diff --git a/CHANGELOG.md b/CHANGELOG.md index 8463c1ce..55b9073d 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -28,6 +28,16 @@ v1.10.4 (2024-07-08) - Added `dmcas_misc.json` template file for model card generation. - Updated generation of `ModelProperties.json` to allow for model card generation immediately upon upload. +v1.10.4 (2024-07-08) +---------- +**Improvements** +- Added example Jupyter notebook for OpenAI models. + +**Buxfixes** +- Dropped support for Python 3.6 and Python 3.7, as those are no longer officially supported versions. +- Added `dmcas_misc.json` template file for model card generation. +- Updated generation of `ModelProperties.json` to allow for model card generation immediately upon upload. + v1.10.3 (2024-04-12) -------------------- **Bugfixes** diff --git a/examples/data/hmeqModels/DecisionTreeClassifier/DecisionTreeClassifier.pickle b/examples/data/hmeqModels/DecisionTreeClassifier/DecisionTreeClassifier.pickle index 6e8c7a94..75461a8f 100644 Binary files a/examples/data/hmeqModels/DecisionTreeClassifier/DecisionTreeClassifier.pickle and b/examples/data/hmeqModels/DecisionTreeClassifier/DecisionTreeClassifier.pickle differ diff --git a/examples/data/hmeqModels/DecisionTreeClassifier/DecisionTreeClassifier.zip b/examples/data/hmeqModels/DecisionTreeClassifier/DecisionTreeClassifier.zip index be1098b3..f5731130 100644 Binary files a/examples/data/hmeqModels/DecisionTreeClassifier/DecisionTreeClassifier.zip and b/examples/data/hmeqModels/DecisionTreeClassifier/DecisionTreeClassifier.zip differ diff --git a/examples/data/hmeqModels/DecisionTreeClassifier/ModelProperties.json b/examples/data/hmeqModels/DecisionTreeClassifier/ModelProperties.json index 55ea28af..3f18304f 100644 --- a/examples/data/hmeqModels/DecisionTreeClassifier/ModelProperties.json +++ b/examples/data/hmeqModels/DecisionTreeClassifier/ModelProperties.json @@ -2,16 +2,19 @@ "name": "DecisionTreeClassifier", "description": "Description for the DecisionTreeClassifier model.", "scoreCodeType": "python", - "trainTable": "", + "trainTable": "cas-shared-default/Public/DecisionTreeClassifier_train_data", "trainCodeType": "Python", - "algorithm": "", + "algorithm": "DecisionTreeClassifier", "function": "Classification", "targetVariable": "BAD", "targetEvent": "1", - "targetLevel": "BINARY", + "targetLevel": "Binary", "eventProbVar": "P_1", "modeler": "sasdemo", "tool": "Python 3", - "toolVersion": "3.8.16", - "properties": [] + "toolVersion": "3.11.7", + "properties": [], + "eventPercentage": "0.20182166826462", + "selectionStatistic": "_RASE_", + "selectionStatisticValue": "0.33514616799162" } \ No newline at end of file diff --git a/examples/data/hmeqModels/DecisionTreeClassifier/dmcas_fitstat.json b/examples/data/hmeqModels/DecisionTreeClassifier/dmcas_fitstat.json index 163db488..3b0f0cdb 100644 --- a/examples/data/hmeqModels/DecisionTreeClassifier/dmcas_fitstat.json +++ b/examples/data/hmeqModels/DecisionTreeClassifier/dmcas_fitstat.json @@ -222,13 +222,14 @@ "_MCE_": 0.88710450623202, "_ASE_": 0.11232295391947, "_MCLL_": 0.99896429012045, - "_KS_": null, + "_KS_": 1.0, "_KSPostCutoff_": null, "_DIV_": 4172.0, "_TAU_": null, "_KSCut_": null, - "_C_": null, - "_PartInd_": null + "_C_": 0.85770092470509, + "_PartInd_": null, + "_KS2_": 0.71540184941018 }, "rowNumber": 2, "header": null @@ -243,14 +244,15 @@ "_DataRole_": "TEST", "_MCE_": 0.99328859060402, "_ASE_": 0.10439243746467, - "_MCLL_": 0.18029189766592, - "_KS_": null, + "_MCLL_": 0.1674139049754, + "_KS_": 1.0, "_KSPostCutoff_": null, "_DIV_": 1788.0, "_TAU_": null, "_KSCut_": null, - "_C_": null, - "_PartInd_": null + "_C_": 0.83803076656035, + "_PartInd_": null, + "_KS2_": 0.6760615331207 }, "rowNumber": 3, "header": null diff --git a/examples/data/hmeqModels/DecisionTreeClassifier/dmcas_lift.json b/examples/data/hmeqModels/DecisionTreeClassifier/dmcas_lift.json index 1ba21ee6..47531d9d 100644 --- a/examples/data/hmeqModels/DecisionTreeClassifier/dmcas_lift.json +++ b/examples/data/hmeqModels/DecisionTreeClassifier/dmcas_lift.json @@ -1569,24 +1569,24 @@ "_column_": "predict", "_RespBest_": 100.0, "_DataRole_": "TEST", - "_CumResp_": 21.8566849075543, - "_PctRespBest_": 98.8888888888889, - "_Gain_": 3.37133698151086, - "_CumLift_": 4.37133698151086, + "_CumResp_": 21.7739353186039, + "_PctRespBest_": 90.0, + "_Gain_": 3.35478706372078, + "_CumLift_": 4.35478706372078, "_Event_": "1", "_TargetName_": null, - "_PctResp_": 21.6138328530259, + "_PctResp_": 19.5965417867435, "_GainBest_": 19.0, "_CumLiftBest_": 20.0, - "_Lift_": 4.37133698151086, - "_CumPctResp_": 21.6138328530259, - "_NEventsBest_": 89.0, - "_CumPctRespBest_": 98.8888888888889, + "_Lift_": 4.35478706372078, + "_CumPctResp_": 19.5965417867435, + "_NEventsBest_": 81.0, + "_CumPctRespBest_": 90.0, "_Value_": 1.0, - "_NEvents_": 19.4524495677233, + "_NEvents_": 17.6368876080691, "_LiftBest_": 20.0, "_PartInd_": "2", - "_Resp_": 21.8566849075543, + "_Resp_": 21.7739353186039, "_Column_": "actual" }, "rowNumber": 44 @@ -1600,24 +1600,24 @@ "_column_": "predict", "_RespBest_": 0.0, "_DataRole_": "TEST", - "_CumResp_": 43.7133698151086, + "_CumResp_": 43.5478706372078, "_PctRespBest_": 0.0, - "_Gain_": 3.37133698151086, - "_CumLift_": 4.37133698151086, + "_Gain_": 3.35478706372078, + "_CumLift_": 4.35478706372078, "_Event_": "1", "_TargetName_": null, - "_PctResp_": 21.6138328530259, + "_PctResp_": 19.5965417867435, "_GainBest_": 9.0, "_CumLiftBest_": 10.0, - "_Lift_": 4.37133698151086, - "_CumPctResp_": 21.6138328530259, + "_Lift_": 4.35478706372078, + "_CumPctResp_": 19.5965417867435, "_NEventsBest_": 0.0, - "_CumPctRespBest_": 49.4444444444444, + "_CumPctRespBest_": 45.0, "_Value_": 1.0, - "_NEvents_": 19.4524495677233, + "_NEvents_": 17.6368876080691, "_LiftBest_": 0.0, "_PartInd_": "2", - "_Resp_": 21.8566849075543, + "_Resp_": 21.7739353186039, "_Column_": "actual" }, "rowNumber": 45 @@ -1631,24 +1631,24 @@ "_column_": "predict", "_RespBest_": 0.0, "_DataRole_": "TEST", - "_CumResp_": 65.5700547226629, + "_CumResp_": 65.3218059558117, "_PctRespBest_": 0.0, - "_Gain_": 3.37133698151086, - "_CumLift_": 4.37133698151086, + "_Gain_": 3.35478706372078, + "_CumLift_": 4.35478706372078, "_Event_": "1", "_TargetName_": null, - "_PctResp_": 21.6138328530259, + "_PctResp_": 19.5965417867435, "_GainBest_": 5.66666666666666, "_CumLiftBest_": 6.66666666666666, - "_Lift_": 4.37133698151086, - "_CumPctResp_": 21.6138328530259, + "_Lift_": 4.35478706372078, + "_CumPctResp_": 19.5965417867435, "_NEventsBest_": 0.0, - "_CumPctRespBest_": 32.9629629629629, + "_CumPctRespBest_": 30.0, "_Value_": 1.0, - "_NEvents_": 19.4524495677233, + "_NEvents_": 17.6368876080691, "_LiftBest_": 0.0, "_PartInd_": "2", - "_Resp_": 21.8566849075543, + "_Resp_": 21.7739353186039, "_Column_": "actual" }, "rowNumber": 46 @@ -1662,24 +1662,24 @@ "_column_": "predict", "_RespBest_": 0.0, "_DataRole_": "TEST", - "_CumResp_": 84.4115743592542, + "_CumResp_": 84.0954069961703, "_PctRespBest_": 0.0, - "_Gain_": 3.22057871796271, - "_CumLift_": 4.22057871796271, + "_Gain_": 3.20477034980851, + "_CumLift_": 4.20477034980851, "_Event_": "1", "_TargetName_": null, - "_PctResp_": 18.6321694184069, + "_PctResp_": 16.8962409363227, "_GainBest_": 4.0, "_CumLiftBest_": 5.0, - "_Lift_": 3.76830392731826, - "_CumPctResp_": 20.8684169943711, + "_Lift_": 3.75472020807172, + "_CumPctResp_": 18.9214665741383, "_NEventsBest_": 0.0, - "_CumPctRespBest_": 24.7222222222222, + "_CumPctRespBest_": 22.5, "_Value_": 0.0, - "_NEvents_": 16.7689524765662, + "_NEvents_": 15.2066168426904, "_LiftBest_": 0.0, "_PartInd_": "2", - "_Resp_": 18.8415196365913, + "_Resp_": 18.7736010403586, "_Column_": "actual" }, "rowNumber": 47 @@ -1693,24 +1693,24 @@ "_column_": "predict", "_RespBest_": 0.0, "_DataRole_": "TEST", - "_CumResp_": 85.3940381601416, + "_CumResp_": 85.0977973115377, "_PctRespBest_": 0.0, - "_Gain_": 2.41576152640566, - "_CumLift_": 3.41576152640566, + "_Gain_": 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+ "_NEvents_": 0.8119361554476, "_LiftBest_": 0.0, "_PartInd_": "2", - "_Resp_": 0.98246380088733, + "_Resp_": 1.00239031536741, "_Column_": "actual" }, "rowNumber": 53 @@ -1879,24 +1879,24 @@ "_column_": "predict", "_RespBest_": 