
| 21 |
+
daily_ratio: 0.8
|
| 22 |
+
velocity: 1.2
|
| 23 |
+
time_anomaly: 0.6
|
| 24 |
+
graph_anomaly: 1.0
|
| 25 |
+
retry: 0.8
|
| 26 |
+
kyc: 0.5
|
| 27 |
+
user_risk: 0.8
|
| 28 |
+
|
| 29 |
+
random_seed: 42
|
croissant.json
ADDED
|
@@ -0,0 +1,796 @@
|
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|
| 1 |
+
{
|
| 2 |
+
"@context": {
|
| 3 |
+
"@vocab": "https://schema.org/",
|
| 4 |
+
"sc": "https://schema.org/",
|
| 5 |
+
"cr": "http://mlcommons.org/croissant/",
|
| 6 |
+
"dct": "http://purl.org/dc/terms/",
|
| 7 |
+
"prov": "http://www.w3.org/ns/prov#",
|
| 8 |
+
"rai": "http://mlcommons.org/croissant/RAI/",
|
| 9 |
+
"field": "cr:field",
|
| 10 |
+
"recordSet": "cr:recordSet",
|
| 11 |
+
"source": "cr:source",
|
| 12 |
+
"fileObject": "cr:fileObject",
|
| 13 |
+
"fileSet": "cr:fileSet",
|
| 14 |
+
"extract": "cr:extract",
|
| 15 |
+
"containedIn": "cr:containedIn",
|
| 16 |
+
"includes": "cr:includes",
|
| 17 |
+
"conformsTo": "dct:conformsTo",
|
| 18 |
+
"citeAs": "cr:citeAs"
|
| 19 |
+
},
|
| 20 |
+
"@type": "sc:Dataset",
|
| 21 |
+
"name": "Temporal Twins Benchmark",
|
| 22 |
+
"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.",
|
| 23 |
+
"url": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins",
|
| 24 |
+
"license": "https://creativecommons.org/licenses/by/4.0/",
|
| 25 |
+
"isBasedOn": {
|
| 26 |
+
"@type": "sc:SoftwareSourceCode",
|
| 27 |
+
"name": "Temporal Twins benchmark code",
|
| 28 |
+
"url": "TODO_CODE_REPOSITORY_URL",
|
| 29 |
+
"license": "https://www.apache.org/licenses/LICENSE-2.0",
|
| 30 |
+
"identifier": "Apache-2.0"
|
| 31 |
+
},
|
| 32 |
+
"conformsTo": "http://mlcommons.org/croissant/1.1",
|
| 33 |
+
"citation": "TODO: Replace with the final Temporal Twins paper citation. Paper URL: TODO_PAPER_URL",
|
| 34 |
+
"citeAs": "Temporal Twins Benchmark (synthetic UPI-style temporal fraud benchmark), paper URL TODO_PAPER_URL, code repository TODO_CODE_REPOSITORY_URL.",
|
| 35 |
+
"creator": [
|
| 36 |
+
{
|
| 37 |
+
"@type": "sc:Organization",
|
| 38 |
+
"name": "Temporal Twins Benchmark Contributors"
|
| 39 |
+
}
|
| 40 |
+
],
|
| 41 |
+
"dateCreated": "2026-05-04",
|
| 42 |
+
"version": "1.0.0",
|
| 43 |
+
"keywords": [
|
| 44 |
+
"synthetic financial transactions",
|
| 45 |
+
"UPI-style benchmark",
|
| 46 |
+
"temporal fraud detection",
|
| 47 |
+
"matched temporal twins",
|
| 48 |
+
"matched-prefix evaluation",
|
| 49 |
+
"sequence modeling",
|
| 50 |
+
"dynamic graph learning",
|
| 51 |
+
"reproducible benchmark"
|
| 52 |
+
],
|
| 53 |
+
"distribution": [
|
| 54 |
+
{
|
| 55 |
+
"@id": "transactions-archive",
|
| 56 |
+
"@type": "cr:FileObject",
|
| 57 |
+
"name": "Transactions archive",
|
| 58 |
+
"description": "Hosted archive containing synthetic transaction files for oracle_calib, easy, medium, and hard across seeds 0 through 4.",
|
| 59 |
+
"contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/tree/main/data",
|
| 60 |
+
"encodingFormat": "application/zip"
|
| 61 |
+
},
|
| 62 |
+
{
|
| 63 |
+
"@id": "matched-prefix-archive",
|
| 64 |
+
"@type": "cr:FileObject",
|
| 65 |
+
"name": "Matched-prefix examples archive",
|
| 66 |
+
"description": "Hosted release archive containing matched-prefix fraud/benign evaluation examples under release/data/*/seed_*/matched_pairs.parquet.",
|
| 67 |
+
"contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins",
|
| 68 |
+
"encodingFormat": "application/zip"
|
| 69 |
+
},
|
| 70 |
+
{
|
| 71 |
+
"@id": "configs-archive",
|
| 72 |
+
"@type": "cr:FileObject",
|
| 73 |
+
"name": "Configs archive",
|
| 74 |
+
"description": "Hosted release archive containing benchmark configuration files under release/configs/*.yaml.",
|
| 75 |
+
"contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins",
|
| 76 |
+
"encodingFormat": "application/zip"
|
| 77 |
+
},
|
| 78 |
+
{
|
| 79 |
+
"@id": "results-archive",
|
| 80 |
+
"@type": "cr:FileObject",
|
| 81 |
+
"name": "Results archive",
|
| 82 |
+
"description": "Hosted release archive containing the deterministic 5-seed paper-suite outputs under release/results/.",
|
| 83 |
+
"contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins",
|
| 84 |
+
"encodingFormat": "application/zip"
|
| 85 |
+
},
|
| 86 |
+
{
|
| 87 |
+
"@id": "metadata-files",
|
| 88 |
+
"@type": "cr:FileSet",
|
| 89 |
+
"name": "Metadata files",
|
| 90 |
+
"description": "Metadata payload for the public release, including this Croissant file and companion notes.",
|
| 91 |
+
"containedIn": {
|
| 92 |
+
"@id": "results-archive"
|
| 93 |
+
},
|
| 94 |
+
"includes": "release/metadata/*"
|
| 95 |
+
},
|
| 96 |
+
{
|
| 97 |
+
"@id": "transactions-files",
|
| 98 |
+
"@type": "cr:FileSet",
|
| 99 |
+
"name": "Synthetic transactions parquet files",
|
| 100 |
+
"description": "Expected synthetic transaction files for benchmark modes oracle_calib, easy, medium, and hard across seeds 0 through 4.",
|
| 101 |
+
"containedIn": {
|
| 102 |
+
"@id": "transactions-archive"
|
| 103 |
+
},
|
| 104 |
+
"includes": "release/data/*/seed_*/transactions.parquet",
|
| 105 |
+
"encodingFormat": "application/x-parquet"
|
| 106 |
+
},
|
| 107 |
+
{
|
| 108 |
+
"@id": "matched-prefix-files",
|
| 109 |
+
"@type": "cr:FileSet",
|
| 110 |
+
"name": "Matched-prefix example parquet files",
|
| 111 |
+
"description": "Expected matched-prefix benchmark examples for the release. Each file contains fraud and benign twin examples evaluated at the same local prefix index.",
|
| 112 |
+
"containedIn": {
|
| 113 |
+
"@id": "matched-prefix-archive"
|
| 114 |
+
},
|
| 115 |
+
"includes": "release/data/*/seed_*/matched_pairs.parquet",
|
| 116 |
+
"encodingFormat": "application/x-parquet"
|
| 117 |
+
},
|
| 118 |
+
{
|
| 119 |
+
"@id": "config-files",
|
| 120 |
+
"@type": "cr:FileSet",
|
| 121 |
+
"name": "Benchmark config files",
|
| 122 |
+
"description": "YAML configuration files for the public release.",
|
| 123 |
+
"containedIn": {
|
| 124 |
+
"@id": "configs-archive"
|
| 125 |
+
},
|
| 126 |
+
"includes": "release/configs/*.yaml"
|
| 127 |
+
},
|
| 128 |
+
{
|
| 129 |
+
"@id": "paper-suite-runs-csv",
|
| 130 |
+
"@type": "cr:FileObject",
|
| 131 |
+
"name": "Per-run paper-suite results",
|
| 132 |
+
"description": "Per-run deterministic results for the final 5-seed paper-scale suite.",
|
| 133 |
+
"contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/raw/main/results/paper_suite_runs.csv",
|
| 134 |
+
"containedIn": {
|
| 135 |
+
"@id": "results-archive"
|
| 136 |
+
},
|
| 137 |
+
"encodingFormat": "text/csv"
|
| 138 |
+
},
|
| 139 |
+
{
|
| 140 |
+
"@id": "paper-suite-summary-csv",
|
| 141 |
+
"@type": "cr:FileObject",
|
| 142 |
+
"name": "Paper-suite summary results",
|
| 143 |
+
"description": "Mean and standard deviation summary of the deterministic 5-seed paper suite.",
|
| 144 |
+
"contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/raw/main/results/paper_suite_summary.csv",
|
| 145 |
+
"containedIn": {
|
| 146 |
+
"@id": "results-archive"
|
| 147 |
+
},
|
| 148 |
+
"encodingFormat": "text/csv"
|
| 149 |
+
},
|
| 150 |
+
{
|
| 151 |
+
"@id": "paper-suite-runtime-csv",
|
| 152 |
+
"@type": "cr:FileObject",
|
| 153 |
+
"name": "Paper-suite runtime summary",
|
| 154 |
+
"description": "Runtime and StaticGNN evaluation diagnostics for the final paper suite.",
|
| 155 |
+
"contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/raw/main/results/paper_suite_runtime.csv",
|
| 156 |
+
"containedIn": {
|
| 157 |
+
"@id": "results-archive"
|
| 158 |
+
},
|
| 159 |
+
"encodingFormat": "text/csv"
|
| 160 |
+
},
|
| 161 |
+
{
|
| 162 |
+
"@id": "paper-suite-failed-checks-csv",
|
| 163 |
+
"@type": "cr:FileObject",
|
| 164 |
+
"name": "Paper-suite failed gate checks",
|
| 165 |
+
"description": "Gate-check and advisory-check outcomes for each run in the final paper suite.",
|
| 166 |
+
"contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/raw/main/results/paper_suite_failed_checks.csv",
|
| 167 |
+
"containedIn": {
|
| 168 |
+
"@id": "results-archive"
|
| 169 |
+
},
|
| 170 |
+
"encodingFormat": "text/csv"
|
| 171 |
+
},
|
| 172 |
+
{
|
| 173 |
+
"@id": "croissant-file",
|
| 174 |
+
"@type": "cr:FileObject",
|
| 175 |
+
"name": "Temporal Twins Croissant metadata",
|
| 176 |
+
"description": "MLCommons Croissant 1.1 metadata for the full Temporal Twins benchmark collection.",
|
| 177 |
+
"contentUrl": "https://huggingface.co/datasets/temporal-twins-benchmark/temporal-twins/raw/main/metadata/temporal_twins_croissant.json",
|
| 178 |
+
"containedIn": {
|
| 179 |
+
"@id": "metadata-files"
|
| 180 |
+
},
|
| 181 |
+
"encodingFormat": "application/ld+json"
|
| 182 |
+
}
|
| 183 |
+
],
|
| 184 |
+
"recordSet": [
|
| 185 |
+
{
|
| 186 |
+
"@id": "transactions",
|
| 187 |
+
"@type": "cr:RecordSet",
|
| 188 |
+
"name": "transactions",
|
| 189 |
+
"description": "Synthetic UPI-style transactions spanning oracle_calib, easy, medium, and hard, with deterministic seeds 0 through 4.",
|
| 190 |
+
"field": [
|
| 191 |
+
{
|
| 192 |
+
"@id": "transactions/sender_id",
|
| 193 |
+
"@type": "cr:Field",
|
| 194 |
+
"name": "sender_id",
|
| 195 |
+
"description": "Synthetic sender account identifier.",
|
| 196 |
+
"dataType": "sc:Text",
|
| 197 |
+
"source": {
|
| 198 |
+
"fileSet": {
|
| 199 |
+
"@id": "transactions-files"
|
| 200 |
+
},
|
| 201 |
+
"extract": {
|
| 202 |
+
"column": "sender_id"
|
| 203 |
+
}
|
