Remove unused grading task params

Retrieve valid scores for a classifier
Assign closest score when AI algorithm returns a score not in the rubric.

openassessment/assessment/api/ai_worker.py

@@ -31,8 +31,7 @@ def get_grading_task_params(grading_workflow_uuid):
         dict with keys:
             * essay_text (unicode): The text of the essay submission.
             * classifier_set (dict): Maps criterion names to serialized classifiers.
-            * course_id (unicode): The course ID that the training task is associated with.
-            * item_id (unicode): Identifies the item that the AI will be training to grade.
+            * valid_scores (dict): Maps criterion names to a list of valid scores for that criterion.
             * algorithm_id (unicode): ID of the algorithm used to perform training.
 
     Raises:
@@ -67,12 +66,19 @@ def get_grading_task_params(grading_workflow_uuid):
         raise AIGradingInternalError(msg)
 
     try:
+        classifiers = list(classifier_set.classifiers.select_related().all())
+
         return {
             'essay_text': workflow.essay_text,
-            'classifier_set': classifier_set.classifiers_dict,
-            'course_id': workflow.course_id,
-            'item_id': workflow.item_id,
+            'classifier_set': {
+                classifier.criterion.name: classifier.download_classifier_data()
+                for classifier in classifiers
+            },
             'algorithm_id': workflow.algorithm_id,
+            'valid_scores': {
+                classifier.criterion.name: classifier.valid_scores
+                for classifier in classifiers
+            }
         }
     except (DatabaseError, ClassifierSerializeError, IncompleteClassifierSet, ValueError) as ex:
         msg = (

openassessment/assessment/models/ai.py

@@ -7,6 +7,7 @@ import logging
 import itertools
 from django.conf import settings
 from django.core.files.base import ContentFile
+from django.core.cache import cache
 from django.db import models, transaction, DatabaseError
 from django.utils.timezone import now
 from django.core.exceptions import ObjectDoesNotExist
@@ -266,6 +267,23 @@ class AIClassifier(models.Model):
         """
         return json.loads(self.classifier_data.read())  # pylint:disable=E1101
 
+    @property
+    def valid_scores(self):
+        """
+        Return a list of valid scores for the rubric criterion associated
+        with this classifier.
+
+        Returns:
+            list of integer scores, in ascending order.
+
+        """
+        cache_key = u"openassessment.assessment.ai.classifier.{pk}.valid_scores".format(pk=self.pk)
+        valid_scores = cache.get(cache_key)
+        if valid_scores is None:
+            valid_scores = sorted([option.points for option in self.criterion.options.all()])
+            cache.set(cache_key, valid_scores)
+        return valid_scores
+
 
 class AIWorkflow(models.Model):
     """

openassessment/assessment/models/base.py

@@ -243,9 +243,7 @@ class Rubric(models.Model):
         # Find the IDs for the options matching the specified point value
         option_id_set = set()
         for criterion_name, option_points in criterion_points.iteritems():
-            if (criterion_name in rubric_points_dict and 
-                option_points in rubric_points_dict[criterion_name]
-            ):
+            if (criterion_name in rubric_points_dict and option_points in rubric_points_dict[criterion_name]):
                 option_id = rubric_points_dict[criterion_name][option_points]
                 option_id_set.add(option_id)
             else:

openassessment/assessment/test/test_ai_worker.py

@@ -226,11 +226,22 @@ class AIWorkerGradingTest(CacheResetTest):
             'essay_text': ANSWER,
             'classifier_set': CLASSIFIERS,
             'algorithm_id': ALGORITHM_ID,
-            'course_id': STUDENT_ITEM.get('course_id'),
-            'item_id': STUDENT_ITEM.get('item_id')
+            'valid_scores': {
+                u"vøȼȺƀᵾłȺɍɏ": [0, 1, 2],
+                u"ﻭɼค๓๓คɼ": [0, 1, 2]
+            }
         }
         self.assertItemsEqual(params, expected_params)
 
+    def test_get_grading_task_params_num_queries(self):
+        with self.assertNumQueries(5):
+            ai_worker_api.get_grading_task_params(self.workflow_uuid)
+
+        # The second time through we should be caching the queries
+        # to determine the valid scores for a classifier
+        with self.assertNumQueries(3):
+            ai_worker_api.get_grading_task_params(self.workflow_uuid)
+
     def test_get_grading_task_params_no_workflow(self):
         with self.assertRaises(AIGradingRequestError):
             ai_worker_api.get_grading_task_params("invalid_uuid")

openassessment/assessment/test/test_worker.py

@@ -3,6 +3,7 @@
 Tests for AI worker tasks.
 """
 from contextlib import contextmanager
+import itertools
 import mock
 from django.test.utils import override_settings
 from submissions import api as sub_api
@@ -45,15 +46,30 @@ class ErrorStubAIAlgorithm(AIAlgorithm):
         raise ScoreError("Test error!")
 
