# cython: embedsignature=True
from libc.string cimport memset
from preshed.maps cimport PreshMap
from preshed.counter cimport PreshCounter
from .strings cimport slice_unicode
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, TAG, DEP
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 .senses import STRINGS as SENSE_STRINGS
from unidecode import unidecode
# Compiler crashes on memory view coercion without this. Should report bug.
from cython.view cimport array as cvarray
cimport numpy as np
np.import_array()
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
elif feat_name == TAG:
return token.tag
elif feat_name == DEP:
return token.dep
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.data[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):
"""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)
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)
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]
@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
cdef Tokens sent = Tokens(self.vocab, self._string[self.data[0].idx:])
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, 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 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):
"""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
cdef int set_parse(self, const TokenC* parsed) except -1:
# TODO: This method is fairly misleading atm. It's used by GreedyParser
# to actually apply the parse calculated. Need to rethink this.
self._py_tokens = [None] * self.length
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
# Get LexemeC for newly merged token
cdef UniStr new_orth_c
slice_unicode(&new_orth_c, self._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
# Clear cached Python objects
self._py_tokens = [None] * self.length
# Return the merged Python object
return self[start]
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
cpdef bint check_flag(self, attr_id_t flag_id) except -1:
return check_flag(self.c.lex, flag_id)
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)
property string:
def __get__(self):
if (self.i+1) == self._seq.length:
return self._string[self.c.idx:]
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 sense:
def __get__(self):
return self.c.sense
property repvec:
def __get__(self):
cdef int length = self.vocab.repvec_length
repvec_view = <float[:length,]>self.c.lex.repvec
return numpy.asarray(repvec_view)
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)
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)
tokens = []
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:
tokens.append(Token.cinit(self.vocab, self._string,
ptr, ptr - (self.c - self.i), self.array_len,
self._seq))
ptr -= 1
else:
ptr -= 1
tokens.reverse()
for t in tokens:
yield t
property children:
def __get__(self):
yield from self.lefts
yield from self.rights
property subtree:
def __get__(self):
for word in self.lefts:
yield from word.subtree
yield self
for word in self.rights:
yield from word.subtree
property left_edge:
def __get__(self):
return Token.cinit(self.vocab, self._string,
(self.c - self.i) + self.c.l_edge, self.c.l_edge,
self.array_len, self._seq)
property right_edge:
def __get__(self):
return Token.cinit(self.vocab, self._string,
(self.c - self.i) + self.c.r_edge, self.c.r_edge,
self.array_len, self._seq)
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)
property conjuncts:
def __get__(self):
"""Get a list of conjoined words"""
cdef Token word
conjs = []
if self.c.pos != CONJ and self.c.pos != PUNCT:
seen_conj = False
for word in reversed(list(self.lefts)):
if word.c.pos == CONJ:
seen_conj = True
elif seen_conj and word.c.pos == self.c.pos:
conjs.append(word)
conjs.reverse()
conjs.append(self)
if seen_conj:
return conjs
elif self is not self.head and self in self.head.conjuncts:
return self.head.conjuncts
else:
return []
property ent_type:
def __get__(self):
return self.c.ent_type
property ent_iob:
def __get__(self):
return self.c.ent_iob
property ent_type_:
def __get__(self):
return self.vocab.strings[self.c.ent_type]
property ent_iob_:
def __get__(self):
iob_strings = ('', 'I', 'O', 'B')
return iob_strings[self.c.ent_iob]
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.vocab.strings[self.c.tag]
property dep_:
def __get__(self):
return self.vocab.strings[self.c.dep]
property sense_:
def __get__(self):
return SENSE_STRINGS[self.c.sense]
_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__
"""