mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 19:39:13 +03:00
cae4457c38
* Add logic to filter out warning IDs via environment variable Usage: SPACY_WARNING_EXCLUDE=W001,W007 * Add warnings for empty vectors * Add warning if no word vectors are used in .similarity methods For example, if only tensors are available in small models – should hopefully clear up some confusion around this * Capture warnings in tests * Rename SPACY_WARNING_EXCLUDE to SPACY_WARNING_IGNORE
287 lines
9.9 KiB
Python
287 lines
9.9 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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from ...tokens import Doc
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from ...vocab import Vocab
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from ...attrs import LEMMA
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import pytest
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import numpy
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@pytest.mark.parametrize('text', [["one", "two", "three"]])
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def test_doc_api_compare_by_string_position(en_vocab, text):
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doc = get_doc(en_vocab, text)
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# Get the tokens in this order, so their ID ordering doesn't match the idx
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token3 = doc[-1]
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token2 = doc[-2]
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token1 = doc[-1]
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token1, token2, token3 = doc
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assert token1 < token2 < token3
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assert not token1 > token2
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assert token2 > token1
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assert token2 <= token3
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assert token3 >= token1
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def test_doc_api_getitem(en_tokenizer):
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text = "Give it back! He pleaded."
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tokens = en_tokenizer(text)
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assert tokens[0].text == 'Give'
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assert tokens[-1].text == '.'
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with pytest.raises(IndexError):
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tokens[len(tokens)]
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def to_str(span):
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return '/'.join(token.text for token in span)
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span = tokens[1:1]
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assert not to_str(span)
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span = tokens[1:4]
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assert to_str(span) == 'it/back/!'
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span = tokens[1:4:1]
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assert to_str(span) == 'it/back/!'
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with pytest.raises(ValueError):
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tokens[1:4:2]
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with pytest.raises(ValueError):
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tokens[1:4:-1]
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span = tokens[-3:6]
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assert to_str(span) == 'He/pleaded'
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span = tokens[4:-1]
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assert to_str(span) == 'He/pleaded'
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span = tokens[-5:-3]
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assert to_str(span) == 'back/!'
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span = tokens[5:4]
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assert span.start == span.end == 5 and not to_str(span)
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span = tokens[4:-3]
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assert span.start == span.end == 4 and not to_str(span)
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span = tokens[:]
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assert to_str(span) == 'Give/it/back/!/He/pleaded/.'
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span = tokens[4:]
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assert to_str(span) == 'He/pleaded/.'
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span = tokens[:4]
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assert to_str(span) == 'Give/it/back/!'
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span = tokens[:-3]
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assert to_str(span) == 'Give/it/back/!'
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span = tokens[-3:]
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assert to_str(span) == 'He/pleaded/.'
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span = tokens[4:50]
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assert to_str(span) == 'He/pleaded/.'
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span = tokens[-50:4]
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assert to_str(span) == 'Give/it/back/!'
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span = tokens[-50:-40]
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assert span.start == span.end == 0 and not to_str(span)
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span = tokens[40:50]
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assert span.start == span.end == 7 and not to_str(span)
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span = tokens[1:4]
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assert span[0].orth_ == 'it'
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subspan = span[:]
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assert to_str(subspan) == 'it/back/!'
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subspan = span[:2]
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assert to_str(subspan) == 'it/back'
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subspan = span[1:]
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assert to_str(subspan) == 'back/!'
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subspan = span[:-1]
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assert to_str(subspan) == 'it/back'
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subspan = span[-2:]
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assert to_str(subspan) == 'back/!'
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subspan = span[1:2]
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assert to_str(subspan) == 'back'
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subspan = span[-2:-1]
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assert to_str(subspan) == 'back'
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subspan = span[-50:50]
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assert to_str(subspan) == 'it/back/!'
