def test_get_lengths_returns_correct_background_length(self):
instance = IndexedBackgroundInstance(self.base_instance,
self.background_instances)
assert instance.get_lengths() == {
'word_sequence_length': 3,
'background_sentences': 2
}
def test_pad_truncates_background_from_left(self):
instance = IndexedBackgroundInstance(self.base_instance,
self.background_instances)
instance.pad({'word_sequence_length': 1, 'background_sentences': 1})
assert instance.indexed_instance.word_indices == [2]
assert len(instance.background_instances) == 1
assert instance.background_instances[0].word_indices == [4]
def test_as_training_data_produces_correct_numpy_arrays_with_background_instances(
self):
instance = IndexedMultipleTrueFalseInstance([
IndexedBackgroundInstance(IndexedTrueFalseInstance([1, 2], False),
[
IndexedTrueFalseInstance([2], None),
IndexedTrueFalseInstance([3], None)
]),
IndexedBackgroundInstance(IndexedTrueFalseInstance([3, 4], False),
[
IndexedTrueFalseInstance([5], None),
IndexedTrueFalseInstance([6], None)
]),
IndexedBackgroundInstance(IndexedTrueFalseInstance([5, 6], False),
[
IndexedTrueFalseInstance([8], None),
IndexedTrueFalseInstance([9], None)
]),
IndexedBackgroundInstance(IndexedTrueFalseInstance([7, 8], True), [
IndexedTrueFalseInstance([11], None),
IndexedTrueFalseInstance([12], None)
]),
], 3)
(word_arrays, background_arrays), label = instance.as_training_data()
assert numpy.all(label == numpy.asarray([0, 0, 0, 1]))
assert numpy.all(
word_arrays == numpy.asarray([[1, 2], [3, 4], [5, 6], [7, 8]]))
assert numpy.all(background_arrays == numpy.asarray(
[[[2], [3]], [[5], [6]], [[8], [9]], [[11], [12]]]))
def test_pad_adds_padded_background_at_end(self):
instance = IndexedBackgroundInstance(self.base_instance,
self.background_instances[1:])
instance.pad({'word_sequence_length': 2, 'background_sentences': 2})
assert instance.indexed_instance.word_indices == [1, 2]
assert len(instance.background_instances) == 2
assert instance.background_instances[0].word_indices == [4, 5]
assert instance.background_instances[1].word_indices == [0, 0]
def test_pad_works_with_complex_contained_instance(self):
instance = IndexedBackgroundInstance(self.qa_instance,
self.background_instances[1:])
instance.pad({
'word_sequence_length': 3,
'answer_length': 1,
'num_options': 2,
'background_sentences': 2,
})
assert instance.indexed_instance.question_indices == [1, 2, 3]
assert len(instance.indexed_instance.option_indices) == 2
assert instance.indexed_instance.option_indices[0] == [3]
assert instance.indexed_instance.option_indices[1] == [4]
assert len(instance.background_instances) == 2
assert instance.background_instances[0].word_indices == [0, 4, 5]
assert instance.background_instances[1].word_indices == [0, 0, 0]
def test_as_training_data_produces_correct_numpy_arrays_with_complex_contained_instance(
self):
# We need the background array to always be _second_, not last.
instance = IndexedBackgroundInstance(self.qa_instance,
self.background_instances)
instance.pad({
'word_sequence_length': 2,
'answer_length': 2,
'num_options': 3,
'background_sentences': 2,
})
(question_array, background_array,
answer_array), label = instance.as_training_data()
assert numpy.all(label == numpy.asarray([0, 1, 0]))
assert numpy.all(question_array == numpy.asarray([2, 3]))
assert numpy.all(
answer_array == numpy.asarray([[2, 3], [0, 4], [5, 6]]))
assert numpy.all(background_array == numpy.asarray([[3, 4], [4, 5]]))
def test_as_training_data_produces_correct_numpy_arrays(self):
instance = IndexedBackgroundInstance(self.base_instance,
self.background_instances)
instance.pad({'word_sequence_length': 3, 'background_sentences': 2})
(word_array, background_array), label = instance.as_training_data()
assert numpy.all(label == numpy.asarray([0, 1]))
assert numpy.all(word_array == numpy.asarray([0, 1, 2]))
assert numpy.all(
background_array == numpy.asarray([[2, 3, 4], [0, 4, 5]]))
instance.indexed_instance.label = False
_, label = instance.as_training_data()
assert numpy.all(label == numpy.asarray([1, 0]))
instance.label = True # we ignore this label, only using the indexed_instance label
_, label = instance.as_training_data()
assert numpy.all(label == numpy.asarray([1, 0]))
def test_pad_truncates_from_right_on_background(self):
instance = IndexedBackgroundInstance(self.base_instance,
self.background_instances[1:])
instance.pad({'word_sequence_length': 1, 'background_sentences': 1})
assert instance.indexed_instance.word_indices == [2]
assert instance.background_instances[0].word_indices == [5]
def test_pad_adds_zeros_on_left_to_background(self):
instance = IndexedBackgroundInstance(self.base_instance,
self.background_instances[1:])
instance.pad({'word_sequence_length': 3, 'background_sentences': 1})
assert instance.indexed_instance.word_indices == [0, 1, 2]
assert instance.background_instances[0].word_indices == [0, 4, 5]
def test_get_lengths_returns_max_of_background_and_word_indices(self):
instance = IndexedBackgroundInstance(self.base_instance,
self.background_instances)
assert instance.get_lengths()['word_sequence_length'] == 3
