def main():
'''
Initialize everything and train
'''
params = get_parser().parse_args()
print(params)
if torch.cuda.is_available() and not params.cuda:
print(
"WARNING: You have a CUDA device, so you should probably run with --cuda"
)
device = 'cuda:0' if torch.cuda.is_available() and params.cuda else 'cpu'
params.device = device
init_seed(params)
test_data, pretrain_data, cell_type_name_map = init_dataset()
avg_score_direct = np.zeros((len(test_data), 5))
for idx, unlabeled_data in enumerate(test_data):
print(unlabeled_data.metadata)
if unlabeled_data.metadata == 'Brain_Myeloid':
continue
#leave one tissue out
labeled_data = test_data[:idx] + test_data[idx + 1:]
n_clusters = len(np.unique(unlabeled_data.y))
mars = MARS(n_clusters,
params,
labeled_data,
unlabeled_data,
pretrain_data[idx],
hid_dim_1=1000,
hid_dim_2=100)
adata, landmarks, scores = mars.train(evaluation_mode=True)
mars.name_cell_types(adata, landmarks, cell_type_name_map)
#adata.write(params.MODEL_DIR+tissue+'/'+tissue+'.h5ad')
avg_score_direct[idx, 0] = scores['accuracy']
avg_score_direct[idx, 1] = scores['f1_score']
avg_score_direct[idx, 2] = scores['nmi']
avg_score_direct[idx, 3] = scores['adj_rand']
avg_score_direct[idx, 4] = scores['adj_mi']
print('{}: Acc {}, F1_score {}, NMI {}, Adj_Rand {}, Adj_MI {}'.format(
unlabeled_data.metadata, scores['accuracy'], scores['f1_score'],
scores['nmi'], scores['adj_rand'], scores['adj_mi']))
avg_score_direct = np.mean(avg_score_direct, axis=0)
print('\nAverage: Acc {}, F1_score {}, NMI {}, Adj_Rand {}, Adj_MI {}\n'.
format(avg_score_direct[0], avg_score_direct[1], avg_score_direct[2],
avg_score_direct[3], avg_score_direct[4]))
from args_parser import get_parser
from model.mars import MARS
from model.experiment_dataset import ExperimentDataset
import warnings
warnings.filterwarnings('ignore')
## set seed
torch.manual_seed(0)
import random
random.seed(0)
np.random.seed(0)
## pytorch params
params, unknown = get_parser().parse_known_args()
params.cuda = True
device = 'cuda:0' if torch.cuda.is_available() and params.cuda else 'cpu'
params.device = device
def run_MARS(train_adata,
test_adata,
result_dir,
prefix="MARS",
celltype_col="cell.type"):
'''Run MARS pipeline
'''
adata = train_adata.concatenate(test_adata)
import args_parser
from abalone import FieldTemplate
from abalone.AbaloneModel import AbaloneAgent
from graphics.QSyncManager import iteration_queue
from graphics.Qt5PlayableGraphic import Qt5PlayableGraphic
args = args_parser.get_parser(add_graphic_option=False).parse_args()
if __name__ == '__main__':
abalone_model = AbaloneAgent(edge_size=args.board_size,
vector_generator=FieldTemplate.basic_start)
graphic_agent = Qt5PlayableGraphic()
graphic_agent.init_ui(abalone_model.game_vector)
handler_queue = graphic_agent.handler_queue
while True:
for event in iteration_queue(handler_queue):
pass
import random
import args_parser
from abalone import FieldTemplate
from abalone.AbaloneModel import AbaloneAgent
from abalone.env.AbaloneEnvironment import AbaloneEnvironment
from graphics.qt5.Qt5Graphic import Qt5Graphic
from graphics.text.TextGraphic import TextGraphic
def random_policy(action_space: int) -> list:
return [random.randrange(0, action_space), random.randrange(0, action_space), random.randrange(0, action_space)]
args = args_parser.get_parser().parse_args()
if __name__ == '__main__':
abalone_model = AbaloneAgent(edge_size=args.board_size,
vector_generator=FieldTemplate.basic_start)
env = AbaloneEnvironment(abalone_model)
graphic = TextGraphic() if args.graphic == "text" else Qt5Graphic()
graphic.init_ui(env.abalone_model.game_vector)
game_success, game_total = 0, 0
total_game, max_turns = 0, 0
while True:
_, info = env.action(random_policy(env.action_space))
success, drops, _, _, end = info
game_success += sum(success)
game_total += 3