diff --git a/README.md b/README.md index 8c4b3b3..8fe7950 100644 --- a/README.md +++ b/README.md @@ -15,8 +15,8 @@ RiskQueue scores transactions, quantifies the dollars exposed, and places the mo | Rare-event ranking | Operational outcome at 750 reviews | Decision policy | Engineering | |---|---|---|---| -| **0.130 average precision** | **97.1% fraud value captured** with expected-loss ranking | **$9,034 modeled cost** at the validation-selected cost threshold | **10.90 ms p95** for batch 1,000 | -| 3.49% demo fraud rate | vs. 95.1% using probability alone | vs. $34,205 at threshold 0.50 | In-process local benchmark; 57 tests pass | +| **0.113 average precision** | **96.9% fraud value captured** with expected-loss ranking | **$8,280 modeled cost** at the validation-selected cost threshold | **10.90 ms p95** for batch 1,000 | +| 3.49% demo fraud rate | vs. 92.3% using probability alone | vs. $38,906 at threshold 0.50 | In-process local benchmark; 60 tests pass | ## Results at a glance @@ -28,21 +28,21 @@ Fraud is a small share of transactions, so accuracy is not a useful headline. Pr | Held-out demo model | Average precision | ROC-AUC | Brier score | |---|---:|---:|---:| -| Logistic Regression | 0.097 | **0.749** | 0.158 | -| Histogram Gradient Boosting demo fallback | **0.130** | 0.745 | **0.033** | +| Logistic Regression + sigmoid calibration | 0.097 | **0.749** | 0.033 | +| Histogram Gradient Boosting demo fallback + sigmoid calibration | **0.113** | 0.734 | **0.033** | ### 2. What happens when analysts can review only a limited queue? ![Analyst capacity versus fraud value](artifacts/figures/capacity_value.png) -The chart measures how much fraudulent transaction value appears in the first *k* reviews. At 750 demo reviews, probability ranking captures 95.1% of fraudulent value, while expected-loss ranking captures 97.1%. The comparison matters because a slightly less likely but much larger transaction can deserve earlier review when the objective is dollars protected. +The chart measures how much fraudulent transaction value appears in the first *k* reviews. At 750 demo reviews, probability ranking captures 92.3% of fraudulent value, while expected-loss ranking captures 96.9%. The comparison matters because a slightly less likely but much larger transaction can deserve earlier review when the objective is dollars protected. | Reviews | Probability queue | Expected-loss queue | Improvement | |---:|---:|---:|---:| -| 100 | 53.6% | **68.3%** | +14.7 points | -| 250 | 79.0% | **85.5%** | +6.5 points | -| 500 | 87.2% | **94.2%** | +7.0 points | -| 750 | 95.1% | **97.1%** | +2.0 points | +| 100 | 55.5% | **68.3%** | +12.8 points | +| 250 | 76.9% | **84.1%** | +7.2 points | +| 500 | 86.9% | **94.7%** | +7.8 points | +| 750 | 92.3% | **96.9%** | +4.6 points | ### 3. Why is 0.50 not automatically the right threshold? @@ -52,9 +52,9 @@ The score estimates risk; it does not decide the operating policy by itself. The | Policy | Threshold | Reviews | Precision | Recall | Modeled cost | |---|---:|---:|---:|---:|---:| -| Default | 0.50 | 2 | 100.0% | 3.3% | $34,205 | -| F1-optimal | 0.15 | 78 | 16.7% | 21.3% | $18,613 | -| Cost-optimized | **0.03** | 291 | 9.3% | **44.3%** | **$9,034** | +| Default | 0.50 | 0 | 0.0% | 0.0% | $38,906 | +| F1-optimal | 0.12 | 75 | 16.0% | 19.7% | $18,628 | +| Cost-optimized | **0.04** | 312 | 8.7% | **44.3%** | **$8,280** | ### 4. Would the system notice changed transaction behavior? @@ -90,6 +90,8 @@ flowchart LR The primary split uses whole time steps: earliest 70% for training, next 15% for validation and final 15% for test. History features update state only after a row is transformed. `isFlaggedFraud` and the four balance columns never enter the model feature list. +After validation average precision selects the base model, sigmoid calibration is fitted on that chronological validation period. Thresholds are selected from the calibrated validation probabilities, and the calibrated model is evaluated once on the final test period. + ## Explore in three minutes ```bash diff --git a/artifacts/figures/capacity_results.csv b/artifacts/figures/capacity_results.csv index d3507bb..c2edd1b 100644 --- a/artifacts/figures/capacity_results.csv +++ b/artifacts/figures/capacity_results.csv @@ -7,16 +7,16 @@ probability,500,500.0,0.076,0.6229508196721312,33823.16,0.8693496268468743 probability,750,750.0,0.06,0.7377049180327869,35914.66,0.9231070151142697 probability,1000,1000.0,0.052,0.8524590163934426,38237.840000000004,0.9828192260992316 expected_loss,25,25.0,0.24,0.09836065573770492,16538.129999999997,0.4250761059654123 -expected_loss,50,50.0,0.16,0.13114754098360656,20359.809999999998,0.5233039499021752 -expected_loss,100,100.0,0.15,0.2459016393442623,27137.33,0.697505132847448 -expected_loss,250,250.0,0.092,0.3770491803278688,31930.4,0.8207004113474742 +expected_loss,50,50.0,0.18,0.14754098360655737,22220.8,0.5711365877179726 +expected_loss,100,100.0,0.14,0.22950819672131148,26564.61,0.6827846301419721 +expected_loss,250,250.0,0.088,0.36065573770491804,32707.73,0.8406799622066157 expected_loss,500,500.0,0.072,0.5901639344262295,36863.61,0.9474976790379345 -expected_loss,750,750.0,0.058666666666666666,0.7213114754098361,37817.25,0.972008889053387 -expected_loss,1000,1000.0,0.052,0.8524590163934426,38650.560000000005,0.9934272821765538 +expected_loss,750,750.0,0.056,0.6885245901639344,37719.46,0.9694954130798423 +expected_loss,1000,1000.0,0.049,0.8032786885245902,38517.51,0.9900075257773298 expected_review_value,25,25.0,0.24,0.09836065573770492,16538.129999999997,0.4250761059654123 -expected_review_value,50,50.0,0.16,0.13114754098360656,20359.809999999998,0.5233039499021752 -expected_review_value,100,100.0,0.15,0.2459016393442623,27137.33,0.697505132847448 -expected_review_value,250,250.0,0.092,0.3770491803278688,31930.4,0.8207004113474742 +expected_review_value,50,50.0,0.18,0.14754098360655737,22220.8,0.5711365877179726 +expected_review_value,100,100.0,0.14,0.22950819672131148,26564.61,0.6827846301419721 +expected_review_value,250,250.0,0.088,0.36065573770491804,32707.73,0.8406799622066157 expected_review_value,500,500.0,0.072,0.5901639344262295,36863.61,0.9474976790379345 -expected_review_value,750,750.0,0.058666666666666666,0.7213114754098361,37817.25,0.972008889053387 -expected_review_value,1000,1000.0,0.052,0.8524590163934426,38650.560000000005,0.9934272821765538 +expected_review_value,750,750.0,0.056,0.6885245901639344,37719.46,0.9694954130798423 +expected_review_value,1000,1000.0,0.049,0.8032786885245902,38517.51,0.9900075257773298 diff --git a/artifacts/figures/capacity_value.png b/artifacts/figures/capacity_value.png index da3bab4..e6c367d 100644 Binary files a/artifacts/figures/capacity_value.png and b/artifacts/figures/capacity_value.png differ diff --git a/artifacts/figures/dashboard_overview.png b/artifacts/figures/dashboard_overview.png index d998aae..6d39cb1 100644 Binary files a/artifacts/figures/dashboard_overview.png and b/artifacts/figures/dashboard_overview.png differ diff --git a/artifacts/figures/error_analysis.csv b/artifacts/figures/error_analysis.csv index f7cea3b..55c0eae 100644 --- a/artifacts/figures/error_analysis.csv +++ b/artifacts/figures/error_analysis.csv @@ -1,10 +1,10 @@ dimension,segment,transactions,fraud_cases,recall,false_positives,fraud_value -type,CASH_IN,186,5,0.2,16,2051.48 -type,CASH_OUT,414,29,0.7241379310344828,255,15869.330000000002 -type,DEBIT,135,1,0.0,4,1198.31 -type,PAYMENT,742,7,0.14285714285714285,101,400.48 -type,TRANSFER,272,19,0.9473684210526315,214,19386.68 -amount_bucket,"(1.0190000000000001, 46.03]",438,11,0.2727272727272727,37,288.7 -amount_bucket,"(113.37, 275.26]",437,12,0.75,157,2225.91 -amount_bucket,"(275.26, 6700.6]",437,27,0.8148148148148148,280,35560.24 -amount_bucket,"(46.03, 113.37]",437,11,0.6363636363636364,116,831.43 +type,CASH_IN,186,5,0.0,0,2051.48 +type,CASH_OUT,414,29,0.41379310344827586,128,15869.330000000002 +type,DEBIT,135,1,0.0,0,1198.31 +type,PAYMENT,742,7,0.14285714285714285,23,400.48 +type,TRANSFER,272,19,0.7368421052631579,134,19386.68 +amount_bucket,"(1.0190000000000001, 46.03]",438,11,0.0,9,288.7 +amount_bucket,"(113.37, 275.26]",437,12,0.3333333333333333,58,2225.91 +amount_bucket,"(275.26, 6700.6]",437,27,0.7407407407407407,194,35560.24 +amount_bucket,"(46.03, 113.37]",437,11,0.2727272727272727,24,831.43 diff --git a/artifacts/figures/model_diagnostics.png b/artifacts/figures/model_diagnostics.png index 6fa647e..c52bcfd 100644 Binary files a/artifacts/figures/model_diagnostics.png and b/artifacts/figures/model_diagnostics.png differ diff --git a/artifacts/figures/precision_recall.png b/artifacts/figures/precision_recall.png index 7314ff7..2300158 100644 Binary files a/artifacts/figures/precision_recall.png and b/artifacts/figures/precision_recall.png differ diff --git a/artifacts/figures/review_queue.csv b/artifacts/figures/review_queue.csv index 5ba1fa9..75fbbca 100644 --- a/artifacts/figures/review_queue.csv +++ b/artifacts/figures/review_queue.csv @@ -1,751 +1,751 @@ transaction_id,step,type,amount,fraud_probability,expected_loss,risk_band,rank,isFraud -demo-011612,348,CASH_OUT,3031.45,0.6391437818934423,1937.5324176208758,high,1,1 -demo-010274,306,CASH_OUT,4771.87,0.39693988212055603,1894.1455152946176,guarded,2,0 -demo-010322,307,CASH_OUT,3780.52,0.4586556275743472,1733.9567731573711,high,3,0 -demo-011024,330,CASH_OUT,4210.22,0.2815364943213422,1185.3305791216014,guarded,4,0 -demo-010276,306,CASH_OUT,5768.83,0.20280216362447115,1169.931205581758,guarded,5,0 -demo-011902,357,TRANSFER,4874.63,0.1881295409162225,917.0619040364458,guarded,6,1 -demo-010959,328,CASH_OUT,4973.46,0.17958803999336334,893.1739333853928,guarded,7,0 -demo-011484,345,TRANSFER,4041.15,0.20425568974700578,825.4278806211124,guarded,8,0 -demo-010814,323,TRANSFER,1630.12,0.4719855419642954,769.3930716668372,high,9,0 -demo-011222,335,CASH_OUT,1930.38,0.3767229672532071,727.2184815262459,guarded,10,0 -demo-010514,313,CASH_OUT,3427.36,0.2107727148043141,722.393971811714,guarded,11,0 -demo-010898,326,TRANSFER,1678.31,0.4114156284957513,690.4829634607044,high,12,1 -demo-011770,353,TRANSFER,3645.57,0.17732988273305259,646.4685005951345,guarded,13,1 -demo-010979,329,TRANSFER,1400.55,0.4463661816162728,625.1581556626709,high,14,0 -demo-011614,348,CASH_OUT,2645.57,0.2239964640718304,592.5983254545124,guarded,15,0 -demo-011216,335,CASH_OUT,2491.87,0.23357702167837294,582.0435730096872,guarded,16,0 -demo-011689,351,CASH_OUT,6700.6,0.08643261755569402,579.1503971936834,low,17,0 -demo-010393,309,TRANSFER,2887.78,0.1790959238012817,517.1896268348653,guarded,18,0 -demo-010462,312,CASH_OUT,1749.26,0.2922334931303382,511.1923601931754,guarded,19,0 -demo-011542,346,CASH_OUT,1947.9,0.2602925145216644,507.02378903675014,guarded,20,1 -demo-010885,326,CASH_OUT,1360.27,0.3672312928298511,499.53371069766155,guarded,21,1 -demo-010775,322,TRANSFER,806.98,0.6117483591728898,493.66869088533866,high,22,0 -demo-011808,354,CASH_OUT,1091.99,0.4455902114493307,486.58005500055464,high,23,0 -demo-011710,351,TRANSFER,3678.4,0.12834979265153373,472.1218772894017,guarded,24,0 -demo-010926,327,TRANSFER,1444.47,0.325179806200668,469.7124746626789,guarded,25,0 -demo-011258,336,CASH_OUT,2440.57,0.14743321483734895,359.82108113558877,guarded,26,0 -demo-010564,315,TRANSFER,1928.62,0.1860373421670273,358.7953388501722,guarded,27,0 -demo-011695,351,TRANSFER,801.77,0.4157109711205334,333.3045853153101,high,28,0 -demo-010880,325,CASH_OUT,1377.09,0.24049742577753758,331.1866000639892,guarded,29,0 -demo-010873,325,TRANSFER,850.3,0.38120682747255197,324.1401653999109,guarded,30,0 -demo-010330,308,CASH_OUT,2178.18,0.14836112699809417,323.15723960470876,guarded,31,0 -demo-011768,353,CASH_OUT,1137.37,0.28089543117800425,319.48203655892667,guarded,32,0 -demo-011387,341,PAYMENT,813.02,0.3777287428228512,307.10102248983446,guarded,33,0 -demo-011067,331,CASH_OUT,1261.98,0.24254318687046902,306.0846509667945,guarded,34,0 -demo-010444,311,CASH_OUT,956.9,0.317578943367305,303.89129090817414,guarded,35,0 -demo-011501,345,TRANSFER,1223.08,0.24457586401574816,299.13584776038124,guarded,36,0 -demo-011777,354,CASH_OUT,2525.09,0.11581142341679401,292.43426715551243,guarded,37,0 -demo-011118,332,TRANSFER,810.64,0.359482172041246,291.41062794351564,guarded,38,0 -demo-011319,338,TRANSFER,2774.07,0.10452501946729732,289.9597207536455,guarded,39,1 -demo-011140,333,TRANSFER,1060.22,0.2641079067512695,280.01248489583094,guarded,40,0 -demo-010765,321,TRANSFER,746.29,0.3678932802667345,274.5550761302613,guarded,41,0 -demo-011601,348,CASH_OUT,2913.24,0.0891808414177673,259.8051944518964,low,42,0 -demo-011276,337,TRANSFER,611.83,0.4090097842233084,250.2444562813468,high,43,0 -demo-011592,348,CASH_OUT,737.9,0.3388634236807321,250.0473203340122,guarded,44,0 -demo-011291,337,CASH_OUT,659.95,0.3745275920311578,247.16948436096263,guarded,45,0 -demo-011332,338,CASH_OUT,1047.61,0.23524478335561066,246.44478749117127,guarded,46,1 -demo-010728,320,TRANSFER,3314.46,0.07320602626613629,242.63844581805807,low,47,0 -demo-010928,327,CASH_OUT,1186.46,0.20417913628936737,242.2503780418828,guarded,48,0 -demo-011324,338,CASH_OUT,830.98,0.27870944740124737,231.60197660148853,guarded,49,0 -demo-011654,349,CASH_OUT,1344.3,0.17142241266297706,230.44314934284006,guarded,50,0 -demo-010861,325,TRANSFER,677.78,0.3361514466773588,227.83672752898025,guarded,51,0 -demo-011091,332,TRANSFER,640.54,0.35133399832410983,225.0434792865253,guarded,52,0 -demo-010929,327,CASH_OUT,964.76,0.22910159345811634,221.02805330465233,guarded,53,0 -demo-011877,356,TRANSFER,639.98,0.3395235057169872,217.28825318875747,guarded,54,0 -demo-011909,357,TRANSFER,819.83,0.263046529723771,215.6534364634392,guarded,55,0 -demo-011580,347,CASH_OUT,1860.99,0.11098534651050004,206.5426200025755,guarded,56,1 -demo-011204,335,CASH_OUT,2768.68,0.07315300379549035,202.53725854849822,low,57,0 -demo-011206,335,TRANSFER,1341.12,0.15059788285179293,201.9698326501965,guarded,58,0 -demo-011734,352,TRANSFER,1190.84,0.16791298724059855,199.95750172559437,guarded,59,0 -demo-011057,331,TRANSFER,1329.42,0.15026591514440904,199.76651291128027,guarded,60,0 -demo-011634,349,TRANSFER,776.52,0.25656382094427693,199.2269382396499,guarded,61,1 -demo-011342,339,CASH_OUT,924.29,0.20895551390987815,193.13549195176128,guarded,62,0 -demo-010925,327,TRANSFER,690.02,0.27579086050893215,190.30120956837337,guarded,63,0 -demo-011359,340,TRANSFER,666.89,0.2829460479142587,188.69388989354,guarded,64,1 -demo-011972,359,CASH_OUT,918.35,0.20195978092858763,185.46976481576846,guarded,65,0 -demo-011322,338,TRANSFER,3363.65,0.05361855056479928,180.3540376072871,low,66,0 -demo-010810,323,TRANSFER,854.85,0.20570314827749228,175.84533630501429,guarded,67,0 -demo-011356,339,PAYMENT,870.56,0.19765627692120488,172.0716484365241,guarded,68,0 -demo-011274,337,CASH_OUT,1131.09,0.15203768499848871,171.9683051249406,guarded,69,0 -demo-010352,308,CASH_OUT,1209.72,0.13549689018025574,163.91329798885897,guarded,70,0 -demo-010915,327,PAYMENT,780.64,0.20895029545224716,163.11495864184224,guarded,71,0 -demo-011187,334,TRANSFER,645.24,0.25078820014086484,161.81857825889162,guarded,72,0 -demo-010653,317,PAYMENT,759.44,0.21179443863172687,160.84516847447867,guarded,73,0 -demo-010543,314,TRANSFER,2207.19,0.0728044573588694,160.69327023792295,low,74,0 -demo-011053,331,CASH_OUT,1057.59,0.15006417979551057,158.706375909934,guarded,75,0 -demo-011178,334,TRANSFER,925.83,0.17068244256020781,158.0229257955172,guarded,76,1 -demo-010770,321,TRANSFER,1201.21,0.13086504938187002,157.1964059679961,guarded,77,0 -demo-011936,358,CASH_OUT,2060.46,0.07526755424690391,155.08578482357564,low,78,0 -demo-010290,306,CASH_OUT,652.95,0.23569419676672684,153.8965257788343,guarded,79,0 -demo-011452,343,TRANSFER,2495.31,0.05988905962232408,149.4417693661815,low,80,0 -demo-011543,346,CASH_OUT,683.97,0.2182188353217816,149.25513679503896,guarded,81,0 -demo-010878,325,CASH_OUT,724.96,0.20350941931851158,147.53618862914817,guarded,82,0 -demo-010533,314,TRANSFER,449.99,0.3254770614540954,146.46142288372837,guarded,83,0 -demo-011551,346,CASH_OUT,726.46,0.2010718831648972,146.07068024397122,guarded,84,0 -demo-010547,314,PAYMENT,901.08,0.15989293329688745,144.07632433515934,guarded,85,0 -demo-010628,317,CASH_OUT,1051.45,0.1349542935705185,141.89769197472168,guarded,86,0 -demo-011186,334,CASH_OUT,1022.18,0.13478812137087243,137.77772190287837,guarded,87,1 -demo-011980,359,TRANSFER,706.64,0.19421722130741645,137.24165726467277,guarded,88,0 -demo-011045,330,TRANSFER,839.11,0.16165377581903867,135.64529982751355,guarded,89,0 -demo-011311,338,CASH_OUT,913.75,0.1422801869390391,130.00852081554697,guarded,90,0 -demo-011376,340,TRANSFER,927.09,0.1399085343698513,129.70780312894544,guarded,91,0 -demo-010461,312,TRANSFER,1226.08,0.10258141353902361,125.77301951192607,guarded,92,0 -demo-011454,343,CASH_OUT,952.39,0.12902468560628633,122.88182032457104,guarded,93,1 -demo-011315,338,TRANSFER,572.72,0.21257804442103698,121.74769760081631,guarded,94,1 -demo-010897,326,TRANSFER,905.52,0.13257227181208422,120.0468435712785,guarded,95,0 -demo-011447,343,CASH_OUT,1658.04,0.07177548825350162,119.00663054383583,low,96,0 -demo-011783,354,TRANSFER,1775.41,0.06698742814488397,118.93014980270846,low,97,0 -demo-010820,323,CASH_OUT,1288.04,0.08750675155867166,112.71219627763143,low,98,0 -demo-010887,326,TRANSFER,1304.35,0.08371070068203837,109.18805243461674,low,99,0 -demo-011413,342,CASH_OUT,676.89,0.16090206904828583,108.91300151809419,guarded,100,0 -demo-011350,339,CASH_OUT,1249.31,0.08578521353554223,107.17232512208827,low,101,0 -demo-011566,346,CASH_OUT,991.41,0.10471968614633875,103.8201440423417,guarded,102,0 -demo-011949,358,TRANSFER,670.81,0.1526828313288402,102.42117008369928,guarded,103,0 -demo-010942,328,CASH_OUT,1278.76,0.07852583491210435,100.41569665220256,low,104,0 -demo-010480,312,PAYMENT,844.77,0.1141948656523113,96.46839665710301,guarded,105,0 -demo-010558,315,CASH_OUT,711.2,0.13492329928803043,95.95745045364725,guarded,106,0 -demo-011135,333,TRANSFER,617.08,0.15513394355545237,95.73005388919856,guarded,107,0 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b/artifacts/figures/score_distribution.png differ diff --git a/artifacts/figures/scored_test.csv b/artifacts/figures/scored_test.csv index 3993546..78ad9d8 100644 --- a/artifacts/figures/scored_test.csv +++ b/artifacts/figures/scored_test.csv @@ -1,1750 +1,1750 @@ transaction_id,step,type,amount,nameOrig,nameDest,isFraud,isFlaggedFraud,sender_count_1h,sender_count_6h,sender_count_24h,sender_total_amount_24h,sender_average_amount_24h,sender_historical_median_amount,sender_unique_recipients_24h,sender_hours_since_prior,amount_to_sender_median,recipient_incoming_count_24h,recipient_unique_senders_24h,recipient_total_received_24h,recipient_hours_since_prior,prior_pair_count,first_time_recipient,hour,day_index,fraud_probability,expected_loss,risk_band -demo-010251,306,PAYMENT,111.4,C0000361,M0000673,0,0,0.0,0.0,0.0,0.0,0.0,148.45,0.0,40.0,0.750421017177501,1.0,1.0,125.76,13.0,0.0,1.0,18,12,0.0013927338323821816,0.15515054892737504,low 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-demo-011497,345,PAYMENT,81.54,C0000482,M0001896,0,0,1.0,1.0,3.0,1027.21,342.40333333333336,213.29500000000002,3.0,1.0,0.38228744227478373,0.0,0.0,0.0,46.0,0.0,1.0,9,14,0.0019132581391537477,0.1560070686665966,low -demo-011498,345,CASH_IN,46.93,C0000050,M0001122,0,0,0.0,0.0,1.0,96.63,96.63,72.47,1.0,8.0,0.6475783082654891,0.0,0.0,0.0,116.0,0.0,1.0,9,14,0.001979503000950979,0.09289807583462945,low -demo-011499,345,CASH_OUT,153.93,C0001035,M0001896,0,0,0.0,1.0,1.0,916.44,916.44,155.54,1.0,2.0,0.9896489648964898,0.0,0.0,0.0,46.0,0.0,1.0,9,14,0.001462661676447294,0.225147511855532,low -demo-011500,345,PAYMENT,151.21,C0001435,M0001941,0,0,0.0,0.0,0.0,0.0,0.0,52.14,0.0,26.0,2.900076716532413,1.0,1.0,63.53,8.0,0.0,1.0,9,14,0.0017591768818641356,0.26600513630667594,low -demo-011501,345,TRANSFER,1223.08,C0001467,M0001906,0,0,0.0,0.0,0.0,0.0,0.0,61.72,0.0,61.0,19.81659105638367,1.0,1.0,35.09,13.0,0.0,1.0,9,14,0.24457586401574816,299.13584776038124,guarded -demo-011502,345,CASH_IN,224.83,C0000176,M0001927,0,0,0.0,0.0,1.0,113.98,113.98,113.98,1.0,16.0,1.972539041937182,1.0,1.0,141.34,12.0,0.0,1.0,9,14,0.0025784794685300297,0.5797195389096066,low -demo-011503,345,PAYMENT,88.46,C0000285,M0000134,0,0,0.0,0.0,1.0,105.94,105.94,108.6,1.0,18.0,0.814548802946593,0.0,0.0,0.0,36.0,0.0,1.0,9,14,0.0028683385116907824,0.2537332247441666,low -demo-011504,345,CASH_OUT,179.04,C0000647,M0000061,0,0,0.0,0.0,1.0,24.73,24.73,214.10000000000002,1.0,7.0,0.8362447454460531,0.0,0.0,0.0,63.0,0.0,1.0,9,14,0.007528163534922648,1.3478423992925508,low -demo-011505,345,PAYMENT,326.71,C0001371,M0001155,0,0,0.0,0.0,1.0,94.12,94.12,94.12,1.0,18.0,3.471206969825754,0.0,0.0,0.0,181.0,0.0,1.0,9,14,0.006534594852027985,2.134917484106063,low -demo-011506,345,PAYMENT,869.23,C0000861,M0001496,0,0,0.0,0.0,0.0,0.0,0.0,182.22,0.0,63.0,4.770222807595215,0.0,0.0,0.0,77.0,0.0,1.0,9,14,0.04864675844775145,42.285221845539,low -demo-011507,345,CASH_OUT,439.51,C0000684,M0001676,0,0,0.0,0.0,2.0,70.55,35.275,61.78,2.0,15.0,7.114114600194237,0.0,0.0,0.0,55.0,0.0,1.0,9,14,0.01904327726861345,8.369710792328297,low -demo-011508,345,PAYMENT,44.69,C0001388,M0001283,0,0,0.0,1.0,2.0,244.72,122.36,28.135,2.0,5.0,1.5884130087080148,0.0,0.0,0.0,103.0,0.0,1.0,9,14,0.002966082211704097,0.13255421404105608,low -demo-011509,345,CASH_IN,114.02,C0000994,M0000627,0,0,0.0,0.0,1.0,340.8,340.8,180.45,1.0,20.0,0.6318647824882239,1.0,1.0,94.31,2.0,0.0,1.0,9,14,0.00047185935771032207,0.05380140396613092,low -demo-011510,345,PAYMENT,170.32,C0001477,M0001865,0,0,0.0,0.0,1.0,445.31,445.31,199.27,1.0,12.0,0.8547197270035629,1.0,1.0,676.89,3.0,0.0,1.0,9,14,0.0005907770399419724,0.10062114544291673,low -demo-011511,345,CASH_OUT,98.87,C0001295,M0001181,0,0,0.0,0.0,0.0,0.0,0.0,128.64,0.0,64.0,0.7685789800995027,0.0,0.0,0.0,46.0,0.0,1.0,9,14,0.015485437234313646,1.5310451793565902,low -demo-011512,345,PAYMENT,243.35,C0001497,M0001363,0,0,0.0,0.0,0.0,0.0,0.0,69.04,0.0,76.0,3.5247682502896867,1.0,1.0,340.8,20.0,0.0,1.0,9,14,0.0047724141231725034,1.1613669768740287,low -demo-011513,345,CASH_OUT,275.29,C0000836,M0000512,0,0,0.0,0.0,0.0,0.0,0.0,114.36,0.0,100.0,2.4072228051766356,1.0,1.0,213.31,8.0,0.0,1.0,9,14,0.08589063258438752,23.644832244156042,low -demo-011514,345,PAYMENT,353.59,C0000561,M0001517,0,0,0.0,0.0,0.0,0.0,0.0,70.64,0.0,57.0,5.005520951302378,0.0,0.0,0.0,61.0,0.0,1.0,9,14,0.005347175060180916,1.8907076295293699,low -demo-011515,345,CASH_OUT,58.37,C0000092,M0000259,0,0,0.0,0.0,0.0,0.0,0.0,118.99000000000001,0.0,37.0,0.4905454239852088,0.0,0.0,0.0,172.0,0.0,1.0,9,14,0.005963699053743573,0.34810111376701236,low -demo-011516,345,PAYMENT,9.35,C0001094,M0001741,0,0,0.0,0.0,0.0,0.0,0.0,114.75,0.0,51.0,0.08148148148148147,0.0,0.0,0.0,33.0,0.0,1.0,9,14,0.0007630273824212031,0.0071343060256382484,low -demo-011517,345,CASH_IN,50.47,C0000398,M0001704,0,0,0.0,0.0,1.0,99.81,99.81,78.165,1.0,8.0,0.6456854090705558,1.0,1.0,381.82,22.0,0.0,1.0,9,14,0.0009188313991527436,0.046373420715238964,low -demo-011518,345,DEBIT,84.94,C0000016,M0001365,0,0,0.0,0.0,0.0,0.0,0.0,60.67,0.0,26.0,1.400032965221691,0.0,0.0,0.0,33.0,0.0,1.0,9,14,0.004036192016612752,0.3428341498910871,low -demo-011519,345,PAYMENT,8.68,C0001204,M0001962,0,0,0.0,0.0,0.0,0.0,0.0,156.7,0.0,111.0,0.05539246968730058,0.0,0.0,0.0,70.0,0.0,1.0,9,14,0.0009224276861433562,0.008006672315724331,low -demo-011520,345,PAYMENT,38.54,C0000636,M0001820,0,0,0.0,0.0,1.0,168.84,168.84,232.1,1.0,10.0,0.16604911676001724,0.0,0.0,0.0,34.0,0.0,1.0,9,14,0.001915907778305869,0.0738390857759082,low -demo-011521,345,PAYMENT,410.25,C0000829,M0000139,0,0,0.0,1.0,1.0,178.63,178.63,98.19,1.0,4.0,4.178124045218454,0.0,0.0,0.0,42.0,0.0,1.0,9,14,0.007956767827632029,3.26426400128604,low -demo-011522,345,PAYMENT,160.6,C0001175,M0000441,0,0,0.0,0.0,0.0,0.0,0.0,278.58,0.0,95.0,0.5764950822025989,0.0,0.0,0.0,53.0,0.0,1.0,9,14,0.0066765798602760095,1.072258725560327,low -demo-011523,345,PAYMENT,98.01,C0000493,M0000242,0,0,0.0,0.0,0.0,0.0,0.0,549.755,0.0,187.0,0.1782794153759402,0.0,0.0,0.0,101.0,0.0,1.0,9,14,0.0023379662882978707,0.22914407591607433,low -demo-011524,345,TRANSFER,52.88,C0001225,M0000641,0,0,0.0,0.0,0.0,0.0,0.0,230.13,0.0,61.0,0.22978316603658802,0.0,0.0,0.0,39.0,0.0,1.0,9,14,0.015493083722155916,0.8192742672276049,low -demo-011525,345,DEBIT,393.54,C0000874,M0001177,0,0,0.0,0.0,0.0,0.0,0.0,76.09,0.0,44.0,5.172033118675253,0.0,0.0,0.0,62.0,0.0,1.0,9,14,0.0036066594412016433,1.4193647564904948,low -demo-011526,345,TRANSFER,117.46,C0000851,M0000287,0,0,0.0,0.0,0.0,0.0,0.0,130.32,0.0,110.0,0.9013198281154082,1.0,1.0,38.36,11.0,0.0,1.0,9,14,0.16191601473157455,19.018655090370746,guarded -demo-011527,345,TRANSFER,222.86,C0000402,M0000172,0,0,0.0,0.0,2.0,652.29,326.145,198.35,2.0,8.0,1.123569447945551,0.0,0.0,0.0,27.0,0.0,1.0,9,14,0.04050453565090817,9.026840815161394,low -demo-011528,345,DEBIT,137.22,C0001183,M0001288,0,0,0.0,0.0,0.0,0.0,0.0,59.25,0.0,123.0,2.3159493670886078,1.0,1.0,257.37,24.0,0.0,1.0,9,14,0.004120069837897543,0.5653559831563008,low -demo-011529,345,DEBIT,102.14,C0000361,M0001262,0,0,0.0,0.0,0.0,0.0,0.0,129.925,0.0,39.0,0.7861458533769482,1.0,1.0,87.6,8.0,0.0,1.0,9,14,0.0014590954454456859,0.14903200879782236,low -demo-011530,345,PAYMENT,532.21,C0000928,M0000732,0,0,0.0,0.0,0.0,0.0,0.0,66.25,0.0,32.0,8.03335849056604,0.0,0.0,0.0,59.0,0.0,1.0,9,14,0.009147578244472682,4.868432617490806,low -demo-011531,345,PAYMENT,169.22,C0001242,M0001438,0,0,0.0,1.0,1.0,78.84,78.84,101.955,1.0,2.0,1.6597518513069491,0.0,0.0,0.0,55.0,0.0,1.0,9,14,0.004605363201085562,0.7793195608876987,low -demo-011532,345,CASH_OUT,302.09,C0000174,M0001183,0,0,0.0,0.0,0.0,0.0,0.0,99.55,0.0,39.0,3.03455549974887,1.0,1.0,154.85,24.0,0.0,1.0,9,14,0.017484222352767437,5.281808730547515,low -demo-011533,346,CASH_OUT,177.64,C0000600,M0000829,0,0,0.0,0.0,0.0,0.0,0.0,164.68,0.0,74.0,1.0786980811270341,0.0,0.0,0.0,25.0,0.0,1.0,10,14,0.08058077709625452,14.314369243378653,low -demo-011534,346,CASH_OUT,339.57,C0001016,M0000136,0,0,0.0,0.0,1.0,19.34,19.34,122.80000000000001,1.0,24.0,2.765228013029316,1.0,1.0,39.75,8.0,0.0,1.0,10,14,0.04025133413047224,13.668145530684457,low -demo-011535,346,PAYMENT,63.42,C0000410,M0001512,0,0,0.0,0.0,0.0,0.0,0.0,117.89,0.0,154.0,0.5379591144287047,0.0,0.0,0.0,42.0,0.0,1.0,10,14,0.004529706823871603,0.2872740067699371,low -demo-011536,346,PAYMENT,104.48,C0000041,M0001249,0,0,0.0,0.0,0.0,0.0,0.0,74.825,0.0,56.0,1.3963247577681257,0.0,0.0,0.0,56.0,0.0,1.0,10,14,0.012099078660868673,1.264111738487559,low -demo-011537,346,CASH_OUT,129.11,C0000613,M0000227,0,0,0.0,0.0,1.0,572.72,572.72,147.19,1.0,8.0,0.8771655683130648,0.0,0.0,0.0,65.0,0.0,1.0,10,14,0.004288840305177916,0.5537321718015208,low -demo-011538,346,PAYMENT,44.17,C0000745,M0000375,0,0,0.0,0.0,0.0,0.0,0.0,114.33,0.0,58.0,0.3863377941047844,1.0,1.0,1341.12,11.0,0.0,1.0,10,14,0.005349675473292163,0.23629516565531486,low -demo-011539,346,PAYMENT,26.24,C0001380,M0000602,0,0,0.0,0.0,0.0,0.0,0.0,107.61500000000001,0.0,34.0,0.24383217952887606,0.0,0.0,0.0,39.0,0.0,1.0,10,14,0.0016055030764908825,0.042128400727120756,low -demo-011540,346,TRANSFER,141.01,C0001210,M0000358,0,0,0.0,0.0,1.0,15.45,15.45,136.99,1.0,18.0,1.0293452076793925,2.0,2.0,2981.3,10.0,0.0,1.0,10,14,0.01304410332749323,1.8393490102098202,low -demo-011541,346,CASH_OUT,129.9,C0000271,M0001615,0,0,0.0,0.0,0.0,0.0,0.0,143.28,0.0,64.0,0.9066164154103853,0.0,0.0,0.0,83.0,0.0,1.0,10,14,0.017410282781514554,2.2615957333187406,low -demo-011542,346,CASH_OUT,1947.9,C0000415,M0001867,1,0,0.0,0.0,1.0,76.33,76.33,121.04499999999999,1.0,10.0,16.092362344582597,1.0,1.0,249.6,23.0,0.0,1.0,10,14,0.2602925145216644,507.02378903675014,guarded -demo-011543,346,CASH_OUT,683.97,C0000277,M0000176,0,0,0.0,0.0,0.0,0.0,0.0,296.74,0.0,27.0,2.304947091730134,0.0,0.0,0.0,26.0,0.0,1.0,10,14,0.2182188353217816,149.25513679503896,guarded -demo-011544,346,PAYMENT,69.74,C0001366,M0000478,0,0,0.0,0.0,0.0,0.0,0.0,145.17,0.0,67.0,0.4804022869738927,0.0,0.0,0.0,140.0,0.0,1.0,10,14,0.003523342309315889,0.24571789265169008,low -demo-011545,346,CASH_OUT,116.59,C0000530,M0000570,0,0,0.0,0.0,1.0,11.27,11.27,37.68,1.0,21.0,3.0942144373673037,0.0,0.0,0.0,46.0,0.0,1.0,10,14,0.024979279703451814,2.9123342206254472,low -demo-011546,346,CASH_OUT,426.92,C0001336,M0001983,0,0,0.0,0.0,1.0,36.74,36.74,60.855000000000004,1.0,15.0,7.015364390764933,1.0,1.0,74.15,24.0,0.0,1.0,10,14,0.01723599046382167,7.3583890488147485,low -demo-011547,346,PAYMENT,934.65,C0000090,M0001242,0,0,0.0,0.0,1.0,172.55,172.55,102.13,1.0,14.0,9.151571526485851,0.0,0.0,0.0,42.0,0.0,1.0,10,14,0.02232856042161364,20.86938899806119,low -demo-011548,346,PAYMENT,223.19,C0000838,M0001908,0,0,0.0,2.0,2.0,194.86,97.43,112.59,2.0,4.0,1.9823252509103828,1.0,1.0,8.89,22.0,0.0,1.0,10,14,0.006678830622477562,1.490648206630767,low -demo-011549,346,CASH_OUT,23.05,C0000112,M0000494,0,0,0.0,0.0,0.0,0.0,0.0,55.735,0.0,64.0,0.4135641876738136,0.0,0.0,0.0,85.0,0.0,1.0,10,14,0.006101299498395228,0.14063495343801,low -demo-011550,346,DEBIT,242.57,C0000831,M0000135,0,0,0.0,0.0,0.0,0.0,0.0,48.91,0.0,31.0,4.959517481087713,0.0,0.0,0.0,78.0,0.0,1.0,10,14,0.0027588890411437378,0.6692237147102365,low -demo-011551,346,CASH_OUT,726.46,C0000016,M0000961,0,0,1.0,1.0,1.0,84.94,84.94,71.12,1.0,1.0,10.214566929133857,0.0,0.0,0.0,74.0,0.0,1.0,10,14,0.2010718831648972,146.07068024397122,guarded 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-demo-011577,347,CASH_IN,155.14,C0000216,M0000475,0,0,0.0,0.0,0.0,0.0,0.0,148.74,0.0,25.0,1.0430281027295951,0.0,0.0,0.0,82.0,0.0,1.0,11,14,0.0019717846700175834,0.30590267370652785,low -demo-011578,347,PAYMENT,11.01,C0000143,M0001764,0,0,0.0,0.0,0.0,0.0,0.0,115.875,0.0,94.0,0.09501618122977346,0.0,0.0,0.0,59.0,0.0,1.0,11,14,0.0014844784723990103,0.016344107981113104,low -demo-011579,347,DEBIT,138.66,C0000101,M0000833,0,0,0.0,0.0,0.0,0.0,0.0,239.43,0.0,134.0,0.5791254228793384,0.0,0.0,0.0,54.0,0.0,1.0,11,14,0.0009480037805999437,0.1314502042179882,low -demo-011580,347,CASH_OUT,1860.99,C0000529,M0000018,1,0,0.0,0.0,1.0,2768.68,2768.68,52.21,1.0,12.0,35.64432101130052,0.0,0.0,0.0,180.0,0.0,1.0,11,14,0.11098534651050004,206.5426200025755,guarded -demo-011581,347,TRANSFER,92.69,C0001224,M0000771,0,0,0.0,0.0,1.0,49.42,49.42,160.49,1.0,12.0,0.5775437721976446,0.0,0.0,0.0,99.0,0.0,1.0,11,14,0.020325626102912515,1.883982283478961,low 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-demo-011592,348,CASH_OUT,737.9,C0001221,M0000952,0,0,0.0,0.0,0.0,0.0,0.0,489.72,0.0,73.0,1.5067793841378745,1.0,1.0,330.02,11.0,0.0,1.0,12,14,0.3388634236807321,250.0473203340122,guarded -demo-011593,348,PAYMENT,43.38,C0000820,M0001417,0,0,1.0,1.0,2.0,54.010000000000005,27.005000000000003,82.87,2.0,1.0,0.5234704959575238,0.0,0.0,0.0,41.0,0.0,1.0,12,14,0.005499768077766508,0.23857993921351114,low -demo-011594,348,TRANSFER,321.75,C0000605,M0000104,0,0,0.0,0.0,0.0,0.0,0.0,98.665,0.0,55.0,3.2610348147772763,0.0,0.0,0.0,62.0,0.0,1.0,12,14,0.06968535794348486,22.421263918316253,low -demo-011595,348,CASH_OUT,341.14,C0000839,M0000885,0,0,0.0,0.0,1.0,22.86,22.86,84.41,1.0,19.0,4.041464281483236,0.0,0.0,0.0,28.0,0.0,1.0,12,14,0.07164664723558092,24.441537237946076,low -demo-011596,348,CASH_IN,275.6,C0000577,M0000883,0,0,0.0,0.0,1.0,39.45,39.45,143.54,1.0,19.0,1.9200222934373696,0.0,0.0,0.0,53.0,0.0,1.0,12,14,0.004740424193150937,1.3064609076323983,low 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+demo-011998,359,PAYMENT,402.26,C0000847,M0001210,0,0,0.0,0.0,0.0,0.0,0.0,174.02,0.0,52.0,2.3115733823698426,0.0,0.0,0.0,261.0,0.0,1.0,23,14,0.014745104465272132,5.9313657222003675,low +demo-011999,359,CASH_OUT,130.25,C0000664,M0000305,0,0,0.0,0.0,1.0,70.59,70.59,280.235,1.0,14.0,0.4647884810962228,0.0,0.0,0.0,59.0,0.0,1.0,23,14,0.02013474874937986,2.622551024606727,low diff --git a/artifacts/figures/summary.json b/artifacts/figures/summary.json index af26c6a..95eadc4 100644 --- a/artifacts/figures/summary.json +++ b/artifacts/figures/summary.json @@ -3,55 +3,55 @@ "transactions": 1749, "fraud_cases": 61, "fraud_rate": 0.03487707261292167, - "best_model": "Histogram Gradient Boosting (demo fallback)", + "best_model": "Histogram Gradient Boosting (demo fallback) + sigmoid calibration", "metrics": { - "Logistic Regression": { + "Logistic Regression + sigmoid calibration": { "average_precision": 0.09678051961425205, "roc_auc": 0.7490288244891616, - "brier": 0.157796436469829, - "log_loss": 0.4925102062200482, - "precision": 0.08027522935779817, - "recall": 0.5737704918032787, - "f1": 0.14084507042253522 + "brier": 0.033402351083231, + "log_loss": 0.1466957539220205, + "precision": 0.0, + "recall": 0.0, + "f1": 0.0 }, - "Histogram Gradient Boosting (demo fallback)": { + "Histogram Gradient Boosting (demo fallback) + sigmoid calibration": { "average_precision": 0.11323123436653515, "roc_auc": 0.7341309921529018, - "brier": 0.03387015187361776, - "log_loss": 0.1535594455001957, - "precision": 0.5, - "recall": 0.01639344262295082, - "f1": 0.031746031746031744 + "brier": 0.03277462561063421, + "log_loss": 0.14180052051565034, + "precision": 0.0, + "recall": 0.0, + "f1": 0.0 } }, - "selected_threshold": 0.01, + "selected_threshold": 0.04, "threshold_policies": [ { "policy": "Default 0.50", "threshold": 0.5, - "reviews": 2, - "precision": 0.5, - "recall": 0.01639344262295082, - "f1": 0.031746031746031744, - "modeled_cost": 35882.829999999994 + "reviews": 0, + "precision": 0.0, + "recall": 0.0, + "f1": 0.0, + "modeled_cost": 38906.27999999999 }, { "policy": "F1-optimal", - "threshold": 0.08, - "reviews": 142, - "precision": 0.13380281690140844, - "recall": 0.3114754098360656, - "f1": 0.18719211822660098, - "modeled_cost": 11513.67 + "threshold": 0.12, + "reviews": 75, + "precision": 0.16, + "recall": 0.19672131147540983, + "f1": 0.17647058823529413, + "modeled_cost": 18627.940000000002 }, { "policy": "Cost-optimized", - "threshold": 0.01, - "reviews": 631, - "precision": 0.06497622820919176, - "recall": 0.6721311475409836, - "f1": 0.11849710982658959, - "modeled_cost": 7104.86 + "threshold": 0.04, + "reviews": 312, + "precision": 0.08653846153846154, + "recall": 0.4426229508196721, + "f1": 0.1447721179624665, + "modeled_cost": 8280.369999999999 } ], "capacity_results": [ @@ -131,28 +131,28 @@ "strategy": "expected_loss", "capacity": 50, "reviews": 50.0, - "precision": 0.16, - "fraud_recall": 0.13114754098360656, - "fraud_amount_captured": 20359.809999999998, - "fraud_value_capture": 0.5233039499021752 + "precision": 0.18, + "fraud_recall": 0.14754098360655737, + "fraud_amount_captured": 22220.8, + "fraud_value_capture": 0.5711365877179726 }, { "strategy": "expected_loss", "capacity": 100, "reviews": 100.0, - "precision": 0.15, - "fraud_recall": 0.2459016393442623, - "fraud_amount_captured": 27137.33, - "fraud_value_capture": 0.697505132847448 + "precision": 0.14, + "fraud_recall": 0.22950819672131148, + "fraud_amount_captured": 26564.61, + "fraud_value_capture": 0.6827846301419721 }, { "strategy": "expected_loss", "capacity": 250, "reviews": 250.0, - "precision": 0.092, - "fraud_recall": 0.3770491803278688, - "fraud_amount_captured": 31930.4, - "fraud_value_capture": 0.8207004113474742 + "precision": 0.088, + "fraud_recall": 0.36065573770491804, + "fraud_amount_captured": 32707.73, + "fraud_value_capture": 0.8406799622066157 }, { "strategy": "expected_loss", @@ -167,19 +167,19 @@ "strategy": "expected_loss", "capacity": 750, "reviews": 750.0, - "precision": 0.058666666666666666, - "fraud_recall": 0.7213114754098361, - "fraud_amount_captured": 37817.25, - "fraud_value_capture": 0.972008889053387 + "precision": 0.056, + "fraud_recall": 0.6885245901639344, + "fraud_amount_captured": 37719.46, + "fraud_value_capture": 0.9694954130798423 }, { "strategy": "expected_loss", "capacity": 1000, "reviews": 1000.0, - "precision": 0.052, - "fraud_recall": 0.8524590163934426, - "fraud_amount_captured": 38650.560000000005, - "fraud_value_capture": 0.9934272821765538 + "precision": 0.049, + "fraud_recall": 0.8032786885245902, + "fraud_amount_captured": 38517.51, + "fraud_value_capture": 0.9900075257773298 }, { "strategy": "expected_review_value", @@ -194,28 +194,28 @@ "strategy": "expected_review_value", "capacity": 50, "reviews": 50.0, - "precision": 0.16, - "fraud_recall": 0.13114754098360656, - "fraud_amount_captured": 20359.809999999998, - "fraud_value_capture": 0.5233039499021752 + "precision": 0.18, + "fraud_recall": 0.14754098360655737, + "fraud_amount_captured": 22220.8, + "fraud_value_capture": 0.5711365877179726 }, { "strategy": "expected_review_value", "capacity": 100, "reviews": 100.0, - "precision": 0.15, - "fraud_recall": 0.2459016393442623, - "fraud_amount_captured": 27137.33, - "fraud_value_capture": 0.697505132847448 + "precision": 0.14, + "fraud_recall": 0.22950819672131148, + "fraud_amount_captured": 26564.61, + "fraud_value_capture": 0.6827846301419721 }, { "strategy": "expected_review_value", "capacity": 