Merge pull request #353 from edx/will/refactor-training-api-call

Will/refactor training api call

apps/openassessment/assessment/api/ai.py

@@ -8,7 +8,9 @@ from openassessment.assessment.serializers import (
 from openassessment.assessment.errors import (
     AITrainingRequestError, AITrainingInternalError
 )
-from openassessment.assessment.models import AITrainingWorkflow, InvalidOptionSelection
+from openassessment.assessment.models import (
+    AITrainingWorkflow, InvalidOptionSelection, NoTrainingExamples
+)
 from openassessment.assessment.worker import training as training_tasks
 
 
@@ -89,6 +91,8 @@ def train_classifiers(rubric_dict, examples, algorithm_id):
     # Create the workflow model
     try:
         workflow = AITrainingWorkflow.start_workflow(examples, algorithm_id)
+    except NoTrainingExamples as ex:
+        raise AITrainingRequestError(ex)
     except:
         msg = (
             u"An unexpected error occurred while creating "

apps/openassessment/assessment/api/ai_worker.py

@@ -7,7 +7,7 @@ from django.db import DatabaseError
 from openassessment.assessment.models import (
     AITrainingWorkflow, AIClassifierSet,
     ClassifierUploadError, ClassifierSerializeError,
-    IncompleteClassifierSet
+    IncompleteClassifierSet, NoTrainingExamples
 )
 from openassessment.assessment.errors import (
     AITrainingRequestError, AITrainingInternalError
@@ -185,8 +185,7 @@ def create_classifiers(training_workflow_uuid, classifier_set):
 
     Args:
         training_workflow_uuid (str): The UUID of the training workflow.
-        classifier_set (dict): Mapping of criterion names to serialized classifiers.
-            (binary classifiers should be base-64 encoded).
+        classifier_set (dict): Mapping of criteria names to serialized classifiers.
 
     Returns:
         None
@@ -201,47 +200,32 @@ def create_classifiers(training_workflow_uuid, classifier_set):
 
         # If the task is executed multiple times, the classifier set may already
         # have been created.  If so, log a warning then return immediately.
-        if workflow.classifier_set is not None:
+        if workflow.is_complete:
             msg = u"AI training workflow with UUID {} already has trained classifiers."
             logger.warning(msg)
-            return
-
-        # Retrieve the rubric model
-        rubric = workflow.rubric
-        if rubric is None:
-            msg = (
-                u"The AI training workflow with UUID {} does not have "
-                u"a rubric associated with it, which means it has no "
-                u"training examples."
-            ).format(training_workflow_uuid)
-            logger.exception(msg)
-            raise AITrainingInternalError(msg)
-
-        try:
-            workflow.classifier_set = AIClassifierSet.create_classifier_set(
-                classifier_set, rubric, workflow.algorithm_id
-            )
-        except IncompleteClassifierSet as ex:
-            msg = (
-                u"An error occurred while creating the classifier set "
-                u"for the training workflow with UUID {uuid}: {ex}"
-            ).format(uuid=training_workflow_uuid, ex=ex)
-            raise AITrainingRequestError(msg)
-        except (ClassifierSerializeError, ClassifierUploadError, DatabaseError) as ex:
-            msg = (
-                u"An unexpected error occurred while creating the classifier "
-                u"set for training workflow UUID {uuid}: {ex}"
-            ).format(uuid=training_workflow_uuid, ex=ex)
-            logger.exception(msg)
-            raise AITrainingInternalError(msg)
-
-        workflow.completed_at = now()
-        workflow.save()
+        else:
+            workflow.complete(classifier_set)
     except AITrainingWorkflow.DoesNotExist:
         msg = (
             u"Could not retrieve AI training workflow with UUID {}"
         ).format(training_workflow_uuid)
         raise AITrainingRequestError(msg)
+    except NoTrainingExamples as ex:
+        logger.exception(ex)
+        raise AITrainingInternalError(ex)
+    except IncompleteClassifierSet as ex:
+        msg = (
+            u"An error occurred while creating the classifier set "
+            u"for the training workflow with UUID {uuid}: {ex}"
+        ).format(uuid=training_workflow_uuid, ex=ex)
+        raise AITrainingRequestError(msg)
+    except (ClassifierSerializeError, ClassifierUploadError, DatabaseError) as ex:
+        msg = (
+            u"An unexpected error occurred while creating the classifier "
+            u"set for training workflow UUID {uuid}: {ex}"
+        ).format(uuid=training_workflow_uuid, ex=ex)
+        logger.exception(msg)
+        raise AITrainingInternalError(msg)
     except DatabaseError:
         msg = (
             u"An unexpected error occurred while creating the classifier set "