| 204 |
+
}
|
| 205 |
+
},
|
| 206 |
+
{
|
| 207 |
+
"@id": "transactions/receiver_id",
|
| 208 |
+
"@type": "cr:Field",
|
| 209 |
+
"name": "receiver_id",
|
| 210 |
+
"description": "Synthetic receiver account identifier.",
|
| 211 |
+
"dataType": "sc:Text",
|
| 212 |
+
"source": {
|
| 213 |
+
"fileSet": {
|
| 214 |
+
"@id": "transactions-files"
|
| 215 |
+
},
|
| 216 |
+
"extract": {
|
| 217 |
+
"column": "receiver_id"
|
| 218 |
+
}
|
| 219 |
+
}
|
| 220 |
+
},
|
| 221 |
+
{
|
| 222 |
+
"@id": "transactions/timestamp",
|
| 223 |
+
"@type": "cr:Field",
|
| 224 |
+
"name": "timestamp",
|
| 225 |
+
"description": "Synthetic event timestamp used to order transactions within each sender history.",
|
| 226 |
+
"dataType": "sc:Number",
|
| 227 |
+
"source": {
|
| 228 |
+
"fileSet": {
|
| 229 |
+
"@id": "transactions-files"
|
| 230 |
+
},
|
| 231 |
+
"extract": {
|
| 232 |
+
"column": "timestamp"
|
| 233 |
+
}
|
| 234 |
+
}
|
| 235 |
+
},
|
| 236 |
+
{
|
| 237 |
+
"@id": "transactions/amount",
|
| 238 |
+
"@type": "cr:Field",
|
| 239 |
+
"name": "amount",
|
| 240 |
+
"description": "Synthetic transaction amount.",
|
| 241 |
+
"dataType": "sc:Number",
|
| 242 |
+
"source": {
|
| 243 |
+
"fileSet": {
|
| 244 |
+
"@id": "transactions-files"
|
| 245 |
+
},
|
| 246 |
+
"extract": {
|
| 247 |
+
"column": "amount"
|
| 248 |
+
}
|
| 249 |
+
}
|
| 250 |
+
},
|
| 251 |
+
{
|
| 252 |
+
"@id": "transactions/risk_score",
|
| 253 |
+
"@type": "cr:Field",
|
| 254 |
+
"name": "risk_score",
|
| 255 |
+
"description": "Synthetic noisy risk score emitted by the simulator's risk engine.",
|
| 256 |
+
"dataType": "sc:Number",
|
| 257 |
+
"source": {
|
| 258 |
+
"fileSet": {
|
| 259 |
+
"@id": "transactions-files"
|
| 260 |
+
},
|
| 261 |
+
"extract": {
|
| 262 |
+
"column": "risk_score"
|
| 263 |
+
}
|
| 264 |
+
}
|
| 265 |
+
},
|
| 266 |
+
{
|
| 267 |
+
"@id": "transactions/failed",
|
| 268 |
+
"@type": "cr:Field",
|
| 269 |
+
"name": "failed",
|
| 270 |
+
"description": "Indicator for whether the synthetic transaction attempt failed.",
|
| 271 |
+
"dataType": "sc:Boolean",
|
| 272 |
+
"source": {
|
| 273 |
+
"fileSet": {
|
| 274 |
+
"@id": "transactions-files"
|
| 275 |
+
},
|
| 276 |
+
"extract": {
|
| 277 |
+
"column": "failed"
|
| 278 |
+
}
|
| 279 |
+
}
|
| 280 |
+
},
|
| 281 |
+
{
|
| 282 |
+
"@id": "transactions/is_fraud",
|
| 283 |
+
"@type": "cr:Field",
|
| 284 |
+
"name": "is_fraud",
|
| 285 |
+
"description": "Delayed synthetic fraud label attached to specific transactions.",
|
| 286 |
+
"dataType": "sc:Boolean",
|
| 287 |
+
"source": {
|
| 288 |
+
"fileSet": {
|
| 289 |
+
"@id": "transactions-files"
|
| 290 |
+
},
|
| 291 |
+
"extract": {
|
| 292 |
+
"column": "is_fraud"
|
| 293 |
+
}
|
| 294 |
+
}
|
| 295 |
+
}
|
| 296 |
+
]
|
| 297 |
+
},
|
| 298 |
+
{
|
| 299 |
+
"@id": "matched_prefix_examples",
|
| 300 |
+
"@type": "cr:RecordSet",
|
| 301 |
+
"name": "matched_prefix_examples",
|
| 302 |
+
"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.",
|
| 303 |
+
"field": [
|
| 304 |
+
{
|
| 305 |
+
"@id": "matched_prefix_examples/twin_pair_id",
|
| 306 |
+
"@type": "cr:Field",
|
| 307 |
+
"name": "twin_pair_id",
|
| 308 |
+
"description": "Matched fraud/benign twin pair identifier.",
|
| 309 |
+
"dataType": "sc:Integer",
|
| 310 |
+
"source": {
|
| 311 |
+
"fileSet": {
|
| 312 |
+
"@id": "matched-prefix-files"
|
| 313 |
+
},
|
| 314 |
+
"extract": {
|
| 315 |
+
"column": "twin_pair_id"
|
| 316 |
+
}
|
| 317 |
+
}
|
| 318 |
+
},
|
| 319 |
+
{
|
| 320 |
+
"@id": "matched_prefix_examples/sender_id",
|
| 321 |
+
"@type": "cr:Field",
|
| 322 |
+
"name": "sender_id",
|
| 323 |
+
"description": "Sender evaluated at the matched prefix.",
|
| 324 |
+
"dataType": "sc:Text",
|
| 325 |
+
"source": {
|
| 326 |
+
"fileSet": {
|
| 327 |
+
"@id": "matched-prefix-files"
|
| 328 |
+
},
|
| 329 |
+
"extract": {
|
| 330 |
+
"column": "sender_id"
|
| 331 |
+
}
|
| 332 |
+
}
|
| 333 |
+
},
|
| 334 |
+
{
|
| 335 |
+
"@id": "matched_prefix_examples/matched_local_event_idx",
|
| 336 |
+
"@type": "cr:Field",
|
| 337 |
+
"name": "matched_local_event_idx",
|
| 338 |
+
"description": "Release-facing matched-prefix event index k used for both the fraud twin and its benign control.",
|
| 339 |
+
"dataType": "sc:Integer",
|
| 340 |
+
"source": {
|
| 341 |
+
"fileSet": {
|
| 342 |
+
"@id": "matched-prefix-files"
|
| 343 |
+
},
|
| 344 |
+
"extract": {
|
| 345 |
+
"column": "matched_local_event_idx"
|
| 346 |
+
}
|
| 347 |
+
}
|
| 348 |
+
},
|
| 349 |
+
{
|
| 350 |
+
"@id": "matched_prefix_examples/label",
|
| 351 |
+
"@type": "cr:Field",
|
| 352 |
+
"name": "label",
|
| 353 |
+
"description": "Binary matched-prefix label where 1 denotes the fraud twin example and 0 denotes the benign matched control.",
|
| 354 |
+
"dataType": "sc:Boolean",
|
| 355 |
+
"source": {
|
| 356 |
+
"fileSet": {
|
| 357 |
+
"@id": "matched-prefix-files"
|
| 358 |
+
},
|
| 359 |
+
"extract": {
|
| 360 |
+
"column": "label"
|
| 361 |
+
}
|
| 362 |
+
}
|
| 363 |
+
},
|
| 364 |
+
{
|
| 365 |
+
"@id": "matched_prefix_examples/benchmark_mode",
|
| 366 |
+
"@type": "cr:Field",
|
| 367 |
+
"name": "benchmark_mode",
|
| 368 |
+
"description": "Benchmark mode identifier, e.g. temporal_twins_oracle_calib or temporal_twins.",
|
| 369 |
+
"dataType": "sc:Text",
|
| 370 |
+
"source": {
|
| 371 |
+
"fileSet": {
|
| 372 |
+
"@id": "matched-prefix-files"
|
| 373 |
+
},
|
| 374 |
+
"extract": {
|
| 375 |
+
"column": "benchmark_mode"
|
| 376 |
+
}
|
| 377 |
+
}
|
| 378 |
+
},
|
| 379 |
+
{
|
| 380 |
+
"@id": "matched_prefix_examples/difficulty",
|
| 381 |
+
"@type": "cr:Field",
|
| 382 |
+
"name": "difficulty",
|
| 383 |
+
"description": "Difficulty slice within the release: oracle_calib, easy, medium, or hard.",
|
| 384 |
+
"dataType": "sc:Text",
|
| 385 |
+
"source": {
|
| 386 |
+
"fileSet": {
|
| 387 |
+
"@id": "matched-prefix-files"
|
| 388 |
+
},
|
| 389 |
+
"extract": {
|
| 390 |
+
"column": "difficulty"
|
| 391 |
+
}
|
| 392 |
+
}
|
| 393 |
+
},
|
| 394 |
+
{
|
| 395 |
+
"@id": "matched_prefix_examples/seed",
|
| 396 |
+
"@type": "cr:Field",
|
| 397 |
+
"name": "seed",
|
| 398 |
+
"description": "Deterministic benchmark seed in the final paper-scale suite.",
|
| 399 |
+
"dataType": "sc:Integer",
|
| 400 |
+
"source": {
|
| 401 |
+
"fileSet": {
|
| 402 |
+
"@id": "matched-prefix-files"
|
| 403 |
+
},
|
| 404 |
+
"extract": {
|
| 405 |
+
"column": "seed"
|
| 406 |
+
}
|
| 407 |
+
}
|
| 408 |
+
}
|
| 409 |
+
]
|
| 410 |
+
},
|
| 411 |
+
{
|
| 412 |
+
"@id": "audit_columns",
|
| 413 |
+
"@type": "cr:RecordSet",
|
| 414 |
+
"name": "audit_columns",
|
| 415 |
+
"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.",
|
| 416 |
+
"field": [
|
| 417 |
+
{
|
| 418 |
+
"@id": "audit_columns/twin_role",
|
| 419 |
+
"@type": "cr:Field",
|
| 420 |
+
"name": "twin_role",
|
| 421 |
+
"description": "Twin role label such as fraud, benign, or background; excluded from ordinary model features.",
|
| 422 |
+
"dataType": "sc:Text",
|
| 423 |
+
"source": {
|
| 424 |
+
"fileSet": {
|
| 425 |
+
"@id": "transactions-files"
|
| 426 |
+
},
|
| 427 |
+
"extract": {
|
| 428 |
+
"column": "twin_role"
|
| 429 |
+
}
|
| 430 |
+
}
|
| 431 |
+
},
|
| 432 |
+
{
|
| 433 |
+
"@id": "audit_columns/template_id",
|
| 434 |
+
"@type": "cr:Field",
|
| 435 |
+
"name": "template_id",
|
| 436 |
+
"description": "Identifier for the matched temporal template used to construct a twin pair; excluded from ordinary model features.",
|
| 437 |
+
"dataType": "sc:Integer",
|
| 438 |
+
"source": {
|
| 439 |
+
"fileSet": {
|
| 440 |
+
"@id": "transactions-files"
|
| 441 |
+
},
|
| 442 |
+
"extract": {
|
| 443 |
+
"column": "template_id"
|
| 444 |
+
}
|
| 445 |
+
}
|
| 446 |
+
},
|
| 447 |
+
{
|
| 448 |
+
"@id": "audit_columns/motif_hit_count",
|
| 449 |
+
"@type": "cr:Field",
|
| 450 |
+
"name": "motif_hit_count",
|
| 451 |
+
"description": "Count of motif hits in the generator trace; exposed only for audit or probe logic, not learned baselines.",
|
| 452 |
+
"dataType": "sc:Integer",
|
| 453 |
+
"source": {
|
| 454 |
+
"fileSet": {
|
| 455 |
+
"@id": "transactions-files"
|
| 456 |
+
},
|
| 457 |
+
"extract": {
|
| 458 |
+
"column": "motif_hit_count"
|
| 459 |
+
}
|
| 460 |
+
}
|
| 461 |
+
},
|
| 462 |
+
{
|
| 463 |
+
"@id": "audit_columns/motif_source",
|
| 464 |
+
"@type": "cr:Field",
|
| 465 |
+
"name": "motif_source",
|
| 466 |
+
"description": "Generator-side motif provenance label; excluded from ordinary model features.",
|
| 467 |
+
"dataType": "sc:Text",
|
| 468 |
+
"source": {
|
| 469 |
+
"fileSet": {
|
| 470 |
+
"@id": "transactions-files"
|
| 471 |
+
},
|
| 472 |
+
"extract": {
|
| 473 |
+
"column": "motif_source"
|
| 474 |
+
}
|
| 475 |
+
}
|
| 476 |
+
},
|
| 477 |
+
{
|
| 478 |
+
"@id": "audit_columns/trigger_event_idx",
|
| 479 |
+
"@type": "cr:Field",
|
| 480 |
+
"name": "trigger_event_idx",
|
| 481 |
+
"description": "Internal trigger event index for delayed fraud assignment; excluded from ordinary model features.",
|
| 482 |
+
"dataType": "sc:Integer",
|
| 483 |
+
"source": {
|
| 484 |
+
"fileSet": {
|
| 485 |
+
"@id": "transactions-files"
|
| 486 |
+
},
|
| 487 |
+
"extract": {
|
| 488 |
+
"column": "trigger_event_idx"
|
| 489 |
+
}
|
| 490 |
+
}
|
| 491 |
+
},
|
| 492 |
+
{
|
| 493 |
+
"@id": "audit_columns/label_event_idx",
|
| 494 |
+
"@type": "cr:Field",
|
| 495 |
+
"name": "label_event_idx",
