# coding: utf8 # cython: infer_types=True # cython: bounds_check=False # cython: profile=True from __future__ import unicode_literals cimport cython cimport numpy as np from libc.string cimport memcpy, memset from libc.math cimport sqrt from collections import Counter import numpy import numpy.linalg import struct import srsly from thinc.neural.util import get_array_module, 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 ID, ORTH, NORM, LOWER, SHAPE, PREFIX, SUFFIX, CLUSTER from ..attrs cimport LENGTH, POS, LEMMA, TAG, DEP, HEAD, SPACY, ENT_IOB from ..attrs cimport ENT_TYPE, ENT_ID, ENT_KB_ID, SENT_START, IDX, attr_id_t from ..parts_of_speech cimport CCONJ, PUNCT, NOUN, univ_pos_t from ..attrs import intify_attrs, IDS from ..util import normalize_slice from ..compat import is_config, copy_reg, pickle, basestring_ from ..errors import Errors, Warnings from .. import util from .underscore import Underscore, get_ext_args from ._retokenize import Retokenizer 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 == 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) def _get_chunker(lang): try: cls = util.get_lang_class(lang) except ImportError: return None except KeyError: return None return cls.Defaults.syntax_iterators.get("noun_chunks") 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=[u'hello', u'world', u'!'], >>> spaces=[True, False, False]) DOCS: https://spacy.io/api/doc """ @classmethod def set_extension(cls, name, **kwargs): """Define a custom attribute which becomes available as `Doc._`. name (unicode): 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/doc#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="Doc")) Underscore.doc_extensions[name] = get_ext_args(**kwargs) @classmethod def get_extension(cls, name): """Look up a previously registered extension by name. name (unicode): Name of the extension. RETURNS (tuple): A `(default, method, getter, setter)` tuple. DOCS: https://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 (unicode): Name of the extension. RETURNS (bool): Whether the extension has been registered. DOCS: https://spacy.io/api/doc#has_extension """ return name in Underscore.doc_extensions @classmethod def remove_extension(cls, name): """Remove a previously registered extension. name (unicode): Name of the extension. RETURNS (tuple): A `(default, method, getter, setter)` tuple of the removed extension. DOCS: https://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, orths_and_spaces=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 (list or None): A list of unicode strings to add to the document as words. If `None`, defaults to empty list. spaces (list or None): 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. RETURNS (Doc): The newly constructed object. DOCS: https://spacy.io/api/doc#init """ self.vocab = vocab size = 20 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.is_tagged = False self.is_parsed = False 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 = _get_chunker(self.vocab.lang) cdef unicode orth cdef bint has_space if orths_and_spaces is None and words is not None: if spaces is None: spaces = [True] * len(words) elif len(spaces) != len(words): raise ValueError(Errors.E027) orths_and_spaces = zip(words, spaces) if orths_and_spaces is not None: for orth_space in orths_and_spaces: if isinstance(orth_space, unicode): orth = orth_space has_space = True elif isinstance(orth_space, bytes): raise ValueError(Errors.E028.format(value=orth_space)) else: orth, has_space = orth_space # Note that we pass self.mem here --- we have ownership, if LexemeC # must be created. self.push_back( self.vocab.get(self.mem, orth), has_space) # Tough to decide on policy for this. Is an empty doc tagged and parsed? # There's no information we'd like to add to it, so I guess so? if self.length == 0: self.is_tagged = True self.is_parsed = True @property def _(self): """Custom extension attributes registered via `set_extension`.""" return Underscore(Underscore.doc_extensions, self) @property def is_sentenced(self): """Check if the document has sentence boundaries assigned. This is defined as having at least one of the following: a) An entry "sents" in doc.user_hooks"; b) Doc.is_parsed is set to True; c) At least one token other than the first where sent_start is not None. """ if "sents" in self.user_hooks: return True if self.is_parsed: return True if len(self) < 2: return True for i in range(1, self.length): if self.c[i].sent_start == -1 or self.c[i].sent_start == 1: return True return False @property def is_nered(self): """Check if the document has named entities set. Will return True if *any* of the tokens has a named entity tag set (even if the others are unknown values), or if the document is empty. """ if len(self) == 0: return True for i in range(self.length): if self.c[i].ent_iob != 0: return True return False 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://spacy.io/api/doc#getitem """ if isinstance(i, slice): start, stop = 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://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://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): if is_config(python3=True): return self.__unicode__() return self.__bytes__() 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): """Create a `Span` object from the slice `doc.text[start : end]`. doc (Doc): The parent document. 