diff --git a/.claude/launch.json b/.claude/launch.json deleted file mode 100644 index aa7fa0d..0000000 --- a/.claude/launch.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "version": "0.0.1", - "configurations": [ - { - "name": "sentinel", - "runtimeExecutable": "streamlit", - "runtimeArgs": ["run", "app.py", "--server.headless", "true"], - "port": 8501 - } - ] -} diff --git a/Readme.md b/Readme.md index 61948be..75ee31e 100644 --- a/Readme.md +++ b/Readme.md @@ -10,14 +10,14 @@ Fraud-Detection-MLOps detects fraud in payment transactions (PaySim + Sparkov), ## The headline: the leakage fix is the story -The original `train.py` used `train_test_split(..., stratify=y)` — a **random** split on time-series fraud data, which leaks future transaction patterns into training. The Day-1 audit ([docs/MLOPS_AUDIT.md](docs/MLOPS_AUDIT.md)) found a second leak too: the benchmark fell back to the *training* file instead of the held-out test file. +The original `train.py` used `train_test_split(..., stratify=y)` — a **random** split on time-series fraud data, which leaks future transaction patterns into training. The Day-1 audit found a second leak too: the benchmark fell back to the *training* file instead of the held-out test file. | Metric (Sparkov OOT, vs AutoGluon 0.952) | Before fix | After fix | |---|---:|---:| | **Honest AUC** | **0.9210** (leaked) | **0.7949** (honest baseline) | | Apparent gap to AutoGluon | −0.031 (mirage) | −0.157 (real) | -The fix: `temporal_split_per_source()` sorts each source chronologically and takes the final 20% as test ([docs/DATA_SPLIT.md](docs/DATA_SPLIT.md)). **The ~0.126 AUC drop was the data leakage. The 0.7949 is the number that was always real** — and the rest of the sprint earns it back honestly. +The fix: `temporal_split_per_source()` sorts each source chronologically and takes the final 20% as test. **The ~0.126 AUC drop was the data leakage. The 0.7949 is the number that was always real** — and the rest of the sprint earns it back honestly. --- @@ -25,10 +25,10 @@ The fix: `temporal_split_per_source()` sorts each source chronologically and tak A 30-trial Optuna sweep + source-balanced sample weights closed the entire gap to AutoGluon — **no new features, no ensembling, no SHAP** (that's the joint Fraud-Detection project's territory; Fraud-Detection-MLOps is deliberately MLOps-only). -| Model | OOT AUC (sparkov_test.csv) | Δ vs AutoGluon 0.952 | Source | -|---|---:|---:|---| -| Day-1 honest baseline (temporal split) | 0.7949 | −0.157 | `results/baseline_metrics.json` | -| **Day-5 champion (Optuna + source-balanced)** | **0.9520** | **−0.00004** (tied) | `results/day05/day05_leaderboard.csv` | +| Model | OOT AUC (sparkov_test.csv) | Δ vs AutoGluon 0.952 | +|---|---:|---:| +| Day-1 honest baseline (temporal split) | 0.7949 | −0.157 | +| **Day-5 champion (Optuna + source-balanced)** | **0.9520** | **−0.00004** (tied) | Champion: XGBoost, per-source temporal split + source-balanced weights (paysim 0.60×, sparkov 2.95×) + Optuna tuning. Stored at `models/fraud_model_tuned_fixed.pkl` (MLflow run `day05_targeted_fix_v1`). @@ -36,12 +36,12 @@ Champion: XGBoost, per-source temporal split + source-balanced weights (paysim 0 ## The four MLOps capabilities (what a notebook doesn't have) -| Capability | Headline metric | Source | -|---|---|---| -| **Dask distributed features** | Pandas 1.15M rows/s vs Dask 0.26M rows/s on one host, **bit-exact within 5.5e-12** | `results/throughput_speedup.csv` | -| **MLflow registry rollback** | **3.9 ms** median alias flip; 11.9 ms full audited rollback | `results/registry_rollback_times.csv` | -| **KS+PSI drift detection** | **0-day** detection lag on a synthetic 2σ shift; **precision 1.0, recall 1.0** on the 7-day window | `results/drift_replay_summary.json` | -| **Auto-retrain + shadow-promote** | drift → train → shadow-eval → promote in **median 6.85 s**; 3/3 events auto-promoted | `results/drift_retrain_events.csv` | +| Capability | Headline metric | +|---|---| +| **Dask distributed features** | Pandas 1.15M rows/s vs Dask 0.26M rows/s on one host, **bit-exact within 5.5e-12** | +| **MLflow registry rollback** | **3.9 ms** median alias flip; 11.9 ms full audited rollback | +| **KS+PSI drift detection** | **0-day** detection lag on a synthetic 2σ shift; **precision 1.0, recall 1.0** on the 7-day window | +| **Auto-retrain + shadow-promote** | drift → train → shadow-eval → promote in **median 6.85 s**; 3/3 events auto-promoted | Dask "loses" the single-host throughput race but is the *scaling primitive* — and it's bit-exact with Pandas, which is the property that lets you swap engines without changing results. @@ -57,7 +57,7 @@ Day 6 ran Claude Opus 4.6 as an LLM fraud judge on the same 200-row OOT sample ( | Naive notebook XGBoost | 0.626 | 0.415 | 73 µs | $0.43 | | Claude Opus 4.6 LLM-judged | 0.622 | 0.351 | 1.82 s | **$1,250,691** | -Two findings: (1) the LLM is **30,400× slower** and **2.9M× more expensive**, and ranks 0.29 AUC worse — it caught only textbook patterns (large online txns at night) and missed the "card skimmed at POS → grocery abuse" behavioral class. (2) **The naive notebook XGBoost (0.626) ties the LLM (0.622)** — the 0.29-AUC jump to the champion comes from the *discipline layers*, not from "XGBoost vs LLM". The discipline is the model. Source: `results/day06/frontier_comparison.csv`. The LLM judge ran in simulate mode (no API key on host); `--mode api` is the same code path with a real key. +Two findings: (1) the LLM is **30,400× slower** and **2.9M× more expensive**, and ranks 0.29 AUC worse — it caught only textbook patterns (large online txns at night) and missed the "card skimmed at POS → grocery abuse" behavioral class. (2) **The naive notebook XGBoost (0.626) ties the LLM (0.622)** — the 0.29-AUC jump to the champion comes from the *discipline layers*, not from "XGBoost vs LLM". The discipline is the model. The LLM judge ran in simulate mode (no API key on host); `--mode api` is the same code path with a real key. ### MLOps ablation (L0 → L3, full OOT) | Layer | OOT AUC | Δ vs prev | @@ -67,7 +67,7 @@ Two findings: (1) the LLM is **30,400× slower** and **2.9M× more expensive**, | L2 + source-balanced weights | 0.696 | +0.039 | | L3 + Optuna (champion) | **0.948** | **+0.252** | -Total L0→L3 gain: **+0.401 OOT AUC**. Every layer is load-bearing; Optuna is the biggest single contributor. Source: `results/day06/ablation_modelling.csv`. +Total L0→L3 gain: **+0.401 OOT AUC**. Every layer is load-bearing; Optuna is the biggest single contributor. --- @@ -159,9 +159,6 @@ Fraud-Detection-MLOps/ │ └── frontier/{llm_judge,compare_models,ablation}.py ├── tests/ # 31-test suite ├── models/fraud_model_tuned_fixed.pkl # champion -├── results/ # baseline, throughput, rollback, drift, day05/, day06/ -├── reports/ # day01..day07 phase reports -├── docs/ # MLOPS_AUDIT.md, DATA_SPLIT.md └── data/raw/{paysim,sparkov,sparkov_test}.csv ``` diff --git a/docs/DATA_SPLIT.md b/docs/DATA_SPLIT.md deleted file mode 100644 index 0a3d8ad..0000000 --- a/docs/DATA_SPLIT.md +++ /dev/null @@ -1,76 +0,0 @@ -# Sentinel — Train/Test Split Rationale - -## Decision - -Sentinel uses a **per-source temporal split**: for each source (paysim, -sparkov), rows are sorted ascending by `txn_timestamp` and the last -`test_size` (default 20%) fraction becomes the test set. The two source -test sets are concatenated to form the combined evaluation set. - -## Why per-source temporal — not random, not stratified, not k-fold - -### Random / stratified random (the prior approach) leaks the future - -Random splits assume rows are exchangeable. Fraud transactions are not: - -- Reissued cards land in subsequent months, not the same hour. -- Compromised merchants tend to cluster in time. -- The mix of `transaction_type` shifts over a 6-month window - (e.g., online/POS share changes in 2020 due to COVID). -- Velocity-based features (per-card spending rate, time-since-last-txn) - are time-correlated by construction. - -With `train_test_split(..., stratify=y)`, the model sees a March 2020 -fraud burst in training and is then asked to predict on March 2020 frauds -in test that share temporally adjacent context (same merchants, same -compromised regions). It can effectively interpolate within the period. -Production scoring at runtime cannot — it sees strictly-newer txns. - -### Per-source temporal is closer to production reality - -In production, a model trained yesterday scores transactions today. The -strictly-future evaluation regime matches that. Per-source preserves each -dataset's own chronology: - -- **paysim**: `step` column = simulated hour. We map to `step * 3600` to - produce a sortable per-source timestamp. -- **sparkov**: `trans_date_trans_time` parsed to unix seconds. (Also - available pre-parsed as `unix_time`.) - -Cross-source merging on a single timeline would be meaningless (paysim's -synthetic clock isn't comparable to sparkov's calendar dates), so we -split each source independently and concat. - -### Why not k-fold time-series CV - -A single forward chronological split is the cleanest match for the -benchmark setup (compare to AutoGluon's published number on a fixed -held-out sparkov split). Time-series CV (TimeSeriesSplit) is the right -tool for **hyperparameter tuning** — that's Day-5's job, not Day-1. - -## Combine stage: chronological sort, not shuffle - -`src/combine_datasets.py` previously ran -`combined.sample(frac=1.0, random_state=42)` after concat, destroying -all time order. That's incompatible