def test_predict_multiple_representations(self, model: Regressor): lm = model # Multiple representations filtered only items with score >= 2 alg = LinearPredictor({'Plot': ['tfidf', 'embedding'], 'Genre': ['tfidf', 'embedding'], 'imdbRating': [0]}, lm, only_greater_eq=2) user_ratings = self.ratings.query('from_id == "A000"') alg.process_rated(user_ratings, self.movies_dir) alg.fit() # predict with filter_list res_filtered = alg.predict(user_ratings, self.movies_dir, filter_list=self.filter_list) item_scored_set = set(res_filtered['to_id']) self.assertEqual(len(item_scored_set), len(self.filter_list)) self.assertCountEqual(item_scored_set, self.filter_list) # predict without filter_list res_all_unrated = alg.predict(user_ratings, self.movies_dir) item_rated_set = set(user_ratings['to_id']) item_scored_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_scored = item_scored_set.intersection(item_rated_set) self.assertEqual(len(rated_in_scored), 0)
def test_rank_single_representation(self, model: Regressor): lm = model # Single representation alg = LinearPredictor({'Plot': ['tfidf']}, lm) user_ratings = self.ratings.query('from_id == "A000"') alg.process_rated(user_ratings, self.movies_dir) alg.fit() # rank with filter_list res_filtered = alg.rank(user_ratings, self.movies_dir, 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(user_ratings, self.movies_dir) item_rated_set = set(user_ratings['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 = 5 res_n_recs = alg.rank(user_ratings, self.movies_dir, n_recs) self.assertEqual(len(res_n_recs), n_recs) item_rated_set = set(user_ratings['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)