spaCy/spacy/tokens.pyx

# cython: profile=True
from preshed.maps cimport PreshMap
from preshed.counter cimport PreshCounter

from .lexeme cimport *
cimport cython

import numpy as np
cimport numpy as np

POS = 0
ENTITY = 0

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 class Tokens:
    """A sequence of references to Lexeme objects.

    The Tokens class provides fast and memory-efficient access to lexical features,
    and can efficiently export the data to a numpy array.

    >>> from spacy.en import EN
    >>> tokens = EN.tokenize('An example sentence.')
    """
    def __init__(self, Language lang, string_length=0):
        self.lang = lang
        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):
        bounds_check(i, self.length, PADDING)
        return Token(self.lang, i, self.data[i].idx, self.data[i].pos,
                     self.data[i].lemma, self.data[i].lex[0])

    def __iter__(self):
        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
        self.length += 1
        return idx + t.lex.length

    @cython.boundscheck(False)
    cpdef np.ndarray[long, ndim=2] get_array(self, list 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_attr(self.data[i].lex, feature)
        return output

    def count_by(self, attr_id_t attr_id):
        cdef int i
        cdef attr_t attr
        cdef size_t count

        cdef PreshCounter counts = PreshCounter(2 ** 8)
        for i in range(self.length):
            if attr_id == LEMMA:
                attr = self.data[i].lemma
            else:
                attr = get_attr(self.data[i].lex, 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:
    def __init__(self, Language lang, int i, int idx,
                 int pos, int lemma, dict lex):
        self.lang = lang
        self.idx = idx
        self.pos = pos
        self.i = i
        self.id = lex['id']

        self.lemma = lemma
        
        self.cluster = lex['cluster']
        self.length = lex['length']
        self.postype = lex['pos_type']
        self.sensetype = 0
        self.sic = lex['sic']
        self.norm = lex['dense']
        self.shape = lex['shape']
        self.suffix = lex['suffix']
        self.prefix = lex['prefix']

        self.prob = lex['prob']
        self.flags = lex['flags']

    property string:
        def __get__(self):
            if self.sic == 0:
                return ''
            cdef bytes utf8string = self.lang.lexicon.strings[self.sic]
            return utf8string.decode('utf8')

    property lemma:
        def __get__(self):
            if self.lemma == 0:
                return self.string
            cdef bytes utf8string = self.lang.lexicon.strings[self.lemma]
            return utf8string.decode('utf8')

    property pos:
        def __get__(self):
            return self.lang.pos_tagger.tag_names[self.pos]