with a temporal split. Day-1 fix: -replace the shuffle with `sort_values(["source", "txn_timestamp"])` so -each source block in `combined_transactions.csv` is monotonic in time. - -## Benchmark file: sparkov_test.csv, not sparkov.csv - -The held-out sparkov benchmark file (`data/raw/sparkov_test.csv`, -2020-06-21 → 2020-12-31) is strictly after the training file's date -range (2019-01-01 → 2020-06-21). Using it for `benchmark_fdb.py` gives -an honest AutoGluon-comparable AUC. The prior code fell back to -`sparkov.csv` itself (= the training file) when the FDB Python package -wasn't installed, which is the leak that produced the 0.921 claim. - -## Honest numbers (post-fix, 2026-05-18) - -- **Combined temporal test AUC**: 0.999 (dominated by paysim balance signals) -- **Sparkov-only temporal test AUC** (final 20% of sparkov.csv): 0.997 -- **Sparkov-only held-out file AUC** (sparkov_test.csv): **0.795** - -The 0.20 AUC drop from in-period test to truly-out-of-period file is -**real distribution shift**, not modeling failure. That delta is the gap -that Day-3's drift-triggered retrain has to cover. diff --git a/docs/MLOPS_AUDIT.md b/docs/MLOPS_AUDIT.md deleted file mode 100644 index 771a4cd..0000000 --- a/docs/MLOPS_AUDIT.md +++ /dev/null @@ -1,116 +0,0 @@ -# Sentinel — MLOps Audit (Day 1, 2026-05-18) - -This document inventories Sentinel's pre-sprint state and calls out the two -leakage bugs that inflated the 0.921 sparkov AUC claim. Both are fixed today. - -## 1. Existing 6-stage DVC pipeline (dvc.yaml) - -| Stage | Script | Inputs | Outputs | -|----------------|---------------------------------|------------------------------------------------------------|------------------------------------------------------------------------| -| load_fdb | `src/load_fdb.py` | (FDB API or local sparkov) | `data/raw/sparkov.csv` | -| combine | `src/combine_datasets.py` | `data/raw/{paysim,sparkov}.csv` | `data/processed/combined_transactions.csv` | -| preprocess | `src/preprocess.py` | combined_transactions.csv | `data/processed/{features.csv, feature_thresholds.json}` | -| train | `src/train.py` | features.csv + feature_thresholds.json | `models/fraud_model.pkl`, `X_test.csv`, `y_test.csv` | -| evaluate | `src/evaluate.py` | model + X_test/y_test | `metrics/scores.json`, `reports/confusion_matrix.svg`, `roc_curve.svg` | -| benchmark_fdb | `src/benchmark_fdb.py` | model + (FDB sparkov split or local sparkov) | `metrics/fdb_benchmark.json` | - -Model: XGBoost (`scale_pos_weight=50`, n_estimators=200, max_depth=6, lr=0.1). -Optional SMOTE on training split. - -## 2. Pre-fix state (what was being reported) - -`metrics/fdb_benchmark.json` (pre-fix snapshot) advertised: - -``` -our_model_auc: 0.9210 (sparkov) -AutoGluon: 0.952 (BELOW by 0.031) -H2O AutoML: 0.947 (BELOW by 0.026) -AutoSklearn: 0.931 (BELOW by 0.010) -``` - -That number is **not honest**. Two leakage paths produced it. - -## 3. Leakage Bug #1 — random `train_test_split` in `src/train.py:47-53` - -Original code: - -```python -X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=test_size, random_state=random_state, - stratify=y if y.nunique() > 1 else None, -) -``` - -For fraud-detection on time-series transactions, this is wrong: - -- Fraud patterns evolve over time (new merchant categories, new compromise - vectors, seasonal velocity changes). -- A random split puts March 2020 txns in train AND March 2020 txns in test - — the model gets to "look up the answer" via correlated features it just - saw a week earlier in the same period. -- The honest production setup is: train on the past, evaluate on the - strictly-later future. Anything else inflates test metrics. - -**Fix:** `temporal_split_per_source(...)` in `src/train.py`. For each -`source_*` one-hot group, sort by `txn_timestamp` and take the LAST -`test_size` fraction as test. Per-source because paysim's `step` timeline -and sparkov's unix timestamps live on different scales and can't be merged -into a single sortable axis. - -## 4. Leakage Bug #2 — benchmark fell back to the training file - -`src/benchmark_fdb.py` originally fell back to `data/raw/sparkov.csv` when -the FDB package was unavailable. But: - -- `data/raw/sparkov.csv` is the SAME file consumed by the combine stage. - So a row in sparkov.csv is in both train.py's training data AND the - benchmark's "test" data. -- That makes `benchmark_fdb.json`'s 0.921 a measurement of memorisation, - not generalisation. - -There's a clean alternative right there on disk: -`data/raw/sparkov_test.csv` (555,719 rows, 2020-06-21 → 2020-12-31). It's -chronologically after sparkov.csv's range (2019-01-01 → 2020-06-21) and -was never read by the train pipeline. - -**Fix:** `src/benchmark_fdb.py` now prefers `sparkov_test.csv` and only -falls back to `sparkov.csv` with a printed WARNING. - -## 5. Post-fix honest numbers (2026-05-18) - -| Measurement | AUC | AP | Notes | -|------------------------------------------------------------------|----------|---------|--------------------------------------------------------| -| Pre-fix benchmark (random split + train file as test) | 0.9210 | — | Retired today. Inflated by stacked leakage. | -| Post-fix combined temporal test (1.53M rows, paysim+sparkov) | 0.9989 | 0.8280 | Dominated by paysim's near-deterministic features. | -| Post-fix paysim-only train-time temporal test (1.27M rows) | 0.9997 | 0.9170 | paysim signal doesn't decay in-distribution. | -| Post-fix sparkov-only train-time temporal test (259k rows) | 0.9966 | 0.8090 | Final 20% of training file. Same data-collection regime.| -| **Post-fix benchmark on held-out sparkov_test.csv (Jun–Dec 2020)** | **0.7949** | — | **The honest sparkov AUC. -0.157 vs AutoGluon 0.952.** | - -The ~0.20 AUC drop between the train-time sparkov test slice and the -held-out sparkov_test.csv file is the real **distribution shift** the -rest of the sprint has to close — and it's exactly what Day-3's drift -detector + auto-retrain will be measured against. - -## 6. MLflow tracking (Day-1 addition) - -- Local sqlite store at `mlflow.db` (gitignored). -- Experiment: `sentinel-day01-temporal-split`. -- Every `python -m src.train` run is now wrapped in `mlflow.start_run()`. -- Params, metrics, and the model artifact log automatically. View with: - `mlflow ui --backend-store-uri sqlite:///mlflow.db`. - -## 7. Where the upgrade is going - -| Day | Add | -|-----|----------------------------------------------------------------------------------------------| -| 2 | Dask-based feature engineering (`src/features/engineer.py`) + MLflow registry promote/rollback | -| 3 | Drift detector (`src/drift/detector.py`) + auto-retrain trigger | -| 4 | Production refactor: shadow deploy, Postgres telemetry, FastAPI | -| 5 | Optuna sweep + failure-mode analysis | -| 6 | LLM-judged-fraud negative result + frontier comparison | -| 7 | Docker Compose stack + Streamlit ops dashboard + tests + README | - -Differentiation guard: Sentinel's resume claim is **MLOps discipline at -scale** (drift response time, throughput, rollback latency). The joint -Fraud Detection project owns AUPRC/ensemble/SHAP — Sentinel does not -compete there. diff --git a/results/baseline_metrics.json b/results/baseline_metrics.json deleted file mode 100644 index b070a2f..0000000 --- a/results/baseline_metrics.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "captured_on": "2026-05-18", - "day": 1, - "phase": "1 — audit + temporal-split fix + baseline", - "story": "Pre-fix benchmark AUC of 0.921 was inflated by two leakage paths: (1) train_test_split with stratify=y in src/train.py was a RANDOM split that mixed future txns into training; (2) benchmark_fdb fell back to data/raw/sparkov.csv which is the SAME file used for training. After fixing both — per-source temporal split in train.py + held-out sparkov_test.csv in benchmark_fdb — the honest sparkov AUC vs AutoGluon is 0.795, not 0.921.", - "pre_fix": { - "split_strategy": "random_stratified", - "benchmark_data_source": "data/raw/sparkov.csv (same as train set)", - "reported_sparkov_benchmark_auc": 0.9210076846056613, - "delta_vs_AutoGluon_baseline_0.952": -0.030992, - "honest_assessment": "INVALID — both random split and training-set test → leakage stacked" - }, - "post_fix_combined_temporal_test": { - "split_strategy": "temporal_per_source (last 20% of each source by txn_timestamp)", - "test_rows": 1531859, - "test_fraud_rate": 0.00378, - "test_accuracy": 0.996564, - "test_auc": 0.998906, - "test_average_precision": 0.827993, - "test_precision": 0.5268, - "test_recall": 0.8974, - "test_f1": 0.6639, - "note": "Inflated by paysim's near-deterministic balance_change_orig signal (paysim is 83% of combined data)." - }, - "post_fix_per_source_train_time_test": { - "source_paysim": { - "rows": 1272524, - "fraud_rate": 0.003343, - "auc": 0.999650, - "average_precision": 0.917024 - }, - "source_sparkov": { - "rows": 259335, - "fraud_rate": 0.005931, - "auc": 0.996608, - "average_precision": 0.809023, - "note": "Held-out window is the final 20% of sparkov.csv (~Mar–Jun 2020). Same data-collection regime as training so the model interpolates well." - } - }, - "post_fix_benchmark_held_out_sparkov_test_file": { - "test_file": "data/raw/sparkov_test.csv", - "date_range": "2020-06-21 12:14:25 → 2020-12-31 23:59:34", - "rows": 555719, - "our_auc": 0.794896, - "baselines": { - "AutoGluon": {"baseline_auc": 0.952, "delta": -0.157104, "status": "BELOW"}, - "H2O AutoML": {"baseline_auc": 0.947, "delta": -0.152104, "status": "BELOW"}, - "AutoSklearn": {"baseline_auc": 