mirror of https://github.com/explosion/spaCy.git
365 lines
13 KiB
Python
365 lines
13 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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from spacy.gold import biluo_tags_from_offsets, offsets_from_biluo_tags, Example, DocAnnotation
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from spacy.gold import spans_from_biluo_tags, GoldParse, iob_to_biluo
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from spacy.gold import GoldCorpus, docs_to_json, align
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from spacy.lang.en import English
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from spacy.tokens import Doc
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from spacy.util import compounding, minibatch
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from .util import make_tempdir
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import pytest
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import srsly
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def test_gold_biluo_U(en_vocab):
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words = ["I", "flew", "to", "London", "."]
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spaces = [True, True, True, False, True]
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doc = Doc(en_vocab, words=words, spaces=spaces)
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entities = [(len("I flew to "), len("I flew to London"), "LOC")]
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tags = biluo_tags_from_offsets(doc, entities)
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assert tags == ["O", "O", "O", "U-LOC", "O"]
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def test_gold_biluo_BL(en_vocab):
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words = ["I", "flew", "to", "San", "Francisco", "."]
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spaces = [True, True, True, True, False, True]
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doc = Doc(en_vocab, words=words, spaces=spaces)
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entities = [(len("I flew to "), len("I flew to San Francisco"), "LOC")]
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tags = biluo_tags_from_offsets(doc, entities)
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assert tags == ["O", "O", "O", "B-LOC", "L-LOC", "O"]
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def test_gold_biluo_BIL(en_vocab):
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words = ["I", "flew", "to", "San", "Francisco", "Valley", "."]
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spaces = [True, True, True, True, True, False, True]
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doc = Doc(en_vocab, words=words, spaces=spaces)
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entities = [(len("I flew to "), len("I flew to San Francisco Valley"), "LOC")]
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tags = biluo_tags_from_offsets(doc, entities)
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assert tags == ["O", "O", "O", "B-LOC", "I-LOC", "L-LOC", "O"]
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def test_gold_biluo_overlap(en_vocab):
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words = ["I", "flew", "to", "San", "Francisco", "Valley", "."]
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spaces = [True, True, True, True, True, False, True]
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doc = Doc(en_vocab, words=words, spaces=spaces)
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entities = [
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(len("I flew to "), len("I flew to San Francisco Valley"), "LOC"),
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(len("I flew to "), len("I flew to San Francisco"), "LOC"),
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]
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with pytest.raises(ValueError):
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biluo_tags_from_offsets(doc, entities)
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def test_gold_biluo_misalign(en_vocab):
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words = ["I", "flew", "to", "San", "Francisco", "Valley."]
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spaces = [True, True, True, True, True, False]
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doc = Doc(en_vocab, words=words, spaces=spaces)
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entities = [(len("I flew to "), len("I flew to San Francisco Valley"), "LOC")]
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tags = biluo_tags_from_offsets(doc, entities)
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assert tags == ["O", "O", "O", "-", "-", "-"]
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def test_roundtrip_offsets_biluo_conversion(en_tokenizer):
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text = "I flew to Silicon Valley via London."
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biluo_tags = ["O", "O", "O", "B-LOC", "L-LOC", "O", "U-GPE", "O"]
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offsets = [(10, 24, "LOC"), (29, 35, "GPE")]
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doc = en_tokenizer(text)
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biluo_tags_converted = biluo_tags_from_offsets(doc, offsets)
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assert biluo_tags_converted == biluo_tags
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offsets_converted = offsets_from_biluo_tags(doc, biluo_tags)
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assert offsets_converted == offsets
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def test_biluo_spans(en_tokenizer):
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doc = en_tokenizer("I flew to Silicon Valley via London.")
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biluo_tags = ["O", "O", "O", "B-LOC", "L-LOC", "O", "U-GPE", "O"]
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spans = spans_from_biluo_tags(doc, biluo_tags)
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assert len(spans) == 2
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assert spans[0].text == "Silicon Valley"
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assert spans[0].label_ == "LOC"
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assert spans[1].text == "London"
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assert spans[1].label_ == "GPE"
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def test_gold_ner_missing_tags(en_tokenizer):
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doc = en_tokenizer("I flew to Silicon Valley via London.")
