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smlindauersmlindauer
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Update H2O binary model example
1 parent 9bf6282 commit ab6387b

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6 files changed

+227
-84
lines changed

6 files changed

+227
-84
lines changed

‎examples/data/hmeqModels/H2OBinaryGLM/dmcas_epscorecode.sas

Lines changed: 5 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -11,7 +11,7 @@ method score(double LOAN, double MORTDUE, double VALUE, varchar(100) REASON, var
1111
resultCode = revision = 0;
1212
if null(pm) then do;
1313
pm = _new_ pymas();
14-
resultCode = pm.useModule('model_exec_abdc9fb5-243b-4979-b244-9c698579af04', 1);
14+
resultCode = pm.useModule('model_exec_0f38bb00-011c-414e-991b-822e1e1460f7', 1);
1515
if resultCode then do;
1616
resultCode = pm.appendSrcLine('import h2o');
1717
resultCode = pm.appendSrcLine('import gzip, shutil, os');
@@ -21,21 +21,18 @@ method score(double LOAN, double MORTDUE, double VALUE, varchar(100) REASON, var
2121
resultCode = pm.appendSrcLine('import pandas as pd');
2222
resultCode = pm.appendSrcLine('import numpy as np');
2323
resultCode = pm.appendSrcLine('');
24-
resultCode = pm.appendSrcLine('');
25-
resultCode = pm.appendSrcLine('global _thisModelFit');
26-
resultCode = pm.appendSrcLine('');
2724
resultCode = pm.appendSrcLine('h2o.init()');
2825
resultCode = pm.appendSrcLine('');
29-
resultCode = pm.appendSrcLine('_thisModelFit = h2o.load_model("/models/resources/viya/cd6cd3c5-174f-4f51-9f43-92a1d695b0e7/glmFit.pickle")');
26+
resultCode = pm.appendSrcLine('_thisModelFit = h2o.load_model("/models/resources/viya/e34d30a4-66dd-4648-ad75-c6e92f0b01f1/glmFit.pickle")');
3027
resultCode = pm.appendSrcLine('');
3128
resultCode = pm.appendSrcLine('def scoreglmFit(LOAN, MORTDUE, VALUE, REASON, JOB, YOJ, DEROG, DELINQ, CLAGE, NINQ, CLNO, DEBTINC):');
3229
resultCode = pm.appendSrcLine(' "Output: EM_EVENTPROBABILITY, EM_CLASSIFICATION"');
3330
resultCode = pm.appendSrcLine('');
3431
resultCode = pm.appendSrcLine(' try:');
35-
resultCode = pm.appendSrcLine(' _thisModelFit');
32+
resultCode = pm.appendSrcLine(' global _thisModelFit');
3633
resultCode = pm.appendSrcLine(' except NameError:');
3734
resultCode = pm.appendSrcLine('');
38-
resultCode = pm.appendSrcLine(' _thisModelFit = h2o.load_model("/models/resources/viya/cd6cd3c5-174f-4f51-9f43-92a1d695b0e7/glmFit.pickle")');
35+
resultCode = pm.appendSrcLine(' _thisModelFit = h2o.load_model("/models/resources/viya/e34d30a4-66dd-4648-ad75-c6e92f0b01f1/glmFit.pickle")');
3936
resultCode = pm.appendSrcLine('');
4037
resultCode = pm.appendSrcLine(' inputArray = pd.DataFrame([[LOAN, MORTDUE, VALUE, REASON, JOB, YOJ, DEROG, DELINQ, CLAGE, NINQ, CLNO, DEBTINC]],');
4138
resultCode = pm.appendSrcLine(' columns=["LOAN", "MORTDUE", "VALUE", "REASON", "JOB", "YOJ", "DEROG", "DELINQ", "CLAGE", "NINQ", "CLNO", "DEBTINC"],');
@@ -49,7 +46,7 @@ method score(double LOAN, double MORTDUE, double VALUE, varchar(100) REASON, var
4946
resultCode = pm.appendSrcLine(' EM_CLASSIFICATION = prediction[1][0]');
5047
resultCode = pm.appendSrcLine('');
5148
resultCode = pm.appendSrcLine(' return(EM_EVENTPROBABILITY, EM_CLASSIFICATION)');
52-
revision = pm.publish(pm.getSource(), 'model_exec_abdc9fb5-243b-4979-b244-9c698579af04');
49+
revision = pm.publish(pm.getSource(), 'model_exec_0f38bb00-011c-414e-991b-822e1e1460f7');
5350

5451
if ( revision < 1 ) then do;
5552
logr.log( 'e', 'py.publish() failed.');

