"""
Tests for grades.scores module.
"""
# pylint: disable=protected-access
from collections import namedtuple
import ddt
from django.test import TestCase
import itertools
from lms.djangoapps.grades.models import BlockRecord
import lms.djangoapps.grades.scores as scores
from lms.djangoapps.grades.transformer import GradesTransformer
from opaque_keys.edx.locator import BlockUsageLocator, CourseLocator
from openedx.core.djangoapps.content.block_structure.block_structure import BlockData
from xmodule.graders import ProblemScore
class TestScoredBlockTypes(TestCase):
"""
Tests for the possibly_scored function.
"""
possibly_scored_block_types = {
'course', 'chapter', 'sequential', 'vertical',
'library_content', 'split_test', 'conditional', 'library', 'randomize',
'problem', 'drag-and-drop-v2', 'openassessment', 'lti', 'lti_consumer',
'videosequence', 'problemset', 'acid_parent', 'done', 'wrapper', 'edx_sga',
}
def test_block_types_possibly_scored(self):
self.assertSetEqual(
self.possibly_scored_block_types,
scores._block_types_possibly_scored()
)
def test_possibly_scored(self):
course_key = CourseLocator(u'org', u'course', u'run')
for block_type in self.possibly_scored_block_types:
usage_key = BlockUsageLocator(course_key, block_type, 'mock_block_id')
self.assertTrue(scores.possibly_scored(usage_key))
@ddt.ddt
class TestGetScore(TestCase):
"""
Tests for get_score
"""
display_name = 'test_name'
location = 'test_location'
SubmissionValue = namedtuple('SubmissionValue', 'exists, weighted_earned, weighted_possible')
CSMValue = namedtuple('CSMValue', 'exists, raw_earned, raw_possible')
PersistedBlockValue = namedtuple('PersistedBlockValue', 'exists, raw_possible, weight, graded')
ContentBlockValue = namedtuple('ContentBlockValue', 'raw_possible, weight, explicit_graded')
ExpectedResult = namedtuple(
'ExpectedResult', 'raw_earned, raw_possible, weighted_earned, weighted_possible, weight, graded, attempted'
)
def _create_submissions_scores(self, submission_value):
"""
Creates a stub result from the submissions API for the given values.
"""
if submission_value.exists:
return {self.location: (submission_value.weighted_earned, submission_value.weighted_possible)}
else:
return {}
def _create_csm_scores(self, csm_value):
"""
Creates a stub result from courseware student module for the given values.
"""
if csm_value.exists:
stub_csm_record = namedtuple('stub_csm_record', 'correct, total')
return {self.location: stub_csm_record(correct=csm_value.raw_earned, total=csm_value.raw_possible)}
else:
return {}
def _create_persisted_block(self, persisted_block_value):
"""
Creates and returns a minimal BlockRecord object with the give values.
"""
if persisted_block_value.exists:
return BlockRecord(
self.location,
persisted_block_value.weight,
persisted_block_value.raw_possible,
persisted_block_value.graded,
)
else:
return None
def _create_block(self, content_block_value):
"""
Creates and returns a minimal BlockData object with the give values.
"""
block = BlockData(self.location)
block.display_name = self.display_name
block.weight = content_block_value.weight
block_grades_transformer_data = block.transformer_data.get_or_create(GradesTransformer)
block_grades_transformer_data.max_score = content_block_value.raw_possible
setattr(
block_grades_transformer_data,
GradesTransformer.EXPLICIT_GRADED_FIELD_NAME,
content_block_value.explicit_graded,
)
return block
@ddt.data(
# submissions _trumps_ other values; weighted and graded from persisted-block _trumps_ latest content values
(
SubmissionValue(exists=True, weighted_earned=50, weighted_possible=100),
CSMValue(exists=True, raw_earned=10, raw_possible=40),
PersistedBlockValue(exists=True, raw_possible=5, weight=40, graded=True),
ContentBlockValue(raw_possible=1, weight=20, explicit_graded=False),
ExpectedResult(
raw_earned=None, raw_possible=None,
weighted_earned=50, weighted_possible=100,
weight=40, graded=True, attempted=True,
),
),
# same as above, except submissions doesn't exist; CSM values used
(
SubmissionValue(exists=False, weighted_earned=50, weighted_possible=100),
CSMValue(exists=True, raw_earned=10, raw_possible=40),
PersistedBlockValue(exists=True, raw_possible=5, weight=40, graded=True),
ContentBlockValue(raw_possible=1, weight=20, explicit_graded=False),
ExpectedResult(
raw_earned=10, raw_possible=40,
weighted_earned=10, weighted_possible=40,
weight=40, graded=True, attempted=True,
),
),
# CSM values exist, but with NULL earned score treated as not-attempted
(
SubmissionValue(exists=False, weighted_earned=50, weighted_possible=100),
CSMValue(exists=True, raw_earned=None, raw_possible=40),
PersistedBlockValue(exists=True, raw_possible=5, weight=40, graded=True),
ContentBlockValue(raw_possible=1, weight=20, explicit_graded=False),
ExpectedResult(
raw_earned=0, raw_possible=40,
weighted_earned=0, weighted_possible=40,
weight=40, graded=True, attempted=False,
),
),
# neither submissions nor CSM exist; Persisted values used
(