|
| 496 |
+
"description": "Internal event index at which the delayed fraud label is attached; excluded from ordinary model features.",
|
| 497 |
+
"dataType": "sc:Integer",
|
| 498 |
+
"source": {
|
| 499 |
+
"fileSet": {
|
| 500 |
+
"@id": "transactions-files"
|
| 501 |
+
},
|
| 502 |
+
"extract": {
|
| 503 |
+
"column": "label_event_idx"
|
| 504 |
+
}
|
| 505 |
+
}
|
| 506 |
+
},
|
| 507 |
+
{
|
| 508 |
+
"@id": "audit_columns/label_delay",
|
| 509 |
+
"@type": "cr:Field",
|
| 510 |
+
"name": "label_delay",
|
| 511 |
+
"description": "Internal delay between trigger and labeled event; excluded from ordinary model features.",
|
| 512 |
+
"dataType": "sc:Integer",
|
| 513 |
+
"source": {
|
| 514 |
+
"fileSet": {
|
| 515 |
+
"@id": "transactions-files"
|
| 516 |
+
},
|
| 517 |
+
"extract": {
|
| 518 |
+
"column": "label_delay"
|
| 519 |
+
}
|
| 520 |
+
}
|
| 521 |
+
},
|
| 522 |
+
{
|
| 523 |
+
"@id": "audit_columns/fraud_source",
|
| 524 |
+
"@type": "cr:Field",
|
| 525 |
+
"name": "fraud_source",
|
| 526 |
+
"description": "Internal fraud-source annotation such as motif or fallback; excluded from ordinary model features.",
|
| 527 |
+
"dataType": "sc:Text",
|
| 528 |
+
"source": {
|
| 529 |
+
"fileSet": {
|
| 530 |
+
"@id": "transactions-files"
|
| 531 |
+
},
|
| 532 |
+
"extract": {
|
| 533 |
+
"column": "fraud_source"
|
| 534 |
+
}
|
| 535 |
+
}
|
| 536 |
+
},
|
| 537 |
+
{
|
| 538 |
+
"@id": "audit_columns/dynamic_fraud_state",
|
| 539 |
+
"@type": "cr:Field",
|
| 540 |
+
"name": "dynamic_fraud_state",
|
| 541 |
+
"description": "Latent generator-side fraud-state variable used for mechanistic analysis; excluded from ordinary model features.",
|
| 542 |
+
"dataType": "sc:Number",
|
| 543 |
+
"source": {
|
| 544 |
+
"fileSet": {
|
| 545 |
+
"@id": "transactions-files"
|
| 546 |
+
},
|
| 547 |
+
"extract": {
|
| 548 |
+
"column": "dynamic_fraud_state"
|
| 549 |
+
}
|
| 550 |
+
}
|
| 551 |
+
}
|
| 552 |
+
]
|
| 553 |
+
},
|
| 554 |
+
{
|
| 555 |
+
"@id": "paper_suite_summary_results",
|
| 556 |
+
"@type": "cr:RecordSet",
|
| 557 |
+
"name": "paper_suite_summary_results",
|
| 558 |
+
"description": "Deterministic 5-seed summary results for the final paper-scale Temporal Twins suite.",
|
| 559 |
+
"field": [
|
| 560 |
+
{
|
| 561 |
+
"@id": "paper_suite_summary_results/benchmark_group",
|
| 562 |
+
"@type": "cr:Field",
|
| 563 |
+
"name": "benchmark_group",
|
| 564 |
+
"description": "Benchmark slice summarized in the row, e.g. oracle_calib, easy, medium, or hard.",
|
| 565 |
+
"dataType": "sc:Text",
|
| 566 |
+
"source": {
|
| 567 |
+
"fileObject": {
|
| 568 |
+
"@id": "paper-suite-summary-csv"
|
| 569 |
+
},
|
| 570 |
+
"extract": {
|
| 571 |
+
"column": "benchmark_group"
|
| 572 |
+
}
|
| 573 |
+
}
|
| 574 |
+
},
|
| 575 |
+
{
|
| 576 |
+
"@id": "paper_suite_summary_results/matched_eval_pairs_mean",
|
| 577 |
+
"@type": "cr:Field",
|
| 578 |
+
"name": "matched_eval_pairs_mean",
|
| 579 |
+
"description": "Mean number of matched-prefix evaluation pairs across seeds.",
|
| 580 |
+
"dataType": "sc:Number",
|
| 581 |
+
"source": {
|
| 582 |
+
"fileObject": {
|
| 583 |
+
"@id": "paper-suite-summary-csv"
|
| 584 |
+
},
|
| 585 |
+
"extract": {
|
| 586 |
+
"column": "matched_eval_pairs_mean"
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
},
|
| 590 |
+
{
|
| 591 |
+
"@id": "paper_suite_summary_results/static_agg_auc_mean",
|
| 592 |
+
"@type": "cr:Field",
|
| 593 |
+
"name": "static_agg_auc_mean",
|
| 594 |
+
"description": "Mean ROC-AUC of the static aggregate shortcut audit.",
|
| 595 |
+
"dataType": "sc:Number",
|
| 596 |
+
"source": {
|
| 597 |
+
"fileObject": {
|
| 598 |
+
"@id": "paper-suite-summary-csv"
|
| 599 |
+
},
|
| 600 |
+
"extract": {
|
| 601 |
+
"column": "static_agg_auc_mean"
|
| 602 |
+
}
|
| 603 |
+
}
|
| 604 |
+
},
|
| 605 |
+
{
|
| 606 |
+
"@id": "paper_suite_summary_results/audit_roc_auc_mean",
|
| 607 |
+
"@type": "cr:Field",
|
| 608 |
+
"name": "audit_roc_auc_mean",
|
| 609 |
+
"description": "Mean oracle or probe ROC-AUC depending on benchmark mode.",
|
| 610 |
+
"dataType": "sc:Number",
|
| 611 |
+
"source": {
|
| 612 |
+
"fileObject": {
|
| 613 |
+
"@id": "paper-suite-summary-csv"
|
| 614 |
+
},
|
| 615 |
+
"extract": {
|
| 616 |
+
"column": "audit_roc_auc_mean"
|
| 617 |
+
}
|
| 618 |
+
}
|
| 619 |
+
},
|
| 620 |
+
{
|
| 621 |
+
"@id": "paper_suite_summary_results/raw_roc_auc_mean",
|
| 622 |
+
"@type": "cr:Field",
|
| 623 |
+
"name": "raw_roc_auc_mean",
|
| 624 |
+
"description": "Mean raw motif oracle or probe ROC-AUC depending on benchmark mode.",
|
| 625 |
+
"dataType": "sc:Number",
|
| 626 |
+
"source": {
|
| 627 |
+
"fileObject": {
|
| 628 |
+
"@id": "paper-suite-summary-csv"
|
| 629 |
+
},
|
| 630 |
+
"extract": {
|
| 631 |
+
"column": "raw_roc_auc_mean"
|
| 632 |
+
}
|
| 633 |
+
}
|
| 634 |
+
},
|
| 635 |
+
{
|
| 636 |
+
"@id": "paper_suite_summary_results/xgb_roc_auc_mean",
|
| 637 |
+
"@type": "cr:Field",
|
| 638 |
+
"name": "xgb_roc_auc_mean",
|
| 639 |
+
"description": "Mean XGBoost ROC-AUC across seeds.",
|
| 640 |
+
"dataType": "sc:Number",
|
| 641 |
+
"source": {
|
| 642 |
+
"fileObject": {
|
| 643 |
+
"@id": "paper-suite-summary-csv"
|
| 644 |
+
},
|
| 645 |
+
"extract": {
|
| 646 |
+
"column": "xgb_roc_auc_mean"
|
| 647 |
+
}
|
| 648 |
+
}
|
| 649 |
+
},
|
| 650 |
+
{
|
| 651 |
+
"@id": "paper_suite_summary_results/static_gnn_roc_auc_mean",
|
| 652 |
+
"@type": "cr:Field",
|
| 653 |
+
"name": "static_gnn_roc_auc_mean",
|
| 654 |
+
"description": "Mean StaticGNN ROC-AUC across seeds.",
|
| 655 |
+
"dataType": "sc:Number",
|
| 656 |
+
"source": {
|
| 657 |
+
"fileObject": {
|
| 658 |
+
"@id": "paper-suite-summary-csv"
|
| 659 |
+
},
|
| 660 |
+
"extract": {
|
| 661 |
+
"column": "static_gnn_roc_auc_mean"
|
| 662 |
+
}
|
| 663 |
+
}
|
| 664 |
+
},
|
| 665 |
+
{
|
| 666 |
+
"@id": "paper_suite_summary_results/seqgru_clean_roc_auc_mean",
|
| 667 |
+
"@type": "cr:Field",
|
| 668 |
+
"name": "seqgru_clean_roc_auc_mean",
|
| 669 |
+
"description": "Mean clean SeqGRU ROC-AUC across seeds.",
|
| 670 |
+
"dataType": "sc:Number",
|
| 671 |
+
"source": {
|
| 672 |
+
"fileObject": {
|
| 673 |
+
"@id": "paper-suite-summary-csv"
|
| 674 |
+
},
|
| 675 |
+
"extract": {
|
| 676 |
+
"column": "seqgru_clean_roc_auc_mean"
|
| 677 |
+
}
|
| 678 |
+
}
|
| 679 |
+
},
|
| 680 |
+
{
|
| 681 |
+
"@id": "paper_suite_summary_results/seqgru_shuffle_delta_mean",
|
| 682 |
+
"@type": "cr:Field",
|
| 683 |
+
"name": "seqgru_shuffle_delta_mean",
|
| 684 |
+
"description": "Mean change in SeqGRU ROC-AUC under shuffled event order.",
|
| 685 |
+
"dataType": "sc:Number",
|
| 686 |
+
"source": {
|
| 687 |
+
"fileObject": {
|
| 688 |
+
"@id": "paper-suite-summary-csv"
|
| 689 |
+
},
|
| 690 |
+
"extract": {
|
| 691 |
+
"column": "seqgru_shuffle_delta_mean"
|
| 692 |
+
}
|
| 693 |
+
}
|
| 694 |
+
},
|
| 695 |
+
{
|
| 696 |
+
"@id": "paper_suite_summary_results/tgn_clean_roc_auc_mean",
|
| 697 |
+
"@type": "cr:Field",
|
| 698 |
+
"name": "tgn_clean_roc_auc_mean",
|
| 699 |
+
"description": "Mean TGN ROC-AUC across seeds.",
|
| 700 |
+
"dataType": "sc:Number",
|
| 701 |
+
"source": {
|
| 702 |
+
"fileObject": {
|
| 703 |
+
"@id": "paper-suite-summary-csv"
|
| 704 |
+
},
|
| 705 |
+
"extract": {
|
| 706 |
+
"column": "tgn_clean_roc_auc_mean"
|
| 707 |
+
}
|
| 708 |
+
}
|
| 709 |
+
},
|
| 710 |
+
{
|
| 711 |
+
"@id": "paper_suite_summary_results/tgat_clean_roc_auc_mean",
|
| 712 |
+
"@type": "cr:Field",
|
| 713 |
+
"name": "tgat_clean_roc_auc_mean",
|
| 714 |
+
"description": "Mean TGAT ROC-AUC across seeds.",
|
| 715 |
+
"dataType": "sc:Number",
|
| 716 |
+
"source": {
|
| 717 |
+
"fileObject": {
|
| 718 |
+
"@id": "paper-suite-summary-csv"
|
| 719 |
+
},
|
| 720 |
+
"extract": {
|
| 721 |
+
"column": "tgat_clean_roc_auc_mean"
|
| 722 |
+
}
|
| 723 |
+
}
|
| 724 |
+
},
|
| 725 |
+
{
|
| 726 |
+
"@id": "paper_suite_summary_results/dyrep_clean_roc_auc_mean",
|
| 727 |
+
"@type": "cr:Field",
|
| 728 |
+
"name": "dyrep_clean_roc_auc_mean",
|
| 729 |
+
"description": "Mean DyRep ROC-AUC across seeds.",
|
| 730 |
+
"dataType": "sc:Number",
|
| 731 |
+
"source": {
|
| 732 |
+
"fileObject": {
|
| 733 |
+
"@id": "paper-suite-summary-csv"
|
| 734 |
+
},
|
| 735 |
+
"extract": {
|
| 736 |
+
"column": "dyrep_clean_roc_auc_mean"
|
| 737 |
+
}
|
| 738 |
+
}
|
| 739 |
+
},
|
| 740 |
+
{
|
| 741 |
+
"@id": "paper_suite_summary_results/jodie_clean_roc_auc_mean",
|
| 742 |
+
"@type": "cr:Field",
|
| 743 |
+
"name": "jodie_clean_roc_auc_mean",
|
| 744 |
+
"description": "Mean JODIE ROC-AUC across seeds.",
|
| 745 |
+
"dataType": "sc:Number",
|
| 746 |
+
"source": {
|
| 747 |
+
"fileObject": {
|
| 748 |
+
"@id": "paper-suite-summary-csv"
|
| 749 |
+
},
|
| 750 |
+
"extract": {
|
| 751 |
+
"column": "jodie_clean_roc_auc_mean"
|
| 752 |
+
}
|
| 753 |
+
}
|
| 754 |
+
}
|
| 755 |
+
]
|
| 756 |
+
}
|
| 757 |
+
],
|
| 758 |
+
"rai:dataLimitations": [
|
| 759 |
+
"Temporal Twins is fully synthetic and is not representative of real UPI fraud prevalence, transaction mix, or institutional controls.",
|
| 760 |
+
"The benchmark is designed to isolate temporal-order reasoning under matched static controls rather than to reproduce a production fraud environment.",
|
| 761 |
+
"Standard-mode probe scores are informative benchmark probes, not upper bounds on real-world fraud detectability."