0.0, "_DataRole_": "TEST", - "_CumResp_": 91.2888209654655, + "_CumResp_": 91.1121392037423, "_PctRespBest_": 0.0, - "_Gain_": 0.65979674482664, - "_CumLift_": 1.65979674482664, + "_Gain_": 0.65658434915895, + "_CumLift_": 1.65658434915895, "_Event_": "1", "_TargetName_": null, - "_PctResp_": 0.97154753643303, + "_PctResp_": 0.90215128383067, "_GainBest_": 0.81818181818181, "_CumLiftBest_": 1.81818181818181, - "_Lift_": 0.19649276017746, - "_CumPctResp_": 8.20677279386509, + "_Lift_": 0.20047806307348, + "_CumPctResp_": 7.45462957121528, "_NEventsBest_": 0.0, - "_CumPctRespBest_": 8.98989898989899, + "_CumPctRespBest_": 8.18181818181818, "_Value_": 0.0, - "_NEvents_": 0.87439278278972, + "_NEvents_": 0.8119361554476, "_LiftBest_": 0.0, "_PartInd_": "2", - 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"rowNumber": 55 @@ -1941,24 +1941,24 @@ "_column_": "predict", "_RespBest_": 0.0, "_DataRole_": "TEST", - "_CumResp_": 93.2537485672402, + "_CumResp_": 93.1169198344771, "_PctRespBest_": 0.0, - "_Gain_": 0.43467305488061, - "_CumLift_": 1.43467305488061, + "_Gain_": 0.43256799745349, + "_CumLift_": 1.43256799745349, "_Event_": "1", "_TargetName_": null, - "_PctResp_": 0.97154753643303, + "_PctResp_": 0.90215128383067, "_GainBest_": 0.53846153846153, "_CumLiftBest_": 1.53846153846153, - "_Lift_": 0.19649276017746, - "_CumPctResp_": 7.09366121579861, + "_Lift_": 0.20047806307348, + "_CumPctResp_": 6.44655598854072, "_NEventsBest_": 0.0, - "_CumPctRespBest_": 7.6068376068376, + "_CumPctRespBest_": 6.92307692307692, "_Value_": 0.0, - "_NEvents_": 0.87439278278972, + "_NEvents_": 0.8119361554476, "_LiftBest_": 0.0, "_PartInd_": "2", - "_Resp_": 0.98246380088733, + "_Resp_": 1.00239031536741, "_Column_": "actual" }, "rowNumber": 56 @@ -1972,24 +1972,24 @@ "_column_": "predict", "_RespBest_": 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a/examples/data/hmeqModels/DecisionTreeClassifier/score_DecisionTreeClassifier.py +++ b/examples/data/hmeqModels/DecisionTreeClassifier/score_DecisionTreeClassifier.py @@ -16,73 +16,40 @@ def score(LOAN, MORTDUE, VALUE, YOJ, DEROG, DELINQ, CLAGE, NINQ, CLNO, DEBTINC): global model except NameError: with open(Path(settings.pickle_path) / "DecisionTreeClassifier.pickle", "rb") as pickle_model: - model = pickle.load(pickle_model) + model = pickle.load(pickle_model) - - try: - if math.isnan(LOAN): - LOAN = 18607.96979865772 - except TypeError: - LOAN = 18607.96979865772 - try: - if math.isnan(MORTDUE): - MORTDUE = 73760.817199559 - except TypeError: - MORTDUE = 73760.817199559 - try: - if math.isnan(VALUE): - VALUE = 101776.04874145007 - except TypeError: - VALUE = 101776.04874145007 - try: - if math.isnan(YOJ): - YOJ = 8.922268135904499 - except TypeError: - YOJ = 8.922268135904499 - try: - if math.isnan(DEROG): - DEROG = 0.2545696877380046 - except TypeError: - DEROG = 0.2545696877380046 - try: - if math.isnan(DELINQ): - DELINQ = 0.4494423791821561 - except TypeError: - DELINQ = 0.4494423791821561 - try: - if math.isnan(CLAGE): - CLAGE = 179.7662751900465 - except TypeError: - CLAGE = 179.7662751900465 - try: - if math.isnan(NINQ): - NINQ = 1.1860550458715597 - except TypeError: - NINQ = 1.1860550458715597 - try: - if math.isnan(CLNO): - CLNO = 21.29609620076682 - except TypeError: - CLNO = 21.29609620076682 - try: - if math.isnan(DEBTINC): - DEBTINC = 33.779915349235246 - except TypeError: - DEBTINC = 33.779915349235246 - - input_array = pd.DataFrame([[LOAN, MORTDUE, VALUE, YOJ, DEROG, DELINQ, CLAGE, NINQ, CLNO, DEBTINC]], - columns=["LOAN", "MORTDUE", "VALUE", "YOJ", "DEROG", "DELINQ", "CLAGE", "NINQ", "CLNO", "DEBTINC"], - dtype=float) - prediction = model.predict_proba(input_array) + index=None + if not isinstance(LOAN, pd.Series): + index=[0] + input_array = pd.DataFrame( + {"LOAN": LOAN, "MORTDUE": MORTDUE, "VALUE": VALUE, "YOJ": YOJ, "DEROG": DEROG, + "DELINQ": DELINQ, "CLAGE": CLAGE, "NINQ": NINQ, "CLNO": CLNO, "DEBTINC": + DEBTINC}, index=index + ) + input_array = impute_missing_values(input_array) + prediction = model.predict_proba(input_array).tolist() # Check for numpy values and convert to a CAS readable representation if isinstance(prediction, np.ndarray): - prediction = prediction.tolist()[0] - - if prediction[0] > prediction[1]: - EM_CLASSIFICATION = "1" + prediction = prediction.tolist() + + if input_array.shape[0] == 1: + if prediction[0][1] > 0.5: + EM_CLASSIFICATION = "1" + else: + EM_CLASSIFICATION = "0" + return EM_CLASSIFICATION, prediction[0][1] else: - EM_CLASSIFICATION = "0" - - return EM_CLASSIFICATION, prediction[0] \ No newline at end of file + df = pd.DataFrame(prediction) + proba = df[1] + classifications = np.where(df[1] > 0.5, '1', '0') + return pd.DataFrame({'EM_CLASSIFICATION': classifications, 'EM_EVENTPROBABILITY': proba}) + +def impute_missing_values(data): + impute_values = \ + {'DEBTINC': 33.779915349235246, 'LOAN': 18607.96979865772, 'DELINQ': + 0.4494423791821561, 'YOJ': 8.922268135904499, 'NINQ': 1.1860550458715597, + 'VALUE': 101776.04874145007, 'DEROG': 0.2545696877380046, 'CLNO': + 21.29609620076682, 'CLAGE': 179.7662751900465, 'MORTDUE': 73760.817199559} + return data.replace(' .', np.nan).fillna(impute_values).apply(pd.to_numeric, errors='ignore') diff --git a/examples/data/hmeqModels/GradientBoosting/GradientBoosting.pickle b/examples/data/hmeqModels/GradientBoosting/GradientBoosting.pickle index 28b393bc..e2d06bbe 100644 Binary files a/examples/data/hmeqModels/GradientBoosting/GradientBoosting.pickle and b/examples/data/hmeqModels/GradientBoosting/GradientBoosting.pickle differ diff --git a/examples/data/hmeqModels/GradientBoosting/GradientBoosting.zip b/examples/data/hmeqModels/GradientBoosting/GradientBoosting.zip index 697552f4..796a8bec 100644 Binary files a/examples/data/hmeqModels/GradientBoosting/GradientBoosting.zip and b/examples/data/hmeqModels/GradientBoosting/GradientBoosting.zip differ diff --git a/examples/data/hmeqModels/GradientBoosting/ModelProperties.json b/examples/data/hmeqModels/GradientBoosting/ModelProperties.json index 4bd7f38b..abe138d7 100644 --- a/examples/data/hmeqModels/GradientBoosting/ModelProperties.json +++ b/examples/data/hmeqModels/GradientBoosting/ModelProperties.json @@ -2,16 +2,19 @@ "name": "GradientBoosting", "description": "Description for the GradientBoosting model.", "scoreCodeType": "python", - "trainTable": "", + "trainTable": "cas-shared-default/Public/GradientBoosting_train_data", "trainCodeType": "Python", - "algorithm": "", + "algorithm": "GradientBoostingClassifier", "function": "Classification", "targetVariable": "BAD", "targetEvent": "1", - "targetLevel": "BINARY", + "targetLevel": "Binary", "eventProbVar": "P_1", "modeler": "sasdemo", "tool": "Python 3", - "toolVersion": "3.8.16", - "properties": [] + "toolVersion": "3.11.7", + "properties": [], + "eventPercentage": "0.20182166826462", + "selectionStatistic": "_RASE_", + "selectionStatisticValue": "0.297599900859" } \ No newline at end of file diff --git a/examples/data/hmeqModels/GradientBoosting/dmcas_fitstat.json b/examples/data/hmeqModels/GradientBoosting/dmcas_fitstat.json index c8894406..bbf36ebb 100644 --- a/examples/data/hmeqModels/GradientBoosting/dmcas_fitstat.json +++ b/examples/data/hmeqModels/GradientBoosting/dmcas_fitstat.json @@ -222,35 +222,37 @@ "_MCE_": 0.7986577181208, "_ASE_": 0.08856570099128, "_MCLL_": 0.01103827946881, - "_KS_": null, + "_KS_": 1.0, "_KSPostCutoff_": null, "_DIV_": 4172.0, "_TAU_": null, "_KSCut_": null, - "_C_": null, - "_PartInd_": null + "_C_": 0.99851205124364, + "_PartInd_": null, + "_KS2_": 0.99702410248728 }, "rowNumber": 2, "header": null }, { "dataMap": { - "_RASE_": 0.30459073530239, + "_RASE_": 0.3041562329411, "_NObs_": 1788.0, "_GINI_": null, "_GAMMA_": null, "_formattedPartition_": " 2", "_DataRole_": "TEST", - "_MCE_": 0.91946308724832, - "_ASE_": 0.09277551603205, - "_MCLL_": 0.06438996345867, - "_KS_": null, + "_MCE_": 0.91890380313199, + "_ASE_": 0.09251101403692, + "_MCLL_": 0.06438996345872, + "_KS_": 1.0, "_KSPostCutoff_": null, "_DIV_": 1788.0, "_TAU_": null, "_KSCut_": null, - "_C_": null, - "_PartInd_": null + "_C_": 0.92167277167277, + "_PartInd_": null, + "_KS2_": 0.84334554334554 }, "rowNumber": 3, "header": null diff --git