def test_sequence_classification_explain_callable():
explainer_string = "I love you , I like you"
seq_explainer = SequenceClassificationExplainer(explainer_string, MODEL, TOKENIZER)
seq_explainer.run()
run_method_predicted_index = seq_explainer.predicted_class_index
seq_explainer()
call_method_predicted_index = seq_explainer.predicted_class_index
assert call_method_predicted_index == run_method_predicted_index
def test_sequence_classification_explain_on_cls_name():
explainer_string = "I love you , I like you"
seq_explainer = SequenceClassificationExplainer(explainer_string, MODEL, TOKENIZER)
attributions = seq_explainer.run(class_name="NEGATIVE")
assert seq_explainer.predicted_class_index == 1
assert seq_explainer.predicted_class_index != seq_explainer.selected_index
assert (
seq_explainer.predicted_class_name
!= seq_explainer.id2label[seq_explainer.selected_index]
)
assert seq_explainer.predicted_class_name != "NEGATIVE"
assert seq_explainer.predicted_class_name == "POSITIVE"
def test_sequence_classification_no_text_given():
explainer_string = "I love you , I hate you"
seq_explainer = SequenceClassificationExplainer(explainer_string, MODEL, TOKENIZER)
attributions = seq_explainer.run()
assert isinstance(attributions, LIGAttributions)
actual_tokens = [token for token, _ in attributions.word_attributions]
expected_tokens = [
"BOS_TOKEN",
"I",
"love",
"you",
",",
"I",
"hate",
"you",
"EOS_TOKEN",
]
assert actual_tokens == expected_tokens
def test_sequence_classification_explain_on_cls_name_not_in_dict():
explainer_string = "I love you , I like you"
seq_explainer = SequenceClassificationExplainer(explainer_string, MODEL, TOKENIZER)
attributions = seq_explainer.run(class_name="UNKNOWN")
assert seq_explainer.selected_index == 1
assert seq_explainer.predicted_class_index == 1
