def test_fetch_song_features__with_3_ids(self):
lp = MLearnipy(username, auth=token())
# creates a fake python object for further testing
lp.user_playlist_tracks = MagicMock(return_value=playlist)
lp.audio_features = MagicMock(return_value=mock_audio_features_any * 3)
assert lp._fetch_song_features(([pl_1_song_id] * 3)) == mock_audio_features_any * 3
def sp():
learnipy = MLearnipy(username, auth=token())
# creates a fake python object for further testing
learnipy.user_playlist_tracks = MagicMock(return_value=playlist)
learnipy.audio_features = MagicMock(return_value=mock_audio_features)
# sets default username for the instance
learnipy.default_username = username
return learnipy
def sppp():
learnipy = MLearnipy(username, auth=token())
# creates a fake python object for further testing
learnipy.user_playlist_tracks = MagicMock(return_value=playlist)
learnipy.audio_features = MagicMock(return_value=mock_audio_features)
learnipy.fetch_filtered_features = MagicMock(
return_value=[{'id': [pl_1_song_id] * 2, 'energy': [pl_1_song_energy] * 2}, [playlist_id]*2])
# sets default username for the instance
learnipy.default_username = username
return learnipy
def test_fetch_song_features__with_120_ids(self):
lp = MLearnipy(username, auth=token())
# creates a fake python object for further testing
lp.audio_features = MagicMock(return_value=mock_audio_features_any * 60)
assert lp._fetch_song_features(([pl_1_song_id] * 120)) == mock_audio_features_any * 120
def test_find_in_list_of_tuples_no_value(self):
assert MLearnipy.find_in_list_of_tuples([('axa','alfa'),('bxb', 'beta')], '', 0,1) == None
def main():
token = fetch_token()
if token:
sp = MLearnipy(username, auth=token)
unsorted_playlist, all_playlists = sp.get_target_and_all_other_pls(selected_features)
# Form a data-frame for ml analysis
already_sorted_set = DatasetFormer(all_playlists, 'playlist_id', ['id'])
incorrectly_sorted_set = DatasetFormer(unsorted_playlist, 'playlist_id', ['id'])
# playlists_for_unsorted_songs contains ids for playlists to which unsorted songs have to be moved
# elements of playlists_for_unsorted_songs are in the same order as songs in incorrectly_sorted_set
playlists_for_unsorted_songs = predict_playlists_for_unsorted_songs(already_sorted_set, incorrectly_sorted_set)
logger.debug("predicted list: {}".format(playlists_for_unsorted_songs))
logger.debug("names: {}".format(already_sorted_set.target_names))
logger.debug("ids : {}".format(already_sorted_set.targets_as_ids))
remaped_playlist = already_sorted_set.remap_list_of_targets_to_initial_value(playlists_for_unsorted_songs)
logger.debug("Songs remapped: {}".format(remaped_playlist))
# returns destination playlist from their respective ids
pl_names = sp.last_fetch_of_all_pls
sp.print_separator(' Decluter suggestions ')
# gets all song ids
song_ids_list = incorrectly_sorted_set.popped_entries['id']
raw_track_list = sp.resolve_song_names_from_id_list(song_ids_list)
tracks = list(
map(make_track_names, raw_track_list))
for index,playlist in list(enumerate(remaped_playlist, start=0)):
# todo add song names here:
print('#{}\t{}\t->\t{}'.format(index, tracks[index], sp.find_in_list_of_tuples(pl_names, playlist, 0, 1)))
sp.print_separator(' Chose songs you want to move ')
for index, playlist in list(enumerate(remaped_playlist, start=0)):
track = tracks[index]
pl_name = sp.find_in_list_of_tuples(pl_names, playlist, 0, 1)
print('Move:\t{}\n#{}\t{} -> {}\n'.format((raw_track_list[index][2]),index, track, pl_name))
# asks if user want to move a song to a predicted playlist
if user_confirms():
print('Moving...')
# todo: add moving here
sp.user_playlist_add_tracks(username, playlist, [song_ids_list[index]])
print('Do you wish to delete the song you just moved?')
if user_confirms():
print('Deleting...')
# todo: delete songs here
sp.user_playlist_remove_all_occurrences_of_tracks(username, unsorted_playlist['playlist_id'][0], [song_ids_list[index]])
else:
pass
else:
pass
sp.print_separator('')
sp.print_separator(' Decluterfy finished ')