# cython: infer_types=True, bounds_check=False, profile=True cimport cython cimport numpy as np from libc.string cimport memcpy from libc.math cimport sqrt from libc.stdint cimport int32_t, uint64_t import copy from collections import Counter from enum import Enum import itertools import numpy import srsly from thinc.api import get_array_module from thinc.util import copy_array import warnings from .span cimport Span from .token cimport Token from ..lexeme cimport Lexeme, EMPTY_LEXEME from ..typedefs cimport attr_t, flags_t from ..attrs cimport attr_id_t from ..attrs cimport LENGTH, POS, LEMMA, TAG, MORPH, DEP, HEAD, SPACY, ENT_IOB from ..attrs cimport ENT_TYPE, ENT_ID, ENT_KB_ID, SENT_START, IDX, NORM from ..attrs import intify_attr, IDS from ..compat import copy_reg, pickle from ..errors import Errors, Warnings from ..morphology import Morphology from .. import util from .underscore import Underscore, get_ext_args from ._retokenize import Retokenizer from ._serialize import ALL_ATTRS as DOCBIN_ALL_ATTRS DEF PADDING = 5 cdef int bounds_check(int i, int length, int padding) except -1: if (i + padding) < 0: raise IndexError(Errors.E026.format(i=i, length=length)) if (i - padding) >= length: raise IndexError(Errors.E026.format(i=i, length=length)) cdef attr_t get_token_attr(const TokenC* token, attr_id_t feat_name) nogil: if feat_name == LEMMA: return token.lemma elif feat_name == NORM: if not token.norm: return token.lex.norm return token.norm elif feat_name == POS: return token.pos elif feat_name == TAG: return token.tag elif feat_name == MORPH: return token.morph elif feat_name == DEP: return token.dep elif feat_name == HEAD: return token.head elif feat_name == SENT_START: return token.sent_start elif feat_name == SPACY: return token.spacy elif feat_name == ENT_IOB: return token.ent_iob elif feat_name == ENT_TYPE: return token.ent_type elif feat_name == ENT_ID: return token.ent_id elif feat_name == ENT_KB_ID: return token.ent_kb_id elif feat_name == IDX: return token.idx else: return Lexeme.get_struct_attr(token.lex, feat_name) cdef attr_t get_token_attr_for_matcher(const TokenC* token, attr_id_t feat_name) nogil: if feat_name == SENT_START: if token.sent_start == 1: return True else: return False else: return get_token_attr(token, feat_name) class SetEntsDefault(str, Enum): blocked = "blocked" missing = "missing" outside = "outside" unmodified = "unmodified" @classmethod def values(cls): return list(cls.__members__.keys()) cdef class Doc: """A sequence of Token objects. Access sentences and named entities, export annotations to numpy arrays, losslessly serialize to compressed binary strings. The `Doc` object holds an array of `TokenC` structs. The Python-level `Token` and `Span` objects are views of this array, i.e. they don't own the data themselves. EXAMPLE: Construction 1 >>> doc = nlp(u'Some text') Construction 2 >>> from spacy.tokens import Doc >>> doc = Doc(nlp.vocab, words=["hello", "world", "!"], spaces=[True, False, False]) DOCS: https://nightly.spacy.io/api/doc """ @classmethod def set_extension(cls, name, **kwargs): """Define a custom attribute which becomes available as `Doc._`. name (str): Name of the attribute to set. default: Optional default value of the attribute. getter (callable): Optional getter function. setter (callable): Optional setter function. method (callable): Optional method for method extension. force (bool): Force overwriting existing attribute. DOCS: https://nightly.spacy.io/api/doc#set_extension USAGE: https://nightly.spacy.io/usage/processing-pipelines#custom-components-attributes """ if cls.has_extension(name) and not kwargs.get("force", False): raise ValueError(Errors.E090.format(name=name, obj="Doc")) Underscore.doc_extensions[name] = get_ext_args(**kwargs) @classmethod def get_extension(cls, name): """Look up a previously registered extension by name. name (str): Name of the extension. RETURNS (tuple): A `(default, method, getter, setter)` tuple. DOCS: https://nightly.spacy.io/api/doc#get_extension """ return Underscore.doc_extensions.get(name) @classmethod def has_extension(cls, name): """Check whether an extension has been registered. name (str): Name of the extension. RETURNS (bool): Whether the extension has been registered. DOCS: https://nightly.spacy.io/api/doc#has_extension """ return name in Underscore.doc_extensions @classmethod def remove_extension(cls, name): """Remove a previously registered extension. name (str): Name of the extension. RETURNS (tuple): A `(default, method, getter, setter)` tuple of the removed extension. DOCS: https://nightly.spacy.io/api/doc#remove_extension """ if not cls.has_extension(name): raise ValueError(Errors.E046.format(name=name)) return Underscore.doc_extensions.pop(name) def __init__( self, Vocab vocab, words=None, spaces=None, *, user_data=None, tags=None, pos=None, morphs=None, lemmas=None, heads=None, deps=None, sent_starts=None, ents=None, ): """Create a Doc object. vocab (Vocab): A vocabulary object, which must match any models you want to use (e.g. tokenizer, parser, entity recognizer). words (Optional[List[str]]): A list of unicode strings to add to the document as words. If `None`, defaults to empty list. spaces (Optional[List[bool]]): A list of boolean values, of the same length as words. True means that the word is followed by a space, False means it is not. If `None`, defaults to `[True]*len(words)` user_data (dict or None): Optional extra data to attach to the Doc. tags (Optional[List[str]]): A list of unicode strings, of the same length as words, to assign as token.tag. Defaults to None. pos (Optional[List[str]]): A list of unicode strings, of the same length as words, to assign as token.pos. Defaults to None. morphs (Optional[List[str]]): A list of unicode strings, of the same length as words, to assign as token.morph. Defaults to None. lemmas (Optional[List[str]]): A list of unicode strings, of the same length as words, to assign as token.lemma. Defaults to None. heads (Optional[List[int]]): A list of values, of the same length as words, to assign as heads. Head indices are the position of the head in the doc. Defaults to None. deps (Optional[List[str]]): A list of unicode strings, of the same length as words, to assign as token.dep. Defaults to None. sent_starts (Optional[List[Union[bool, None]]]): A list of values, of the same length as words, to assign as token.is_sent_start. Will be overridden by heads if heads is provided. Defaults to None. ents (Optional[List[str]]): A list of unicode strings, of the same length as words, as IOB tags to assign as token.ent_iob and token.ent_type. Defaults to None. DOCS: https://nightly.spacy.io/api/doc#init """ self.vocab = vocab size = max(20, (len(words) if words is not None else 0)) self.mem = Pool() # Guarantee self.lex[i-x], for any i >= 0 and x < padding is in bounds # However, we need to remember the true starting places, so that we can # realloc. data_start = self.mem.alloc(size + (PADDING*2), sizeof(TokenC)) cdef int i for i in range(size + (PADDING*2)): data_start[i].lex = &EMPTY_LEXEME data_start[i].l_edge = i data_start[i].r_edge = i self.c = data_start + PADDING self.max_length = size self.length = 0 self.sentiment = 0.0 self.cats = {} self.user_hooks = {} self.user_token_hooks = {} self.user_span_hooks = {} self.tensor = numpy.zeros((0,), dtype="float32") self.user_data = {} if user_data is None else user_data self._vector = None self.noun_chunks_iterator = self.vocab.get_noun_chunks cdef bint has_space if words is None and spaces is not None: raise ValueError("words must be set if spaces is set") elif spaces is None and words is not None: self.has_unknown_spaces = True else: self.has_unknown_spaces = False words = words if words is not None else [] spaces = spaces if spaces is not None else ([True] * len(words)) if len(spaces) != len(words): raise ValueError(Errors.E027) cdef const LexemeC* lexeme for word, has_space in zip(words, spaces): if isinstance(word, unicode): lexeme = self.vocab.get(self.mem, word) elif isinstance(word, bytes): raise ValueError(Errors.E028.format(value=word)) else: lexeme = self.vocab.get_by_orth(self.mem, word) self.push_back(lexeme, has_space) if heads is not None: heads = [head - i for i, head in enumerate(heads)] if deps and not heads: heads = [0] * len(deps) if sent_starts is not None: for i in range(len(sent_starts)): if sent_starts[i] is True: sent_starts[i] = 1 elif sent_starts[i] is False: sent_starts[i] = -1 elif sent_starts[i] is None or sent_starts[i] not in [-1, 0, 1]: sent_starts[i] = 0 ent_iobs = None ent_types = None if ents is not None: iob_strings = Token.iob_strings() # make valid IOB2 out of IOB1 or IOB2 for i, ent in enumerate(ents): if ent is "": ents[i] = None elif ent is not None and not isinstance(ent, str): raise ValueError(Errors.E177.format(tag=ent)) if i < len(ents) - 1: # OI -> OB if (ent is None or ent.startswith("O")) and \ (ents[i+1] is not None and ents[i+1].startswith("I")): ents[i+1] = "B" + ents[i+1][1:] # B-TYPE1 I-TYPE2 or I-TYPE1 I-TYPE2 -> B/I-TYPE1 B-TYPE2 if ent is not None and ents[i+1] is not None and \ (ent.startswith("B") or ent.startswith("I")) and \ ents[i+1].startswith("I") and \ ent[1:] != ents[i+1][1:]: ents[i+1] = "B" + ents[i+1][1:] ent_iobs = [] ent_types = [] for ent in ents: if ent is None: ent_iobs.append(iob_strings.index("")) ent_types.append("") elif ent == "O": ent_iobs.append(iob_strings.index(ent)) ent_types.append("") else: if len(ent) < 3 or ent[1] != "-": raise ValueError(Errors.E177.format(tag=ent)) ent_iob, ent_type = ent.split("-", 1) if ent_iob not in iob_strings: raise ValueError(Errors.E177.format(tag=ent)) ent_iob = iob_strings.index(ent_iob) ent_iobs.append(ent_iob) ent_types.append(ent_type) headings = [] values = [] annotations = [pos, heads, deps, lemmas, tags, morphs, sent_starts, ent_iobs, ent_types] possible_headings = [POS, HEAD, DEP, LEMMA, TAG, MORPH, SENT_START, ENT_IOB, ENT_TYPE] for a, annot in enumerate(annotations): if annot is not None: if len(annot) != len(words): raise ValueError(Errors.E189) headings.append(possible_headings[a]) if annot is not heads and annot is not sent_starts and annot is not ent_iobs: values.extend(annot) for value in values: self.vocab.strings.add(value) # if there are any other annotations, set them if headings: attrs = self.to_array(headings) j = 0 for annot in annotations: if annot: if annot is heads or annot is sent_starts or annot is ent_iobs: for i in range(len(words)): if attrs.ndim == 1: attrs[i] = annot[i] else: attrs[i, j] = annot[i] elif annot is morphs: for i in range(len(words)): morph_key = vocab.morphology.add(morphs[i]) if attrs.ndim == 1: attrs[i] = morph_key else: attrs[i, j] = morph_key else: for i in range(len(words)): if attrs.ndim == 1: attrs[i] = self.vocab.strings[annot[i]] else: attrs[i, j] = self.vocab.strings[annot[i]] j += 1 self.from_array(headings, attrs) @property def _(self): """Custom extension attributes registered via `set_extension`.""" return Underscore(Underscore.doc_extensions, self) @property def is_tagged(self): warnings.warn(Warnings.W107.format(prop="is_tagged", attr="TAG"), DeprecationWarning) return self.has_annotation("TAG") @property def is_parsed(self): warnings.warn(Warnings.W107.format(prop="is_parsed", attr="DEP"), DeprecationWarning) return self.has_annotation("DEP") @property def is_nered(self): warnings.warn(Warnings.W107.format(prop="is_nered", attr="ENT_IOB"), DeprecationWarning) return self.has_annotation("ENT_IOB") @property def is_sentenced(self): warnings.warn(Warnings.W107.format(prop="is_sentenced", attr="SENT_START"), DeprecationWarning) return self.has_annotation("SENT_START") def has_annotation(self, attr, *, require_complete=False): """Check whether the doc contains annotation on a token attribute. attr (Union[int, str]): The attribute string name or int ID. require_complete (bool): Whether to check that the attribute is set on every token in the doc. RETURNS (bool): Whether annotation is present. DOCS: https://nightly.spacy.io/api/doc#has_annotation """ # empty docs are always annotated if self.length == 0: return True cdef int i cdef int range_start = 0 attr = intify_attr(attr) # adjust attributes if attr == HEAD: # HEAD does not have an unset state, so rely on DEP attr = DEP elif attr == self.vocab.strings["IS_SENT_START"]: # as in Matcher, allow IS_SENT_START as an alias of SENT_START attr = SENT_START # special cases for sentence boundaries if attr == SENT_START: if "sents" in self.user_hooks: return True # docs of length 1 always have sentence boundaries if self.length == 1: return True range_start = 1 if require_complete: return all(Token.get_struct_attr(&self.c[i], attr) for i in range(range_start, self.length)) else: return any(Token.get_struct_attr(&self.c[i], attr) for i in range(range_start, self.length)) def __getitem__(self, object i): """Get a `Token` or `Span` object. i (int or tuple) The index of the token, or the slice of the document to get. RETURNS (Token or Span): The token at `doc[i]]`, or the span at `doc[start : end]`. EXAMPLE: >>> doc[i] Get the `Token` object at position `i`, where `i` is an integer. Negative indexing is supported, and follows the usual Python semantics, i.e. `doc[-2]` is `doc[len(doc) - 2]`. >>> doc[start : end]] Get a `Span` object, starting at position `start` and ending at position `end`, where `start` and `end` are token indices. For instance, `doc[2:5]` produces a span consisting of tokens 2, 3 and 4. Stepped slices (e.g. `doc[start : end : step]`) are not supported, as `Span` objects must be contiguous (cannot have gaps). You can use negative indices and open-ended ranges, which