|
| 762 |
+
],
|
| 763 |
+
"rai:dataBiases": [
|
| 764 |
+
"Behavioral patterns are simulator-defined and reflect the assumptions of the Temporal Twins generator rather than observed user behavior.",
|
| 765 |
+
"Difficulty slices intentionally reshape motif strength, noise, delay, and adversarial perturbations, so conclusions should be interpreted as benchmark-relative rather than population-representative."
|
| 766 |
+
],
|
| 767 |
+
"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.",
|
| 768 |
+
"rai:dataUseCases": [
|
| 769 |
+
"Intended for temporal machine learning benchmark research, including sequence models, dynamic graph models, matched-control evaluation, and shortcut auditing.",
|
| 770 |
+
"Suitable for studying whether a model uses causal temporal order rather than static transaction summaries."
|
| 771 |
+
],
|
| 772 |
+
"rai:dataSocialImpact": [
|
| 773 |
+
"Positive use may include more rigorous evaluation of temporal fraud-detection methods under matched static controls.",
|
| 774 |
+
"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."
|
| 775 |
+
],
|
| 776 |
+
"rai:hasSyntheticData": true,
|
| 777 |
+
"prov:wasGeneratedBy": {
|
| 778 |
+
"@type": "prov:Activity",
|
| 779 |
+
"name": "Temporal Twins synthetic UPI transaction generator",
|
| 780 |
+
"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.",
|
| 781 |
+
"prov:used": [
|
| 782 |
+
{
|
| 783 |
+
"@type": "prov:Entity",
|
| 784 |
+
"name": "Temporal Twins benchmark code repository",
|
| 785 |
+
"url": "TODO_CODE_REPOSITORY_URL",
|
| 786 |
+
"license": "https://www.apache.org/licenses/LICENSE-2.0",
|
| 787 |
+
"identifier": "Apache-2.0"
|
| 788 |
+
},
|
| 789 |
+
{
|
| 790 |
+
"@type": "prov:Entity",
|
| 791 |
+
"name": "Temporal Twins paper",
|
| 792 |
+
"url": "TODO_PAPER_URL"
|
| 793 |
+
}
|
| 794 |
+
]
|
| 795 |
+
}
|
| 796 |
+
}
|
data/.DS_Store
ADDED
|
Binary file (10.2 kB). View file
|
|
|
data/README_GENERATION.md
ADDED
|
@@ -0,0 +1,120 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Generating Release Data Files
|
| 2 |
+
|
| 3 |
+
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.
|
| 4 |
+
|
| 5 |
+
## Expected Outputs Per Seed
|
| 6 |
+
|
| 7 |
+
Each directory `release/data/<mode>/seed_<seed>/` is expected to contain:
|
| 8 |
+
|
| 9 |
+
- `transactions.parquet`
|
| 10 |
+
- `matched_pairs.parquet`
|
| 11 |
+
- `audit_summary.csv`
|
| 12 |
+
- `schema.json`
|
| 13 |
+
- `config.yaml`
|
| 14 |
+
|
| 15 |
+
Where:
|
| 16 |
+
|
| 17 |
+
- `<mode>` is one of `oracle_calib`, `easy`, `medium`, `hard`
|
| 18 |
+
- `<seed>` is one of `0`, `1`, `2`, `3`, `4`
|
| 19 |
+
|
| 20 |
+
## Benchmark Mapping
|
| 21 |
+
|
| 22 |
+
- `oracle_calib` uses `benchmark_mode = "temporal_twins_oracle_calib"` and `difficulty = "easy"`
|
| 23 |
+
- `easy` uses `benchmark_mode = "temporal_twins"` and `difficulty = "easy"`
|
| 24 |
+
- `medium` uses `benchmark_mode = "temporal_twins"` and `difficulty = "medium"`
|
| 25 |
+
- `hard` uses `benchmark_mode = "temporal_twins"` and `difficulty = "hard"`
|
| 26 |
+
|
| 27 |
+
## Exact Export Command
|
| 28 |
+
|
| 29 |
+
Run this command from the repository root:
|
| 30 |
+
|
| 31 |
+
```bash
|
| 32 |
+
PYTHONPATH=. python3 - <<'PY'
|
| 33 |
+
from pathlib import Path
|
| 34 |
+
import json
|
| 35 |
+
import pandas as pd
|
| 36 |
+
import yaml
|
| 37 |
+
|
| 38 |
+
from src.core.config_loader import load_config
|
| 39 |
+
from experiments.run_all import (
|
| 40 |
+
build_matched_control_tables,
|
| 41 |
+
generate_single_difficulty,
|
| 42 |
+
report_matched_control_audits,
|
| 43 |
+
set_global_determinism,
|
| 44 |
+
)
|
| 45 |
+
|
| 46 |
+
release_root = Path("release/data")
|
| 47 |
+
seeds = [0, 1, 2, 3, 4]
|
| 48 |
+
mode_specs = [
|
| 49 |
+
("oracle_calib", "temporal_twins_oracle_calib", "easy"),
|
| 50 |
+
("easy", "temporal_twins", "easy"),
|
| 51 |
+
("medium", "temporal_twins", "medium"),
|
| 52 |
+
("hard", "temporal_twins", "hard"),
|
| 53 |
+
]
|
| 54 |
+
|
| 55 |
+
base_cfg = load_config("config/default.yaml")
|
| 56 |
+
base_cfg.num_users = 350
|
| 57 |
+
base_cfg.simulation_days = 45
|
| 58 |
+
|
| 59 |
+
for release_mode, benchmark_mode, difficulty in mode_specs:
|
| 60 |
+
for seed in seeds:
|
| 61 |
+
cfg = base_cfg.model_copy(deep=True)
|
| 62 |
+
cfg.benchmark_mode = benchmark_mode
|
| 63 |
+
cfg.random_seed = seed
|
| 64 |
+
set_global_determinism(seed)
|
| 65 |
+
|
| 66 |
+
df = generate_single_difficulty(
|
| 67 |
+
cfg,
|
| 68 |
+
difficulty=difficulty,
|
| 69 |
+
seed=seed,
|
| 70 |
+
benchmark_mode=benchmark_mode,
|
| 71 |
+
)
|
| 72 |
+
matched_examples, pair_rows, pair_counts = build_matched_control_tables(df)
|
| 73 |
+
audit = report_matched_control_audits(matched_examples, pair_rows, pair_counts)
|
| 74 |
+
|
| 75 |
+
out_dir = release_root / release_mode / f"seed_{seed}"
|
| 76 |
+
out_dir.mkdir(parents=True, exist_ok=True)
|
| 77 |
+
|
| 78 |
+
matched_export = matched_examples.rename(
|
| 79 |
+
columns={"eval_local_event_idx": "matched_local_event_idx"}
|
| 80 |
+
).copy()
|
| 81 |
+
matched_export["benchmark_mode"] = benchmark_mode
|
| 82 |
+
matched_export["difficulty"] = release_mode
|
| 83 |
+
matched_export["seed"] = seed
|
| 84 |
+
|
| 85 |
+
df.to_parquet(out_dir / "transactions.parquet", index=False)
|
| 86 |
+
matched_export.to_parquet(out_dir / "matched_pairs.parquet", index=False)
|
| 87 |
+
pd.DataFrame([audit]).to_csv(out_dir / "audit_summary.csv", index=False)
|
| 88 |
+
|
| 89 |
+
schema = {
|
| 90 |
+
"transactions_columns": {k: str(v) for k, v in df.dtypes.items()},
|
| 91 |
+
"matched_pairs_columns": {k: str(v) for k, v in matched_export.dtypes.items()},
|
| 92 |
+
"files": [
|
| 93 |
+
"transactions.parquet",
|
| 94 |
+
"matched_pairs.parquet",
|
| 95 |
+
"audit_summary.csv",
|
| 96 |
+
"schema.json",
|
| 97 |
+
"config.yaml",
|
| 98 |
+
],
|
| 99 |
+
}
|
| 100 |
+
(out_dir / "schema.json").write_text(json.dumps(schema, indent=2) + "\\n")
|
| 101 |
+
(out_dir / "config.yaml").write_text(
|
| 102 |
+
yaml.safe_dump(
|
| 103 |
+
{
|
| 104 |
+
**cfg.model_dump(),
|
| 105 |
+
"benchmark_mode": benchmark_mode,
|
| 106 |
+
"difficulty": difficulty,
|
| 107 |
+
"release_mode": release_mode,
|
| 108 |
+
"seed": seed,
|
| 109 |
+
"fast_mode": False,
|
| 110 |
+
"n_checkpoints": 8,
|
| 111 |
+
},
|
| 112 |
+
sort_keys=False,
|
| 113 |
+
)
|
| 114 |
+
)
|
| 115 |
+
PY
|
| 116 |
+
```
|
| 117 |
+
|
| 118 |
+
## Paper Result Reproduction
|
| 119 |
+
|
| 120 |
+
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`.
