spaCy/spacy/tokens/doc.pyx
2016-01-18 16:03:19 +01:00

576 lines
20 KiB
Cython

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
import six
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 .span 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 = <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
data_start[i].l_edge = i
data_start[i].r_edge = i
self.c = 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.c[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.c[i], i, self)
def __len__(self):
return self.length
def __unicode__(self):
return u''.join([t.text_with_ws for t in self])
def __bytes__(self):
return u''.join([t.text_with_ws for t in self]).encode('utf-8')
def __str__(self):
if six.PY3:
return self.__unicode__()
return self.__bytes__()
def __repr__(self):
return self.__str__()
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 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 self.text_with_ws
@property
def text_with_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.c[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.c[i].ent_type = 0
self.c[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.c[i].ent_type = 0
self.c[i].ent_iob = 2
else:
# Mark (inside) as I
for i in range(start, end):
self.c[i].ent_type = ent_type
self.c[i].ent_iob = 1
# Set start as B
self.c[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', 'root']
np_deps = [self.vocab.strings[label] for label in labels]
conj = self.vocab.strings['conj']
np_label = self.vocab.strings['NP']
for i in range(self.length):
word = &self.c[i]
if word.pos == NOUN and word.dep in np_deps:
yield Span(self, word.l_edge, i+1, label=np_label)
elif word.pos == NOUN and word.dep == conj:
head = word+word.head
while head.dep == conj and head.head < 0:
head += head.head
# If the head is an NP, and we're coordinated to it, we're an NP
if head.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.c[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.c[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.c[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.c[i], attr_id), 1)
else:
for i in range(self.length):
if not exclude(self[i]):
attr = get_token_attr(&self.c[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.c - PADDING
data_start = <TokenC*>self.mem.realloc(data_start, n * sizeof(TokenC))
self.c = data_start + PADDING
cdef int i
for i in range(self.length, self.max_length + PADDING):
self.c[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.c[i] = parsed[i]
assert self.c[i].l_edge <= i
assert self.c[i].r_edge >= i
def from_array(self, attrs, array):
cdef int i, col
cdef attr_id_t attr_id
cdef TokenC* tokens = self.c
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
if not self.is_parsed and tokens[i].head != 0:
self.is_parsed = True
elif attr_id == TAG:
for i in range(length):
self.vocab.morphology.assign_tag(&tokens[i],
self.vocab.morphology.reverse_index[values[i]])
if not self.is_tagged and tokens[i].tag != 0:
self.is_tagged = True
elif attr_id == POS:
for i in range(length):
tokens[i].pos = <univ_pos_t>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]
else:
raise ValueError("Unknown attribute ID: %d" % attr_id)
set_children_from_heads(self.c, 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
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 start = token_by_start(self.c, self.length, start_idx)
if start == -1:
return None
cdef int end = token_by_end(self.c, self.length, end_idx)
if end == -1:
return None
# Currently we have the token index, we want the range-end index
end += 1
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.c[start]
token.spacy = self.c[end-1].spacy
if tag in self.vocab.morphology.tag_map:
self.vocab.morphology.assign_tag(token, tag)
else:
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
# We update token.lex after keeping span root and dep, since
# setting token.lex will change span.start and span.end properties
# as it modifies the character offsets in the doc
token.lex = lex
for i in range(self.length):
self.c[i].head += i
# Set the head of the merged token, and its dep relation, from the Span
token.head = self.c[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.c[i].head
if start <= head_idx < end:
self.c[i].head = start
elif head_idx >= end:
self.c[i].head -= offset
# Now compress the token array
for i in range(end, self.length):
self.c[i - offset] = self.c[i]
for i in range(self.length - offset, self.length):
memset(&self.c[i], 0, sizeof(TokenC))
self.c[i].lex = &EMPTY_LEXEME
self.length -= offset
for i in range(self.length):
# ...And, set heads back to a relative position
self.c[i].head -= i
# Set the left/right children, left/right edges
set_children_from_heads(self.c, self.length)
# Clear the cached Python objects
self._py_tokens = [None] * self.length
# Return the merged Python object
return self[start]
cdef int token_by_start(const TokenC* tokens, int length, int start_char) except -2:
cdef int i
for i in range(length):
if tokens[i].idx == start_char:
return i
else:
return -1
cdef int token_by_end(const TokenC* tokens, int length, int end_char) except -2:
cdef int i
for i in range(length):
if tokens[i].idx + tokens[i].lex.length == end_char:
return i
else:
return -1
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
# Set sentence starts
for i in range(length):
if tokens[i].head == 0 and tokens[i].dep != 0:
tokens[tokens[i].l_edge].sent_start = True