def test_get_lr(test_ctx):
# Tests that the likelihood ratio values are not empty for extreme values and are realistic.
with mock_install_continuous_data(test_ctx):
r = SimilarityRecommender(test_ctx)
cache = r._get_cache({})
assert r.get_lr(0.0001, cache) is not None
assert r.get_lr(10.0, cache) is not None
assert r.get_lr(0.001, cache) > r.get_lr(5.0, cache)
def test_compute_clients_dist(test_ctx):
# Test the distance function computation.
with mock_install_continuous_data(test_ctx):
r = SimilarityRecommender(test_ctx)
test_clients = [
{
"client_id": "test-client-002",
"activeAddons": [],
"geo_city": "sfo-us",
"subsession_length": 1,
"locale": "en-US",
"os": "windows",
"bookmark_count": 1,
"tab_open_count": 1,
"total_uri": 1,
"unique_tlds": 1,
},
{
"client_id": "test-client-003",
"activeAddons": [],
"geo_city": "brasilia-br",
"subsession_length": 1,
"locale": "br-PT",
"os": "windows",
"bookmark_count": 10,
"tab_open_count": 1,
"total_uri": 1,
"unique_tlds": 1,
},
{
"client_id": "test-client-004",
"activeAddons": [],
"geo_city": "brasilia-br",
"subsession_length": 100,
"locale": "br-PT",
"os": "windows",
"bookmark_count": 10,
"tab_open_count": 10,
"total_uri": 100,
"unique_tlds": 10,
},
]
per_client_test = []
# Compute a different set of distances for each set of clients.
cache = r._get_cache({})
for tc in test_clients:
test_distances = r.compute_clients_dist(tc, cache)
assert len(test_distances) == len(CONTINUOUS_FEATURE_FIXTURE_DATA)
per_client_test.append(test_distances[2][0])
# Ensure the different clients also had different distances to a specific donor.
assert per_client_test[0] >= per_client_test[1] >= per_client_test[2]
