Merge pull request #161 from sassoftware/1.9.1

v1.9.1

Author: smlindauer

examples/data/hmeqModels/H2OBinaryGLM/glmFit.pickle

@@ -13,6 +13,6 @@
     },
     {
         "role": "scoreResource",
-        "name": "glmfit.pickle"
+        "name": "glmfit.mojo"
     }
 ]

examples/data/hmeqModels/H2OBinaryGLM/glmfit_binary.zip

@@ -13,18 +13,15 @@
 
 h2o.init()
 
-with gzip.open(Path(settings.pickle_path) / "{model_file_name}", "r") as fileIn, open(Path(settings.pickle_path) / "glmfit.zip", "wb") as fileOut:
-    shutil.copyfileobj(fileIn, fileOut)
-os.chmod(Path(settings.pickle_path) / "glmfit.zip", 0o777)
-model = h2o.import_mojo(Path(settings.pickle_path) / "glmfit.zip")
+model = h2o.import_mojo(Path(settings.pickle_path))
 
 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(Path(settings.pickle_path) / "glmfit.zip")
+        model = h2o.import_mojo(Path(settings.pickle_path))
 
     try:
         if math.isnan(LOAN):

examples/data/hmeqModels/H2OBinaryGLM/glmfit_mojo.zip

@@ -648,7 +648,11 @@
     "\n",
     "# Serialize the models to a pickle format\n",
     "for (mod, prefix, path) in zip(model, model_prefix, zip_folder):\n",
-    "    pzmm.PickleModel.pickle_trained_model(trained_model=mod, model_prefix=prefix, pickle_path=path)"
+    "    pzmm.PickleModel.pickle_trained_model(\n",
+    "        model_prefix=prefix,\n",
+    "        trained_model=mod,\n",
+    "        pickle_path=path\n",
+    "    )"
    ]
   },
   {