def test_recommendations_via_manager(): # noqa
ctx = get_test_ctx()
EXPECTED_RESULTS = [('ghi', 3430.0), ('def', 3320.0), ('ijk', 3200.0),
('hij', 3100.0), ('lmn', 420.0),
('klm', 409.99999999999994), ('jkl', 400.0),
('abc', 23.0), ('fgh', 22.0), ('efg', 21.0)]
factory = MockRecommenderFactory()
class MockProfileFetcher:
def get(self, client_id):
return {'client_id': client_id}
ctx['recommender_factory'] = factory
ctx['profile_fetcher'] = MockProfileFetcher()
ctx['utils'] = Mocker()
ctx['clock'] = Clock()
ctx['cache'] = JSONCache(ctx)
manager = RecommendationManager(ctx.child())
recommendation_list = manager.recommend({'client_id': 'some_ignored_id'},
10,
extra_data={'branch': 'ensemble'})
assert isinstance(recommendation_list, list)
assert recommendation_list == EXPECTED_RESULTS
def test_recommendation_strategy():
"""The recommendation manager is currently very naive and just
selects the first recommender which returns 'True' to
can_recommend()."""
EXPECTED_ADDONS = ["expected_id", "other-id"]
# Create a stub ProfileFetcher that always returns the same
# client data.
class StubFetcher:
def get(self, client_id):
return {'client_id': '00000'}
# Configure the recommender so that only the second model
# can recommend and return the expected addons.
factory = MockRecommenderFactory(
legacy=lambda: StubRecommender(False, []),
collaborative=lambda: StubRecommender(True, EXPECTED_ADDONS),
similarity=lambda: StubRecommender(False, []),
locale=lambda: StubRecommender(False, []))
# Make sure the recommender returns the expected addons.
ctx = get_test_ctx()
ctx['recommender_factory'] = factory
ctx['profile_fetcher'] = StubFetcher()
ctx['utils'] = Mocker()
ctx['clock'] = Clock()
ctx['cache'] = JSONCache(ctx)
manager = RecommendationManager(ctx.child())
results = manager.recommend("client-id",
10,
extra_data={'branch': 'linear'})
assert results == EXPECTED_ADDONS
def test_fixed_client_id_empty_list(test_ctx):
ctx = install_mocks(test_ctx)
ctx = install_mock_curated_data(ctx)
manager = RecommendationManager(ctx.child())
recommendation_list = manager.recommend(EMPTY_TEST_CLIENT_IDS[0], 10)
assert len(recommendation_list) == 0
def test_intervention_control(test_ctx):
ctx = install_mocks(test_ctx)
ctx = install_mock_curated_data(ctx)
manager = RecommendationManager(ctx.child())
recommendation_list = manager.recommend('some_ignored_id',
10,
extra_data={'branch': INTERVENTION_CONTROL})
assert len(recommendation_list) == 0
def test_none_profile_returns_empty_list(test_ctx):
ctx = install_mocks(test_ctx)
class MockProfileFetcher:
def get(self, client_id):
return None
ctx["profile_fetcher"] = MockProfileFetcher()
rec_manager = RecommendationManager(ctx)
assert rec_manager.recommend("random-client-id", 10) == []
def test_none_profile_returns_empty_list(monkeypatch):
monkeypatch.setattr(hbase_client.HBaseClient, 'get_client_profile',
lambda x, y: None)
monkeypatch.setattr(hbase_client.HBaseClient, '_get_hbase_hostname',
lambda x: 'master-ip-address')
fetcher = ProfileFetcher()
rec_manager = RecommendationManager(fetcher, ("fake-recommender", ))
assert rec_manager.recommend("random-client-id", 10) == []
def test_intervention_b(test_ctx):
"""The recommendation manager is currently very naive and just
selects the first recommender which returns 'True' to
can_recommend()."""
