def test_calculate_beta_distribution_passes():
weighted_score, weighted_attempts = algorithm.calculate_weighted_score_and_attempts(
1., 1., 1., 1.)
try:
algorithm.calculate_beta_distribution_mean(weighted_score,
weighted_attempts)
assert True
except:
assert False
def test_calculate_beta_distribution_mean_score_is_nonegative():
score = -1.
attempts = 2.
try:
algorithm.calculate_beta_distribution_mean(score, attempts)
assert False
except Exception as error:
assert error.__class__.__name__ == 'ValueError'
assert str(error) == f"{score} < 0 : score should be non-negative"
def test_calculate_beta_distribution_mean_score_lt_attempts():
score = 2.
attempts = 1.
try:
algorithm.calculate_beta_distribution_mean(score, attempts)
assert False
except Exception as error:
assert error.__class__.__name__ == 'ValueError'
assert str(
error
) == f"{score} > {attempts} : score should be less than or equal to attempts"
def test_calculate_beta_distribution_mean_attempts_is_float():
score = 1.
attempts = 2
try:
algorithm.calculate_beta_distribution_mean(score, attempts)
assert False
except Exception as error:
assert error.__class__.__name__ == 'TypeError'
assert str(
error
) == f"attempts should be a float, a {score.__class__.__name__} was provided"
def test_calculate_beta_distribution_mean_is_1_thirds():
score = 0.
attempts = 1.
mastery = algorithm.calculate_beta_distribution_mean(score, attempts)
assert mastery == 1 / 3
def test_calculate_beta_distribution_mean_is_half():
score = 1.
attempts = 2.
mastery = algorithm.calculate_beta_distribution_mean(score, attempts)
assert mastery == 0.5
def test_calculate_beta_distribution_mean_in_range_0_to_1():
score = 1.
attempts = 2.
mastery = algorithm.calculate_beta_distribution_mean(score, attempts)
assert mastery == pytest.approx(0.5, abs=0.5)
def test_calculate_beta_distribution_mean_returns_float():
score = 1.
attempts = 2.
mastery = algorithm.calculate_beta_distribution_mean(score, attempts)
assert isinstance(mastery, float)
def handler(event, context):
try:
topic_id_list = event['topicIds']
questions = event['questions']
return_results = event['returnResults']
except KeyError:
raise Exception('[BAD REQUEST]: Invalid event')
study_guide_id_list = helper.get_study_guide_id_list(
topic_id_list)
topic_id_for_study_guide_id = helper.get_topic_id(
study_guide_id_list)
response = {'nextQuestion': {},
'results': []}
if not questions:
response['nextQuestion'] = algorithm.choose_initial_question(
topic_id_for_study_guide_id, study_guide_id_list)
return _build_response(200, response)
question_id_list = _create_question_id_list(questions)
study_guide_score_and_attempts, topic_score_and_attempts = \
_accumulate_score_and_attempts(
study_guide_id_list, topic_id_list, questions)
_add_weighted_score_and_attempts(
study_guide_score_and_attempts, topic_score_and_attempts,
study_guide_id_list, topic_id_for_study_guide_id)
if return_results:
results_list = []
for study_guide_id in study_guide_id_list:
topic_id = topic_id_for_study_guide_id[study_guide_id]
weighted_score = study_guide_score_and_attempts[
study_guide_id]['weighted_score']
weighted_attempts = study_guide_score_and_attempts[
study_guide_id]['weighted_attempts']
mastery = float(algorithm.calculate_beta_distribution_mean(
weighted_score, weighted_attempts))
mastery_band, band_confidence = algorithm.calculate_mastery_band_and_confidence(
mastery, weighted_score, weighted_attempts)
results_list.append({
'studyGuideId': study_guide_id,
'topicId': topic_id,
'band': mastery_band,
'masteryScore': mastery * 100,
'confidenceScore': band_confidence * 100
})
response['results'] = results_list
else:
confidence_intervals_list = algorithm.calculate_confidence_intervals_list(
study_guide_id_list, study_guide_score_and_attempts)
response['nextQuestion'] = algorithm.choose_next_question(
topic_id_for_study_guide_id, study_guide_id_list,
confidence_intervals_list, question_id_list)
return _build_response(200, response)
def test_calculate_beta_distribution_mean_1_out_of_1():
weighted_score = 1.
weighted_attempts = 1.
mastery = algorithm.calculate_beta_distribution_mean(
weighted_score, weighted_attempts)
assert mastery == pytest.approx(0.66, abs=0.01)