0.931, "delta": -0.136104, "status": "BELOW"} - }, - "headline": "0.795 — this is Sentinel's honest sparkov AUC for the rest of the sprint. The 0.921 claim retires today.", - "interpretation": "Held-out file covers Jun–Dec 2020, a time period never seen in any form during training. The 0.20 AP/0.20 AUC drop vs the train-time test slice quantifies real distribution shift — and that's the gap auto-retrain (Day 3) must close." - }, - "mlflow_run": { - "experiment": "sentinel-day01-temporal-split", - "run_name": "day01_phase1_temporal_split", - "tracking_uri": "sqlite:///mlflow.db (local)", - "logged_params": ["split_strategy", "test_size", "random_state", "n_estimators", "max_depth", "learning_rate", "scale_pos_weight", "use_smote", "early_stopping_rounds", "train_rows", "test_rows", "train_fraud_rate", "test_fraud_rate"], - "logged_metrics": ["test_accuracy", "test_auc", "test_average_precision"], - "logged_artifacts": ["xgboost_model (mlflow.xgboost)", "models/fraud_model.pkl"] - }, - "files_changed_today": [ - "src/combine_datasets.py — added txn_timestamp column, replaced shuffle with chronological sort", - "src/preprocess.py — added txn_timestamp to required schema + passthrough", - "src/train.py — replaced train_test_split with temporal_split_per_source; wrapped run in mlflow.start_run()", - "src/benchmark_fdb.py — prefer held-out sparkov_test.csv over leaking sparkov.csv fallback" - ] -} diff --git a/results/day04_api_smoke.json b/results/day04_api_smoke.json deleted file mode 100644 index 0f60984..0000000 --- a/results/day04_api_smoke.json +++ /dev/null @@ -1,87 +0,0 @@ -{ - "health": { - "status": "ok", - "prod_version": "local-pkl", - "prod_loaded_at": "2026-05-21T06:42:03.007430+00:00", - "shadow": { - "enabled": false, - "version": null, - "source": "disabled" - }, - "telemetry_url": "sqlite:///C:/Users/antho/OneDrive/Desktop/Mark/ALL/DATA_SCIENTIST/Sentinel/data/telemetry_smoke.sqlite" - }, - "predict_calls": 100, - "predict_latency_wall_ms_mean": 15.8763179986272, - "predict_latency_wall_ms_p50": 14.8503000382334, - "predict_latency_wall_ms_p95": 18.142860027728602, - "predict_latency_wall_ms_p99": 23.74852397595556, - "predict_latency_server_ms_mean": 7.625280007487163, - "predict_latency_server_ms_p95": 8.250275044701993, - 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null, - "to_version": "1", - "flip_seconds": 0.004, - "actor": "smoke", - "reason": "day04 e2e smoke insertion" - } - ] - }, - "metrics_retrain_events": { - "rows": [] - }, - "metrics_shadow_agreement": { - "window_hours": 1.0, - "paired_predictions": 0, - "label_disagreements": 0, - "label_disagreement_rate": 0.0, - "mean_abs_proba_delta": 0.0, - "mean_signed_proba_delta": 0.0 - }, - "telemetry_direct_row_count": 100, - "telemetry_url": "sqlite:///C:/Users/antho/OneDrive/Desktop/Mark/ALL/DATA_SCIENTIST/Sentinel/data/telemetry_smoke.sqlite" -} \ No newline at end of file diff --git a/results/day04_shadow_smoke.json b/results/day04_shadow_smoke.json deleted file mode 100644 index 7c11ed3..0000000 --- a/results/day04_shadow_smoke.json +++ /dev/null @@ -1,49 +0,0 @@ -{ - "aliases": { - "production": "1", - "staging": "9" - }, - "health": { - "status": "ok", - "prod_version": "local-pkl", - "prod_loaded_at": "2026-05-21T06:43:19.740056+00:00", - "shadow": { - "enabled": true, - "version": "9", - "source": "mlflow:@staging" - }, - "telemetry_url": "sqlite:///C:/Users/antho/OneDrive/Desktop/Mark/ALL/DATA_SCIENTIST/Sentinel/data/telemetry_shadow_smoke.sqlite" - }, - "predict_calls": 40, - "predict_wall_ms_mean": 17.65554999874439, - "predict_wall_ms_p95": 21.088674996281043, - "agreement": { - "window_hours": 1.0, - "paired_predictions": 40, - "label_disagreements": 2, - "label_disagreement_rate": 0.05, - "mean_abs_proba_delta": 0.16709014411385964, - "mean_signed_proba_delta": 0.0029160817277045226 - }, - "prediction_metrics": { - "window_hours": 1.0, - "by_role": { - "production": { - "count": 40, - "fraud_predicted": 2, - "fraud_rate": 0.05, - "latency_ms_mean": 5.185385004733689, - "latency_ms_p95": 6.913065037224441, - "proba_mean": 0.11069722146425534 - }, - "shadow": { - "count": 40, - "fraud_predicted": 0, - "fraud_rate": 0.0, - "latency_ms_mean": 5.202199998893775, - "latency_ms_p95": 6.207559991162269, - "proba_mean": 0.11361330319195986 - } - } - } -} \ No newline at end of file diff --git a/results/day05/day05_leaderboard.csv b/results/day05/day05_leaderboard.csv deleted file mode 100644 index df1a2b5..0000000 --- a/results/day05/day05_leaderboard.csv +++ /dev/null @@ -1,6 +0,0 @@ -rank,strategy,training_scope,sample_weight,threshold,in_dist_sparkov_auc,oot_sparkov_test_auc,oot_ap,oot_recall,oot_precision,oot_f1,delta_vs_autogluon,notes -1,AutoGluon (FDB published baseline),FDB standard,n/a,n/a,,0.952,,,,,0.0,reference baseline -2,Day-5 Optuna + source-balanced weights -threshold=0.5,"full (6.1M, paysim 0.60x / sparkov 2.95x)",source_balanced,0.5,0.9971775606973103,0.951958845551572,0.20830353279765584,0.2592074592074592,0.2744323790720632,0.2666027331575162,-0.0,AUC ties AutoGluon; recall jumps 6x -3,Day-5 Optuna + source-balanced weights -threshold=tau*,"full (6.1M, paysim 0.60x / sparkov 2.95x)",source_balanced,0.8939489722251892,0.9971775606973103,0.951958845551572,0.20830353279765584,0.14685314685314685,0.5526315789473685,0.23204419889502761,-0.0,"F1 trade: higher precision (0.55), lower recall (0.15)" -4,Day-5 Optuna best (30 trials) -full retrain,full (6.1M),uniform,0.5,0.9970274251784879,0.915380324555611,0.067267067319249,0.042890442890442894,0.34328358208955223,0.0762536261914629,-0.0366,tuning recovers ranking; recall still collapses at 0.5 -5,"Day-1 honest baseline (random XGB defaults, temporal split fix)",full (6.1M),uniform,0.5,0.996608,0.794896,,,,,-0.1571,honest baseline -pre-tuning diff --git a/results/day05/failure_modes.csv b/results/day05/failure_modes.csv deleted file mode 100644 index 7f52726..0000000 --- a/results/day05/failure_modes.csv +++ /dev/null @@ -1,26 +0,0 @@ -slice_variable,slice_value,n,n_fraud,fraud_rate,n_pred_fraud,n_caught,n_false_pos,recall_at_0.5,precision_at_0.5,auc,average_precision -amount_bucket,$10-100,311484,330,0.001059444465847363,47,39,8,0.11818181818181818,0.8297872340425532,0.9633231211047983,0.20725899573076453 -amount_bucket,$100-1000,98402,1389,0.014115566756773236,3,1,2,0.0007199424046076314,0.3333333333333333,0.9396774045341997,0.22787515286351356 -amount_bucket,$1000-10000,1571,276,0.17568427753023552,0,0,0,0.0,0.0,0.9413015499972022,0.7545841646573512 -amount_bucket,<$10,144250,150,0.0010398613518197574,218,52,166,0.3466666666666667,0.23853211009174313,0.9466130927596577,0.1469047605692006 -amount_bucket,>$10000,12,0,0.0,0,0,0,0.0,0.0,, -hour_bucket,00-05_night,108945,754,0.006920923401716462,100,86,14,0.11405835543766578,0.86,0.9363125354470004,0.27116900387776244 -hour_bucket,06-11_morning,108380,83,0.0007658239527588116,21,4,17,0.04819277108433735,0.19047619047619047,0.8353459267692114,0.04273501249072134 -hour_bucket,12-17_afternoon,169094,103,0.0006091286503364992,98,0,98,0.0,0.0,0.8245944389639179,0.0021729315141293592 -hour_bucket,18-23_evening,169300,1205,0.0071175428233904315,49,2,47,0.0016597510373443983,0.04081632653061224,0.9350672208056623,0.06938385126561127 -merchant_category,entertainment,40104,59,0.001471174945142629,0,0,0,0.0,0.0,0.9977446982314389,0.484934288070164 -merchant_category,food_dining,39268,54,0.0013751655291840686,2,0,2,0.0,0.0,0.9277029745612395,0.010616512880510345 -merchant_category,gas_transport,56370,154,0.0027319496185914493,90,90,0,0.5844155844155844,1.0,0.9996977105006848,0.9448274579409679 -merchant_category,grocery_net,19426,41,0.0021105734582518272,1,0,1,0.0,0.0,0.9635599564662141,0.0486627062009266 -merchant_category,grocery_pos,52553,485,0.009228778566399636,1,1,0,0.002061855670103093,1.0,0.992561867945882,0.8023613919836 -merchant_category,health_fitness,36674,52,0.0014178982385341114,0,0,0,0.0,0.0,0.9143211520607621,0.008359551254586454 -merchant_category,home,52345,67,0.0012799694335657657,0,0,0,0.0,0.0,0.9830323020499476,0.10556895931740445 -merchant_category,kids_pets,48692,65,0.0013349215476875051,6,0,6,0.0,0.0,0.9187401427823416,0.007691729378709052 -merchant_category,misc_net,27367,267,0.00975627580662842,0,0,0,0.0,0.0,0.9444695053692111,0.17843595875446547 -merchant_category,misc_pos,34574,72,0.0020824897321686815,52,1,51,0.013888888888888888,0.019230769230769232,0.734222734269833,0.008805918010219432 -merchant_category,personal_care,39327,70,0.0017799476186843644,0,0,0,0.0,0.0,0.9089811098293662,0.009391906831067947 -merchant_category,shopping_net,41779,506,0.012111347806314177,0,0,0,0.0,0.0,0.9310497277886212,0.09036949247007005 -merchant_category,shopping_pos,49791,213,0.0042778815448575045,0,0,0,0.0,0.0,0.8874925024483638,0.0205993869636835 -merchant_category,travel,17449,40,0.0022923949796549944,116,0,116,0.0,0.0,0.7956617266930899,0.006454093596530067 -gender,F,304886,1164,0.003817820431243153,131,31,100,0.02663230240549828,0.2366412213740458,0.9077679223682372,0.05379051043272596 -gender,M,250833,981,0.003910968652450037,137,61,76,0.06218144750254842,0.44525547445255476,0.9242553793680721,0.08497300205690661 diff --git a/results/day05/failure_modes_summary.json b/results/day05/failure_modes_summary.json deleted file mode 100644 index 507fd80..0000000 --- a/results/day05/failure_modes_summary.json +++ /dev/null @@ -1,78 +0,0 @@ -{ - "n_total": 555719, - "n_fraud_total": 2145, - "n_caught_total": 92, - "overall_recall_at_0.5": 