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subspan = span[50:-50]
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assert subspan.start == subspan.end == 4 and not to_str(subspan)
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@pytest.mark.parametrize('text', ["Give it back! He pleaded.",
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" Give it back! He pleaded. "])
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def test_doc_api_serialize(en_tokenizer, text):
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tokens = en_tokenizer(text)
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new_tokens = get_doc(tokens.vocab).from_bytes(tokens.to_bytes())
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assert tokens.text == new_tokens.text
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assert [t.text for t in tokens] == [t.text for t in new_tokens]
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assert [t.orth for t in tokens] == [t.orth for t in new_tokens]
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new_tokens = get_doc(tokens.vocab).from_bytes(
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tokens.to_bytes(tensor=False), tensor=False)
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assert tokens.text == new_tokens.text
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assert [t.text for t in tokens] == [t.text for t in new_tokens]
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assert [t.orth for t in tokens] == [t.orth for t in new_tokens]
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new_tokens = get_doc(tokens.vocab).from_bytes(
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tokens.to_bytes(sentiment=False), sentiment=False)
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assert tokens.text == new_tokens.text
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assert [t.text for t in tokens] == [t.text for t in new_tokens]
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assert [t.orth for t in tokens] == [t.orth for t in new_tokens]
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def test_doc_api_set_ents(en_tokenizer):
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text = "I use goggle chrone to surf the web"
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tokens = en_tokenizer(text)
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assert len(tokens.ents) == 0
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tokens.ents = [(tokens.vocab.strings['PRODUCT'], 2, 4)]
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assert len(list(tokens.ents)) == 1
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assert [t.ent_iob for t in tokens] == [0, 0, 3, 1, 0, 0, 0, 0]
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assert tokens.ents[0].label_ == 'PRODUCT'
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assert tokens.ents[0].start == 2
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assert tokens.ents[0].end == 4
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def test_doc_api_merge(en_tokenizer):
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text = "WKRO played songs by the beach boys all night"
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# merge 'The Beach Boys'
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doc = en_tokenizer(text)
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assert len(doc) == 9
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doc.merge(doc[4].idx, doc[6].idx + len(doc[6]), tag='NAMED', lemma='LEMMA',
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ent_type='TYPE')
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assert len(doc) == 7
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assert doc[4].text == 'the beach boys'
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assert doc[4].text_with_ws == 'the beach boys '
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assert doc[4].tag_ == 'NAMED'
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# merge 'all night'
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doc = en_tokenizer(text)
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assert len(doc) == 9
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doc.merge(doc[7].idx, doc[8].idx + len(doc[8]), tag='NAMED', lemma='LEMMA',
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ent_type='TYPE')
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assert len(doc) == 8
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assert doc[7].text == 'all night'
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assert doc[7].text_with_ws == 'all night'
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def test_doc_api_merge_children(en_tokenizer):
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"""Test that attachments work correctly after merging."""
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text = "WKRO played songs by the beach boys all night"
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doc = en_tokenizer(text)
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assert len(doc) == 9
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doc.merge(doc[4].idx, doc[6].idx + len(doc[6]), tag='NAMED', lemma='LEMMA',
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ent_type='TYPE')
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for word in doc:
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if word.i < word.head.i:
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assert word in list(word.head.lefts)
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elif word.i > word.head.i:
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assert word in list(word.head.rights)
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def test_doc_api_merge_hang(en_tokenizer):
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text = "through North and South Carolina"
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doc = en_tokenizer(text)
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doc.merge(18, 32, tag='', lemma='', ent_type='ORG')
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doc.merge(8, 32, tag='', lemma='', ent_type='ORG')
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def test_doc_api_retokenizer(en_tokenizer):
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doc = en_tokenizer("WKRO played songs by the beach boys all night")
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with doc.retokenize() as retokenizer:
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retokenizer.merge(doc[4:7])
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assert len(doc) == 7
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assert doc[4].text == 'the beach boys'
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def test_doc_api_retokenizer_attrs(en_tokenizer):
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doc = en_tokenizer("WKRO played songs by the beach boys all night")
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# test both string and integer attributes and values
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attrs = {LEMMA: 'boys', 'ENT_TYPE': doc.vocab.strings['ORG']}