a/examples/data/hmeqModels/GradientBoosting/dmcas_lift.json b/examples/data/hmeqModels/GradientBoosting/dmcas_lift.json index 7e6cd318..521a5d98 100644 --- a/examples/data/hmeqModels/GradientBoosting/dmcas_lift.json +++ b/examples/data/hmeqModels/GradientBoosting/dmcas_lift.json @@ -1564,29 +1564,29 @@ "dataMap": { "_NObs_": 90.0, "_Depth_": 5.0, - "_CumRespBest_": 60.4026845637584, + "_CumRespBest_": 60.0, "_formattedPartition_": " 2", "_column_": "predict", - "_RespBest_": 60.4026845637584, + "_RespBest_": 60.0, "_DataRole_": "TEST", - "_CumResp_": 25.0662437382743, + "_CumResp_": 25.07204610951, "_PctRespBest_": 100.0, - "_Gain_": 4.01324874765487, - "_CumLift_": 5.01324874765487, + "_Gain_": 4.01440922190201, + "_CumLift_": 5.01440922190201, "_Event_": "1", "_TargetName_": null, - "_PctResp_": 41.4985590778098, - "_GainBest_": 11.0805369127516, - "_CumLiftBest_": 12.0805369127516, - "_Lift_": 5.01324874765487, - "_CumPctResp_": 41.4985590778098, + "_PctResp_": 41.7867435158501, + "_GainBest_": 11.0, + "_CumLiftBest_": 12.0, + "_Lift_": 5.01440922190201, + "_CumPctResp_": 41.7867435158501, "_NEventsBest_": 90.0, "_CumPctRespBest_": 100.0, "_Value_": 1.0, - "_NEvents_": 37.3487031700288, - "_LiftBest_": 12.0805369127516, + "_NEvents_": 37.6080691642651, + "_LiftBest_": 12.0, "_PartInd_": "2", - "_Resp_": 25.0662437382743, + "_Resp_": 25.07204610951, "_Column_": "actual" }, "rowNumber": 44 @@ -1598,26 +1598,26 @@ "_CumRespBest_": 100.0, "_formattedPartition_": " 2", "_column_": "predict", - "_RespBest_": 39.5973154362416, + "_RespBest_": 40.0, "_DataRole_": "TEST", - "_CumResp_": 50.1324874765487, - "_PctRespBest_": 65.5555555555555, - "_Gain_": 4.01324874765487, - "_CumLift_": 5.01324874765487, + "_CumResp_": 50.1440922190201, + "_PctRespBest_": 66.6666666666666, + "_Gain_": 4.01440922190201, + "_CumLift_": 5.01440922190201, "_Event_": "1", "_TargetName_": null, - "_PctResp_": 41.4985590778098, + "_PctResp_": 41.7867435158501, "_GainBest_": 9.0, "_CumLiftBest_": 10.0, - "_Lift_": 5.01324874765487, - "_CumPctResp_": 41.4985590778098, - "_NEventsBest_": 59.0, - "_CumPctRespBest_": 82.7777777777777, + "_Lift_": 5.01440922190201, + "_CumPctResp_": 41.7867435158501, + "_NEventsBest_": 60.0, + "_CumPctRespBest_": 83.3333333333333, "_Value_": 1.0, - "_NEvents_": 37.3487031700288, - "_LiftBest_": 7.91946308724832, + "_NEvents_": 37.6080691642651, + "_LiftBest_": 8.0, "_PartInd_": "2", - "_Resp_": 25.0662437382743, + "_Resp_": 25.07204610951, "_Column_": "actual" }, "rowNumber": 45 @@ -1631,24 +1631,24 @@ "_column_": "predict", "_RespBest_": 0.0, "_DataRole_": "TEST", - "_CumResp_": 75.1987312148231, + "_CumResp_": 75.2161383285302, "_PctRespBest_": 0.0, - "_Gain_": 4.01324874765487, - "_CumLift_": 5.01324874765487, + "_Gain_": 4.01440922190201, + "_CumLift_": 5.01440922190201, "_Event_": "1", "_TargetName_": null, - "_PctResp_": 41.4985590778098, + "_PctResp_": 41.7867435158501, "_GainBest_": 5.66666666666666, "_CumLiftBest_": 6.66666666666666, - "_Lift_": 5.01324874765487, - "_CumPctResp_": 41.4985590778098, + "_Lift_": 5.01440922190201, + "_CumPctResp_": 41.7867435158501, "_NEventsBest_": 0.0, - "_CumPctRespBest_": 55.1851851851851, + "_CumPctRespBest_": 55.5555555555555, "_Value_": 1.0, - "_NEvents_": 37.3487031700288, + "_NEvents_": 37.6080691642651, "_LiftBest_": 0.0, "_PartInd_": "2", - "_Resp_": 25.0662437382743, + "_Resp_": 25.07204610951, "_Column_": "actual" }, "rowNumber": 46 @@ -1662,24 +1662,24 @@ "_column_": "predict", "_RespBest_": 0.0, "_DataRole_": "TEST", - "_CumResp_": 96.6745688350278, + "_CumResp_": 96.6967383761277, "_PctRespBest_": 0.0, - "_Gain_": 3.83372844175139, - "_CumLift_": 4.83372844175139, + "_Gain_": 3.83483691880638, + "_CumLift_": 4.83483691880638, "_Event_": "1", "_TargetName_": null, - "_PctResp_": 35.5544422823389, + "_PctResp_": 35.801000079329, "_GainBest_": 4.0, "_CumLiftBest_": 5.0, - "_Lift_": 4.29516752404095, - "_CumPctResp_": 40.0125298789421, + "_Lift_": 4.29612000951948, + "_CumPctResp_": 40.2903076567198, "_NEventsBest_": 0.0, - "_CumPctRespBest_": 41.3888888888888, + "_CumPctRespBest_": 41.6666666666666, "_Value_": 0.0, - "_NEvents_": 31.998998054105, + "_NEvents_": 32.2209000713961, "_LiftBest_": 0.0, "_PartInd_": "2", - "_Resp_": 21.4758376202047, + "_Resp_": 21.4806000475974, "_Column_": "actual" }, "rowNumber": 47 @@ -1693,24 +1693,24 @@ "_column_": "predict", "_RespBest_": 0.0, "_DataRole_": "TEST", - "_CumResp_": 96.8841548328202, + "_CumResp_": 96.9049271339347, "_PctRespBest_": 0.0, - "_Gain_": 2.8753661933128, - "_CumLift_": 3.87536619331281, + "_Gain_": 2.87619708535739, + "_CumLift_": 3.87619708535739, "_Event_": "1", "_TargetName_": null, "_PctResp_": 0.34698126301179, "_GainBest_": 3.0, "_CumLiftBest_": 4.0, - "_Lift_": 0.04191719955847, - "_CumPctResp_": 32.079420155756, + "_Lift_": 0.04163775156141, + "_CumPctResp_": 32.3016423779782, "_NEventsBest_": 0.0, - "_CumPctRespBest_": 33.1111111111111, + "_CumPctRespBest_": 33.3333333333333, "_Value_": 0.0, "_NEvents_": 0.31228313671061, "_LiftBest_": 0.0, "_PartInd_": "2", - "_Resp_": 0.20958599779236, + "_Resp_": 0.20818875780707, "_Column_": "actual" }, "rowNumber": 48 @@ -1724,24 +1724,24 @@ "_column_": "predict", "_RespBest_": 0.0, "_DataRole_": "TEST", - "_CumResp_": 97.0937408306126, + "_CumResp_": 97.1131158917418, "_PctRespBest_": 0.0, - "_Gain_": 2.23645802768708, - "_CumLift_": 3.23645802768708, + "_Gain_": 2.23710386305806, + "_CumLift_": 3.23710386305806, "_Event_": "1", "_TargetName_": null, "_PctResp_": 0.34698126301179, "_GainBest_": 2.33333333333333, "_CumLiftBest_": 3.33333333333333, - "_Lift_": 0.04191719955847, - "_CumPctResp_": 26.7906803402986, + "_Lift_": 0.04163775156141, + "_CumPctResp_": 26.9758655254838, "_NEventsBest_": 0.0, - "_CumPctRespBest_": 27.5925925925925, + "_CumPctRespBest_": 27.7777777777777, "_Value_": 0.0, "_NEvents_": 0.31228313671061, "_LiftBest_": 0.0, "_PartInd_": "2", - "_Resp_": 0.20958599779236, + "_Resp_": 0.20818875780707, "_Column_": "actual" }, "rowNumber": 49 @@ -1755,24 +1755,24 @@ "_column_": "predict", "_RespBest_": 0.0, "_DataRole_": "TEST", - "_CumResp_": 97.3033268284049, + "_CumResp_": 97.3213046495489, "_PctRespBest_": 0.0, - "_Gain_": 1.78009505224014, - "_CumLift_": 2.78009505224014, + "_Gain_": 1.78060870427282, + "_CumLift_": 2.78060870427282, "_Event_": "1", "_TargetName_": null, "_PctResp_": 0.34698126301179, "_GainBest_": 1.85714285714285, "_CumLiftBest_": 2.85714285714285, - "_Lift_": 0.04191719955847, - "_CumPctResp_": 23.0130090435433, + "_Lift_": 0.04163775156141, + "_CumPctResp_": 23.1717392022735, "_NEventsBest_": 0.0, - "_CumPctRespBest_": 23.6507936507936, + "_CumPctRespBest_": 23.8095238095238, "_Value_": 0.0, "_NEvents_": 0.31228313671061, "_LiftBest_": 0.0, "_PartInd_": "2", - "_Resp_": 0.20958599779236, + "_Resp_": 0.20818875780707, "_Column_": "actual" }, "rowNumber": 50 @@ -1786,24 +1786,24 @@ "_column_": "predict", "_RespBest_": 0.0, "_DataRole_": "TEST", - "_CumResp_": 97.5129128261973, + "_CumResp_": 97.529493407356, "_PctRespBest_": 0.0, - "_Gain_": 1.43782282065493, - "_CumLift_": 2.43782282065493, + "_Gain_": 1.4382373351839, + "_CumLift_": 2.4382373351839, "_Event_": "1", "_TargetName_": null, "_PctResp_": 0.34698126301179, - "_GainBest_": 1.49999999999999, + "_GainBest_": 1.5, "_CumLiftBest_": 2.5, - "_Lift_": 0.04191719955847, - "_CumPctResp_": 20.1797555709769, + "_Lift_": 0.04163775156141, + "_CumPctResp_": 20.3186444598658, "_NEventsBest_": 0.0, - "_CumPctRespBest_": 20.6944444444444, + "_CumPctRespBest_": 20.8333333333333, "_Value_": 0.0, "_NEvents_": 0.31228313671061, "_LiftBest_": 0.0, "_PartInd_": "2", - "_Resp_": 0.20958599779236, + "_Resp_": 0.20818875780707, "_Column_": "actual" }, "rowNumber": 51 @@ -1817,24 +1817,24 @@ "_column_": "predict", "_RespBest_": 0.0, "_DataRole_": "TEST", - "_CumResp_": 97.7224988239896, + "_CumResp_": 97.7376821651631, "_PctRespBest_": 0.0, - "_Gain_": 1.17161108497754, - "_CumLift_": 2.17161108497754, + "_Gain_": 1.17194849255918, + "_CumLift_": 2.17194849255918, "_Event_": "1", "_TargetName_": null, "_PctResp_": 0.34698126301179, "_GainBest_": 1.22222222222222, "_CumLiftBest_": 2.22222222222222, - "_Lift_": 0.04191719955847, - "_CumPctResp_": 