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https://github.com/explosion/spaCy.git
synced 2025-10-31 07:57:35 +03:00
Fix lowercase augmentation (#7336)
* Fix aborted/skipped augmentation for `spacy.orth_variants.v1` if lowercasing was enabled for an example * Simplify `spacy.orth_variants.v1` for `Example` vs. `GoldParse` * Preserve reference tokenization in `spacy.lower_case.v1`
This commit is contained in:
Adriane Boyd
GitHub
parent
cd70c3cb79
commit
3f3e8110dc
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@ -38,19 +38,59 @@ def doc(nlp):
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@pytest.mark.filterwarnings("ignore::UserWarning")
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def test_make_orth_variants(nlp, doc):
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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]) as output_file:
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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 for now
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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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@ -66,6 +106,21 @@ def test_lowercase_augmenter(nlp, doc):
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assert ref_ent.text == orig_ent.text.lower()
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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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@ -1,12 +1,10 @@
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from typing import Callable, Iterator, Dict, List, Tuple, TYPE_CHECKING
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import random
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import itertools
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import copy
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from functools import partial
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from pydantic import BaseModel, StrictStr
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from ..util import registry
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from ..tokens import Doc
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from .example import Example
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if TYPE_CHECKING:
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@ -71,7 +69,7 @@ def lower_casing_augmenter(
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else:
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example_dict = example.to_dict()
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doc = nlp.make_doc(example.text.lower())
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example_dict["token_annotation"]["ORTH"] = [t.lower_ for t in doc]
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example_dict["token_annotation"]["ORTH"] = [t.lower_ for t in example.reference]
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yield example.from_dict(doc, example_dict)
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@ -88,24 +86,15 @@ def orth_variants_augmenter(
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else:
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raw_text = example.text
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orig_dict = example.to_dict()
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if not orig_dict["token_annotation"]:
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yield example
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else:
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variant_text, variant_token_annot = make_orth_variants(
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nlp,
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raw_text,
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orig_dict["token_annotation"],
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orth_variants,
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lower=raw_text is not None and random.random() < lower,
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)
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if variant_text:
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doc = nlp.make_doc(variant_text)
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else:
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doc = Doc(nlp.vocab, words=variant_token_annot["ORTH"])
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variant_token_annot["ORTH"] = [w.text for w in doc]
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variant_token_annot["SPACY"] = [w.whitespace_ for w in doc]
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orig_dict["token_annotation"] = variant_token_annot
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yield example.from_dict(doc, orig_dict)
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variant_text, variant_token_annot = make_orth_variants(
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nlp,
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raw_text,
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orig_dict["token_annotation"],
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orth_variants,
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lower=raw_text is not None and random.random() < lower,
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)
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orig_dict["token_annotation"] = variant_token_annot
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yield example.from_dict(nlp.make_doc(variant_text), orig_dict)
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def make_orth_variants(
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@ -116,88 +105,53 @@ def make_orth_variants(
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*,
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lower: bool = False,
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) -> Tuple[str, Dict[str, List[str]]]:
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orig_token_dict = copy.deepcopy(token_dict)
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ndsv = orth_variants.get("single", [])
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ndpv = orth_variants.get("paired", [])
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words = token_dict.get("ORTH", [])
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tags = token_dict.get("TAG", [])
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# keep unmodified if words or tags are not defined
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if words and tags:
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if lower:
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words = [w.lower() for w in words]
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# single variants
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punct_choices = [random.choice(x["variants"]) for x in ndsv]
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for word_idx in range(len(words)):
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for punct_idx in range(len(ndsv)):
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if (
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tags[word_idx] in ndsv[punct_idx]["tags"]
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and words[word_idx] in ndsv[punct_idx]["variants"]
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):
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words[word_idx] = punct_choices[punct_idx]
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# paired variants
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punct_choices = [random.choice(x["variants"]) for x in ndpv]
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for word_idx in range(len(words)):
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for punct_idx in range(len(ndpv)):
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if tags[word_idx] in ndpv[punct_idx]["tags"] and words[
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word_idx
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] in itertools.chain.from_iterable(ndpv[punct_idx]["variants"]):
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# backup option: random left vs. right from pair
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pair_idx = random.choice([0, 1])
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# best option: rely on paired POS tags like `` / ''
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if len(ndpv[punct_idx]["tags"]) == 2:
