Merge pull request #122 from edx/ajpal/fix-attempts-per-completed

Ajpal/fix attempts per completed

analytics_data_api/constants/engagement_entity_types.py

-DISCUSSION = 'discussion'
-PROBLEM = 'problem'
-VIDEO = 'video'
-INDIVIDUAL_TYPES = [DISCUSSION, PROBLEM, VIDEO]
-
-PROBLEMS = 'problems'
-VIDEOS = 'videos'
-AGGREGATE_TYPES = [DISCUSSION, PROBLEMS, VIDEOS]

analytics_data_api/constants/engagement_events.py

-from analytics_data_api.constants import engagement_entity_types
-
 ATTEMPTED = 'attempted'
 ATTEMPTS_PER_COMPLETED = 'attempts_per_completed'
 COMPLETED = 'completed'
 CONTRIBUTED = 'contributed'
 VIEWED = 'viewed'
 
-# map entity types to events
-EVENTS = {
-    engagement_entity_types.DISCUSSION: [CONTRIBUTED],
-    engagement_entity_types.PROBLEM: [ATTEMPTED, ATTEMPTS_PER_COMPLETED, COMPLETED],
-    engagement_entity_types.PROBLEMS: [ATTEMPTED, COMPLETED],
-    engagement_entity_types.VIDEO: [VIEWED],
-    engagement_entity_types.VIDEOS: [VIEWED],
-}
+DISCUSSION = 'discussion'
+PROBLEM = 'problem'
+VIDEO = 'video'
+PROBLEMS = 'problems'
+VIDEOS = 'videos'
+
+INDIVIDUAL_EVENTS = [
+    'problem_attempts_per_completed',
+    'problem_attempted',
+    'problem_completed',
+    'discussion_contributed',
+    'video_viewed'
+]
+
+EVENTS = [
+    'problem_attempts_per_completed',
+    'problems_attempted',
+    'problems_completed',
+    'discussion_contributions',
+    'videos_viewed'
+]

analytics_data_api/constants/engagement_types.py

-from analytics_data_api.constants.engagement_entity_types import DISCUSSION, PROBLEM, VIDEO
-from analytics_data_api.constants.engagement_events import ATTEMPTED, COMPLETED, CONTRIBUTED, VIEWED
+from analytics_data_api.constants.engagement_events import (ATTEMPTED, ATTEMPTS_PER_COMPLETED, COMPLETED,
+                                                            CONTRIBUTED, DISCUSSION, PROBLEM, VIDEO, VIEWED)
 
 
 class EngagementType(object):
@@ -12,6 +12,7 @@ class EngagementType(object):
     # Defines the current canonical set of engagement types used in the Learner
     # Analytics API.
     ALL_TYPES = (
+        'problem_attempts_per_completed',
         'problems_attempted',
         'problems_completed',
         'videos_viewed',
@@ -30,6 +31,9 @@ class EngagementType(object):
             if event_type == ATTEMPTED:
                 self.name = 'problems_attempted'
                 self.is_counted_by_entity = True
+            if event_type == ATTEMPTS_PER_COMPLETED:
+                self.name = 'problem_attempts_per_completed'
+                self.is_counted_by_entity = True
             if event_type == COMPLETED:
                 self.name = 'problems_completed'
                 self.is_counted_by_entity = True

analytics_data_api/management/commands/generate_fake_course_data.py

@@ -8,7 +8,7 @@ import random
 from django.core.management.base import BaseCommand
 from django.utils import timezone
 from analytics_data_api.v0 import models
-from analytics_data_api.constants import engagement_entity_types, engagement_events
+from analytics_data_api.constants import engagement_events
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -194,16 +194,17 @@ class Command(BaseCommand):
         current = start_date
         while current < end_date:
             current = current + datetime.timedelta(days=1)
-            for entity_type in engagement_entity_types.INDIVIDUAL_TYPES:
-                for event in engagement_events.EVENTS[entity_type]:
-                    num_events = random.randint(0, max_value)
-                    if num_events:
-                        for _ in xrange(num_events):
-                            count = random.randint(0, max_value / 20)
-                            entity_id = 'an-id-{}-{}'.format(entity_type, event)
-                            models.ModuleEngagement.objects.create(
-                                course_id=course_id, username=username, date=current,
-                                entity_type=entity_type, entity_id=entity_id, event=event, count=count)
+            for metric in engagement_events.INDIVIDUAL_EVENTS:
+                num_events = random.randint(0, max_value)
+                if num_events:
+                    for _ in xrange(num_events):
+                        count = random.randint(0, max_value / 20)
+                        entity_type = metric.split('_', 1)[0]
+                        event = metric.split('_', 1)[1]
+                        entity_id = 'an-id-{}-{}'.format(entity_type, event)
+                        models.ModuleEngagement.objects.create(
+                            course_id=course_id, username=username, date=current,
+                            entity_type=entity_type, entity_id=entity_id, event=event, count=count)
             logger.info("Done!")
 