‎examples/data/hmeqModels/H2OBinaryGLM/dmcas_packagescorecode.sas

Lines changed: 5 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -8,7 +8,7 @@ method score(double LOAN, double MORTDUE, double VALUE, varchar(100) REASON, var
88
resultCode = revision = 0;
99
if null(pm) then do;
1010
pm = _new_ pymas();
11-
resultCode = pm.useModule('model_exec_abdc9fb5-243b-4979-b244-9c698579af04', 1);
11+
resultCode = pm.useModule('model_exec_0f38bb00-011c-414e-991b-822e1e1460f7', 1);
1212
if resultCode then do;
1313
resultCode = pm.appendSrcLine('import h2o');
1414
resultCode = pm.appendSrcLine('import gzip, shutil, os');
@@ -18,21 +18,18 @@ method score(double LOAN, double MORTDUE, double VALUE, varchar(100) REASON, var
1818
resultCode = pm.appendSrcLine('import pandas as pd');
1919
resultCode = pm.appendSrcLine('import numpy as np');
2020
resultCode = pm.appendSrcLine('');
21-
resultCode = pm.appendSrcLine('');
22-
resultCode = pm.appendSrcLine('global _thisModelFit');
23-
resultCode = pm.appendSrcLine('');
2421
resultCode = pm.appendSrcLine('h2o.init()');
2522
resultCode = pm.appendSrcLine('');
26-
resultCode = pm.appendSrcLine('_thisModelFit = h2o.load_model("/models/resources/viya/cd6cd3c5-174f-4f51-9f43-92a1d695b0e7/glmFit.pickle")');
23+
resultCode = pm.appendSrcLine('_thisModelFit = h2o.load_model("/models/resources/viya/e34d30a4-66dd-4648-ad75-c6e92f0b01f1/glmFit.pickle")');
2724
resultCode = pm.appendSrcLine('');
2825
resultCode = pm.appendSrcLine('def scoreglmFit(LOAN, MORTDUE, VALUE, REASON, JOB, YOJ, DEROG, DELINQ, CLAGE, NINQ, CLNO, DEBTINC):');
2926
resultCode = pm.appendSrcLine(' "Output: EM_EVENTPROBABILITY, EM_CLASSIFICATION"');
3027
resultCode = pm.appendSrcLine('');
3128
resultCode = pm.appendSrcLine(' try:');
32-
resultCode = pm.appendSrcLine(' _thisModelFit');
29+
resultCode = pm.appendSrcLine(' global _thisModelFit');
3330
resultCode = pm.appendSrcLine(' except NameError:');
3431
resultCode = pm.appendSrcLine('');
35-
resultCode = pm.appendSrcLine(' _thisModelFit = h2o.load_model("/models/resources/viya/cd6cd3c5-174f-4f51-9f43-92a1d695b0e7/glmFit.pickle")');
32+
resultCode = pm.appendSrcLine(' _thisModelFit = h2o.load_model("/models/resources/viya/e34d30a4-66dd-4648-ad75-c6e92f0b01f1/glmFit.pickle")');
3633
resultCode = pm.appendSrcLine('');
3734
resultCode = pm.appendSrcLine(' inputArray = pd.DataFrame([[LOAN, MORTDUE, VALUE, REASON, JOB, YOJ, DEROG, DELINQ, CLAGE, NINQ, CLNO, DEBTINC]],');
3835
resultCode = pm.appendSrcLine(' columns=["LOAN", "MORTDUE", "VALUE", "REASON", "JOB", "YOJ", "DEROG", "DELINQ", "CLAGE", "NINQ", "CLNO", "DEBTINC"],');
@@ -46,7 +43,7 @@ method score(double LOAN, double MORTDUE, double VALUE, varchar(100) REASON, var
4643
resultCode = pm.appendSrcLine(' EM_CLASSIFICATION = prediction[1][0]');
4744
resultCode = pm.appendSrcLine('');
4845
resultCode = pm.appendSrcLine(' return(EM_EVENTPROBABILITY, EM_CLASSIFICATION)');
49-
revision = pm.publish(pm.getSource(), 'model_exec_abdc9fb5-243b-4979-b244-9c698579af04');
46+
revision = pm.publish(pm.getSource(), 'model_exec_0f38bb00-011c-414e-991b-822e1e1460f7');
5047

5148
if ( revision < 1 ) then do;
5249
logr.log( 'e', 'py.publish() failed.');

‎examples/data/hmeqModels/H2OBinaryGLM/glmFit.pickle

128 Bytes
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‎examples/data/hmeqModels/H2OBinaryGLM/glmFit.zip

128 Bytes
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‎examples/data/hmeqModels/H2OBinaryGLM/glmFitScore.py

Lines changed: 4 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -6,21 +6,18 @@
66
import pandas as pd
77
import numpy as np
88

9-
10-
global _thisModelFit
11-
129
h2o.init()
1310

14-
_thisModelFit = h2o.load_model('/models/resources/viya/cd6cd3c5-174f-4f51-9f43-92a1d695b0e7/glmFit.pickle')
11+
_thisModelFit = h2o.load_model('/models/resources/viya/e34d30a4-66dd-4648-ad75-c6e92f0b01f1/glmFit.pickle')
1512

1613
def scoreglmFit(LOAN, MORTDUE, VALUE, REASON, JOB, YOJ, DEROG, DELINQ, CLAGE, NINQ, CLNO, DEBTINC):
1714
"Output: EM_EVENTPROBABILITY, EM_CLASSIFICATION"
1815

1916
try:
20-
_thisModelFit
17+
global _thisModelFit
2118
except NameError:
2219

23-
_thisModelFit = h2o.load_model('/models/resources/viya/cd6cd3c5-174f-4f51-9f43-92a1d695b0e7/glmFit.pickle')
20+
_thisModelFit = h2o.load_model('/models/resources/viya/e34d30a4-66dd-4648-ad75-c6e92f0b01f1/glmFit.pickle')
2421

2522
inputArray = pd.DataFrame([[LOAN, MORTDUE, VALUE, REASON, JOB, YOJ, DEROG, DELINQ, CLAGE, NINQ, CLNO, DEBTINC]],
2623
columns=['LOAN', 'MORTDUE', 'VALUE', 'REASON', 'JOB', 'YOJ', 'DEROG', 'DELINQ', 'CLAGE', 'NINQ', 'CLNO', 'DEBTINC'],
@@ -33,4 +30,4 @@ def scoreglmFit(LOAN, MORTDUE, VALUE, REASON, JOB, YOJ, DEROG, DELINQ, CLAGE, NI
3330
EM_EVENTPROBABILITY = float(prediction[1][2])
3431
EM_CLASSIFICATION = prediction[1][0]
3532

36-
return(EM_EVENTPROBABILITY, EM_CLASSIFICATION)
33+
return(EM_EVENTPROBABILITY, EM_CLASSIFICATION)

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