def can_access(self, feature_key: str):
feature = self._feature_store.get_feature(feature_key)
if feature:
return evaluate(self._attributes, feature,
self._get_staff_segment())
# If the feature itself does not exist, then allow the user access.
return True
def test_evaluate_partial_overlap(self):
""" Two separate features with 10% rollout. Count users who have access
to both features. Should be 0.1 x 0.1 ~= 0.01 of total population. """
from tests import fixtures
truthy_both = 0
SAMPLE_SIZE = int(10e4)
for email in fixtures.random_emails(SAMPLE_SIZE):
regular_user_attr.update({'email': email})
result_1 = evaluate(regular_user_attr, partial_feature)
result_2 = evaluate(regular_user_attr, partial_feature_2)
if result_1 and result_2:
truthy_both += 1
logger.info(' %d users among %d population saw both features.',
truthy_both, SAMPLE_SIZE)
self.assertAlmostEqual(truthy_both / SAMPLE_SIZE * 100,
partial_feature.rollout_percentage *
partial_feature_2.rollout_percentage / 100,
delta=0.5)
def test_evaluate_partial(self):
from tests import fixtures
truthy = 0
falsy = 0
for email in fixtures.random_emails(10000):
regular_user_attr.update({'email': email})
result = evaluate(regular_user_attr, partial_feature)
if result:
truthy = truthy + 1
else:
falsy = falsy + 1
logger.info("After going through %d records," % (truthy + falsy))
logger.info(" %d %% items were selected.",
100 * truthy / (truthy + falsy))
logger.info(" %d %% was the rollout_percentage." %
(partial_feature.rollout_percentage))
self.assertAlmostEqual(truthy / (truthy + falsy) * 100,
partial_feature.rollout_percentage,
delta=0.5)
def test_evaluate_ga(self):
result = evaluate(regular_user_attr, ga_feature)
self.assertEqual(True, result)
result = evaluate(staff_user_attr, ga_feature)
self.assertEqual(True, result)
def test_evaluate_with_dev(self):
result = evaluate(
regular_user_attr,
indev_feature,
)
self.assertEqual(False, result)