9cf2f9f298
This will remove imports from __future__ that are no longer needed. https://docs.python.org/3.5/library/2to3.html#2to3fixer-future
119 lines
5.0 KiB
Python
119 lines
5.0 KiB
Python
""" Tests for analytics.distributions """
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import pytest
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from django.test import TestCase
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from opaque_keys.edx.locator import CourseLocator
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from six.moves import range
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from lms.djangoapps.instructor_analytics.distributions import AVAILABLE_PROFILE_FEATURES, profile_distribution
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from student.models import CourseEnrollment
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from student.tests.factories import UserFactory
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class TestAnalyticsDistributions(TestCase):
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'''Test analytics distribution gathering.'''
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def setUp(self):
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super(TestAnalyticsDistributions, self).setUp()
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self.course_id = CourseLocator('robot', 'course', 'id')
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self.users = [UserFactory(
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profile__gender=['m', 'f', 'o'][i % 3],
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profile__level_of_education=['a', 'hs', 'el'][i % 3],
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profile__year_of_birth=i + 1930
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) for i in range(30)]
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self.ces = [CourseEnrollment.enroll(user, self.course_id)
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for user in self.users]
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def test_profile_distribution_bad_feature(self):
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feature = 'robot-not-a-real-feature'
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self.assertNotIn(feature, AVAILABLE_PROFILE_FEATURES)
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with pytest.raises(ValueError):
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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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distribution = profile_distribution(self.course_id, feature)
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self.assertEqual(distribution.type, 'EASY_CHOICE')
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self.assertEqual(distribution.data['no_data'], 0)
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self.assertEqual(distribution.data['m'], len(self.users) / 3)
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self.assertEqual(distribution.choices_display_names['m'], 'Male')
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def test_profile_distribution_open_choice(self):
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feature = 'year_of_birth'
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self.assertIn(feature, AVAILABLE_PROFILE_FEATURES)
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distribution = profile_distribution(self.course_id, feature)
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print(distribution)
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self.assertEqual(distribution.type, 'OPEN_CHOICE')
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self.assertTrue(hasattr(distribution, 'choices_display_names'))
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self.assertEqual(distribution.choices_display_names, None)
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self.assertNotIn('no_data', distribution.data)
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self.assertEqual(distribution.data[1930], 1)
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def test_gender_count(self):
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course_enrollments = CourseEnrollment.objects.filter(
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course_id=self.course_id, user__profile__gender='m'
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)
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distribution = profile_distribution(self.course_id, "gender")
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self.assertEqual(distribution.data['m'], len(course_enrollments))
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course_enrollments[0].deactivate()
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distribution = profile_distribution(self.course_id, "gender")
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self.assertEqual(distribution.data['m'], len(course_enrollments) - 1)
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def test_level_of_education_count(self):
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course_enrollments = CourseEnrollment.objects.filter(
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course_id=self.course_id, user__profile__level_of_education='hs'
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)
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distribution = profile_distribution(self.course_id, "level_of_education")
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self.assertEqual(distribution.data['hs'], len(course_enrollments))
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course_enrollments[0].deactivate()
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distribution = profile_distribution(self.course_id, "level_of_education")
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self.assertEqual(distribution.data['hs'], len(course_enrollments) - 1)
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class TestAnalyticsDistributionsNoData(TestCase):
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'''Test analytics distribution gathering.'''
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def setUp(self):
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super(TestAnalyticsDistributionsNoData, self).setUp()
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self.course_id = CourseLocator('robot', 'course', 'id')
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self.users = [UserFactory(
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profile__year_of_birth=i + 1930,
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) for i in range(5)]
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self.nodata_users = [UserFactory(
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profile__year_of_birth=None,
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profile__gender=[None, ''][i % 2]
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) for i in range(4)]
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self.users += self.nodata_users
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self.ces = tuple(CourseEnrollment.enroll(user, self.course_id)
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for user in self.users)
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def test_profile_distribution_easy_choice_nodata(self):
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feature = 'gender'
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self.assertIn(feature, AVAILABLE_PROFILE_FEATURES)
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distribution = profile_distribution(self.course_id, feature)
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print(distribution)
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self.assertEqual(distribution.type, 'EASY_CHOICE')
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self.assertTrue(hasattr(distribution, 'choices_display_names'))
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self.assertNotEqual(distribution.choices_display_names, None)
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self.assertIn('no_data', distribution.data)
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self.assertEqual(distribution.data['no_data'], len(self.nodata_users))
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def test_profile_distribution_open_choice_nodata(self):
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feature = 'year_of_birth'
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self.assertIn(feature, AVAILABLE_PROFILE_FEATURES)
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distribution = profile_distribution(self.course_id, feature)
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print(distribution)
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self.assertEqual(distribution.type, 'OPEN_CHOICE')
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self.assertTrue(hasattr(distribution, 'choices_display_names'))
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self.assertEqual(distribution.choices_display_names, None)
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self.assertIn('no_data', distribution.data)
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self.assertEqual(distribution.data['no_data'], len(self.nodata_users))
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