def test_sequence_classification_explain_on_cls_name_not_in_dict():
explainer_string = "I love you , I like you"
seq_explainer = SequenceClassificationExplainer(DISTILBERT_MODEL,
DISTILBERT_TOKENIZER)
seq_explainer._run(explainer_string, class_name="UNKNOWN")
assert seq_explainer.selected_index == 1
assert seq_explainer.predicted_class_index == 1
def test_sequence_classification_predicted_class_name_no_id2label_defaults_idx(
):
explainer_string = "I love you , I like you"
seq_explainer = SequenceClassificationExplainer(DISTILBERT_MODEL,
DISTILBERT_TOKENIZER)
seq_explainer.id2label = {"test": "value"}
seq_explainer._run(explainer_string)
assert seq_explainer.predicted_class_name == 1
def test_sequence_classification_explain_on_cls_name_with_custom_labels():
explainer_string = "I love you , I like you"
seq_explainer = SequenceClassificationExplainer(
DISTILBERT_MODEL, DISTILBERT_TOKENIZER, custom_labels=["sad", "happy"])
seq_explainer._run(explainer_string, class_name="sad")
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 != "sad"
assert seq_explainer.predicted_class_name == "happy"
def test_sequence_classification_explain_on_cls_name():
explainer_string = "I love you , I like you"
seq_explainer = SequenceClassificationExplainer(DISTILBERT_MODEL,
DISTILBERT_TOKENIZER)
seq_explainer._run(explainer_string, 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_explain_callable():
explainer_string = "I love you , I like you"
seq_explainer = SequenceClassificationExplainer(explainer_string,
DISTILBERT_MODEL,
DISTILBERT_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_run_text_given():
seq_explainer = SequenceClassificationExplainer(DISTILBERT_MODEL,
DISTILBERT_TOKENIZER)
word_attributions = seq_explainer._run("I love you, I just love you")
assert isinstance(word_attributions, list)
actual_tokens = [token for token, _ in word_attributions]
expected_tokens = [
"[CLS]",
"i",
"love",
"you",
",",
"i",
"just",
"love",
"you",
"[SEP]",
]
assert actual_tokens == expected_tokens
def test_sequence_classification_no_text_given():
explainer_string = "I love you , I hate you"
seq_explainer = SequenceClassificationExplainer(explainer_string,
DISTILBERT_MODEL,
DISTILBERT_TOKENIZER)
attributions = seq_explainer._run()
assert isinstance(attributions, LIGAttributions)
actual_tokens = [token for token, _ in attributions.word_attributions]
expected_tokens = [
"[CLS]",
"i",
"love",
"you",
",",
"i",
"hate",
"you",
"[SEP]",
]
assert actual_tokens == expected_tokens
def test_sequence_classification_run_text_given_bert():
explainer_string = "I love you , I hate you"
seq_explainer = SequenceClassificationExplainer(explainer_string,
BERT_MODEL, BERT_TOKENIZER)
attributions = seq_explainer._run("I love you, I just love you")
assert isinstance(attributions, LIGAttributions)
assert seq_explainer.accepts_position_ids == True
actual_tokens = [token for token, _ in attributions.word_attributions]
expected_tokens = [
"[CLS]",
"i",
"love",
"you",
",",
"i",
"just",
"love",
"you",
"[SEP]",
]
assert actual_tokens == expected_tokens