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. DOCS: https://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) cdef int start = token_by_start(self.c, self.length, start_idx) if start == -1: return None cdef int end = token_by_end(self.c, self.length, end_idx) if end == -1: return None # 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://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) return xp.dot(vector, other.vector) / (self.vector_norm * other.vector_norm) @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/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://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://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 (unicode): 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 (unicode): 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://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 = ["%s|%s" % (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: 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)) return tuple(output) def __set__(self, ents): # TODO: # 1. Test basic data-driven ORTH gazetteer # 2. Test more nuanced date and currency regex tokens_in_ents = {} cdef attr_t entity_type cdef attr_t kb_id cdef int ent_start, ent_end for ent_info in ents: entity_type, kb_id, ent_start, ent_end = get_entity_info(ent_info) for token_index in range(ent_start, ent_end): if token_index in tokens_in_ents.keys(): raise ValueError(Errors.E103.format( span1=(tokens_in_ents[token_index][0], tokens_in_ents[token_index][1], self.vocab.strings[tokens_in_ents[token_index][2]]), span2=(ent_start, ent_end, self.vocab.strings[entity_type]))) tokens_in_ents[token_index] = (ent_start, ent_end, entity_type, kb_id) cdef int i for i in range(self.length): # default values entity_type = 0 kb_id = 0 # Set ent_iob to Missing (0) bij default unless this token was nered before ent_iob = 0 if self.c[i].ent_iob != 0: ent_iob = 2 # overwrite if the token was part of a specified entity if i in tokens_in_ents.keys(): ent_start, ent_end, entity_type, kb_id = tokens_in_ents[i] if entity_type is None or entity_type <= 0: # Blocking this token from being overwritten by downstream NER ent_iob = 3 elif ent_start == i: # Marking the start of an entity ent_iob = 3 else: # Marking the inside of an entity ent_iob = 1 self.c[i].ent_type = entity_type self.c[i].ent_kb_id = kb_id self.c[i].ent_iob = ent_iob @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://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. To improve accuracy on informal texts, spaCy calculates sentence boundaries from the syntactic dependency parse. If the parser is disabled, the `sents` iterator will be unavailable. YIELDS (Span): Sentences in the document. DOCS: https://spacy.io/api/doc#sents """ if not self.is_sentenced: 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 (unicode): 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 == 0: # Flip these to false when we see the first token. self.is_tagged = False self.is_parsed = False 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, basestring_): # 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' py_attr_ids = [(IDS[id_.upper()] if hasattr(id_, "upper") else id_) for id_ in py_attr_ids] # 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://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): 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 cdef void set_parse(self, const TokenC* parsed) nogil: # TODO: This method is fairly misleading atm. It's used by Parser # to actually apply the parse calculated. Need to rethink this. # Probably we should use from_array? self.is_parsed = True for i in range(self.length): self.c[i] = parsed[i] 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://spacy.io/api/doc#from_array """ # Handle scalar/list inputs of strings/ints for py_attr_ids # See also #3064 if isinstance(attrs, basestring_): # 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)) if SENT_START in attrs and HEAD in attrs: raise ValueError(Errors.E032) cdef int i, col, abs_head_index cdef attr_id_t attr_id cdef TokenC* tokens = self.c cdef int length = len(array) # 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)) # 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 = numpy.int32(array[i, col]) + 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=numpy.int32(array[i, col]))) # Do TAG first. This lets subsequent loop override stuff like POS, LEMMA if TAG in attrs: col = attrs.index(TAG) for i in range(length): if array[i, col] != 0: self.vocab.morphology.assign_tag(&tokens[i], array[i, col]) # Now load the data for i in range(length): token = &self.c[i] for j in range(n_attrs): if attr_ids[j] != TAG: Token.set_struct_attr(token, attr_ids[j], array[i, j]) # Set flags self.is_parsed = bool(self.is_parsed or HEAD in attrs) self.is_tagged = bool(self.is_tagged or TAG in attrs or POS in attrs) # If document is parsed, set children if self.is_parsed: set_children_from_heads(self.c, length) return self 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://spacy.io/api/doc#get_lca_matrix """ return numpy.asarray(_get_lca_matrix(self, 0, len(self))) def to_disk(self, path, **kwargs): """Save the current state to a directory. path (unicode or 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 (list): String names of serialization fields to exclude. DOCS: https://spacy.io/api/doc#to_disk """ path = util.ensure_path(path) with path.open("wb") as file_: file_.write(self.to_bytes(**kwargs)) def from_disk(self, path, **kwargs): """Loads