have their normal Python semantics. DOCS: https://nightly.spacy.io/api/doc#getitem """ if isinstance(i, slice): start, stop = util.normalize_slice(len(self), i.start, i.stop, i.step) return Span(self, start, stop, label=0) if i < 0: i = self.length + i bounds_check(i, self.length, PADDING) return Token.cinit(self.vocab, &self.c[i], i, self) def __iter__(self): """Iterate over `Token` objects, from which the annotations can be easily accessed. This is the main way of accessing `Token` objects, which are the main way annotations are accessed from Python. If faster- than-Python speeds are required, you can instead access the annotations as a numpy array, or access the underlying C data directly from Cython. DOCS: https://nightly.spacy.io/api/doc#iter """ cdef int i for i in range(self.length): yield Token.cinit(self.vocab, &self.c[i], i, self) def __len__(self): """The number of tokens in the document. RETURNS (int): The number of tokens in the document. DOCS: https://nightly.spacy.io/api/doc#len """ return self.length def __unicode__(self): return "".join([t.text_with_ws for t in self]) def __bytes__(self): return "".join([t.text_with_ws for t in self]).encode("utf-8") def __str__(self): return self.__unicode__() def __repr__(self): return self.__str__() @property def doc(self): return self def char_span(self, int start_idx, int end_idx, label=0, kb_id=0, vector=None, alignment_mode="strict"): """Create a `Span` object from the slice `doc.text[start_idx : end_idx]`. Returns None if no valid `Span` can be created. doc (Doc): The parent document. start_idx (int): The index of the first character of the span. end_idx (int): The index of the first character after the span. label (uint64 or string): A label to attach to the Span, e.g. for named entities. kb_id (uint64 or string): An ID from a KB to capture the meaning of a named entity. vector (ndarray[ndim=1, dtype='float32']): A meaning representation of the span. alignment_mode (str): How character indices are aligned to token boundaries. Options: "strict" (character indices must be aligned with token boundaries), "contract" (span of all tokens completely within the character span), "expand" (span of all tokens at least partially covered by the character span). Defaults to "strict". RETURNS (Span): The newly constructed object. DOCS: https://nightly.spacy.io/api/doc#char_span """ if not isinstance(label, int): label = self.vocab.strings.add(label) if not isinstance(kb_id, int): kb_id = self.vocab.strings.add(kb_id) if alignment_mode not in ("strict", "contract", "expand"): alignment_mode = "strict" cdef int start = token_by_char(self.c, self.length, start_idx) if start < 0 or (alignment_mode == "strict" and start_idx != self[start].idx): return None # end_idx is exclusive, so find the token at one char before cdef int end = token_by_char(self.c, self.length, end_idx - 1) if end < 0 or (alignment_mode == "strict" and end_idx != self[end].idx + len(self[end])): return None # Adjust start and end by alignment_mode if alignment_mode == "contract": if self[start].idx < start_idx: start += 1 if end_idx < self[end].idx + len(self[end]): end -= 1 # if no tokens are completely within the span, return None if end < start: return None elif alignment_mode == "expand": # Don't consider the trailing whitespace to be part of the previous # token if start_idx == self[start].idx + len(self[start]): start += 1 # Currently we have the token index, we want the range-end index end += 1 cdef Span span = Span(self, start, end, label=label, kb_id=kb_id, vector=vector) return span def similarity(self, other): """Make a semantic similarity estimate. The default estimate is cosine similarity using an average of word vectors. other (object): The object to compare with. By default, accepts `Doc`, `Span`, `Token` and `Lexeme` objects. RETURNS (float): A scalar similarity score. Higher is more similar. DOCS: https://nightly.spacy.io/api/doc#similarity """ if "similarity" in self.user_hooks: return self.user_hooks["similarity"](self, other) if isinstance(other, (Lexeme, Token)) and self.length == 1: if self.c[0].lex.orth == other.orth: return 1.0 elif isinstance(other, (Span, Doc)) and len(self) == len(other): similar = True for i in range(self.length): if self[i].orth != other[i].orth: similar = False break if similar: return 1.0 if self.vocab.vectors.n_keys == 0: warnings.warn(Warnings.W007.format(obj="Doc")) if self.vector_norm == 0 or other.vector_norm == 0: warnings.warn(Warnings.W008.format(obj="Doc")) return 0.0 vector = self.vector xp = get_array_module(vector) result = xp.dot(vector, other.vector) / (self.vector_norm * other.vector_norm) # ensure we get a scalar back (numpy does this automatically but cupy doesn't) return result.item() @property def has_vector(self): """A boolean value indicating whether a word vector is associated with the object. RETURNS (bool): Whether a word vector is associated with the object. DOCS: https://nightly.spacy.io/api/doc#has_vector """ if "has_vector" in self.user_hooks: return self.user_hooks["has_vector"](self) elif self.vocab.vectors.data.size: return True elif self.tensor.size: return True else: return False property vector: """A real-valued meaning representation. Defaults to an average of the token vectors. RETURNS (numpy.ndarray[ndim=1, dtype='float32']): A 1D numpy array representing the document's semantics. DOCS: https://nightly.spacy.io/api/doc#vector """ def __get__(self): if "vector" in self.user_hooks: return self.user_hooks["vector"](self) if self._vector is not None: return self._vector xp = get_array_module(self.vocab.vectors.data) if not len(self): self._vector = xp.zeros((self.vocab.vectors_length,), dtype="f") return self._vector elif self.vocab.vectors.data.size > 0: self._vector = sum(t.vector for t in self) / len(self) return self._vector elif self.tensor.size > 0: self._vector = self.tensor.mean(axis=0) return self._vector else: return xp.zeros((self.vocab.vectors_length,), dtype="float32") def __set__(self, value): self._vector = value property vector_norm: """The L2 norm of the document's vector representation. RETURNS (float): The L2 norm of the vector representation. DOCS: https://nightly.spacy.io/api/doc#vector_norm """ def __get__(self): if "vector_norm" in self.user_hooks: return self.user_hooks["vector_norm"](self) cdef float value cdef double norm = 0 if self._vector_norm is None: norm = 0.0 for value in self.vector: norm += value * value self._vector_norm = sqrt(norm) if norm != 0 else 0 return self._vector_norm def __set__(self, value): self._vector_norm = value @property def text(self): """A unicode representation of the document text. RETURNS (str): The original verbatim text of the document. """ return "".join(t.text_with_ws for t in self) @property def text_with_ws(self): """An alias of `Doc.text`, provided for duck-type compatibility with `Span` and `Token`. RETURNS (str): The original verbatim text of the document. """ return self.text property ents: """The named entities in the document. Returns a tuple of named entity `Span` objects, if the entity recognizer has been applied. RETURNS (tuple): Entities in the document, one `Span` per entity. DOCS: https://nightly.spacy.io/api/doc#ents """ def __get__(self): cdef int i cdef const TokenC* token cdef int start = -1 cdef attr_t label = 0 cdef attr_t kb_id = 0 output = [] for i in range(self.length): token = &self.c[i] if token.ent_iob == 1: if start == -1: seq = [f"{t.text}|{t.ent_iob_}" for t in self[i-5:i+5]] raise ValueError(Errors.E093.format(seq=" ".join(seq))) elif token.ent_iob == 2 or token.ent_iob == 0 or \ (token.ent_iob == 3 and token.ent_type == 0): if start != -1: output.append(Span(self, start, i, label=label, kb_id=kb_id)) start = -1 label = 0 kb_id = 0 elif token.ent_iob == 3: if start != -1: output.append(Span(self, start, i, label=label, kb_id=kb_id)) start = i label = token.ent_type kb_id = token.ent_kb_id if start != -1: output.append(Span(self, start, self.length, label=label, kb_id=kb_id)) # remove empty-label spans output = [o for o in output if o.label_ != ""] return tuple(output) def __set__(self, ents): # TODO: # 1. Test basic data-driven ORTH gazetteer # 2. Test more nuanced date and currency regex cdef attr_t entity_type, kb_id cdef int ent_start, ent_end ent_spans = [] for ent_info in ents: entity_type_, kb_id, ent_start, ent_end = get_entity_info(ent_info) if isinstance(entity_type_, str): self.vocab.strings.add(entity_type_) span = Span(self, ent_start, ent_end, label=entity_type_, kb_id=kb_id) ent_spans.append(span) self.set_ents(ent_spans, default=SetEntsDefault.outside) def set_ents(self, entities, *, blocked=None, missing=None, outside=None, default=SetEntsDefault.outside): """Set entity annotation. entities (List[Span]): Spans with labels to set as entities. blocked (Optional[List[Span]]): Spans to set as 'blocked' (never an entity) for spacy's built-in NER component. Other components may ignore this setting. missing (Optional[List[Span]]): Spans with missing/unknown entity information. outside (Optional[List[Span]]): Spans outside of entities (O in IOB). default (str): How to set entity annotation for tokens outside of any provided spans. Options: "blocked", "missing", "outside" and "unmodified" (preserve current state). Defaults to "outside". """ if default not in SetEntsDefault.values(): raise ValueError(Errors.E1011.format(default=default, modes=", ".join(SetEntsDefault))) # Ignore spans with missing labels entities = [ent for ent in entities if ent.label > 0] if blocked is None: blocked = tuple() if missing is None: missing = tuple() if outside is None: outside = tuple() # Find all tokens covered by spans and check that none are overlapping cdef int i seen_tokens = set() for span in itertools.chain.from_iterable([entities, blocked, missing, outside]): if not isinstance(span, Span): raise ValueError(Errors.E1012.format(span=span)) for i in range(span.start, span.end): if i in seen_tokens: raise ValueError(Errors.E1010.format(i=i)) seen_tokens.add(i) # Set all specified entity information for span in entities: for i in range(span.start, span.end): if i == span.start: self.c[i].ent_iob = 3 else: self.c[i].ent_iob = 1 self.c[i].ent_type = span.label self.c[i].ent_kb_id = span.kb_id for span in blocked: for i in range(span.start, span.end): self.c[i].ent_iob = 3 self.c[i].ent_type = 0 for span in missing: for i in range(span.start, span.end): self.c[i].ent_iob = 0 self.c[i].ent_type = 0 for span in outside: for i in range(span.start, span.end): self.c[i].ent_iob = 2 self.c[i].ent_type = 0 # Set tokens outside of all provided spans if default != SetEntsDefault.unmodified: for i in range(self.length): if i not in seen_tokens: self.c[i].ent_type = 0 if default == SetEntsDefault.outside: self.c[i].ent_iob = 2 elif default == SetEntsDefault.missing: self.c[i].ent_iob = 0 elif default == SetEntsDefault.blocked: self.c[i].ent_iob = 3 # Fix any resulting inconsistent annotation for i in range(self.length - 1): # I must follow B or I: convert I to B if (self.c[i].ent_iob == 0 or self.c[i].ent_iob == 2) and \ self.c[i+1].ent_iob == 1: self.c[i+1].ent_iob = 3 # Change of type with BI or II: convert second I to B if self.c[i].ent_type != self.c[i+1].ent_type and \ (self.c[i].ent_iob == 3 or self.c[i].ent_iob == 1) and \ self.c[i+1].ent_iob == 1: self.c[i+1].ent_iob = 3 @property def noun_chunks(self): """Iterate over the base noun phrases in the document. Yields base noun-phrase #[code Span] objects, if the document has been syntactically parsed. A base noun phrase, or "NP chunk", is a noun phrase that does not permit other NPs to be nested within it – so no NP-level coordination, no prepositional phrases, and no relative clauses. YIELDS (Span): Noun chunks in the document. DOCS: https://nightly.spacy.io/api/doc#noun_chunks """ # Accumulate the result before beginning to iterate over it. This # prevents the tokenisation from being changed out from under us # during the iteration. The tricky thing here is that Span accepts # its tokenisation changing, so it's okay once we have the Span # objects. See Issue #375. spans = [] if self.noun_chunks_iterator is not None: for start, end, label in self.noun_chunks_iterator(self): spans.append(Span(self, start, end, label=label)) for span in spans: yield span @property def sents(self): """Iterate over the sentences in the document. Yields sentence `Span` objects. Sentence spans have no label. YIELDS (Span): Sentences in the document. DOCS: https://nightly.spacy.io/api/doc#sents """ if not self.has_annotation("SENT_START"): raise ValueError(Errors.E030) if "sents" in self.user_hooks: yield from self.user_hooks["sents"](self) else: start = 0 for i in range(1, self.length): if self.c[i].sent_start == 1: yield Span(self, start, i) start = i if start != self.length: yield Span(self, start, self.length) @property def lang(self): """RETURNS (uint64): ID of the language of the doc's vocabulary.""" return self.vocab.strings[self.vocab.lang] @property def lang_(self): """RETURNS (str): Language of the doc's vocabulary, e.g. 'en'.""" return self.vocab.lang cdef int push_back(self, LexemeOrToken lex_or_tok, bint has_space) except -1: if self.length == self.max_length: self._realloc(self.length * 2) cdef TokenC* t = &self.c[self.length] if LexemeOrToken is const_TokenC_ptr: t[0] = lex_or_tok[0] else: t.lex = lex_or_tok if self.length == 0: t.idx = 0 else: t.idx = (t-1).idx + (t-1).lex.length + (t-1).spacy t.l_edge = self.length t.r_edge = self.length if t.lex.orth == 0: raise ValueError(Errors.E031.format(i=self.length)) t.spacy = has_space self.length += 1 if self.length == 1: # Set token.sent_start to 1 for first token. See issue #2869 self.c[0].sent_start = 1 return t.idx + t.lex.length + t.spacy @cython.boundscheck(False) cpdef np.ndarray to_array(self, object py_attr_ids): """Export given token attributes to a numpy `ndarray`. If `attr_ids` is a sequence of M attributes, the output array will be of shape `(N, M)`, where N is the length of the `Doc` (in tokens). If `attr_ids` is a single attribute, the output shape will be (N,). You can specify attributes by integer ID (e.g. spacy.attrs.LEMMA) or string name (e.g. 