|
data/_export_summary.csv
ADDED
|
@@ -0,0 +1,21 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
release_mode,seed,transactions,matched_examples,matched_pairs,audit_examples
|
| 2 |
+
oracle_calib,0,35408,2240,1120,2240
|
| 3 |
+
oracle_calib,1,31994,2106,1053,2106
|
| 4 |
+
oracle_calib,2,35922,2390,1195,2390
|
| 5 |
+
oracle_calib,3,32228,2222,1111,2222
|
| 6 |
+
oracle_calib,4,32108,2276,1138,2276
|
| 7 |
+
easy,0,46386,3398,1699,3398
|
| 8 |
+
easy,1,40462,3132,1566,3132
|
| 9 |
+
easy,2,44958,3558,1779,3558
|
| 10 |
+
easy,3,46312,3374,1687,3374
|
| 11 |
+
easy,4,41482,3296,1648,3296
|
| 12 |
+
medium,0,77692,3184,1592,3184
|
| 13 |
+
medium,1,76870,3168,1584,3168
|
| 14 |
+
medium,2,80150,3174,1587,3174
|
| 15 |
+
medium,3,80172,3148,1574,3148
|
| 16 |
+
medium,4,78456,3144,1572,3144
|
| 17 |
+
hard,0,81978,2600,1300,2600
|
| 18 |
+
hard,1,97936,2656,1328,2656
|
| 19 |
+
hard,2,86358,2624,1312,2624
|
| 20 |
+
hard,3,76070,2614,1307,2614
|
| 21 |
+
hard,4,76406,2618,1309,2618
|
data/easy/.DS_Store
ADDED
|
Binary file (8.2 kB). View file
|
|
|
data/easy/seed_0/audit_summary.csv
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
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
|
| 2 |
+
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
|
data/easy/seed_0/config.yaml
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
num_users: 350
|
| 2 |
+
simulation_days: 45
|
| 3 |
+
fraud_ratio: 0.05
|
| 4 |
+
benchmark_mode: temporal_twins
|
| 5 |
+
user_params:
|
| 6 |
+
lambda_mean: 5.0
|
| 7 |
+
lambda_std: 1.0
|
| 8 |
+
mu_mean: 7.5
|
| 9 |
+
mu_std: 1.0
|
| 10 |
+
sigma_mean: 0.5
|
| 11 |
+
sigma_std: 0.2
|
| 12 |
+
upi_limits:
|
| 13 |
+
max_txn_amount: 100000.0
|
| 14 |
+
daily_limit: 100000.0
|
| 15 |
+
risk_model:
|
| 16 |
+
weights:
|
| 17 |
+
amount_ratio: 1.0
|
| 18 |
+
daily_ratio: 0.8
|
| 19 |
+
velocity: 1.2
|
| 20 |
+
time_anomaly: 0.6
|
| 21 |
+
graph_anomaly: 1.0
|
| 22 |
+
retry: 0.8
|
| 23 |
+
kyc: 0.5
|
| 24 |
+
user_risk: 0.8
|
| 25 |
+
random_seed: 0
|
| 26 |
+
difficulty: easy
|
| 27 |
+
release_mode: easy
|
| 28 |
+
seed: 0
|
| 29 |
+
fast_mode: false
|
| 30 |
+
n_checkpoints: 8
|
data/easy/seed_0/matched_pairs.parquet
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d08d99bb61bfc154f2020f20458f4ba34084737ca12c191784d00e4c5f9a968b
|
| 3 |
+
size 81815
|
data/easy/seed_0/schema.json
ADDED
|
@@ -0,0 +1,84 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"transactions_columns": {
|
| 3 |
+
"txn_id": "int32",
|
| 4 |
+
"sender_id": "int64",
|
| 5 |
+
"receiver_id": "int64",
|
| 6 |
+
"amount": "float32",
|
| 7 |
+
"timestamp": "float32",
|
| 8 |
+
"txn_type": "int8",
|
| 9 |
+
"is_fraud": "int8",
|
| 10 |
+
"fraud_type": "str",
|
| 11 |
+
"is_retry": "int8",
|
| 12 |
+
"risk_score": "float32",
|
| 13 |
+
"fail_prob": "float32",
|
| 14 |
+
"failed": "int8",
|
| 15 |
+
"twin_pair_id": "int64",
|
| 16 |
+
"template_id": "int64",
|
| 17 |
+
"twin_role": "str",
|
| 18 |
+
"twin_label": "int8",
|
| 19 |
+
"motif_source": "int8",
|
| 20 |
+
"motif_chain_state": "float32",
|
| 21 |
+
"motif_strength": "float32",
|
| 22 |
+
"dynamic_fraud_state": "float32",
|
| 23 |
+
"fraud_source": "str",
|
| 24 |
+
"motif_hit_count": "int32",
|
| 25 |
+
"trigger_event_idx": "int32",
|
| 26 |
+
"label_event_idx": "int32",
|
| 27 |
+
"label_delay": "int32",
|
| 28 |
+
"is_fallback_label": "int8",
|
| 29 |
+
"risk_noisy": "float32",
|
| 30 |
+
"neighbor_score": "float32",
|
| 31 |
+
"pair_freq": "float32",
|
| 32 |
+
"txn_count_10": "float32",
|
| 33 |
+
"amount_sum_10": "float32"
|
| 34 |
+
},
|
| 35 |
+
"matched_pairs_columns": {
|
| 36 |
+
"pair_event_id": "int64",
|
| 37 |
+
"twin_pair_id": "int64",
|
| 38 |
+
"template_id": "int64",
|
| 39 |
+
"matched_local_event_idx": "int64",
|
| 40 |
+
"prefix_txn_count": "int64",
|
| 41 |
+
"sender_id": "int64",
|
| 42 |
+
"label": "int64",
|
| 43 |
+
"twin_role": "str",
|
| 44 |
+
"matched_sender_id": "int64",
|
| 45 |
+
"total_txn_count": "int64",
|
| 46 |
+
"eval_timestamp": "float64",
|
| 47 |
+
"account_age": "float64",
|
| 48 |
+
"active_age": "float64",
|
| 49 |
+
"benchmark_mode": "str",
|
| 50 |
+
"difficulty": "str",
|
| 51 |
+
"seed": "int64"
|
| 52 |
+
},
|
| 53 |
+
"audit_summary_columns": {
|
| 54 |
+
"pair_total_txn_count_diff_mean": "float64",
|
| 55 |
+
"pair_total_txn_count_diff_max": "float64",
|
| 56 |
+
"auc_total_txn_count": "float64",
|
| 57 |
+
"auc_local_event_idx": "float64",
|
| 58 |
+
"auc_prefix_txn_count": "float64",
|
| 59 |
+
"auc_timestamp": "float64",
|
| 60 |
+
"auc_account_age": "float64",
|
| 61 |
+
"auc_active_age": "float64",
|
| 62 |
+
"fraud_label_event_idx_mean": "float64",
|
| 63 |
+
"fraud_label_event_idx_max": "float64",
|
| 64 |
+
"benign_eval_event_idx_mean": "float64",
|
| 65 |
+
"benign_eval_event_idx_max": "float64",
|
| 66 |
+
"pair_event_idx_diff_mean": "float64",
|
| 67 |
+
"pair_event_idx_diff_max": "float64",
|
| 68 |
+
"pair_active_age_diff_mean": "float64",
|
| 69 |
+
"pair_active_age_diff_max": "float64",
|
| 70 |
+
"pair_timestamp_diff_mean": "float64",
|
| 71 |
+
"pair_timestamp_diff_max": "float64",
|
| 72 |
+
"benign_motif_hit_rate": "float64",
|
| 73 |
+
"benign_motif_hit_pairs": "int64",
|
| 74 |
+
"matched_control_examples": "int64",
|
| 75 |
+
"matched_control_pair_events": "int64"
|
| 76 |
+
},
|
| 77 |
+
"files": [
|
| 78 |
+
"transactions.parquet",
|
| 79 |
+
"matched_pairs.parquet",
|
| 80 |
+
"audit_summary.csv",
|
| 81 |
+
"schema.json",
|
| 82 |
+
"config.yaml"
|
| 83 |
+
]
|
| 84 |
+
}
|
data/easy/seed_0/transactions.parquet
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:8024a68ece6179d8d3af63ffd66eb8a444c79737760049dda816a03e4a25dbd7
|
| 3 |
+
size 1332635
|
data/easy/seed_1/audit_summary.csv
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
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
|
| 2 |
+
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
|
data/easy/seed_1/config.yaml
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
num_users: 350
|
| 2 |
+
simulation_days: 45
|
| 3 |
+
fraud_ratio: 0.05
|
| 4 |
+
benchmark_mode: temporal_twins
|
| 5 |
+
user_params:
|
| 6 |
+
lambda_mean: 5.0
|
| 7 |
+
lambda_std: 1.0
|
| 8 |
+
mu_mean: 7.5
|
| 9 |
+
mu_std: 1.0
|
| 10 |
+
sigma_mean: 0.5
|
| 11 |
+
sigma_std: 0.2
|
| 12 |
+
upi_limits:
|
| 13 |
+
max_txn_amount: 100000.0
|
| 14 |
+
daily_limit: 100000.0
|
| 15 |
+
risk_model:
|
| 16 |
+
weights:
|
| 17 |
+
amount_ratio: 1.0
|
| 18 |
+
daily_ratio: 0.8
|
| 19 |
+
velocity: 1.2
|
| 20 |
+
time_anomaly: 0.6
|
| 21 |
+
graph_anomaly: 1.0
|
| 22 |
+
retry: 0.8
|
| 23 |
+
kyc: 0.5
|
| 24 |
+
user_risk: 0.8
|
| 25 |
+
random_seed: 1
|
| 26 |
+
difficulty: easy
|
| 27 |
+
release_mode: easy
|
| 28 |
+
seed: 1
|
| 29 |
+
fast_mode: false
|
| 30 |
+
n_checkpoints: 8
|
data/easy/seed_1/matched_pairs.parquet
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7503b30b1b597adc9507fb10361d8ef0c0ca6e5add89c502843caba3a0aa9e74
|
| 3 |
+
size 77643
|
data/easy/seed_1/schema.json
ADDED
|
@@ -0,0 +1,84 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"transactions_columns": {
|
| 3 |
+
"txn_id": "int32",
|
| 4 |
+
"sender_id": "int64",
|
| 5 |
+
"receiver_id": "int64",
|
| 6 |
+
"amount": "float32",
|
| 7 |
+
"timestamp": "float32",
|
| 8 |
+
"txn_type": "int8",
|
| 9 |
+
"is_fraud": "int8",
|
| 10 |
+
"fraud_type": "str",
|
| 11 |
+
"is_retry": "int8",
|
| 12 |
+
"risk_score": "float32",
|
| 13 |
+
"fail_prob": "float32",
|
| 14 |
+
"failed": "int8",
|
| 15 |
+
"twin_pair_id": "int64",
|
| 16 |
+
"template_id": "int64",
|
| 17 |
+
"twin_role": "str",
|
| 18 |
+
"twin_label": "int8",
|
| 19 |
+
"motif_source": "int8",
|
| 20 |
+
"motif_chain_state": "float32",
|
| 21 |
+
"motif_strength": "float32",
|
| 22 |
+
"dynamic_fraud_state": "float32",
|
| 23 |
+
"fraud_source": "str",
|
| 24 |
+
"motif_hit_count": "int32",
|
| 25 |
+
"trigger_event_idx": "int32",
|
| 26 |
+
"label_event_idx": "int32",
|
| 27 |
+
"label_delay": "int32",
|
| 28 |
+
"is_fallback_label": "int8",
|
| 29 |
+
"risk_noisy": "float32",
|
| 30 |
+
"neighbor_score": "float32",
|
| 31 |
+
"pair_freq": "float32",
|
| 32 |
+
"txn_count_10": "float32",
|
| 33 |
+
"amount_sum_10": "float32"
|
| 34 |
+
},
|
| 35 |
+
"matched_pairs_columns": {
|
| 36 |
+
"pair_event_id": "int64",
|
| 37 |
+
"twin_pair_id": "int64",
|
| 38 |
+
"template_id": "int64",
|
| 39 |
+
"matched_local_event_idx": "int64",
|
| 40 |
+
"prefix_txn_count": "int64",
|
| 41 |
+
"sender_id": "int64",
|
| 42 |
+
"label": "int64",
|
| 43 |
+
"twin_role": "str",
|
| 44 |
+