state from a directory. Modifies the object in place and returns it. path (unicode or 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://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, **kwargs) def to_bytes(self, exclude=tuple(), **kwargs): """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://spacy.io/api/doc#to_bytes """ array_head = [LENGTH, SPACY, LEMMA, ENT_IOB, ENT_TYPE, ENT_ID, NORM, ENT_KB_ID] if self.is_tagged: array_head.extend([TAG, POS]) # If doc parsed add head and dep attribute if self.is_parsed: array_head.extend([HEAD, DEP]) # Otherwise add sent_start else: array_head.append(SENT_START) strings = set() for token in self: strings.add(token.tag_) strings.add(token.lemma_) strings.add(token.dep_) strings.add(token.ent_type_) strings.add(token.ent_kb_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), } for key in kwargs: if key in serializers or key in ("user_data", "user_data_keys", "user_data_values"): raise ValueError(Errors.E128.format(arg=key)) 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_bytes(serializers, exclude) def from_bytes(self, bytes_data, exclude=tuple(), **kwargs): """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://spacy.io/api/doc#from_bytes """ 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, } for key in kwargs: if key in deserializers or key in ("user_data",): raise ValueError(Errors.E128.format(arg=key)) msg = util.from_bytes(bytes_data, deserializers, exclude) # 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) 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://spacy.io/api/doc#retokenize USAGE: https://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 merge(self, int start_idx, int end_idx, *args, **attributes): """Retokenize the document, such that the span at `doc.text[start_idx : end_idx]` is merged into a single token. If `start_idx` and `end_idx `do not mark start and end token boundaries, the document remains unchanged. start_idx (int): Character index of the start of the slice to merge. end_idx (int): Character index after the end of the slice to merge. **attributes: Attributes to assign to the merged token. By default, attributes are inherited from the syntactic root of the span. RETURNS (Token): The newly merged token, or `None` if the start and end indices did not fall at token boundaries. """ cdef unicode tag, lemma, ent_type warnings.warn(Warnings.W013.format(obj="Doc"), DeprecationWarning) # TODO: ENT_KB_ID ? if len(args) == 3: warnings.warn(Warnings.W003, DeprecationWarning) tag, lemma, ent_type = args attributes[TAG] = tag attributes[LEMMA] = lemma attributes[ENT_TYPE] = ent_type elif not args: fix_attributes(self, attributes) elif args: raise ValueError(Errors.E034.format(n_args=len(args), args=repr(args), kwargs=repr(attributes))) remove_label_if_necessary(attributes) attributes = intify_attrs(attributes, strings_map=self.vocab.strings) cdef int start = token_by_start(self.c, self.length, start_idx) if start == -1: return None cdef int end = token_by_end(self.c, self.length, end_idx) if end == -1: return None # Currently we have the token index, we want the range-end index end += 1 with self.retokenize() as retokenizer: retokenizer.merge(self[start:end], attrs=attributes) return self[start] def print_tree(self, light=False, flat=False): raise ValueError(Errors.E105) def to_json(self, underscore=None): """Convert a Doc to JSON. The format it produces will be the new format for the `spacy train` command (not implemented yet). 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://spacy.io/api/doc#to_json """ data = {"text": self.text} if self.is_nered: data["ents"] = [{"start": ent.start_char, "end": ent.end_char, "label": ent.label_} for ent in self.ents] if self.is_sentenced: 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"] = [] for token in self: token_data = {"id": token.i, "start": token.idx, "end": token.idx + len(token)} if self.is_tagged: token_data["pos"] = token.pos_ token_data["tag"] = token.tag_ if self.is_parsed: 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 cdef int token_by_start(const TokenC* tokens, int length, int start_char) except -2: cdef int i for i in range(length): if 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: cdef int i for i in range(length): if tokens[i].idx + tokens[i].lex.length == end_char: return i else: return -1 cdef int set_children_from_heads(TokenC* tokens, int length) except -1: 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(length): 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, length, loop_count) if loop_count > 10: warnings.warn(Warnings.W026) break loop_count += 1 # Set sentence starts for i in range(length): if tokens[i].head == 0 and tokens[i].dep != 0: tokens[tokens[i].l_edge].sent_start = True cdef int _set_lr_kids_and_edges(TokenC* tokens, int length, 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(length): child = &tokens[i] head = &tokens[i + child.head] if child < head and loop_count == 0: 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(length-1, -1, -1): child = &tokens[i] head = &tokens[i + child.head] if child > head and loop_count == 0: 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(length): if tokens[i].head == 0 and tokens[i].dep != 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(length): if (i > 0 and i in sent_starts) or i == length - 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 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