def _attempt(self):
missing_numbers = self._debut_board.get_missing_numbers()
empty_cells = self._debut_board.get_empty_cell_locations()
number_combinations = permutations(missing_numbers,
len(missing_numbers))
for combo in number_combinations:
cloned_board = self._debut_board.clone()
cell_index = 0
for empty_cell in empty_cells:
try:
cloned_board.set_cell_value(empty_cell.get_x(),
empty_cell.get_y(),
combo[cell_index])
cell_index = cell_index + 1
except NumberAssignmentError:
break
if cloned_board.has_empty_cell():
continue
evaluator = Evaluator()
evaluator.evaluate(cloned_board)
if evaluator.is_complete() and evaluator.is_valid():
self._result.complete = True
self._result.success = True
self._result.solution = cloned_board
return
def run(self):
"""В функции создаётся объект
модели. Данные передаются в модель.
Модель возвращает результат
"""
evaluator = Evaluator()
evaluator.fit(self.list1, self.list2)
results = evaluator.evaluate()
self.throw_resalts.emit(results)
predictor = Predictor()
evaluator = Evaluator(predictor, label2idx, word2idx, pos2idx, args)
best_scores = []
best_dev_f1_sum = 0
patience = 0
print('\n'*2)
print('='*10 + 'Phase1, train on train_data, epoch=args.epochs' + '='*10)
print('\n'*2)
for epoch in range(1, args.epochs+1):
loss = train_epoch(train_data, ner_model, optimizer, criterion, args)
print("*"*10 + "epoch:{}, loss:{}".format(epoch, loss) + "*"*10)
eval_result_train = evaluator.evaluate(train_data, ner_model, args, cuda = args.cuda)
print("On train_data: ")
print_scores(eval_result_train, args)
eval_result_dev = evaluator.evaluate(dev_data, ner_model, args, cuda = args.cuda)
print("On dev_data: ")
print_scores(eval_result_dev, args)
if eval_result_dev[0][0] + eval_result_dev[0][1] >= best_dev_f1_sum:
best_scores = [loss, eval_result_train, eval_result_dev]
best_dev_f1_sum = eval_result_dev[0][0] + eval_result_dev[0][1]
save_model(epoch, args, ner_model, best_scores, optimizer,
args.save_model + '/epoch{}_dev_TF1_{}_EF1_{}.model'.format(epoch, eval_result_dev[0][0], eval_result_dev[0][1]))
print('Model saved as:', args.save_model + '/epoch{}_dev_TF1_{}_EF1_{}.model'.format(epoch, eval_result_dev[0][0], eval_result_dev[0][1]))
best_epoch = epoch
patience = 0
