From c176649a6abec31d69998f2cd02f576caedc4654 Mon Sep 17 00:00:00 2001
From: Samarth Uday <tedyysam@gmail.com>
Date: Sat, 29 Aug 2026 22:18:17 +0530
Subject: [PATCH] fix: Refresh artifact under corrected methodology, fix
 silently-ignored CSVs

Completes verification of the temporal-validation and walk-forward fixes
against real data, per review checklist.

**Real-data verification (all confirmed on the full 9.5M-row dataset):**

- Retrained via the corrected train_model.py: test-set metrics are
  byte-identical to before (PR-AUC 0.9859, ROC-AUC 0.9998) as expected --
  the trained model/calibrator don't change, only where the threshold is
  selected from. Decision threshold moved from 0.001075 (old, selected from
  the calibration split) to 0.001160 (new, selected from the true held-out
  validation split). Calibration diagnostics are now honestly slightly
  worse (calibrated Brier 0.000078 vs 0.000043 previously) since they're
  now measured on data the calibrator never saw, rather than on its own
  fitting set.
- Walk-forward backtest: 4 real windows spanning the full timeline,
  PR-AUC 0.938 -> 0.990 as the training window expands (previously: 0
  windows, RuntimeError). Added to README as a new Temporal Stability table.
- Unseen-entity evaluation re-run against the fresh artifact; added the
  fixed-production-threshold table to docs/research/unseen_entity_generalization.md
  -- one threshold (0.001160) applied to both subgroups lands each within
  noise of the intended 0.5% alert budget independently, recall is
  indistinguishable (99.3% vs 99.2%), precision differs 11x consistent with
  the subgroups' 12x prevalence gap.
- `python scripts/run_experiments.py --generalization` run end-to-end
  against a real 284K-row sample of SAML-D (fast mode) to verify the full
  6-stage orchestration and --features/--artifact passthrough work
  correctly; real committed reports/ were untouched (sample used isolated
  /tmp paths) and diffed byte-identical against docs/assets/ afterward.

**Bug found while verifying the above:** docs/assets/ablation_results.csv
and docs/assets/typology_results.csv (and their reports/ counterparts) were
never actually committed, despite prior commit messages claiming otherwise
-- a blanket `*.csv` rule in .gitignore (meant for large raw datasets) was
silently swallowing them since the first commit that tried to add them.
This meant /api/results would return empty ablation/typology tables on any
truly fresh clone, even though the PNG figures and JSON files (unaffected
by the csv rule) worked fine -- the fallback-to-docs/assets resilience only
half-worked. Fixed: `!docs/assets/*.csv` exception added, both files
committed for the first time.

All 63 tests pass, ruff clean, API smoke-tested against the refreshed
artifact (health/model-info/results all correct, decision_threshold and
PR-AUC match the retrained artifact).

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
---
 .gitignore                                    |   3 +-
 README.md                                     |  18 +++++
 docs/assets/ablation_results.csv              |   5 ++
 docs/assets/calibration_curve.png             | Bin 51143 -> 51147 bytes
 docs/assets/model_metrics.json                |   8 +--
 docs/assets/typology_results.csv              |  29 ++++++++
 docs/assets/unseen_entity_results.json        |  25 +++++++
 docs/assets/walk_forward_results.csv          |   5 ++
 docs/research/unseen_entity_generalization.md |  21 ++++++
 reports/model_metrics.json                    |   8 +--
 reports/unseen_entity_results.json            |  68 ++++++++++++++++++
 11 files changed, 181 insertions(+), 9 deletions(-)
 create mode 100644 docs/assets/ablation_results.csv
 create mode 100644 docs/assets/typology_results.csv
 create mode 100644 docs/assets/walk_forward_results.csv
 create mode 100644 reports/unseen_entity_results.json

diff --git a/.gitignore b/.gitignore
index 721f485..2ef5452 100644
--- a/.gitignore
+++ b/.gitignore
@@ -44,4 +44,5 @@ logs/
 htmlcov/
 