'LEMMA' or 'lemma'). attr_ids (list[]): A list of attributes (int IDs or string names). RETURNS (numpy.ndarray[long, ndim=2]): A feature matrix, with one row per word, and one column per attribute indicated in the input `attr_ids`. EXAMPLE: >>> from spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA >>> doc = nlp(text) >>> # All strings mapped to integers, for easy export to numpy >>> np_array = doc.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA]) """ cdef int i, j cdef attr_id_t feature cdef np.ndarray[attr_t, ndim=2] output # Handle scalar/list inputs of strings/ints for py_attr_ids # See also #3064 if isinstance(py_attr_ids, str): # Handle inputs like doc.to_array('ORTH') py_attr_ids = [py_attr_ids] elif not hasattr(py_attr_ids, "__iter__"): # Handle inputs like doc.to_array(ORTH) py_attr_ids = [py_attr_ids] # Allow strings, e.g. 'lemma' or 'LEMMA' try: py_attr_ids = [(IDS[id_.upper()] if hasattr(id_, "upper") else id_) for id_ in py_attr_ids] except KeyError as msg: keys = [k for k in IDS.keys() if not k.startswith("FLAG")] raise KeyError(Errors.E983.format(dict="IDS", key=msg, keys=keys)) from None # Make an array from the attributes --- otherwise our inner loop is # Python dict iteration. cdef np.ndarray attr_ids = numpy.asarray(py_attr_ids, dtype="i") output = numpy.ndarray(shape=(self.length, len(attr_ids)), dtype=numpy.uint64) c_output = output.data c_attr_ids = attr_ids.data cdef TokenC* token cdef int nr_attr = attr_ids.shape[0] for i in range(self.length): token = &self.c[i] for j in range(nr_attr): c_output[i*nr_attr + j] = get_token_attr(token, c_attr_ids[j]) # Handle 1d case return output if len(attr_ids) >= 2 else output.reshape((self.length,)) def count_by(self, attr_id_t attr_id, exclude=None, object counts=None): """Count the frequencies of a given attribute. Produces a dict of `{attribute (int): count (ints)}` frequencies, keyed by the values of the given attribute ID. attr_id (int): The attribute ID to key the counts. RETURNS (dict): A dictionary mapping attributes to integer counts. DOCS: https://nightly.spacy.io/api/doc#count_by """ cdef int i cdef attr_t attr cdef size_t count if counts is None: counts = Counter() output_dict = True else: output_dict = False # Take this check out of the loop, for a bit of extra speed if exclude is None: for i in range(self.length): counts[get_token_attr(&self.c[i], attr_id)] += 1 else: for i in range(self.length): if not exclude(self[i]): counts[get_token_attr(&self.c[i], attr_id)] += 1 if output_dict: return dict(counts) def _realloc(self, new_size): if new_size < self.max_length: return self.max_length = new_size n = new_size + (PADDING * 2) # What we're storing is a "padded" array. We've jumped forward PADDING # places, and are storing the pointer to that. This way, we can access # words out-of-bounds, and get out-of-bounds markers. # Now that we want to realloc, we need the address of the true start, # so we jump the pointer back PADDING places. cdef TokenC* data_start = self.c - PADDING data_start = self.mem.realloc(data_start, n * sizeof(TokenC)) self.c = data_start + PADDING cdef int i for i in range(self.length, self.max_length + PADDING): self.c[i].lex = &EMPTY_LEXEME def from_array(self, attrs, array): """Load attributes from a numpy array. Write to a `Doc` object, from an `(M, N)` array of attributes. attrs (list) A list of attribute ID ints. array (numpy.ndarray[ndim=2, dtype='int32']): The attribute values. RETURNS (Doc): Itself. DOCS: https://nightly.spacy.io/api/doc#from_array """ # Handle scalar/list inputs of strings/ints for py_attr_ids # See also #3064 if isinstance(attrs, str): # Handle inputs like doc.to_array('ORTH') attrs = [attrs] elif not hasattr(attrs, "__iter__"): # Handle inputs like doc.to_array(ORTH) attrs = [attrs] # Allow strings, e.g. 'lemma' or 'LEMMA' attrs = [(IDS[id_.upper()] if hasattr(id_, "upper") else id_) for id_ in attrs] if array.dtype != numpy.uint64: warnings.warn(Warnings.W028.format(type=array.dtype)) cdef int i, col cdef int32_t abs_head_index cdef attr_id_t attr_id cdef TokenC* tokens = self.c cdef int length = len(array) if length != len(self): raise ValueError(Errors.E971.format(array_length=length, doc_length=len(self))) # Get set up for fast loading cdef Pool mem = Pool() cdef int n_attrs = len(attrs) # attrs should not be empty, but make sure to avoid zero-length mem alloc assert n_attrs > 0 attr_ids = mem.alloc(n_attrs, sizeof(attr_id_t)) for i, attr_id in enumerate(attrs): attr_ids[i] = attr_id if len(array.shape) == 1: array = array.reshape((array.size, 1)) cdef np.ndarray transposed_array = numpy.ascontiguousarray(array.T) values = transposed_array.data stride = transposed_array.shape[1] # Check that all heads are within the document bounds if HEAD in attrs: col = attrs.index(HEAD) for i in range(length): # cast index to signed int abs_head_index = values[col * stride + i] abs_head_index += i if abs_head_index < 0 or abs_head_index >= length: raise ValueError( Errors.E190.format( index=i, value=array[i, col], rel_head_index=abs_head_index-i ) ) # Verify ENT_IOB are proper integers if ENT_IOB in attrs: iob_strings = Token.iob_strings() col = attrs.index(ENT_IOB) n_iob_strings = len(iob_strings) for i in range(length): value = values[col * stride + i] if value < 0 or value >= n_iob_strings: raise ValueError( Errors.E982.format( values=iob_strings, value=value ) ) # Now load the data for i in range(length): token = &self.c[i] for j in range(n_attrs): value = values[j * stride + i] if attr_ids[j] == MORPH: # add morph to morphology table self.vocab.morphology.add(self.vocab.strings[value]) Token.set_struct_attr(token, attr_ids[j], value) # If document is parsed, set children and sentence boundaries if HEAD in attrs and DEP in attrs: col = attrs.index(DEP) if array[:, col].any(): set_children_from_heads(self.c, 0, length) return self @staticmethod def from_docs(docs, ensure_whitespace=True, attrs=None): """Concatenate multiple Doc objects to form a new one. Raises an error if the `Doc` objects do not all share the same `Vocab`. docs (list): A list of Doc objects. ensure_whitespace (bool): Insert a space between two adjacent docs whenever the first doc does not end in whitespace. attrs (list): Optional list of attribute ID