17.976113981203, + "_Lift_": 0.04163775156141, + "_CumPctResp_": 18.0995707713265, "_NEventsBest_": 0.0, - "_CumPctRespBest_": 18.395061728395, + "_CumPctRespBest_": 18.5185185185185, "_Value_": 0.0, "_NEvents_": 0.31228313671061, "_LiftBest_": 0.0, "_PartInd_": "2", - "_Resp_": 0.20958599779236, + "_Resp_": 0.20818875780707, "_Column_": "actual" }, "rowNumber": 52 @@ -1848,24 +1848,24 @@ "_column_": "predict", "_RespBest_": 0.0, "_DataRole_": "TEST", - "_CumResp_": 97.932084821782, + "_CumResp_": 97.9458709229701, "_PctRespBest_": 0.0, - "_Gain_": 0.95864169643564, - "_CumLift_": 1.95864169643564, + "_Gain_": 0.9589174184594, + "_CumLift_": 1.9589174184594, "_Event_": "1", "_TargetName_": null, "_PctResp_": 0.34698126301179, - "_GainBest_": 0.99999999999999, + "_GainBest_": 1.0, "_CumLiftBest_": 2.0, - "_Lift_": 0.04191719955847, - "_CumPctResp_": 16.2132007093839, + "_Lift_": 0.04163775156141, + "_CumPctResp_": 16.324311820495, "_NEventsBest_": 0.0, - "_CumPctRespBest_": 16.5555555555555, + "_CumPctRespBest_": 16.6666666666666, "_Value_": 0.0, "_NEvents_": 0.31228313671061, "_LiftBest_": 0.0, "_PartInd_": "2", - "_Resp_": 0.20958599779236, + "_Resp_": 0.20818875780707, "_Column_": "actual" }, "rowNumber": 53 @@ -1879,24 +1879,24 @@ "_column_": "predict", "_RespBest_": 0.0, "_DataRole_": "TEST", - "_CumResp_": 98.1416708195744, + "_CumResp_": 98.1540596807772, "_PctRespBest_": 0.0, - "_Gain_": 0.78439401490135, - "_CumLift_": 1.78439401490135, + "_Gain_": 0.78461926692322, + "_CumLift_": 1.78461926692322, "_Event_": "1", "_TargetName_": null, "_PctResp_": 0.34698126301179, "_GainBest_": 0.81818181818181, "_CumLiftBest_": 1.81818181818181, - "_Lift_": 0.04191719955847, - "_CumPctResp_": 14.77081712335, + "_Lift_": 0.04163775156141, + "_CumPctResp_": 14.8718272243601, "_NEventsBest_": 0.0, - "_CumPctRespBest_": 15.050505050505, + "_CumPctRespBest_": 15.1515151515151, "_Value_": 0.0, "_NEvents_": 0.31228313671061, "_LiftBest_": 0.0, "_PartInd_": "2", - "_Resp_": 0.20958599779236, + "_Resp_": 0.20818875780707, "_Column_": "actual" }, "rowNumber": 54 @@ -1910,24 +1910,24 @@ "_column_": "predict", "_RespBest_": 0.0, "_DataRole_": "TEST", - "_CumResp_": 98.3512568173667, + "_CumResp_": 98.3622484385843, "_PctRespBest_": 0.0, - "_Gain_": 0.63918761362277, - "_CumLift_": 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TypeError: - LOAN = 18607.96979865772 - try: - if math.isnan(MORTDUE): - MORTDUE = 73760.817199559 - except TypeError: - MORTDUE = 73760.817199559 - try: - if math.isnan(VALUE): - VALUE = 101776.04874145007 - except TypeError: - VALUE = 101776.04874145007 - try: - if math.isnan(YOJ): - YOJ = 8.922268135904499 - except TypeError: - YOJ = 8.922268135904499 - try: - if math.isnan(DEROG): - DEROG = 0.2545696877380046 - except TypeError: - DEROG = 0.2545696877380046 - try: - if math.isnan(DELINQ): - DELINQ = 0.4494423791821561 - except TypeError: - DELINQ = 0.4494423791821561 - try: - if math.isnan(CLAGE): - CLAGE = 179.7662751900465 - except TypeError: - CLAGE = 179.7662751900465 - try: - if math.isnan(NINQ): - NINQ = 1.1860550458715597 - except TypeError: - NINQ = 1.1860550458715597 - try: - if math.isnan(CLNO): - CLNO = 21.29609620076682 - except TypeError: - CLNO = 21.29609620076682 - try: - if math.isnan(DEBTINC): - DEBTINC = 33.779915349235246 - except TypeError: - DEBTINC = 33.779915349235246 - - input_array = pd.DataFrame([[LOAN, MORTDUE, VALUE, YOJ, DEROG, DELINQ, CLAGE, NINQ, CLNO, DEBTINC]], - columns=["LOAN", "MORTDUE", "VALUE", "YOJ", "DEROG", "DELINQ", "CLAGE", "NINQ", "CLNO", "DEBTINC"], - dtype=float) - prediction = model.predict_proba(input_array) + index=None + if not isinstance(LOAN, pd.Series): + index=[0] + input_array = pd.DataFrame( + {"LOAN": LOAN, "MORTDUE": MORTDUE, "VALUE": VALUE, "YOJ": YOJ, "DEROG": DEROG, + "DELINQ": DELINQ, "CLAGE": CLAGE, "NINQ": NINQ, "CLNO": CLNO, "DEBTINC": + DEBTINC}, index=index + ) + input_array = impute_missing_values(input_array) + prediction = model.predict_proba(input_array).tolist() # Check for numpy values and convert to a CAS readable representation if isinstance(prediction, np.ndarray): - prediction = prediction.tolist()[0] - - if prediction[0] > prediction[1]: - EM_CLASSIFICATION = "1" + prediction = prediction.tolist() + + if input_array.shape[0] == 1: + if prediction[0][1] > 0.5: + EM_CLASSIFICATION = "1" + else: + EM_CLASSIFICATION = "0" + return EM_CLASSIFICATION, prediction[0][1] else: - EM_CLASSIFICATION = "0" - - return EM_CLASSIFICATION, prediction[0] \ No newline at end of file + df = pd.DataFrame(prediction) + proba = df[1] + classifications = np.where(df[1] > 0.5, '1', '0') + return pd.DataFrame({'EM_CLASSIFICATION': classifications, 'EM_EVENTPROBABILITY': proba}) + +def impute_missing_values(data): + impute_values = \ + {'DEBTINC': 33.779915349235246, 'LOAN': 18607.96979865772, 'DELINQ': + 0.4494423791821561, 'YOJ': 8.922268135904499, 'NINQ': 1.1860550458715597, + 'VALUE': 101776.04874145007, 'DEROG': 0.2545696877380046, 'CLNO': + 21.29609620076682, 'CLAGE': 179.7662751900465, 'MORTDUE': 73760.817199559} + return data.replace(' .', np.nan).fillna(impute_values).apply(pd.to_numeric, errors='ignore') diff --git a/examples/data/hmeqModels/RandomForest/ModelProperties.json b/examples/data/hmeqModels/RandomForest/ModelProperties.json index 632af1c8..0f345631 100644 --- a/examples/data/hmeqModels/RandomForest/ModelProperties.json +++ b/examples/data/hmeqModels/RandomForest/ModelProperties.json @@ -2,16 +2,19 @@ "name": "RandomForest", "description": "Description for the RandomForest model.", "scoreCodeType": "python", - "trainTable": "", + "trainTable": "cas-shared-default/Public/RandomForest_train_data", "trainCodeType": "Python", - "algorithm": "", + "algorithm": "RandomForestClassifier", "function": "Classification", "targetVariable": "BAD", "targetEvent": "1", - "targetLevel": "BINARY", + "targetLevel": "Binary", "eventProbVar": "P_1", "modeler": "sasdemo", "tool": "Python 3", - "toolVersion": "3.8.16", - "properties": [] + "toolVersion": "3.11.7", + "properties": [], + "eventPercentage": "0.20182166826462", + "selectionStatistic": "_RASE_", + "selectionStatisticValue": "0.29557962253594" } \ No newline at end of file diff --git a/examples/data/hmeqModels/RandomForest/RandomForest.pickle b/examples/data/hmeqModels/RandomForest/RandomForest.pickle index 4d3520f3..23616a2d 100644 Binary files a/examples/data/hmeqModels/RandomForest/RandomForest.pickle and b/examples/data/hmeqModels/RandomForest/RandomForest.pickle differ diff --git a/examples/data/hmeqModels/RandomForest/RandomForest.zip b/examples/data/hmeqModels/RandomForest/RandomForest.zip index bf078447..1304bb8b 100644 Binary files a/examples/data/hmeqModels/RandomForest/RandomForest.zip and b/examples/data/hmeqModels/RandomForest/RandomForest.zip differ diff --git a/examples/data/hmeqModels/RandomForest/dmcas_fitstat.json b/examples/data/hmeqModels/RandomForest/dmcas_fitstat.json index 340c403d..231eb4c2 100644 --- a/examples/data/hmeqModels/RandomForest/dmcas_fitstat.json +++ b/examples/data/hmeqModels/RandomForest/dmcas_fitstat.json @@ -213,44 +213,46 @@ }, { "dataMap": { - "_RASE_": 0.29542948856089, + "_RASE_": 0.29557962253594, "_NObs_": 4172.0, "_GINI_": null, "_GAMMA_": null, "_formattedPartition_": " 1", "_DataRole_": "TRAIN", - "_MCE_": 0.86529242569511, - "_ASE_": 0.08727858271135, - "_MCLL_": 0.22076558898701, - "_KS_": null, + "_MCE_": 0.86577181208053, + "_ASE_": 0.08736731325849, + "_MCLL_": 0.22076558898696, + "_KS_": 1.0, "_KSPostCutoff_": null, "_DIV_": 4172.0, "_TAU_": null, "_KSCut_": null, - "_C_": null, - "_PartInd_": null + "_C_": 0.92719298245614, + "_PartInd_": null, + "_KS2_": 0.85438596491228 }, "rowNumber": 2, "header": null }, { "dataMap": { - "_RASE_": 0.30199032656328, + "_RASE_": 0.30658438978848, "_NObs_": 1788.0, "_GINI_": null, "_GAMMA_": null, "_formattedPartition_": " 2", "_DataRole_": "TEST", - "_MCE_": 0.94351230425055, - "_ASE_": 0.0911981573378, - "_MCLL_": 0.01287799269577, - "_KS_": null, + "_MCE_": 0.95022371364653, + "_ASE_": 0.09399398806198, + "_MCLL_": 0.02575598538522, + "_KS_": 1.0, "_KSPostCutoff_": null, "_DIV_": 1788.0, "_TAU_": null, "_KSCut_": null, - "_C_": null, - "_PartInd_": null + "_C_": 