Add Span.to_array method

spacy/tokens/span.pxd

@@ -15,5 +15,5 @@ cdef class Span:
     cdef public _vector
     cdef public _vector_norm
 
-
     cpdef int _recalculate_indices(self) except -1
+    cpdef np.ndarray to_array(self, object features)

spacy/tokens/span.pyx

@@ -7,7 +7,7 @@ import numpy
 import numpy.linalg
 from libc.math cimport sqrt
 
-from .doc cimport token_by_start, token_by_end
+from .doc cimport token_by_start, token_by_end, get_token_attr
 from ..structs cimport TokenC, LexemeC
 from ..typedefs cimport flags_t, attr_t, hash_t
 from ..attrs cimport attr_id_t
@@ -135,6 +135,28 @@ cdef class Span:
             return 0.0
         return numpy.dot(self.vector, other.vector) / (self.vector_norm * other.vector_norm)
 
+    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 document.
+        The values will be 32-bit integers.
+
+        attr_ids (list[int]): A list of attribute ID ints.
+        RETURNS (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.uint64)
+        output = numpy.ndarray(shape=(self.length, len(attr_ids)), dtype=numpy.uint64)
+        for i in range(self.start, self.end):
+            for j, feature in enumerate(attr_ids):
+                output[i, j] = get_token_attr(&self.doc.c[i], feature)
+        return output
+
     cpdef int _recalculate_indices(self) except -1:
         if self.end > self.doc.length \
         or self.doc.c[self.start].idx != self.start_char \