def test_overall_quality(self):
popularity = self._popularity(59)
rating = self._rating(4)
irrelevant = self._measurement("Some other quantity", 42, self.source,
1)
pop = popularity.normalized_value
rat = rating.normalized_value
assert 0.5 == pop
assert 1.0 / 3 == rat
l = [popularity, rating, irrelevant]
quality = Measurement.overall_quality(l)
assert (0.7 * rat) + (0.3 * pop) == quality
# Mess with the weights.
assert (0.5 * rat) + (0.5 * pop) == Measurement.overall_quality(
l, 0.5, 0.5)
# Adding a non-popularity measurement that is _equated_ to
# popularity via a percentile scale modifies the
# normalized value -- we don't care exactly how, only that
# it's taken into account.
oclc = DataSource.lookup(self._db, DataSource.OCLC)
popularityish = self._measurement(Measurement.HOLDINGS, 400, oclc, 10)
new_quality = Measurement.overall_quality(l + [popularityish])
assert quality != new_quality
def test_overall_quality_with_popularity_and_quality_but_not_rating(self):
pop = self._popularity(4)
qual = self._quality(0.5)
# We would expect the final quality score to be 1/2 of the quality
# score we got from the metadata wrangler, and 1/2 of the normalized
# value of the 4-star rating.
expect = (pop.normalized_value / 2) + (0.5 / 2)
assert expect == Measurement.overall_quality([pop, qual], 0.5, 0.5)
def test_overall_quality_with_popularity_quality_and_rating(self):
pop = self._popularity(4)
rat = self._rating(4)
quality_score = 0.66
qual = self._quality(quality_score)
# The popularity and rating are scaled appropriately and
# added together.
expect_1 = (pop.normalized_value * 0.75) + (rat.normalized_value *
0.25)
# Then the whole thing is divided in half and added to half of the
# quality score
expect_total = expect_1 / 2 + (quality_score / 2)
assert expect_total == Measurement.overall_quality([pop, rat, qual],
0.75, 0.25)
def test_overall_quality_is_zero_if_no_relevant_measurements(self):
irrelevant = self._measurement("Some other quantity", 42, self.source,
1)
assert 0 == Measurement.overall_quality([irrelevant])
def test_overall_quality_takes_weights_into_account(self):
rating1 = self._rating(10, weight=10)
rating2 = self._rating(1, weight=1)
assert 0.91 == round(Measurement.overall_quality([rating1, rating2]),
2)
def test_overall_quality_based_solely_on_popularity_if_no_rating(self):
pop = self._popularity(59)
assert 0.5 == Measurement.overall_quality([pop])