 
+class InvalidScoreAlgorithm(AIAlgorithm):
+    """
+    Stub implementation that returns a score that isn't in the rubric.
+    """
+    SCORE_CYCLE = itertools.cycle([-100, 0.7, 1.2, 100])
+
+    def train_classifier(self, examples):
+        return {}
+
+    def score(self, text, classifier):
+        return self.SCORE_CYCLE.next()
+
+
 ALGORITHM_ID = u"test-stub"
 ERROR_STUB_ALGORITHM_ID = u"error-stub"
 UNDEFINED_CLASS_ALGORITHM_ID = u"undefined_class"
 UNDEFINED_MODULE_ALGORITHM_ID = u"undefined_module"
+INVALID_SCORE_ALGORITHM_ID = u"invalid_score"
 AI_ALGORITHMS = {
     ALGORITHM_ID: '{module}.StubAIAlgorithm'.format(module=__name__),
     ERROR_STUB_ALGORITHM_ID: '{module}.ErrorStubAIAlgorithm'.format(module=__name__),
     UNDEFINED_CLASS_ALGORITHM_ID: '{module}.NotDefinedAIAlgorithm'.format(module=__name__),
-    UNDEFINED_MODULE_ALGORITHM_ID: 'openassessment.not.valid.NotDefinedAIAlgorithm'
+    UNDEFINED_MODULE_ALGORITHM_ID: 'openassessment.not.valid.NotDefinedAIAlgorithm',
+    INVALID_SCORE_ALGORITHM_ID: '{module}.InvalidScoreAlgorithm'.format(module=__name__),
 }
 
 
@@ -109,9 +125,7 @@ class AITrainingTaskTest(CeleryTaskTest):
         Create a training workflow in the database.
         """
         examples = deserialize_training_examples(EXAMPLES, RUBRIC)
-
         workflow = AITrainingWorkflow.start_workflow(examples, self.COURSE_ID, self.ITEM_ID, self.ALGORITHM_ID)
-
         self.workflow_uuid = workflow.uuid
 
     def test_unknown_algorithm(self):
@@ -252,6 +266,32 @@ class AIGradingTaskTest(CeleryTaskTest):
         workflow.classifier_set = classifier_set
         workflow.save()
 
+    @mock.patch('openassessment.assessment.api.ai_worker.create_assessment')
+    @override_settings(ORA2_AI_ALGORITHMS=AI_ALGORITHMS)
+    def test_algorithm_gives_invalid_score(self, mock_create_assessment):
+        # If an algorithm provides a score that isn't in the rubric,
+        # we should choose the closest valid score.
+        self._set_algorithm_id(INVALID_SCORE_ALGORITHM_ID)
+
+        # The first score given by the algorithm should be below the minimum valid score
+        # The second score will be between two valid scores (0 and 1), rounding up
+        grade_essay(self.workflow_uuid)
+        expected_scores = {
+            u"vøȼȺƀᵾłȺɍɏ": 0,
+            u"ﻭɼค๓๓คɼ": 1
+        }
+        mock_create_assessment.assert_called_with(self.workflow_uuid, expected_scores)
+
+        # The third score will be between two valid scores (1 and 2), rounding down
+        # The final score will be greater than the maximum score
+        self._reset_workflow()
+        grade_essay(self.workflow_uuid)
+        expected_scores = {
+            u"vøȼȺƀᵾłȺɍɏ": 1,
+            u"ﻭɼค๓๓คɼ": 2
+        }
+        mock_create_assessment.assert_called_with(self.workflow_uuid, expected_scores)
+
     @mock.patch('openassessment.assessment.worker.grading.ai_worker_api.get_grading_task_params')
     @override_settings(ORA2_AI_ALGORITHMS=AI_ALGORITHMS)
     def test_retrieve_params_error(self, mock_call):
@@ -277,6 +317,39 @@ class AIGradingTaskTest(CeleryTaskTest):
         with self.assert_retry(grade_essay, AIGradingInternalError):
             grade_essay(self.workflow_uuid)
 
