mirror of https://github.com/explosion/spaCy.git
246 lines
10 KiB
Python
246 lines
10 KiB
Python
import random
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from contextlib import contextmanager
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import pytest
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from spacy.lang.en import English
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from spacy.pipeline._parser_internals.nonproj import contains_cycle
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from spacy.tokens import Doc, DocBin, Span
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from spacy.training import Corpus, Example
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from spacy.training.augment import (
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create_lower_casing_augmenter,
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create_orth_variants_augmenter,
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make_whitespace_variant,
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)
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from ..util import make_tempdir
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@contextmanager
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def make_docbin(docs, name="roundtrip.spacy"):
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with make_tempdir() as tmpdir:
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output_file = tmpdir / name
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DocBin(docs=docs).to_disk(output_file)
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yield output_file
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@pytest.fixture
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def nlp():
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return English()
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@pytest.fixture
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def doc(nlp):
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# fmt: off
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words = ["Sarah", "'s", "sister", "flew", "to", "Silicon", "Valley", "via", "London", "."]
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tags = ["NNP", "POS", "NN", "VBD", "IN", "NNP", "NNP", "IN", "NNP", "."]
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pos = ["PROPN", "PART", "NOUN", "VERB", "ADP", "PROPN", "PROPN", "ADP", "PROPN", "PUNCT"]
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ents = ["B-PERSON", "I-PERSON", "O", "", "O", "B-LOC", "I-LOC", "O", "B-GPE", "O"]
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cats = {"TRAVEL": 1.0, "BAKING": 0.0}
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# fmt: on
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doc = Doc(nlp.vocab, words=words, tags=tags, pos=pos, ents=ents)
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doc.cats = cats
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return doc
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@pytest.mark.filterwarnings("ignore::UserWarning")
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def test_make_orth_variants(nlp):
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single = [
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{"tags": ["NFP"], "variants": ["…", "..."]},
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{"tags": [":"], "variants": ["-", "—", "–", "--", "---", "——"]},
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]
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# fmt: off
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words = ["\n\n", "A", "\t", "B", "a", "b", "…", "...", "-", "—", "–", "--", "---", "——"]
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tags = ["_SP", "NN", "\t", "NN", "NN", "NN", "NFP", "NFP", ":", ":", ":", ":", ":", ":"]
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# fmt: on
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spaces = [True] * len(words)
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spaces[0] = False
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spaces[2] = False
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doc = Doc(nlp.vocab, words=words, spaces=spaces, tags=tags)
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augmenter = create_orth_variants_augmenter(
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level=0.2, lower=0.5, orth_variants={"single": single}
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)
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with make_docbin([doc] * 10) as output_file:
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reader = Corpus(output_file, augmenter=augmenter)
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# Due to randomness, only test that it works without errors
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list(reader(nlp))
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# check that the following settings lowercase everything
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augmenter = create_orth_variants_augmenter(
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level=1.0, lower=1.0, orth_variants={"single": single}
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)
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with make_docbin([doc] * 10) as output_file:
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reader = Corpus(output_file, augmenter=augmenter)
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for example in reader(nlp):
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for token in example.reference:
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assert token.text == token.text.lower()
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# check that lowercasing is applied without tags
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doc = Doc(nlp.vocab, words=words, spaces=[True] * len(words))
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augmenter = create_orth_variants_augmenter(
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level=1.0, lower=1.0, orth_variants={"single": single}
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)
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with make_docbin([doc] * 10) as output_file:
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reader = Corpus(output_file, augmenter=augmenter)
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for example in reader(nlp):
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for ex_token, doc_token in zip(example.reference, doc):
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assert ex_token.text == doc_token.text.lower()
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# check that no lowercasing is applied with lower=0.0
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doc = Doc(nlp.vocab, words=words, spaces=[True] * len(words))
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augmenter = create_orth_variants_augmenter(