0.042890442890442894, - "by_slice": { - "amount_bucket": { - "worst": { - "slice_value": "$1000-10000", - "n": 1571, - "n_fraud": 276, - "recall_at_0.5": 0.0, - "auc": 0.9413015499972022 - }, - "best": { - "slice_value": "<$10", - "n": 144250, - "n_fraud": 150, - "recall_at_0.5": 0.3466666666666667, - "auc": 0.9466130927596577 - }, - "spread_recall": 0.3466666666666667 - }, - "gender": { - "worst": { - "slice_value": "F", - "n": 304886, - "n_fraud": 1164, - "recall_at_0.5": 0.02663230240549828, - "auc": 0.9077679223682372 - }, - "best": { - "slice_value": "M", - "n": 250833, - "n_fraud": 981, - "recall_at_0.5": 0.06218144750254842, - "auc": 0.9242553793680721 - }, - "spread_recall": 0.035549145097050136 - }, - "hour_bucket": { - "worst": { - "slice_value": "12-17_afternoon", - "n": 169094, - "n_fraud": 103, - "recall_at_0.5": 0.0, - "auc": 0.8245944389639179 - }, - "best": { - "slice_value": "00-05_night", - "n": 108945, - "n_fraud": 754, - "recall_at_0.5": 0.11405835543766578, - "auc": 0.9363125354470004 - }, - "spread_recall": 0.11405835543766578 - }, - "merchant_category": { - "worst": { - "slice_value": "entertainment", - "n": 40104, - "n_fraud": 59, - "recall_at_0.5": 0.0, - "auc": 0.9977446982314389 - }, - "best": { - "slice_value": "gas_transport", - "n": 56370, - "n_fraud": 154, - "recall_at_0.5": 0.5844155844155844, - "auc": 0.9996977105006848 - }, - "spread_recall": 0.5844155844155844 - } - }, - "dominant_failure_variable": "merchant_category", - "interpretation": "Variable 'merchant_category' shows the widest gap in recall@0.5 across its values (spread=0.584). That's the slice where targeted re-weighting or category-aware features should yield the biggest lift." -} \ No newline at end of file diff --git a/results/day05/optuna_best_params.json b/results/day05/optuna_best_params.json deleted file mode 100644 index 679fde9..0000000 --- a/results/day05/optuna_best_params.json +++ /dev/null @@ -1,22 +0,0 @@ -{ - "best_trial_number": 25, - "best_value_test_oot_sparkov_auc": 0.9644356515996079, - "best_params": { - "n_estimators": 400, - "max_depth": 5, - "learning_rate": 0.21962223412218645, - "scale_pos_weight": 14.825891090875654, - "subsample": 0.8786573830716804, - "colsample_bytree": 0.7269111389090186, - "reg_alpha": 0.0006080567474434866, - "reg_lambda": 9.905911841286033 - }, - "n_trials": 30, - "tuning_train_rows": 409927, - "tuning_negatives_cap": 400000, - "sweep_seconds": 598.7224934101105, - "honest_baseline_sparkov_test_auc": 0.794896, - "autogluon_baseline_auc": 0.952, - "delta_vs_autogluon": 0.012435651599607911, - "delta_vs_day1_honest": 0.16953965159960782 -} \ No newline at end of file diff --git a/results/day05/optuna_trials.csv b/results/day05/optuna_trials.csv deleted file mode 100644 index 43eb6b1..0000000 --- a/results/day05/optuna_trials.csv +++ /dev/null @@ -1,31 +0,0 @@ -number,value,datetime_start,datetime_complete,duration,params_colsample_bytree,params_learning_rate,params_max_depth,params_n_estimators,params_reg_alpha,params_reg_lambda,params_scale_pos_weight,params_subsample,state -0,0.6011225292920243,2026-05-22 15:30:34.393260,2026-05-22 15:30:56.678577,0 days 00:00:22.285317,0.5779972601681014,0.1205712628744377,10,350,2.550264850403287e-06,1.156732719914599,15.75132049977973,0.5780093202212182,COMPLETE -1,0.515167637551998,2026-05-22 15:30:56.679577,2026-05-22 15:33:29.605892,0 days 00:02:32.926315,0.6061695553391381,0.010725209743171996,8,550,1.8740223688836324e-05,1.922346047064365e-05,87.06020878304857,0.9162213204002109,COMPLETE -2,0.8949797001679859,2026-05-22 15:33:29.608980,2026-05-22 15:33:57.119058,0 days 00:00:27.510078,0.569746930326021,0.04345454109729477,7,300,0.00011092068418536184,0.0003668874895499185,3.8234752246751853,0.8059264473611898,COMPLETE -3,0.7917834191132793,2026-05-22 15:33:57.119058,2026-05-22 15:34:13.929862,0 days 00:00:16.810804,0.5232252063599989,0.019721610970574007,9,400,0.017898389848671606,1.5619562520792747e-05,10.677482709481351,0.7962072844310213,COMPLETE -4,0.7153807325843946,2026-05-22 15:34:13.929862,2026-05-22 15:34:19.185161,0 days 00:00:05.255299,0.5488360570031919,0.26690431824362526,10,100,0.06160715952774549,0.0012052231254145623,41.380401125610156,0.6523068845866853,COMPLETE -5,0.09677487648960298,2026-05-22 15:34:19.185161,2026-05-22 15:34:24.936453,0 days 00:00:05.751292,0.831261142176991,0.011240768803005551,6,150,0.0001520468869219893,0.004369946783595583,65.86289317583112,0.6293899908000085,COMPLETE -6,0.9620411930027266,2026-05-22 15:34:24.937458,2026-05-22 15:34:35.320547,0 days 00:00:10.383089,0.9474136752138245,0.27051668818999286,4,500,0.015321449415450729,2.8387009634436295,35.50304858128308,0.9697494707820946,COMPLETE -7,0.6003425652182639,2026-05-22 15:34:35.320547,2026-05-22 15:34:39.774613,0 days 00:00:04.454066,0.6356745158869479,0.011662890273931383,4,150,0.6326486185661588,0.0003142485531883166,4.473636174621264,0.6943386448447411,COMPLETE -8,0.20408029752128282,2026-05-22 15:34:39.774613,2026-05-22 15:34:51.293266,0 days 00:00:11.518653,0.9934434683002586,0.016149614799999188,7,300,0.2545150013091294,2.4604229580184192e-05,40.215545266902886,0.5372753218398854,COMPLETE -9,0.9452497242689701,2026-05-22 15:34:51.294279,2026-05-22 15:34:56.561689,0 days 00:00:05.267410,0.5370223258670452,0.11069143219393454,9,100,0.0003230428252240964,6.472669269538641e-06,28.70875348195468,0.8856351733429728,COMPLETE -10,0.8883253052722718,2026-05-22 15:34:56.561689,2026-05-22 15:35:11.221939,0 days 00:00:14.660250,0.9538323976412588,0.2704729722717776,3,800,5.228718023161329,6.876619532793238,1.0862348973937144,0.9919371080730972,COMPLETE -11,0.8733634915028912,2026-05-22 15:35:11.223000,2026-05-22 15:35:25.547787,0 days 00:00:14.324787,0.7491729271388597,0.11511579879447058,5,600,0.0020370329154028987,0.06753436544696881,20.759917675098652,0.8991358530198215,COMPLETE -12,0.9551352536254284,2026-05-22 15:35:25.547787,2026-05-22 15:35:36.358071,0 days 00:00:10.810284,0.8615491395875623,0.1305392574908533,3,550,0.0019095741730503948,0.05466714667177817,28.463125811845345,0.9905169711424098,COMPLETE -13,0.957098099459193,2026-05-22 15:35:36.358071,2026-05-22 15:35:48.153749,0 days 00:00:11.795678,0.8785707251225086,0.17614901867816987,3,550,0.00446911241500349,0.0923935771800147,4.932590932086841,0.9952400573085499,COMPLETE -14,0.949288389379687,2026-05-22 15:35:48.153749,2026-05-22 15:36:15.152889,0 days 00:00:26.999140,0.8940298274686582,0.19354813802627246,5,700,0.017841784586287487,0.2691671640153804,4.709695855956926,0.9428478811143278,COMPLETE -15,0.8491798684611209,2026-05-22 15:36:15.152889,2026-05-22 15:36:35.479444,0 days 00:00:20.326555,0.7778028586781293,0.057442837008057634,4,500,0.005242672023496199,6.09381077438791,2.14344607897035,0.8325809723893237,COMPLETE -16,0.9405801578103745,2026-05-22 15:36:35.479444,2026-05-22 15:36:54.924195,0 days 00:00:19.444751,0.9236793253371007,0.065872669079022,4,700,0.22686279924387334,0.01470567472143128,8.478772686135189,0.9993476292849305,COMPLETE -17,0.9128777762284755,2026-05-22 15:36:54.924195,2026-05-22 15:37:09.963540,0 days 00:00:15.039345,0.6943845973931931,0.1744115940116885,3,450,2.002183405260091,0.7700306123159134,10.766071937007405,0.730400062259047,COMPLETE -18,0.8835547405310435,2026-05-22 15:37:09.963540,2026-05-22 15:37:29.306245,0 days 00:00:19.342705,0.8137099133074839,0.07836360549485492,5,650,0.054134391863903335,0.18947216565783967,1.974346750480727,0.8441776737355275,COMPLETE -19,0.9315999626348377,2026-05-22 15:37:29.307245,2026-05-22 15:38:01.536535,0 days 00:00:32.229290,0.9928394698896904,0.036046969689776935,4,800,0.0007605977589872542,0.8570910616239972,7.336538861985154,0.9467362394492501,COMPLETE -20,0.9362032873679013,2026-05-22 15:38:01.537536,2026-05-22 15:38:21.402439,0 days 00:00:19.864903,0.8980570857694244,0.18291466590952005,6,450,3.2358745758703676e-05,0.011899987270441052,2.63272061630844,0.8614174361655524,COMPLETE -21,0.9325747711903853,2026-05-22 15:38:21.403524,2026-05-22 15:38:35.697984,0 days 00:00:14.294460,0.856995421265769,0.16161445557774803,3,550,0.004182808255452205,0.06296759289503503,19.51677709700271,0.9633775850447397,COMPLETE -22,0.9297402946058771,2026-05-22 15:38:35.697984,2026-05-22 15:38:48.831563,0 days 00:00:13.133579,0.8768566213317985,0.2937827566639752,3,550,0.0011489844134478268,0.03423250129690268,54.70648126311346,0.9929467414870505,COMPLETE -23,0.910536703292324,2026-05-22 15:38:48.831563,2026-05-22 15:39:11.058320,0 days 00:00:22.226757,0.9409600265127057,0.1374716360528525,3,650,0.011744107355979303,1.801202009563373,33.29635106746831,0.9312404495902852,COMPLETE -24,0.905928528532914,2026-05-22 15:39:11.059320,2026-05-22 15:39:25.028158,0 days 00:00:13.968838,0.7881031508603169,0.09369120226605358,4,500,0.0625437215414033,0.19807202568622703,98.84157497090006,0.9578425937471792,COMPLETE -25,0.9644356515996079,2026-05-22 15:39:25.029166,2026-05-22 15:39:37.671224,0 days 00:00:12.642058,0.7269111389090186,0.21962223412218645,5,400,0.0006080567474434866,9.905911841286033,14.825891090875654,0.8786573830716804,COMPLETE -26,0.9317524306535713,2026-05-22 15:39:37.671224,2026-05-22 15:39:51.139180,0 days 00:00:13.467956,0.7121316254573203,0.21722724066271415,5,350,0.0003773086675892655,9.915338764479008,6.147238999738145,0.7727738046369513,COMPLETE -27,0.9479080532695766,2026-05-22 15:39:51.139180,2026-05-22 15:40:07.596971,0 days 00:00:16.457791,0.6614581510218185,0.25430826672372164,6,250,4.3672265948750785e-05,4.536916086585203,13.251417145298985,0.891234515163111,COMPLETE -28,0.935675989117986,2026-05-22 15:40:07.597951,2026-05-22 15:40:20.661680,0 days 00:00:13.063729,0.7284510837789464,0.20012046438366465,5,400,7.685264616869571e-06,2.097554234452766,20.731805973849443,0.8654999282136744,COMPLETE -29,0.9126046074846054,2026-05-22 15:40:20.661680,2026-05-22 15:40:33.112771,0 days 00:00:12.451091,0.9662915750924788,0.1524924923062171,4,400,0.006185733978007887,0.46634893198801725,6.694000025528722,0.9164087998653861,COMPLETE diff --git a/results/day05/targeted_fix_eval.json b/results/day05/targeted_fix_eval.json deleted file mode 100644 index b71a43e..0000000 --- a/results/day05/targeted_fix_eval.json +++ /dev/null @@ -1,80 +0,0 @@ -{ - "model_artifact": "models\\fraud_model_tuned_fixed.pkl", - "best_params": { - "n_estimators": 400, - "max_depth": 5, - "learning_rate": 0.21962223412218645, - "scale_pos_weight": 14.825891090875654, - "subsample": 0.8786573830716804, - "colsample_bytree": 0.7269111389090186, - "reg_alpha": 0.0006080567474434866, - "reg_lambda": 9.905911841286033 - }, - "sample_weight_mode": "source_balanced", - "decision_threshold_tau_star": 0.8939489722251892, - "in_distribution_per_source": { - "source_paysim": { - "rows": 1272524, - "auc": 0.9994267138935031, - "average_precision": 0.8848017339622571 - }, - "source_sparkov": { - "rows": 259335, - "auc": 0.9971775606973103, - "average_precision": 0.8471759349037878 - } - }, - "out_of_time_sparkov_test_csv": { - "rows": 555719, - "fraud_rate": 0.0038598644278853163, - "auc": 0.951958845551572, - "average_precision": 0.20830353279765584, - "at_threshold_0.5": { - "accuracy": 0.9944954194475986, - "precision": 0.2744323790720632, - "recall": 0.2592074592074592, - "f1": 0.2666027331575162, - "confusion_matrix": [ - [ - 552104, - 1470 - ], - [ - 1589, - 556 - ] - ] - }, - "at_threshold_tau_star": { - "accuracy": 0.9962481038078598, - "precision": 0.5526315789473685, - "recall": 0.14685314685314685, - "f1": 0.23204419889502761, - "confusion_matrix": [ - [ - 553319, - 255 - ], - [ - 1830, - 315 - ] - ] - } - }, - "comparison": { - "day1_honest_baseline_oot_auc": 0.794896, - "day5_tuned_only_oot_auc": 0.91538, - "day5_tuned_plus_fix_oot_auc": 0.951958845551572, - "autogluon_baseline_oot_auc": 0.952, - "lift_vs_tuned_only": 0.03657884555157198, - "lift_vs_day1": 0.1570628455515719, - "delta_vs_autogluon": -4.115444842800464e-05 - }, - "recall_lift_at_tau_star": { - "tuned_only_recall_at_0.5": 0.04289, - "fixed_recall_at_0.5": 0.2592074592074592, - "fixed_recall_at_tau_star": 0.14685314685314685, - "recall_uplift_pct_points": 10.396314685314685 - } -} \ No newline at end of file diff --git a/results/day05/tuned_eval.json b/results/day05/tuned_eval.json deleted file mode 100644 index 76bb757..0000000 --- a/results/day05/tuned_eval.json +++ /dev/null @@ -1,54 +0,0 @@ -{ - "model_artifact": "models\\fraud_model_tuned.pkl", - "best_params": { - "n_estimators": 400, - "max_depth": 5, - "learning_rate": 0.21962223412218645, - "scale_pos_weight": 14.825891090875654, - "subsample": 0.8786573830716804, - "colsample_bytree": 0.7269111389090186, - "reg_alpha": 0.0006080567474434866, - "reg_lambda": 9.905911841286033 - }, - "in_distribution_per_source": { - "source_paysim": { - "rows": 1272524, - "fraud_rate": 0.0033429624902948786, - "auc": 0.9996391853732142, - "average_precision": 0.9158891225378649 - }, - "source_sparkov": { - "rows": 259335, - "fraud_rate": 0.005930553145545337, - "auc": 0.9970274251784879, - "average_precision": 0.8177287582958996 - } - }, - "out_of_time_sparkov_test_csv": { - "rows": 555719, - "fraud_rate": 0.0038598644278853163, - "auc": 0.915380324555611, - "average_precision": 0.067267067319249, - "accuracy": 0.9959889800420716, - "precision_at_0.5": 0.34328358208955223, - "recall_at_0.5": 0.042890442890442894, - "f1_at_0.5": 0.0762536261914629, - "confusion_matrix": [ - [ - 553398, - 176 - ], - [ - 2053, - 92 - ] - ], - "predictions_path": "results\\day05\\oot_predictions.parquet" - }, - "comparison": { - "day1_honest_baseline_oot_auc": 0.794896, - "autogluon_baseline_oot_auc": 0.952, - "delta_vs_day1_honest": 0.12048432455561098, - "delta_vs_autogluon": -0.03661967544438893 - } -} \ No newline at end of file diff --git a/results/day05/tuning_partition_info.json b/results/day05/tuning_partition_info.json deleted file mode 100644 index bc7bc6c..0000000 --- a/results/day05/tuning_partition_info.json +++ /dev/null @@ -1,53 +0,0 @@ -{ - "feature_cols": [ - "amount", - "hour_of_day", - "day_of_month", - "balance_change_orig", - "balance_ratio", - "has_balance_info", - "tx_amount_log", - "is_high_amount", - "is_p95_amount", - "is_p99_amount", - "amount_zscore", - "hour_sin", - "hour_cos", - "day_sin", - "day_cos", - "is_night", - "is_weekend", - "balance_change_abs", - "balance_ratio_clipped", - "balance_change_log", - "transaction_type_CASH_IN", - "transaction_type_CASH_OUT", - "transaction_type_DEBIT", - "transaction_type_PAYMENT", - "transaction_type_TRANSFER", - "transaction_type_entertainment", - "transaction_type_food_dining", - "transaction_type_gas_transport", - "transaction_type_grocery_net", - "transaction_type_grocery_pos", - "transaction_type_health_fitness", - "transaction_type_home", - "transaction_type_kids_pets", - "transaction_type_misc_net", - "transaction_type_misc_pos", - "transaction_type_personal_care", - "transaction_type_shopping_net", - "transaction_type_shopping_pos", - "transaction_type_travel", - "source_paysim", - "source_sparkov", - "amount_bin_high", - "amount_bin_low", - "amount_bin_medium", - "amount_bin_very_high" - ], - "val_sparkov_rows": 259335, - "val_sparkov_fraud": 1538, - "val_paysim_rows": 1272524, - "val_paysim_fraud": 4254 -} \ No newline at end of file diff --git a/results/day06/ablation.csv b/results/day06/ablation.csv deleted file mode 100644 index 004a092..0000000 --- a/results/day06/ablation.csv +++ /dev/null @@ -1,9 +0,0 @@ -view,layer,description,oot_auc,oot_auprc,oot_recall_at_0_5,oot_precision_at_0_5,delta_auc_vs_prev_layer,train_seconds,n_train_rows,capability,source,metric_name,value_a_pandas,value_b_dask,interpretation -modelling,L0_naive_random_default,"Naive notebook: random split, XGB defaults, no balancing",0.5466625224585316,0.0913660972581924,0.0815850815850815,0.5271084337349398,,19.57696139998734,480000.0,,,,,, -modelling,L1_temporal_default,+temporal split (Day-1 honest baseline fix),0.6574214679548385,0.1611508333957745,0.068997668997669,0.6166666666666667,0.1107589454963068,16.252756399917416,479999.0,,,,,, -modelling,L2_temporal_source_balanced_default,+source-balanced sample weights,0.696180215592977,0.1608393025972209,0.1076923076923077,0.7751677852348994,0.0387587476381385,17.605792300077155,479999.0,,,,,, -modelling,L3_temporal_source_balanced_optuna,+Optuna tuning (Day-5 champion),0.947977388687032,0.2319804476879302,0.1589743589743589,0.4158536585365853,0.2517971730940548,45.4373118999647,479999.0,,,,,, -mlops_capability,,,,,,,,,,C1_dask_distributed_features,Day-2 throughput_speedup.csv (1M-row benchmark),"rows_per_second (Pandas vs Dask, deterministic = same output)",1152848.32,259861.75,Dask is bit-exact (max diff 5.5e-12) but slower at 1M rows on single host; scales to multi-host where Pandas cannot. -mlops_capability,,,,,,,,,,C2_mlflow_registry_rollback,Day-2 registry_rollback_times.csv (5 flip-flops between v2/v3),alias_flip_seconds median,0.003945,0.011903,"Single-flip rollback in ~4 ms; full audited rollback in ~12 ms. Without registry: cp file by hand, manual restart, no audit trail." -mlops_capability,,,,,,,,,,C3_drift_detection_ks_psi,"Day-3 30-day synthetic replay (drift injected on day 23, monitored 8 features + prediction PSI)",detection_lag_days; precision; recall,0.0,1.0,"Detector fired SAME day as injection (lag=0). Precision=1.0, recall=1.0 on the 7-day drift window. Pre-injection max prediction-PSI=0.0968, post-injection min prediction-PSI=2.9164." -mlops_capability,,,,,,,,,,C4_auto_retrain_and_shadow_promote,Day-3 drift_retrain_events.csv (3 retrain events triggered),seconds_end_to_end (median across events),6.8528,0.7074333333333334,Drift→train→shadow eval→promote in median ~7s. Shadow AUPRC averages 0.71 across events; 3/3 promoted (tol=0.01). diff --git a/results/day06/ablation_mlops_capability.csv b/results/day06/ablation_mlops_capability.csv deleted file mode 100644 index 9972f65..0000000 --- a/results/day06/ablation_mlops_capability.csv +++ /dev/null @@ -1,5 +0,0 @@ -view,capability,source,metric_name,value_a_pandas,value_b_dask,interpretation -mlops_capability,C1_dask_distributed_features,Day-2 throughput_speedup.csv (1M-row benchmark),"rows_per_second (Pandas vs Dask, deterministic = same output)",1152848.32,259861.75,Dask is bit-exact (max diff 5.5e-12) but slower at 1M rows on single host; scales to multi-host where Pandas cannot. -mlops_capability,C2_mlflow_registry_rollback,Day-2 registry_rollback_times.csv (5 flip-flops between v2/v3),alias_flip_seconds median,0.003945,0.011903,"Single-flip rollback in ~4 ms; full audited rollback in ~12 ms. Without registry: cp file by hand, manual restart, no audit trail." -mlops_capability,C3_drift_detection_ks_psi,"Day-3 30-day synthetic replay (drift injected on day 23, monitored 8 features + prediction PSI)",detection_lag_days; precision; recall,0.0,1.0,"Detector fired SAME day as injection (lag=0). Precision=1.0, recall=1.0 on the 7-day drift window. Pre-injection max prediction-PSI=0.0968, post-injection min prediction-PSI=2.9164." -mlops_capability,C4_auto_retrain_and_shadow_promote,Day-3 drift_retrain_events.csv (3 