 # Large data
-*.csv
\ No newline at end of file
+*.csv
+!docs/assets/*.csv
\ No newline at end of file
diff --git a/README.md b/README.md
index 22e2e14..1a4d6c4 100644
--- a/README.md
+++ b/README.md
@@ -94,6 +94,24 @@ The model detects specific money-laundering patterns with high recall:
 - **98%+ detection**: Behavioural Change, Cycle, Deposit-Send, Scatter-Gather, Gather-Scatter, Stacked Bipartite, Single Large
 - **Limitations**: Over-Invoicing (86% recall) detected less reliably due to low transaction prevalence
 
+### Temporal Stability
+
+Walk-forward backtesting splits the full timeline into 14 chronological blocks
+and evaluates 4 expanding out-of-time windows (train grows, calibration/test
+roll forward each time):
+
+| Window | PR-AUC | Precision @ 0.5% | Recall @ 0.5% | Lift @ 0.5% |
+|---|---:|---:|---:|---:|
+| W1 | 0.9379 | 18.39% | 97.05% | 194.1x |
+| W2 | 0.9658 | 21.61% | 98.19% | 196.4x |
+| W3 | 0.9774 | 20.54% | 99.08% | 198.1x |
+| W4 | 0.9901 | 23.49% | 99.38% | 198.7x |
+
+Performance is stable-to-improving across expanding out-of-time windows, not
+just in the single held-out test split — later windows benefit from more
+accumulated transaction history for the network features (degree, lifetime
+counts, counterparty concentration).
+
 ### Analysis Visualizations
 
