cimport numpy as np from libc.math cimport sqrt import numpy from thinc.api import get_array_module import warnings import copy from .doc cimport token_by_start, token_by_end, get_token_attr, _get_lca_matrix from ..structs cimport TokenC, LexemeC from ..typedefs cimport flags_t, attr_t, hash_t from ..attrs cimport attr_id_t from ..parts_of_speech cimport univ_pos_t from ..attrs cimport * from ..lexeme cimport Lexeme from ..symbols cimport dep from ..util import normalize_slice from ..errors import Errors, Warnings from .underscore import Underscore, get_ext_args cdef class Span: """A slice from a Doc object. DOCS: https://spacy.io/api/span """ @classmethod def set_extension(cls, name, **kwargs): """Define a custom attribute which becomes available as `Span._`. 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://spacy.io/api/span#set_extension USAGE: https://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="Span")) Underscore.span_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://spacy.io/api/span#get_extension """ return Underscore.span_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://spacy.io/api/span#has_extension """ return name in Underscore.span_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://spacy.io/api/span#remove_extension """ if not cls.has_extension(name): raise ValueError(Errors.E046.format(name=name)) return Underscore.span_extensions.pop(name) def __cinit__(self, Doc doc, int start, int end, label=0, vector=None, vector_norm=None, kb_id=0): """Create a `Span` object from the slice `doc[start : end]`. doc (Doc): The parent document. start (int): The index of the first token of the span. end (int): The index of the first token after the span. label (int or str): A label to attach to the Span, e.g. for named entities. vector (ndarray[ndim=1, dtype='float32']): A meaning representation of the span. vector_norm (float): The L2 norm of the span's vector representation. kb_id (uint64): An identifier from a Knowledge Base to capture the meaning of a named entity. DOCS: https://spacy.io/api/span#init """ if not (0 <= start <= end <= len(doc)): raise IndexError(Errors.E035.format(start=start, end=end, length=len(doc))) self.doc = doc if isinstance(label, str): label = doc.vocab.strings.add(label) if isinstance(kb_id, str): kb_id = doc.vocab.strings.add(kb_id) if label not in doc.vocab.strings: raise ValueError(Errors.E084.format(label=label)) start_char = doc[start].idx if start < doc.length else len(doc.text) if start == end: end_char = start_char else: end_char = doc[end - 1].idx + len(doc[end - 1]) self.c = SpanC( label=label, kb_id=kb_id, start=start, end=end, start_char=start_char, end_char=end_char, ) self._vector = vector self._vector_norm = vector_norm def __richcmp__(self, Span other, int op): if other is None: if op == 0 or op == 1 or op == 2: return False else: return True self_tuple = (self.c.start_char, self.c.end_char, self.c.label, self.c.kb_id, self.doc) other_tuple = (other.c.start_char, other.c.end_char, other.c.label, other.c.kb_id, other.doc) # < if op == 0: return self_tuple < other_tuple # <= elif op == 1: return self_tuple <= other_tuple # == elif op == 2: return self_tuple == other_tuple # != elif op == 3: return self_tuple != other_tuple # > elif op == 4: return self_tuple > other_tuple # >= elif op == 5: return self_tuple >= other_tuple def __hash__(self): return hash((self.doc, self.c.start_char, self.c.end_char, self.c.label, self.c.kb_id)) def __len__(self): """Get the number of tokens in the span. RETURNS (int): The number of tokens in the span. DOCS: https://spacy.io/api/span#len """ if self.c.end < self.c.start: return 0 return self.c.end - self.c.start def __repr__(self): return self.text def __getitem__(self, object i): """Get a `Token` or a `Span` object i (int or tuple): The index of the token within the span, or slice of the span to get. RETURNS (Token or Span): The token at `span[i]`. DOCS: https://spacy.io/api/span#getitem """ if isinstance(i, slice): start, end = normalize_slice(len(self), i.start, i.stop, i.step) return Span(self.doc, start + self.start, end + self.start) else: if i < 0: token_i = self.c.end + i else: token_i = self.c.start + i if self.c.start <= token_i < self.c.end: return self.doc[token_i] else: raise IndexError(Errors.E1002) def __iter__(self): """Iterate over `Token` objects. YIELDS (Token): A `Token` object. DOCS: https://spacy.io/api/span#iter """ for i in range(self.c.start, self.c.end): yield self.doc[i] def __reduce__(self): raise NotImplementedError(Errors.E112) @property def _(self): """Custom extension attributes registered via `set_extension`.""" return Underscore(Underscore.span_extensions, self, start=self.c.start_char, end=self.c.end_char) def as_doc(self, *, bint copy_user_data=False, array_head=None, array=None): """Create a `Doc` object with a copy of the `Span`'s data. copy_user_data (bool): Whether or not to copy the original doc's user data. array_head (tuple): `Doc` array attrs, can be passed in to speed up computation. array (ndarray): `Doc` as array, can be passed in to speed up computation. RETURNS (Doc): The `Doc` copy of the span. DOCS: https://spacy.io/api/span#as_doc """ words = [t.text for t in self] spaces = [bool(t.whitespace_) for t in self] cdef Doc doc = Doc(self.doc.vocab, words=words, spaces=spaces) if array_head is None: array_head = self.doc._get_array_attrs() if array is None: array = self.doc.to_array(array_head) array = array[self.start : self.end] self._fix_dep_copy(array_head, array) # Fix initial IOB so the entities are valid for doc.ents below. if len(array) > 0 and ENT_IOB in array_head: ent_iob_col = array_head.index(ENT_IOB) if array[0][ent_iob_col] == 1: array[0][ent_iob_col] = 3 doc.from_array(array_head, array) # Set partial entities at the beginning or end of the span to have # missing entity annotation. Note: the initial partial entity could be # detected from the IOB annotation but the final partial entity can't, # so detect and remove both in the same way by checking self.ents. span_ents = {(ent.start, ent.end) for ent in self.ents} doc_ents = doc.ents if len(doc_ents) > 0: # Remove initial partial ent if (doc_ents[0].start + self.start, doc_ents[0].end + self.start) not in span_ents: doc.set_ents([], missing=[doc_ents[0]], default="unmodified") # Remove final partial ent if (doc_ents[-1].start + self.start, doc_ents[-1].end + self.start) not in span_ents: doc.set_ents([], missing=[doc_ents[-1]], default="unmodified") doc.noun_chunks_iterator = self.doc.noun_chunks_iterator doc.user_hooks = self.doc.user_hooks doc.user_span_hooks = self.doc.user_span_hooks doc.user_token_hooks = self.doc.user_token_hooks doc.vector = self.vector doc.vector_norm = self.vector_norm doc.tensor = self.doc.tensor[self.start : self.end] for key, value in self.doc.cats.items(): if hasattr(key, "__len__") and len(key) == 3: cat_start, cat_end, cat_label = key if cat_start == self.start_char and cat_end == self.end_char: doc.cats[cat_label] = value if copy_user_data: user_data = {} char_offset = self.start_char for key, value in self.doc.user_data.items(): if isinstance(key, tuple) and len(key) == 4 and key[0] == "._.": 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 user_data[(data_type, name, start, end)] = copy.copy(value) else: user_data[key] = copy.copy(value) doc.user_data = user_data return doc def _fix_dep_copy(self, attrs, array): """ Rewire dependency links to make sure their heads fall into the span while still keeping the correct number of sentences. """ cdef int length = len(array) cdef attr_t value cdef int i, head_col, ancestor_i old_to_new_root = dict() if HEAD in attrs: head_col = attrs.index(HEAD) for i in range(length): # if the HEAD refers to a token outside this span, find a more appropriate ancestor token = self[i] ancestor_i = token.head.i - self.c.start # span offset if ancestor_i not in range(length): if DEP in attrs: array[i, attrs.index(DEP)] = dep # try finding an ancestor within this span ancestors = token.ancestors for ancestor in ancestors: ancestor_i = ancestor.i - self.c.start if ancestor_i in range(length): array[i, head_col] = ancestor_i - i # if there is no appropriate ancestor, define a new artificial root value = array[i, head_col] if (i+value) not in range(length): new_root = old_to_new_root.get(ancestor_i, None) if new_root is not None: # take the same artificial root as a previous token from the same sentence array[i, head_col] = new_root - i else: # set this token as the new artificial root array[i, head_col] = 0 old_to_new_root[ancestor_i] = i return array def get_lca_matrix(self): """Calculates a matrix of Lowest Common Ancestors (LCA) for a given `Span`, where LCA[i, j] is the index of the lowest common ancestor among the tokens span[i] and span[j]. If they have no common ancestor within the