RuntimeError: generator raised StopIteration

[n0obcoder]

i am getting the following error while training the model using the command

python train.py -d path/to/dataset/

  File "D:\daftar\recomendation systems\sequence-based-recommendations-master\neural_networks\rnn_base.py", line 405, in _gen_mini_batch
    sequence, user_id = next(sequence_generator)
StopIteration

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "train.py", line 60, in <module>
    main()
  File "train.py", line 57, in main
    validation_metrics=args.metrics.split(','))
  File "D:\daftar\recomendation systems\sequence-based-recommendations-master\neural_networks\rnn_base.py", line 324, in train
    metrics = self._compute_validation_metrics(metrics)
  File "D:\daftar\recomendation systems\sequence-based-recommendations-master\neural_networks\rnn_base.py", line 365, in _compute_validation_metrics
    for batch_input, goal in self._gen_mini_batch(self.dataset.validation_set(epochs=1), test=True):
RuntimeError: generator raised StopIteration

What might be causing this error?

[n0obcoder]

i noticed that this error is occuring when all the 500 test examples are yielded and there is calling the next() method on this causes this error. But i still don't know how can i resolve this error.

[n0obcoder]

i fixed it by the help of https://stackoverflow.com/questions/51700960/runtimeerror-generator-raised-stopiteration-every-time-i-try-to-run-app

i changed

while True:
	j = 0
	sequences = []
	batch_size = self.batch_size
	if test:
		batch_size = 1
	while j < batch_size:
		
		sequence, user_id = next(sequence_generator)

		# print('sequence: ', sequence)
		# print('user_id: ', user_id, 'test: ', test)
		# pdb.set_trace()

		# finds the lengths of the different subsequences
		if not test:
			seq_lengths = sorted(random.sample(range(2, len(sequence)), min([batch_size - j, len(sequence) - 2, max_reuse_sequence])))
		else:
			seq_lengths = [int(len(sequence) / 2)] 

		skipped_seq = 0
		for l in seq_lengths:
			target = self.target_selection(sequence[l:], test=test)
			if len(target) == 0:
				skipped_seq += 1
				continue
			start = max(0, l - self.max_length) # sequences cannot be longer than self.max_lenght
			sequences.append([user_id, sequence[start:l], target])

		j += len(seq_lengths) - skipped_seq

	if test:
		# sequence[seq_lengths[0]:] is the sequence (ratings included) GT here
		# [i[0] for i in sequence[seq_lengths[0]:]]  is the GT here (ratings excluded)
		yield self._prepare_input(sequences), [i[0] for i in sequence[seq_lengths[0]:]] 
	else:
		yield self._prepare_input(sequences)

to

while True:
	try:
		j = 0
		sequences = []
		batch_size = self.batch_size
		if test:
			batch_size = 1
		while j < batch_size:
			
			sequence, user_id = next(sequence_generator)

			# print('sequence: ', sequence)
			# print('user_id: ', user_id, 'test: ', test)
			# pdb.set_trace()

			# finds the lengths of the different subsequences
			if not test:
				seq_lengths = sorted(random.sample(range(2, len(sequence)), min([batch_size - j, len(sequence) - 2, max_reuse_sequence])))
			else:
				seq_lengths = [int(len(sequence) / 2)] 

			skipped_seq = 0
			for l in seq_lengths:
				target = self.target_selection(sequence[l:], test=test)
				if len(target) == 0:
					skipped_seq += 1
					continue
				start = max(0, l - self.max_length) # sequences cannot be longer than self.max_lenght
				sequences.append([user_id, sequence[start:l], target])

			j += len(seq_lengths) - skipped_seq

		if test:
			# sequence[seq_lengths[0]:] is the sequence (ratings included) GT here
			# [i[0] for i in sequence[seq_lengths[0]:]]  is the GT here (ratings excluded)
			yield self._prepare_input(sequences), [i[0] for i in sequence[seq_lengths[0]:]] 
		else:
			yield self._prepare_input(sequences)
	except StopIteration:
		return