diff --git a/src/test/java/org/jpmml/h2o/XGBoostMojoModelConverterTest.java b/src/test/java/org/jpmml/h2o/XGBoostMojoModelConverterTest.java index 18a160b..3c11f01 100644 --- a/src/test/java/org/jpmml/h2o/XGBoostMojoModelConverterTest.java +++ b/src/test/java/org/jpmml/h2o/XGBoostMojoModelConverterTest.java @@ -32,7 +32,7 @@ public XGBoostMojoModelConverterTest(){ public void evaluateAudit() throws Exception { FieldName[] targetFields = {FieldName.create("Adjusted")}; - evaluate("XGBoost", "Audit", excludeFields(targetFields), new PMMLEquivalence(3e-6, 3e-6)); + evaluate("XGBoost", "Audit", excludeFields(targetFields), new PMMLEquivalence(6e-6, 6e-6)); } @Test diff --git a/src/test/resources/csv/GBMPoissonVisit.csv b/src/test/resources/csv/GBMPoissonVisit.csv index 8e1aa06..38b116f 100644 --- a/src/test/resources/csv/GBMPoissonVisit.csv +++ b/src/test/resources/csv/GBMPoissonVisit.csv @@ -1,2264 +1,2264 @@ docvis -5.2883774866117825 -6.122085709079099 -4.929716249421078 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+453,7 @@ Adjusted,probability(0),probability(1) 0,0.9413227646252216,0.05867723537477835 1,0.7150985824629208,0.284901417537079 0,0.9400077142859884,0.05999228571401152 -1,0.7336945015946115,0.2663054984053885 +0,0.7336945015946115,0.2663054984053885 0,0.9556305252115592,0.044369474788440916 1,0.483152764282081,0.516847235717919 1,0.13888083406472174,0.8611191659352783 @@ -1005,7 +1005,7 @@ Adjusted,probability(0),probability(1) 0,0.9566907627155868,0.04330923728441318 0,0.9679498074296844,0.03205019257031569 0,0.9131733998166672,0.0868266001833329 -1,0.7324537415778838,0.2675462584221162 +0,0.7324537415778838,0.2675462584221162 0,0.931994234028389,0.06800576597161105 0,0.9999996166479792,3.8335202079276796e-07 0,0.9900550143878944,0.009944985612105528 @@ -1739,7 +1739,7 @@ Adjusted,probability(0),probability(1) 0,0.781602896731486,0.21839710326851405 0,0.9713335811368102,0.028666418863189724 0,0.984850467590936,0.015149532409063925 -1,0.7350867151284486,0.26491328487155136 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a/src/test/resources/csv/RandomForestAuto.csv +++ b/src/test/resources/csv/RandomForestAuto.csv @@ -1,393 +1,393 @@ mpg -16.68823533899644 -14.661764705882351 -16.727941176470587 -16.061274472404932 -16.90980395148782 -13.982620295356302 -13.894385001238653 -14.173796765944536 -13.747326177709244 -14.808823529411766 -14.573529411764708 -13.926470588235292 -15.014705882352944 -15.779411764705882 -24.052941154031192 -21.75 -19.455882352941178 -20.980392119463747 -27.729411854463464 -26.0 -24.411764705882355 -24.64705882352941 -24.529411764705888 -25.088235294117645 -20.953921598546646 -11.0 -11.320855589473947 -12.227718353271484 -10.882352941176471 -27.788235383875232 -26.873161876902863 -24.629411809584674 -18.926470588235293 -17.264705882352942 -17.720588235294112 +16.788854486802045 +14.762957068050607 +17.205882352941178 +16.577089758480295 +16.841795640833237 +14.527662950403549 +14.174721773932962 +14.233545303344727 +13.998251185697667 +14.880604126874138 +14.939427656285904 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a/src/test/resources/csv/RandomForestIris.csv b/src/test/resources/csv/RandomForestIris.csv index d7e33bd..8af256c 100644 --- a/src/test/resources/csv/RandomForestIris.csv +++ b/src/test/resources/csv/RandomForestIris.csv @@ -1,151 +1,151 @@ Species,probability(setosa),probability(versicolor),probability(virginica) -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -setosa,1,0.0,0.0 -versicolor,0,0.9166666666666666,0.08333333333333333 -versicolor,0,1.0,0.0 -versicolor,0,0.9166666666666666,0.08333333333333333 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,0.9166666666666666,0.08333333333333333 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,0.9166666666666666,0.08333333333333333 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,0.6363636363636364,0.3636363636363637 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,0.7272727272727273,0.2727272727272727 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,0.9090909090909092,0.09090909090909093 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -versicolor,0,1.0,0.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.3636363636363637,0.6363636363636364 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.4545454545454545,0.5454545454545454 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.16666666666666666,0.8333333333333334 -virginica,0,0.1111111111111111,0.8888888888888888 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.3636363636363637,0.6363636363636364 -virginica,0,0.3,0.7 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.3636363636363637,0.6363636363636364 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.08333333333333333,0.9166666666666666 -virginica,0,0.0,1.0 -virginica,0,0.0,1.0 -virginica,0,0.09090909090909093,0.9090909090909092 +setosa,0.9971374044578676,0.0,0.002862595542132259 +setosa,0.9971374044578676,0.0,0.002862595542132259 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+setosa,0.9971374044578676,0.0,0.002862595542132259 +setosa,0.9971374044578676,0.0,0.002862595542132259 +setosa,0.9971374044578676,0.0,0.002862595542132259 +setosa,0.9971374044578676,0.0,0.002862595542132259 +setosa,0.9971374044578676,0.0,0.002862595542132259 +setosa,0.9971374044578676,0.0,0.002862595542132259 +setosa,0.9971374044578676,0.0,0.002862595542132259 +setosa,0.9971374044578676,0.0,0.002862595542132259 +setosa,0.9971374044578676,0.0,0.002862595542132259 +setosa,0.9971374044578676,0.0,0.002862595542132259 +setosa,0.9971374044578676,0.0,0.002862595542132259 +setosa,0.9971374044578676,0.0,0.002862595542132259 +setosa,0.9971374044578676,0.0,0.002862595542132259 +setosa,0.9971374044578676,0.0,0.002862595542132259 +setosa,0.9971374044578676,0.0,0.002862595542132259 +setosa,0.9971374044578676,0.0,0.002862595542132259 +setosa,0.9971374044578676,0.0,0.002862595542132259 +setosa,0.9971374044578676,0.0,0.002862595542132259 +setosa,0.9971374044578676,0.0,0.002862595542132259 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+virginica,0.0,0.0,1.0 +virginica,0.0,0.0,1.0 +virginica,0.0,0.09126984118349038,0.9087301588165096 +virginica,0.0,0.0,1.0 +virginica,0.0,0.0,1.0 +virginica,0.0,0.18181818181818185,0.8181818181818182 +virginica,0.0,0.0,1.0 +virginica,0.0,0.0,1.0 +virginica,0.0,0.0,1.0 +virginica,0.0,0.1111111111111111,0.8888888888888888 +virginica,0.0,0.18181818181818185,0.8181818181818182 +virginica,0.0,0.0,1.0 +virginica,0.0,0.0,1.0 +virginica,0.0,0.0,1.0 +virginica,0.0,0.2738095235504711,0.7261904764495288 +virginica,0.0,0.0,1.0 +virginica,0.0,0.0,1.0 +virginica,0.0,0.0,1.0 +virginica,0.0,0.0,1.0 +virginica,0.0,0.0,1.0 +virginica,0.0,0.0,1.0 +virginica,0.0,0.0,1.0 +virginica,0.0,0.09126984118349038,0.9087301588165096 +virginica,0.0,0.0,1.0 +virginica,0.0,0.0,1.0 +virginica,0.0,0.0,1.0 diff --git