span, LCA[i, j] will be -1. RETURNS (np.array[ndim=2, dtype=numpy.int32]): LCA matrix with shape (n, n), where n = len(self). DOCS: https://spacy.io/api/span#get_lca_matrix """ return numpy.asarray(_get_lca_matrix(self.doc, self.c.start, self.c.end)) 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://spacy.io/api/span#similarity """ if "similarity" in self.doc.user_span_hooks: return self.doc.user_span_hooks["similarity"](self, other) if len(self) == 1 and hasattr(other, "orth"): if self[0].orth == other.orth: return 1.0 elif isinstance(other, (Doc, Span)) and len(self) == len(other): similar = True for i in range(len(self)): if self[i].orth != getattr(other[i], "orth", None): similar = False break if similar: return 1.0 if self.vocab.vectors.n_keys == 0: warnings.warn(Warnings.W007.format(obj="Span")) if self.vector_norm == 0.0 or other.vector_norm == 0.0: warnings.warn(Warnings.W008.format(obj="Span")) 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() cpdef np.ndarray to_array(self, object py_attr_ids): """Given a list of M attribute IDs, export the tokens to a numpy `ndarray` of shape `(N, M)`, where `N` is the length of the document. The values will be 32-bit integers. attr_ids (list[int]): A list of attribute ID ints. 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`. """ cdef int i, j cdef attr_id_t feature cdef np.ndarray[attr_t, ndim=2] output # Make an array from the attributes - otherwise our inner loop is Python # dict iteration cdef np.ndarray[attr_t, ndim=1] attr_ids = numpy.asarray(py_attr_ids, dtype=numpy.uint64) cdef int length = self.end - self.start output = numpy.ndarray(shape=(length, len(attr_ids)), dtype=numpy.uint64) for i in range(self.start, self.end): for j, feature in enumerate(attr_ids): output[i-self.start, j] = get_token_attr(&self.doc.c[i], feature) return output @property def vocab(self): """RETURNS (Vocab): The Span's Doc's vocab.""" return self.doc.vocab @property def sent(self): """Obtain the sentence that contains this span. If the given span crosses sentence boundaries, return only the first sentence to which it belongs. RETURNS (Span): The sentence span that the span is a part of. """ if "sent" in self.doc.user_span_hooks: return self.doc.user_span_hooks["sent"](self) elif "sents" in self.doc.user_hooks: for sentence in self.doc.user_hooks["sents"](self.doc): if sentence.start <= self.start < sentence.end: return sentence # Use `sent_start` token attribute to find sentence boundaries cdef int n = 0 if self.doc.has_annotation("SENT_START"): # Find start of the sentence start = self.start while self.doc.c[start].sent_start != 1 and start > 0: start += -1 # Find end of the sentence - can be within the entity end = self.start + 1 while end < self.doc.length and self.doc.c[end].sent_start != 1: end += 1 n += 1 if n >= self.doc.length: break return self.doc[start:end] else: raise ValueError(Errors.E030) @property def sents(self): """Obtain the sentences that contain this span. If the given span crosses sentence boundaries, return all sentences it is a part of. RETURNS (Iterable[Span]): All sentences that the span is a part of. DOCS: https://spacy.io/api/span#sents """ cdef int start cdef int i if "sents" in self.doc.user_span_hooks: yield from self.doc.user_span_hooks["sents"](self) elif "sents" in self.doc.user_hooks: for sentence in self.doc.user_hooks["sents"](self.doc): if sentence.end > self.start: if sentence.start < self.end or sentence.start == self.start == self.end: yield sentence else: break else: if not self.doc.has_annotation("SENT_START"): raise ValueError(Errors.E030) # Use `sent_start` token attribute to find sentence boundaries # Find start of the 1st sentence of the Span start = self.start while self.doc.c[start].sent_start != 1 and start > 0: start -= 1 # Now, find all the sentences in the span for i in range(start + 1, self.doc.length): if self.doc.c[i].sent_start == 1: yield Span(self.doc, start, i) start = i if start >= self.end: break if start < self.end: yield Span(self.doc, start, self.end) @property def ents(self): """The named entities in the span. Returns a tuple of named entity `Span` objects, if the entity recognizer has been applied. RETURNS (tuple): Entities in the span, one `Span` per entity. DOCS: https://spacy.io/api/span#ents """ cdef Span ent ents = [] for ent in self.doc.ents: if ent.c.start >= self.c.start: if ent.c.end <= self.c.end: ents.append(ent) else: break return ents @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://spacy.io/api/span#has_vector """ if "has_vector" in self.doc.user_span_hooks: return self.doc.user_span_hooks["has_vector"](self) elif self.vocab.vectors.size > 0: return any(token.has_vector for token in self) elif self.doc.tensor.size > 0: return True else: return False @property def vector(self): """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 span's semantics. DOCS: https://spacy.io/api/span#vector """ if "vector" in self.doc.user_span_hooks: return self.doc.user_span_hooks["vector"](self) if self._vector is None: if not len(self): xp = get_array_module(self.vocab.vectors.data) self._vector = xp.zeros((self.vocab.vectors_length,), dtype="f") else: self._vector = sum(t.vector for t in self) / len(self) return self._vector @property def vector_norm(self): """The L2 norm of the span's vector representation. RETURNS (float): The L2 norm of the vector representation. DOCS: https://spacy.io/api/span#vector_norm """ if "vector_norm" in self.doc.user_span_hooks: return self.doc.user_span_hooks["vector"](self) if self._vector_norm is None: vector = self.vector total = (vector*vector).sum() xp = get_array_module(vector) self._vector_norm = xp.sqrt(total) if total != 0. else 0. return self._vector_norm @property def tensor(self): """The span's slice of the doc's tensor. RETURNS (ndarray[ndim=2, dtype='float32']): A 2D numpy or cupy array representing the span's semantics. """ if self.doc.tensor is None: return None return self.doc.tensor[self.start : self.end] @property def sentiment(self): """RETURNS (float): A scalar value indicating the positivity or negativity of the span. """ if "sentiment" in self.doc.user_span_hooks: return self.doc.user_span_hooks["sentiment"](self) else: return sum([token.sentiment for token in self]) / len(self) @property def text(self): """RETURNS (str): The original verbatim text of the span.""" text = self.text_with_ws if len(self) > 0 and self[-1].whitespace_: text = text[:-1] return text @property def text_with_ws(self): """The text content of the span with a trailing whitespace character if the last token has one. RETURNS (str): The text content of the span (with trailing whitespace). """ return "".join([t.text_with_ws for t in self]) @property def noun_chunks(self): """Iterate over the base noun phrases in the span. Yields base noun-phrase #[code Span] objects, if the language has a noun chunk iterator. Raises a NotImplementedError otherwise. 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 span. DOCS: https://spacy.io/api/span#noun_chunks """ for span in self.doc.noun_chunks: if span.start >= self.start and span.end <= self.end: yield span @property def root(self): """The token with the shortest path to the root of the sentence (or the root itself). If multiple tokens are equally high in the tree, the first token is taken. RETURNS (Token): The root token. DOCS: https://spacy.io/api/span#root """ if "root" in self.doc.user_span_hooks: return self.doc.user_span_hooks["root"](self) # This should probably be called 'head', and the other one called # 'gov'. But we went with 'head' elsewhere, and now we're stuck =/ cdef int i # First, we scan through the Span, and check whether there's a word # with head==0, i.e. a sentence root. If so, we can return it. The # longer the span, the more likely it contains a sentence root, and # in this case we return in linear time. for i in range(self.c.start, self.c.end): if self.doc.c[i].head == 0: return self.doc[i] # If we don't have a sentence root, we do something that's not so # algorithmically clever, but I think should be quite fast, # especially for short spans. # For each word, we count the path length, and arg min this measure. # We could use better tree logic to save steps here...But I # think this should be okay. cdef int current_best = self.doc.length cdef int root = -1 for i in range(self.c.start, self.c.end): if self.c.start <= (i+self.doc.c[i].head) < self.c.end: continue words_to_root = _count_words_to_root(&self.doc.c[i], self.doc.length) if words_to_root < current_best: current_best = words_to_root root = i if root == -1: return self.doc[self.c.start] else: return self.doc[root] def char_span(self, int start_idx, int end_idx, label=0, kb_id=0, vector=None): """Create a `Span` object from the slice `span.text[start : end]`. start (int): The index of the first character of