0.91299448930562, + "_PartInd_": null, + "_KS2_": 0.82598897861125 }, "rowNumber": 3, "header": null diff --git a/examples/data/hmeqModels/RandomForest/dmcas_lift.json b/examples/data/hmeqModels/RandomForest/dmcas_lift.json index 446aa845..2e6edfb7 100644 --- a/examples/data/hmeqModels/RandomForest/dmcas_lift.json +++ b/examples/data/hmeqModels/RandomForest/dmcas_lift.json @@ -924,29 +924,29 @@ "dataMap": { "_NObs_": 209.0, "_Depth_": 5.0, - "_CumRespBest_": 34.7176079734219, + "_CumRespBest_": 34.8333333333333, "_formattedPartition_": " 1", "_column_": "predict", - "_RespBest_": 34.7176079734219, + "_RespBest_": 34.8333333333333, "_DataRole_": "TRAIN", - "_CumResp_": 23.1725601912863, + "_CumResp_": 23.1670625494853, "_PctRespBest_": 100.0, - "_Gain_": 3.63451203825727, - "_CumLift_": 4.63451203825727, + "_Gain_": 3.63341250989707, + "_CumLift_": 4.63341250989707, "_Event_": "1", "_TargetName_": null, - "_PctResp_": 66.7458432304038, - "_GainBest_": 5.94352159468438, - "_CumLiftBest_": 6.94352159468438, - "_Lift_": 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"TRAIN", - "_CumResp_": 92.6902407651454, + "_CumResp_": 92.6682501979414, "_PctRespBest_": 0.0, - "_Gain_": 3.63451203825727, - "_CumLift_": 4.63451203825727, + "_Gain_": 3.63341250989707, + "_CumLift_": 4.63341250989707, "_Event_": "1", "_TargetName_": null, - "_PctResp_": 66.7458432304038, + "_PctResp_": 66.5083135391924, "_GainBest_": 4.0, "_CumLiftBest_": 5.0, - "_Lift_": 4.63451203825727, - "_CumPctResp_": 66.7458432304038, + "_Lift_": 4.63341250989707, + "_CumPctResp_": 66.5083135391924, "_NEventsBest_": 0.0, - "_CumPctRespBest_": 72.0095693779904, + "_CumPctRespBest_": 71.7703349282296, "_Value_": 1.0, - "_NEvents_": 139.498812351543, + "_NEvents_": 139.002375296912, "_LiftBest_": 0.0, "_PartInd_": "1", - "_Resp_": 23.1725601912863, + "_Resp_": 23.1670625494853, "_Column_": "actual" }, "rowNumber": 26 @@ -1053,24 +1053,24 @@ "_column_": "predict", "_RespBest_": 0.0, "_DataRole_": "TRAIN", - "_CumResp_": 93.7605379465844, + "_CumResp_": 93.7397397397397, "_PctRespBest_": 0.0, - 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0.0, "_PartInd_": "1", - "_Resp_": 0.41702832400506, + "_Resp_": 0.41841841841841, "_Column_": "actual" }, "rowNumber": 31 @@ -1208,24 +1208,24 @@ "_column_": "predict", "_RespBest_": 0.0, "_DataRole_": "TRAIN", - "_CumResp_": 95.8456795666098, + "_CumResp_": 95.8318318318318, "_PctRespBest_": 0.0, - "_Gain_": 0.91691359133219, - "_CumLift_": 1.91691359133219, + "_Gain_": 0.91663663663663, + "_CumLift_": 1.91663663663663, "_Event_": "1", "_TargetName_": null, "_PctResp_": 1.2012012012012, "_GainBest_": 1.0, "_CumLiftBest_": 2.0, - "_Lift_": 0.08340566480101, - "_CumPctResp_": 27.6072244493297, + "_Lift_": 0.08368368368368, + "_CumPctResp_": 27.5115306694254, "_NEventsBest_": 0.0, - "_CumPctRespBest_": 28.8038277511961, + "_CumPctRespBest_": 28.7081339712918, "_Value_": 0.0, "_NEvents_": 2.51051051051051, "_LiftBest_": 0.0, "_PartInd_": "1", - "_Resp_": 0.41702832400506, + "_Resp_": 0.41841841841841, "_Column_": "actual" }, "rowNumber": 32 @@ -1239,24 +1239,24 @@ "_column_": "predict", 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"_Specificity_": 0.85409252669039, - "_FHALF_": 0.33892617449664, + "_Specificity_": 0.84796700058927, + "_FHALF_": 0.30087897227856, "_PartInd_": "2", - "_C_": 0.92214430256088, - "_GINI_": 0.84428860512176, - "_GAMMA_": 0.99662287385198, - "_TAU_": 0.09088382539068, - "_FNR_": 0.00980392156862 + "_C_": 0.91299448930562, + "_GINI_": 0.82598897861125, + "_GAMMA_": 0.99197430533413, + "_TAU_": 0.07984273694304, + "_FNR_": 0.02197802197802 }, "rowNumber": 300 } diff --git a/examples/data/hmeqModels/RandomForest/score_RandomForest.py b/examples/data/hmeqModels/RandomForest/score_RandomForest.py index 1e074ad5..4eed0c9d 100644 --- a/examples/data/hmeqModels/RandomForest/score_RandomForest.py +++ b/examples/data/hmeqModels/RandomForest/score_RandomForest.py @@ -16,73 +16,40 @@ def score(LOAN, MORTDUE, VALUE, YOJ, DEROG, DELINQ, CLAGE, NINQ, CLNO, DEBTINC): global model except NameError: with open(Path(settings.pickle_path) / "RandomForest.pickle", "rb") as pickle_model: - model = pickle.load(pickle_model) + model = pickle.load(pickle_model) - - try: - if math.isnan(LOAN): - LOAN = 18607.96979865772 - except TypeError: - LOAN = 18607.96979865772 - try: - if math.isnan(MORTDUE): - MORTDUE = 73760.817199559 - except TypeError: - MORTDUE = 73760.817199559 - try: - if math.isnan(VALUE): - VALUE = 101776.04874145007 - except TypeError: - VALUE = 101776.04874145007 - try: - if math.isnan(YOJ): - YOJ = 8.922268135904499 - except TypeError: - YOJ = 8.922268135904499 - try: - if math.isnan(DEROG): - DEROG = 0.2545696877380046 - except TypeError: - DEROG = 0.2545696877380046 - try: - if math.isnan(DELINQ): - DELINQ = 0.4494423791821561 - except TypeError: - DELINQ = 0.4494423791821561 - try: - if math.isnan(CLAGE): - CLAGE = 179.7662751900465 - except TypeError: - CLAGE = 179.7662751900465 - try: - if math.isnan(NINQ): - NINQ = 1.1860550458715597 - except TypeError: - NINQ = 1.1860550458715597 - try: - if math.isnan(CLNO): - CLNO = 21.29609620076682 - except TypeError: - CLNO = 21.29609620076682 - try: - if math.isnan(DEBTINC): - DEBTINC = 33.779915349235246 - except TypeError: - DEBTINC = 33.779915349235246 - - input_array = pd.DataFrame([[LOAN, MORTDUE, VALUE, YOJ, DEROG, DELINQ, CLAGE, NINQ, CLNO, DEBTINC]], - columns=["LOAN", "MORTDUE", "VALUE", "YOJ", "DEROG", "DELINQ", "CLAGE", "NINQ", "CLNO", "DEBTINC"], - dtype=float) - prediction = model.predict_proba(input_array) + index=None + if not isinstance(LOAN, pd.Series): + index=[0] + input_array = pd.DataFrame( + {"LOAN": LOAN, "MORTDUE": MORTDUE, "VALUE": VALUE, "YOJ": YOJ, "DEROG": DEROG, + "DELINQ": DELINQ, "CLAGE": CLAGE, "NINQ": NINQ, "CLNO": CLNO, "DEBTINC": + DEBTINC}, index=index + ) + input_array = impute_missing_values(input_array) + prediction = model.predict_proba(input_array).tolist() # Check for numpy values and convert to a CAS readable representation if isinstance(prediction, np.ndarray): - prediction = prediction.tolist()[0] - - if prediction[0] > prediction[1]: - EM_CLASSIFICATION = "1" + prediction = prediction.tolist() + + if input_array.shape[0] == 1: + if prediction[0][1] > 0.5: + EM_CLASSIFICATION = "1" + else: + EM_CLASSIFICATION = "0" + return EM_CLASSIFICATION, prediction[0][1] else: - EM_CLASSIFICATION = "0" - - return EM_CLASSIFICATION, prediction[0] \ No newline at end of file + df = pd.DataFrame(prediction) + proba = df[1] + classifications = np.where(df[1] > 0.5, '1', '0') + return pd.DataFrame({'EM_CLASSIFICATION': classifications, 'EM_EVENTPROBABILITY': proba}) + +def impute_missing_values(data): + impute_values = \ + {'DEBTINC': 33.779915349235246, 'LOAN': 18607.96979865772, 'DELINQ': + 0.4494423791821561, 'YOJ': 8.922268135904499, 'NINQ': 1.1860550458715597, + 'VALUE': 101776.04874145007, 'DEROG': 0.2545696877380046, 'CLNO': + 21.29609620076682, 'CLAGE': 179.7662751900465, 'MORTDUE': 73760.817199559} + return data.replace(' .', np.nan).fillna(impute_values).apply(pd.to_numeric, errors='ignore') diff --git a/examples/pzmm_regression_model_import.ipynb b/examples/pzmm_regression_model_import.ipynb index c0c2ad53..59c73a6e 100644 --- a/examples/pzmm_regression_model_import.ipynb +++ b/examples/pzmm_regression_model_import.ipynb @@ -494,13 +494,10 @@ } ], "metadata": { - "interpreter": { - "hash": "f9708d3f38eeab835578f0695c8890716ee809285281a28db6e379a5abca1310" - }, "kernelspec": { - "display_name": "dev-py38", + "display_name": "base", "language": "python", - "name": "dev-py38" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -512,7 +509,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.11.7" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/examples/score_testing_example.ipynb b/examples/score_testing_example.ipynb new file mode 100644 index 00000000..cfc172e7 --- /dev/null +++ b/examples/score_testing_example.ipynb @@ -0,0 +1,72 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "# Implementing Score Testing\n", + "\n", + "_**Note:** Before running this example, you will need to run an example that creates a model on a Viya server and copy the UUID. This will be used as a