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level=1.0, lower=0.0, orth_variants={"single": single}
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)
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with make_docbin([doc] * 10) as output_file:
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reader = Corpus(output_file, augmenter=augmenter)
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for example in reader(nlp):
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for ex_token, doc_token in zip(example.reference, doc):
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assert ex_token.text == doc_token.text
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def test_lowercase_augmenter(nlp, doc):
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augmenter = create_lower_casing_augmenter(level=1.0)
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with make_docbin([doc]) as output_file:
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reader = Corpus(output_file, augmenter=augmenter)
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corpus = list(reader(nlp))
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eg = corpus[0]
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assert eg.reference.text == doc.text.lower()
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assert eg.predicted.text == doc.text.lower()
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ents = [(e.start, e.end, e.label) for e in doc.ents]
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assert [(e.start, e.end, e.label) for e in eg.reference.ents] == ents
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for ref_ent, orig_ent in zip(eg.reference.ents, doc.ents):
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assert ref_ent.text == orig_ent.text.lower()
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assert [t.ent_iob for t in doc] == [t.ent_iob for t in eg.reference]
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assert [t.pos_ for t in eg.reference] == [t.pos_ for t in doc]
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# check that augmentation works when lowercasing leads to different
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# predicted tokenization
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words = ["A", "B", "CCC."]
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doc = Doc(nlp.vocab, words=words)
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with make_docbin([doc]) as output_file:
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reader = Corpus(output_file, augmenter=augmenter)
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corpus = list(reader(nlp))
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eg = corpus[0]
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assert eg.reference.text == doc.text.lower()
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assert eg.predicted.text == doc.text.lower()
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assert [t.text for t in eg.reference] == [t.lower() for t in words]
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assert [t.text for t in eg.predicted] == [
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t.text for t in nlp.make_doc(doc.text.lower())
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]
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@pytest.mark.filterwarnings("ignore::UserWarning")
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def test_custom_data_augmentation(nlp, doc):
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def create_spongebob_augmenter(randomize: bool = False):
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def augment(nlp, example):
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text = example.text
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if randomize:
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ch = [c.lower() if random.random() < 0.5 else c.upper() for c in text]
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else:
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ch = [c.lower() if i % 2 else c.upper() for i, c in enumerate(text)]
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example_dict = example.to_dict()
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doc = nlp.make_doc("".join(ch))
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example_dict["token_annotation"]["ORTH"] = [t.text for t in doc]
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yield example
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yield example.from_dict(doc, example_dict)
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return augment
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with make_docbin([doc]) as output_file:
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reader = Corpus(output_file, augmenter=create_spongebob_augmenter())
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corpus = list(reader(nlp))
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orig_text = "Sarah 's sister flew to Silicon Valley via London . "
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augmented = "SaRaH 's sIsTeR FlEw tO SiLiCoN VaLlEy vIa lOnDoN . "
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assert corpus[0].text == orig_text
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assert corpus[0].reference.text == orig_text
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assert corpus[0].predicted.text == orig_text
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assert corpus[1].text == augmented
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assert corpus[1].reference.text == augmented
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assert corpus[1].predicted.text == augmented
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ents = [(e.start, e.end, e.label) for e in doc.ents]
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assert [(e.start, e.end, e.label) for e in corpus[0].reference.ents] == ents
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assert [(e.start, e.end, e.label) for e in corpus[1].reference.ents] == ents
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def test_make_whitespace_variant(nlp):
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# fmt: off
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text = "They flew to New York City.\nThen they drove to Washington, D.C."
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words = ["They", "flew", "to", "New", "York", "City", ".", "\n", "Then", "they", "drove", "to", "Washington", ",", "D.C."]
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spaces = [True, True, True, True, True, False, False, False, True, True, True, True, False, True, False]
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tags = ["PRP", "VBD", "IN", "NNP", "NNP", "NNP", ".", "_SP", "RB", "PRP", "VBD", "IN", "NNP", ",", "NNP"]
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lemmas = ["they", "fly", "to", "New", "York", "City", ".", "\n", "then", "they", "drive", "to", "Washington", ",", "D.C."]
