gistfile1.txt

-bash-4.2$ pytest
========================================= test session starts ==========================================
platform linux2 -- Python 2.7.12, pytest-3.2.3, py-1.4.34, pluggy-0.4.0
rootdir: /home/hadoop/taar, inifile:
plugins: cov-2.5.1
collected 24 items

test_collaborativerecommender.py ....
test_legacyrecommender.py ...
test_localerecommender.py ....
test_profile_fetcher.py ..
test_recommendation_manager.py .F
test_similarityrecommender.py ..F..F
test_utils.py ...

=============================================== FAILURES ===============================================
_____________________________________ test_recommendation_strategy _____________________________________

    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

test_recommendation_manager.py:55:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = <taar.recommenders.recommendation_manager.RecommendationManager object at 0x7f3f56c81810>
client_id = 'client-id', limit = 10, extra_data = {}

    def recommend(self, client_id, limit, extra_data={}):
        """Return recommendations for the given client.

            The recommendation logic will go through each recommender and pick the
            first one that "can_recommend".

            :param client_id: the client unique id.
            :param limit: the maximum number of recommendations to return.
            :param extra_data: a dictionary with extra client data.
            """
        # Get the info for the requested client id.
        client_info = self.profile_fetcher.get(client_id)
        if client_info is None:
            return []

        # Compute the recommendation.
        for r in self.recommenders:
>           if r.can_recommend(client_info, extra_data):
E           TypeError: can_recommend() takes exactly 2 arguments (3 given)

/mnt/anaconda2/lib/python2.7/site-packages/mozilla_taar-0.0.16.dev9+ga3157d4-py2.7.egg/taar/recommenders/recommendation_manager.py:54: TypeError
_________________________________________ test_recommendations _________________________________________

instantiate_mocked_s3_bucket = s3.ServiceResource()

    def test_recommendations(instantiate_mocked_s3_bucket):
        # Create a new instance of a SimilarityRecommender.
        r = SimilarityRecommender()

>       recommendations = r.recommend(generate_a_fake_taar_client(), 10)

test_similarityrecommender.py:124:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/mnt/anaconda2/lib/python2.7/site-packages/mozilla_taar-0.0.16.dev9+ga3157d4-py2.7.egg/taar/recommenders/similarity_recommender.py:151: in recommend
    donor_set_ranking, indices = self.get_similar_donors(client_data)
/mnt/anaconda2/lib/python2.7/site-packages/mozilla_taar-0.0.16.dev9+ga3157d4-py2.7.egg/taar/recommenders/similarity_recommender.py:133: in get_similar_donors
    lambda x, y: distance.hamming(x, y))
/mnt/anaconda2/lib/python2.7/site-packages/scipy/spatial/distance.py:2020: in cdist
    XA = _copy_array_if_base_present(_convert_to_double(XA))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

X = array([[u'nowhere-us', u'en-US', u'mac'],
       [u'pompei-it', u'it-IT', u'Linux'],
       [u'brasilia-br', u'br-PT', u'windows']], dtype=object)

    def _convert_to_double(X):
        if X.dtype != np.double:
>           X = X.astype(np.double)
E           ValueError: could not convert string to float: windows

/mnt/anaconda2/lib/python2.7/site-packages/scipy/spatial/distance.py:143: ValueError
_______________________________________ test_distance_functions ________________________________________

instantiate_mocked_s3_bucket = s3.ServiceResource()

    def test_distance_functions(instantiate_mocked_s3_bucket):
        # Tests the similarity functions via expected output when passing modified client data.
        r = SimilarityRecommender()

        # Generate a fake client.
        test_client = generate_a_fake_taar_client()
>       recs = r.recommend(test_client)
E       TypeError: recommend() takes at least 3 arguments (2 given)

test_similarityrecommender.py:157: TypeError
================================= 3 failed, 21 passed in 3.22 seconds ==================================
-bash-4.2$