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biluo_tags = [None, "O", "O", "B-LOC", "L-LOC", "O", "U-GPE", "O"]
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gold = GoldParse(doc, entities=biluo_tags) # noqa: F841
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def test_iob_to_biluo():
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good_iob = ["O", "O", "B-LOC", "I-LOC", "O", "B-PERSON"]
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good_biluo = ["O", "O", "B-LOC", "L-LOC", "O", "U-PERSON"]
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bad_iob = ["O", "O", '"', "B-LOC", "I-LOC"]
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converted_biluo = iob_to_biluo(good_iob)
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assert good_biluo == converted_biluo
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with pytest.raises(ValueError):
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iob_to_biluo(bad_iob)
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def test_roundtrip_docs_to_json():
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text = "I flew to Silicon Valley via London."
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tags = ["PRP", "VBD", "IN", "NNP", "NNP", "IN", "NNP", "."]
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heads = [1, 1, 1, 4, 2, 1, 5, 1]
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deps = ["nsubj", "ROOT", "prep", "compound", "pobj", "prep", "pobj", "punct"]
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biluo_tags = ["O", "O", "O", "B-LOC", "L-LOC", "O", "U-GPE", "O"]
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cats = {"TRAVEL": 1.0, "BAKING": 0.0}
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nlp = English()
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doc = nlp(text)
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for i in range(len(tags)):
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doc[i].tag_ = tags[i]
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doc[i].dep_ = deps[i]
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doc[i].head = doc[heads[i]]
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doc.ents = spans_from_biluo_tags(doc, biluo_tags)
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doc.cats = cats
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doc.is_tagged = True
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doc.is_parsed = True
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# roundtrip to JSON
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with make_tempdir() as tmpdir:
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json_file = tmpdir / "roundtrip.json"
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srsly.write_json(json_file, [docs_to_json(doc)])
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goldcorpus = GoldCorpus(train=str(json_file), dev=str(json_file))
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reloaded_example = next(goldcorpus.train_dataset(nlp))
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goldparse = reloaded_example.gold
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assert len(doc) == goldcorpus.count_train()
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assert text == reloaded_example.text
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assert tags == goldparse.tags
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assert deps == goldparse.labels
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assert heads == goldparse.heads
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assert biluo_tags == goldparse.ner
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assert "TRAVEL" in goldparse.cats
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assert "BAKING" in goldparse.cats
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assert cats["TRAVEL"] == goldparse.cats["TRAVEL"]
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assert cats["BAKING"] == goldparse.cats["BAKING"]
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# roundtrip to JSONL train dicts
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with make_tempdir() as tmpdir:
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jsonl_file = tmpdir / "roundtrip.jsonl"
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srsly.write_jsonl(jsonl_file, [docs_to_json(doc)])
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goldcorpus = GoldCorpus(str(jsonl_file), str(jsonl_file))
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reloaded_example = next(goldcorpus.train_dataset(nlp))
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goldparse = reloaded_example.gold
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assert len(doc) == goldcorpus.count_train()
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assert text == reloaded_example.text
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assert tags == goldparse.tags
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assert deps == goldparse.labels
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assert heads == goldparse.heads
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assert biluo_tags == goldparse.ner
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assert "TRAVEL" in goldparse.cats
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assert "BAKING" in goldparse.cats
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assert cats["TRAVEL"] == goldparse.cats["TRAVEL"]
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assert cats["BAKING"] == goldparse.cats["BAKING"]
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# roundtrip to JSONL tuples
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with make_tempdir() as tmpdir:
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jsonl_file = tmpdir / "roundtrip.jsonl"
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# write to JSONL train dicts
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srsly.write_jsonl(jsonl_file, [docs_to_json(doc)])
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goldcorpus = GoldCorpus(str(jsonl_file), str(jsonl_file))
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# load and rewrite as JSONL tuples
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srsly.write_jsonl(jsonl_file, goldcorpus.train_examples)
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goldcorpus = GoldCorpus(str(jsonl_file), str(jsonl_file))
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reloaded_example = next(goldcorpus.train_dataset(nlp))
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goldparse = reloaded_example.gold
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assert len(doc) == goldcorpus.count_train()
