# coding: utf8 # cython: infer_types=True # cython: bounds_check=False from __future__ import unicode_literals cimport cython cimport numpy as np import numpy import numpy.linalg import struct import dill from libc.string cimport memcpy, memset from libc.math cimport sqrt from .span cimport Span from .token cimport Token from .span cimport Span from .token cimport Token from .printers import parse_tree from ..lexeme cimport Lexeme, EMPTY_LEXEME from ..typedefs cimport attr_t, flags_t from ..attrs import intify_attrs from ..attrs cimport attr_id_t from ..attrs cimport ID, ORTH, NORM, LOWER, SHAPE, PREFIX, SUFFIX, LENGTH, CLUSTER from ..attrs cimport LENGTH, POS, LEMMA, TAG, DEP, HEAD, SPACY, ENT_IOB, ENT_TYPE from ..attrs cimport SENT_START from ..parts_of_speech cimport CCONJ, PUNCT, NOUN, univ_pos_t from ..util import normalize_slice from ..compat import is_config from .. import about from .. import util DEF PADDING = 5 cdef int bounds_check(int i, int length, int padding) except -1: if (i + padding) < 0: raise IndexError if (i - padding) >= length: raise IndexError 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 == 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 else: return Lexeme.get_struct_attr(token.lex, 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(u'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]) """ def __init__(self, Vocab vocab, words=None, spaces=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)` RETURNS (Doc): The newly constructed object. """ 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 = {} self._py_tokens = [] 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( "Arguments 'words' and 'spaces' should be sequences of the " "same length, or 'spaces' should be left default at None. " "spaces should be a sequence of booleans, with True meaning " "that the word owns a ' ' character following it.") 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( "orths_and_spaces expects either List(unicode) or " "List((unicode, bool)). Got bytes instance: %s" % (str(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 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. """ 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) if self._py_tokens[i] is not None: return self._py_tokens[i] else: 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. EXAMPLE: >>> for token in doc """ cdef int i for i in range(self.length): if self._py_tokens[i] is not None: yield self._py_tokens[i] else: 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. EXAMPLE: >>> len(doc) """ return self.length def __unicode__(self): return u''.join([t.text_with_ws for t in self]) def __bytes__(self): return u''.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, 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. vector (ndarray[ndim=1, dtype='float32']): A meaning representation of the span. RETURNS (Span): The newly constructed object. """ if not isinstance(label, int): label = self.vocab.strings.add(label) 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, 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. """ if 'similarity' in self.user_hooks: return self.user_hooks['similarity'](self, other) if self.vector_norm == 0 or other.vector_norm == 0: return 0.0 return numpy.dot(self.vector, other.vector) / (self.vector_norm * other.vector_norm) property has_vector: """A boolean value indicating whether a word vector is associated with the object. RETURNS (bool): Whether a word vector is associated with the object. """ def __get__(self): if 'has_vector' in self.user_hooks: return self.user_hooks['has_vector'](self) elif any(token.has_vector for token in self): return True elif self.tensor is not None: 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. """ def __get__(self): if 'vector' in self.user_hooks: return self.user_hooks['vector'](self) if self._vector is not None: return self._vector elif self.has_vector and len(self): self._vector = sum(t.vector for t in self) / len(self) return self._vector elif self.tensor is not None: self._vector = self.tensor.mean(axis=0) return self._vector else: return numpy.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. """ 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 text: """A unicode representation of the document text. RETURNS (unicode): The original verbatim text of the document. """ def __get__(self): return u''.join(t.text_with_ws for t in self) property text_with_ws: """An alias of `Doc.text`, provided for duck-type compatibility with `Span` and `Token`. RETURNS (unicode): The original verbatim text of the document. """ def __get__(self): return self.text property ents: """Iterate over the entities in the document. Yields named-entity `Span` objects, if the entity recognizer has been applied to the document. YIELDS (Span): Entities in the document. EXAMPLE: Iterate over the span to get individual Token objects, or access the label: >>> tokens = nlp(u'Mr. Best flew to New York on Saturday morning.') >>> ents = list(tokens.ents) >>> assert ents[0].label == 346 >>> assert ents[0].label_ == 'PERSON' >>> assert ents[0].orth_ == 'Best' >>> assert ents[0].text == 'Mr. Best' """ def __get__(self): cdef int i cdef const TokenC* token cdef int start = -1 cdef attr_t label = 0 output = [] for i in range(self.length): token = &self.c[i] if token.ent_iob == 1: assert start != -1 elif token.ent_iob == 2 or token.ent_iob == 0: if start != -1: output.append(Span(self, start, i, label=label)) start = -1 label = 0 elif token.ent_iob == 3: if start != -1: output.append(Span(self, start, i, label=label)) start = i label = token.ent_type if start != -1: output.append(Span(self, start, self.length, label=label)) return tuple(output) def __set__(self, ents): # TODO: # 1. Allow negative matches # 2. Ensure pre-set NERs are not over-written during statistical prediction # 3. Test basic data-driven ORTH gazetteer # 4. Test more nuanced date and currency regex cdef int i for i in range(self.length): self.c[i].ent_type = 0 # At this point we don't know whether the NER has run over the # Doc. If the ent_iob is missing, leave it missing. if self.c[i].ent_iob != 0: self.c[i].ent_iob = 2 # Means O. Non-O are set from ents. cdef attr_t ent_type cdef int start, end for ent_info in ents: if isinstance(ent_info, Span): ent_id = ent_info.ent_id ent_type = ent_info.label start = ent_info.start end = ent_info.end elif len(ent_info) == 3: ent_type, start, end = ent_info else: ent_id, ent_type, start, end = ent_info if ent_type is None or ent_type < 0: # Mark as O for i in range(start, end): self.c[i].ent_type = 0 self.c[i].ent_iob = 2 else: # Mark (inside) as I for i in range(start, end): self.c[i].ent_type = ent_type self.c[i].ent_iob = 1 # Set start as B self.c[start].ent_iob = 3 property noun_chunks: """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. """ def __get__(self): if not self.is_parsed: raise ValueError( "noun_chunks requires the dependency parse, which " "requires data to be installed. For more info, see the " "documentation: \n%s\n" % about.__docs_models__) # 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 = [] 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 sents: """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. EXAMPLE: >>> doc = nlp("This is a sentence. Here's another...") >>> assert [s.root.text for s in doc.sents] == ["is", "'s"] """ def __get__(self): if 'sents' in self.user_hooks: yield from self.user_hooks['sents'](self) return if not self.is_parsed: raise ValueError( "sentence boundary detection requires the dependency parse, which " "requires data to be installed. For more info, see the " "documentation: \n%s\n" % about.__docs_models__) cdef int i start = 0 for i in range(1, self.length): if self.c[i].sent_start: yield Span(self, start, i) start = i if start != self.length: yield Span(self, start, self.length) 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 assert t.lex.orth != 0 t.spacy = has_space self.length += 1 self._py_tokens.append(None) return t.idx + t.lex.length + t.spacy @cython.boundscheck(False) 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`. 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 # 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) output = numpy.ndarray(shape=(self.length, len(attr_ids)), dtype=numpy.uint64) for i in range(self.length): for j, feature in enumerate(attr_ids): output[i, j] = get_token_attr(&self.c[i], feature) return output def count_by(self, attr_id_t attr_id, exclude=None, PreshCounter 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. EXAMPLE: >>> from spacy import attrs >>> doc = nlp(u'apple apple orange banana') >>> tokens.count_by(attrs.ORTH) {12800L: 1, 11880L: 2, 7561L: 1} >>> tokens.to_array([attrs.ORTH]) array([[11880], [11880], [7561], [12800]]) """ cdef int i cdef attr_t attr cdef size_t count if counts is None: counts = PreshCounter() 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.inc(get_token_attr(&self.c[i], attr_id), 1) else: for i in range(self.length): if not exclude(self[i]): attr = get_token_attr(&self.c[i], attr_id) counts.inc(attr, 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): if SENT_START in attrs and HEAD in attrs: raise ValueError( "Conflicting attributes specified in doc.from_array():\n" "(HEAD, SENT_START)\n" "The HEAD attribute currently sets sentence boundaries implicitly,\n" "based on the tree structure. This means the HEAD attribute would " "potentially override the sentence boundaries set by SENT_START.\n" "See https://github.com/spacy-io/spaCy/issues/235 for details and " "workarounds, and to propose solutions.") cdef int i, col 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) attr_ids = mem.alloc(n_attrs, sizeof(attr_id_t)) for i, attr_id in enumerate(attrs): attr_ids[i] = attr_id # Now load the data for i in range(self.length): token = &self.c[i] for j in range(n_attrs): Token.set_struct_attr(token, attr_ids[j], array[i, j]) # Auxiliary loading logic for col, attr_id in enumerate(attrs): if attr_id == TAG: for i in range(length): if array[i, col] != 0: self.vocab.morphology.assign_tag(&tokens[i], array[i, col]) set_children_from_heads(self.c, self.length) self.is_parsed = bool(HEAD in attrs or DEP in attrs) self.is_tagged = bool(TAG in attrs or POS in attrs) return self def to_disk(self, path, **exclude): """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. """ with path.open('wb') as file_: file_.write(self.to_bytes(**exclude)) def from_disk(self, path, **exclude): """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. RETURNS (Doc): The modified `Doc` object. """ with path.open('rb') as file_: bytes_data = file_.read() self.from_bytes(bytes_data, **exclude) def to_bytes(self, **exclude): """Serialize, i.e. export the document contents to a binary string. RETURNS (bytes): A losslessly serialized copy of the `Doc`, including all annotations. """ array_head = [LENGTH,SPACY,TAG,LEMMA,HEAD,DEP,ENT_IOB,ENT_TYPE] 