def test_restaurant_dataset():
with open(restaurant_data, 'r') as file:
raw_sentences = adapter.read_sentences(file)
assert len(raw_sentences) == 3041
stats = {'rejected': 0, 'conflicts': 0}
sentences = adapter.validate_sentences(raw_sentences, stats)
sentences = list(sentences)
assert len(sentences) == 1978
assert stats['rejected'] == 1063
assert stats['conflicts'] == 91
generate = adapter.generate_classifier_examples
examples = [example
for sentence in sentences
for example in generate(sentence)]
assert len(examples) == 3602
# Check distribution of polarities
count = lambda label: sum(True for example in examples
if example.sentiment == label)
ratios = np.array([count(label) for label in Sentiment]) / len(examples)
# The labels are in the order: [neutral, negative, positive]
assert ratios.round(2).tolist() == [0.18, 0.22, 0.6]
file_path = os.path.join(data_dir,
'classifier-semeval-restaurant-train.bin')
absa.utils.save(examples, file_path)
def test_validate_sentence():
xml_text = """
<sentences>
<sentence id="1">test 1</sentence>
<sentence id="2">
<text>test 2</text>
<aspectTerms>
<aspectTerm polarity="conflict"/>
</aspectTerms>
</sentence>
<sentence id="3">
<text>test 3</text>
<aspectTerms>
<aspectTerm polarity="neutral"/>
<aspectTerm polarity="conflict"/>
</aspectTerms>
</sentence>
</sentences>
"""
file = io.StringIO(xml_text)
sentences = adapter.read_sentences(file)
stats = {'rejected': 0, 'conflicts': 0}
validated_sentence, = adapter.validate_sentences(sentences, stats)
assert validated_sentence.find('text').text == 'test 3'
assert stats == {'rejected': 2, 'conflicts': 2}
def test_read_sentences():
xml_text = """
<sentences>
<sentence id="1">test 1</sentence>
<sentence id="2">test 2</sentence>
<sentence id="3">test 3</sentence>
</sentences>
"""
file = io.StringIO(xml_text)
sentences = adapter.read_sentences(file)
assert len(sentences) == 3
def test_generate_classifier_examples():
text = 'I charge it at night and skip taking the cord ' \
'with me because of the good battery life.'
xml_text = f"""
<sentences>
<sentence id="1">
<text>{text}</text>
<aspectTerms>
<aspectTerm term="cord" polarity="neutral" from="41" to="45"/>
<aspectTerm term="battery life" polarity="positive" from="74" to="86"/>
</aspectTerms>
</sentence>
</sentences>
"""
file = io.StringIO(xml_text)
sentence, = adapter.read_sentences(file)
examples = adapter.generate_classifier_examples(sentence)
example_1, example_2 = examples
assert example_1.aspect == 'cord'
assert example_1.sentiment == Sentiment.neutral
assert example_2.aspect == 'battery life'
assert example_2.sentiment == Sentiment.positive
assert example_1.text == example_2.text == text.lower()