fix MM project properties so performance UI capabilities work

Author: Cloud User

src/sasctl/_services/model_management.py

@@ -174,11 +174,16 @@ def create_performance_definition(cls,
 
         # Performance data cannot be captured unless certain project properties
         # have been configured.
-        for required in ['targetVariable', 'targetLevel',
-                         'predictionVariable']:
+        for required in ['targetVariable', 'targetLevel']:
             if getattr(project, required, None) is None:
                 raise ValueError("Project %s must have the '%s' property set."
                                  % (project.name, required))
+        if project['function'] == 'classification' and project['eventProbabilityVariable'] == None:
+            raise ValueError("Project %s must have the 'eventProbabilityVariable' property set."
+                                 % (project.name))
+        if project['function'] == 'prediction' and project['predictionVariable'] == None:
+            raise ValueError("Project %s must have the 'predictionVariable' property set."
+                                 % (project.name))
 
         request = {'projectId': project.id,
                    'name': name or model.name + ' Performance',

src/sasctl/tasks.py

@@ -49,8 +49,8 @@ def _sklearn_to_dict(model):
                 'RandomForestClassifier': 'Forest',
                 'DecisionTreeClassifier': 'Decision tree',
                 'DecisionTreeRegressor': 'Decision tree',
-                'classifier': 'Classification',
-                'regressor': 'Prediction'}
+                'classifier': 'classification',
+                'regressor': 'prediction'}
 
     if hasattr(model, '_final_estimator'):
         estimator = type(model._final_estimator)
@@ -302,17 +302,27 @@ def get_version(x):
         else:
             prediction_variable = None
 
-        project = mr.create_project(project, repo_obj,
+        # As of Viya 3.4 the 'predictionVariable' parameter is not set during
+        # project creation.  Update the project if necessary.
+        if model.get('function') == 'prediction':   #Predications require predictionVariable
+            project = mr.create_project(project, repo_obj,
                                     variables=vars,
                                     function=model.get('function'),
                                     targetLevel=target_level,
                                     predictionVariable=prediction_variable)
 
-        # As of Viya 3.4 the 'predictionVariable' parameter is not set during
-        # project creation.  Update the project if necessary.
-        if project.get('predictionVariable') != prediction_variable:
-            project['predictionVariable'] = prediction_variable
-            mr.update_project(project)
+            if project.get('predictionVariable') != prediction_variable:
+                project['predictionVariable'] = prediction_variable
+                mr.update_project(project)
+        else:  #Classifications require eventProbabilityVariable 
+            project = mr.create_project(project, repo_obj,
+                                    variables=vars,
+                                    function=model.get('function'),
+                                    targetLevel=target_level,
+                                    eventProbabilityVariable=prediction_variable)
+            if project.get('eventProbabilityVariable') != prediction_variable:
+                project['eventProbabilityVariable'] = prediction_variable
+                mr.update_project(project)
 
     model = mr.create_model(model, project)
 
@@ -506,9 +516,12 @@ def update_model_performance(data, model, label, refresh=True):
                          "regression and binary classification projects.  "
                          "Received project with '%s' target level.  Should be "
                          "'Interval' or 'Binary'.", project.get('targetLevel'))
-    elif project.get('predictionVariable', '') == '':
+    elif project.get('predictionVariable', '') == '' and project.get('function', '').lower() == 'prediction':
         raise ValueError("Project '%s' does not have a prediction variable "
                          "specified." % project)
+    elif project.get('eventProbabilityVariable', '') == '' and project.get('function', '').lower() == 'classification':
+        raise ValueError("Project '%s' does not have an Event Probability variable "
+                         "specified." % project)
 
     # Find the performance definition for the model
     # As of Viya 3.4, no way to search by model or project

tests/unit/test_model_management.py

@@ -50,13 +50,20 @@ def test_create_performance_definition():
                 with pytest.raises(ValueError):
                     # Project missing some required properties
                     get_project.return_value = copy.deepcopy(PROJECT)
-                    get_project.return_value['predictionVariable'] = 'predicted'
+                    get_project.return_value['function'] = 'classification'
+                    _ = mm.create_performance_definition('model', 'TestLibrary', 'TestData')
+
+                with pytest.raises(ValueError):
+                    # Project missing some required properties
+                    get_project.return_value = copy.deepcopy(PROJECT)
+                    get_project.return_value['function'] = 'prediction'
                     _ = mm.create_performance_definition('model', 'TestLibrary', 'TestData')
 
                 get_project.return_value = copy.deepcopy(PROJECT)
                 get_project.return_value['targetVariable'] = 'target'
                 get_project.return_value['targetLevel'] = 'interval'
                 get_project.return_value['predictionVariable'] = 'predicted'
+                get_project.return_value['function'] = 'prediction'
                 _ = mm.create_performance_definition('model', 'TestLibrary',
                                                      'TestData',
                                                      max_bins=3,

tests/unit/test_tasks.py

@@ -22,27 +22,27 @@ def test_sklearn_metadata():
 
     info = _sklearn_to_dict(LinearRegression())
     assert info['algorithm'] == 'Linear regression'
-    assert info['function'] == 'Prediction'
+    assert info['function'] == 'prediction'
 
     info = _sklearn_to_dict(LogisticRegression())
     assert info['algorithm'] == 'Logistic regression'
-    assert info['function'] == 'Classification'
+    assert info['function'] == 'classification'
 
     info = _sklearn_to_dict(SVC())
     assert info['algorithm'] == 'Support vector machine'
-    assert info['function'] == 'Classification'
+    assert info['function'] == 'classification'
 
     info = _sklearn_to_dict(GradientBoostingClassifier())
     assert info['algorithm'] == 'Gradient boosting'
-    assert info['function'] == 'Classification'
+    assert info['function'] == 'classification'
 
     info = _sklearn_to_dict(DecisionTreeClassifier())
     assert info['algorithm'] == 'Decision tree'
-    assert info['function'] == 'Classification'
+    assert info['function'] == 'classification'
 
     info = _sklearn_to_dict(RandomForestClassifier())
     assert info['algorithm'] == 'Forest'
-    assert info['function'] == 'Classification'
+    assert info['function'] == 'classification'
 
 
 def test_parse_module_url():
@@ -96,6 +96,13 @@ def test_save_performance_project_types():
                 project.return_value = {'function': 'Prediction',
                                         'targetLevel': 'Binary'}
                 update_model_performance(None, None, None)
+            
+            # Classification variable required
+            with pytest.raises(ValueError):
+                project.return_value = {'function': 'classification',
+                                        'targetLevel': 'Binary'}
+                update_model_performance(None, None, None)
+
 
     # Check projects w/ invalid properties