ints or attribute name strings. RETURNS (Doc): A doc that contains the concatenated docs, or None if no docs were given. DOCS: https://nightly.spacy.io/api/doc#from_docs """ if not docs: return None vocab = {doc.vocab for doc in docs} if len(vocab) > 1: raise ValueError(Errors.E999) (vocab,) = vocab if attrs is None: attrs = Doc._get_array_attrs() else: if any(isinstance(attr, str) for attr in attrs): # resolve attribute names attrs = [intify_attr(attr) for attr in attrs] # intify_attr returns None for invalid attrs attrs = list(attr for attr in set(attrs) if attr) # filter duplicates, remove None if present if SPACY not in attrs: attrs.append(SPACY) concat_words = [] concat_spaces = [] concat_user_data = {} char_offset = 0 for doc in docs: concat_words.extend(t.text for t in doc) concat_spaces.extend(bool(t.whitespace_) for t in doc) for key, value in doc.user_data.items(): if isinstance(key, tuple) and len(key) == 4: data_type, name, start, end = key if start is not None or end is not None: start += char_offset if end is not None: end += char_offset concat_user_data[(data_type, name, start, end)] = copy.copy(value) else: warnings.warn(Warnings.W101.format(name=name)) else: warnings.warn(Warnings.W102.format(key=key, value=value)) char_offset += len(doc.text) if ensure_whitespace and not (len(doc) > 0 and doc[-1].is_space): char_offset += 1 arrays = [doc.to_array(attrs) for doc in docs] if ensure_whitespace: spacy_index = attrs.index(SPACY) for i, array in enumerate(arrays[:-1]): if len(array) > 0 and not docs[i][-1].is_space: array[-1][spacy_index] = 1 token_offset = -1 for doc in docs[:-1]: token_offset += len(doc) if not (len(doc) > 0 and doc[-1].is_space): concat_spaces[token_offset] = True concat_array = numpy.concatenate(arrays) concat_doc = Doc(vocab, words=concat_words, spaces=concat_spaces, user_data=concat_user_data) concat_doc.from_array(attrs, concat_array) return concat_doc def get_lca_matrix(self): """Calculates a matrix of Lowest Common Ancestors (LCA) for a given `Doc`, where LCA[i, j] is the index of the lowest common ancestor among token i and j. RETURNS (np.array[ndim=2, dtype=numpy.int32]): LCA matrix with shape (n, n), where n = len(self). DOCS: https://nightly.spacy.io/api/doc#get_lca_matrix """ return numpy.asarray(_get_lca_matrix(self, 0, len(self))) def copy(self): cdef Doc other = Doc(self.vocab) other._vector = copy.deepcopy(self._vector) other._vector_norm = copy.deepcopy(self._vector_norm) other.tensor = copy.deepcopy(self.tensor) other.cats = copy.deepcopy(self.cats) other.user_data = copy.deepcopy(self.user_data) other.sentiment = self.sentiment other.has_unknown_spaces = self.has_unknown_spaces other.user_hooks = dict(self.user_hooks) other.user_token_hooks = dict(self.user_token_hooks) other.user_span_hooks = dict(self.user_span_hooks) other.length = self.length other.max_length = self.max_length buff_size = other.max_length + (PADDING*2) tokens = other.mem.alloc(buff_size, sizeof(TokenC)) memcpy(tokens, self.c - PADDING, buff_size * sizeof(TokenC)) other.c = &tokens[PADDING] return other def to_disk(self, path, *, exclude=tuple()): """Save the current state to a directory. path (str / Path): A path to a directory, which will be created if it doesn't exist. Paths may be either strings or Path-like objects. exclude (Iterable[str]): String names of serialization fields to exclude. DOCS: https://nightly.spacy.io/api/doc#to_disk """ path = util.ensure_path(path) with path.open("wb") as file_: file_.write(self.to_bytes(exclude=exclude)) def from_disk(self, path, *, exclude=tuple()): """Loads state from a directory. Modifies the object in place and returns it. path (str / Path): A path to a directory. Paths may be either strings or `Path`-like objects. exclude (list): String names of serialization fields to exclude. RETURNS (Doc): The modified `Doc` object. DOCS: https://nightly.spacy.io/api/doc#from_disk """ path = util.ensure_path(path) with path.open("rb") as file_: bytes_data = file_.read() return self.from_bytes(bytes_data, exclude=exclude) def to_bytes(self, *, exclude=tuple()): """Serialize, i.e. export the document contents to a binary string. exclude (list): String names of serialization fields to exclude. RETURNS (bytes): A losslessly serialized copy of the `Doc`, including all annotations. DOCS: https://nightly.spacy.io/api/doc#to_bytes """ return srsly.msgpack_dumps(self.to_dict(exclude=exclude)) def from_bytes(self, bytes_data, *, exclude=tuple()): """Deserialize, i.e. import the document contents from a binary string. data (bytes): The string to load from. exclude (list): String names of serialization fields to exclude. RETURNS (Doc): Itself. DOCS: https://nightly.spacy.io/api/doc#from_bytes """ return self.from_dict(srsly.msgpack_loads(bytes_data), exclude=exclude) def to_dict(self, *, exclude=tuple()): """Export the document contents to a dictionary for serialization. exclude (list): String names of serialization fields to exclude. RETURNS (bytes): A losslessly serialized copy of the `Doc`, including all annotations. DOCS: https://nightly.spacy.io/api/doc#to_bytes """ array_head = Doc._get_array_attrs() strings = set() for token in self: strings.add(token.tag_) strings.add(token.lemma_) strings.add(str(token.morph)) strings.add(token.dep_) strings.add(token.ent_type_) strings.add(token.ent_kb_id_) strings.add(token.ent_id_) strings.add(token.norm_) # Msgpack doesn't distinguish between lists and tuples, which is # vexing for user data. As a best guess, we *know* that within # keys, we must have tuples. In values we just have to hope # users don't mind getting a list instead of a tuple. serializers = { "text": lambda: self.text, "array_head": lambda: array_head, "array_body": lambda: self.to_array(array_head), "sentiment": lambda: self.sentiment, "tensor": lambda: self.tensor, "cats": lambda: self.cats, "strings": lambda: list(strings), "has_unknown_spaces": lambda: self.has_unknown_spaces } if "user_data" not in exclude and self.user_data: user_data_keys, user_data_values = list(zip(*self.user_data.items())) if "user_data_keys" not in exclude: serializers["user_data_keys"] = lambda: srsly.msgpack_dumps(user_data_keys) if "user_data_values" not in exclude: serializers["user_data_values"] = lambda: srsly.msgpack_dumps(user_data_values) return util.to_dict(serializers, exclude) def from_dict(self, msg, *, exclude=tuple()): """Deserialize, i.e. import the document contents from a binary string. data (bytes): The string to load from. exclude (list): String names of serialization fields to exclude. RETURNS (Doc): Itself. DOCS: https://nightly.spacy.io/api/doc#from_dict """ if self.length != 0: raise ValueError(Errors.E033.format(length=self.length)) deserializers = { "text": lambda b: None, "array_head": lambda b: None, "array_body": lambda b: None, "sentiment": lambda b: None, "tensor": lambda b: None, "cats": lambda b: None, "strings": lambda b: None, "user_data_keys": lambda b: None, "user_data_values": lambda b: None, "has_unknown_spaces": lambda b: None } # Msgpack doesn't distinguish between lists and tuples, which is # vexing for user data. As a best guess, we *know* that within # keys, we must have tuples. In values we just have to hope # users don't mind getting a list instead of a tuple. if "user_data" not in exclude and "user_data_keys" in msg: user_data_keys = srsly.msgpack_loads(msg["user_data_keys"], use_list=False) user_data_values = srsly.msgpack_loads(msg["user_data_values"]) for key, value in zip(user_data_keys, user_data_values): self.user_data[key] = value cdef int i, start, end, has_space if "sentiment" not in exclude and "sentiment" in msg: self.sentiment = msg["sentiment"] if "tensor" not in exclude and "tensor" in msg: self.tensor = msg["tensor"] if "cats" not in exclude and "cats" in msg: self.cats = msg["cats"] if "strings" not in exclude and "strings" in msg: for s in msg["strings"]: self.vocab.strings.add(s) if "has_unknown_spaces" not in exclude and "has_unknown_spaces" in msg: self.has_unknown_spaces = msg["has_unknown_spaces"] start = 0 cdef const LexemeC* lex cdef unicode orth_ text = msg["text"] attrs = msg["array_body"] for i in range(attrs.shape[0]): end = start + attrs[i, 0] has_space = attrs[i, 1] orth_ = text[start:end] lex = self.vocab.get(self.mem, orth_) self.push_back(lex, has_space) start = end + has_space self.from_array(msg["array_head"][2:], attrs[:, 2:]) return self def extend_tensor(self, tensor): """Concatenate a new tensor onto the doc.tensor object. The doc.tensor attribute holds dense feature vectors computed by the models in the pipeline. Let's say a document with 30 words has a tensor with 128 dimensions per word. doc.tensor.shape will be (30, 128). After calling doc.extend_tensor with an array of shape (30, 64), doc.tensor == (30, 192). """ xp = get_array_module(self.tensor) if self.tensor.size == 0: self.tensor.resize(tensor.shape, refcheck=False) copy_array(self.tensor, tensor) else: self.tensor = xp.hstack((self.tensor, tensor)) def retokenize(self): """Context manager to handle retokenization of the Doc. Modifications to the Doc's tokenization are stored, and then made all at once when the context manager exits. This is much more efficient, and less error-prone. All views of the Doc (Span and Token) created before the retokenization are invalidated, although they may accidentally continue to work. DOCS: https://nightly.spacy.io/api/doc#retokenize USAGE: https://nightly.spacy.io/usage/linguistic-features#retokenization """ return Retokenizer(self) def _bulk_merge(self, spans, attributes): """Retokenize the document, such that the spans given as arguments are merged into single tokens. The spans need to be in document order, and no span intersection is allowed. spans (Span[]): Spans to merge, in document order, with all span intersections empty. Cannot be empty. attributes (Dictionary[]): Attributes to assign to the merged tokens. By default, must be the same length as spans, empty dictionaries are allowed. attributes are inherited from the syntactic root of the span. RETURNS (Token): The first newly merged token. """ cdef unicode tag, lemma, ent_type attr_len = len(attributes) span_len = len(spans) if not attr_len == span_len: raise ValueError(Errors.E121.format(attr_len=attr_len, span_len=span_len)) with self.retokenize() as retokenizer: for i, span in enumerate(spans): fix_attributes(self, attributes[i]) remove_label_if_necessary(attributes[i]) retokenizer.merge(span, attributes[i]) def to_json(self, underscore=None): """Convert a Doc to JSON. underscore (list): Optional list of string names of custom doc._. attributes. Attribute values need to be JSON-serializable. Values will be added to an "_" key in the data, e.g. "_": {"foo": "bar"}. RETURNS (dict): The data in spaCy's JSON format. DOCS: https://nightly.spacy.io/api/doc#to_json """ data = {"text": self.text} if self.has_annotation("ENT_IOB"): data["ents"] = [{"start": ent.start_char, "end": ent.end_char, "label": ent.label_} for ent in self.ents] if self.has_annotation("SENT_START"): sents = list(self.sents) data["sents"] = [{"start": sent.start_char, "end": sent.end_char} for sent in sents] if self.cats: data["cats"] = self.cats data["tokens"] = [] attrs = ["TAG", "MORPH", "POS", "LEMMA", "DEP"] include_annotation = {attr: self.has_annotation(attr) for attr in attrs} for token in self: token_data = {"id": token.i, "start": token.idx, "end": token.idx + len(token)} if include_annotation["TAG"]: token_data["tag"] = token.tag_ if include_annotation["POS"]: token_data["pos"] = token.pos_ if include_annotation["MORPH"]: token_data["morph"] = token.morph_ if include_annotation["LEMMA"]: token_data["lemma"] = token.lemma_ if include_annotation["DEP"]: token_data["dep"] = token.dep_ token_data["head"] = token.head.i data["tokens"].append(token_data) if underscore: data["_"] = {} for attr in underscore: if not self.has_extension(attr): raise ValueError(Errors.E106.format(attr=attr, opts=underscore)) value = self._.get(attr) if not srsly.is_json_serializable(value): raise ValueError(Errors.E107.format(attr=attr, value=repr(value))) data["_"][attr] = value return data def to_utf8_array(self, int nr_char=-1): """Encode word strings to utf8, and export to a fixed-width array of characters. Characters are placed into the array in the order: 0, -1, 1, -2, etc For example, if the array is sliced array[:, :8], the array will contain the first 4 characters and last 4 characters of each word --- with the middle characters clipped out. The value 255 is used as a pad value. """ byte_strings = [token.orth_.encode('utf8') for token in self] if nr_char == -1: nr_char = max(len(bs) for bs in byte_strings) cdef np.ndarray output = numpy.zeros((len(byte_strings), nr_char), dtype='uint8') output.fill(255) cdef int i, j, start_idx, end_idx cdef bytes byte_string cdef unsigned char utf8_char for i, byte_string in enumerate(byte_strings): j = 0 start_idx = 0 end_idx = len(byte_string) - 1 while j < nr_char and start_idx <= end_idx: output[i, j] = byte_string[start_idx] start_idx += 1 j += 1 if j < nr_char and start_idx <= end_idx: output[i, j] = byte_string[end_idx] end_idx -= 1 j += 1 return output @staticmethod def _get_array_attrs(): attrs = [LENGTH, SPACY] attrs.extend(intify_attr(x) for x in DOCBIN_ALL_ATTRS) return tuple(attrs) cdef int token_by_start(const TokenC* tokens, int length, int start_char) except -2: cdef int i = token_by_char(tokens, length, start_char) if i >= 0 and tokens[i].idx == start_char: return i else: return -1 cdef int token_by_end(const TokenC* tokens, int length, int end_char) except -2: # end_char is exclusive, so find the token