     def generate_learner_engagement_range_data(self, course_id, start_date, end_date, max_value=100):
@@ -211,19 +212,15 @@ class Command(BaseCommand):
         models.ModuleEngagementMetricRanges.objects.all().delete()
 
         logger.info("Generating engagement range data...")
-        for entity_type in engagement_entity_types.AGGREGATE_TYPES:
-            for event in engagement_events.EVENTS[entity_type]:
-                metric = '{0}_{1}'.format(entity_type, event)
-
-                low_ceil = random.random() * max_value * 0.5
-                models.ModuleEngagementMetricRanges.objects.create(
-                    course_id=course_id, start_date=start_date, end_date=end_date, metric=metric,
-                    range_type='low', low_value=0, high_value=low_ceil)
-
-                high_floor = random.random() * max_value * 0.5 + low_ceil
-                models.ModuleEngagementMetricRanges.objects.create(
-                    course_id=course_id, start_date=start_date, end_date=end_date, metric=metric,
-                    range_type='high', low_value=high_floor, high_value=max_value)
+        for event in engagement_events.EVENTS:
+            low_ceil = random.random() * max_value * 0.5
+            models.ModuleEngagementMetricRanges.objects.create(
+                course_id=course_id, start_date=start_date, end_date=end_date, metric=event,
+                range_type='low', low_value=0, high_value=low_ceil)
+            high_floor = random.random() * max_value * 0.5 + low_ceil
+            models.ModuleEngagementMetricRanges.objects.create(
+                course_id=course_id, start_date=start_date, end_date=end_date, metric=event,
+                range_type='high', low_value=high_floor, high_value=max_value)
 
     def generate_tags_distribution_data(self, course_id):
         logger.info("Deleting existed tags distribution data...")

analytics_data_api/v0/serializers.py

@@ -3,7 +3,6 @@ from django.conf import settings
 from rest_framework import pagination, serializers
 
 from analytics_data_api.constants import (
-    engagement_entity_types,
     engagement_events,
     enrollment_modes,
     genders,
@@ -458,23 +457,16 @@ class CourseLearnerMetadataSerializer(serializers.Serializer):
             'date_range': DateRangeSerializer(query_set[0] if len(query_set) else None).data
         }
 
-        # go through each entity and event type combination and fill in the ranges
-        for entity_type in engagement_entity_types.AGGREGATE_TYPES:
-            for event in engagement_events.EVENTS[entity_type]:
-                metric = '{0}_{1}'.format(entity_type, event)
-                # It's assumed that there may be any combination of low, normal,
-                # and high ranges in the database for the given course.  Some
-                # edge cases result from a lack of available data; in such
-                # cases, only some ranges may be returned.
-                low_range_queryset = query_set.filter(metric=metric, range_type='low')
-                normal_range_queryset = query_set.filter(metric=metric, range_type='normal')
-                high_range_queryset = query_set.filter(metric=metric, range_type='high')
-                engagement_ranges.update({
-                    metric: EnagementRangeMetricSerializer({
-                        'low_range': low_range_queryset[0] if len(low_range_queryset) else None,
-                        'normal_range': normal_range_queryset[0] if len(normal_range_queryset) else None,
-                        'high_range': high_range_queryset[0] if len(high_range_queryset) else None,
-                    }).data
-                })
+        for metric in engagement_events.EVENTS:
+            low_range_queryset = query_set.filter(metric=metric, range_type='low')
+            normal_range_queryset = query_set.filter(metric=metric, range_type='normal')
+            high_range_queryset = query_set.filter(metric=metric, range_type='high')
+            engagement_ranges.update({
+                metric: EnagementRangeMetricSerializer({
+                    'low_range': low_range_queryset[0] if len(low_range_queryset) else None,
+                    'normal_range': normal_range_queryset[0] if len(normal_range_queryset) else None,
+                    'high_range': high_range_queryset[0] if len(high_range_queryset) else None,
+                }).data
+            })
 
