spaCy/spacy/tokens.pyx

449 lines
14 KiB
Cython

# 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, ORTH, NORM, LOWER, SHAPE, PREFIX, SUFFIX, LENGTH, CLUSTER
from .typedefs cimport POS, LEMMA
from .parts_of_speech import UNIV_POS_NAMES
from unidecode import unidecode
cimport numpy
import numpy
cimport cython
from cpython.mem cimport PyMem_Malloc, PyMem_Free
from libc.string cimport memcpy
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 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 Tokens:
"""
Container class for annotated text. Constructed via English.__call__ or
Tokenizer.__call__.
"""
def __cinit__(self, Vocab vocab, unicode string):
self.vocab = vocab
self._string = string
string_length = len(string)
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
self.is_tagged = False
self.is_parsed = False
self._py_tokens = []
self._tag_strings = tuple() # These will be set by the POS tagger and parser
self._dep_strings = tuple() # The strings are arbitrary and model-specific.
def __getitem__(self, object i):
"""Retrieve a token.
The Python Token objects are created lazily from internal C data, and
cached in _py_tokens
Returns:
token (Token):
"""
if i < 0:
i = self.length + i
bounds_check(i, self.length, PADDING)
return Token.cinit(self.vocab, self._string,
&self.data[i], i, self.length,
self, self._tag_strings, self._dep_strings)
def __iter__(self):
"""Iterate over the tokens.
Yields:
token (Token):
"""
for i in range(self.length):
yield Token.cinit(self.vocab, self._string,
&self.data[i], i, self.length,
self, self._tag_strings, self._dep_strings)
def __len__(self):
return self.length
def __unicode__(self):
cdef const TokenC* last = &self.data[self.length - 1]
return self._string[:last.idx + last.lex.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
self._py_tokens.append(None)
return idx + t.lex.length
@cython.boundscheck(False)
cpdef long[:,:] 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 numpy.ndarray[long, ndim=2] output
# Make an array from the attributes --- otherwise our inner loop is Python
# dict iteration.
cdef numpy.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):
"""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
cdef PreshCounter counts = PreshCounter(2 ** 8)
for i in range(self.length):
if exclude is not None and exclude(self[i]):
continue
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
@property
def sents(self):
"""This is really only a place-holder for a proper solution."""
cdef int i
cdef Tokens sent = Tokens(self.vocab, self._string[self.data[0].idx:])
#cdef attr_t period = self.vocab.strings['.']
#cdef attr_t question = self.vocab.strings['?']
#cdef attr_t exclamation = self.vocab.strings['!']
spans = []
start = None
for i in range(self.length):
if start is None:
start = i
if self.data[i].sent_end:
spans.append((start, i+1))
start = None
#if self.data[i].lex.orth == period or self.data[i].lex.orth == exclamation or \
# self.data[i].lex.orth == question:
# spans.append((start, i+1))
# start = None
if start is not None:
spans.append((start, self.length))
return spans
cdef class Token:
"""An individual token --- i.e. a word, a punctuation symbol, etc. Created
via Tokens.__getitem__ and Tokens.__iter__.
