Improve efficiency of Doc.to_array

spacy/tokens/doc.pyx

@@ -1,6 +1,7 @@
 # coding: utf8
 # cython: infer_types=True
 # cython: bounds_check=False
+# cython: profile=True
 from __future__ import unicode_literals
 
 cimport cython
@@ -567,7 +568,6 @@ cdef class Doc:
         """
         cdef int i, j
         cdef attr_id_t feature
-        cdef np.ndarray[attr_t, ndim=1] attr_ids
         cdef np.ndarray[attr_t, ndim=2] output
         # Handle scalar/list inputs of strings/ints for py_attr_ids
         if not hasattr(py_attr_ids, '__iter__') \
@@ -579,12 +579,17 @@ cdef class Doc:
                        for id_ in py_attr_ids]
         # Make an array from the attributes --- otherwise our inner loop is
         # Python dict iteration.
-        attr_ids = numpy.asarray(py_attr_ids, dtype=numpy.uint64)
+        cdef np.ndarray attr_ids = numpy.asarray(py_attr_ids, dtype='i')
         output = numpy.ndarray(shape=(self.length, len(attr_ids)),
                                dtype=numpy.uint64)
+        c_output = <attr_t*>output.data
+        c_attr_ids = <attr_id_t*>attr_ids.data
+        cdef TokenC* token
+        cdef int nr_attr = attr_ids.shape[0]
         for i in range(self.length):
-            for j, feature in enumerate(attr_ids):
-                output[i, j] = get_token_attr(&self.c[i], feature)
+            token = &self.c[i]
+            for j in range(nr_attr):
+                c_output[i*nr_attr + j] = get_token_attr(token, c_attr_ids[j])
         # Handle 1d case
         return output if len(attr_ids) >= 2 else output.reshape((self.length,))