spaCy/spacy/pipeline/attributeruler.py

import srsly
from typing import List, Dict, Union, Iterable, Any, Optional
from pathlib import Path

from .pipe import Pipe
from ..errors import Errors
from ..language import Language
from ..matcher import Matcher
from ..symbols import IDS
from ..tokens import Doc, Span
from ..tokens._retokenize import normalize_token_attrs, set_token_attrs
from ..vocab import Vocab
from .. import util


MatcherPatternType = List[Dict[Union[int, str], Any]]
AttributeRulerPatternType = Dict[str, Union[MatcherPatternType, Dict, int]]


@Language.factory(
    "attribute_ruler", default_config={"pattern_dicts": None, "validate": False}
)
def make_attribute_ruler(
    nlp: Language,
    name: str,
    pattern_dicts: Optional[Iterable[AttributeRulerPatternType]],
    validate: bool,
):
    return AttributeRuler(
        nlp.vocab, name, pattern_dicts=pattern_dicts, validate=validate
    )


class AttributeRuler(Pipe):
    """Set token-level attributes for tokens matched by Matcher patterns.
    Additionally supports importing patterns from tag maps and morph rules.

    DOCS: https://spacy.io/api/attributeruler
    """

    def __init__(
        self,
        vocab: Vocab,
        name: str = "attribute_ruler",
        *,
        pattern_dicts: Optional[Iterable[AttributeRulerPatternType]] = None,
        validate: bool = False,
    ) -> None:
        """Initialize the AttributeRuler.

        vocab (Vocab): The vocab.
        name (str): The pipe name. Defaults to "attribute_ruler".
        pattern_dicts (Iterable[Dict]): A list of pattern dicts with the keys as
        the arguments to AttributeRuler.add (`patterns`/`attrs`/`index`) to add
        as patterns.

        RETURNS (AttributeRuler): The AttributeRuler component.

        DOCS: https://spacy.io/api/attributeruler#init
        """
        self.name = name
        self.vocab = vocab
        self.matcher = Matcher(self.vocab, validate=validate)
        self.attrs = []
        self._attrs_unnormed = []  # store for reference
        self.indices = []

        if pattern_dicts:
            self.add_patterns(pattern_dicts)

    def __call__(self, doc: Doc) -> Doc:
        """Apply the AttributeRuler to a Doc and set all attribute exceptions.

        doc (Doc): The document to process.
        RETURNS (Doc): The processed Doc.

        DOCS: https://spacy.io/api/attributeruler#call
        """
        matches = self.matcher(doc)

        for match_id, start, end in matches:
            span = Span(doc, start, end, label=match_id)
            attrs = self.attrs[span.label]
            index = self.indices[span.label]
            try:
                token = span[index]
            except IndexError:
                raise ValueError(
                    Errors.E1001.format(
                        patterns=self.matcher.get(span.label),
                        span=[t.text for t in span],
                        index=index,
                    )
                ) from None
            set_token_attrs(token, attrs)
        return doc

    def pipe(self, stream, *, batch_size=128):
        """Apply the pipe to a stream of documents. This usually happens under
        the hood when the nlp object is called on a text and all components are
        applied to the Doc.

        stream (Iterable[Doc]): A stream of documents.
        batch_size (int): The number of documents to buffer.
        YIELDS (Doc): Processed documents in order.

        DOCS: https://spacy.io/attributeruler/pipe#pipe
        """
        for doc in stream:
            doc = self(doc)
            yield doc

    def load_from_tag_map(
        self, tag_map: Dict[str, Dict[Union[int, str], Union[int, str]]]
    ) -> None:
        """Load attribute ruler patterns from a tag map.

        tag_map (dict): The tag map that maps fine-grained tags to
            coarse-grained tags and morphological features.

        DOCS: https://spacy.io/api/attributeruler#load_from_morph_rules
        """
        for tag, attrs in tag_map.items():
            pattern = [{"TAG": tag}]
            attrs, morph_attrs = _split_morph_attrs(attrs)
            morph = self.vocab.morphology.add(morph_attrs)
            attrs["MORPH"] = self.vocab.strings[morph]
            self.add([pattern], attrs)

    def load_from_morph_rules(
        self, morph_rules: Dict[str, Dict[str, Dict[Union[int, str], Union[int, str]]]]
    ) -> None:
        """Load attribute ruler patterns from morph rules.

        morph_rules (dict): The morph rules that map token text and
            fine-grained tags to coarse-grained tags, lemmas and morphological
            features.

        DOCS: https://spacy.io/api/attributeruler#load_from_morph_rules
        """
        for tag in morph_rules:
            for word in morph_rules[tag]:
                pattern = [{"ORTH": word, "TAG": tag}]
                attrs = morph_rules[tag][word]
                attrs, morph_attrs = _split_morph_attrs(attrs)
                morph = self.vocab.morphology.add(morph_attrs)
                attrs["MORPH"] = self.vocab.strings[morph]
                self.add([pattern], attrs)

    def add(
        self, patterns: Iterable[MatcherPatternType], attrs: Dict, index: int = 0
    ) -> None:
        """Add Matcher patterns for tokens that should be modified with the
        provided attributes. The token at the specified index within the
        matched span will be assigned the attributes.

        patterns (Iterable[List[Dict]]): A list of Matcher patterns.
        attrs (Dict): The attributes to assign to the target token in the
            matched span.
        index (int): The index of the token in the matched span to modify. May
            be negative to index from the end of the span. Defaults to 0.

