spaCy/spacy/training/augment.py

from typing import Callable, Iterator, Dict, List, Tuple, TYPE_CHECKING
import random
import itertools
import copy
from functools import partial
from pydantic import BaseModel, StrictStr

from ..util import registry
from ..tokens import Doc
from .example import Example

if TYPE_CHECKING:
    from ..language import Language  # noqa: F401


class OrthVariantsSingle(BaseModel):
    tags: List[StrictStr]
    variants: List[StrictStr]


class OrthVariantsPaired(BaseModel):
    tags: List[StrictStr]
    variants: List[List[StrictStr]]


class OrthVariants(BaseModel):
    paired: List[OrthVariantsPaired] = {}
    single: List[OrthVariantsSingle] = {}


@registry.augmenters("spacy.orth_variants.v1")
def create_orth_variants_augmenter(
    level: float, lower: float, orth_variants: OrthVariants
) -> Callable[["Language", Example], Iterator[Example]]:
    """Create a data augmentation callback that uses orth-variant replacement.
    The callback can be added to a corpus or other data iterator during training.

    level (float): The percentage of texts that will be augmented.
    lower (float): The percentage of texts that will be lowercased.
    orth_variants (Dict[str, dict]): A dictionary containing the single and
        paired orth variants. Typically loaded from a JSON file.
    RETURNS (Callable[[Language, Example], Iterator[Example]]): The augmenter.
    """
    return partial(
        orth_variants_augmenter, orth_variants=orth_variants, level=level, lower=lower
    )


@registry.augmenters("spacy.lower_case.v1")
def create_lower_casing_augmenter(
    level: float,
) -> Callable[["Language", Example], Iterator[Example]]:
    """Create a data augmentation callback that converts documents to lowercase.
    The callback can be added to a corpus or other data iterator during training.

    level (float): The percentage of texts that will be augmented.
    RETURNS (Callable[[Language, Example], Iterator[Example]]): The augmenter.
    """
    return partial(lower_casing_augmenter, level=level)


def dont_augment(nlp: "Language", example: Example) -> Iterator[Example]:
    yield example


def lower_casing_augmenter(
    nlp: "Language", example: Example, *, level: float
) -> Iterator[Example]:
    if random.random() >= level:
        yield example
    else:
        example_dict = example.to_dict()
        doc = nlp.make_doc(example.text.lower())
        example_dict["token_annotation"]["ORTH"] = [t.lower_ for t in doc]
        yield example.from_dict(doc, example_dict)


def orth_variants_augmenter(
    nlp: "Language",
    example: Example,
    orth_variants: dict,
    *,
    level: float = 0.0,
    lower: float = 0.0,
) -> Iterator[Example]:
    if random.random() >= level:
        yield example
    else:
        raw_text = example.text
        orig_dict = example.to_dict()
        if not orig_dict["token_annotation"]:
            yield example
        else:
            variant_text, variant_token_annot = make_orth_variants(
                nlp,
                raw_text,
                orig_dict["token_annotation"],
                orth_variants,
                lower=raw_text is not None and random.random() < lower,
            )
            if variant_text:
                doc = nlp.make_doc(variant_text)
            else:
                doc = Doc(nlp.vocab, words=variant_token_annot["ORTH"])
                variant_token_annot["ORTH"] = [w.text for w in doc]
                variant_token_annot["SPACY"] = [w.whitespace_ for w in doc]
            orig_dict["token_annotation"] = variant_token_annot
            yield example.from_dict(doc, orig_dict)


def make_orth_variants(
    nlp: "Language",
    raw: str,
    token_dict: Dict[str, List[str]],
    orth_variants: Dict[str, List[Dict[str, List[str]]]],
    *,
    lower: bool = False,
) -> Tuple[str, Dict[str, List[str]]]:
    orig_token_dict = copy.deepcopy(token_dict)
    ndsv = orth_variants.get("single", [])
    ndpv = orth_variants.get("paired", [])
    words = token_dict.get("ORTH", [])
    tags = token_dict.get("TAG", [])
    # keep unmodified if words or tags are not defined
    if words and tags:
        if lower:
            words = [w.lower() for w in words]
        # single variants
        punct_choices = [random.choice(x["variants"]) for x in ndsv]
        for word_idx in range(len(words)):
            for punct_idx in range(len(ndsv)):
                if (
                    tags[word_idx] in ndsv[punct_idx]["tags"]
                    and words[word_idx] in ndsv[punct_idx]["variants"]
                ):
                    words[word_idx] = punct_choices[punct_idx]
        # paired variants
        punct_choices = [random.choice(x["variants"]) for x in ndpv]
        for word_idx in range(len(words)):
            for punct_idx in range(len(ndpv)):
                if tags[word_idx] in ndpv[punct_idx]["tags"] and words[
                    word_idx
                ] in itertools.chain.from_iterable(ndpv[punct_idx]["variants"]):
                    # backup option: random left vs. right from pair
                    pair_idx = random.choice([0, 1])
                    # best option: rely on paired POS tags like `` / ''
                    if len(ndpv[punct_idx]["tags"]) == 2:
                        pair_idx = ndpv[punct_idx]["tags"].index(tags[word_idx])
                    # next best option: rely on position in variants
                    # (may not be unambiguous, so order of variants matters)
                    else:
                        for pair in ndpv[punct_idx]["variants"]:
                            if words[word_idx] in pair:
                                pair_idx = pair.index(words[word_idx])
                    words[word_idx] = punct_choices[punct_idx][pair_idx]
        token_dict["ORTH"] = words
        token_dict["TAG"] = tags
    # modify raw
    if raw is not None:
        variants = []
        for single_variants in ndsv:
            variants.extend(single_variants["variants"])
        for paired_variants in ndpv:
            variants.extend(
                list(itertools.chain.from_iterable(paired_variants["variants"]))
            )
        # store variants in reverse length order to be able to prioritize
        # longer matches (e.g., "---" before "--")
        variants = sorted(variants, key=lambda x: len(x))
        variants.reverse()
        variant_raw = ""
        raw_idx = 0
        # add initial whitespace
        while raw_idx < len(raw) and raw[raw_idx].isspace():
            variant_raw += raw[raw_idx]
            raw_idx += 1
        for word in words:
            match_found = False
            # skip whitespace words
            if word.isspace():
                match_found = True
            # add identical word
            elif word not in variants and raw[raw_idx:].startswith(word):
                variant_raw += word
                raw_idx += len(word)
                match_found = True
            # add variant word
            else:
                for variant in variants:
                    if not match_found and raw[raw_idx:].startswith(variant):
                        raw_idx += len(variant)
                        variant_raw += word
                        match_found = True
            # something went wrong, abort
            # (add a warning message?)
            if not match_found:
                return raw, orig_token_dict
            # add following whitespace
            while raw_idx < len(raw) and raw[raw_idx].isspace():
                variant_raw += raw[raw_idx]
                raw_idx += 1
        raw = variant_raw
    return raw, token_dict