spaCy/spacy/gold/new_example.pyx

import numpy
from ..tokens.doc cimport Doc
from ..attrs import IDS
from .align cimport Alignment
from .annotation import TokenAnnotation, DocAnnotation
from .iob_utils import biluo_to_iob, biluo_tags_from_offsets
from .align import Alignment
from ..errors import Errors, AlignmentError


cpdef Doc annotations2doc(Doc predicted, tok_annot, doc_annot):
    # TODO: Improve and test this
    words = tok_annot.get("ORTH", [tok.text for tok in predicted])
    attrs, array = _annot2array(predicted.vocab.strings, tok_annot, doc_annot)
    output = Doc(predicted.vocab, words=words)
    if array.size:
        output = output.from_array(attrs, array)
    output.cats.update(doc_annot.get("cats", {}))
    return output


cdef class NewExample:
    def __init__(self, Doc predicted, Doc reference, *, Alignment alignment=None):
        """ Doc can either be text, or an actual Doc """
        msg = "Example.__init__ got None for '{arg}'. Requires Doc."
        if predicted is None:
            raise TypeError(msg.format(arg="predicted"))
        if reference is None:
            raise TypeError(msg.format(arg="reference"))
        self.x = predicted
        self.y = reference
        self._alignment = alignment

    @property
    def predicted(self):
        return self.x
    
    @property
    def reference(self):
        return self.y
 
    @classmethod
    def from_dict(cls, Doc predicted, dict example_dict):
        if example_dict is None:
            raise ValueError("Example.from_dict expected dict, received None")
        if not isinstance(predicted, Doc):
            raise TypeError(f"Argument 1 should be Doc. Got {type(predicted)}")
        example_dict = _fix_legacy_dict_data(predicted, example_dict)
        tok_dict, doc_dict = _parse_example_dict_data(example_dict)
        return NewExample(
            predicted,
            annotations2doc(predicted, tok_dict, doc_dict)
        )
    
    @property
    def alignment(self):
        if self._alignment is None:
            if self.doc is None:
                return None
            spacy_words = [token.orth_ for token in self.predicted]
            gold_words = [token.orth_ for token in self.reference]
            if gold_words == []:
                gold_words = spacy_words
            self._alignment = Alignment(spacy_words, gold_words)
        return self._alignment

    def get_aligned(self, field):
        raise NotImplementedError

    def to_dict(self):
        """ Note that this method does NOT export the doc, only the annotations ! """
        token_dict = self._token_annotation
        doc_dict = self._doc_annotation
        return {"token_annotation": token_dict, "doc_annotation": doc_dict}

    def text(self):
        return self.x.text


def _annot2array(strings, tok_annot, doc_annot):
    attrs = []
    values = []
    for key, value in tok_annot.items():
        if key not in IDS:
            raise ValueError(f"Unknown attr: {key}")
        elif key == "ORTH":
            pass
        elif key == "HEAD":
            attrs.append(key)
            values.append([h-i for i, h in enumerate(value)])
        else:
            attrs.append(key)
            values.append([strings.add(v) for v in value])
    # TODO: Calculate token.ent_kb_id from doc_annot["links"].
    # We need to fix this and the doc.ents thing, both should be doc
    # annotations.
    array = numpy.array(values, dtype="uint64")
    return attrs, array.T


def _parse_example_dict_data(example_dict):
    return (
        example_dict["token_annotation"],
        example_dict["doc_annotation"]
    )


def _fix_legacy_dict_data(predicted, example_dict):
    token_dict = example_dict.get("token_annotation", {})
    doc_dict = example_dict.get("doc_annotation", {})
    for key, value in example_dict.items():
        if key in ("token_annotation", "doc_annotation"):
            pass
        elif key in ("cats", "links"):
            doc_dict[key] = value
        else:
            token_dict[key] = value
    # Remap keys
    remapping = {
        "words": "ORTH",
        "tags": "TAG",
        "pos": "POS",
        "lemmas": "LEMMA",
        "deps": "DEP",
        "heads": "HEAD",
        "sent_starts": "SENT_START",
        "morphs": "MORPH",
    }
    old_token_dict = token_dict
    token_dict = {}
    for key, value in old_token_dict.items():
        if key in remapping:
            token_dict[remapping[key]] = value
        elif key in ("ner", "entities") and value:
            # Arguably it would be smarter to put this in the doc annotation?
            words = token_dict.get("words", [t.text for t in predicted])
            ent_iobs, ent_types = _parse_ner_tags(predicted, words, value)
            token_dict["ENT_IOB"] = ent_iobs
            token_dict["ENT_TYPE"] = ent_types
    return {
        "token_annotation": token_dict,
        "doc_annotation": doc_dict
    }


def _parse_ner_tags(predicted, words, biluo_or_offsets):
    if isinstance(biluo_or_offsets[0], (list, tuple)):
        # Convert to biluo if necessary
        # This is annoying but to convert the offsets we need a Doc
        # that has the target tokenization.
        reference = Doc(
            predicted.vocab,
            words=words
        )
        biluo = biluo_tags_from_offsets(predicted, biluo_or_offsets)
    else:
        biluo = biluo_or_offsets
    ent_iobs = []
    ent_types = []
    for iob_tag in biluo_to_iob(biluo):
        ent_iobs.append(iob_tag.split("-")[0])
        if iob_tag.startswith("I") or iob_tag.startswith("B"):
            ent_types.append(iob_tag.split("-", 1)[1])
        else:
            ent_types.append("")
    return ent_iobs, ent_types


