mirror of
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129 lines
5.2 KiB
Python
129 lines
5.2 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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from ..util import get_doc
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import pytest
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def test_spans_merge_tokens(en_tokenizer):
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text = "Los Angeles start."
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heads = [1, 1, 0, -1]
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tokens = en_tokenizer(text)
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doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads)
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assert len(doc) == 4
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assert doc[0].head.text == 'Angeles'
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assert doc[1].head.text == 'start'
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doc.merge(0, len('Los Angeles'), tag='NNP', lemma='Los Angeles', ent_type='GPE')
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assert len(doc) == 3
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assert doc[0].text == 'Los Angeles'
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assert doc[0].head.text == 'start'
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doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads)
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assert len(doc) == 4
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assert doc[0].head.text == 'Angeles'
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assert doc[1].head.text == 'start'
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doc.merge(0, len('Los Angeles'), tag='NNP', lemma='Los Angeles', label='GPE')
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assert len(doc) == 3
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assert doc[0].text == 'Los Angeles'
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assert doc[0].head.text == 'start'
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assert doc[0].ent_type_ == 'GPE'
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def test_spans_merge_heads(en_tokenizer):
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text = "I found a pilates class near work."
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heads = [1, 0, 2, 1, -3, -1, -1, -6]
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tokens = en_tokenizer(text)
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doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads)
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assert len(doc) == 8
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doc.merge(doc[3].idx, doc[4].idx + len(doc[4]), tag=doc[4].tag_,
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lemma='pilates class', ent_type='O')
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assert len(doc) == 7
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assert doc[0].head.i == 1
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assert doc[1].head.i == 1
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assert doc[2].head.i == 3
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assert doc[3].head.i == 1
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assert doc[4].head.i in [1, 3]
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assert doc[5].head.i == 4
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def test_span_np_merges(en_tokenizer):
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text = "displaCy is a parse tool built with Javascript"
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heads = [1, 0, 2, 1, -3, -1, -1, -1]
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tokens = en_tokenizer(text)
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doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads)
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assert doc[4].head.i == 1
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doc.merge(doc[2].idx, doc[4].idx + len(doc[4]), tag='NP', lemma='tool',
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ent_type='O')
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assert doc[2].head.i == 1
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text = "displaCy is a lightweight and modern dependency parse tree visualization tool built with CSS3 and JavaScript."
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heads = [1, 0, 8, 3, -1, -2, 4, 3, 1, 1, -9, -1, -1, -1, -1, -2, -15]
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tokens = en_tokenizer(text)
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doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads)
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ents = [(e[0].idx, e[-1].idx + len(e[-1]), e.label_, e.lemma_) for e in doc.ents]
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for start, end, label, lemma in ents:
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merged = doc.merge(start, end, tag=label, lemma=lemma, ent_type=label)
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assert merged != None, (start, end, label, lemma)
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text = "One test with entities like New York City so the ents list is not void"
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heads = [1, 11, -1, -1, -1, 1, 1, -3, 4, 2, 1, 1, 0, -1, -2]
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tokens = en_tokenizer(text)
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doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads)
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for span in doc.ents:
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merged = doc.merge()
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assert merged != None, (span.start, span.end, span.label_, span.lemma_)
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def test_spans_entity_merge(en_tokenizer):
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text = "Stewart Lee is a stand up comedian who lives in England and loves Joe Pasquale.\n"
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heads = [1, 1, 0, 1, 2, -1, -4, 1, -2, -1, -1, -3, -10, 1, -2, -13, -1]
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tags = ['NNP', 'NNP', 'VBZ', 'DT', 'VB', 'RP', 'NN', 'WP', 'VBZ', 'IN', 'NNP', 'CC', 'VBZ', 'NNP', 'NNP', '.', 'SP']
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ents = [('Stewart Lee', 'PERSON', 0, 2), ('England', 'GPE', 10, 11), ('Joe Pasquale', 'PERSON', 13, 15)]
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tokens = en_tokenizer(text)
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doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads, tags=tags, ents=ents)
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assert len(doc) == 17
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for ent in doc.ents:
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label, lemma, type_ = (ent.root.tag_, ent.root.lemma_, max(w.ent_type_ for w in ent))
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ent.merge(label=label, lemma=lemma, ent_type=type_)
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# check looping is ok
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assert len(doc) == 15
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def test_spans_sentence_update_after_merge(en_tokenizer):
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text = "Stewart Lee is a stand up comedian. He lives in England and loves Joe Pasquale."
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heads = [1, 1, 0, 1, 2, -1, -4, -5, 1, 0, -1, -1, -3, -4, 1, -2, -7]
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deps = ['compound', 'nsubj', 'ROOT', 'det', 'amod', 'prt', 'attr',
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'punct', 'nsubj', 'ROOT', 'prep', 'pobj', 'cc', 'conj',
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'compound', 'dobj', 'punct']
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tokens = en_tokenizer(text)
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doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads, deps=deps)
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sent1, sent2 = list(doc.sents)
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init_len = len(sent1)
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init_len2 = len(sent2)
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doc[0:2].merge(label='none', lemma='none', ent_type='none')
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doc[-2:].merge(label='none', lemma='none', ent_type='none')
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assert len(sent1) == init_len - 1
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assert len(sent2) == init_len2 - 1
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def test_spans_subtree_size_check(en_tokenizer):
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text = "Stewart Lee is a stand up comedian who lives in England and loves Joe Pasquale"
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heads = [1, 1, 0, 1, 2, -1, -4, 1, -2, -1, -1, -3, -10, 1, -2]
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deps = ['compound', 'nsubj', 'ROOT', 'det', 'amod', 'prt', 'attr',
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'nsubj', 'relcl', 'prep', 'pobj', 'cc', 'conj', 'compound',
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'dobj']
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tokens = en_tokenizer(text)
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doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads, deps=deps)
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sent1 = list(doc.sents)[0]
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init_len = len(list(sent1.root.subtree))
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doc[0:2].merge(label='none', lemma='none', ent_type='none')
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assert len(list(sent1.root.subtree)) == init_len - 1
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