 ![ROC Curve](docs/assets/roc_curve.png)
diff --git a/docs/assets/ablation_results.csv b/docs/assets/ablation_results.csv
new file mode 100644
index 0000000..98156a9
--- /dev/null
+++ b/docs/assets/ablation_results.csv
@@ -0,0 +1,5 @@
+model,pr_auc,recall_at_0.5%
+Base,0.08984612760924429,0.12868949232585597
++ Behavioral,0.20015470040725364,0.45218417945690675
++ Network,0.943473551428709,0.9769775678866588
+All,0.9859286355765932,0.9929161747343566
diff --git a/docs/assets/calibration_curve.png b/docs/assets/calibration_curve.png
index 0aa2b25035d97fcb2ad42a6f2b551a61e23917c7..68235034a21acb8aab5557a8beb6a9c3901a07b7 100644
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diff --git a/docs/assets/model_metrics.json b/docs/assets/model_metrics.json
index 9434212..c135119 100644
--- a/docs/assets/model_metrics.json
+++ b/docs/assets/model_metrics.json
@@ -33,9 +33,9 @@
   "training_prevalence": 0.001003416857597292,
   "test_prevalence": 0.0011921912179000402,
   "calibration": {
-    "raw_brier_score": 0.0016988252755254507,
-    "calibrated_brier_score": 4.299887950764969e-05,
-    "raw_log_loss": 0.006217300426214933,
-    "calibrated_log_loss": 0.0002347317640669644
+    "raw_brier_score": 0.0016964440001174808,
+    "calibrated_brier_score": 7.803709740983322e-05,
+    "raw_log_loss": 0.006314944475889206,
+    "calibrated_log_loss": 0.00040054231067188084
   }
 }
diff --git a/docs/assets/typology_results.csv b/docs/assets/typology_results.csv
new file mode 100644
index 0000000..818de64
--- /dev/null
+++ b/docs/assets/typology_results.csv
@@ -0,0 +1,29 @@
+typology,transactions,positives,recall_at_0.5%
+Behavioural_Change_1,62,62,0.9838709677419355
+Behavioural_Change_2,61,61,1.0
+Cash_Withdrawal,240,240,1.0
+Cycle,49,49,0.9795918367346939
+Deposit-Send,163,163,0.9754601226993865
+Fan_In,36,36,1.0
+Layered_Fan_In,88,88,1.0
+Layered_Fan_Out,100,100,0.98
+Normal_Cash_Deposits,33430,0,0.0
+Normal_Cash_Withdrawal,45245,0,0.0
+Normal_Fan_In,304332,0,0.0
+Normal_Fan_Out,344619,0,0.0
+Normal_Foward,6336,0,0.0
+Normal_Group,78557,0,0.0
+Normal_Mutual,18740,0,0.0
+Normal_Periodical,31681,0,0.0
+Normal_Plus_Mutual,34939,0,0.0
+Normal_Small_Fan_Out,518275,0,0.0
+Normal_single_large,3065,0,0.0
+Over-Invoicing,7,7,0.8571428571428571
+Scatter-Gather,46,46,0.9782608695652174
+Smurfing,151,151,1.0
+Stacked Bipartite,128,128,1.0
+Structuring,363,363,1.0
+Single_large,34,34,0.9705882352941176
+Bipartite,26,26,1.0
+Gather-Scatter,82,82,0.9878048780487805
+Fan_Out,58,58,1.0
diff --git a/docs/assets/unseen_entity_results.json b/docs/assets/unseen_entity_results.json
index 28d2e21..98643d9 100644
--- a/docs/assets/unseen_entity_results.json
+++ b/docs/assets/unseen_entity_results.json
@@ -39,5 +39,30 @@
       "recall_at_alert_rate": 0.992,
       "lift_at_alert_rate": 198.36478273217836
     }
+  },
+  "fixed_production_threshold": {
+    "decision_threshold": 0.0011600581929087639,
+    "both_parties_seen": {
+      "label": "Both parties seen",
+      "metrics": {
+        "threshold": 0.0011600581929087639,
+        "precision": 0.3958333333333333,
+        "recall": 0.992989165073295,
+        "lift": 180.84663798597833,
+        "alert_rate": 0.005490780343675977,
+        "alerts": 3936
+      }
+    },
+    "unseen_entity": {
+      "label": "Unseen entity",
+      "metrics": {
+        "threshold": 0.0011600581929087639,
+        "precision": 0.035137432700481724,
+        "recall": 0.992,
+        "lift": 197.91510342873337,
+        "alert_rate": 0.005012250115399638,
+        "alerts": 3529
+      }
+    }
   }
 }
diff --git a/docs/assets/walk_forward_results.csv b/docs/assets/walk_forward_results.csv
new file mode 100644
index 0000000..35d2f21
--- /dev/null
+++ b/docs/assets/walk_forward_results.csv
@@ -0,0 +1,5 @@
+window,pr_auc,recall_at_0.5%,precision_at_0.5%,lift_at_0.5%
+W1,0.9379450719376109,0.9705197827773467,0.18386243386243387,194.10153168240834
+W2,0.9658401319348904,0.9819156061620897,0.21606484893146646,196.36922824815977
+W3,0.977403742060776,0.9907735982966643,0.2053847285567162,198.140726093725
+W4,0.9901104009196656,0.99375,0.23489437139902497,198.7222531023785
diff --git a/docs/research/unseen_entity_generalization.md b/docs/research/unseen_entity_generalization.md
index e571761..652a991 100644
--- a/docs/research/unseen_entity_generalization.md
+++ b/docs/research/unseen_entity_generalization.md
@@ -63,6 +63,27 @@ synthetic dataset. That is a plausible property of real laundering rings too
 "the model doesn't know what to do with new accounts" from "new accounts are
 inherently rarer positives here."
 
+## Under the actual production threshold
+
+The table above gives each subgroup its own top-K -- the best possible
+ranking within that population alone. That answers "how good is ranking
+within this population," not "what happens to unseen accounts under the
+policy actually deployed." Applying the artifact's single fixed
+`decision_threshold` (0.001160, chosen on the validation split) to both
+subgroups instead:
+
+| Population | Alert rate | Precision | Recall | Lift |
+|---|---:|---:|---:|---:|
+| Both parties seen | 0.549% | 39.58% | 99.30% | 180.8x |
+| Unseen entity | 0.501% | 3.51% | 99.20% | 197.9x |
+
+One threshold, applied identically to both, lands each subgroup within noise
+of its own 0.5% alert budget on its own -- the calibration transfers across
+subgroups even though it was never tuned per-subgroup. Recall is
+indistinguishable (99.3% vs 99.2%). Precision differs by 11x, entirely
+consistent with the 12x prevalence gap between the two populations, not a
+sign the threshold behaves differently for unseen accounts.
+
 ## What this doesn't test
 