a/src/test/resources/csv/StackedEnsembleAuditNA.csv b/src/test/resources/csv/StackedEnsembleAuditNA.csv index c2f1bce..0e5837f 100644 --- a/src/test/resources/csv/StackedEnsembleAuditNA.csv +++ b/src/test/resources/csv/StackedEnsembleAuditNA.csv @@ -1,1900 +1,1900 @@ Adjusted,probability(0),probability(1) -0,0.907200441825658,0.09279955817434207 -0,0.9006719573755863,0.09932804262441372 -0,0.9162310882744108,0.0837689117255893 -1,0.5427304504550212,0.4572695495449789 -1,0.3311521252706817,0.6688478747293183 -0,0.8802438421139238,0.1197561578860762 -1,0.2737601602045153,0.7262398397954847 -0,0.8877417413638865,0.11225825863611352 -0,0.9081024927246428,0.09189750727535706 -0,0.9248018693955171,0.07519813060448302 -0,0.911310108225932,0.08868989177406794 -0,0.8634656000300527,0.13653439996994735 -0,0.9260472859506229,0.07395271404937696 -0,0.924542938158482,0.0754570618415181 -1,0.3467266846275929,0.6532733153724071 -0,0.9248018693955171,0.07519813060448302 -0,0.8436476949514543,0.15635230504854564 -0,0.8873662978192134,0.11263370218078665 -1,0.635500608970422,0.3644993910295779 -1,0.4253490301293852,0.5746509698706148 -0,0.9175744479918406,0.0824255520081594 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+33.09748710622876 +35.634633015270204 +36.34918830893924 +34.7284379522201 +34.63551581841572 +22.77108628459877 +27.548174519644494 +27.07527331189234 +21.627619372922272 +29.114386031697048 +29.679467770640308 +26.61292589423211 +27.106020471326094 +37.850916780187006 +30.859645644507697 +28.811822748248616 +28.83264004666956 diff --git a/src/test/resources/csv/StackedEnsembleIris.csv b/src/test/resources/csv/StackedEnsembleIris.csv index eab68e9..eb50ea6 100644 --- a/src/test/resources/csv/StackedEnsembleIris.csv +++ b/src/test/resources/csv/StackedEnsembleIris.csv @@ -1,151 +1,151 @@ Species,probability(setosa),probability(versicolor),probability(virginica) -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 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-setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -setosa,0.9983049882266616,0.000785697589745191,0.0009093141835931486 -versicolor,0.0009897752111232047,0.9634088748248374,0.03560134996403947 -versicolor,0.0007010361657493825,0.9685637424919836,0.030735221342266836 -versicolor,0.002044692668344685,0.8192707404080684,0.1786845669235871 -versicolor,0.0006965808504708111,0.9688563937667356,0.03044702538279344 -versicolor,0.0007010361657493825,0.9685637424919836,0.030735221342266836 -versicolor,0.0006528712263102293,0.9695187556761804,0.02982837309750938 -versicolor,0.0010458706348282993,0.9623820265607144,0.0365721028044572 -versicolor,0.0009778350135128358,0.9223005898392396,0.07672157514724763 -versicolor,0.0006528712263102293,0.9695187556761804,0.02982837309750938 -versicolor,0.0006980620293840273,0.9688327442055332,0.03046919376508278 -versicolor,0.0006965808504708111,0.9688563937667356,0.03044702538279344 -versicolor,0.0006719524357578434,0.9691012197533904,0.030226827810851706 -versicolor,0.0006357335011981728,0.9698568432739432,0.029507423224858543 -versicolor,0.0009897752111232047,0.9634088748248374,0.03560134996403947 -versicolor,0.00063707355983808,0.9698341894730388,0.02952873696712315 -versicolor,0.0006171910034274809,0.9701814918094842,0.02920131718708823 -versicolor,0.0007010361657493825,0.9685637424919836,0.030735221342266836 -versicolor,0.00063707355983808,0.9698341894730388,0.02952873696712315 -versicolor,0.0007010361657493825,0.9685637424919836,0.030735221342266836 -versicolor,0.0006357335011981728,0.9698568432739432,0.029507423224858543 -versicolor,0.0033494332106932053,0.6477658257319625,0.3488847410573443 -versicolor,0.00063707355983808,0.9698341894730388,0.02952873696712315 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-versicolor,0.0007010361657493825,0.9685637424919836,0.030735221342266836 -versicolor,0.0010458706348282993,0.9623820265607144,0.0365721028044572 -versicolor,0.0006218399882476896,0.9701015111484996,0.029276648863252732 -versicolor,0.00063707355983808,0.9698341894730388,0.02952873696712315 -versicolor,0.0006965808504708111,0.9688563937667356,0.03044702538279344 -versicolor,0.0006847780130486423,0.9690544653950334,0.030260756591918087 -versicolor,0.0006479930909712037,0.969600423065884,0.02975158384314485 -versicolor,0.00063707355983808,0.9698341894730388,0.02952873696712315 -versicolor,0.0006965808504708111,0.9688563937667356,0.03044702538279344 -versicolor,0.00063707355983808,0.9698341894730388,0.02952873696712315 -versicolor,0.00063707355983808,0.9698341894730388,0.02952873696712315 -versicolor,0.00063707355983808,0.9698341894730388,0.02952873696712315 -versicolor,0.00063707355983808,0.9698341894730388,0.02952873696712315 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+26.91247940063477 +42.5367546081543 +32.330928802490234 +28.42300224304199 +30.12629318237305 diff --git a/src/test/resources/csv/XGBoostIris.csv b/src/test/resources/csv/XGBoostIris.csv index 436d682..c073121 100644 --- a/src/test/resources/csv/XGBoostIris.csv +++ b/src/test/resources/csv/XGBoostIris.csv @@ -1,151 +1,151 @@ Species,probability(setosa),probability(versicolor),probability(virginica) -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730406999588012,0.013483433984220028,0.013475850224494934 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.973201334476471,0.013485659845173359,0.013313042931258678 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -setosa,0.9730350375175476,0.01348335575312376,0.013481620699167252 -versicolor,0.013966363854706287,0.9720750451087952,0.013958566822111608 -versicolor,0.01328818965703249,0.9738880395889282,0.012823742814362049 -versicolor,0.015256721526384354,0.9507597088813782,0.03398359939455986 -versicolor,0.0174628347158432,0.9650066494941713,0.01753048785030842 -versicolor,0.01504101138561964,0.9700812697410583,0.01487772073596716 -versicolor,0.01345986220985651,0.9731031060218812,0.013437052257359028 -versicolor,0.01727575995028019,0.9507523775100708,0.03197187930345535 -versicolor,0.021151153370738033,0.9576621651649476,0.02118667773902416 -versicolor,0.014163144864141941,0.9717204570770264,0.01411635894328356 -versicolor,0.011976448819041252,0.976011037826538,0.012012520805001259 -versicolor,0.02675030753016472,0.9463840126991272,0.026865648105740547 -versicolor,0.014317748136818409,0.9715139269828796,0.014168293215334415 -versicolor,0.019362322986125943,0.9612285494804382,0.019409148022532463 -versicolor,0.015015266835689543,0.9700166583061218,0.014968086034059525 -versicolor,0.01345957349985838,0.9730821847915648,0.013458168134093285 -versicolor,0.013350202701985836,0.9733068943023682,0.013342896476387978 -versicolor,0.014317781664431097,0.9715161919593812,0.014166003093123438 -versicolor,0.0106206638738513,0.9787523150444032,0.010627016425132751 -versicolor,0.01936675608158112,0.9613360762596129,0.019297130405902863 -versicolor,0.011955824680626392,0.9760499000549316,0.011994265019893645 -versicolor,0.04189735651016235,0.4866971075534821,0.4714055955410004 -versicolor,0.01344602182507515,0.973128855228424,0.013425123877823351 -versicolor,0.010256592184305193,0.9666810631752014,0.02306234836578369 -versicolor,0.015015266835689543,0.9700166583061218,0.014968086034059525 -versicolor,0.013438492082059385,0.9731460809707642,0.013415437191724777 -versicolor,0.013430160470306872,0.9731542468070984,0.013415548950433731 -versicolor,0.014685499481856825,0.9668574333190918,0.018457045778632164 -versicolor,0.0430716834962368,0.7412854433059692,0.21564285457134247 -versicolor,0.01430349890142679,0.9715713262557985,0.014125214889645575 -versicolor,0.010578399524092674,0.97880220413208,0.010619407519698143 -versicolor,0.013826778158545494,0.9723623394966124,0.013810884207487106 -versicolor,0.013826778158545494,0.9723623394966124,0.013810884207487106 -versicolor,0.0106206638738513,0.9787523150444032,0.010627016425132751 -versicolor,0.030918564647436145,0.5524660348892212,0.4166154265403748 -versicolor,0.014317781664431097,0.9715161919593812,0.014166003093123438 -versicolor,0.0154147082939744,0.9559173583984376,0.028667902573943138 -versicolor,0.01580909825861454,0.9686671495437622,0.015523709356784819 -versicolor,0.017474919557571408,0.9650378823280334,0.01748717948794365 -versicolor,0.013459495268762112,0.9730765819549559,0.013463930226862429 -versicolor,0.011955824680626392,0.9760499000549316,0.011994265019893645 -versicolor,0.0119849294424057,0.9760117530822754,0.012003316543996334 -versicolor,0.014171069487929344,0.9717024564743042,0.014126541092991829 -versicolor,0.010602877475321293,0.9787780046463012,0.0106191448867321 -versicolor,0.02675030753016472,0.9463840126991272,0.026865648105740547 -versicolor,0.012004994787275793,0.975982904434204,0.012012174353003502 -versicolor,0.013459495268762112,0.9730765819549559,0.013463930226862429 -versicolor,0.01345957349985838,0.9730821847915648,0.013458168134093285 -versicolor,0.01343835797160864,0.9731364250183104,0.01342522818595171 -versicolor,0.013165479525923729,0.9735638499259948,0.013270636089146136 -versicolor,0.01345957349985838,0.9730821847915648,0.013458168134093285 -virginica,0.013487150892615318,0.013290702365338802,0.9732220768928528 -virginica,0.012716028839349748,0.028357308357954025,0.9589266777038574 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+virginica,0.01826418749988079,0.022793499752879143,0.9589423537254332 +virginica,0.017446355894207954,0.019626868888735768,0.9629268050193788 +virginica,0.01739388145506382,0.022575555369257927,0.9600305557250975 +virginica,0.023735152557492256,0.03785344958305359,0.9384114146232604 diff --git a/src/test/resources/main.py b/src/test/resources/main.py index bb5b44d..bfac9b7 100644 --- a/src/test/resources/main.py +++ b/src/test/resources/main.py @@ -21,12 +21,12 @@ def split_columns(df): def store_mojo(model, name): model.download_mojo(path = "mojo/" + name) -h2o.connect() +h2o.init(nthreads = 1) def train_stack(df, ntrees): - gbm = H2OGradientBoostingEstimator(ntrees = ntrees, nfolds = 3, fold_assignment = "Modulo", keep_cross_validation_predictions = True) + gbm = H2OGradientBoostingEstimator(ntrees = ntrees, nfolds = 3, fold_assignment = "Modulo", keep_cross_validation_predictions = True, seed = 42) gbm.train(*split_columns(df), training_frame = df) - drf = H2ORandomForestEstimator(ntrees = ntrees, nfolds = 3, fold_assignment = "Modulo", keep_cross_validation_predictions = True) + drf = H2ORandomForestEstimator(ntrees = ntrees, nfolds = 3, fold_assignment = "Modulo", keep_cross_validation_predictions = True, seed = 42) drf.train(*split_columns(df), training_frame = df) return [gbm, drf] @@ -46,18 +46,18 @@ def build_audit(df, classifier, name): audit_df = load_audit("Audit") -build_audit(audit_df, H2OGradientBoostingEstimator(ntrees = 31), "GBMAudit") -build_audit(audit_df, H2OGeneralizedLinearEstimator(family = "binomial", lambda_ = 0), "GLMAudit") -build_audit(audit_df, H2ORandomForestEstimator(ntrees = 31, binomial_double_trees = True), "RandomForestAudit") -build_audit(audit_df, H2OXGBoostEstimator(ntrees = 31), "XGBoostAudit") +build_audit(audit_df, H2OGradientBoostingEstimator(ntrees = 31, seed = 42), "GBMAudit") +build_audit(audit_df, H2OGeneralizedLinearEstimator(family = "binomial", lambda_ = 0, seed = 42), "GLMAudit") +build_audit(audit_df, H2ORandomForestEstimator(ntrees = 31, binomial_double_trees = True, seed = 42), "RandomForestAudit") +build_audit(audit_df, H2OXGBoostEstimator(ntrees = 31, seed = 42), "XGBoostAudit") audit_df = load_audit("AuditNA") -build_audit(audit_df, H2OGradientBoostingEstimator(ntrees = 31), "GBMAuditNA") -build_audit(audit_df, H2OGeneralizedLinearEstimator(family = "binomial"), "GLMAuditNA") -build_audit(audit_df, H2ORandomForestEstimator(ntrees = 31, binomial_double_trees = False), "RandomForestAuditNA") -build_audit(audit_df, H2OStackedEnsembleEstimator(base_models = train_stack(audit_df, 11)), "StackedEnsembleAuditNA") -build_audit(audit_df, H2OXGBoostEstimator(ntrees = 31), "XGBoostAuditNA") +build_audit(audit_df, H2OGradientBoostingEstimator(ntrees = 31, seed = 42), "GBMAuditNA") +build_audit(audit_df, H2OGeneralizedLinearEstimator(family = "binomial", seed = 42), "GLMAuditNA") +build_audit(audit_df, H2ORandomForestEstimator(ntrees = 31, binomial_double_trees = False, seed = 42), "RandomForestAuditNA") +build_audit(audit_df, H2OStackedEnsembleEstimator(base_models = train_stack(audit_df, 11), seed = 42), "StackedEnsembleAuditNA") +build_audit(audit_df, H2OXGBoostEstimator(ntrees = 31, seed = 42), "XGBoostAuditNA") # # Multi-class classification @@ -75,11 +75,11 @@ def build_iris(df, classifier, name): iris_df = load_iris("Iris") -build_iris(iris_df, H2OGradientBoostingEstimator(ntrees = 11), "GBMIris") -build_iris(iris_df, H2OGeneralizedLinearEstimator(family = "multinomial"), "GLMIris") -build_iris(iris_df, H2ORandomForestEstimator(ntrees = 11), "RandomForestIris") -build_iris(iris_df, H2OStackedEnsembleEstimator(base_models = train_stack(iris_df, 5)), "StackedEnsembleIris") -build_iris(iris_df, H2OXGBoostEstimator(ntrees = 11), "XGBoostIris") +build_iris(iris_df, H2OGradientBoostingEstimator(ntrees = 11, seed = 42), "GBMIris") +build_iris(iris_df, H2OGeneralizedLinearEstimator(family = "multinomial", seed = 42), "GLMIris") +build_iris(iris_df, H2ORandomForestEstimator(ntrees = 11, seed = 42), "RandomForestIris") +build_iris(iris_df, H2OStackedEnsembleEstimator(base_models = train_stack(iris_df, 5), seed = 42), "StackedEnsembleIris") +build_iris(iris_df, H2OXGBoostEstimator(ntrees = 11, seed = 42), "XGBoostIris") # # Regression @@ -97,18 +97,18 @@ def build_auto(df, regressor, name): auto_df = load_auto("Auto") -build_auto(auto_df, H2OGradientBoostingEstimator(ntrees = 17), "GBMAuto") -build_auto(auto_df, H2OGeneralizedLinearEstimator(family = "gaussian", lambda_ = 