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assert text == reloaded_example.text
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assert tags == goldparse.tags
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assert deps == goldparse.labels
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assert heads == goldparse.heads
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assert biluo_tags == goldparse.ner
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assert "TRAVEL" in goldparse.cats
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assert "BAKING" in goldparse.cats
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assert cats["TRAVEL"] == goldparse.cats["TRAVEL"]
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assert cats["BAKING"] == goldparse.cats["BAKING"]
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# xfail while we have backwards-compatible alignment
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@pytest.mark.xfail
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@pytest.mark.parametrize(
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"tokens_a,tokens_b,expected",
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[
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(["a", "b", "c"], ["ab", "c"], (3, [-1, -1, 1], [-1, 2], {0: 0, 1: 0}, {})),
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(
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["a", "b", "``", "c"],
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['ab"', "c"],
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(4, [-1, -1, -1, 1], [-1, 3], {0: 0, 1: 0, 2: 0}, {}),
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),
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(["a", "bc"], ["ab", "c"], (4, [-1, -1], [-1, -1], {0: 0}, {1: 1})),
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(
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["ab", "c", "d"],
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["a", "b", "cd"],
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(6, [-1, -1, -1], [-1, -1, -1], {1: 2, 2: 2}, {0: 0, 1: 0}),
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),
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(
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["a", "b", "cd"],
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["a", "b", "c", "d"],
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(3, [0, 1, -1], [0, 1, -1, -1], {}, {2: 2, 3: 2}),
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),
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([" ", "a"], ["a"], (1, [-1, 0], [1], {}, {})),
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],
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)
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def test_align(tokens_a, tokens_b, expected):
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cost, a2b, b2a, a2b_multi, b2a_multi = align(tokens_a, tokens_b)
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assert (cost, list(a2b), list(b2a), a2b_multi, b2a_multi) == expected
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# check symmetry
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cost, a2b, b2a, a2b_multi, b2a_multi = align(tokens_b, tokens_a)
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assert (cost, list(b2a), list(a2b), b2a_multi, a2b_multi) == expected
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def test_goldparse_startswith_space(en_tokenizer):
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text = " a"
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doc = en_tokenizer(text)
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g = GoldParse(doc, words=["a"], entities=["U-DATE"], deps=["ROOT"], heads=[0])
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assert g.words == [" ", "a"]
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assert g.ner == [None, "U-DATE"]
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assert g.labels == [None, "ROOT"]
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def test_gold_constructor():
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"""Test that the GoldParse constructor works fine"""
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nlp = English()
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doc = nlp("This is a sentence")
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gold = GoldParse(doc, cats={"cat1": 1.0, "cat2": 0.0})
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assert gold.cats["cat1"]
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assert not gold.cats["cat2"]
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assert gold.words == ["This", "is", "a", "sentence"]
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def test_gold_orig_annot():
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nlp = English()
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doc = nlp("This is a sentence")
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gold = GoldParse(doc, cats={"cat1": 1.0, "cat2": 0.0})
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assert gold.orig.words == ["This", "is", "a", "sentence"]
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assert gold.cats["cat1"]
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doc_annotation = DocAnnotation(cats={"cat1": 0.0, "cat2": 1.0})
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gold2 = GoldParse.from_annotation(doc, doc_annotation, gold.orig)
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assert gold2.orig.words == ["This", "is", "a", "sentence"]
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assert not gold2.cats["cat1"]
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def test_tuple_format_implicit():
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"""Test tuple format with implicit GoldParse creation"""
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train_data = [
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("Uber blew through $1 million a week", {"entities": [(0, 4, "ORG")]}),
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(
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"Spotify steps up Asia expansion",
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{"entities": [(0, 8, "ORG"), (17, 21, "LOC")]},
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),
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("Google rebrands its business apps", {"entities": [(0, 6, "ORG")]}),
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]
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_train(train_data)
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def test_tuple_format_implicit_invalid():