 Network features (out-degree, counterparty HHI) are computed per-transaction
diff --git a/reports/model_metrics.json b/reports/model_metrics.json
index 9434212..c135119 100644
--- a/reports/model_metrics.json
+++ b/reports/model_metrics.json
@@ -33,9 +33,9 @@
   "training_prevalence": 0.001003416857597292,
   "test_prevalence": 0.0011921912179000402,
   "calibration": {
-    "raw_brier_score": 0.0016988252755254507,
-    "calibrated_brier_score": 4.299887950764969e-05,
-    "raw_log_loss": 0.006217300426214933,
-    "calibrated_log_loss": 0.0002347317640669644
+    "raw_brier_score": 0.0016964440001174808,
+    "calibrated_brier_score": 7.803709740983322e-05,
+    "raw_log_loss": 0.006314944475889206,
+    "calibrated_log_loss": 0.00040054231067188084
   }
 }
diff --git a/reports/unseen_entity_results.json b/reports/unseen_entity_results.json
new file mode 100644
index 0000000..98643d9
--- /dev/null
+++ b/reports/unseen_entity_results.json
@@ -0,0 +1,68 @@
+{
+  "alert_rate": 0.005,
+  "accounts_seen_in_training": 693879,
+  "standard_out_of_time": {
+    "label": "Standard out-of-time (full test set)",
+    "transactions": 1420913,
+    "positives": 1694,
+    "prevalence": 0.0011921912179000402,
+    "metrics": {
+      "pr_auc": 0.9859286355765932,
+      "roc_auc": 0.9998338278230887,
+      "precision_at_alert_rate": 0.23673469387755103,
+      "recall_at_alert_rate": 0.9929161747343566,
+      "lift_at_alert_rate": 198.5710767896297
+    }
+  },
+  "both_parties_seen": {
+    "label": "Both sender and receiver seen during training",
+    "transactions": 716838,
+    "positives": 1569,
+    "prevalence": 0.002188779054681811,
+    "metrics": {
+      "pr_auc": 0.9917797464630843,
+      "roc_auc": 0.9998019722862765,
+      "precision_at_alert_rate": 0.4345885634588563,
+      "recall_at_alert_rate": 0.992989165073295,
+      "lift_at_alert_rate": 198.55296153774358
+    }
+  },
+  "unseen_entity": {
+    "label": "At least one party unseen during training",
+    "transactions": 704075,
+    "positives": 125,
+    "prevalence": 0.00017753790434257713,
+    "metrics": {
+      "pr_auc": 0.7912056121187283,
+      "roc_auc": 0.9997998835144541,
+      "precision_at_alert_rate": 0.03521726782164158,
+      "recall_at_alert_rate": 0.992,
+      "lift_at_alert_rate": 198.36478273217836
+    }
+  },
+  "fixed_production_threshold": {
+    "decision_threshold": 0.0011600581929087639,
+    "both_parties_seen": {
+      "label": "Both parties seen",
+      "metrics": {
+        "threshold": 0.0011600581929087639,
+        "precision": 0.3958333333333333,
+        "recall": 0.992989165073295,
+        "lift": 180.84663798597833,
+        "alert_rate": 0.005490780343675977,
+        "alerts": 3936
+      }
+    },
+    "unseen_entity": {
+      "label": "Unseen entity",
+      "metrics": {
+        "threshold": 0.0011600581929087639,
+        "precision": 0.035137432700481724,
+        "recall": 0.992,
+        "lift": 197.91510342873337,
+        "alert_rate": 0.005012250115399638,
+        "alerts": 3529
+      }
+    }
+  }
+}