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pair_idx = ndpv[punct_idx]["tags"].index(tags[word_idx])
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# next best option: rely on position in variants
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# (may not be unambiguous, so order of variants matters)
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else:
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for pair in ndpv[punct_idx]["variants"]:
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if words[word_idx] in pair:
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pair_idx = pair.index(words[word_idx])
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words[word_idx] = punct_choices[punct_idx][pair_idx]
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# keep unmodified if words are not defined
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if not words:
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return raw, token_dict
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if lower:
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words = [w.lower() for w in words]
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raw = raw.lower()
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# if no tags, only lowercase
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if not tags:
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token_dict["ORTH"] = words
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token_dict["TAG"] = tags
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# modify raw
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if raw is not None:
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variants = []
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for single_variants in ndsv:
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variants.extend(single_variants["variants"])
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for paired_variants in ndpv:
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variants.extend(
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list(itertools.chain.from_iterable(paired_variants["variants"]))
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)
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# store variants in reverse length order to be able to prioritize
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# longer matches (e.g., "---" before "--")
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variants = sorted(variants, key=lambda x: len(x))
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variants.reverse()
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variant_raw = ""
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raw_idx = 0
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# add initial whitespace
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while raw_idx < len(raw) and raw[raw_idx].isspace():
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variant_raw += raw[raw_idx]
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raw_idx += 1
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for word in words:
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match_found = False
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# skip whitespace words
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if word.isspace():
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match_found = True
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# add identical word
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elif word not in variants and raw[raw_idx:].startswith(word):
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variant_raw += word
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raw_idx += len(word)
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match_found = True
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# add variant word
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else:
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for variant in variants:
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if not match_found and raw[raw_idx:].startswith(variant):
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raw_idx += len(variant)
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variant_raw += word
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match_found = True
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# something went wrong, abort
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# (add a warning message?)
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if not match_found:
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return raw, orig_token_dict
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# add following whitespace
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while raw_idx < len(raw) and raw[raw_idx].isspace():
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variant_raw += raw[raw_idx]
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raw_idx += 1
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raw = variant_raw
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return raw, token_dict
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# single variants
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ndsv = orth_variants.get("single", [])
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punct_choices = [random.choice(x["variants"]) for x in ndsv]
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for word_idx in range(len(words)):
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for punct_idx in range(len(ndsv)):
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if (
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tags[word_idx] in ndsv[punct_idx]["tags"]
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and words[word_idx] in ndsv[punct_idx]["variants"]
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):
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words[word_idx] = punct_choices[punct_idx]
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# paired variants
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ndpv = orth_variants.get("paired", [])
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punct_choices = [random.choice(x["variants"]) for x in ndpv]
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for word_idx in range(len(words)):
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for punct_idx in range(len(ndpv)):
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if tags[word_idx] in ndpv[punct_idx]["tags"] and words[
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word_idx
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] in itertools.chain.from_iterable(ndpv[punct_idx]["variants"]):
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# backup option: random left vs. right from pair
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pair_idx = random.choice([0, 1])
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# best option: rely on paired POS tags like `` / ''
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if len(ndpv[punct_idx]["tags"]) == 2:
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pair_idx = ndpv[punct_idx]["tags"].index(tags[word_idx])
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# next best option: rely on position in variants
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# (may not be unambiguous, so order of variants matters)
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else:
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for pair in ndpv[punct_idx]["variants"]:
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if words[word_idx] in pair:
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pair_idx = pair.index(words[word_idx])
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words[word_idx] = punct_choices[punct_idx][pair_idx]
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token_dict["ORTH"] = words
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# construct modified raw text from words and spaces
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raw = ""
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for orth, spacy in zip(token_dict["ORTH"], token_dict["SPACY"]):
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raw += orth
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if spacy:
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raw += " "
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return raw, token_dict
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