        DOCS: https://spacy.io/api/attributeruler#add
        """
        self.matcher.add(len(self.attrs), patterns)
        self._attrs_unnormed.append(attrs)
        attrs = normalize_token_attrs(self.vocab, attrs)
        self.attrs.append(attrs)
        self.indices.append(index)

    def add_patterns(self, pattern_dicts: Iterable[AttributeRulerPatternType]) -> None:
        """Add patterns from a list of pattern dicts with the keys as the
        arguments to AttributeRuler.add.
        pattern_dicts (Iterable[dict]): A list of pattern dicts with the keys
            as the arguments to AttributeRuler.add (patterns/attrs/index) to
            add as patterns.

        DOCS: https://spacy.io/api/attributeruler#add_patterns
        """
        for p in pattern_dicts:
            self.add(**p)

    @property
    def patterns(self) -> List[AttributeRulerPatternType]:
        """All the added patterns."""
        all_patterns = []
        for i in range(len(self.attrs)):
            p = {}
            p["patterns"] = self.matcher.get(i)[1]
            p["attrs"] = self._attrs_unnormed[i]
            p["index"] = self.indices[i]
            all_patterns.append(p)
        return all_patterns

    def to_bytes(self, exclude: Iterable[str] = tuple()) -> bytes:
        """Serialize the AttributeRuler to a bytestring.

        exclude (Iterable[str]): String names of serialization fields to exclude.
        RETURNS (bytes): The serialized object.

        DOCS: https://spacy.io/api/attributeruler#to_bytes
        """
        serialize = {}
        serialize["vocab"] = self.vocab.to_bytes
        patterns = {k: self.matcher.get(k)[1] for k in range(len(self.attrs))}
        serialize["patterns"] = lambda: srsly.msgpack_dumps(patterns)
        serialize["attrs"] = lambda: srsly.msgpack_dumps(self.attrs)
        serialize["indices"] = lambda: srsly.msgpack_dumps(self.indices)
        return util.to_bytes(serialize, exclude)

    def from_bytes(self, bytes_data: bytes, exclude: Iterable[str] = tuple()):
        """Load the AttributeRuler from a bytestring.

        bytes_data (bytes): The data to load.
        exclude (Iterable[str]): String names of serialization fields to exclude.
        returns (AttributeRuler): The loaded object.

        DOCS: https://spacy.io/api/attributeruler#from_bytes
        """
        data = {"patterns": b""}

        def load_patterns(b):
            data["patterns"] = srsly.msgpack_loads(b)

        def load_attrs(b):
            self.attrs = srsly.msgpack_loads(b)

        def load_indices(b):
            self.indices = srsly.msgpack_loads(b)

        deserialize = {
            "vocab": lambda b: self.vocab.from_bytes(b),
            "patterns": load_patterns,
            "attrs": load_attrs,
            "indices": load_indices,
        }
        util.from_bytes(bytes_data, deserialize, exclude)

        if data["patterns"]:
            for key, pattern in data["patterns"].items():
                self.matcher.add(key, pattern)
            assert len(self.attrs) == len(data["patterns"])
            assert len(self.indices) == len(data["patterns"])

        return self

    def to_disk(self, path: Union[Path, str], exclude: Iterable[str] = tuple()) -> None:
        """Serialize the AttributeRuler to disk.

        path (Union[Path, str]): A path to a directory.
        exclude (Iterable[str]): String names of serialization fields to exclude.
        DOCS: https://spacy.io/api/attributeruler#to_disk
        """
        patterns = {k: self.matcher.get(k)[1] for k in range(len(self.attrs))}
        serialize = {
            "vocab": lambda p: self.vocab.to_disk(p),
            "patterns": lambda p: srsly.write_msgpack(p, patterns),
            "attrs": lambda p: srsly.write_msgpack(p, self.attrs),
            "indices": lambda p: srsly.write_msgpack(p, self.indices),
        }
        util.to_disk(path, serialize, exclude)

    def from_disk(
        self, path: Union[Path, str], exclude: Iterable[str] = tuple()
    ) -> None:
        """Load the AttributeRuler from disk.

        path (Union[Path, str]): A path to a directory.
        exclude (Iterable[str]): String names of serialization fields to exclude.
        DOCS: https://spacy.io/api/attributeruler#from_disk
        """
        data = {"patterns": b""}

        def load_patterns(p):
            data["patterns"] = srsly.read_msgpack(p)

        def load_attrs(p):
            self.attrs = srsly.read_msgpack(p)

        def load_indices(p):
            self.indices = srsly.read_msgpack(p)

        deserialize = {
            "vocab": lambda p: self.vocab.from_disk(p),
            "patterns": load_patterns,
            "attrs": load_attrs,
            "indices": load_indices,
        }
        util.from_disk(path, deserialize, exclude)

        if data["patterns"]:
            for key, pattern in data["patterns"].items():
                self.matcher.add(key, pattern)
            assert len(self.attrs) == len(data["patterns"])
            assert len(self.indices) == len(data["patterns"])

        return self


def _split_morph_attrs(attrs):
    """Split entries from a tag map or morph rules dict into to two dicts, one
    with the token-level features (POS, LEMMA) and one with the remaining
    features, which are presumed to be individual MORPH features."""
    other_attrs = {}
    morph_attrs = {}
    for k, v in attrs.items():
        if k in "_" or k in IDS.keys() or k in IDS.values():
            other_attrs[k] = v
        else:
            morph_attrs[k] = v
    return other_attrs, morph_attrs