updated formatting

Author: jlwalke2

src/sasctl/services/model_repository.py

@@ -116,23 +116,28 @@ def get_model_contents(model):
         return get(link.get('href'), headers={'Accept': link.get('itemType', '*/*')})
 
 
-def create_model(model, project, description=None, modeler=None, function=None, algorithm=None, tool=None,
-                 is_champion=False,
-                 properties={}, **kwargs):
+def create_model(model, project, description=None, modeler=None, function=None,
+                 algorithm=None, tool=None, is_champion=False, properties={},
+                 **kwargs):
     """Creates a model into a project or folder.
 
     Parameters
     ----------
     model : str or dict
-        The name or id of the model, or a dictionary representation of the model.
+        The name or id of the model, or a dictionary representation of the
+        model.
     project : str or dict
-        The name or id of the model project, or a dictionary representation of the model project.
+        The name or id of the model project, or a dictionary representation of
+        the model project.
     description : str, optional
         The description of the model.
     modeler : str, optional
-        Name of the user that created the model.  Current user name will be used if unspecified.
+        Name of the user that created the model.  Current user name will be
+        used if unspecified.
     function : str, optional
-        The function of the model, valid values include: analytical, classification, cluster, forecasting, prediction, Text analytics, transformation.
+        The function of the model, valid values include: analytical,
+        classification, cluster, forecasting, prediction, Text analytics,
+        transformation.
     algorithm : str, optional
         The name of the model algorithm.
     tool : str, optional
@@ -148,7 +153,8 @@ def create_model(model, project, description=None, modeler=None, function=None,
     champion : bool, optional
         Indicates whether the project has champion model or not.
     role : str, optional
-        The role of the model, valid values include: plain, champion, challenger.
+        The role of the model, valid values include: plain, champion,
+        challenger.
     location : str, optional,
         The location of this model.
     targetVariable : str, optional
@@ -165,10 +171,11 @@ def create_model(model, project, description=None, modeler=None, function=None,
         Custom model properties that can be set: name, value, type
 
     inputVariables : array_like, optional
-        Model input variables. By default, these are the same as the model project.
+        Model input variables. By default, these are the same as the model
+        project.
     outputVariables : array_like, optional
-        Model output variables. By default, these are the same as the model project.
-
+        Model output variables. By default, these are the same as the model
+        project.
 
     Returns
     -------

src/sasctl/tasks.py

@@ -119,7 +119,8 @@ def register_model(model, name, project, repository=None, input=None, version='l
         model = mr.import_model_from_zip(name, project, zipfile, version=version)
         return model
 
-    # If the model is an scikit-learn model, generate the model dictionary from it and pickle the model for storage
+    # If the model is an scikit-learn model, generate the model dictionary
+    # from it and pickle the model for storage
     elif all(hasattr(model, attr) for attr in ['_estimator_type', 'get_params']):
         # Pickle the model so we can store it
         model_pkl = pickle.dumps(model)
@@ -135,23 +136,36 @@ def register_model(model, name, project, repository=None, input=None, version='l
             assert isinstance(mas_module, PyMAS)
 
             # Include score code files from ESP and MAS
-            files.append({'name': 'dmcas_packagescorecode.sas', 'file': mas_module.score_code(), 'role': 'Score Code'})
-            files.append({'name': 'dmcas_espscorecode.sas', 'file': mas_module.score_code(dest='ESP'), 'role': 'Score Code'})
-
-            model['inputVariables'] = [var.as_model_metadata() for var in mas_module.variables if not var.out]
-            model['outputVariables'] = [var.as_model_metadata() for var in mas_module.variables if var.out]
+            files.append({'name': 'dmcas_packagescorecode.sas',
+                          'file': mas_module.score_code(),
+                          'role': 'Score Code'})
+            files.append({'name': 'dmcas_espscorecode.sas',
+                          'file': mas_module.score_code(dest='ESP'),
+                          'role': 'Score Code'})
+
+            model['inputVariables'] = [var.as_model_metadata()
+                                       for var in mas_module.variables
+                                       if not var.out]
+
+            model['outputVariables'] = [var.as_model_metadata()
+                                        for var in mas_module.variables
+                                        if var.out
+                                        and var.name not in ('rc', 'msg')]
         except ValueError:
-            # PyMAS creation failed, most likely because input data wasn't provided
-            warnings.warn('Unable to determine input/output variables.  Model variables will not be specified.')
-
+            # PyMAS creation failed, most likely because input data wasn't
+            # provided
+            warnings.warn('Unable to determine input/output variables. '
+                          ' Model variables will not be specified.')
     else:
         # Otherwise, the model better be a dictionary of metadata
         assert isinstance(model, dict)
 
     if create_project:
         vars = model.get('inputVariables', []) + model.get('outputVariables', [])
         target_level = 'Interval' if model.get('function') == 'Regression' else None
-        project = mr.create_project(project, repository, variables=vars, targetLevel=target_level)
+        project = mr.create_project(project, repository,
+                                    variables=vars,
+                                    targetLevel=target_level)
 
     model = mr.create_model(model, project)