mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-26 18:06:29 +03:00
291 lines
9.3 KiB
Cython
291 lines
9.3 KiB
Cython
# cython: profile=True
|
|
# cython: embedsignature=True
|
|
|
|
from preshed.maps cimport PreshMap
|
|
from preshed.counter cimport PreshCounter
|
|
|
|
from .vocab cimport EMPTY_LEXEME
|
|
from .typedefs cimport attr_id_t, attr_t
|
|
from .typedefs cimport LEMMA
|
|
from .typedefs cimport ID, SIC, DENSE, SHAPE, PREFIX, SUFFIX, LENGTH, CLUSTER, POS_TYPE
|
|
from .typedefs cimport POS, LEMMA
|
|
|
|
cimport cython
|
|
|
|
import numpy as np
|
|
cimport numpy as np
|
|
|
|
|
|
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
|
|
else:
|
|
return get_lex_attr(token.lex, feat_name)
|
|
|
|
|
|
cdef attr_t get_lex_attr(const Lexeme* 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 == SIC:
|
|
return lex.sic
|
|
elif feat_name == DENSE:
|
|
return lex.dense
|
|
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
|
|
elif feat_name == POS_TYPE:
|
|
return lex.pos_type
|
|
else:
|
|
return 0
|
|
|
|
|
|
cdef class Tokens:
|
|
"""Access and set annotations onto some text.
|
|
"""
|
|
def __init__(self, Vocab vocab, string_length=0):
|
|
self.vocab = vocab
|
|
if string_length >= 3:
|
|
size = int(string_length / 3.0)
|
|
else:
|
|
size = 5
|
|
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
|
|
|
|
def __getitem__(self, i):
|
|
"""Retrieve a token.
|
|
|
|
Returns:
|
|
token (Token):
|
|
"""
|
|
bounds_check(i, self.length, PADDING)
|
|
return Token(self, i)
|
|
|
|
def __iter__(self):
|
|
"""Iterate over the tokens.
|
|
|
|
Yields:
|
|
token (Token):
|
|
"""
|
|
for i in range(self.length):
|
|
yield self[i]
|
|
|
|
def __len__(self):
|
|
return self.length
|
|
|
|
cdef int push_back(self, int idx, LexemeOrToken lex_or_tok) 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
|
|
t.idx = idx
|
|
self.length += 1
|
|
return idx + t.lex.length
|
|
|
|
@cython.boundscheck(False)
|
|
cpdef np.ndarray[long, ndim=2] to_array(self, object 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
|
|
output = np.ndarray(shape=(self.length, len(attr_ids)), dtype=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):
|
|
"""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.SIC)
|
|
{12800L: 1, 11880L: 2, 7561L: 1}
|
|
>>> tokens.to_array([attrs.SIC])
|
|
array([[11880],
|
|
[11880],
|
|
[ 7561],
|
|
[12800]])
|
|
"""
|
|
cdef int i
|
|
cdef attr_t attr
|
|
cdef size_t count
|
|
|
|
cdef PreshCounter counts = PreshCounter(2 ** 8)
|
|
for i in range(self.length):
|
|
attr = get_token_attr(&self.data[i], attr_id)
|
|
counts.inc(attr, 1)
|
|
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
|
|
|
|
|
|
@cython.freelist(64)
|
|
cdef class Token:
|
|
"""An individual token.
|
|
|
|
Internally, the Token is a tuple (i, tokens) --- it delegates to the Tokens
|
|
object.
|
|
"""
|
|
def __init__(self, Tokens tokens, int i):
|
|
self._seq = tokens
|
|
self.i = i
|
|
|
|
def __unicode__(self):
|
|
cdef const TokenC* t = &self._seq.data[self.i]
|
|
cdef int end_idx = t.idx + t.lex.length
|
|
if self.i + 1 == self._seq.length:
|
|
return self.string
|
|
if end_idx == t[1].idx:
|
|
return self.string
|
|
else:
|
|
return self.string + ' '
|
|
|
|
def __len__(self):
|
|
"""The number of unicode code-points in the original string.
|
|
|
|
Returns:
|
|
length (int):
|
|
"""
|
|
return self._seq.data[self.i].lex.length
|
|
|
|
property idx:
|
|
"""The index into the original string at which the token starts.
|
|
|
|
The following is supposed to always be true:
|
|
|
|
>>> original_string[token.idx:token.idx len(token) == token.string
|
|
"""
|
|
def __get__(self):
|
|
return self._seq.data[self.i].idx
|
|
|
|
property cluster:
|
|
"""The Brown cluster ID of the word: en.wikipedia.org/wiki/Brown_clustering
|
|
|
|
Similar words have better-than-chance likelihood of having similar cluster
|
|
IDs, although the clustering is quite noisy. Cluster IDs make good features,
|
|
and help to make models slightly more robust to domain variation.
|
|
|
|
A common trick is to use only the first N bits of a cluster ID in a feature,
|
|
as the more general part of the hierarchical clustering is often more accurate
|
|
than the lower categories.
|
|
|
|
To assist in this, I encode the cluster IDs little-endian, to allow a simple
|
|
bit-mask:
|
|
|
|
>>> six_bits = cluster & (2**6 - 1)
|
|
"""
|
|
def __get__(self):
|
|
return self._seq.data[self.i].lex.cluster
|
|
|
|
property string:
|
|
"""The unicode string of the word, with no whitespace padding."""
|
|
def __get__(self):
|
|
cdef const TokenC* t = &self._seq.data[self.i]
|
|
if t.lex.sic == 0:
|
|
return ''
|
|
cdef bytes utf8string = self._seq.vocab.strings[t.lex.sic]
|
|
return utf8string.decode('utf8')
|
|
|
|
property lemma:
|
|
"""The unicode string of the word's lemma. If no part-of-speech tag is
|
|
assigned, the most common part-of-speech tag of the word is used.
|
|
"""
|
|
def __get__(self):
|
|
cdef const TokenC* t = &self._seq.data[self.i]
|
|
if t.lemma == 0:
|
|
return self.string
|
|
cdef bytes utf8string = self._seq.vocab.strings[t.lemma]
|
|
return utf8string.decode('utf8')
|
|
|
|
property dep_tag:
|
|
"""The ID integer of the word's dependency label. If no parse has been
|
|
assigned, defaults to 0.
|
|
"""
|
|
def __get__(self):
|
|
return self._seq.data[self.i].dep_tag
|
|
|
|
property pos:
|
|
"""The ID integer of the word's part-of-speech tag, from the 13-tag
|
|
Google Universal Tag Set. Constants for this tag set are available in
|
|
spacy.typedefs.
|
|
"""
|
|
def __get__(self):
|
|
return self._seq.data[self.i].pos
|
|
|
|
property fine_pos:
|
|
"""The ID integer of the word's fine-grained part-of-speech tag, as assigned
|
|
by the tagger model. Fine-grained tags include morphological information,
|
|
and other distinctions, and allow a more accurate tagger to be trained.
|
|
"""
|
|
|
|
def __get__(self):
|
|
return self._seq.data[self.i].fine_pos
|
|
|
|
property sic:
|
|
def __get__(self):
|
|
return self._seq.data[self.i].lex.sic
|
|
|
|
property head:
|
|
"""The token predicted by the parser to be the head of the current token."""
|
|
def __get__(self):
|
|
cdef const TokenC* t = &self._seq.data[self.i]
|
|
return Token(self._seq, self.i + t.head)
|