"matched_sender_id": "int64",
|
| 45 |
+
"total_txn_count": "int64",
|
| 46 |
+
"eval_timestamp": "float64",
|
| 47 |
+
"account_age": "float64",
|
| 48 |
+
"active_age": "float64",
|
| 49 |
+
"benchmark_mode": "str",
|
| 50 |
+
"difficulty": "str",
|
| 51 |
+
"seed": "int64"
|
| 52 |
+
},
|
| 53 |
+
"audit_summary_columns": {
|
| 54 |
+
"pair_total_txn_count_diff_mean": "float64",
|
| 55 |
+
"pair_total_txn_count_diff_max": "float64",
|
| 56 |
+
"auc_total_txn_count": "float64",
|
| 57 |
+
"auc_local_event_idx": "float64",
|
| 58 |
+
"auc_prefix_txn_count": "float64",
|
| 59 |
+
"auc_timestamp": "float64",
|
| 60 |
+
"auc_account_age": "float64",
|
| 61 |
+
"auc_active_age": "float64",
|
| 62 |
+
"fraud_label_event_idx_mean": "float64",
|
| 63 |
+
"fraud_label_event_idx_max": "float64",
|
| 64 |
+
"benign_eval_event_idx_mean": "float64",
|
| 65 |
+
"benign_eval_event_idx_max": "float64",
|
| 66 |
+
"pair_event_idx_diff_mean": "float64",
|
| 67 |
+
"pair_event_idx_diff_max": "float64",
|
| 68 |
+
"pair_active_age_diff_mean": "float64",
|
| 69 |
+
"pair_active_age_diff_max": "float64",
|
| 70 |
+
"pair_timestamp_diff_mean": "float64",
|
| 71 |
+
"pair_timestamp_diff_max": "float64",
|
| 72 |
+
"benign_motif_hit_rate": "float64",
|
| 73 |
+
"benign_motif_hit_pairs": "int64",
|
| 74 |
+
"matched_control_examples": "int64",
|
| 75 |
+
"matched_control_pair_events": "int64"
|
| 76 |
+
},
|
| 77 |
+
"files": [
|
| 78 |
+
"transactions.parquet",
|
| 79 |
+
"matched_pairs.parquet",
|
| 80 |
+
"audit_summary.csv",
|
| 81 |
+
"schema.json",
|
| 82 |
+
"config.yaml"
|
| 83 |
+
]
|
| 84 |
+
}
|
data/easy/seed_1/transactions.parquet
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:22f5283274a62e56a22458610709081d8b1bbf6e4793b6363c3ca9e518dc7f3e
|
| 3 |
+
size 1229511
|
data/easy/seed_2/audit_summary.csv
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
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
|
| 2 |
+
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
|
data/easy/seed_2/config.yaml
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
num_users: 350
|
| 2 |
+
simulation_days: 45
|
| 3 |
+
fraud_ratio: 0.05
|
| 4 |
+
benchmark_mode: temporal_twins
|
| 5 |
+
user_params:
|
| 6 |
+
lambda_mean: 5.0
|
| 7 |
+
lambda_std: 1.0
|
| 8 |
+
mu_mean: 7.5
|
| 9 |
+
mu_std: 1.0
|
| 10 |
+
sigma_mean: 0.5
|
| 11 |
+
sigma_std: 0.2
|
| 12 |
+
upi_limits:
|
| 13 |
+
max_txn_amount: 100000.0
|
| 14 |
+
daily_limit: 100000.0
|
| 15 |
+
risk_model:
|
| 16 |
+
weights:
|
| 17 |
+
amount_ratio: 1.0
|
| 18 |
+
daily_ratio: 0.8
|
| 19 |
+
velocity: 1.2
|
| 20 |
+
time_anomaly: 0.6
|
| 21 |
+
graph_anomaly: 1.0
|
| 22 |
+
retry: 0.8
|
| 23 |
+
kyc: 0.5
|
| 24 |
+
user_risk: 0.8
|
| 25 |
+
random_seed: 2
|
| 26 |
+
difficulty: easy
|
| 27 |
+
release_mode: easy
|
| 28 |
+
seed: 2
|
| 29 |
+
fast_mode: false
|
| 30 |
+
n_checkpoints: 8
|
data/easy/seed_2/matched_pairs.parquet
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:583ee815999e1deade36980ead3d9f8cc93a23303e607b925728e153e2e250bd
|
| 3 |
+
size 84709
|
data/easy/seed_2/schema.json
ADDED
|
@@ -0,0 +1,84 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"transactions_columns": {
|
| 3 |
+
"txn_id": "int32",
|
| 4 |
+
"sender_id": "int64",
|
| 5 |
+
"receiver_id": "int64",
|
| 6 |
+
"amount": "float32",
|
| 7 |
+
"timestamp": "float32",
|
| 8 |
+
"txn_type": "int8",
|
| 9 |
+
"is_fraud": "int8",
|
| 10 |
+
"fraud_type": "str",
|
| 11 |
+
"is_retry": "int8",
|
| 12 |
+
"risk_score": "float32",
|
| 13 |
+
"fail_prob": "float32",
|
| 14 |
+
"failed": "int8",
|
| 15 |
+
"twin_pair_id": "int64",
|
| 16 |
+
"template_id": "int64",
|
| 17 |
+
"twin_role": "str",
|
| 18 |
+
"twin_label": "int8",
|
| 19 |
+
"motif_source": "int8",
|
| 20 |
+
"motif_chain_state": "float32",
|
| 21 |
+
"motif_strength": "float32",
|
| 22 |
+
"dynamic_fraud_state": "float32",
|
| 23 |
+
"fraud_source": "str",
|
| 24 |
+
"motif_hit_count": "int32",
|
| 25 |
+
"trigger_event_idx": "int32",
|
| 26 |
+
"label_event_idx": "int32",
|
| 27 |
+
"label_delay": "int32",
|
| 28 |
+
"is_fallback_label": "int8",
|
| 29 |
+
"risk_noisy": "float32",
|
| 30 |
+
"neighbor_score": "float32",
|
| 31 |
+
"pair_freq": "float32",
|
| 32 |
+
"txn_count_10": "float32",
|
| 33 |
+
"amount_sum_10": "float32"
|
| 34 |
+
},
|
| 35 |
+
"matched_pairs_columns": {
|
| 36 |
+
"pair_event_id": "int64",
|
| 37 |
+
"twin_pair_id": "int64",
|
| 38 |
+
"template_id": "int64",
|
| 39 |
+
"matched_local_event_idx": "int64",
|
| 40 |
+
"prefix_txn_count": "int64",
|
| 41 |
+
"sender_id": "int64",
|
| 42 |
+
"label": "int64",
|
| 43 |
+
"twin_role": "str",
|
| 44 |
+
"matched_sender_id": "int64",
|
| 45 |
+
"total_txn_count": "int64",
|
| 46 |
+
"eval_timestamp": "float64",
|
| 47 |
+
"account_age": "float64",
|
| 48 |
+
"active_age": "float64",
|
| 49 |
+
"benchmark_mode": "str",
|
| 50 |
+
"difficulty": "str",
|
| 51 |
+
"seed": "int64"
|
| 52 |
+
},
|
| 53 |
+
"audit_summary_columns": {
|
| 54 |
+
"pair_total_txn_count_diff_mean": "float64",
|
| 55 |
+
"pair_total_txn_count_diff_max": "float64",
|
| 56 |
+
"auc_total_txn_count": "float64",
|
| 57 |
+
"auc_local_event_idx": "float64",
|
| 58 |
+
"auc_prefix_txn_count": "float64",
|
| 59 |
+
"auc_timestamp": "float64",
|
| 60 |
+
"auc_account_age": "float64",
|
| 61 |
+
"auc_active_age": "float64",
|
| 62 |
+
"fraud_label_event_idx_mean": "float64",
|
| 63 |
+
"fraud_label_event_idx_max": "float64",
|
| 64 |
+
"benign_eval_event_idx_mean": "float64",
|
| 65 |
+
"benign_eval_event_idx_max": "float64",
|
| 66 |
+
"pair_event_idx_diff_mean": "float64",
|
| 67 |
+
"pair_event_idx_diff_max": "float64",
|
| 68 |
+
"pair_active_age_diff_mean": "float64",
|
| 69 |
+
"pair_active_age_diff_max": "float64",
|
| 70 |
+
"pair_timestamp_diff_mean": "float64",
|
| 71 |
+
"pair_timestamp_diff_max": "float64",
|
| 72 |
+
"benign_motif_hit_rate": "float64",
|
| 73 |
+
"benign_motif_hit_pairs": "int64",
|
| 74 |
+
"matched_control_examples": "int64",
|
| 75 |
+
"matched_control_pair_events": "int64"
|
| 76 |
+
},
|
| 77 |
+
"files": [
|
| 78 |
+
"transactions.parquet",
|
| 79 |
+
"matched_pairs.parquet",
|
| 80 |
+
"audit_summary.csv",
|
| 81 |
+
"schema.json",
|
| 82 |
+
"config.yaml"
|
| 83 |
+
]
|
| 84 |
+
}
|
data/easy/seed_2/transactions.parquet
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c61849d3d9679ef08fe1f595ced19145f2ecf60c15afc5c1d7998eb245b48f91
|
| 3 |
+
size 1289176
|
data/easy/seed_3/audit_summary.csv
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
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
|
| 2 |
+
0.0,0.0,0.5,0.5,0.5,0.5,0.49999999999999994,0.49999999999999994,38.321280379371665,105.0,38.321280379371665,105.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,3374,1687
|
data/easy/seed_3/config.yaml
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
num_users: 350
|
| 2 |
+
simulation_days: 45
|
| 3 |
+
fraud_ratio: 0.05
|
| 4 |
+
benchmark_mode: temporal_twins
|
| 5 |
+
user_params:
|
| 6 |
+
lambda_mean: 5.0
|
| 7 |
+
lambda_std: 1.0
|
| 8 |
+
mu_mean: 7.5
|
| 9 |
+
mu_std: 1.0
|
| 10 |
+
sigma_mean: 0.5
|
| 11 |
+
sigma_std: 0.2
|
| 12 |
+
upi_limits:
|
| 13 |
+
max_txn_amount: 100000.0
|
| 14 |
+
daily_limit: 100000.0
|
| 15 |
+
risk_model:
|
| 16 |
+
weights:
|
| 17 |
+
amount_ratio: 1.0
|
| 18 |
+
daily_ratio: 0.8
|
| 19 |
+
velocity: 1.2
|
| 20 |
+
time_anomaly: 0.6
|
| 21 |
+
graph_anomaly: 1.0
|
| 22 |
+
retry: 0.8
|
| 23 |
+
kyc: 0.5
|
| 24 |
+
user_risk: 0.8
|
| 25 |
+
random_seed: 3
|
| 26 |
+
difficulty: easy
|
| 27 |
+
release_mode: easy
|
| 28 |
+
seed: 3
|
| 29 |
+
fast_mode: false
|
| 30 |
+
n_checkpoints: 8
|
data/easy/seed_3/matched_pairs.parquet
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6844de17f7fb80b858563672a15dfb38102e0dc1f370a9690b7c0f2ae5a66145
|
| 3 |
+
size 81281
|
data/easy/seed_3/schema.json
ADDED
|
@@ -0,0 +1,84 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"transactions_columns": {
|
| 3 |
+
"txn_id": "int32",
|
| 4 |
+
"sender_id": "int64",
|
| 5 |
+
"receiver_id": "int64",
|
| 6 |
+
"amount": "float32",
|
| 7 |
+
"timestamp": "float32",
|
| 8 |
+
"txn_type": "int8",
|
| 9 |
+
"is_fraud": "int8",
|
| 10 |
+
"fraud_type": "str",
|
| 11 |
+
"is_retry": "int8",
|
| 12 |
+
"risk_score": "float32",
|
| 13 |
+
"fail_prob": "float32",
|
| 14 |
+
"failed": "int8",
|
| 15 |
+
"twin_pair_id": "int64",
|
| 16 |
+
"template_id": "int64",
|
| 17 |
+
"twin_role": "str",
|
| 18 |
+
"twin_label": "int8",
|
| 19 |
+
"motif_source": "int8",
|
| 20 |
+
"motif_chain_state": "float32",
|
| 21 |
+