create_score_definition function parameter._" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "import sys\n", + "\n", + "from sasctl._services.score_definitions import ScoreDefinitions as sd # Importing ScoreDefinitions service\n", + "from sasctl._services.score_execution import ScoreExecution as se # Importing ScoreExecution service" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating the score definition for this model using the model UUID generated two steps before\n", + "score_definition = sd.create_score_definition(\n", + " \"example_score_def_name\", # Name of the score_definition, which can be any string\n", + " \"model_id\", # Use Model UUID generated two steps before\n", + " \"table_name\", # Table name for input data, which must exist in host server or it will throw an HTTP error and prompt you to upload a data file\n", + " # True, # Uncomment 'True' if your Viya version is compatible with CAS Gateway\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Executing the score definition\n", + "score_execution = se.create_score_execution(\n", + " score_definition.get(\"id\") # Score definition id created in the previous cell\n", + ")\n", + "\n", + "# Prints score_execution_id\n", + "print(score_execution)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can view our scored model information within Model Manager under Projects -> Choose your model -> Scoring. \n", + "***" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/sasctl/_services/score_definitions.py b/src/sasctl/_services/score_definitions.py new file mode 100644 index 00000000..6146878e --- /dev/null +++ b/src/sasctl/_services/score_definitions.py @@ -0,0 +1,162 @@ +import requests +from requests import HTTPError +import sys + +from pathlib import Path +import json +from typing import Union, Optional + +from ..core import current_session, delete, get, sasctl_command, RestObj +from .cas_management import CASManagement +from .model_repository import ModelRepository +from .service import Service + + +class ScoreDefinitions(Service): + """ + Used for creating and maintaining score definitions. + + The Score Definitions API is used for creating and maintaining score definitions. + + See Also + -------- + `REST Documentation ` + + """ + + _SERVICE_ROOT = "/scoreDefinitions" + _cas_management = CASManagement() + _model_repository = ModelRepository() + + ( + list_definitions, + get_definition, + update_definition, + delete_definition, + ) = Service._crud_funcs("/definitions", "definition") + + @classmethod + def create_score_definition( + cls, + score_def_name: str, + model: Union[str, dict], + table_name: str, + use_cas_gateway: Optional[bool] = False, + table_file: Union[str, Path] = None, + description: str = "", + server_name: str = "cas-shared-default", + library_name: str = "Public", + model_version: str = "latest", + ): + """Creates the score definition service. + + Parameters + -------- + score_def_name: str + Name of score definition. + model : str or dict + The name or id of the model, or a dictionary representation of the model. + table_name: str + A user-inputted table name in CAS Management. + use_cas_gateway: bool, optional + Determines whether object uses CAS Gateway or not. + table_file: str or Path, optional + A user-provided path to an uploadable file. Defaults to None. + description: str, optional + Description of score definition. Defaults to an empty string. + server_name: str, optional + The server within CAS that the table is in. Defaults to "cas-shared-default". + library_name: str, optional + The library within the CAS server the table exists in. Defaults to "Public". + model_version: str, optional + The user-chosen version of the model with the specified model_id. Defaults to "latest". + + Returns + ------- + RestObj + + """ + # Changes object descriptor type to either use or not use CAS Gateway + object_descriptor_type: str + if use_cas_gateway: + object_descriptor_type = "sas.models.model.python" + else: + object_descriptor_type = "sas.models.model.ds2" + + model = cls._model_repository.get_model(model) + + if not model: + raise HTTPError( + { + f"This model may not exist in a project or the model may not exist at all." + } + ) + model_id = model.id + model_project_id = model.get("projectId") + model_project_version_id = model.get("projectVersionId") + model_name = model.get("name") + # Checking if the model exists and if it's in a project + + try: + inputMapping = [] + for input_item in model.get("inputVariables"): + var = { + "mappingValue": input_item["name"], + "mappingType": "datasource", + "variableName": input_item["name"], + } + inputMapping.append(var) + + except: + print("This model does not have the optional 'inputVariables' parameter.") + + # Optional mapping - Maps the variables in the data to the variables of the score object. It's not necessary to create a score definition. + + table = cls._cas_management.get_table(table_name, library_name, server_name) + if not table and not table_file: + raise HTTPError( + f"This table may not exist in CAS. Please include the `table_file` argument in the function call if it doesn't exist." + ) + elif not table and table_file: + cls._cas_management.upload_file( + str(table_file), table_name + ) # do I need to add a check if the file doesn't exist or does upload_file take care of that? + table = cls._cas_management.get_table(table_name, library_name, server_name) + if not table: + raise HTTPError( + f"The file failed to upload properly or another error occurred." + ) + # Checks if the inputted table exists, and if not, uploads a file to create a new table + + save_score_def = { + "name": model_name, # used to be score_def_name + "description": description, + "objectDescriptor": { + "uri": f"/modelManagement/models/{model_id}", + "name": f"{model_name}({model_version})", + "type": f"{object_descriptor_type}", + }, + "inputData": { + "type": "CASTable", + "serverName": server_name, + "libraryName": library_name, + "tableName": table_name, + }, + "properties": { + "tableBaseName": "", + "modelOrigUri": f"/modelRepository/models/{model_id}", + "projectUri": f"/modelRepository/projects/{model_project_id}", + "projectVersionUri": f"/modelRepository/projects/{model_project_id}/projectVersions/{model_project_version_id}", + "publishDestination": "", + "versionedModel": f"{model_name}({model_version})", + }, + "mappings": inputMapping, + } + # Consolidating all of the model and table information to create the score definition information + + headers_score_def = {"Content-Type": "application/json"} + + return cls.post( + "/definitions", data=json.dumps(save_score_def), headers=headers_score_def + ) + # The response information of the score definition can be seen as a JSON as well as a RestOBJ diff --git a/src/sasctl/_services/score_execution.py b/src/sasctl/_services/score_execution.py new file mode 100644 index 00000000..eeac893e --- /dev/null +++ b/src/sasctl/_services/score_execution.py @@ -0,0 +1,250 @@ +import json +import time +import warnings +from distutils.version import StrictVersion +from typing import Union + +import pandas as pd +from requests import HTTPError + +from .cas_management import CASManagement +from ..core import current_session +from .score_definitions import ScoreDefinitions +from .service import Service + + +class ScoreExecution(Service): + """ + The Score Execution API is used to produce a score by + executing the mapped code generated by score objects using the score definition. + + See Also + -------- + `REST Documentation ` + + """ + + _SERVICE_ROOT = "/scoreExecution" + _cas_management = CASManagement() + _score_definitions = ScoreDefinitions() + _services = Service() + + ( + list_executions, + get_execution, + update_execution, + delete_execution, + ) = Service._crud_funcs("/executions", "execution") + + @classmethod + def create_score_execution( + cls, + score_definition_id: str, + description: str = "", + output_server_name: str = "cas-shared-default", + output_library_name: str = "Public", + output_table_name: str = "", + ): + """Creates the score definition service. + + Parameters + -------- + score_definition_id: str + A score definition id representing score definition existing on the server that needs to