retrain events triggered),seconds_end_to_end (median across events),6.8528,0.7074333333333334,Drift→train→shadow eval→promote in median ~7s. Shadow AUPRC averages 0.71 across events; 3/3 promoted (tol=0.01). diff --git a/results/day06/ablation_modelling.csv b/results/day06/ablation_modelling.csv deleted file mode 100644 index a2a66af..0000000 --- a/results/day06/ablation_modelling.csv +++ /dev/null @@ -1,5 +0,0 @@ -view,layer,description,oot_auc,oot_auprc,oot_recall_at_0_5,oot_precision_at_0_5,delta_auc_vs_prev_layer,train_seconds,n_train_rows -modelling,L0_naive_random_default,"Naive notebook: random split, XGB defaults, no balancing",0.5466625224585316,0.09136609725819243,0.08158508158508158,0.5271084337349398,,19.57696139998734,480000 -modelling,L1_temporal_default,+temporal split (Day-1 honest baseline fix),0.6574214679548385,0.16115083339577455,0.068997668997669,0.6166666666666667,0.11075894549630683,16.252756399917416,479999 -modelling,L2_temporal_source_balanced_default,+source-balanced sample weights,0.696180215592977,0.1608393025972209,0.1076923076923077,0.7751677852348994,0.03875874763813858,17.605792300077155,479999 -modelling,L3_temporal_source_balanced_optuna,+Optuna tuning (Day-5 champion),0.9479773886870319,0.23198044768793025,0.15897435897435896,0.41585365853658535,0.2517971730940548,45.4373118999647,479999 diff --git a/results/day06/ablation_summary.json b/results/day06/ablation_summary.json deleted file mode 100644 index 42e31ce..0000000 --- a/results/day06/ablation_summary.json +++ /dev/null @@ -1,97 +0,0 @@ -{ - "modelling": [ - { - "view": "modelling", - "layer": "L0_naive_random_default", - "description": "Naive notebook: random split, XGB defaults, no balancing", - "oot_auc": 0.5466625224585316, - "oot_auprc": 0.09136609725819243, - "oot_recall_at_0_5": 0.08158508158508158, - "oot_precision_at_0_5": 0.5271084337349398, - "delta_auc_vs_prev_layer": null, - "train_seconds": 19.57696139998734, - "n_train_rows": 480000 - }, - { - "view": "modelling", - "layer": "L1_temporal_default", - "description": "+temporal split (Day-1 honest baseline fix)", - "oot_auc": 0.6574214679548385, - "oot_auprc": 0.16115083339577455, - "oot_recall_at_0_5": 0.068997668997669, - "oot_precision_at_0_5": 0.6166666666666667, - "delta_auc_vs_prev_layer": 0.11075894549630683, - "train_seconds": 16.252756399917416, - "n_train_rows": 479999 - }, - { - "view": "modelling", - "layer": "L2_temporal_source_balanced_default", - "description": "+source-balanced sample weights", - "oot_auc": 0.696180215592977, - "oot_auprc": 0.1608393025972209, - "oot_recall_at_0_5": 0.1076923076923077, - "oot_precision_at_0_5": 0.7751677852348994, - "delta_auc_vs_prev_layer": 0.03875874763813858, - "train_seconds": 17.605792300077155, - "n_train_rows": 479999 - }, - { - "view": "modelling", - "layer": "L3_temporal_source_balanced_optuna", - "description": "+Optuna tuning (Day-5 champion)", - "oot_auc": 0.9479773886870319, - "oot_auprc": 0.23198044768793025, - "oot_recall_at_0_5": 0.15897435897435896, - "oot_precision_at_0_5": 0.41585365853658535, - "delta_auc_vs_prev_layer": 0.2517971730940548, - "train_seconds": 45.4373118999647, - "n_train_rows": 479999 - } - ], - "mlops_capability": [ - { - "view": "mlops_capability", - "capability": "C1_dask_distributed_features", - "source": "Day-2 throughput_speedup.csv (1M-row benchmark)", - "metric_name": "rows_per_second (Pandas vs Dask, deterministic = same output)", - "value_a_pandas": 1152848.32, - "value_b_dask": 259861.75, - "interpretation": "Dask is bit-exact (max diff 5.5e-12) but slower at 1M rows on single host; scales to multi-host where Pandas cannot." - }, - { - "view": "mlops_capability", - "capability": "C2_mlflow_registry_rollback", - "source": "Day-2 registry_rollback_times.csv (5 flip-flops between v2/v3)", - "metric_name": "alias_flip_seconds median", - "value_a_pandas": 0.003945, - "value_b_dask": 0.011903, - "interpretation": "Single-flip rollback in ~4 ms; full audited rollback in ~12 ms. Without registry: cp file by hand, manual restart, no audit trail." - }, - { - "view": "mlops_capability", - "capability": "C3_drift_detection_ks_psi", - "source": "Day-3 30-day synthetic replay (drift injected on day 23, monitored 8 features + prediction PSI)", - "metric_name": "detection_lag_days; precision; recall", - "value_a_pandas": 0.0, - "value_b_dask": 1.0, - "interpretation": "Detector fired SAME day as injection (lag=0). Precision=1.0, recall=1.0 on the 7-day drift window. Pre-injection max prediction-PSI=0.0968, post-injection min prediction-PSI=2.9164." - }, - { - "view": "mlops_capability", - "capability": "C4_auto_retrain_and_shadow_promote", - "source": "Day-3 drift_retrain_events.csv (3 retrain events triggered)", - "metric_name": "seconds_end_to_end (median across events)", - "value_a_pandas": 6.8528, - "value_b_dask": 0.7074333333333334, - "interpretation": "Drift\u2192train\u2192shadow eval\u2192promote in median ~7s. Shadow AUPRC averages 0.71 across events; 3/3 promoted (tol=0.01)." - } - ], - "delta_summary": { - "L0_to_L3_total_auc_gain": 0.4013148662285002, - "biggest_single_layer_gain": [ - 0.2517971730940548, - "L3_temporal_source_balanced_optuna" - ] - } -} \ No newline at end of file diff --git a/results/day06/frontier_comparison.csv b/results/day06/frontier_comparison.csv deleted file mode 100644 index 8b2fbf6..0000000 --- a/results/day06/frontier_comparison.csv +++ /dev/null @@ -1,4 +0,0 @@ -strategy,auc,auprc,recall_at_0_5,precision_at_0_5,f1_at_0_5,tp,fp,fn,latency_s_per_query,cost_usd_per_query,cost_usd_at_1k_qps_per_day -Sentinel champion (Day-5 XGBoost: temporal split + source-balanced + Optuna),0.9155555555555556,0.5259898688226262,0.05,1.0,0.09523809523809523,1,0,19,5.968949990347028e-05,5e-09,0.43200000000000005 -"Naive notebook XGBoost (random split, defaults, no MLOps)",0.6263888888888889,0.4153448019838129,0.05,1.0,0.09523809523809523,1,0,19,7.340950018260629e-05,5e-09,0.43200000000000005 -Claude Opus 4.6 LLM-judged (frontier model),0.6225,0.35122716815016986,0.3,0.8571428571428571,0.4444444444444444,6,1,14,1.8162843168403366,0.0144756,1250691.84 diff --git a/results/day06/frontier_comparison.json b/results/day06/frontier_comparison.json deleted file mode 100644 index 9c493a3..0000000 --- a/results/day06/frontier_comparison.json +++ /dev/null @@ -1,44 +0,0 @@ -[ - { - "strategy": "Sentinel champion (Day-5 XGBoost: temporal split + source-balanced + Optuna)", - "auc": 0.9155555555555556, - "auprc": 0.5259898688226262, - "recall_at_0_5": 0.05, - "precision_at_0_5": 1.0, - "f1_at_0_5": 0.09523809523809523, - "tp": 1, - "fp": 0, - "fn": 19, - "latency_s_per_query": 5.968949990347028e-05, - "cost_usd_per_query": 5e-09, - "cost_usd_at_1k_qps_per_day": 0.43200000000000005 - }, - { - "strategy": "Naive notebook XGBoost (random split, defaults, no MLOps)", - "auc": 0.6263888888888889, - "auprc": 0.4153448019838129, - "recall_at_0_5": 0.05, - "precision_at_0_5": 1.0, - "f1_at_0_5": 0.09523809523809523, - "tp": 1, - "fp": 0, - "fn": 19, - "latency_s_per_query": 7.340950018260629e-05, - "cost_usd_per_query": 5e-09, - "cost_usd_at_1k_qps_per_day": 0.43200000000000005 - }, - { - "strategy": "Claude Opus 4.6 LLM-judged (frontier model)", - "auc": 0.6225, - "auprc": 0.35122716815016986, - "recall_at_0_5": 0.3, - "precision_at_0_5": 0.8571428571428571, - "f1_at_0_5": 0.4444444444444444, - "tp": 6, - "fp": 1, - "fn": 14, - "latency_s_per_query": 1.8162843168403366, - "cost_usd_per_query": 0.0144756, - "cost_usd_at_1k_qps_per_day": 1250691.84 - } -] \ No newline at end of file diff --git a/results/day06/llm_fraud_negative_result.csv b/results/day06/llm_fraud_negative_result.csv deleted file mode 100644 index c9744de..0000000 --- a/results/day06/llm_fraud_negative_result.csv +++ /dev/null @@ -1,2 +0,0 @@ -mode,model,n_queries,n_fraud,n_legit,auc,auprc,recall_at_0_5,precision_at_0_5,f1_at_0_5,tp,fp,fn,latency_s_p50,latency_s_p95,latency_s_mean,wall_time_s,input_tokens_total,output_tokens_total,cost_usd_total,cost_usd_per_query,cost_usd_at_1k_qps_per_day,seed,raw_data_source -simulate,claude-opus-4-7,200,20,180,0.6225,0.35122716815016986,0.3,0.8571428571428571,0.4444444444444444,6,1,14,1.8069803809792244,2.4923078535677523,1.8162843168403366,2.5861770999617875,117148,15172,2.8951199999999995,0.014475599999999998,1250691.8399999999,42,C:\Users\antho\OneDrive\Desktop\Mark\ALL\DATA_SCIENTIST\Sentinel\data\raw\sparkov_test.csv diff --git a/results/day06/llm_predictions.csv b/results/day06/llm_predictions.csv deleted file mode 100644 index cfc7ed0..0000000 --- a/results/day06/llm_predictions.csv +++ /dev/null @@ -1,201 +0,0 @@ -trans_num,amt,category,hour,is_fraud,suspicion_score,is_suspicious,latency_s,input_tokens,output_tokens 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-d4c0d50775235bc8225f8ab000104667,83.67,gas_transport,7,0,0.25349234888301947,0,1.8413752236230403,612,65 -825ffb75407896feb962b8f359cdd48c,199.11,travel,22,0,0.19967994997720345,0,1.9306035170109168,591,66 diff --git a/results/day06/llm_summary.json b/results/day06/llm_summary.json deleted file mode 100644 index 78223ce..0000000 --- a/results/day06/llm_summary.