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import pytest
from nltk import config_megam
from nltk.classify.rte_classify import RTEFeatureExtractor, rte_classifier, rte_features
from nltk.corpus import rte as rte_corpus
expected_from_rte_feature_extration = """
alwayson => True
ne_hyp_extra => 0
ne_overlap => 1
neg_hyp => 0
neg_txt => 0
word_hyp_extra => 3
word_overlap => 3
alwayson => True
ne_hyp_extra => 0
ne_overlap => 1
neg_hyp => 0
neg_txt => 0
word_hyp_extra => 2
word_overlap => 1
alwayson => True
ne_hyp_extra => 1
ne_overlap => 1
neg_hyp => 0
neg_txt => 0
word_hyp_extra => 1
word_overlap => 2
alwayson => True
ne_hyp_extra => 1
ne_overlap => 0
neg_hyp => 0
neg_txt => 0
word_hyp_extra => 6
word_overlap => 2
alwayson => True
ne_hyp_extra => 1
ne_overlap => 0
neg_hyp => 0
neg_txt => 0
word_hyp_extra => 4
word_overlap => 0
alwayson => True
ne_hyp_extra => 1
ne_overlap => 0
neg_hyp => 0
neg_txt => 0
word_hyp_extra => 3
word_overlap => 1
"""
class TestRTEClassifier:
# Test the feature extraction method.
def test_rte_feature_extraction(self):
pairs = rte_corpus.pairs(["rte1_dev.xml"])[:6]
test_output = [
f"{key:<15} => {rte_features(pair)[key]}"
for pair in pairs
for key in sorted(rte_features(pair))
]
expected_output = expected_from_rte_feature_extration.strip().split("\n")
# Remove null strings.
expected_output = list(filter(None, expected_output))
assert test_output == expected_output
# Test the RTEFeatureExtractor object.
def test_feature_extractor_object(self):
rtepair = rte_corpus.pairs(["rte3_dev.xml"])[33]
extractor = RTEFeatureExtractor(rtepair)
assert extractor.hyp_words == {"member", "China", "SCO."}
assert extractor.overlap("word") == set()
assert extractor.overlap("ne") == {"China"}
assert extractor.hyp_extra("word") == {"member"}
# Test the RTE classifier training.
def test_rte_classification_without_megam(self):
# Use a sample size for unit testing, since we
# don't need to fully train these classifiers
clf = rte_classifier("IIS", sample_N=100)
clf = rte_classifier("GIS", sample_N=100)
def test_rte_classification_with_megam(self):
try:
config_megam()
except (LookupError, AttributeError) as e:
pytest.skip("Skipping tests with dependencies on MEGAM")
clf = rte_classifier("megam", sample_N=100)
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