be executed. + description: str, optional + Description of score execution. Defaults to an empty string. + output_server_name: str, optional + The name of the output server the output table and output library is stored in. Defaults to "cas-shared-default". + output_library_name: str, optional + The name of the output library the output table is stored in. Defaults to "Public". + output_table_name: str, optional + The name of the output table the score execution or analysis output will be stored in. Defaults to an empty string. + + Returns + ------- + RestObj + + """ + + # Gets information about the scoring object from the score definition + score_definition = cls._score_definitions.get_definition(score_definition_id) + if not score_definition: + raise HTTPError + score_exec_name = score_definition.get("name") + model_uuid = score_definition.get("objectDescriptor").get("uri").split("/")[-1] + model_uri = f"/modelManagement/models/{model_uuid}" + model_name = score_definition.get("objectDescriptor").get("name") + model_input_library = score_definition.get("inputData").get("libraryName") + model_table_name = score_definition.get("inputData").get("tableName") + + # Defining a default output table name if none is provided + if not output_table_name: + output_table_name = f"{model_name}_{score_definition_id}" + + headers_score_exec = {"Content-Type": "application/json"} + + create_score_exec = { + "name": f"{score_exec_name}", + "description": description, + "hints": { + "objectURI": f"{model_uri}", + "inputTableName": f"{model_table_name}", + "inputLibraryName": f"{model_input_library}", + }, + "scoreDefinitionId": f"{score_definition_id}", + "outputTable": { + "tableName": output_table_name, + "libraryName": output_library_name, + "serverName": output_server_name, + }, + } + + # Creating the score execution + score_execution = cls.post( + "executions", + data=json.dumps(create_score_exec), + headers=headers_score_exec, + ) + + return score_execution + + @classmethod + def poll_score_execution_state( + cls, score_execution: Union[dict, str], timeout: int = 300 + ): + """Checks the state of the score execution. + + Parameters + -------- + score_execution: str or dict + A running score_execution. + timeout: int + Time limit for checking the score_execution state. + + Returns + ------- + String + + """ + if type(score_execution) is str: + exec_id = score_execution + else: + exec_id = score_execution.get("id") + + start_poll = time.time() + while time.time() - start_poll < timeout: + score_execution_state = cls.get(f"executions/{exec_id}/state") + if score_execution_state == "completed": + print("Score execution state is 'completed'") + return "completed" + elif score_execution_state == "failed": + # TODO: Grab score execution logs and return those + print("The score execution state is failed.") + return "failed" + elif time.time() - start_poll > timeout: + print("The score execution is still running, but polling time ran out.") + return "timeout" + + @classmethod + def get_score_execution_results( + cls, + score_execution: Union[dict, str], + ): + """Generates an output table for the score_execution results. + + Parameters + -------- + score_execution: str or dict + A running score_execution. + + Returns + ------- + Table reference + + """ + try: + import swat + except ImportError: + swat = None + + if type(score_execution) is str: + score_execution = cls.get_execution(score_execution) + + server_name = score_execution.get("outputTable").get("serverName") + library_name = score_execution.get("outputTable").get("libraryName") + table_name = score_execution.get("outputTable").get("tableName") + + # If swat is not available, then + if not swat: + output_table = cls._no_gateway_get_results( + server_name, library_name, table_name + ) + return output_table + else: + session = current_session() + cas = session.as_swat() + response = cas.loadActionSet("gateway") + if not response: + output_table = cls._no_gateway_get_results( + server_name, library_name, table_name + ) + return output_table + else: + gateway_code = f""" +import pandas as pd +import numpy as np + +table = gateway.read_table({{"caslib": "{library_name}", "name": "{table_name}"}}) + +gateway.return_table("Execution Results", df = table, label = "label", title = "title")""" + + output_table = cas.gateway.runlang( + code=gateway_code, single=True, timeout_millis=10000 + ) + output_table = pd.DataFrame(output_table["Execution Results"]) + return output_table + + @classmethod + def _no_gateway_get_results(cls, server_name, library_name, table_name): + """Helper method that builds the output table. + + Parameters + -------- + server_name: str + CAS server where original table is stored. + library_name: CAS library where original table is stored. + table_name: Table that contains row and columns information to build the output table + + Returns + ------- + Pandas Dataframe + + """ + if pd.__version__ >= StrictVersion("1.0.3"): + from pandas import json_normalize + else: + from pandas.io.json import json_normalize + + warnings.warn( + "Without swat installed, the amount of rows from the output table that " + "can be collected are memory limited by the CAS worker." + ) + + output_columns = cls._cas_management.get( + f"servers/{server_name}/" + f"caslibs/{library_name}/" + f"tables/{table_name}/columns?limit=10000" + ) + columns = json_normalize(output_columns.json(), "items") + column_names = columns["names"].to_list() + + output_rows = cls._services.get( + f"casRowSets/servers/{server_name}" + f"caslibs/{library_name}" + f"tables/{table_name}/rows?limit=10000" + ) + output_table = pd.DataFrame( + json_normalize(output_rows.json()["items"])["cells"].to_list(), + columns=column_names, + ) + return output_table diff --git a/src/sasctl/services.py b/src/sasctl/services.py index 6b93c80e..38e67bdb 100644 --- a/src/sasctl/services.py +++ b/src/sasctl/services.py @@ -21,6 +21,8 @@ from ._services.report_images import ReportImages as report_images from ._services.reports import Reports as reports from ._services.saslogon import SASLogon as saslogon +from ._services.score_definitions import ScoreDefinitions as score_definitions +from ._services.score_execution import ScoreExecution as score_execution from ._services.sentiment_analysis import SentimentAnalysis as sentiment_analysis from ._services.text_categorization import TextCategorization as text_categorization from ._services.text_parsing import TextParsing as text_parsing diff --git a/src/sasctl/tasks.py b/src/sasctl/tasks.py index 5324a06b..1da7d3fb 100644 --- a/src/sasctl/tasks.py +++ b/src/sasctl/tasks.py @@ -12,6 +12,8 @@ import os import re import sys +from pathlib import Path +from typing import Union from warnings import warn import pandas as pd @@ -29,7 +31,10 @@ from .services import model_management as mm from .services import model_publish as mp from .services import model_repository as mr - +from .services import score_definitions as sd +from .services import score_execution as se +from .utils.misc import installed_packages +from .utils.pymas import from_pickle logger = logging.getLogger(__name__) @@ -964,3 +969,32 @@ def get_project_kpis( kpiTableDf = kpiTableDf.apply(lambda x: x.str.strip()).replace([".", ""], None) return kpiTableDf + + +def score_model_with_cas( + score_def_name: str, + model: Union[str, dict], + table_name: str, + table_file: Union[str, Path] = None, + description: str = "", + server_name: str = "cas-shared-default", + library_name: str = "Public", + model_version: str = "latest", + use_cas_gateway: bool = False, +): + score_definition = sd.create_score_definition( + score_def_name, + model, + table_name, + table_file=table_file, + description=description, + server_name=server_name, + library_name=library_name, + model_version=model_version, + use_cas_gateway=use_cas_gateway, + ) + score_execution = se.create_score_execution(score_definition.id) + score_execution_poll = se.poll_score_execution_state(score_execution) + print(score_execution_poll) + score_results = se.get_score_execution_results(score_execution) + return score_results diff --git a/tests/unit/test_score_definitions.py b/tests/unit/test_score_definitions.py new file mode 100644 index 00000000..8a75294a --- /dev/null +++ b/tests/unit/test_score_definitions.py @@ -0,0 +1,158 @@ +#!