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"""Test that an error is thrown for an implicit invalid GoldParse field"""
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train_data = [
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("Uber blew through $1 million a week", {"frumble": [(0, 4, "ORG")]}),
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(
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"Spotify steps up Asia expansion",
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{"entities": [(0, 8, "ORG"), (17, 21, "LOC")]},
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),
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("Google rebrands its business apps", {"entities": [(0, 6, "ORG")]}),
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]
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with pytest.raises(TypeError):
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_train(train_data)
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def _train(train_data):
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nlp = English()
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ner = nlp.create_pipe("ner")
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ner.add_label("ORG")
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ner.add_label("LOC")
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nlp.add_pipe(ner)
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optimizer = nlp.begin_training()
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for i in range(5):
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losses = {}
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batches = minibatch(train_data, size=compounding(4.0, 32.0, 1.001))
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for batch in batches:
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nlp.update(batch, sgd=optimizer, losses=losses)
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tokens_1 = {
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"ids": [1, 2, 3],
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"words": ["Hi", "there", "everyone"],
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"tags": ["INTJ", "ADV", "PRON"],
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}
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tokens_2 = {
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"ids": [1, 2, 3, 4],
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"words": ["It", "is", "just", "me"],
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"tags": ["PRON", "AUX", "ADV", "PRON"],
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}
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text0 = "Hi there everyone It is just me"
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def test_merge_sents():
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nlp = English()
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example = Example()
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example.add_token_annotation(**tokens_1)
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example.add_token_annotation(**tokens_2)
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assert len(example.get_gold_parses(merge=False, vocab=nlp.vocab)) == 2
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assert len(example.get_gold_parses(merge=True, vocab=nlp.vocab)) == 1 # this shouldn't change the original object
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merged_example = example.merge_sents()
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token_annotation_1 = example.token_annotations[0]
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assert token_annotation_1.ids == [1, 2, 3]
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assert token_annotation_1.words == ["Hi", "there", "everyone"]
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assert token_annotation_1.tags == ["INTJ", "ADV", "PRON"]
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token_annotation_m = merged_example.token_annotations[0]
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assert token_annotation_m.ids == [1, 2, 3, 4, 5, 6, 7]
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assert token_annotation_m.words == ["Hi", "there", "everyone", "It", "is", "just", "me"]
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assert token_annotation_m.tags == ["INTJ", "ADV", "PRON", "PRON", "AUX", "ADV", "PRON"]
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def test_tuples_to_example():
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ex = Example()
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ex.add_token_annotation(**tokens_1)
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ex.add_token_annotation(**tokens_2)
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ex.add_doc_annotation(cats={"TRAVEL": 1.0, "BAKING": 0.0})
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ex_dict = ex.to_dict()
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token_dicts = [
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{
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"ids": [1, 2, 3],
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"words": ["Hi", "there", "everyone"],
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"tags": ["INTJ", "ADV", "PRON"],
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"heads": [],
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"deps": [],
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"entities": [],
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"morphology": [],
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"brackets": [],
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},
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{
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"ids": [1, 2, 3, 4],
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"words": ["It", "is", "just", "me"],
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"tags": ["PRON", "AUX", "ADV", "PRON"],
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"heads": [],
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"deps": [],
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"entities": [],
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"morphology": [],
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"brackets": [],
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},
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]
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doc_dict = {"cats": {"TRAVEL": 1.0, "BAKING": 0.0}, "links": {}}
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assert ex_dict == {"token_annotations": token_dicts, "doc_annotation": doc_dict}
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