def test_rank(self):
# test not personalized
alg = NXPageRank()
# rank with filter_list
res_filtered = alg.rank('A000', self.graph, filter_list=self.filter_list)
item_ranked_set = set(res_filtered['to_id'])
self.assertEqual(len(item_ranked_set), len(self.filter_list))
self.assertCountEqual(item_ranked_set, self.filter_list)
# rank without filter_list
res_all_unrated = alg.rank('A000', self.graph)
item_rated_set = set(self.ratings.query('from_id == "A000"')['to_id'])
item_ranked_set = set(res_all_unrated['to_id'])
# We expect this to be empty, since the alg should rank only unrated items (unless in filter list)
rated_in_ranked = item_ranked_set.intersection(item_rated_set)
self.assertEqual(len(rated_in_ranked), 0)
# rank with n_recs specified
n_recs = 1
res_n_recs = alg.rank('A000', self.graph, n_recs)
self.assertEqual(len(res_n_recs), n_recs)
item_rated_set = set(self.ratings.query('from_id == "A000"')['to_id'])
item_ranked_set = set(res_n_recs['to_id'])
# We expect this to be empty, since the alg should rank only unrated items (unless in filter list)
rated_in_ranked = item_ranked_set.intersection(item_rated_set)
self.assertEqual(len(rated_in_ranked), 0)
# test personalized
alg = NXPageRank(personalized=True)
result_personalized = alg.rank('A000', self.graph)
alg = NXPageRank()
result_not_personalized = alg.rank('A000', self.graph)
result_personalized = np.array(result_personalized)
result_not_personalized = np.array(result_not_personalized)
result_personalized.sort(axis=0)
result_not_personalized.sort(axis=0)
self.assertFalse(np.array_equal(result_personalized, result_not_personalized))
def test_page_rank_with_feature_selection(self):
# the PageRank algorithm is tested with the NXTopKPageRank Feature Selection algorithm
# since the Feature Selection is already tested in the dedicated test file
# this test only checks that the PageRank run works while defining a Feature Selection algorithm
movies_dir = os.path.join(dir_test_files, 'complex_contents', 'movies_codified/')
user_dir = os.path.join(dir_test_files, 'complex_contents', 'users_codified/')
df = pd.DataFrame.from_dict({'from_id': ["1", "1", "2", "2", "2", "3", "4", "4"],
'to_id': ["tt0113228", "tt0113041", "tt0113228", "tt0112346",
"tt0112453", "tt0112453", "tt0112346", "tt0112453"],
'score': [0.8, 0.7, -0.4, 1.0, 0.4, 0.1, -0.3, 0.7]})
# only one property from the dbpedia repr extracted
graph_with_properties: NXFullGraph = NXFullGraph(df,
user_contents_dir=user_dir,
item_contents_dir=movies_dir,
item_exo_representation='dbpedia',
user_exo_representation='local',
item_exo_properties=None,
user_exo_properties=['1']
)
# fs standard algorithm
alg = NXPageRank(feature_selection=NXTopKPageRank())
result = alg.rank('4', graph_with_properties)
self.assertEqual(len(result), 2)
# fs personalized algorithm
alg = NXPageRank(personalized=True, feature_selection=NXTopKPageRank())
result_personalized = alg.rank('4', graph_with_properties)
self.assertEqual(len(result_personalized), 2)
# fs personalized algorithm and filter list
alg = NXPageRank(personalized=True, feature_selection=NXTopKPageRank())
result_personalized = alg.rank('4', graph_with_properties, filter_list=['tt0113228'])
self.assertEqual(len(result_personalized), 1)
# fs personalized algorithm and empty filter list
alg = NXPageRank(personalized=True, feature_selection=NXTopKPageRank())
result_personalized = alg.rank('4', graph_with_properties, filter_list=[])
self.assertEqual(len(result_personalized), 0)