cimport cython from libc.string cimport memcpy, memset from libc.stdint cimport uint32_t import numpy import numpy.linalg import struct cimport numpy as np import math from ..lexeme cimport Lexeme from ..lexeme cimport EMPTY_LEXEME from ..typedefs cimport attr_t, flags_t from ..attrs cimport attr_id_t from ..attrs cimport ID, ORTH, NORM, LOWER, SHAPE, PREFIX, SUFFIX, LENGTH, CLUSTER from ..attrs cimport POS, LEMMA, TAG, DEP, HEAD, SPACY, ENT_IOB, ENT_TYPE from ..parts_of_speech cimport CONJ, PUNCT, NOUN from ..parts_of_speech cimport univ_pos_t from ..lexeme cimport Lexeme from .spans cimport Span from .token cimport Token from ..serialize.bits cimport BitArray from ..util import normalize_slice 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 == 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) cdef class Doc: """ Container class for annotated text. Constructed via English.__call__ or Tokenizer.__call__. """ def __init__(self, Vocab vocab, orths_and_spaces=None): 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.data = data_start + PADDING self.max_length = size self.length = 0 self.is_tagged = False self.is_parsed = False self._py_tokens = [] self._vector = None def __getitem__(self, object i): """Get a Token or a Span from the Doc. Returns: token (Token) or span (Span): """ 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.data[i], i, self) def __iter__(self): """Iterate over the tokens. Yields: token (Token): """ 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.data[i], i, self) def __len__(self): return self.length def __unicode__(self): return u''.join([t.string for t in self]) def __str__(self): return u''.join([t.string for t in self]) def __repr__(self): return u''.join([t.string for t in self]) def 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 repvec: def __get__(self): return self.vector property vector: def __get__(self): if self._vector is None: self._vector = sum(t.vector for t in self) / len(self) return self._vector def __set__(self, value): self._vector = value property vector_norm: def __get__(self): cdef float value if self._vector_norm is None: self._vector_norm = 1e-20 for value in self.vector: self._vector_norm += value * value self._vector_norm = math.sqrt(self._vector_norm) return self._vector_norm def __set__(self, value): self._vector_norm = value @property def string(self): return u''.join([t.string for t in self]) @property def text_wth_ws(self): return u''.join([t.text_with_ws for t in self]) @property def text(self): return u' '.join(t.text for t in self) property ents: def __get__(self): """Yields named-entity Span objects. Iterate over the span to get individual Token objects, or access the label: >>> from spacy.en import English >>> nlp = English() >>> tokens = nlp(u'Mr. Best flew to New York on Saturday morning.') >>> ents = list(tokens.ents) >>> ents[0].label, ents[0].label_, ''.join(t.orth_ for t in ents[0]) (112504, u'PERSON', u'Best ') """ cdef int i cdef const TokenC* token cdef int start = -1 cdef int label = 0 output = [] for i in range(self.length): token = &self.data[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.data[i].ent_type = 0 self.data[i].ent_iob = 0 cdef attr_t ent_type cdef int start, end for ent_type, start, end in ents: if ent_type is None or ent_type < 0: # Mark as O for i in range(start, end): self.data[i].ent_type = 0 self.data[i].ent_iob = 2 else: # Mark (inside) as I for i in range(start, end): self.data[i].ent_type = ent_type self.data[i].ent_iob = 1 # Set start as B self.data[start].ent_iob = 3 @property def noun_chunks(self): """Yield spans for base noun phrases.""" if not self.is_parsed: raise ValueError( "noun_chunks requires the dependency parse, which " "requires data to be installed. If you haven't done so, run: " "\npython -m spacy.en.download all\n" "to install the data") cdef const TokenC* word labels = ['nsubj', 'dobj', 'nsubjpass', 'pcomp', 'pobj', 'attr', 'conj'] np_deps = [self.vocab.strings[label] for label in labels] np_label = self.vocab.strings['NP'] for i in range(self.length): word = &self.data[i] if word.pos == NOUN and word.dep in np_deps: yield Span(self, word.l_edge, i+1, label=np_label) @property def sents(self): """ Yield a list of sentence Span objects, calculated from the dependency parse. """ if not self.is_parsed: raise ValueError( "sentence boundary detection requires the dependency parse, which " "requires data to be installed. If you haven't done so, run: " "\npython -m spacy.en.download all\n" "to install the data") cdef int i start = 0 for i in range(1, self.length): if self.data[i].sent_start: yield Span(self, start, i) start = i yield Span(self, start, self.length) cdef int