/usr/bin/env python +# encoding: utf-8 +# +# Copyright © 2024, SAS Institute Inc., Cary, NC, USA. All Rights Reserved. +# SPDX-License-Identifier: Apache-2.0 + +import json +import os +import pickle +import random +import tempfile +import unittest +import copy +from pathlib import Path +from unittest import mock + +import numpy as np +import pandas as pd +import pytest +from requests import HTTPError + +import sasctl.pzmm as pzmm +from sasctl import current_session +from sasctl.core import RestObj, VersionInfo, request +from sasctl._services.score_definitions import ScoreDefinitions as sd + + +class CustomMock: + def __init__(self, json_info, id=""): + self.json_info = json_info + self.id = id + + def get(self, key1, key2=None, key3=None): + if key2 is None and key3 is None: + return self.json_info[key1] + else: + return self.json_info[key1][key2][key3] + + +def test_create_score_definition(): + """ + Test Cases: + - Valid model id with input mapping and valid table name + - Invalid model id + - Valid table name without input mapping + - Invalid table name with invalid file + - Invalid table name with valid file + - Invalid table name without table file argument + + """ + # Mocking a session to allow the post call to go through + with mock.patch("sasctl.core.Session._get_authorization_token"): + current_session("example.com", "username", "password") + + # Mocking the REST API calls and methods + with mock.patch( + "sasctl._services.model_repository.ModelRepository.get_model" + ) as get_model: + with mock.patch( + "sasctl._services.cas_management.CASManagement.get_table" + ) as get_table: + with mock.patch( + "sasctl._services.cas_management.CASManagement.upload_file" + ) as upload_file: + with mock.patch( + "sasctl._services.score_definitions.ScoreDefinitions.post" + ) as post: + # Invalid model id test case + get_model.return_value = None + with pytest.raises(HTTPError): + sd.create_score_definition( + score_def_name="test_create_sd", + model="12345", + table_name="test_table", + ) + + # Valid model id but invalid table name with no table_file argument test case + get_model_mock = CustomMock( + json_info={ + "id": "12345", + "projectId": "54321", + "projectVersionId": "67890", + "name": "test_model", + }, + ) + get_model.return_value = get_model_mock + get_table.return_value = None + with pytest.raises(HTTPError): + sd.create_score_definition( + score_def_name="test_create_sd", + model="12345", + table_name="test_table", + ) + + # Invalid table name with a table_file argument that doesn't work test case + get_table.return_value = None + upload_file.return_value = None + get_table.return_value = None + with pytest.raises(HTTPError): + sd.create_score_definition( + score_def_name="test_create_sd", + model="12345", + table_name="test_table", + table_file="test_path", + ) + + # Valid table_file argument that successfully creates a table test case + get_table.return_value = None + upload_file.return_value = RestObj + get_table_mock = CustomMock( + json_info={"tableName": "test_table"}, + ) + get_table.return_value = get_table_mock + response = sd.create_score_definition( + score_def_name="test_create_sd", + model="12345", + table_name="test_table", + table_file="test_path", + ) + assert response + + # Valid table_name argument test case + get_table.return_value = get_table_mock + response = sd.create_score_definition( + score_def_name="test_create_sd", + model="12345", + table_name="test_table", + table_file="test_path", + ) + assert response + + # Checking response with inputVariables in model elements + get_model_mock = CustomMock( + json_info={ + "id": "12345", + "projectId": "54321", + "projectVersionId": "67890", + "name": "test_model", + "inputVariables": [ + {"name": "first"}, + {"name": "second"}, + {"name": "third"}, + ], + }, + ) + get_model.return_value = get_model_mock + get_table.return_value = get_table_mock + response = sd.create_score_definition( + score_def_name="test_create_sd", + model="12345", + table_name="test_table", + ) + assert response + assert post.call_count == 3 + + data = post.call_args + json_data = json.loads(data.kwargs["data"]) + assert json_data["mappings"] != [] diff --git a/tests/unit/test_score_execution.py b/tests/unit/test_score_execution.py new file mode 100644 index 00000000..f0090155 --- /dev/null +++ b/tests/unit/test_score_execution.py @@ -0,0 +1,111 @@ +#!/usr/bin/env python +# encoding: utf-8 +# +# Copyright © 2024, SAS Institute Inc., Cary, NC, USA. All Rights Reserved. +# SPDX-License-Identifier: Apache-2.0 + +import json +import os +import pickle +import random +import tempfile +import unittest +import copy +from pathlib import Path +from unittest import mock +from unittest.mock import MagicMock + +import numpy as np +import pandas as pd +import pytest +from requests import HTTPError + +import sasctl.pzmm as pzmm +from sasctl import current_session +from sasctl.core import RestObj, VersionInfo +from sasctl._services.score_definitions import ScoreDefinitions as sd +from sasctl._services.score_execution import ScoreExecution as se + + +# Creating a CustomMock for list_executions to avoid a TypeError when comparing status code from mock with >= 400 in score_execution +class CustomMock: + def __init__(self, json_info): + self.json_info = json_info + + def get(self, key1, key2=None, key3=None): + if key2 is None and key3 is None: + return self.json_info[key1] + elif key2 and not key3: + return self.json_info[key1][key2] + else: + return self.json_info[key1][key2][key3] + + +def test_create_score_execution(): + """ + Test Cases: + - Valid score definition id and invalid list_executions argument + - Invalid score definition id + - Valid list_executions argument with execution already running but invalid delete_execution argument + - Valid list_executions argument with execution already running but valid delete_execution argument + - Valid list_executions argument without execution already running + - With output table specified within create_score_execution arguments + + """ + + # Mocking a session to allow the post call to go through + with mock.patch("sasctl.core.Session._get_authorization_token"): + current_session("example.com", "username", "password") + # Mocking what a successful score execution post would be if the user specified an output table + TARGET = {"outputTable": {"tableName": "example_table"}} + # Creating a target to compare post call responses to + target = copy.deepcopy(TARGET) + + # Mocking the REST API calls and functions + with mock.patch( + "sasctl._services.score_definitions.ScoreDefinitions.get_definition" + ) as get_definition: + with mock.patch("sasctl._services.score_execution.ScoreExecution.post") as post: + # Invalid score definition id test case + get_definition.return_value = None + with pytest.raises(HTTPError): + se.create_score_execution(score_definition_id="12345") + + # Valid score definition id and invalid list_executions argument test case + get_definition_mock = CustomMock( + json_info={ + "inputData": { + "libraryName": "cas-shared-default", + "tableName": "test_table", + }, + "name": "score_def_name", + "objectDescriptor": { + "name": "test_model", + "type": "sas.publish.example", + "uri": "/modelPublish/models/example", + }, + }, + ) + get_definition.return_value = get_definition_mock + response = se.create_score_execution(score_definition_id="3456") + assert response + + # Checking whether the output table name remained the default empty string or the default changed as writted in score_execution + data = post.call_args + json_data = json.loads(data.kwargs["data"]) + assert json_data["outputTable"]["tableName"] != "" + + # With output table specified within create_score_execution arguments test case + response = se.create_score_execution( + score_definition_id="12345", output_table_name="example_table" + ) + assert response + assert post.call_count == 2 + + # Checking whether specified output table name or the default output table name is in the response + data = post.call_args + json_data = json.loads(data.kwargs["data"]) + assert ( + target["outputTable"]["tableName"] + == json_data["outputTable"]["tableName"] + )