refactor distributions (minor)
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@@ -5,7 +5,9 @@ Profile Distributions
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from django.db.models import Count
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from student.models import CourseEnrollment, UserProfile
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AVAILABLE_PROFILE_FEATURES = ['gender', 'level_of_education', 'year_of_birth']
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_EASY_CHOICE_FEATURES = ('gender', 'level_of_education')
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_OPEN_CHOICE_FEATURES = ('year_of_birth',)
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AVAILABLE_PROFILE_FEATURES = _EASY_CHOICE_FEATURES + _OPEN_CHOICE_FEATURES
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def profile_distribution(course_id, feature):
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@@ -26,24 +28,16 @@ def profile_distribution(course_id, feature):
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OPEN_CHOICE - choices with a larger domain e.g. year_of_birth
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"""
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EASY_CHOICE_FEATURES = ['gender', 'level_of_education']
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OPEN_CHOICE_FEATURES = ['year_of_birth']
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def raise_not_implemented():
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raise NotImplementedError("feature requested not implemented but is advertised in AVAILABLE_PROFILE_FEATURES {}" .format(feature))
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feature_results = {}
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if not feature in AVAILABLE_PROFILE_FEATURES:
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raise ValueError("unsupported feature requested for distribution '{}'".format(feature))
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if feature in EASY_CHOICE_FEATURES:
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if feature in _EASY_CHOICE_FEATURES:
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if feature == 'gender':
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raw_choices = UserProfile.GENDER_CHOICES
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elif feature == 'level_of_education':
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raw_choices = UserProfile.LEVEL_OF_EDUCATION_CHOICES
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else:
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raise raise_not_implemented()
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choices = [(short, full) for (short, full) in raw_choices] + [('no_data', 'No Data')]
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@@ -53,14 +47,12 @@ def profile_distribution(course_id, feature):
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count = CourseEnrollment.objects.filter(course_id=course_id, user__profile__gender=short).count()
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elif feature == 'level_of_education':
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count = CourseEnrollment.objects.filter(course_id=course_id, user__profile__level_of_education=short).count()
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else:
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raise raise_not_implemented()
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data[short] = count
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feature_results['data'] = data
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feature_results['type'] = 'EASY_CHOICE'
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feature_results['display_names'] = dict(choices)
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elif feature in OPEN_CHOICE_FEATURES:
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elif feature in _OPEN_CHOICE_FEATURES:
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profiles = UserProfile.objects.filter(user__courseenrollment__course_id=course_id)
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query_distribution = profiles.values(feature).annotate(Count(feature)).order_by()
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# query_distribution is of the form [{'featureval': 'value1', 'featureval__count': 4}, {'featureval': 'value2', 'featureval__count': 2}, ...]
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@@ -77,7 +69,5 @@ def profile_distribution(course_id, feature):
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feature_results['data'] = distribution
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feature_results['type'] = 'OPEN_CHOICE'
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else:
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raise raise_not_implemented()
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return feature_results
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@@ -14,10 +14,10 @@ class TestAnalyticsDistributions(TestCase):
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def setUp(self):
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self.course_id = 'some/robot/course/id'
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self.users = tuple(UserFactory(
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self.users = [UserFactory(
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profile__gender=['m', 'f', 'o'][i % 3],
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profile__year_of_birth=i + 1930
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) for i in xrange(30))
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) for i in xrange(30)]
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self.ces = tuple(CourseEnrollment.objects.create(course_id=self.course_id, user=user) for user in self.users)
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@@ -27,13 +27,6 @@ class TestAnalyticsDistributions(TestCase):
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self.assertNotIn(feature, AVAILABLE_PROFILE_FEATURES)
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profile_distribution(self.course_id, feature)
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@raises(NotImplementedError)
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def test_profile_distribution_not_implemented_feature(self):
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feature = 'ROBOT_DO_NOT_USE_FEATURE'
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AVAILABLE_PROFILE_FEATURES.append(feature)
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self.assertIn(feature, AVAILABLE_PROFILE_FEATURES)
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profile_distribution(self.course_id, feature)
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def test_profile_distribution_easy_choice(self):
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feature = 'gender'
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self.assertIn(feature, AVAILABLE_PROFILE_FEATURES)
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