0), "GLMAuto") -build_auto(auto_df, H2ORandomForestEstimator(ntrees = 17), "RandomForestAuto") -build_auto(auto_df, H2OXGBoostEstimator(ntrees = 17), "XGBoostAuto") +build_auto(auto_df, H2OGradientBoostingEstimator(ntrees = 17, seed = 42), "GBMAuto") +build_auto(auto_df, H2OGeneralizedLinearEstimator(family = "gaussian", lambda_ = 0, seed = 42), "GLMAuto") +build_auto(auto_df, H2ORandomForestEstimator(ntrees = 17, seed = 42), "RandomForestAuto") +build_auto(auto_df, H2OXGBoostEstimator(ntrees = 17, seed = 42), "XGBoostAuto") auto_df = load_auto("AutoNA") -build_auto(auto_df, H2OGradientBoostingEstimator(ntrees = 17), "GBMAutoNA") -build_auto(auto_df, H2OGeneralizedLinearEstimator(family = "gaussian"), "GLMAutoNA") -build_auto(auto_df, H2ORandomForestEstimator(ntrees = 17), "RandomForestAutoNA") -build_auto(auto_df, H2OStackedEnsembleEstimator(base_models = train_stack(auto_df, 7)), "StackedEnsembleAutoNA") -build_auto(auto_df, H2OXGBoostEstimator(ntrees = 17), "XGBoostAutoNA") +build_auto(auto_df, H2OGradientBoostingEstimator(ntrees = 17, seed = 42), "GBMAutoNA") +build_auto(auto_df, H2OGeneralizedLinearEstimator(family = "gaussian", seed = 42), "GLMAutoNA") +build_auto(auto_df, H2ORandomForestEstimator(ntrees = 17, seed = 42), "RandomForestAutoNA") +build_auto(auto_df, H2OStackedEnsembleEstimator(base_models = train_stack(auto_df, 7), seed = 42), "StackedEnsembleAutoNA") +build_auto(auto_df, H2OXGBoostEstimator(ntrees = 17, seed = 42), "XGBoostAutoNA") def load_visit(name): return load_csv(name + ".csv", ["edlevel", "outwork", "female", "married", "kids"]) @@ -122,10 +122,10 @@ def build_visit(df, regressor, name): visit_df = load_visit("Visit") -build_visit(visit_df, H2OGradientBoostingEstimator(ntrees = 31, distribution = "poisson"), "GBMPoissonVisit") -build_visit(visit_df, H2OGradientBoostingEstimator(ntrees = 31, distribution = "tweedie"), "GBMTweedieVisit") +build_visit(visit_df, H2OGradientBoostingEstimator(ntrees = 31, distribution = "poisson", seed = 42), "GBMPoissonVisit") +build_visit(visit_df, H2OGradientBoostingEstimator(ntrees = 31, distribution = "tweedie", seed = 42), "GBMTweedieVisit") visit_df = load_visit("VisitNA") -build_visit(visit_df, H2OGradientBoostingEstimator(ntrees = 31, distribution = "poisson"), "GBMPoissonVisitNA") -build_visit(visit_df, H2OGradientBoostingEstimator(ntrees = 31, distribution = "gamma"), "GBMGammaVisitNA") \ No newline at end of file +build_visit(visit_df, H2OGradientBoostingEstimator(ntrees = 31, distribution = "poisson", seed = 42), "GBMPoissonVisitNA") +build_visit(visit_df, H2OGradientBoostingEstimator(ntrees = 31, distribution = "gamma", seed = 42), "GBMGammaVisitNA") \ No newline at end of file diff --git a/src/test/resources/mojo/GBMAudit.zip b/src/test/resources/mojo/GBMAudit.zip index 438a29c..7fd35db 100644 Binary files a/src/test/resources/mojo/GBMAudit.zip and b/src/test/resources/mojo/GBMAudit.zip differ diff --git a/src/test/resources/mojo/GBMAuditNA.zip b/src/test/resources/mojo/GBMAuditNA.zip index 8ee51fe..628a64e 100644 Binary files a/src/test/resources/mojo/GBMAuditNA.zip and b/src/test/resources/mojo/GBMAuditNA.zip differ diff --git a/src/test/resources/mojo/GBMAuto.zip b/src/test/resources/mojo/GBMAuto.zip index 55ae0cf..aa48894 100644 Binary files a/src/test/resources/mojo/GBMAuto.zip and b/src/test/resources/mojo/GBMAuto.zip differ diff --git a/src/test/resources/mojo/GBMAutoNA.zip 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