SubmissionValue(exists=False, weighted_earned=50, weighted_possible=100),
CSMValue(exists=False, raw_earned=10, raw_possible=40),
PersistedBlockValue(exists=True, raw_possible=5, weight=40, graded=True),
ContentBlockValue(raw_possible=1, weight=20, explicit_graded=False),
ExpectedResult(
raw_earned=0, raw_possible=5,
weighted_earned=0, weighted_possible=40,
weight=40, graded=True, attempted=False,
),
),
# none of submissions, CSM, or persisted exist; Latest content values used
(
SubmissionValue(exists=False, weighted_earned=50, weighted_possible=100),
CSMValue(exists=False, raw_earned=10, raw_possible=40),
PersistedBlockValue(exists=False, raw_possible=5, weight=40, graded=True),
ContentBlockValue(raw_possible=1, weight=20, explicit_graded=False),
ExpectedResult(
raw_earned=0, raw_possible=1,
weighted_earned=0, weighted_possible=20,
weight=20, graded=False, attempted=False,
),
),
)
@ddt.unpack
def test_get_score(self, submission_value, csm_value, persisted_block_value, block_value, expected_result):
score = scores.get_score(
self._create_submissions_scores(submission_value),
self._create_csm_scores(csm_value),
self._create_persisted_block(persisted_block_value),
self._create_block(block_value),
)
expected_score = ProblemScore(**expected_result._asdict())
self.assertEquals(score, expected_score)
@ddt.ddt
class TestWeightedScore(TestCase):
"""
Tests the helper method: weighted_score
"""
@ddt.data(
(0, 0, 1),
(5, 0, 0),
(10, 0, None),
(0, 5, None),
(5, 10, None),
(10, 10, None),
)
@ddt.unpack
def test_cannot_compute(self, raw_earned, raw_possible, weight):
self.assertEquals(
scores.weighted_score(raw_earned, raw_possible, weight),
(raw_earned, raw_possible),
)
@ddt.data(
(0, 5, 0, (0, 0)),
(5, 5, 0, (0, 0)),
(2, 5, 1, (.4, 1)),
(5, 5, 1, (1, 1)),
(5, 5, 3, (3, 3)),
(2, 4, 6, (3, 6)),
)
@ddt.unpack
def test_computed(self, raw_earned, raw_possible, weight, expected_score):
self.assertEquals(
scores.weighted_score(raw_earned, raw_possible, weight),
expected_score,
)
def test_assert_on_invalid_r_possible(self):
with self.assertRaises(AssertionError):
scores.weighted_score(raw_earned=1, raw_possible=None, weight=1)
@ddt.ddt
class TestInternalGetGraded(TestCase):
"""
Tests the internal helper method: _get_explicit_graded
"""
def _create_block(self, explicit_graded_value):
"""
Creates and returns a minimal BlockData object with the give value
for explicit_graded.
"""
block = BlockData('any_key')
setattr(
block.transformer_data.get_or_create(GradesTransformer),
GradesTransformer.EXPLICIT_GRADED_FIELD_NAME,
explicit_graded_value,
)
return block
@ddt.data(None, True, False)
def test_with_no_persisted_block(self, explicitly_graded_value):
block = self._create_block(explicitly_graded_value)
self.assertEquals(
scores._get_graded_from_block(None, block),
explicitly_graded_value is not False, # defaults to True unless explicitly False
)
@ddt.data(
*itertools.product((True, False), (True, False, None))
)
@ddt.unpack
def test_with_persisted_block(self, persisted_block_value, block_value):
block = self._create_block(block_value)
block_record = BlockRecord(block.location, 0, 0, persisted_block_value)
self.assertEquals(
scores._get_graded_from_block(block_record, block),
block_record.graded, # persisted value takes precedence
)
@ddt.ddt
class TestInternalGetScoreFromBlock(TestCase):
"""
Tests the internal helper method: _get_score_from_persisted_or_latest_block
"""
def _create_block(self, raw_possible):
"""
Creates and returns a minimal BlockData object with the give value
for raw_possible.
"""
block = BlockData('any_key')
block.transformer_data.get_or_create(GradesTransformer).max_score = raw_possible
return block
def _verify_score_result(self, persisted_block, block, weight, expected_r_possible):
"""
Verifies the result of _get_score_from_persisted_or_latest_block is as expected.
"""
# pylint: disable=unbalanced-tuple-unpacking
(
raw_earned, raw_possible, weighted_earned, weighted_possible, attempted
) = scores._get_score_from_persisted_or_latest_block(persisted_block, block, weight)
self.assertEquals(raw_earned, 0.0)
self.assertEquals(raw_possible, expected_r_possible)
self.assertEquals(weighted_earned, 0.0)
if weight is None or expected_r_possible == 0:
self.assertEquals(weighted_possible, expected_r_possible)
else:
self.assertEquals(weighted_possible, weight)
self.assertFalse(attempted)
@ddt.data(
*itertools.product((0, 1, 5), (None, 0, 1, 5))
)
@ddt.unpack
def test_with_no_persisted_block(self, block_r_possible, weight):
block = self._create_block(block_r_possible)
self._verify_score_result(None, block, weight, block_r_possible)
@ddt.data(
*itertools.product((0, 1, 5), (None, 0, 1, 5), (None, 0, 1, 5))
)
@ddt.unpack
def test_with_persisted_block(self, persisted_block_r_possible, block_r_possible, weight):
block = self._create_block(block_r_possible)
block_record = BlockRecord(block.location, 0, persisted_block_r_possible, False)
self._verify_score_result(block_record, block, weight, persisted_block_r_possible)