250, "reviews": 250.0, - "precision": 0.092, - "fraud_recall": 0.3770491803278688, - "fraud_amount_captured": 31930.4, - "fraud_value_capture": 0.8207004113474742 + "precision": 0.088, + "fraud_recall": 0.36065573770491804, + "fraud_amount_captured": 32707.73, + "fraud_value_capture": 0.8406799622066157 }, { "strategy": "expected_review_value", @@ -230,19 +230,19 @@ "strategy": "expected_review_value", "capacity": 750, "reviews": 750.0, - "precision": 0.058666666666666666, - "fraud_recall": 0.7213114754098361, - "fraud_amount_captured": 37817.25, - "fraud_value_capture": 0.972008889053387 + "precision": 0.056, + "fraud_recall": 0.6885245901639344, + "fraud_amount_captured": 37719.46, + "fraud_value_capture": 0.9694954130798423 }, { "strategy": "expected_review_value", "capacity": 1000, "reviews": 1000.0, - "precision": 0.052, - "fraud_recall": 0.8524590163934426, - "fraud_amount_captured": 38650.560000000005, - "fraud_value_capture": 0.9934272821765538 + "precision": 0.049, + "fraud_recall": 0.8032786885245902, + "fraud_amount_captured": 38517.51, + "fraud_value_capture": 0.9900075257773298 } ], "monitoring": { diff --git a/artifacts/figures/threshold_cost.png b/artifacts/figures/threshold_cost.png index f929d7e..5d97bf3 100644 Binary files a/artifacts/figures/threshold_cost.png and b/artifacts/figures/threshold_cost.png differ diff --git a/artifacts/figures/threshold_policies.csv b/artifacts/figures/threshold_policies.csv index d6c7953..ba020d8 100644 --- a/artifacts/figures/threshold_policies.csv +++ b/artifacts/figures/threshold_policies.csv @@ -1,4 +1,4 @@ policy,threshold,reviews,precision,recall,f1,modeled_cost -Default 0.50,0.5,2,0.5,0.01639344262295082,0.031746031746031744,35882.829999999994 -F1-optimal,0.08,142,0.13380281690140844,0.3114754098360656,0.18719211822660098,11513.67 -Cost-optimized,0.01,631,0.06497622820919176,0.6721311475409836,0.11849710982658959,7104.86 +Default 0.50,0.5,0,0.0,0.0,0.0,38906.27999999999 +F1-optimal,0.12,75,0.16,0.19672131147540983,0.17647058823529413,18627.940000000002 +Cost-optimized,0.04,312,0.08653846153846154,0.4426229508196721,0.1447721179624665,8280.369999999999 diff --git a/docs/RESULTS.md b/docs/RESULTS.md index 2f0158f..f776cd0 100644 --- a/docs/RESULTS.md +++ b/docs/RESULTS.md @@ -8,7 +8,7 @@ The checked-in report is a **deterministic synthetic demo**, included so the rep 2. Logistic regression versus nonlinear gradient boosting. 3. Average precision, ROC-AUC, Brier, log loss, precision, recall, and F1. 4. 0.50, validation F1-optimal, and validation cost-optimized thresholds. -5. Probability versus expected-loss review queues across five capacity levels. +5. Probability versus expected-loss review queues across seven capacity levels. 6. A shifted-batch PSI monitoring demonstration. 7. A local in-process API benchmark: batch 1,000 median 7.50 ms and p95 10.90 ms. diff --git a/riskqueue/modeling/train.py b/riskqueue/modeling/train.py index ce92994..4593543 100644 --- a/riskqueue/modeling/train.py +++ b/riskqueue/modeling/train.py @@ -12,6 +12,11 @@ from sklearn.pipeline import Pipeline from sklearn.preprocessing import OneHotEncoder, StandardScaler +try: + from sklearn.frozen import FrozenEstimator +except ImportError: # scikit-learn 1.5 uses the older prefit calibration API. + FrozenEstimator = None + from riskqueue.features.schema import CATEGORICAL_FEATURES, MODEL_FEATURES, assert_leakage_safe @@ -91,6 +96,14 @@ def train_boosted(frame: pd.DataFrame, labels) -> TrainedModel: def calibrate_prefit(model: TrainedModel, validation: pd.DataFrame, labels) -> TrainedModel: - calibrated = CalibratedClassifierCV(model.estimator, method="sigmoid", cv="prefit") + if FrozenEstimator is None: + calibrated = CalibratedClassifierCV(model.estimator, method="sigmoid", cv="prefit") + else: + indices = np.arange(len(validation)) + calibrated = CalibratedClassifierCV( + FrozenEstimator(model.estimator), + method="sigmoid", + cv=[(indices, indices)], + ) calibrated.fit(validation[model.feature_names], labels) return TrainedModel(f"{model.name} + sigmoid calibration", calibrated, model.feature_names) diff --git a/riskqueue/reporting.py b/riskqueue/reporting.py index bd15613..41a8da6 100644 --- a/riskqueue/reporting.py +++ b/riskqueue/reporting.py @@ -32,6 +32,8 @@ def generate_report_artifacts( model_probabilities: dict[str, np.ndarray], output: str | Path, threshold_source: tuple[np.ndarray, np.ndarray, np.ndarray] | None = None, + *, + selected_model: str, ) -> dict: output = Path(output) output.mkdir(parents=True, exist_ok=True) @@ -39,6 +41,10 @@ def generate_report_artifacts( metrics = { name: classification_metrics(labels, probs) for name, probs in model_probabilities.items() } + if selected_model not in metrics: + raise ValueError(f"Selected model is missing probabilities: {selected_model}") + best_name = selected_model + best_probs = model_probabilities[best_name] fig, ax = plt.subplots(figsize=(8.4, 5.2)) prevalence = labels.mean() @@ -63,9 +69,6 @@ def generate_report_artifacts( ax.legend(frameon=False) _finish(fig, output / "precision_recall.png") - best_name = max(metrics, key=lambda name: metrics[name]["average_precision"]) - best_probs = model_probabilities[best_name] - # Diagnostics used by the model-performance dashboard. fig, axes = plt.subplots(1, 3, figsize=(14, 4.2)) false_positive_rate, true_positive_rate, _ = roc_curve(labels, best_probs) diff --git a/scripts/run_demo.py b/scripts/run_demo.py index c2b6613..cb15f2a 100644 --- a/scripts/run_demo.py +++ b/scripts/run_demo.py @@ -3,10 +3,12 @@ import argparse from pathlib import Path +from sklearn.metrics import average_precision_score + from riskqueue.data.splits import temporal_split from riskqueue.demo import make_demo_transactions from riskqueue.features.pipeline import build_features -from riskqueue.modeling.train import train_boosted, train_logistic +from riskqueue.modeling.train import calibrate_prefit, train_boosted, train_logistic from riskqueue.reporting import generate_report_artifacts @@ -17,27 +19,37 @@ def main() -> None: args = parser.parse_args() frame = build_features(make_demo_transactions(args.rows)) split = temporal_split(frame) - models = [ + base_models = [ train_logistic(split.train, split.train.isFraud), train_boosted(split.train, split.train.isFraud), ] - validation_probs = {m.name: m.predict_proba(split.validation) for m in models} - test_probs = {m.name: m.predict_proba(split.test) for m in models} - best = max( - models, - key=lambda m: __import__("sklearn.metrics").metrics.average_precision_score( - split.validation.isFraud, validation_probs[m.name] + base_validation_probs = { + model.name: model.predict_proba(split.validation) for model in base_models + } + selected_base = max( + base_models, + key=lambda model: average_precision_score( + split.validation.isFraud, base_validation_probs[model.name] ), ) + calibrated = { + model.name: calibrate_prefit(model, split.validation, split.validation.isFraud) + for model in base_models + } + models = list(calibrated.values()) + test_probs = {m.name: m.predict_proba(split.test) for m in models} + best = calibrated[selected_base.name] + validation_probs = best.predict_proba(split.validation) summary = generate_report_artifacts( split.test, test_probs, args.output, ( split.validation.isFraud.to_numpy(), - validation_probs[best.name], + validation_probs, split.validation.amount.to_numpy(), ), + selected_model=best.name, ) print(f"Generated report for {summary['transactions']:,} held-out demo transactions") print(f"Best demo model: {summary['best_model']}") diff --git a/scripts/train_model.py b/scripts/train_model.py index 7dafe1e..2954864 100644 --- a/scripts/train_model.py +++ b/scripts/train_model.py @@ -10,7 +10,7 @@ from riskqueue.features.pipeline import build_features from riskqueue.modeling.artifacts import ModelMetadata, save_artifact from riskqueue.modeling.evaluate import classification_metrics -from riskqueue.modeling.train import train_boosted, train_logistic +from riskqueue.modeling.train import calibrate_prefit, train_boosted, train_logistic from riskqueue.reporting import generate_report_artifacts @@ -21,14 +21,26 @@ def main() -> None: args = parser.parse_args() featured = build_features(load_paysim(args.csv, sample_rows=args.sample_rows)) split = temporal_split(featured) - models = [ + base_models = [ train_logistic(split.train, split.train.isFraud), train_boosted(split.train, split.train.isFraud), ] - validation = {m.name: m.predict_proba(split.validation) for m in models} - best = max( - models, key=lambda m: average_precision_score(split.validation.isFraud, validation[m.name]) + base_validation = { + model.name: model.predict_proba(split.validation) for model in base_models + } + selected_base = max( + base_models, + key=lambda model: average_precision_score( + split.validation.isFraud, base_validation[model.name] + ), ) + calibrated = { + model.name: calibrate_prefit(model, split.validation, split.validation.isFraud) + for model in base_models + } + models = list(calibrated.values()) + best = calibrated[selected_base.name] + validation = best.predict_proba(split.validation) test_probabilities = {m.name: m.predict_proba(split.test) for m in models} summary = generate_report_artifacts( split.test, @@ -36,9 +48,10 @@ def main() -> None: "artifacts/figures", ( split.validation.isFraud.to_numpy(), - validation[best.name], + validation, split.validation.amount.to_numpy(), ), + selected_model=best.name, ) metrics = classification_metrics( split.test.isFraud, test_probabilities[best.name], summary["selected_threshold"] diff --git a/tests/test_generated_results.py b/tests/test_generated_results.py new file mode 100644 index 0000000..eeed8e6 --- /dev/null +++ b/tests/test_generated_results.py @@ -0,0 +1,39 @@ +import json +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] + + +def test_generated_results_use_calibrated_models(): + summary = json.loads((ROOT / "artifacts/figures/summary.json").read_text()) + + assert "sigmoid calibration" in summary["best_model"] + assert all("sigmoid calibration" in name for name in summary["metrics"]) + + +def test_readme_headlines_match_generated_results(): + summary = json.loads((ROOT / "artifacts/figures/summary.json").read_text()) + readme = (ROOT / "README.md").read_text() + best_metrics = summary["metrics"][summary["best_model"]] + expected_loss = next( + row + for row in summary["capacity_results"] + if row["strategy"] == "expected_loss" and row["capacity"] == 750 + ) + probability = next( + row + for row in summary["capacity_results"] + if row["strategy"] == "probability" and row["capacity"] == 750 + ) + cost_policy = next( + row for row in summary["threshold_policies"] if row["policy"] == "Cost-optimized" + ) + default_policy = next( + row for row in summary["threshold_policies"] if row["policy"] == "Default 0.50" + ) + + assert f"**{best_metrics['average_precision']:.3f} average precision**" in readme + assert f"**{expected_loss['fraud_value_capture']:.1%} fraud value captured**" in readme + assert f"vs. {probability['fraud_value_capture']:.1%} using probability alone" in readme + assert f"**${cost_policy['modeled_cost']:,.0f} modeled cost**" in readme + assert f"vs. ${default_policy['modeled_cost']:,.0f} at threshold 0.50" in readme diff --git a/tests/test_modeling.py b/tests/test_modeling.py new file mode 100644 index 0000000..2987faf --- /dev/null +++ b/tests/test_modeling.py @@ -0,0 +1,22 @@ +import numpy as np +import pandas as pd +from sklearn.linear_model import LogisticRegression + +from riskqueue.modeling.train import TrainedModel, calibrate_prefit + + +def test_prefit_calibration_uses_validation_probabilities(): + training = pd.DataFrame({"signal": [-3.0, -2.0, -1.0, 1.0, 2.0, 3.0]}) + labels = np.array([0, 0, 0, 1, 1, 1]) + estimator = LogisticRegression().fit(training, labels) + coefficients = estimator.coef_.copy() + model = TrainedModel("test model", estimator, ["signal"]) + + calibrated = calibrate_prefit(model, training, labels) + probabilities = calibrated.predict_proba(training) + + np.testing.assert_array_equal(estimator.coef_, coefficients) + assert calibrated.name == "test model + sigmoid calibration" + assert probabilities.shape == (len(training),) + assert np.isfinite(probabilities).all() + assert ((0 <= probabilities) & (probabilities <= 1)).all()