         return engagement_ranges

analytics_data_api/v0/tests/views/test_engagement_timelines.py

@@ -9,8 +9,8 @@ import pytz
 from rest_framework import status
 
 from analyticsdataserver.tests import TestCaseWithAuthentication
-from analytics_data_api.constants.engagement_entity_types import DISCUSSION, PROBLEM, VIDEO
-from analytics_data_api.constants.engagement_events import ATTEMPTED, COMPLETED, CONTRIBUTED, VIEWED
+from analytics_data_api.constants.engagement_events import (ATTEMPTED, COMPLETED, CONTRIBUTED, DISCUSSION,
+                                                            PROBLEM, VIDEO, VIEWED)
 from analytics_data_api.v0 import models
 from analytics_data_api.v0.tests.views import DemoCourseMixin, VerifyCourseIdMixin
 

analytics_data_api/v0/tests/views/test_learners.py

@@ -17,7 +17,7 @@ from django.conf import settings
 from django.core import management
 
 from analyticsdataserver.tests import TestCaseWithAuthentication
-from analytics_data_api.constants import engagement_entity_types, engagement_events
+from analytics_data_api.constants import engagement_events
 from analytics_data_api.v0.models import ModuleEngagementMetricRanges
 from analytics_data_api.v0.tests.views import DemoCourseMixin, VerifyCourseIdMixin
 
@@ -571,19 +571,10 @@ class CourseLearnerMetadataTests(DemoCourseMixin, VerifyCourseIdMixin,
         empty_range = {
             range_type: None for range_type in ['below_average', 'average', 'above_average']
         }
-        for metric in self.engagement_metrics:
+        for metric in engagement_events.EVENTS:
             empty_engagement_ranges['engagement_ranges'][metric] = copy.deepcopy(empty_range)
         return empty_engagement_ranges
 
-    @property
-    def engagement_metrics(self):
-        """ Convenience method for getting the metric types. """
-        metrics = []
-        for entity_type in engagement_entity_types.AGGREGATE_TYPES:
-            for event in engagement_events.EVENTS[entity_type]:
-                metrics.append('{0}_{1}'.format(entity_type, event))
-        return metrics
-
     def test_no_engagement_ranges(self):
         response = self._get(self.course_id)
         self.assertEqual(response.status_code, 200)
@@ -627,7 +618,7 @@ class CourseLearnerMetadataTests(DemoCourseMixin, VerifyCourseIdMixin,
         }
 
         max_value = 1000.0
-        for metric_type in self.engagement_metrics:
+        for metric_type in engagement_events.EVENTS:
             low_ceil = 100.5
             G(ModuleEngagementMetricRanges, course_id=self.course_id, start_date=start_date, end_date=end_date,
               metric=metric_type, range_type='low', low_value=0, high_value=low_ceil)
@@ -649,12 +640,8 @@ class CourseLearnerMetadataTests(DemoCourseMixin, VerifyCourseIdMixin,
         self.assertDictContainsSubset(expected, json.loads(response.content))
 
     def test_engagement_ranges_fields(self):
-        actual_entity_types = engagement_entity_types.INDIVIDUAL_TYPES
-        expected_entity_types = ['discussion', 'problem', 'video']
-        self.assertEqual(actual_entity_types, expected_entity_types)
-        actual_events = []
-        for entity_type in actual_entity_types:
-            for event in engagement_events.EVENTS[entity_type]:
-                actual_events.append(event)
-        expected_events = ['contributed', 'attempted', 'attempts_per_completed', 'completed', 'viewed']
-        self.assertEqual(actual_events, expected_events)
+        expected_events = engagement_events.EVENTS
+        response = json.loads(self._get(self.course_id).content)
+        self.assertTrue('engagement_ranges' in response)
+        for event in expected_events:
+            self.assertTrue(event in response['engagement_ranges'])