mirror of
https://github.com/explosion/spaCy.git
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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
153 lines
4.7 KiB
Python
153 lines
4.7 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 ...attrs import ORTH, LENGTH
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from ...tokens import Doc
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from ...vocab import Vocab
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import pytest
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@pytest.fixture
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def doc(en_tokenizer):
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text = "This is a sentence. This is another sentence. And a third."
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heads = [1, 0, 1, -2, -3, 1, 0, 1, -2, -3, 0, 1, -2, -1]
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deps = ['nsubj', 'ROOT', 'det', 'attr', 'punct', 'nsubj', 'ROOT', 'det',
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'attr', 'punct', 'ROOT', 'det', 'npadvmod', 'punct']
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tokens = en_tokenizer(text)
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return get_doc(tokens.vocab, [t.text for t in tokens], heads=heads, deps=deps)
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@pytest.fixture
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def doc_not_parsed(en_tokenizer):
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text = "This is a sentence. This is another sentence. And a third."
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tokens = en_tokenizer(text)
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d = get_doc(tokens.vocab, [t.text for t in tokens])
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d.is_parsed = False
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return d
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def test_spans_sent_spans(doc):
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sents = list(doc.sents)
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assert sents[0].start == 0
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assert sents[0].end == 5
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assert len(sents) == 3
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assert sum(len(sent) for sent in sents) == len(doc)
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def test_spans_root(doc):
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span = doc[2:4]
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assert len(span) == 2
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assert span.text == 'a sentence'
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assert span.root.text == 'sentence'
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assert span.root.head.text == 'is'
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def test_spans_string_fn(doc):
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span = doc[0:4]
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assert len(span) == 4
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assert span.text == 'This is a sentence'
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assert span.upper_ == 'THIS IS A SENTENCE'
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assert span.lower_ == 'this is a sentence'
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def test_spans_root2(en_tokenizer):
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text = "through North and South Carolina"
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heads = [0, 3, -1, -2, -4]
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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[-2:].root.text == 'Carolina'
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def test_spans_span_sent(doc, doc_not_parsed):
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"""Test span.sent property"""
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assert len(list(doc.sents))
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assert doc[:2].sent.root.text == 'is'
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assert doc[:2].sent.text == 'This is a sentence .'
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assert doc[6:7].sent.root.left_edge.text == 'This'
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# test on manual sbd
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doc_not_parsed[0].is_sent_start = True
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doc_not_parsed[5].is_sent_start = True
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assert doc_not_parsed[1:3].sent == doc_not_parsed[0:5]
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assert doc_not_parsed[10:14].sent == doc_not_parsed[5:]
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def test_spans_lca_matrix(en_tokenizer):
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"""Test span's lca matrix generation"""
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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[:2].get_lca_matrix()
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assert(lca[0, 0] == 0)
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assert(lca[0, 1] == -1)
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assert(lca[1, 0] == -1)
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assert(lca[1, 1] == 1)
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def test_span_similarity_match():
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doc = Doc(Vocab(), words=['a', 'b', 'a', 'b'])
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span1 = doc[:2]
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span2 = doc[2:]
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with pytest.warns(None):
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assert span1.similarity(span2) == 1.0
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assert span1.similarity(doc) == 0.0
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assert span1[:1].similarity(doc.vocab['a']) == 1.0
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def test_spans_default_sentiment(en_tokenizer):
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"""Test span.sentiment property's default averaging behaviour"""
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text = "good stuff bad stuff"
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tokens = en_tokenizer(text)
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tokens.vocab[tokens[0].text].sentiment = 3.0
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tokens.vocab[tokens[2].text].sentiment = -2.0
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doc = get_doc(tokens.vocab, [t.text for t in tokens])
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assert doc[:2].sentiment == 3.0 / 2
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assert doc[-2:].sentiment == -2. / 2
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assert doc[:-1].sentiment == (3.+-2) / 3.
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def test_spans_override_sentiment(en_tokenizer):
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"""Test span.sentiment property's default averaging behaviour"""
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text = "good stuff bad stuff"
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tokens = en_tokenizer(text)
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tokens.vocab[tokens[0].text].sentiment = 3.0
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tokens.vocab[tokens[2].text].sentiment = -2.0
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doc = get_doc(tokens.vocab, [t.text for t in tokens])
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doc.user_span_hooks['sentiment'] = lambda span: 10.0
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assert doc[:2].sentiment == 10.0
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assert doc[-2:].sentiment == 10.0
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assert doc[:-1].sentiment == 10.0
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def test_spans_are_hashable(en_tokenizer):
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"""Test spans can be hashed."""
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text = "good stuff bad stuff"
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tokens = en_tokenizer(text)
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span1 = tokens[:2]
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span2 = tokens[2:4]
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assert hash(span1) != hash(span2)
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span3 = tokens[0:2]
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assert hash(span3) == hash(span1)
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def test_spans_by_character(doc):
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span1 = doc[1:-2]
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span2 = doc.char_span(span1.start_char, span1.end_char, label='GPE')
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assert span1.start_char == span2.start_char
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assert span1.end_char == span2.end_char
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assert span2.label_ == 'GPE'
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def test_span_to_array(doc):
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span = doc[1:-2]
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arr = span.to_array([ORTH, LENGTH])
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assert arr.shape == (len(span), 2)
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assert arr[0, 0] == span[0].orth
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assert arr[0, 1] == len(span[0])
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#def test_span_as_doc(doc):
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# span = doc[4:10]
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# span_doc = span.as_doc()
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# assert span.text == span_doc.text.strip()
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