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heads = [1, 1, 1, 4, 5, 2, 1, 10, 10, 10, 10, 10, 11, 12, 12]
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deps = ["nsubj", "ROOT", "prep", "compound", "compound", "pobj", "punct", "dep", "advmod", "nsubj", "ROOT", "prep", "pobj", "punct", "appos"]
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ents = ["O", "", "O", "B-GPE", "I-GPE", "I-GPE", "O", "O", "O", "O", "O", "O", "B-GPE", "O", "B-GPE"]
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# fmt: on
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doc = Doc(
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nlp.vocab,
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words=words,
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spaces=spaces,
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tags=tags,
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lemmas=lemmas,
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heads=heads,
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deps=deps,
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ents=ents,
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)
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assert doc.text == text
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example = Example(nlp.make_doc(text), doc)
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# whitespace is only added internally in entity spans
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mod_ex = make_whitespace_variant(nlp, example, " ", 3)
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assert mod_ex.reference.ents[0].text == "New York City"
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mod_ex = make_whitespace_variant(nlp, example, " ", 4)
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assert mod_ex.reference.ents[0].text == "New York City"
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mod_ex = make_whitespace_variant(nlp, example, " ", 5)
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assert mod_ex.reference.ents[0].text == "New York City"
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mod_ex = make_whitespace_variant(nlp, example, " ", 6)
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assert mod_ex.reference.ents[0].text == "New York City"
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# add a space at every possible position
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for i in range(len(doc) + 1):
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mod_ex = make_whitespace_variant(nlp, example, " ", i)
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assert mod_ex.reference[i].is_space
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# adds annotation when the doc contains at least partial annotation
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assert [t.tag_ for t in mod_ex.reference] == tags[:i] + ["_SP"] + tags[i:]
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assert [t.lemma_ for t in mod_ex.reference] == lemmas[:i] + [" "] + lemmas[i:]
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assert [t.dep_ for t in mod_ex.reference] == deps[:i] + ["dep"] + deps[i:]
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# does not add partial annotation if doc does not contain this feature
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assert not mod_ex.reference.has_annotation("POS")
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assert not mod_ex.reference.has_annotation("MORPH")
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# produces well-formed trees
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assert not contains_cycle([t.head.i for t in mod_ex.reference])
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assert len(list(doc.sents)) == 2
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if i == 0:
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assert mod_ex.reference[i].head.i == 1
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else:
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assert mod_ex.reference[i].head.i == i - 1
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# adding another space also produces well-formed trees
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for j in (3, 8, 10):
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mod_ex2 = make_whitespace_variant(nlp, mod_ex, "\t\t\n", j)
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assert not contains_cycle([t.head.i for t in mod_ex2.reference])
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assert len(list(doc.sents)) == 2
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assert mod_ex2.reference[j].head.i == j - 1
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# entities are well-formed
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assert len(doc.ents) == len(mod_ex.reference.ents)
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# there is one token with missing entity information
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assert any(t.ent_iob == 0 for t in mod_ex.reference)
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for ent in mod_ex.reference.ents:
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assert not ent[0].is_space
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assert not ent[-1].is_space
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# no modifications if:
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# partial dependencies
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example.reference[0].dep_ = ""
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mod_ex = make_whitespace_variant(nlp, example, " ", 5)
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assert mod_ex.text == example.reference.text
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example.reference[0].dep_ = "nsubj" # reset
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# spans
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example.reference.spans["spans"] = [example.reference[0:5]]
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mod_ex = make_whitespace_variant(nlp, example, " ", 5)
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assert mod_ex.text == example.reference.text
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del example.reference.spans["spans"] # reset
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# links
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example.reference.ents = [Span(doc, 0, 2, label="ENT", kb_id="Q123")]
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mod_ex = make_whitespace_variant(nlp, example, " ", 5)
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assert mod_ex.text == example.reference.text
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