mirror of https://github.com/explosion/spaCy.git
394 lines
14 KiB
Cython
394 lines
14 KiB
Cython
cimport cython
|
|
from libc.string cimport memcpy, memset
|
|
|
|
import numpy
|
|
|
|
from ..lexeme cimport EMPTY_LEXEME
|
|
from ..strings cimport slice_unicode
|
|
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 import UNIV_POS_NAMES
|
|
from ..parts_of_speech cimport CONJ, PUNCT
|
|
from ..lexeme cimport check_flag
|
|
from .spans import Span
|
|
from ..structs cimport UniStr
|
|
from .token cimport Token
|
|
|
|
|
|
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 get_lex_attr(token.lex, feat_name)
|
|
|
|
|
|
cdef attr_t get_lex_attr(const LexemeC* lex, attr_id_t feat_name) nogil:
|
|
if feat_name < (sizeof(flags_t) * 8):
|
|
return check_flag(lex, feat_name)
|
|
elif feat_name == ID:
|
|
return lex.id
|
|
elif feat_name == ORTH:
|
|
return lex.orth
|
|
elif feat_name == LOWER:
|
|
return lex.lower
|
|
elif feat_name == NORM:
|
|
return lex.norm
|
|
elif feat_name == SHAPE:
|
|
return lex.shape
|
|
elif feat_name == PREFIX:
|
|
return lex.prefix
|
|
elif feat_name == SUFFIX:
|
|
return lex.suffix
|
|
elif feat_name == LENGTH:
|
|
return lex.length
|
|
elif feat_name == CLUSTER:
|
|
return lex.cluster
|
|
else:
|
|
return 0
|
|
|
|
|
|
cdef class Doc:
|
|
"""
|
|
Container class for annotated text. Constructed via English.__call__ or
|
|
Tokenizer.__call__.
|
|
"""
|
|
def __init__(self, Vocab vocab):
|
|
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 = <TokenC*>self.mem.alloc(size + (PADDING*2), sizeof(TokenC))
|
|
cdef int i
|
|
for i in range(size + (PADDING*2)):
|
|
data_start[i].lex = &EMPTY_LEXEME
|
|
self.data = data_start + PADDING
|
|
self.max_length = size
|
|
self.length = 0
|
|
self.is_tagged = False
|
|
self.is_parsed = False
|
|
self._py_tokens = []
|
|
|
|
def __getitem__(self, object i):
|
|
"""Get a token.
|
|
|
|
Returns:
|
|
token (Token):
|
|
"""
|
|
if isinstance(i, slice):
|
|
if i.step is not None:
|
|
raise ValueError("Stepped slices not supported in Span objects."
|
|
"Try: list(doc)[start:stop:step] instead.")
|
|
return Span(self, i.start, i.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):
|
|
"""
|
|
for i in range(self.length):
|
|
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])
|
|
|
|
@property
|
|
def string(self):
|
|
return unicode(self)
|
|
|
|
@property
|
|
def ents(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
|
|
for i in range(self.length):
|
|
token = &self.data[i]
|
|
if token.ent_iob == 1:
|
|
assert start != -1
|
|
pass
|
|
elif token.ent_iob == 2:
|
|
if start != -1:
|
|
yield Span(self, start, i, label=label)
|
|
start = -1
|
|
label = 0
|
|
elif token.ent_iob == 3:
|
|
if start != -1:
|
|
yield Span(self, start, i, label=label)
|
|
start = i
|
|
label = token.ent_type
|
|
if start != -1:
|
|
yield Span(self, start, self.length, label=label)
|
|
|
|
@property
|
|
def sents(self):
|
|
"""
|
|
Yield a list of sentence Span objects, calculated from the dependency parse.
|
|
"""
|
|
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 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.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[long, ndim=2] output
|
|
# Make an array from the attributes --- otherwise our inner loop is Python
|
|
# dict iteration.
|
|
cdef np.ndarray[long, ndim=1] attr_ids = numpy.asarray(py_attr_ids)
|
|
output = numpy.ndarray(shape=(self.length, len(attr_ids)), dtype=numpy.int)
|
|
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(self.length)
|
|
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):
|
|
attr = get_token_attr(&self.data[i], attr_id)
|
|
counts.inc(attr, 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 = <TokenC*>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.
|
|
self.is_parsed = True
|
|
for i in range(self.length):
|
|
self.data[i] = parsed[i]
|
|
|
|
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 unicode string = self.string
|
|
# Get LexemeC for newly merged token
|
|
cdef UniStr new_orth_c
|
|
slice_unicode(&new_orth_c, string, start_idx, end_idx)
|
|
cdef const LexemeC* lex = self.vocab.get(self.mem, &new_orth_c)
|
|
# House the new merged token where it starts
|
|
cdef TokenC* token = &self.data[start]
|
|
# Update fields
|
|
token.lex = lex
|
|
# 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]
|
|
# Fix dependencies
|
|
# Begin by setting all the head indices to absolute token positions
|
|
# This is easier to work with for now than the offsets
|
|
for i in range(self.length):
|
|
self.data[i].head += i
|
|
# Find the head of the merged token, and its dep relation
|
|
outer_heads = {}
|
|
for i in range(start, end):
|
|
head_idx = self.data[i].head
|
|
if head_idx == i or head_idx < start or head_idx >= end:
|
|
# Don't consider "heads" which are actually dominated by a word
|
|
# in the region we're merging
|
|
gp = head_idx
|
|
while self.data[gp].head != gp:
|
|
if start <= gp < end:
|
|
break
|
|
gp = self.data[gp].head
|
|
else:
|
|
# If we have multiple words attaching to the same head,
|
|
# but with different dep labels, we're preferring the last
|
|
# occurring dep label. Shrug. What else could we do, I guess?
|
|
outer_heads[head_idx] = self.data[i].dep
|
|
|
|
token.head, token.dep = max(outer_heads.items())
|
|
# 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
|
|
# TODO: Fix left and right deps
|
|
# 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
|
|
|
|
# Return the merged Python object
|
|
return self[start]
|
|
|
|
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)
|
|
for col, attr_id in enumerate(attrs):
|
|
values = array[:, col]
|
|
if attr_id == HEAD:
|
|
for i in range(length):
|
|
tokens[i].head = values[i]
|
|
elif attr_id == TAG:
|
|
for i in range(length):
|
|
tokens[i].tag = 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]
|