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, 'user_data': lambda: self.user_data } return util.to_bytes(serializers, exclude) def from_bytes(self, bytes_data, **exclude): """Deserialize, i.e. import the document contents from a binary string. data (bytes): The string to load from. RETURNS (Doc): Itself. """ if self.length != 0: raise ValueError("Cannot load into non-empty Doc") deserializers = { 'text': lambda b: None, 'array_head': lambda b: None, 'array_body': lambda b: None, 'sentiment': lambda b: None, 'tensor': lambda b: None, 'user_data': lambda user_data: self.user_data.update(user_data) } msg = util.from_bytes(bytes_data, deserializers, exclude) cdef attr_t[:, :] attrs cdef int i, start, end, has_space self.sentiment = msg['sentiment'] self.tensor = msg['tensor'] 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 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): The character index of the start of the slice to merge. end_idx (int): The 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 token 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 if len(args) == 3: # TODO: Warn deprecation tag, lemma, ent_type = args attributes[TAG] = tag attributes[LEMMA] = lemma attributes[ENT_TYPE] = ent_type elif not args: if "label" in attributes and 'ent_type' not in attributes: if isinstance(attributes["label"], int): attributes[ENT_TYPE] = attributes["label"] else: attributes[ENT_TYPE] = self.vocab.strings[attributes["label"]] if 'ent_type' in attributes: attributes[ENT_TYPE] = attributes['ent_type'] elif args: raise ValueError( "Doc.merge received %d non-keyword arguments. " "Expected either 3 arguments (deprecated), or 0 (use keyword arguments). " "Arguments supplied:\n%s\n" "Keyword arguments:%s\n" % (len(args), repr(args), repr(attributes))) # More deprecated attribute handling =/ if 'label' in attributes: attributes['ent_type'] = attributes.pop('label') 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 cdef Span span = self[start:end] # Get LexemeC for newly merged token new_orth = ''.join([t.text_with_ws for t in span]) if span[-1].whitespace_: new_orth = new_orth[:-len(span[-1].whitespace_)] cdef const LexemeC* lex = self.vocab.get(self.mem, new_orth) # House the new merged token where it starts cdef TokenC* token = &self.c[start] token.spacy = self.c[end-1].spacy for attr_name, attr_value in attributes.items(): if attr_name == TAG: self.vocab.morphology.assign_tag(token, attr_value) else: Token.set_struct_attr(token, attr_name, attr_value) # Begin by setting all the head indices to absolute token positions # This is easier to work with for now than the offsets # Before thinking of something simpler, beware the case where a dependency # bridges over the entity. Here the alignment of the tokens changes. span_root = span.root.i token.dep = span.root.dep # We update token.lex after keeping span root and dep, since # setting token.lex will change span.start and span.end properties # as it modifies the character offsets in the doc token.lex = lex for i in range(self.length): self.c[i].head += i # Set the head of the merged token, and its dep relation, from the Span token.head = self.c[span_root].head # Adjust deps before shrinking tokens # Tokens which point into the merged token should now point to it # Subtract the offset from all tokens which point to >= end offset = (end - start) - 1 for i in range(self.length): head_idx = self.c[i].head if start <= head_idx < end: self.c[i].head = start elif head_idx >= end: self.c[i].head -= offset # Now compress the token array for i in range(end, self.length): self.c[i - offset] = self.c[i] for i in range(self.length - offset, self.length): memset(&self.c[i], 0, sizeof(TokenC)) self.c[i].lex = &EMPTY_LEXEME self.length -= offset for i in range(self.length): # ...And, set heads back to a relative position self.c[i].head -= i # Set the left/right children, left/right edges set_children_from_heads(self.c, self.length) # Clear the cached Python objects self._py_tokens = [None] * self.length # Return the merged Python object return self[start] def print_tree(self, light=False, flat=False): """Returns the parse trees in JSON (dict) format. light (bool): Don't include lemmas or entities. flat (bool): Don't include arcs or modifiers. RETURNS (dict): Parse tree as dict. EXAMPLE: >>> doc = nlp('Bob brought Alice the pizza. Alice ate the pizza.') >>> trees = doc.print_tree() >>> trees[1] {'modifiers': [ {'modifiers': [], 'NE': 'PERSON', 'word': 'Alice', 'arc': 'nsubj', 'POS_coarse': 'PROPN', 'POS_fine': 'NNP', 'lemma': 'Alice'}, {'modifiers': [ {'modifiers': [], 'NE': '', 'word': 'the', 'arc': 'det', 'POS_coarse': 'DET', 'POS_fine': 'DT', 'lemma': 'the'}], 'NE': '', 'word': 'pizza', 'arc': 'dobj', 'POS_coarse': 'NOUN', 'POS_fine': 'NN', 'lemma': 'pizza'}, {'modifiers': [], 'NE': '', 'word': '.', 'arc': 'punct', 'POS_coarse': 'PUNCT', 'POS_fine': '.', 'lemma': '.'}], 'NE': '', 'word': 'ate', 'arc': 'ROOT', 'POS_coarse': 'VERB', 'POS_fine': 'VBD', 'lemma': 'eat'} """ return parse_tree(self, light=light, flat=flat) 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 # Set left edges for i in range(length): child = &tokens[i] head = &tokens[i + child.head] if child < head: if child.l_edge < head.l_edge: head.l_edge = child.l_edge head.l_kids += 1 # 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: if child.r_edge > head.r_edge: head.r_edge = child.r_edge head.r_kids += 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