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Make small changes to Doc.to_array

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* Change type-check logic to 'hasattr' (Python type-checking is brittle)
* Small 'house style' edits, mostly making code more terse.
This commit is contained in:
Matthew Honnibal 2017-10-20 11:17:00 +02:00 committed by GitHub
parent fbccc8c87d
commit c0799430a7
1 changed files with 17 additions and 20 deletions
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37
spacy/tokens/doc.pyx
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@ -477,11 +477,11 @@ cdef class Doc:
cpdef np.ndarray to_array(self, object py_attr_ids):
"""Export given token attributes to a numpy `ndarray`.
If `attr_ids` is a sequence of M attributes, the output array will
be of shape `(N, M)`, where N is the length of the `Doc`
(in tokens). If `attr_ids` is a single attribute, the output shape will
be (N,). You can specify attributes by integer ID (e.g. spacy.attrs.LEMMA)
or string name (e.g. 'LEMMA' or 'lemma').
If `attr_ids` is a sequence of M attributes, the output array will
be of shape `(N, M)`, where N is the length of the `Doc`
(in tokens). If `attr_ids` is a single attribute, the output shape will
be (N,). You can specify attributes by integer ID (e.g. spacy.attrs.LEMMA)
or string name (e.g. 'LEMMA' or 'lemma').
Example:
from spacy import attrs
@ -499,28 +499,25 @@ cdef class Doc:
"""
cdef int i, j
cdef attr_id_t feature
cdef np.ndarray[attr_t, ndim=1] attr_ids, output_1D
cdef np.ndarray[attr_t, ndim=2] output
cdef np.ndarray[attr_t, ndim=1] output_1D
# Handle scalar/list inputs of strings/ints for py_attr_ids
if( type(py_attr_ids) is not list and type(py_attr_ids) is not tuple ):
py_attr_ids = [ py_attr_ids ]
py_attr_ids_input = []
for py_attr_id in py_attr_ids:
if( type(py_attr_id) is int ):
py_attr_ids_input.append(py_attr_id)
else:
py_attr_ids_input.append(IDS[py_attr_id.upper()])
# Make an array from the attributes --- otherwise our inner loop is Python
if not hasattr(py_attr_ids, '__iter__'):
py_attr_ids = [py_attr_ids]
# Allow strings, e.g. 'lemma' or 'LEMMA'
convert_id = lambda id_: IDS[id_.upper()] if hasattr(id_, 'upper') else id_
# Make an array from the attributes --- otherwise inner loop would be Python
# dict iteration.
cdef np.ndarray[attr_t, ndim=1] attr_ids = numpy.asarray(py_attr_ids_input, dtype=numpy.int32)
attr_ids = numpy.asarray((convert_id(id_) for id_ in 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)
if( len(attr_ids) == 1 ):
output_1D = output.reshape((self.length))
return output_1D
return output
# Handle 1d case
return output if len(attr_ids) >= 2 else output.reshape((self.length,))
def count_by(self, attr_id_t attr_id, exclude=None, PreshCounter counts=None):
"""