"""
def __cinit__(self, Vocab vocab, unicode string):
self.vocab = vocab
self._string = string
def __dealloc__(self):
if self._owns_c_data:
# Cast through const, if we own the data
PyMem_Free(<void*>self.c)
def __len__(self):
return self.c.lex.length
def __unicode__(self):
return self.string
cdef int take_ownership_of_c_data(self) except -1:
owned_data = <TokenC*>PyMem_Malloc(sizeof(TokenC) * self.array_len)
memcpy(owned_data, self.c, sizeof(TokenC) * self.array_len)
self.c = owned_data
self._owns_c_data = True
def nbor(self, int i=1):
return Token.cinit(self.vocab, self._string,
self.c, self.i, self.array_len,
self._seq, self._tag_strings, self._dep_strings)
property string:
def __get__(self):
cdef int next_idx = (self.c + 1).idx
if next_idx < self.c.idx:
next_idx = self.c.idx + self.c.lex.length
return self._string[self.c.idx:next_idx]
property prob:
def __get__(self):
return self.c.lex.prob
property idx:
def __get__(self):
return self.c.idx
property cluster:
def __get__(self):
return self.c.lex.cluster
property orth:
def __get__(self):
return self.c.lex.orth
property lower:
def __get__(self):
return self.c.lex.lower
property norm:
def __get__(self):
return self.c.lex.norm
property shape:
def __get__(self):
return self.c.lex.shape
property prefix:
def __get__(self):
return self.c.lex.prefix
property suffix:
def __get__(self):
return self.c.lex.suffix
property lemma:
def __get__(self):
return self.c.lemma
property pos:
def __get__(self):
return self.c.pos
property tag:
def __get__(self):
return self.c.tag
property dep:
def __get__(self):
return self.c.dep
property repvec:
def __get__(self):
return numpy.asarray(<float[:300,]> self.c.lex.repvec)
property n_lefts:
def __get__(self):
cdef int n = 0
cdef const TokenC* ptr = self.c - self.i
while ptr != self.c:
if ptr + ptr.head == self.c:
n += 1
ptr += 1
return n
property n_rights:
def __get__(self):
cdef int n = 0
cdef const TokenC* ptr = self.c + (self.array_len - self.i)
while ptr != self.c:
if ptr + ptr.head == self.c:
n += 1
ptr -= 1
return n
property lefts:
def __get__(self):
"""The leftward immediate children of the word, in the syntactic
dependency parse.
"""
cdef const TokenC* ptr = self.c - self.i
while ptr < self.c:
# If this head is still to the right of us, we can skip to it
# No token that's between this token and this head could be our
# child.
if (ptr.head >= 1) and (ptr + ptr.head) < self.c:
ptr += ptr.head
elif ptr + ptr.head == self.c:
yield Token.cinit(self.vocab, self._string,
ptr, ptr - (self.c - self.i), self.array_len,
self._seq, self._tag_strings, self._dep_strings)
ptr += 1
else:
ptr += 1
property rights:
def __get__(self):
"""The rightward immediate children of the word, in the syntactic
dependency parse."""
cdef const TokenC* ptr = (self.c - self.i) + (self.array_len - 1)
while ptr > self.c:
# If this head is still to the right of us, we can skip to it
# No token that's between this token and this head could be our
# child.
if (ptr.head < 0) and ((ptr + ptr.head) > self.c):
ptr += ptr.head
elif ptr + ptr.head == self.c:
yield Token.cinit(self.vocab, self._string,
ptr, ptr - (self.c - self.i), self.array_len,
self._seq, self._tag_strings, self._dep_strings)
ptr -= 1
else:
ptr -= 1
property head:
def __get__(self):
"""The token predicted by the parser to be the head of the current token."""
return Token.cinit(self.vocab, self._string,
self.c + self.c.head, self.i + self.c.head, self.array_len,
self._seq, self._tag_strings, self._dep_strings)
property whitespace_:
def __get__(self):
return self.string[self.c.lex.length:]
property orth_:
def __get__(self):
return self.vocab.strings[self.c.lex.orth]
property lower_:
def __get__(self):
return self.vocab.strings[self.c.lex.lower]
property norm_:
def __get__(self):
return self.vocab.strings[self.c.lex.norm]
property shape_:
def __get__(self):
return self.vocab.strings[self.c.lex.shape]
property prefix_:
def __get__(self):
return self.vocab.strings[self.c.lex.prefix]
property suffix_:
def __get__(self):
return self.vocab.strings[self.c.lex.suffix]
property lemma_:
def __get__(self):
return self.vocab.strings[self.c.lemma]
property pos_:
def __get__(self):
return _pos_id_to_string[self.c.pos]
property tag_:
def __get__(self):
return self._tag_strings[self.c.tag]
property dep_:
def __get__(self):
return self._dep_strings[self.c.dep]
_pos_id_to_string = {id_: string for string, id_ in UNIV_POS_NAMES.items()}
_parse_unset_error = """Text has not been parsed, so cannot be accessed.
Check that the parser data is installed. Run "python -m spacy.en.download" if not.
Check whether parse=False in the call to English.__call__
"""