ctx = install_mocks(test_ctx)
ctx = install_mock_curated_data(ctx)
manager = RecommendationManager(ctx.child())
recommendation_list = manager.recommend('some_ignored_id',
4,
extra_data={'branch': INTERVENTION_B})
assert isinstance(recommendation_list, list)
assert len(recommendation_list) == 4
def test_fixed_client_id_empty_list(test_ctx):
ctx = install_mocks(test_ctx)
ctx = install_mock_curated_data(ctx)
manager = RecommendationManager(ctx.child())
recommendation_list = manager.recommend(EMPTY_TEST_CLIENT_IDS[0],
10,
extra_data={'branch': INTERVENTION_A})
assert len(recommendation_list) == 0
recommendation_list = manager.recommend(EMPTY_TEST_CLIENT_IDS[0],
10,
extra_data={'branch': INTERVENTION_B})
assert len(recommendation_list) == 0
def test_intervention_names(test_ctx):
# verify that intervention names can use hyphens instead of
# underscore
ctx = install_mocks(test_ctx)
ctx = install_mock_curated_data(ctx)
manager = RecommendationManager(ctx.child())
recommendation_list = manager.recommend(TEST_CLIENT_IDS[0],
10,
extra_data={'branch': 'intervention-a'})
assert len(recommendation_list) == 10
recommendation_list = manager.recommend(TEST_CLIENT_IDS[0],
10,
extra_data={'branch': 'intervention-b'})
assert len(recommendation_list) == 10
def get_client_recommendations(request):
form = AddonRecommendationsForm()
recommendations = []
if request.method == 'POST':
form = AddonRecommendationsForm(data=request.POST)
if form.is_valid():
# Use addon recommender.
value = form.cleaned_data['client_id']
num_items = form.cleaned_data['num_items']
recommendation_manager = RecommendationManager()
recommendations = recommendation_manager.recommend(value,
num_items)
context = {
'form': form,
'recommendations': recommendations
}
return render(request, template_name="taar/index.html", context=context)
def test_recommendation_strategy():
EXPECTED_ADDONS = ["expected_id", "other-id"]
# Create a stub ProfileFetcher that always returns the same
# client data.
class StubFetcher:
def get(self, client_id):
return {}
# Configure the recommender so that only the second model
# can recommend and return the expected addons.
recommenders = (
StubRecommender(False, []),
StubRecommender(True, EXPECTED_ADDONS),
StubRecommender(False, []),
)
# Make sure the recommender returns the expected addons.
manager = RecommendationManager(StubFetcher(), recommenders)
assert manager.recommend("client-id", 10) == EXPECTED_ADDONS
def test_simple_recommendation(test_ctx):
ctx = install_mocks(test_ctx)
EXPECTED_RESULTS = [
("ghi", 3430.0),
("def", 3320.0),
("ijk", 3200.0),
("hij", 3100.0),
("lmn", 420.0),
("klm", 409.99999999999994),
("jkl", 400.0),
("abc", 23.0),
("fgh", 22.0),
("efg", 21.0),
]
manager = RecommendationManager(ctx.child())
recommendation_list = manager.recommend("some_ignored_id", 10)
assert isinstance(recommendation_list, list)
assert recommendation_list == EXPECTED_RESULTS
def test_intervention_a(test_ctx):
ctx = install_mocks(test_ctx)
EXPECTED_RESULTS = [('ghi', 3430.0),
('def', 3320.0),
('ijk', 3200.0),
('hij', 3100.0),
('lmn', 420.0),
('klm', 409.99999999999994),
('jkl', 400.0),
('abc', 23.0),
('fgh', 22.0),
('efg', 21.0)]
manager = RecommendationManager(ctx.child())
recommendation_list = manager.recommend('some_ignored_id',
10,
extra_data={'branch': INTERVENTION_A})
assert isinstance(recommendation_list, list)
assert recommendation_list == EXPECTED_RESULTS
def test_experimental_randomization(test_ctx):
ctx = install_mocks(test_ctx)
ctx = install_mock_curated_data(ctx)
manager = RecommendationManager(ctx.child())
raw_list = manager.recommend(TEST_CLIENT_IDS[0], 10)
# Clobber the experiment probability to be 100% to force a
# reordering.
ctx["TAAR_EXPERIMENT_PROB"] = 1.0
manager = RecommendationManager(ctx.child())
rand_list = manager.recommend(TEST_CLIENT_IDS[0], 10)
'''
The two lists should be :
* different (guid, weight) lists (possibly just order)
* same length
'''
assert (reduce(
operator.and_,
[(t1[0] == t2[0] and t1[1] == t2[1])
for t1, t2 in zip(rand_list, raw_list)],
) is False)
assert len(rand_list) == len(raw_list)
def test_none_profile_returns_empty_list(test_ctx):
ctx = install_mocks(test_ctx)
rec_manager = RecommendationManager(ctx)
assert rec_manager.recommend("random-client-id", 10) == []
def test_none_profile_returns_empty_list():
ctx = get_test_ctx()
ctx['clock'] = Clock()
ctx['cache'] = JSONCache(ctx)
rec_manager = RecommendationManager(ctx)
assert rec_manager.recommend("random-client-id", 10) == []