spaCy/spacy/training/alignment_array.pyx

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from typing import List
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import numpy
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from ..errors import Errors
from libc.stdint cimport int32_t
cdef class AlignmentArray:
"""AlignmentArray is similar to Thinc's Ragged with two simplfications:
indexing returns numpy arrays and this type can only be used for CPU arrays.
However, these changes make AlignmentArray more efficient for indexing in a
tight loop."""
__slots__ = []
def __init__(self, alignment: List[List[int]]):
cdef int data_len = 0
cdef int outer_len
cdef int idx
self._starts_ends = numpy.zeros(len(alignment) + 1, dtype='int32')
cdef int32_t* starts_ends_ptr = <int32_t*>self._starts_ends.data
for idx, outer in enumerate(alignment):
outer_len = len(outer)
starts_ends_ptr[idx + 1] = starts_ends_ptr[idx] + outer_len
data_len += outer_len
self._lengths = None
self._data = numpy.empty(data_len, dtype="int32")
idx = 0
cdef int32_t* data_ptr = <int32_t*>self._data.data
for outer in alignment:
for inner in outer:
data_ptr[idx] = inner
idx += 1
def __getitem__(self, idx):
starts = self._starts_ends[:-1]
ends = self._starts_ends[1:]
if isinstance(idx, int):
start = starts[idx]
end = ends[idx]
elif isinstance(idx, slice):
if not (idx.step is None or idx.step == 1):
raise ValueError(Errors.E1027)
start = starts[idx]
if len(start) == 0:
return self._data[0:0]
start = start[0]
end = ends[idx][-1]
else:
raise ValueError(Errors.E1028)
return self._data[start:end]
@property
def data(self):
return self._data
@property
def lengths(self):
if self._lengths is None:
self._lengths = self.ends - self.starts
return self._lengths
@property
def ends(self):
return self._starts_ends[1:]
@property
def starts(self):
return self._starts_ends[:-1]