def get_scored_tracks(cls, parameters, track, initial_tracks):
tracks = []
trial_count = 0
finder = Track()
base_track = track
pa = ParameterAdapter()
conditions = pa.parameters_to_conditions(parameters)
while trial_count < RecommendApi.TRACK_TRIAL_LIMIT and len(tracks) <= RecommendApi.TRACK_COUNT_BASE:
try:
# get tracks by criticizes
if len(tracks) > 0:
conditions["offset"] = len(tracks)
if trial_count == 0:
tracks += initial_tracks
new_tracks = finder.find(conditions)
tracks += list(filter(lambda t: t.id not in [t.id for t in tracks], new_tracks))
# filter by inputed parameters
if track:
tracks = list(filter(lambda t: pa.filter_by_parameters(parameters, track, t), tracks))
except HTTPError as ex:
pass
trial_count += 1
sleep(0.5)
scored = tracks
if len(tracks) > 0:
if track is None:
base_track = tracks[0]
evaluator = Track.make_evaluator(TrackCriticizePattern)
scored = evaluator.calc_score(tracks, base_track)
scored = scored[:RecommendApi.TRACK_COUNT_BASE]
return scored