apps/openassessment/assessment/models/ai.py

@@ -40,6 +40,19 @@ class ClassifierSerializeError(Exception):
     pass
 
 
+class NoTrainingExamples(Exception):
+    """
+    No training examples were provided to the workflow.
+    """
+    def __init__(self, workflow_uuid=None):
+        msg = u"No training examples were provided"
+        if workflow_uuid is not None:
+            msg = u"{msg} to the training workflow with UUID {uuid}".format(
+                msg=msg, uuid=workflow_uuid
+            )
+        super(NoTrainingExamples, self).__init__(msg)
+
+
 class AIClassifierSet(models.Model):
     """
     A set of trained classifiers (immutable).
@@ -242,7 +255,13 @@ class AITrainingWorkflow(models.Model):
         Returns:
             AITrainingWorkflow
 
+        Raises:
+            NoTrainingExamples
+
         """
+        if len(examples) == 0:
+            raise NoTrainingExamples()
+
         workflow = AITrainingWorkflow.objects.create(algorithm_id=algorithm_id)
         workflow.training_examples.add(*examples)
         workflow.save()
@@ -256,6 +275,9 @@ class AITrainingWorkflow(models.Model):
         Returns:
             Rubric or None (if no training examples are available)
 
+        Raises:
+            NoTrainingExamples
+
         """
         # We assume that all the training examples we have been provided are using
         # the same rubric (this is enforced by the API call that deserializes
@@ -264,4 +286,38 @@ class AITrainingWorkflow(models.Model):
         if first_example:
             return first_example[0].rubric
         else:
-            return None
+            raise NoTrainingExamples(workflow_uuid=self.uuid)
+
+    @property
+    def is_complete(self):
+        """
+        Check whether the workflow is complete (classifiers have been trained).
+
+        Returns:
+            bool
+
+        """
+        return self.completed_at is not None
+
+    def complete(self, classifier_set):
+        """
+        Add a classifier set to the workflow and mark it complete.
+
+        Args:
+            classifier_set (dict): Mapping of criteria names to serialized classifiers.
+
+        Returns:
+            None
+
+        Raises:
+            NoTrainingExamples
+            IncompleteClassifierSet
+            ClassifierSerializeError
+            ClassifierUploadError
+            DatabaseError
+        """
+        self.classifier_set = AIClassifierSet.create_classifier_set(
+            classifier_set, self.rubric, self.algorithm_id
+        )
+        self.completed_at = now()
+        self.save()

apps/openassessment/assessment/test/test_ai.py

@@ -108,6 +108,11 @@ class AITrainingTest(CacheResetTest):
         with self.assertRaises(AITrainingRequestError):
             ai_api.train_classifiers(RUBRIC, mutated_examples, self.ALGORITHM_ID)
 
+    def test_train_classifiers_no_examples(self):
+        # Empty list of training examples
+        with self.assertRaises(AITrainingRequestError):
+            ai_api.train_classifiers(RUBRIC, [], self.ALGORITHM_ID)
+
     @override_settings(ORA2_AI_ALGORITHMS=AI_ALGORITHMS)
     @mock.patch.object(AITrainingWorkflow.objects, 'create')
     def test_start_workflow_database_error(self, mock_create):