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with doc.retokenize() as retokenizer:
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retokenizer.merge(doc[4:7], attrs=attrs)
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assert len(doc) == 7
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assert doc[4].text == 'the beach boys'
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assert doc[4].lemma_ == 'boys'
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assert doc[4].ent_type_ == 'ORG'
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def test_doc_api_sents_empty_string(en_tokenizer):
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doc = en_tokenizer("")
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doc.is_parsed = True
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sents = list(doc.sents)
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assert len(sents) == 0
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def test_doc_api_runtime_error(en_tokenizer):
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# Example that caused run-time error while parsing Reddit
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text = "67% of black households are single parent \n\n72% of all black babies born out of wedlock \n\n50% of all black kids don\u2019t finish high school"
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deps = ['nsubj', 'prep', 'amod', 'pobj', 'ROOT', 'amod', 'attr', '',
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'nummod', 'prep', 'det', 'amod', 'pobj', 'acl', 'prep', 'prep',
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'pobj', '', 'nummod', 'prep', 'det', 'amod', 'pobj', 'aux', 'neg',
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'ROOT', 'amod', 'dobj']
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tokens = en_tokenizer(text)
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doc = get_doc(tokens.vocab, [t.text for t in tokens], deps=deps)
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nps = []
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for np in doc.noun_chunks:
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while len(np) > 1 and np[0].dep_ not in ('advmod', 'amod', 'compound'):
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np = np[1:]
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if len(np) > 1:
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nps.append((np.start_char, np.end_char, np.root.tag_, np.text, np.root.ent_type_))
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for np in nps:
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start, end, tag, lemma, ent_type = np
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doc.merge(start, end, tag=tag, lemma=lemma, ent_type=ent_type)
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def test_doc_api_right_edge(en_tokenizer):
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"""Test for bug occurring from Unshift action, causing incorrect right edge"""
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text = "I have proposed to myself, for the sake of such as live under the government of the Romans, to translate those books into the Greek tongue."
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heads = [2, 1, 0, -1, -1, -3, 15, 1, -2, -1, 1, -3, -1, -1, 1, -2, -1, 1,
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-2, -7, 1, -19, 1, -2, -3, 2, 1, -3, -26]
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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[6].text == 'for'
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subtree = [w.text for w in doc[6].subtree]
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assert subtree == ['for', 'the', 'sake', 'of', 'such', 'as',
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'live', 'under', 'the', 'government', 'of', 'the', 'Romans', ',']
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assert doc[6].right_edge.text == ','
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def test_doc_api_has_vector():
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vocab = Vocab()
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vocab.reset_vectors(width=2)
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vocab.set_vector('kitten', vector=numpy.asarray([0., 2.], dtype='f'))
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doc = Doc(vocab, words=['kitten'])
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assert doc.has_vector
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def test_doc_api_similarity_match():
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doc = Doc(Vocab(), words=['a'])
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with pytest.warns(None):
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assert doc.similarity(doc[0]) == 1.0
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assert doc.similarity(doc.vocab['a']) == 1.0
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doc2 = Doc(doc.vocab, words=['a', 'b', 'c'])
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with pytest.warns(None):
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assert doc.similarity(doc2[:1]) == 1.0
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assert doc.similarity(doc2) == 0.0
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def test_lowest_common_ancestor(en_tokenizer):
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tokens = en_tokenizer('the lazy dog slept')
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doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=[2, 1, 1, 0])
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lca = doc.get_lca_matrix()
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assert(lca[1, 1] == 1)
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assert(lca[0, 1] == 2)
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assert(lca[1, 2] == 2)
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def test_parse_tree(en_tokenizer):
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"""Tests doc.print_tree() method."""
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text = 'I like New York in Autumn.'
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heads = [1, 0, 1, -2, -3, -1, -5]
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tags = ['PRP', 'IN', 'NNP', 'NNP', 'IN', 'NNP', '.']
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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)
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# full method parse_tree(text) is a trivial composition
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trees = doc.print_tree()
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assert len(trees) > 0
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tree = trees[0]
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assert all(k in list(tree.keys()) for k in ['word', 'lemma', 'NE', 'POS_fine', 'POS_coarse', 'arc', 'modifiers'])
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assert tree['word'] == 'like' # check root is correct
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