Add updated model content for H2O model example.

Author: smlindauer

examples/data/hmeqModels/H2OBinaryGLM/glmFit.pickle

@@ -1,12 +1,12 @@
 [
     {
         "name": "EM_CLASSIFICATION",
-        "level": "interval",
-        "type": "decimal",
-        "length": 8
+        "level": "nominal",
+        "type": "string",
+        "length": 1
     },
     {
-        "name": "EM_PROBABILITY",
+        "name": "EM_EVENTPROBABILITY",
         "level": "interval",
         "type": "decimal",
         "length": 8

examples/data/hmeqModels/H2OBinaryGLM/glmfit_binary.zip

@@ -4,26 +4,22 @@
 import numpy as np
 from pathlib import Path
 
-import settings
-
 import h2o
 import gzip
 import shutil
 import os
 
 h2o.init()
 
-model = h2o.load(str(Path(settings.pickle_path) / glmfit.pickle))
+model = h2o.load(str(Path("/models/resources/viya/b3b0fcef-81d8-4da1-a78d-8284dbb590a8/glmfit.pickle")))
 
 def score(LOAN, MORTDUE, VALUE, REASON, JOB, YOJ, DEROG, DELINQ, CLAGE, NINQ, CLNO, DEBTINC):
     "Output: EM_CLASSIFICATION, EM_EVENTPROBABILITY"
 
     try:
         global model
     except NameError:
-        model = h2o.load(str(Path(settings.pickle_path) / glmfit.pickle))
-
-
+        model = h2o.load(str(Path("/models/resources/viya/b3b0fcef-81d8-4da1-a78d-8284dbb590a8/glmfit.pickle")))
 
     try:
         if math.isnan(LOAN):
@@ -88,7 +84,7 @@ def score(LOAN, MORTDUE, VALUE, REASON, JOB, YOJ, DEROG, DELINQ, CLAGE, NINQ, CL
                                columns=["LOAN", "MORTDUE", "VALUE", "REASON", "JOB", "YOJ", "DEROG", "DELINQ", "CLAGE", "NINQ", "CLNO", "DEBTINC"],
                                dtype=float,
                                index=[0])
-    column_types = ['"LOAN" : "numeric"', '"MORTDUE" : "numeric"', '"VALUE" : "numeric"', '"REASON" : "string"', '"JOB" : "string"', '"YOJ" : "numeric"', '"DEROG" : "numeric"', '"DELINQ" : "numeric"', '"CLAGE" : "numeric"', '"NINQ" : "numeric"', '"CLNO" : "numeric"', '"DEBTINC" : "numeric"']
+    column_types = {"LOAN" : "numeric", "MORTDUE" : "numeric", "VALUE" : "numeric", "REASON" : "string", "JOB" : "string", "YOJ" : "numeric", "DEROG" : "numeric", "DELINQ" : "numeric", "CLAGE" : "numeric", "NINQ" : "numeric", "CLNO" : "numeric", "DEBTINC" : "numeric"}
     h2o_array = h2o.H2OFrame(input_array, column_types=column_types)
     prediction = model.predict(h2o_array)
     prediction = h2o.as_list(prediction, use_pandas=False)

examples/data/hmeqModels/H2OBinaryGLM/outputVar.json

@@ -1,12 +1,12 @@
 [
     {
         "name": "EM_CLASSIFICATION",
-        "level": "interval",
-        "type": "decimal",
-        "length": 8
+        "level": "nominal",
+        "type": "string",
+        "length": 1
     },
     {
-        "name": "EM_PROBABILITY",
+        "name": "EM_EVENTPROBABILITY",
         "level": "interval",
         "type": "decimal",
         "length": 8

examples/data/hmeqModels/H2OBinaryGLM/score_glmfit_binary.py

@@ -4,26 +4,22 @@
 import numpy as np
 from pathlib import Path
 
-import settings
-
 import h2o
 import gzip
 import shutil
 import os
 
 h2o.init()
 
-model = h2o.import_mojo(str(Path(settings.pickle_path) / glmfit.mojo))
+model = h2o.import_mojo(str(Path("/models/resources/viya/1758598b-cff6-4993-9ac2-8199d4ec6564/glmfit.mojo")))
 
 def score(LOAN, MORTDUE, VALUE, REASON, JOB, YOJ, DEROG, DELINQ, CLAGE, NINQ, CLNO, DEBTINC):
     "Output: EM_CLASSIFICATION, EM_EVENTPROBABILITY"
 
     try:
         global model
     except NameError:
-        model = h2o.import_mojo(str(Path(settings.pickle_path) / glmfit.mojo))
-
-
+        model = h2o.import_mojo(str(Path("/models/resources/viya/1758598b-cff6-4993-9ac2-8199d4ec6564/glmfit.mojo")))
 
     try:
         if math.isnan(LOAN):
@@ -88,7 +84,7 @@ def score(LOAN, MORTDUE, VALUE, REASON, JOB, YOJ, DEROG, DELINQ, CLAGE, NINQ, CL
                                columns=["LOAN", "MORTDUE", "VALUE", "REASON", "JOB", "YOJ", "DEROG", "DELINQ", "CLAGE", "NINQ", "CLNO", "DEBTINC"],
                                dtype=float,
                                index=[0])
-    column_types = ['"LOAN" : "numeric"', '"MORTDUE" : "numeric"', '"VALUE" : "numeric"', '"REASON" : "string"', '"JOB" : "string"', '"YOJ" : "numeric"', '"DEROG" : "numeric"', '"DELINQ" : "numeric"', '"CLAGE" : "numeric"', '"NINQ" : "numeric"', '"CLNO" : "numeric"', '"DEBTINC" : "numeric"']
+    column_types = {"LOAN" : "numeric", "MORTDUE" : "numeric", "VALUE" : "numeric", "REASON" : "string", "JOB" : "string", "YOJ" : "numeric", "DEROG" : "numeric", "DELINQ" : "numeric", "CLAGE" : "numeric", "NINQ" : "numeric", "CLNO" : "numeric", "DEBTINC" : "numeric"}
     h2o_array = h2o.H2OFrame(input_array, column_types=column_types)
     prediction = model.predict(h2o_array)
     prediction = h2o.as_list(prediction, use_pandas=False)