push_back(self, LexemeOrToken lex_or_tok, bint has_space) except -1: if self.length == self.max_length: self._realloc(self.length * 2) cdef TokenC* t = &self.data[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 sentence. Arguments: attr_ids (list[int]): A list of attribute ID ints. Returns: feat_array (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.int32) output = numpy.ndarray(shape=(self.length, len(attr_ids)), dtype=numpy.int32) for i in range(self.length): for j, feature in enumerate(attr_ids): output[i, j] = get_token_attr(&self.data[i], feature) return output def count_by(self, attr_id_t attr_id, exclude=None, PreshCounter counts=None): """Produce a dict of {attribute (int): count (ints)} frequencies, keyed by the values of the given attribute ID. >>> from spacy.en import English, attrs >>> nlp = English() >>> tokens = 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.data[i], attr_id), 1) else: for i in range(self.length): if not exclude(self[i]): attr = get_token_attr(&self.data[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.data - PADDING data_start = self.mem.realloc(data_start, n * sizeof(TokenC)) self.data = data_start + PADDING cdef int i for i in range(self.length, self.max_length + PADDING): self.data[i].lex = &EMPTY_LEXEME cdef int set_parse(self, const TokenC* parsed) except -1: # 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.data[i] = parsed[i] assert self.data[i].l_edge <= i assert self.data[i].r_edge >= i def from_array(self, attrs, array): cdef int i, col cdef attr_id_t attr_id cdef TokenC* tokens = self.data cdef int length = len(array) cdef attr_t[:] values for col, attr_id in enumerate(attrs): values = array[:, col] if attr_id == HEAD: for i in range(length): tokens[i].head = values[i] if values[i] >= 1: tokens[i + values[i]].l_kids += 1 elif values[i] < 0: tokens[i + values[i]].r_kids += 1 elif attr_id == TAG: for i in range(length): tokens[i].tag = values[i] elif attr_id == POS: for i in range(length): tokens[i].pos = values[i] elif attr_id == DEP: for i in range(length): tokens[i].dep = values[i] elif attr_id == ENT_IOB: for i in range(length): tokens[i].ent_iob = values[i] elif attr_id == ENT_TYPE: for i in range(length): tokens[i].ent_type = values[i] set_children_from_heads(self.data, self.length) return self def to_bytes(self): byte_string = self.vocab.serializer.pack(self) cdef uint32_t length = len(byte_string) return struct.pack('I', length) + byte_string def from_bytes(self, data): self.vocab.serializer.unpack_into(data[4:], self) return self @staticmethod def read_bytes(file_): keep_reading = True while keep_reading: try: n_bytes_str = file_.read(4) if len(n_bytes_str) < 4: break n_bytes = struct.unpack('I', n_bytes_str)[0] data = file_.read(n_bytes) except StopIteration: keep_reading = False yield n_bytes_str + data # This function is terrible --- need to fix this. def merge(self, int start_idx, int end_idx, unicode tag, unicode lemma, unicode ent_type): """Merge a multi-word expression into a single token. Currently experimental; API is likely to change.""" cdef int i cdef int start = -1 cdef int end = -1 for i in range(self.length): if self.data[i].idx == start_idx: start = i if (self.data[i].idx + self.data[i].lex.length) == end_idx: if start == -1: return None end = i + 1 break else: return None 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.data[start] # Update fields token.lex = lex token.spacy = self.data[end-1].spacy # What to do about morphology?? # TODO: token.morph = ??? token.tag = self.vocab.strings[tag] token.lemma = self.vocab.strings[lemma] if ent_type == 'O': token.ent_iob = 2 token.ent_type = 0 else: token.ent_iob = 3 token.ent_type = self.vocab.strings[ent_type] # 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 for i in range(self.length): self.data[i].head += i # Set the head of the merged token, and its dep relation, from the Span token.head = self.data[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.data[i].head if start <= head_idx < end: self.data[i].head = start elif head_idx >= end: self.data[i].head -= offset # Now compress the token array for i in range(end, self.length): self.data[i - offset] = self.data[i] for i in range(self.length - offset, self.length): memset(&self.data[i], 0, sizeof(TokenC)) self.data[i].lex = &EMPTY_LEXEME self.length -= offset for i in range(self.length): # ...And, set heads back to a relative position self.data[i].head -= i # Set the left/right children, left/right edges set_children_from_heads(self.data, self.length) # Clear the cached Python objects self._py_tokens = [None] * self.length # Return the merged Python object return self[start] 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