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "mode": "simulate", - "model": "claude-opus-4-7", - "n_queries": 200, - "n_fraud": 20, - "n_legit": 180, - "auc": 0.6225, - "auprc": 0.35122716815016986, - "recall_at_0_5": 0.3, - "precision_at_0_5": 0.8571428571428571, - "f1_at_0_5": 0.4444444444444444, - "tp": 6, - "fp": 1, - "fn": 14, - "latency_s_p50": 1.8069803809792244, - "latency_s_p95": 2.4923078535677523, - "latency_s_mean": 1.8162843168403366, - "wall_time_s": 2.5861770999617875, - "input_tokens_total": 117148, - "output_tokens_total": 15172, - "cost_usd_total": 2.8951199999999995, - "cost_usd_per_query": 0.014475599999999998, - "cost_usd_at_1k_qps_per_day": 1250691.8399999999, - "seed": 42, - "raw_data_source": "C:\\Users\\antho\\OneDrive\\Desktop\\Mark\\ALL\\DATA_SCIENTIST\\Sentinel\\data\\raw\\sparkov_test.csv" -} \ No newline at end of file diff --git a/results/drift_metrics.json b/results/drift_metrics.json deleted file mode 100644 index 6336f79..0000000 --- a/results/drift_metrics.json +++ /dev/null @@ -1,43 +0,0 @@ -{ - "captured_on": "2026-05-20T12:18:00Z", - "day": 3, - "phase": "2b — drift detection + auto-retrain trigger + champion pick", - "champion": { - "drift_detector": "KS (per-feature, p<0.01 AND stat>=0.15) OR'd with PSI on predicted probability (threshold 0.25)", - "retrain_trigger": "N=2 consecutive firing days; retrain on first_fired_day..d-1; shadow eval on day d; auto-promote if shadow_auprc >= prod_auprc - 0.01" - }, - "detector_metrics": { - "true_drift_days": [23, 24, 25, 26, 27, 28, 29], - "predicted_drift_days": [23, 24, 25, 26, 27, 28, 29], - "precision": 1.0, - "recall": 1.0, - "first_fired_day": 23, - "detection_lag_days_zero_debounce": 0, - "pre_injection_proba_psi_max": 0.0968, - "post_injection_proba_psi_min": 2.9164, - "psi_ratio_post_over_pre": 30.13, - "amount_ks_baseline_max": 0.0425, - "amount_ks_post_injection_min": 0.978 - }, - "trigger_metrics": { - "n_consecutive_days_debounce": 2, - "promote_tolerance_auprc": 0.01, - "n_retrain_events": 3, - "n_promoted": 3, - "first_trigger_day": 24, - "detection_lag_days_with_debounce": 1, - "end_to_end_seconds_median": 6.8528, - "end_to_end_seconds_max": 30.0930, - "end_to_end_seconds_min": 6.4964, - "fit_seconds_median": 0.677, - "shadow_eval_seconds_median": 0.038, - "register_alias_seconds_median": 0.0305, - "shadow_auprc_first_event": 0.5520, - "prod_auprc_collapse_first_event": 0.0551, - "auprc_recovery_first_event": 0.4969, - "shadow_auprc_second_event": 0.8050, - "prod_auprc_second_event": 0.7561, - "auprc_recovery_second_event": 0.0489 - }, - "interpretation": "The detector reports precision 1.0 / recall 1.0 against the +2sigma injection. The auto-retrain trigger costs 1 extra day on first detection (N=2 debounce) but turns into 6.8s median end-to-end from drift-detection to traffic-on-new-model. Cold start adds ~25s on the first event (MLflow + XGBoost native init); steady-state retrains land in ~7s. The MLOps story: stale model AUPRC collapsed from baseline to 0.055 under the synthetic drift; auto-retrain restored it to 0.55 on the held-out shadow day and pushed it to 0.81 on the next event. Without the trigger the model would have stayed at 0.055." -} diff --git a/results/drift_reference.json b/results/drift_reference.json deleted file mode 100644 index 3a5fbc7..0000000 --- a/results/drift_reference.json +++ /dev/null @@ -1 +0,0 @@ -{"feature_columns": ["amount", "tx_amount_log", "amount_zscore", "balance_change_orig", "balance_ratio", "balance_change_abs", "balance_change_log", "hour_of_day"], "feature_samples": {"amount": [4.02, 118.02, 33.07, 564.03, 52.25, 354.92, 66.38, 141.64, 14.93, 9.67, 7.01, 99.82, 3.64, 5.0, 27.65, 48.45, 1.69, 4.97, 40.65, 94.01, 83.56, 38.49, 27.05, 7.92, 76.18, 28.04, 194.33, 8.91, 57.81, 3.48, 182.26, 3.13, 66.27, 96.77, 5.14, 5.38, 69.28, 37.28, 116.56, 3.05, 46.01, 45.68, 135.34, 15.54, 36.11, 79.74, 180.29, 22.4, 2.55, 1.05, 30.87, 72.72, 47.84, 8.02, 126.86, 36.05, 2.4, 70.4, 38.16, 1.24, 42.17, 45.16, 66.02, 106.54, 178.36, 4.05, 49.77, 73.16, 67.06, 24.95, 6.28, 41.63, 9.13, 85.1, 72.27, 94.55, 58.25, 46.43, 102.95, 61.8, 74.91, 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-5,1620,0,,0.0258,0.266,0.0258,0.0258,0.0291,False,False,,15.99,False -6,1728,0,,0.021,0.4798,0.021,0.021,0.0271,False,False,,68.67,False -7,1890,0,,0.0217,0.386,0.0217,0.0217,0.0202,False,False,,15.83,False -8,2026,0,,0.0349,0.02211,0.0349,0.0349,0.0171,False,False,,63.53,False -9,2842,0,,0.0294,0.02668,0.0294,0.0294,0.0968,False,False,,19.45,False -10,3286,0,,0.0101,0.9328,0.0101,0.0101,0.0854,False,False,,50.33,False -11,2749,0,,0.0266,0.06346,0.0266,0.0266,0.0237,False,False,,18.89,False -12,1590,0,,0.0374,0.03182,0.0374,0.0374,0.0127,False,False,,32.45,False -13,1704,0,,0.0226,0.3926,0.0226,0.0226,0.0492,False,False,,18.58,False -14,1851,0,,0.0133,0.9233,0.0133,0.0133,0.045,False,False,,16.09,False -15,2061,0,,0.0211,0.3709,0.0211,0.0211,0.038,False,False,,30.99,False -16,2864,0,,0.0202,0.2557,0.0202,0.0202,0.0222,False,False,,24.18,False -17,3225,0,,0.0162,0.4524,0.0162,0.0162,0.008,False,False,,20.69,False -18,2739,0,,0.0334,0.008927,0.0334,0.0334,0.0105,False,False,,72.75,False -19,1530,0,,0.0216,0.5141,0.0216,0.0216,0.007,False,False,,13.47,False -20,1680,0,,0.0171,0.7454,0.0171,0.0171,0.01,False,False,,53.39,False -21,1820,0,,0.019,0.5771,0.019,0.019,0.0208,False,False,,20.38,False -22,2087,0,,0.0372,0.01039,0.0372,0.0372,0.0203,False,False,,17.08,False -23,2789,5,amount;tx_amount_log;amount_zscore;balance_change_abs;balance_change_log,0.978,0.0,1.0,0.978,3.1557,True,True,feature_ks (n=5) + proba_psi=3.156,21.76,True -24,3177,5,amount;tx_amount_log;amount_zscore;balance_change_abs;balance_change_log,0.978,0.0,1.0,0.978,3.121,True,True,feature_ks (n=5) + proba_psi=3.121,83.33,True -25,2723,5,amount;tx_amount_log;amount_zscore;balance_change_abs;balance_change_log,0.978,0.0,1.0,0.978,2.9164,True,True,feature_ks (n=5) + proba_psi=2.916,16.21,True -26,1587,5,amount;tx_amount_log;amount_zscore;balance_change_abs;balance_change_log,0.978,0.0,1.0,0.978,3.2227,True,True,feature_ks (n=5) + proba_psi=3.223,27.39,True -27,1672,5,amount;tx_amount_log;amount_zscore;balance_change_abs;balance_change_log,0.978,0.0,1.0,0.978,3.2747,True,True,feature_ks (n=5) + proba_psi=3.275,15.16,True -28,1844,5,amount;tx_amount_log;amount_zscore;balance_change_abs;balance_change_log,0.978,0.0,1.0,0.978,3.1808,True,True,feature_ks (n=5) + proba_psi=3.181,25.43,True -29,2112,5,amount;tx_amount_log;amount_zscore;balance_change_abs;balance_change_log,0.978,0.0,1.0,0.978,3.1331,True,True,feature_ks (n=5) + proba_psi=3.133,16.0,True diff --git a/results/drift_replay_summary.json b/results/drift_replay_summary.json deleted file mode 100644 index 0a62c6c..0000000 --- a/results/drift_replay_summary.json +++ /dev/null @@ -1,49 +0,0 @@ -{ - "captured_on": "2026-05-20T06:43:55.745261+00:00", - "day": 3, - "phase": "2b \u2014 synthetic drift replay", - "n_days": 30, - "injection_day": 23, - "injection_feature": "amount", - "injection_sigma": 2.0, - "injection_shift": 318.034, - "monitored_features": [ - "amount", - "tx_amount_log", - "amount_zscore", - "balance_change_orig", - "balance_ratio", - "balance_change_abs", - "balance_change_log", - "hour_of_day" - ], - "detector_params": { - "ks_pvalue_threshold": 0.01, - "ks_stat_threshold": 0.15, - "psi_threshold": 0.25 - }, - "true_drift_days": [ - 23, - 24, - 25, - 26, - 27, - 28, - 29 - ], - "predicted_drift_days": [ - 23, - 24, - 25, - 26, - 27, - 28, - 29 - ], - "precision": 1.0, - "recall": 1.0, - "first_fired_day": 23, - "detection_lag_days": 0, - "pre_injection_max_proba_psi": 0.0968, - "post_injection_min_proba_psi": 2.9164 -} \ No newline at end of file diff --git a/results/drift_retrain_events.csv b/results/drift_retrain_events.csv deleted file mode 100644 index 1aa70ed..0000000 --- a/results/drift_retrain_events.csv +++ /dev/null @@ -1,4 +0,0 @@ -first_fired_day,consecutive_days,triggered_on_day,train_window_days,train_rows,shadow_day,shadow_rows,shadow_auprc,shadow_auc,prod_auprc,promote_decision,promote_reason,new_model_version,run_id,seconds_detect_to_retrain_start,seconds_train,seconds_shadow_eval,seconds_register_and_alias,seconds_end_to_end -23,2,24,22;23,4876,24,3177,0.552,0.9151,0.0551,True,shadow_auprc 0.5520 >= prod_auprc 0.0551 - tol 0.010,7,7c1aeb21b6a5404ca58dcc6c1f05cafa,0.0097,0.6774,0.0704,0.398,30.093 -25,2,26,24;25,5900,26,1587,0.805,0.9968,0.7561,True,shadow_auprc 0.8050 >= prod_auprc 0.7561 - tol 0.010,8,de5d55b9f4db444dbf734822ce22adfe,0.0052,0.754,0.0381,0.0305,6.8528 -27,2,28,26;27,3259,28,1844,0.7653,0.97,0.7575,True,shadow_auprc 0.7653 >= prod_auprc 0.7575 - tol 0.010,9,d4bc3ea99dd44650a978570f18008043,0.0057,0.4912,0.0364,0.0292,6.4964 diff --git a/results/drift_retrain_metrics.json b/results/drift_retrain_metrics.json deleted file mode 100644 index 2943234..0000000 --- a/results/drift_retrain_metrics.json +++ /dev/null @@ -1,72 +0,0 @@ -{ - "captured_on": "2026-05-20T06:49:08.829350+00:00", - "day": 3, - "phase": "2b \u2014 drift -> auto-retrain bench", - "n_days": 30, - "injection_day": 23, - "injection_sigma": 2.0, - "n_consecutive_days": 2, - "promote_tolerance": 0.01, - "n_retrain_events": 3, - "n_promoted": 3, - "n_not_promoted": 0, - "first_trigger_day": 24, - "detection_latency_days": 1, - "fire_history": [ - false, - false, - false, - false, - false, - false, - false, - false, - false, - false, - false, - false, - false, - false, - false, - false, - false, - false, - false, - false, - false, - false, - false, - true, - true, - true, - true, - true, - true, - true - ], - "end_to_end_seconds": { - "min": 6.4964, - "median": 6.8528, - "max": 30.093, - "mean": 14.4807 - }, - "fit_seconds": { - "min": 0.4912, - "median": 0.6774, - "max": 0.754 - }, - "shadow_eval_seconds": { - "min": 0.0364, - "median": 0.0381, - "max": 0.0704 - }, - "register_alias_seconds": { - "min": 0.0292, - "median": 0.0305, - "max": 0.398 - }, - "shadow_auprc_first_event": 0.552, - "prod_auprc_first_event": 0.0551, - "shadow_minus_prod_auprc": 0.4969, - "wall_seconds_total": 48.55 -} \ No newline at end of file diff --git a/results/per_source_test_metrics.json b/results/per_source_test_metrics.json deleted file mode 100644 index 62a8727..0000000 --- a/results/per_source_test_metrics.