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92 lines
2.5 KiB
Python
92 lines
2.5 KiB
Python
# -*- coding: utf-8 -*-
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import unittest
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from nltk.corpus import rte as rte_corpus
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from nltk.classify.rte_classify import RTEFeatureExtractor, rte_features, rte_classifier
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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 RTEClassifierTest(unittest.TestCase):
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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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"%-15s => %s" % (key, 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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self.assertEqual(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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self.assertEqual(extractor.hyp_words, {'member', 'China', 'SCO.'})
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self.assertEqual(extractor.overlap('word'), set())
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self.assertEqual(extractor.overlap('ne'), {'China'})
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self.assertEqual(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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clf = rte_classifier('IIS')
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clf = rte_classifier('GIS')
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@unittest.skip("Skipping tests with dependencies on MEGAM")
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def test_rte_classification_with_megam(self):
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nltk.config_megam('/usr/local/bin/megam')
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clf = rte_classifier('megam')
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clf = rte_classifier('BFGS')
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