at one char before cdef int i = token_by_char(tokens, length, end_char - 1) if i >= 0 and tokens[i].idx + tokens[i].lex.length == end_char: return i else: return -1 cdef int token_by_char(const TokenC* tokens, int length, int char_idx) except -2: cdef int start = 0, mid, end = length - 1 while start <= end: mid = (start + end) / 2 if char_idx < tokens[mid].idx: end = mid - 1 elif char_idx >= tokens[mid].idx + tokens[mid].lex.length + tokens[mid].spacy: start = mid + 1 else: return mid return -1 cdef int set_children_from_heads(TokenC* tokens, int start, int end) except -1: # note: end is exclusive cdef TokenC* head cdef TokenC* child cdef int i # Set number of left/right children to 0. We'll increment it in the loops. for i in range(start, end): tokens[i].l_kids = 0 tokens[i].r_kids = 0 tokens[i].l_edge = i tokens[i].r_edge = i cdef int loop_count = 0 cdef bint heads_within_sents = False # Try up to 10 iterations of adjusting lr_kids and lr_edges in order to # handle non-projective dependency parses, stopping when all heads are # within their respective sentence boundaries. We have documented cases # that need at least 4 iterations, so this is to be on the safe side # without risking getting stuck in an infinite loop if something is # terribly malformed. while not heads_within_sents: heads_within_sents = _set_lr_kids_and_edges(tokens, start, end, loop_count) if loop_count > 10: warnings.warn(Warnings.W026) break loop_count += 1 # Set sentence starts for i in range(start, end): tokens[i].sent_start = -1 for i in range(start, end): if tokens[i].head == 0: tokens[tokens[i].l_edge].sent_start = 1 cdef int _set_lr_kids_and_edges(TokenC* tokens, int start, int end, int loop_count) except -1: # May be called multiple times due to non-projectivity. See issues #3170 # and #4688. # Set left edges cdef TokenC* head cdef TokenC* child cdef int i, j for i in range(start, end): child = &tokens[i] head = &tokens[i + child.head] if loop_count == 0 and child < head: head.l_kids += 1 if child.l_edge < head.l_edge: head.l_edge = child.l_edge if child.r_edge > head.r_edge: head.r_edge = child.r_edge # Set right edges - same as above, but iterate in reverse for i in range(end-1, start-1, -1): child = &tokens[i] head = &tokens[i + child.head] if loop_count == 0 and child > head: head.r_kids += 1 if child.r_edge > head.r_edge: head.r_edge = child.r_edge if child.l_edge < head.l_edge: head.l_edge = child.l_edge # Get sentence start positions according to current state sent_starts = set() for i in range(start, end): if tokens[i].head == 0: sent_starts.add(tokens[i].l_edge) cdef int curr_sent_start = 0 cdef int curr_sent_end = 0 # Check whether any heads are not within the current sentence for i in range(start, end): if (i > 0 and i in sent_starts) or i == end - 1: curr_sent_end = i for j in range(curr_sent_start, curr_sent_end): if tokens[j].head + j < curr_sent_start or tokens[j].head + j >= curr_sent_end + 1: return False curr_sent_start = i return True cdef int _get_tokens_lca(Token token_j, Token token_k): """Given two tokens, returns the index of the lowest common ancestor (LCA) among the two. If they have no common ancestor, -1 is returned. token_j (Token): a token. token_k (Token): another token. RETURNS (int): index of lowest common ancestor, or -1 if the tokens have no common ancestor. """ if token_j == token_k: return token_j.i elif token_j.head == token_k: return token_k.i elif token_k.head == token_j: return token_j.i token_j_ancestors = set(token_j.ancestors) if token_k in token_j_ancestors: return token_k.i for token_k_ancestor in token_k.ancestors: if token_k_ancestor == token_j: return token_j.i if token_k_ancestor in token_j_ancestors: return token_k_ancestor.i return -1 cdef int [:,:] _get_lca_matrix(Doc doc, int start, int end): """Given a doc and a start and end position defining a set of contiguous tokens within it, returns a matrix of Lowest Common Ancestors (LCA), where LCA[i, j] is the index of the lowest common ancestor among token i and j. If the tokens have no common ancestor within the specified span, LCA[i, j] will be -1. doc (Doc): The index of the token, or the slice of the document start (int): First token to be included in the LCA matrix. end (int): Position of next to last token included in the LCA matrix. RETURNS (int [:, :]): memoryview of numpy.array[ndim=2, dtype=numpy.int32], with shape (n, n), where n = len(doc). """ cdef int [:,:] lca_matrix cdef int j, k n_tokens= end - start lca_mat = numpy.empty((n_tokens, n_tokens), dtype=numpy.int32) lca_mat.fill(-1) lca_matrix = lca_mat for j in range(n_tokens): token_j = doc[start + j] # the common ancestor of token and itself is itself: lca_matrix[j, j] = j # we will only iterate through tokens in the same sentence sent = token_j.sent sent_start = sent.start j_idx_in_sent = start + j - sent_start n_missing_tokens_in_sent = len(sent) - j_idx_in_sent # make sure we do not go past `end`, in cases where `end` < sent.end max_range = min(j + n_missing_tokens_in_sent, end) for k in range(j + 1, max_range): lca = _get_tokens_lca(token_j, doc[start + k]) # if lca is outside of span, we set it to -1 if not start <= lca < end: lca_matrix[j, k] = -1 lca_matrix[k, j] = -1 else: lca_matrix[j, k] = lca - start lca_matrix[k, j] = lca - start return lca_matrix def pickle_doc(doc): bytes_data = doc.to_bytes(exclude=["vocab", "user_data"]) hooks_and_data = (doc.user_data, doc.user_hooks, doc.user_span_hooks, doc.user_token_hooks) return (unpickle_doc, (doc.vocab, srsly.pickle_dumps(hooks_and_data), bytes_data)) def unpickle_doc(vocab, hooks_and_data, bytes_data): user_data, doc_hooks, span_hooks, token_hooks = srsly.pickle_loads(hooks_and_data) doc = Doc(vocab, user_data=user_data).from_bytes(bytes_data, exclude=["user_data"]) doc.user_hooks.update(doc_hooks) doc.user_span_hooks.update(span_hooks) doc.user_token_hooks.update(token_hooks) return doc copy_reg.pickle(Doc, pickle_doc, unpickle_doc) def remove_label_if_necessary(attributes): # More deprecated attribute handling =/ if "label" in attributes: attributes["ent_type"] = attributes.pop("label") def fix_attributes(doc, attributes): if "label" in attributes and "ent_type" not in attributes: if isinstance(attributes["label"], int): attributes[ENT_TYPE] = attributes["label"] else: attributes[ENT_TYPE] = doc.vocab.strings[attributes["label"]] if "ent_type" in attributes: attributes[ENT_TYPE] = attributes["ent_type"] def get_entity_info(ent_info): if isinstance(ent_info, Span): ent_type = ent_info.label ent_kb_id = ent_info.kb_id start = ent_info.start end = ent_info.end elif len(ent_info) == 3: ent_type, start, end = ent_info ent_kb_id = 0 elif len(ent_info) == 4: ent_type, ent_kb_id, start, end = ent_info else: ent_id, ent_kb_id, ent_type, start, end = ent_info return ent_type, ent_kb_id, start, end