class Example:
    def get_aligned(self, field):
        """Return an aligned array for a token annotation field."""
        if self.doc is None:
            return self.token_annotation.get_field(field)
        doc = self.doc
        if field == "word":
            return [token.orth_ for token in doc]
        gold_values = self.token_annotation.get_field(field)
        alignment = self.alignment
        i2j_multi = alignment.i2j_multi
        gold_to_cand = alignment.gold_to_cand
        cand_to_gold = alignment.cand_to_gold

        output = []
        for i, gold_i in enumerate(cand_to_gold):
            if doc[i].text.isspace():
                output.append(None)
            elif gold_i is None:
                if i in i2j_multi:
                    output.append(gold_values[i2j_multi[i]])
                else:
                    output.append(None)
            else:
                output.append(gold_values[gold_i])
        return output

    def split_sents(self):
        """ Split the token annotations into multiple Examples based on
        sent_starts and return a list of the new Examples"""
        if not self.token_annotation.words:
            return [self]
        s_ids, s_words, s_tags, s_pos, s_morphs = [], [], [], [], []
        s_lemmas, s_heads, s_deps, s_ents, s_sent_starts = [], [], [], [], []
        s_brackets = []
        sent_start_i = 0
        t = self.token_annotation
        split_examples = []
        for i in range(len(t.words)):
            if i > 0 and t.sent_starts[i] == 1:
                split_examples.append(
                    Example(
                        doc=Doc(self.doc.vocab, words=s_words),
                        token_annotation=TokenAnnotation(
                            ids=s_ids,
                            words=s_words,
                            tags=s_tags,
                            pos=s_pos,
                            morphs=s_morphs,
                            lemmas=s_lemmas,
                            heads=s_heads,
                            deps=s_deps,
                            entities=s_ents,
                            sent_starts=s_sent_starts,
                            brackets=s_brackets,
                        ),
                        doc_annotation=self.doc_annotation
                    )
                )
                s_ids, s_words, s_tags, s_pos, s_heads = [], [], [], [], []
                s_deps, s_ents, s_morphs, s_lemmas = [], [], [], []
                s_sent_starts, s_brackets = [], []
                sent_start_i = i
            s_ids.append(t.get_id(i))
            s_words.append(t.get_word(i))
            s_tags.append(t.get_tag(i))
            s_pos.append(t.get_pos(i))
            s_morphs.append(t.get_morph(i))
            s_lemmas.append(t.get_lemma(i))
            s_heads.append(t.get_head(i) - sent_start_i)
            s_deps.append(t.get_dep(i))
            s_ents.append(t.get_entity(i))
            s_sent_starts.append(t.get_sent_start(i))
            for b_end, b_label in t.brackets_by_start.get(i, []):
                s_brackets.append((i - sent_start_i, b_end - sent_start_i, b_label))
            i += 1
        split_examples.append(
            Example(
                doc=Doc(self.doc.vocab, words=s_words),
                token_annotation=TokenAnnotation(
                    ids=s_ids,
                    words=s_words,
                    tags=s_tags,
                    pos=s_pos,
                    morphs=s_morphs,
                    lemmas=s_lemmas,
                    heads=s_heads,
                    deps=s_deps,
                    entities=s_ents,
                    sent_starts=s_sent_starts,
                    brackets=s_brackets,
                ),
                doc_annotation=self.doc_annotation
            )
        )
        return split_examples

    @classmethod
    def to_example_objects(cls, examples, make_doc=None, keep_raw_text=False):
        """
        Return a list of Example objects, from a variety of input formats.
        make_doc needs to be provided when the examples contain text strings and keep_raw_text=False
        """
        if isinstance(examples, Example):
            return [examples]
        if isinstance(examples, tuple):
            examples = [examples]
        converted_examples = []
        for ex in examples:
            if isinstance(ex, Example):
                converted_examples.append(ex)
            # convert string to Doc to Example
            elif isinstance(ex, str):
                if keep_raw_text:
                    converted_examples.append(Example(doc=ex))
                else:
                    doc = make_doc(ex)
                    converted_examples.append(Example(doc=doc))
            # convert tuples to Example
            elif isinstance(ex, tuple) and len(ex) == 2:
                doc, gold = ex
                # convert string to Doc
                if isinstance(doc, str) and not keep_raw_text:
                    doc = make_doc(doc)
                converted_examples.append(Example.from_dict(gold, doc=doc))
            # convert Doc to Example
            elif isinstance(ex, Doc):
                converted_examples.append(Example(doc=ex))
            else:
                converted_examples.append(ex)
        return converted_examples

    def _deprecated_get_gold(self, make_projective=False):
        from ..syntax.gold_parse import get_parses_from_example

        _, gold = get_parses_from_example(self, make_projective=make_projective)[0]
        return gold