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