def test_interpret_fails_when_embedding_layer_not_found(self):
inputs = {"sentence": "It was the ending that I hated"}
vocab = Vocabulary()
vocab.add_tokens_to_namespace(
[w for w in inputs["sentence"].split(" ")])
model = FakeModelForTestingInterpret(
vocab, max_tokens=len(inputs["sentence"].split(" ")))
predictor = TextClassifierPredictor(model,
TextClassificationJsonReader())
interpreter = IntegratedGradient(predictor)
with raises(RuntimeError):
interpreter.saliency_interpret_from_json(inputs)
def test_integrated_gradient(self):
inputs = {"sentence": "It was the ending that I hated"}
archive = load_archive(self.FIXTURES_ROOT / 'basic_classifier' /
'serialization' / 'model.tar.gz')
predictor = Predictor.from_archive(archive, 'text_classifier')
interpreter = IntegratedGradient(predictor)
interpretation = interpreter.saliency_interpret_from_json(inputs)
assert interpretation is not None
assert 'instance_1' in interpretation
assert 'grad_input_1' in interpretation['instance_1']
grad_input_1 = interpretation['instance_1']['grad_input_1']
assert len(grad_input_1) == 7 # 7 words in input
# two interpretations should be identical for integrated gradients
repeat_interpretation = interpreter.saliency_interpret_from_json(
inputs)
repeat_grad_input_1 = repeat_interpretation['instance_1'][
'grad_input_1']
for grad, repeat_grad in zip(grad_input_1, repeat_grad_input_1):
assert grad == approx(repeat_grad)
def test_interpret_works_with_custom_embedding_layer(self):
inputs = {"sentence": "It was the ending that I hated"}
vocab = Vocabulary()
vocab.add_tokens_to_namespace(
[w for w in inputs["sentence"].split(" ")])
model = FakeModelForTestingInterpret(
vocab, max_tokens=len(inputs["sentence"].split(" ")))
predictor = FakePredictorForTestingInterpret(
model, TextClassificationJsonReader())
interpreter = IntegratedGradient(predictor)
interpretation = interpreter.saliency_interpret_from_json(inputs)
assert interpretation is not None
assert "instance_1" in interpretation
assert "grad_input_1" in interpretation["instance_1"]
grad_input_1 = interpretation["instance_1"]["grad_input_1"]
assert len(grad_input_1) == 7 # 7 words in input