"motif_strength": "float32",
|
| 22 |
+
"dynamic_fraud_state": "float32",
|
| 23 |
+
"fraud_source": "str",
|
| 24 |
+
"motif_hit_count": "int32",
|
| 25 |
+
"trigger_event_idx": "int32",
|
| 26 |
+
"label_event_idx": "int32",
|
| 27 |
+
"label_delay": "int32",
|
| 28 |
+
"is_fallback_label": "int8",
|
| 29 |
+
"risk_noisy": "float32",
|
| 30 |
+
"neighbor_score": "float32",
|
| 31 |
+
"pair_freq": "float32",
|
| 32 |
+
"txn_count_10": "float32",
|
| 33 |
+
"amount_sum_10": "float32"
|
| 34 |
+
},
|
| 35 |
+
"matched_pairs_columns": {
|
| 36 |
+
"pair_event_id": "int64",
|
| 37 |
+
"twin_pair_id": "int64",
|
| 38 |
+
"template_id": "int64",
|
| 39 |
+
"matched_local_event_idx": "int64",
|
| 40 |
+
"prefix_txn_count": "int64",
|
| 41 |
+
"sender_id": "int64",
|
| 42 |
+
"label": "int64",
|
| 43 |
+
"twin_role": "str",
|
| 44 |
+
"matched_sender_id": "int64",
|
| 45 |
+
"total_txn_count": "int64",
|
| 46 |
+
"eval_timestamp": "float64",
|
| 47 |
+
"account_age": "float64",
|
| 48 |
+
"active_age": "float64",
|
| 49 |
+
"benchmark_mode": "str",
|
| 50 |
+
"difficulty": "str",
|
| 51 |
+
"seed": "int64"
|
| 52 |
+
},
|
| 53 |
+
"audit_summary_columns": {
|
| 54 |
+
"pair_total_txn_count_diff_mean": "float64",
|
| 55 |
+
"pair_total_txn_count_diff_max": "float64",
|
| 56 |
+
"auc_total_txn_count": "float64",
|
| 57 |
+
"auc_local_event_idx": "float64",
|
| 58 |
+
"auc_prefix_txn_count": "float64",
|
| 59 |
+
"auc_timestamp": "float64",
|
| 60 |
+
"auc_account_age": "float64",
|
| 61 |
+
"auc_active_age": "float64",
|
| 62 |
+
"fraud_label_event_idx_mean": "float64",
|
| 63 |
+
"fraud_label_event_idx_max": "float64",
|
| 64 |
+
"benign_eval_event_idx_mean": "float64",
|
| 65 |
+
"benign_eval_event_idx_max": "float64",
|
| 66 |
+
"pair_event_idx_diff_mean": "float64",
|
| 67 |
+
"pair_event_idx_diff_max": "float64",
|
| 68 |
+
"pair_active_age_diff_mean": "float64",
|
| 69 |
+
"pair_active_age_diff_max": "float64",
|
| 70 |
+
"pair_timestamp_diff_mean": "float64",
|
| 71 |
+
"pair_timestamp_diff_max": "float64",
|
| 72 |
+
"benign_motif_hit_rate": "float64",
|
| 73 |
+
"benign_motif_hit_pairs": "int64",
|
| 74 |
+
"matched_control_examples": "int64",
|
| 75 |
+
"matched_control_pair_events": "int64"
|
| 76 |
+
},
|
| 77 |
+
"files": [
|
| 78 |
+
"transactions.parquet",
|
| 79 |
+
"matched_pairs.parquet",
|
| 80 |
+
"audit_summary.csv",
|
| 81 |
+
"schema.json",
|
| 82 |
+
"config.yaml"
|
| 83 |
+
]
|
| 84 |
+
}
|
data/easy/seed_3/transactions.parquet
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d4b28ff442286708fcc9fe214e53187c5a23d14f634b1387f4e2527c2e9ccd25
|
| 3 |
+
size 1357249
|
data/easy/seed_4/audit_summary.csv
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
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
|
| 2 |
+
0.0,0.0,0.5,0.49999999999999994,0.49999999999999994,0.49999999999999994,0.5,0.5,39.30157766990291,100.0,39.30157766990291,100.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,3296,1648
|
data/easy/seed_4/config.yaml
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
num_users: 350
|
| 2 |
+
simulation_days: 45
|
| 3 |
+
fraud_ratio: 0.05
|
| 4 |
+
benchmark_mode: temporal_twins
|
| 5 |
+
user_params:
|
| 6 |
+
lambda_mean: 5.0
|
| 7 |
+
lambda_std: 1.0
|
| 8 |
+
mu_mean: 7.5
|
| 9 |
+
mu_std: 1.0
|
| 10 |
+
sigma_mean: 0.5
|
| 11 |
+
sigma_std: 0.2
|
| 12 |
+
upi_limits:
|
| 13 |
+
max_txn_amount: 100000.0
|
| 14 |
+
daily_limit: 100000.0
|
| 15 |
+
risk_model:
|
| 16 |
+
weights:
|
| 17 |
+
amount_ratio: 1.0
|
| 18 |
+
daily_ratio: 0.8
|
| 19 |
+
velocity: 1.2
|
| 20 |
+
time_anomaly: 0.6
|
| 21 |
+
graph_anomaly: 1.0
|
| 22 |
+
retry: 0.8
|
| 23 |
+
kyc: 0.5
|
| 24 |
+
user_risk: 0.8
|
| 25 |
+
random_seed: 4
|
| 26 |
+
difficulty: easy
|
| 27 |
+
release_mode: easy
|
| 28 |
+
seed: 4
|
| 29 |
+
fast_mode: false
|
| 30 |
+
n_checkpoints: 8
|
data/easy/seed_4/matched_pairs.parquet
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:086f1c0336b7a670b6279207751fc337cf7d0a30cad24d4ae1b98542e94a4884
|
| 3 |
+
size 81766
|
data/easy/seed_4/schema.json
ADDED
|
@@ -0,0 +1,84 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"transactions_columns": {
|
| 3 |
+
"txn_id": "int32",
|
| 4 |
+
"sender_id": "int64",
|
| 5 |
+
"receiver_id": "int64",
|
| 6 |
+
"amount": "float32",
|
| 7 |
+
"timestamp": "float64",
|
| 8 |
+
"txn_type": "int8",
|
| 9 |
+
"is_fraud": "int8",
|
| 10 |
+
"fraud_type": "str",
|
| 11 |
+
"is_retry": "int8",
|
| 12 |
+
"risk_score": "float32",
|
| 13 |
+
"fail_prob": "float32",
|
| 14 |
+
"failed": "int8",
|
| 15 |
+
"twin_pair_id": "int64",
|
| 16 |
+
"template_id": "int64",
|
| 17 |
+
"twin_role": "str",
|
| 18 |
+
"twin_label": "int8",
|
| 19 |
+
"motif_source": "int8",
|
| 20 |
+
"motif_chain_state": "float32",
|
| 21 |
+
"motif_strength": "float32",
|
| 22 |
+
"dynamic_fraud_state": "float32",
|
| 23 |
+
"fraud_source": "str",
|
| 24 |
+
"motif_hit_count": "int32",
|
| 25 |
+
"trigger_event_idx": "int32",
|
| 26 |
+
"label_event_idx": "int32",
|
| 27 |
+
"label_delay": "int32",
|
| 28 |
+
"is_fallback_label": "int8",
|
| 29 |
+
"risk_noisy": "float32",
|
| 30 |
+
"neighbor_score": "float32",
|
| 31 |
+
"pair_freq": "float32",
|
| 32 |
+
"txn_count_10": "float32",
|
| 33 |
+
"amount_sum_10": "float32"
|
| 34 |
+
},
|
| 35 |
+
"matched_pairs_columns": {
|
| 36 |
+
"pair_event_id": "int64",
|
| 37 |
+
"twin_pair_id": "int64",
|
| 38 |
+
"template_id": "int64",
|
| 39 |
+
"matched_local_event_idx": "int64",
|
| 40 |
+
"prefix_txn_count": "int64",
|
| 41 |
+
"sender_id": "int64",
|
| 42 |
+
"label": "int64",
|
| 43 |
+
"twin_role": "str",
|
| 44 |
+
"matched_sender_id": "int64",
|
| 45 |
+
"total_txn_count": "int64",
|
| 46 |
+
"eval_timestamp": "float64",
|
| 47 |
+
"account_age": "float64",
|
| 48 |
+
"active_age": "float64",
|
| 49 |
+
"benchmark_mode": "str",
|
| 50 |
+
"difficulty": "str",
|
| 51 |
+
"seed": "int64"
|
| 52 |
+
},
|
| 53 |
+
"audit_summary_columns": {
|
| 54 |
+
"pair_total_txn_count_diff_mean": "float64",
|
| 55 |
+
"pair_total_txn_count_diff_max": "float64",
|
| 56 |
+
"auc_total_txn_count": "float64",
|
| 57 |
+
"auc_local_event_idx": "float64",
|
| 58 |
+
"auc_prefix_txn_count": "float64",
|
| 59 |
+
"auc_timestamp": "float64",
|
| 60 |
+
"auc_account_age": "float64",
|
| 61 |
+
"auc_active_age": "float64",
|
| 62 |
+
"fraud_label_event_idx_mean": "float64",
|
| 63 |
+
"fraud_label_event_idx_max": "float64",
|
| 64 |
+
"benign_eval_event_idx_mean": "float64",
|
| 65 |
+
"benign_eval_event_idx_max": "float64",
|
| 66 |
+
"pair_event_idx_diff_mean": "float64",
|
| 67 |
+
"pair_event_idx_diff_max": "float64",
|
| 68 |
+
"pair_active_age_diff_mean": "float64",
|
| 69 |
+
"pair_active_age_diff_max": "float64",
|
| 70 |
+
"pair_timestamp_diff_mean": "float64",
|
| 71 |
+
"pair_timestamp_diff_max": "float64",
|
| 72 |
+
"benign_motif_hit_rate": "float64",
|
| 73 |
+
"benign_motif_hit_pairs": "int64",
|
| 74 |
+
"matched_control_examples": "int64",
|
| 75 |
+
"matched_control_pair_events": "int64"
|
| 76 |
+
},
|
| 77 |
+
"files": [
|
| 78 |
+
"transactions.parquet",
|
| 79 |
+
"matched_pairs.parquet",
|
| 80 |
+
"audit_summary.csv",
|
| 81 |
+
"schema.json",
|
| 82 |
+
"config.yaml"
|
| 83 |
+
]
|
| 84 |
+
}
|
data/easy/seed_4/transactions.parquet
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f12641c7b1496ea8a84f9d26fccd9a65f9fdbd4678de0c821a472dfbb6ca9c58
|
| 3 |
+
size 1226851
|
data/hard/.DS_Store
ADDED
|
Binary file (10.2 kB). View file
|
|
|
data/hard/seed_0/audit_summary.csv
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
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
|
| 2 |
+
0.0,0.0,0.5,0.49999999999999994,0.49999999999999994,0.5000000000000001,0.5000000000000001,0.5000000000000001,72.17846153846153,835.0,72.17846153846153,835.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,2600,1300
|
data/hard/seed_0/config.yaml
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
num_users: 350
|
| 2 |
+
simulation_days: 45
|
| 3 |
+
fraud_ratio: 0.05
|
| 4 |
+
benchmark_mode: temporal_twins
|
| 5 |
+
user_params:
|
| 6 |
+
lambda_mean: 5.0
|
| 7 |
+
lambda_std: 1.0
|
| 8 |
+
mu_mean: 7.5
|
| 9 |
+
mu_std: 1.0
|
| 10 |
+
sigma_mean: 0.5
|
| 11 |
+
sigma_std: 0.2
|
| 12 |
+
upi_limits:
|
| 13 |
+
max_txn_amount: 100000.0
|
| 14 |
+
daily_limit: 100000.0
|
| 15 |
+
risk_model:
|
| 16 |
+
weights:
|
| 17 |
+
amount_ratio: 1.0
|
| 18 |
+
daily_ratio: 0.8
|
| 19 |
+
velocity: 1.2
|
| 20 |
+
time_anomaly: 0.6
|
| 21 |
+
graph_anomaly: 1.0
|
| 22 |
+
retry: 0.8
|
| 23 |
+
kyc: 0.5
|
| 24 |
+
user_risk: 0.8
|
