def test_criticize_by_like(self): pa = ParameterAdapter() selected_track = random.sample(self.tracks, 1)[0] # by parameter criticize_type = TrackCriticizeType.Like scored = RecommendApi.get_favorite_tracks([], selected_track, self.tracks) print("tracks: {0}".format(len(scored))) self.print_tracks(map(lambda s: s.item, scored[:10]))
def test_criticize_by_parameter(self): pa = ParameterAdapter() selected_track = random.sample(self.tracks, 1)[0] # by parameter criticize_type = TrackCriticizeType.Parameter post_parameters = {"bpm": "123"} # dummy bpm value parameters = pa.request_to_parameters(criticize_type, selected_track, post_parameters) print(map(lambda p: p.__str__(), parameters)) scored = RecommendApi.get_scored_tracks(parameters, selected_track, self.tracks) print("tracks: {0}".format(len(scored))) self.print_tracks(map(lambda s: s.item, scored[:10]))
def test_criticize_by_pattern(self): pa = ParameterAdapter() selected_track = random.sample(self.tracks, 1)[0] # by parameter criticize_type = TrackCriticizeType.Pattern evaluator = Track.make_evaluator() criticize_patterns = evaluator.make_pattern(self.tracks, selected_track) pattern = random.sample(criticize_patterns, 1)[0] post_parameters = {"pattern": pattern.pattern} parameters = pa.request_to_parameters(criticize_type, selected_track, post_parameters) print(map(lambda p: p.__str__(), parameters)) scored = RecommendApi.get_scored_tracks(parameters, selected_track, self.tracks) print("tracks: {0}".format(len(scored))) self.print_tracks(map(lambda s: s.item, scored[:10]))