+    @mock.patch('openassessment.assessment.worker.grading.ai_worker_api.get_grading_task_params')
+    @override_settings(ORA2_AI_ALGORITHMS=AI_ALGORITHMS)
+    def test_params_missing_criterion_for_valid_scores(self, mock_call):
+        mock_call.return_value = {
+            'essay_text': 'test',
+            'classifier_set': {
+                u"vøȼȺƀᵾłȺɍɏ": {},
+                u"ﻭɼค๓๓คɼ": {}
+            },
+            'algorithm_id': ALGORITHM_ID,
+            'valid_scores': {}
+        }
+        with self.assert_retry(grade_essay, AIGradingInternalError):
+            grade_essay(self.workflow_uuid)
+
+    @mock.patch('openassessment.assessment.worker.grading.ai_worker_api.get_grading_task_params')
+    @override_settings(ORA2_AI_ALGORITHMS=AI_ALGORITHMS)
+    def test_params_valid_scores_empty_list(self, mock_call):
+        mock_call.return_value = {
+            'essay_text': 'test',
+            'classifier_set': {
+                u"vøȼȺƀᵾłȺɍɏ": {},
+                u"ﻭɼค๓๓คɼ": {}
+            },
+            'algorithm_id': ALGORITHM_ID,
+            'valid_scores': {
+                u"vøȼȺƀᵾłȺɍɏ": [],
+                u"ﻭɼค๓๓คɼ": [0, 1, 2]
+            }
+        }
+        with self.assert_retry(grade_essay, AIGradingInternalError):
+            grade_essay(self.workflow_uuid)
+
     def _set_algorithm_id(self, algorithm_id):
         """
         Override the default algorithm ID for the grading workflow.
@@ -291,3 +364,12 @@ class AIGradingTaskTest(CeleryTaskTest):
         workflow = AIGradingWorkflow.objects.get(uuid=self.workflow_uuid)
         workflow.algorithm_id = algorithm_id
         workflow.save()
+
+    def _reset_workflow(self):
+        """
+        Reset the workflow so we can re-use it.
+        """
+        workflow = AIGradingWorkflow.objects.get(uuid=self.workflow_uuid)
+        workflow.completed_at = None
+        workflow.assessment = None
+        workflow.save()

openassessment/assessment/worker/grading.py

@@ -10,7 +10,7 @@ from celery.utils.log import get_task_logger
 from dogapi import dog_stats_api
 from openassessment.assessment.api import ai_worker as ai_worker_api
 from openassessment.assessment.errors import (
-    AIError, AIGradingInternalError, AIGradingRequestError, AIReschedulingInternalError, ANTICIPATED_CELERY_ERRORS
+    AIError, AIGradingInternalError, AIReschedulingInternalError, ANTICIPATED_CELERY_ERRORS
 )
 from .algorithm import AIAlgorithm, AIAlgorithmError
 from openassessment.assessment.models.ai import AIClassifierSet, AIGradingWorkflow
@@ -54,6 +54,7 @@ def grade_essay(workflow_uuid):
         essay_text = params['essay_text']
         classifier_set = params['classifier_set']
         algorithm_id = params['algorithm_id']
+        valid_scores = params['valid_scores']
     except (AIError, KeyError):
         msg = (
             u"An error occurred while retrieving the AI grading task "
@@ -62,6 +63,23 @@ def grade_essay(workflow_uuid):
         logger.exception(msg)
         raise grade_essay.retry()
 
+    # Validate that the we have valid scores for each criterion
+    for criterion_name in classifier_set.keys():
+        msg = None
+        if criterion_name not in valid_scores:
+            msg = (
+                u"Could not find {criterion} in the list of valid scores "
+                u"for grading workflow with UUID {uuid}"
+            ).format(criterion=criterion_name, uuid=workflow_uuid)
+        elif len(valid_scores[criterion_name]) == 0:
+            msg = (
+                u"Valid scores for {criterion} is empty for "
+                u"grading workflow with UUID {uuid}"
+            ).format(criterion=criterion_name, uuid=workflow_uuid)
+        if msg:
+            logger.exception(msg)
+            raise AIGradingInternalError(msg)
+
     # Retrieve the AI algorithm
     try:
         algorithm = AIAlgorithm.algorithm_for_id(algorithm_id)
@@ -76,7 +94,10 @@ def grade_essay(workflow_uuid):
     # Use the algorithm to evaluate the essay for each criterion
     try:
         scores_by_criterion = {
-            criterion_name: algorithm.score(essay_text, classifier)
+            criterion_name: _closest_valid_score(
+                algorithm.score(essay_text, classifier),
+                valid_scores[criterion_name]
+            )
             for criterion_name, classifier in classifier_set.iteritems()
         }
     except AIAlgorithmError:
@@ -222,6 +243,35 @@ def reschedule_grading_tasks(course_id, item_id):
             raise reschedule_grading_tasks.retry()
 
 
+def _closest_valid_score(score, valid_scores):
+    """
+    Return the closest valid score for a given score.
+    This is necessary, since rubric scores may be non-contiguous.
+
+    Args:
+        score (int or float): The score assigned by the algorithm.
+        valid_scores (list of int): Valid scores for this criterion,
+            assumed to be sorted in ascending order.
+
+    Returns:
+        int
+
+    """
+    # If the score is already valid, return it
+    if score in valid_scores:
+        return score
+
+    # Otherwise, find the closest score in the list.
+    closest = valid_scores[0]
+    delta = abs(score - closest)
+    for valid in valid_scores[1:]:
+        new_delta = abs(score - valid)
+        if new_delta < delta:
+            closest = valid
+        delta = new_delta
+    return closest
+
+
 def _log_start_reschedule_grading(course_id=None, item_id=None):
     """
     Sends data about the rescheduling_grading task to datadog