| 25 |
+
random_seed: 0
|
| 26 |
+
difficulty: hard
|
| 27 |
+
release_mode: hard
|
| 28 |
+
seed: 0
|
| 29 |
+
fast_mode: false
|
| 30 |
+
n_checkpoints: 8
|
data/hard/seed_0/matched_pairs.parquet
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:cd7ab1965e4b8961bf5a314170a7ed2a33211b5006ce2ac3fe27327d1cfdaf5d
|
| 3 |
+
size 69658
|
data/hard/seed_0/schema.json
ADDED
|
@@ -0,0 +1,84 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
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|
|
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|
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|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"transactions_columns": {
|
| 3 |
+
"txn_id": "int32",
|
| 4 |
+
"sender_id": "int64",
|
| 5 |
+
"receiver_id": "int64",
|
| 6 |
+
"amount": "float32",
|
| 7 |
+
"timestamp": "float32",
|
| 8 |
+
"txn_type": "int8",
|
| 9 |
+
"is_fraud": "int8",
|
| 10 |
+
"fraud_type": "str",
|
| 11 |
+
"is_retry": "int8",
|
| 12 |
+
"risk_score": "float32",
|
| 13 |
+
"fail_prob": "float32",
|
| 14 |
+
"failed": "int8",
|
| 15 |
+
"twin_pair_id": "int64",
|
| 16 |
+
"template_id": "int64",
|
| 17 |
+
"twin_role": "str",
|
| 18 |
+
"twin_label": "int8",
|
| 19 |
+
"motif_source": "int8",
|
| 20 |
+
"motif_chain_state": "float32",
|
| 21 |
+
"motif_strength": "float32",
|
| 22 |
+
"dynamic_fraud_state": "float32",
|
| 23 |
+
"fraud_source": "str",
|
| 24 |
+
"motif_hit_count": "int32",
|
| 25 |
+
"trigger_event_idx": "int32",
|
| 26 |
+
"label_event_idx": "int32",
|
| 27 |
+
"label_delay": "int32",
|
| 28 |
+
"is_fallback_label": "int8",
|
| 29 |
+
"risk_noisy": "float32",
|
| 30 |
+
"neighbor_score": "float32",
|
| 31 |
+
"pair_freq": "float32",
|
| 32 |
+
"txn_count_10": "float32",
|
| 33 |
+
"amount_sum_10": "float32"
|
| 34 |
+
},
|
| 35 |
+
"matched_pairs_columns": {
|
| 36 |
+
"pair_event_id": "int64",
|
| 37 |
+
"twin_pair_id": "int64",
|
| 38 |
+
"template_id": "int64",
|
| 39 |
+
"matched_local_event_idx": "int64",
|
| 40 |
+
"prefix_txn_count": "int64",
|
| 41 |
+
"sender_id": "int64",
|
| 42 |
+
"label": "int64",
|
| 43 |
+
"twin_role": "str",
|
| 44 |
+
"matched_sender_id": "int64",
|
| 45 |
+
"total_txn_count": "int64",
|
| 46 |
+
"eval_timestamp": "float64",
|
| 47 |
+
"account_age": "float64",
|
| 48 |
+
"active_age": "float64",
|
| 49 |
+
"benchmark_mode": "str",
|
| 50 |
+
"difficulty": "str",
|
| 51 |
+
"seed": "int64"
|
| 52 |
+
},
|
| 53 |
+
"audit_summary_columns": {
|
| 54 |
+
"pair_total_txn_count_diff_mean": "float64",
|
| 55 |
+
"pair_total_txn_count_diff_max": "float64",
|
| 56 |
+
"auc_total_txn_count": "float64",
|
| 57 |
+
"auc_local_event_idx": "float64",
|
| 58 |
+
"auc_prefix_txn_count": "float64",
|
| 59 |
+
"auc_timestamp": "float64",
|
| 60 |
+
"auc_account_age": "float64",
|
| 61 |
+
"auc_active_age": "float64",
|
| 62 |
+
"fraud_label_event_idx_mean": "float64",
|
| 63 |
+
"fraud_label_event_idx_max": "float64",
|
| 64 |
+
"benign_eval_event_idx_mean": "float64",
|
| 65 |
+
"benign_eval_event_idx_max": "float64",
|
| 66 |
+
"pair_event_idx_diff_mean": "float64",
|
| 67 |
+
"pair_event_idx_diff_max": "float64",
|
| 68 |
+
"pair_active_age_diff_mean": "float64",
|
| 69 |
+
"pair_active_age_diff_max": "float64",
|
| 70 |
+
"pair_timestamp_diff_mean": "float64",
|
| 71 |
+
"pair_timestamp_diff_max": "float64",
|
| 72 |
+
"benign_motif_hit_rate": "float64",
|
| 73 |
+
"benign_motif_hit_pairs": "int64",
|
| 74 |
+
"matched_control_examples": "int64",
|
| 75 |
+
"matched_control_pair_events": "int64"
|
| 76 |
+
},
|
| 77 |
+
"files": [
|
| 78 |
+
"transactions.parquet",
|
| 79 |
+
"matched_pairs.parquet",
|
| 80 |
+
"audit_summary.csv",
|
| 81 |
+
"schema.json",
|
| 82 |
+
"config.yaml"
|
| 83 |
+
]
|
| 84 |
+
}
|
data/hard/seed_0/transactions.parquet
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5c6087b6fbbf6fe37ebf59e9e8c364c54ec1ebe6b6d7bd26add4ebcd819f68f0
|
| 3 |
+
size 2281421
|
data/hard/seed_1/audit_summary.csv
ADDED
|
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
|
|
|
| 1 |
+
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
|
| 2 |
+
0.0,0.0,0.49999999999999994,0.5,0.5,0.49999999999999994,0.49999999999999994,0.49999999999999994,92.03990963855422,1822.0,92.03990963855422,1822.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,2656,1328
|
data/hard/seed_1/config.yaml
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
num_users: 350
|
| 2 |
+
simulation_days: 45
|
| 3 |
+
fraud_ratio: 0.05
|
| 4 |
+
benchmark_mode: temporal_twins
|
| 5 |
+
user_params:
|
| 6 |
+
lambda_mean: 5.0
|
| 7 |
+
lambda_std: 1.0
|
| 8 |
+
mu_mean: 7.5
|
| 9 |
+
mu_std: 1.0
|
| 10 |
+
sigma_mean: 0.5
|
| 11 |
+
sigma_std: 0.2
|
| 12 |
+
upi_limits:
|
| 13 |
+
max_txn_amount: 100000.0
|
| 14 |
+
daily_limit: 100000.0
|
| 15 |
+
risk_model:
|
| 16 |
+
weights:
|
| 17 |
+
amount_ratio: 1.0
|
| 18 |
+
daily_ratio: 0.8
|
| 19 |
+
velocity: 1.2
|
| 20 |
+
time_anomaly: 0.6
|
| 21 |
+
graph_anomaly: 1.0
|
| 22 |
+
retry: 0.8
|
| 23 |
+
kyc: 0.5
|
| 24 |
+
user_risk: 0.8
|
| 25 |
+
random_seed: 1
|
| 26 |
+
difficulty: hard
|
| 27 |
+
release_mode: hard
|
| 28 |
+
seed: 1
|
| 29 |
+
fast_mode: false
|
| 30 |
+
n_checkpoints: 8
|
data/hard/seed_1/matched_pairs.parquet
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:69211d9c6d5c6d43b1b2e3ce897c152b2928bd7ff579032af63b398661888fa7
|
| 3 |
+
size 70787
|
data/hard/seed_1/schema.json
ADDED
|
@@ -0,0 +1,84 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"transactions_columns": {
|
| 3 |
+
"txn_id": "int32",
|
| 4 |
+
"sender_id": "int64",
|
| 5 |
+
"receiver_id": "int64",
|
| 6 |
+
"amount": "float32",
|
| 7 |
+
"timestamp": "float32",
|
| 8 |
+
"txn_type": "int8",
|
| 9 |
+
"is_fraud": "int8",
|
| 10 |
+
"fraud_type": "str",
|
| 11 |
+
"is_retry": "int8",
|
| 12 |
+
"risk_score": "float32",
|
| 13 |
+
"fail_prob": "float32",
|
| 14 |
+
"failed": "int8",
|
| 15 |
+
"twin_pair_id": "int64",
|
| 16 |
+
"template_id": "int64",
|
| 17 |
+
"twin_role": "str",
|
| 18 |
+
"twin_label": "int8",
|
| 19 |
+
"motif_source": "int8",
|
| 20 |
+
"motif_chain_state": "float32",
|
| 21 |
+
"motif_strength": "float32",
|
| 22 |
+
"dynamic_fraud_state": "float32",
|
| 23 |
+
"fraud_source": "str",
|
| 24 |
+
"motif_hit_count": "int32",
|
| 25 |
+
"trigger_event_idx": "int32",
|
| 26 |
+
"label_event_idx": "int32",
|
| 27 |
+
"label_delay": "int32",
|
| 28 |
+
"is_fallback_label": "int8",
|
| 29 |
+
"risk_noisy": "float32",
|
| 30 |
+
"neighbor_score": "float32",
|
| 31 |
+
"pair_freq": "float32",
|
| 32 |
+
"txn_count_10": "float32",
|
| 33 |
+
"amount_sum_10": "float32"
|
| 34 |
+
},
|
| 35 |
+
"matched_pairs_columns": {
|
| 36 |
+
"pair_event_id": "int64",
|
| 37 |
+
"twin_pair_id": "int64",
|
| 38 |
+
"template_id": "int64",
|
| 39 |
+
"matched_local_event_idx": "int64",
|
| 40 |
+
"prefix_txn_count": "int64",
|
| 41 |
+
"sender_id": "int64",
|
| 42 |
+
"label": "int64",
|
| 43 |
+
"twin_role": "str",
|
| 44 |
+
"matched_sender_id": "int64",
|
| 45 |
+
"total_txn_count": "int64",
|
| 46 |
+
"eval_timestamp": "float64",
|
| 47 |
+
"account_age": "float64",
|
| 48 |
+
"active_age": "float64",
|
| 49 |
+
"benchmark_mode": "str",
|
| 50 |
+
"difficulty": "str",
|
| 51 |
+
"seed": "int64"
|
| 52 |
+
},
|
| 53 |
+
"audit_summary_columns": {
|
| 54 |
+
"pair_total_txn_count_diff_mean": "float64",
|
| 55 |
+
"pair_total_txn_count_diff_max": "float64",
|
| 56 |
+
"auc_total_txn_count": "float64",
|
| 57 |
+
"auc_local_event_idx": "float64",
|
| 58 |
+
"auc_prefix_txn_count": "float64",
|
| 59 |
+
"auc_timestamp": "float64",
|
| 60 |
+
"auc_account_age": "float64",
|
| 61 |
+
"auc_active_age": "float64",
|
| 62 |
+
"fraud_label_event_idx_mean": "float64",
|
| 63 |
+
"fraud_label_event_idx_max": "float64",
|
| 64 |
+
"benign_eval_event_idx_mean": "float64",
|
| 65 |
+
"benign_eval_event_idx_max": "float64",
|
| 66 |
+
"pair_event_idx_diff_mean": "float64",
|
| 67 |
+
"pair_event_idx_diff_max": "float64",
|
| 68 |
+
"pair_active_age_diff_mean": "float64",
|
| 69 |
+
"pair_active_age_diff_max": "float64",
|
| 70 |
+
"pair_timestamp_diff_mean": "float64",
|
| 71 |
+
"pair_timestamp_diff_max": "float64",
|
| 72 |
+
"benign_motif_hit_rate": "float64",
|
| 73 |
+
"benign_motif_hit_pairs": "int64",
|
| 74 |
+
"matched_control_examples": "int64",
|
| 75 |
+
"matched_control_pair_events": "int64"
|
| 76 |
+
},
|
| 77 |
+
"files": [
|
| 78 |
+
"transactions.parquet",
|
| 79 |
+
"matched_pairs.parquet",
|
| 80 |
+
"audit_summary.csv",
|
| 81 |
+
"schema.json",
|
| 82 |
+
"config.yaml"
|
| 83 |
+
]
|
| 84 |
+
}
|