def test_single_word(self):
data_source = getStream(self.test_words_short)
ts = split_corpus_dataset.TokenizedSplitSingleTarget(
data_source, self.vocab, 1)
tokens_string = next(iter(ts))
expectation = (torch.LongTensor([0]), torch.LongTensor([1])
) # input, target
self.assertEqual(tokens_string, expectation)
def test_two_word_seq(self):
data_source = getStream(self.test_words_short)
ts = split_corpus_dataset.TokenizedSplitSingleTarget(
data_source, self.vocab, 2)
tokens_strings = list(iter(ts))
expectation = [(torch.LongTensor([0, 1]), torch.LongTensor([2])),
(torch.LongTensor([1, 2]), torch.LongTensor([0]))]
self.assertEqual(tokens_strings, expectation)
def test_single_word_len(self):
data_source = getStream(self.test_words_short)
ts = split_corpus_dataset.TokenizedSplitSingleTarget(
data_source, self.vocab, 1)
self.assertEqual(len(ts), len(self.test_words_short) - 1)
def test_two_word_retrieval(self):
data_source = getStream(self.test_words_short)
ts = split_corpus_dataset.TokenizedSplitSingleTarget(
data_source, self.vocab, 2)
words = list(ts.input_words())
self.assertEqual(words, ['a b', 'b c']) # we expect the input words
def test_len_no_output(self):
data_source = getStream(self.test_words_short)
ts = split_corpus_dataset.TokenizedSplitSingleTarget(
data_source, self.vocab, 5)
self.assertEqual(len(ts), 0)
