def get_rating_fn(courses, uc):
if uc.course_id not in courses:
obj = {}
for field in COURSE_RATING_FIELDS:
obj[field] = m.AggregateRating()
courses[uc.course_id] = obj
return courses[uc.course_id]
def test_add_remove_ratings(self):
# This sequence of add/remove ratings reproduces a long-standing bug we
# used to have: sometimes when users saved ratings for a professor, the
# rating would be negative (and the save wouldn't validate due to model
# constraints): https://github.com/UWFlow/rmc/issues/116
# Professor Meredith is a cute kitten. Let's see if she's passionate.
passion = m.AggregateRating()
self.assertRating(passion, expected_rating=0.0, expected_count=0)
# She's adopted by a girl named Taylor. They become happy roommates.
passion.add_rating(1)
self.assertRating(passion, expected_rating=1.0, expected_count=1)
# But Meredith exhibits bad posture. Taylor isn't excited about that,
# so she retracts her old rating for now.
passion.remove_rating(1)
self.assertRating(passion, expected_rating=0.0, expected_count=0)
# Meredith learns how to moves her ears on command and Taylor is happy.
passion.add_rating(1)
self.assertRating(passion, expected_rating=1.0, expected_count=1)
# But Ed is dejected after Meredith gives him a look of disapproval.
passion.add_rating(0)
self.assertRating(passion, expected_rating=0.5, expected_count=2)
# Meredith meowed on about how Taylor can't sing, which Taylor thought
# was Mean, so she changed her old approval to a disapproval.
passion.remove_rating(1)
passion.add_rating(0)
self.assertRating(passion, expected_rating=0.0, expected_count=2)
def interest_from_scores(scores):
# Course directed ratings
# INTEREST
# TODO(Sandy): Revise the use of this question-metric
# how many classes attended
i_count = scores[17]['num_replies']
i_rating = normalize_score(scores[17]) / max(1, i_count)
return m.AggregateRating(rating=i_rating, count=i_count)
def overall_course_from_interest_easiness(i, e):
INTEREST_WEIGHT = 0.5
EASINESS_WEIGHT = 0.5
# OVERALL
oc_count = int(
round(i.count * INTEREST_WEIGHT +
e.count * EASINESS_WEIGHT))
oc_rating = (i.rating * INTEREST_WEIGHT +
e.rating * EASINESS_WEIGHT) / max(1, oc_count)
return m.AggregateRating(rating=oc_rating, count=oc_count)
def easiness_from_scores(scores):
Q11_WEIGHT = 0.5
Q12_WEIGHT = 0.5
# EASINESS
# difficulty of concepts
e1 = normalize_score(scores[11]) * Q11_WEIGHT
e1r = scores[11]['num_replies'] * Q11_WEIGHT
# workload
e2 = normalize_score(scores[12]) * Q12_WEIGHT
e2r = scores[12]['num_replies'] * Q12_WEIGHT
e_count = int(round(e1r + e2r))
e_rating = (e1 + e2) / max(1, e_count)
return m.AggregateRating(rating=e_rating, count=e_count)
def get_rating_fn(courses, uc):
if uc.professor_id is None:
return None
if uc.course_id not in courses:
courses[uc.course_id] = {}
professors = courses[uc.course_id]
if uc.professor_id not in professors:
obj = {}
for field in PROFESSOR_RATING_FIELDS:
obj[field] = m.AggregateRating()
professors[uc.professor_id] = obj
return professors[uc.professor_id]
def get_aggregate_fields_fn(uc):
easiness = uc.easiness
interest = uc.interest
# TODO(mack): add usefulness metric
def calculate_overall_rating(e, i):
return ((e.count * e.rating + i.count * i.rating) /
max(1, (e.count + i.count)))
# heuristic for getting the overall rating:
# 1. the count will max of the count for each attribute
# 2. the rating will be average
overall = m.AggregateRating(
count=max(easiness.count, interest.count),
rating=calculate_overall_rating(easiness, interest),
)
return [
('easiness', easiness),
('interest', interest),
('overall', overall),
]
def clarity_from_scores(scores):
Q1_WEIGHT = 0.2
Q2_WEIGHT = 0.2
Q3_WEIGHT = 0.4
Q4_WEIGHT = 0.2
# CLARITY
# presentation in lectures (organization and clarity)
c1 = normalize_score(scores[1]) * Q1_WEIGHT
c1r = scores[1]['num_replies'] * Q1_WEIGHT
# response to questions
c2 = normalize_score(scores[2]) * Q2_WEIGHT
c2r = scores[2]['num_replies'] * Q2_WEIGHT
# oral presentation (audibility, articulation, english)
c3 = normalize_score(scores[3]) * Q3_WEIGHT
c3r = scores[3]['num_replies'] * Q3_WEIGHT
# visual presentation
# (organization, legibility, effective use of materials)
c4 = normalize_score(scores[4]) * Q4_WEIGHT
c4r = scores[4]['num_replies'] * Q4_WEIGHT
c_count = int(round(c1r + c2r + c3r + c4r))
c_rating = (c1 + c2 + c3 + c4) / max(1, c_count)
return m.AggregateRating(rating=c_rating, count=c_count)
def overall_prof_from_scores(scores):
# OVERALL
# overall appraisal of quality of teaching
op_count = scores[10]['num_replies']
op_rating = normalize_score(scores[10]) / max(1, op_count)
return m.AggregateRating(rating=op_rating, count=op_count)
def passion_from_scores(scores):
# PASSION
# attitude towards teachings the course
p_count = scores[8]['num_replies']
p_rating = normalize_score(scores[8]) / max(1, p_count)
return m.AggregateRating(rating=p_rating, count=p_count)