the span. end (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. RETURNS (Span): The newly constructed object. """ start_idx += self.c.start_char end_idx += self.c.start_char return self.doc.char_span(start_idx, end_idx, label=label, kb_id=kb_id, vector=vector) @property def conjuncts(self): """Tokens that are conjoined to the span's root. RETURNS (tuple): A tuple of Token objects. DOCS: https://spacy.io/api/span#lefts """ return self.root.conjuncts @property def lefts(self): """Tokens that are to the left of the span, whose head is within the `Span`. YIELDS (Token):A left-child of a token of the span. DOCS: https://spacy.io/api/span#lefts """ for token in reversed(self): # Reverse, so we get tokens in order for left in token.lefts: if left.i < self.start: yield left @property def rights(self): """Tokens that are to the right of the Span, whose head is within the `Span`. YIELDS (Token): A right-child of a token of the span. DOCS: https://spacy.io/api/span#rights """ for token in self: for right in token.rights: if right.i >= self.end: yield right @property def n_lefts(self): """The number of tokens that are to the left of the span, whose heads are within the span. RETURNS (int): The number of leftward immediate children of the span, in the syntactic dependency parse. DOCS: https://spacy.io/api/span#n_lefts """ return len(list(self.lefts)) @property def n_rights(self): """The number of tokens that are to the right of the span, whose heads are within the span. RETURNS (int): The number of rightward immediate children of the span, in the syntactic dependency parse. DOCS: https://spacy.io/api/span#n_rights """ return len(list(self.rights)) @property def subtree(self): """Tokens within the span and tokens which descend from them. YIELDS (Token): A token within the span, or a descendant from it. DOCS: https://spacy.io/api/span#subtree """ for word in self.lefts: yield from word.subtree yield from self for word in self.rights: yield from word.subtree property start: def __get__(self): return self.c.start def __set__(self, int start): if start < 0: raise IndexError("TODO") self.c.start = start property end: def __get__(self): return self.c.end def __set__(self, int end): if end < 0: raise IndexError("TODO") self.c.end = end property start_char: def __get__(self): return self.c.start_char def __set__(self, int start_char): if start_char < 0: raise IndexError("TODO") self.c.start_char = start_char property end_char: def __get__(self): return self.c.end_char def __set__(self, int end_char): if end_char < 0: raise IndexError("TODO") self.c.end_char = end_char property label: def __get__(self): return self.c.label def __set__(self, attr_t label): self.c.label = label property kb_id: def __get__(self): return self.c.kb_id def __set__(self, attr_t kb_id): self.c.kb_id = kb_id property ent_id: """RETURNS (uint64): The entity ID.""" def __get__(self): return self.root.ent_id def __set__(self, hash_t key): raise NotImplementedError(Errors.E200.format(attr="ent_id")) property ent_id_: """RETURNS (str): The (string) entity ID.""" def __get__(self): return self.root.ent_id_ def __set__(self, str key): raise NotImplementedError(Errors.E200.format(attr="ent_id_")) @property def orth_(self): """Verbatim text content (identical to `Span.text`). Exists mostly for consistency with other attributes. RETURNS (str): The span's text.""" return self.text @property def lemma_(self): """RETURNS (str): The span's lemma.""" return "".join([t.lemma_ + t.whitespace_ for t in self]).strip() property label_: """RETURNS (str): The span's label.""" def __get__(self): return self.doc.vocab.strings[self.label] def __set__(self, str label_): self.label = self.doc.vocab.strings.add(label_) property kb_id_: """RETURNS (str): The named entity's KB ID.""" def __get__(self): return self.doc.vocab.strings[self.kb_id] def __set__(self, str kb_id_): self.kb_id = self.doc.vocab.strings.add(kb_id_) cdef int _count_words_to_root(const TokenC* token, int sent_length) except -1: # Don't allow spaces to be the root, if there are # better candidates if Lexeme.c_check_flag(token.lex, IS_SPACE) and token.l_kids == 0 and token.r_kids == 0: return sent_length-1 if Lexeme.c_check_flag(token.lex, IS_PUNCT) and token.l_kids == 0 and token.r_kids == 0: return sent_length-1 cdef int n = 0 while token.head != 0: token += token.head n += 1 if n >= sent_length: raise RuntimeError(Errors.E039) return n