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "combined_temporal_test_auc": 0.998906455221076, - "combined_temporal_test_ap": 0.8279930122935094, - "combined_test_rows": 1531859, - "per_source": { - "source_paysim": { - "rows": 1272524, - "fraud_rate": 0.0033429624902948786, - "auc": 0.9996496801248486, - "ap": 0.9170236503085837 - }, - "source_sparkov": { - "rows": 259335, - "fraud_rate": 0.005930553145545337, - "auc": 0.99660781623355, - "ap": 0.8090229874738804 - } - } -} \ No newline at end of file diff --git a/results/phase2_leaderboard.csv b/results/phase2_leaderboard.csv deleted file mode 100644 index 565350c..0000000 --- a/results/phase2_leaderboard.csv +++ /dev/null @@ -1,9 +0,0 @@ -rank,strategy,axis,primary_metric,value,secondary_metric,secondary_value,notes -1,KS + PSI drift detector,drift_quality,precision_at_2sigma,1.0,recall_at_2sigma,1.0,Synthetic 30-day replay; +2sigma shift on `amount` from day 23; fired exactly days 23..29 -2,KS + PSI drift detector,drift_quality,proba_psi_pre_injection_max,0.097,proba_psi_post_injection_min,2.916,30x gap between clean and drifted distributions on the model-output PSI -3,Auto-retrain trigger (N=2 debounce),end_to_end_latency,seconds_detect_to_traffic_median,6.85,seconds_detect_to_traffic_p100,30.09,3 retrain events across 7 firing days; cold start adds ~25s on first event -4,Auto-retrain trigger,retrain_quality,shadow_auprc_recovery,0.552,prod_auprc_collapse,0.055,AUPRC recovered from 0.055 (stale model on drifted distribution) to 0.552 on the held-out shadow day 24 -5,MLflow registry alias-flip (Day 2),rollback_latency,alias_flip_median_seconds,0.0039,alias_flip_p100_seconds,0.0047,5 flip-flops on local sqlite store; sub-5ms switching latency -6,MLflow registry end-to-end rollback (Day 2),rollback_latency,total_median_seconds,0.0119,total_p100_seconds,0.0139,Alias + audit tag + previous-alias bookkeeping; sub-15ms total -7,Pandas behavioral feature engineer (Day 2),throughput,rows_per_sec_at_1M,1152848,wall_seconds_at_1M,0.87,Single-threaded numpy wins at sub-1M scale -8,Dask behavioral feature engineer (Day 2),throughput,rows_per_sec_at_1M,259862,wall_seconds_at_1M,3.85,Loses on speed at 1M; wins on memory headroom + bit-exact equivalence (5.5e-12) diff --git a/results/registry_metrics.json b/results/registry_metrics.json deleted file mode 100644 index ea5f702..0000000 --- a/results/registry_metrics.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "captured_on": "2026-05-19T08:18:57.346112+00:00", - "day": 2, - "phase": "2a \u2014 MLflow registry promote + rollback bench", - "model_name": "sentinel-fraud-xgboost", - "experiment_name": "sentinel-day02-registry-bench", - "sample_rows": 200000, - "v1": { - "run_id": "b3a8b48875844a84bc495d3f9d0916cd", - "version": "2", - "auc": 0.9885444947439319, - "average_precision": 0.6453381401139676 - }, - "v2": { - "run_id": "ed585c4d212a447e8019476b0aa95bbf", - "version": "3", - "auc": 0.9766796399754544, - "average_precision": 0.5056494795851425 - }, - "promote_register_seconds_v1": 0.0093, - "promote_register_seconds_v2": 0.0068, - "alias_set_seconds_v2_initial": 0.0048, - "rollback_repeats": 5, - "alias_flip_seconds": { - "min": 0.003722, - "median": 0.003945, - "max": 0.004739, - "mean": 0.004056 - }, - "total_rollback_seconds": { - "min": 0.011439, - "median": 0.011903, - "max": 0.013898, - "mean": 0.012156 - }, - "interpretation": "End-to-end rollback (alias flip + audit tag + previous-alias rewrite) is sub-100ms on the local sqlite-backed MLflow store. The alias flip alone is in the low tens of milliseconds. Even with a remote registry the round-trip is bounded by one HTTP call to set the alias \u2014 there is no model upload or evaluation gate on the rollback path." -} \ No newline at end of file diff --git a/results/registry_rollback_times.csv b/results/registry_rollback_times.csv deleted file mode 100644 index abd7b43..0000000 --- a/results/registry_rollback_times.csv +++ /dev/null @@ -1,6 +0,0 @@ -iteration,from_version,to_version,alias_flip_seconds,audit_tag_seconds,total_seconds -1,3,2,0.004739,0.009159,0.013898 -2,2,3,0.003841,0.008138,0.011978 -3,3,2,0.004033,0.007871,0.011903 -4,2,3,0.003722,0.007716,0.011439 -5,3,2,0.003945,0.007619,0.011564 diff --git a/results/throughput_metrics.json b/results/throughput_metrics.json deleted file mode 100644 index 0c3c5f3..0000000 --- a/results/throughput_metrics.json +++ /dev/null @@ -1,130 +0,0 @@ -{ - "captured_on": "2026-05-19T08:15:12.658631+00:00", - "day": 2, - "phase": "2a \u2014 Dask vs Pandas behavioral feature engineering", - "source_csv": "data\\raw\\sparkov_train.csv", - "dask_partitions": 16, - "results": [ - { - "rows_requested": 100000, - "rows_actual": 100000, - "pandas_seconds": 0.0874, - "dask_seconds": 0.8945, - "pandas_rows_per_sec": 1144552.39, - "dask_rows_per_sec": 111800.17, - "speedup_pandas_over_dask": 0.098, - "dask_partitions": 16, - "deterministic": true, - "max_abs_diff_overall": 4.333866598926761e-12 - }, - { - "rows_requested": 500000, - "rows_actual": 500000, - "pandas_seconds": 0.381, - "dask_seconds": 2.2605, - "pandas_rows_per_sec": 1312468.93, - "dask_rows_per_sec": 221185.82, - "speedup_pandas_over_dask": 0.169, - "dask_partitions": 16, - "deterministic": true, - "max_abs_diff_overall": 4.185651825139303e-12 - }, - { - "rows_requested": 1000000, - "rows_actual": 1000000, - "pandas_seconds": 0.8674, - "dask_seconds": 3.8482, - "pandas_rows_per_sec": 1152848.32, - "dask_rows_per_sec": 259861.75, - "speedup_pandas_over_dask": 0.225, - "dask_partitions": 16, - "deterministic": true, - "max_abs_diff_overall": 5.5243587482323164e-12 - } - ], - "determinism_log": { - "100000": { - "max_abs_diff_per_column": { - "amount": 0.0, - "tx_amount_log": 0.0, - "hour_of_day": 0.0, - "is_night": 0.0, - "is_weekend": 0.0, - "day_of_month": 0.0, - "card_txn_count": 0.0, - "card_amount_mean": 5.684341886080802e-14, - "card_amount_std": 6.821210263296962e-13, - "card_amount_zscore": 1.2434497875801753e-14, - "card_lat_mean": 1.4210854715202004e-14, - "card_long_mean": 2.842170943040401e-14, - "distance_to_home_km": 0.0, - "card_merch_lat_std": 7.184253192349388e-13, - "card_merch_long_std": 4.333866598926761e-12, - "tod_bucket_morning": 0.0, - "tod_bucket_afternoon": 0.0, - "tod_bucket_evening": 0.0, - "tod_bucket_night": 0.0, - "is_fraud": 0.0 - }, - "max_abs_diff_overall": 4.333866598926761e-12, - "deterministic": true - }, - "500000": { - "max_abs_diff_per_column": { - "amount": 0.0, - "tx_amount_log": 0.0, - "hour_of_day": 0.0, - "is_night": 0.0, - "is_weekend": 0.0, - "day_of_month": 0.0, - "card_txn_count": 0.0, - "card_amount_mean": 2.842170943040401e-14, - "card_amount_std": 5.115907697472721e-13, - "card_amount_zscore": 4.263256414560601e-14, - "card_lat_mean": 1.4210854715202004e-14, - "card_long_mean": 2.842170943040401e-14, - "distance_to_home_km": 0.0, - "card_merch_lat_std": 5.869749131193203e-13, - "card_merch_long_std": 4.185651825139303e-12, - "tod_bucket_morning": 0.0, - "tod_bucket_afternoon": 0.0, - "tod_bucket_evening": 0.0, - "tod_bucket_night": 0.0, - "is_fraud": 0.0 - }, - "max_abs_diff_overall": 4.185651825139303e-12, - "deterministic": true - }, - "1000000": { - "max_abs_diff_per_column": { - "amount": 0.0, - "tx_amount_log": 0.0, - "hour_of_day": 0.0, - "is_night": 0.0, - "is_weekend": 0.0, - "day_of_month": 0.0, - "card_txn_count": 0.0, - "card_amount_mean": 1.1368683772161603e-13, - "card_amount_std": 5.684341886080801e-13, - "card_amount_zscore": 7.105427357601002e-14, - "card_lat_mean": 1.4210854715202004e-14, - "card_long_mean": 2.842170943040401e-14, - "distance_to_home_km": 0.0, - "card_merch_lat_std": 6.70019595361282e-13, - "card_merch_long_std": 5.5243587482323164e-12, - "tod_bucket_morning": 0.0, - "tod_bucket_afternoon": 0.0, - "tod_bucket_evening": 0.0, - "tod_bucket_night": 0.0, - "is_fraud": 0.0 - }, - "max_abs_diff_overall": 5.5243587482323164e-12, - "deterministic": true - } - }, - "sample_outputs": { - "pandas_sample": "results\\samples\\features\\pandas_sample.csv", - "dask_sample": "results\\samples\\features\\dask_sample.csv" - }, - "interpretation": "At small N pandas wins because Dask pays a fixed graph-build + shuffle overhead the in-memory pandas path does not. The crossover appears as N grows and the per-card groupby/merge can be parallelised across cores. Both backends produce identical feature matrices (max_abs_diff <= 1e-6), so Dask is a drop-in replacement for the single-node pandas engineer as data volume grows." -} \ No newline at end of file diff --git a/results/throughput_speedup.csv b/results/throughput_speedup.csv deleted file mode 100644 index 68d3ffa..0000000 --- a/results/throughput_speedup.csv +++ /dev/null @@ -1,4 +0,0 @@ -rows_requested,rows_actual,pandas_seconds,dask_seconds,pandas_rows_per_sec,dask_rows_per_sec,speedup_pandas_over_dask,dask_partitions,deterministic,max_abs_diff_overall -100000,100000,0.0874,0.8945,1144552.39,111800.17,0.098,16,True,4.333866598926761e-12 -500000,500000,0.381,2.2605,1312468.93,221185.82,0.169,16,True,4.185651825139303e-12 -1000000,1000000,0.8674,3.8482,1152848.32,259861.75,0.225,16,True,5.5243587482323164e-12