Support single value for attribute list in doc.to_array

.github/contributors/ramananbalakrishnan.md

@@ -0,0 +1,106 @@
+# spaCy contributor agreement
+
+This spaCy Contributor Agreement (**"SCA"**) is based on the
+[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
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+## Contributor Details
+
+| Field                          | Entry                |
+|------------------------------- | -------------------- |
+| Name                           | Ramanan Balakrishnan |
+| Company name (if applicable)   |                      |
+| Title or role (if applicable)  |                      |
+| Date                           | 2017-10-18           |
+| GitHub username                | ramananbalakrishnan  |
+| Website (optional)             |                      |

spacy/tests/doc/test_array.py

@@ -17,6 +17,26 @@ def test_doc_array_attr_of_token(en_tokenizer, en_vocab):
     assert feats_array[0][0] != feats_array[0][1]
 
 
+def test_doc_stringy_array_attr_of_token(en_tokenizer, en_vocab):
+    text = "An example sentence"
+    tokens = en_tokenizer(text)
+    example = tokens.vocab["example"]
+    assert example.orth != example.shape
+    feats_array = tokens.to_array((ORTH, SHAPE))
+    feats_array_stringy = tokens.to_array(("ORTH", "SHAPE"))
+    assert feats_array_stringy[0][0] == feats_array[0][0]
+    assert feats_array_stringy[0][1] == feats_array[0][1]
+
+
+def test_doc_scalar_attr_of_token(en_tokenizer, en_vocab):
+    text = "An example sentence"
+    tokens = en_tokenizer(text)
+    example = tokens.vocab["example"]
+    assert example.orth != example.shape
+    feats_array = tokens.to_array(ORTH)
+    assert feats_array.shape == (3,)
+
+
 def test_doc_array_tag(en_tokenizer):
     text = "A nice sentence."
     pos = ['DET', 'ADJ', 'NOUN', 'PUNCT']

spacy/tokens/doc.pyx

@@ -16,6 +16,7 @@ from .token cimport Token
 from ..lexeme cimport Lexeme
 from ..lexeme cimport EMPTY_LEXEME
 from ..typedefs cimport attr_t, flags_t
+from ..attrs import IDS
 from ..attrs cimport attr_id_t
 from ..attrs cimport ID, ORTH, NORM, LOWER, SHAPE, PREFIX, SUFFIX, LENGTH, CLUSTER
 from ..attrs cimport POS, LEMMA, TAG, DEP, HEAD, SPACY, ENT_IOB, ENT_TYPE
@@ -474,10 +475,13 @@ cdef class Doc:
 
     @cython.boundscheck(False)
     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.
+        """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').
 
         Example:
             from spacy import attrs
@@ -486,24 +490,33 @@ cdef class Doc:
             np_array = doc.to_array([attrs.LOWER, attrs.POS, attrs.ENT_TYPE, attrs.IS_ALPHA])
 
         Arguments:
-            attr_ids (list[int]): A list of attribute ID ints.
+            attr_ids (list[]): A list of attributes (int IDs or string names).
 
         Returns:
             feat_array (numpy.ndarray[long, ndim=2]):
               A feature matrix, with one row per word, and one column per attribute
-              indicated in the input attr_ids.
+              indicated in the input `attr_ids`.
         """
         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
-        # Make an array from the attributes --- otherwise our inner loop is Python
+        # Handle scalar/list inputs of strings/ints for py_attr_ids
+        if not hasattr(py_attr_ids, '__iter__'):
+            py_attr_ids = [py_attr_ids]
+
+        # Allow strings, e.g. 'lemma' or 'LEMMA'
+        py_attr_ids = [(IDS[id_.upper()] if hasattr(id_, 'upper') else id_)
+                       for id_ in py_attr_ids]
+        # 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, dtype=numpy.int32)
+        attr_ids = numpy.asarray(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)
-        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):
         """

website/docs/api/doc.jade

@@ -176,29 +176,41 @@ p
     +tag method
 
 p
-    |  Export the document annotations to a numpy array of shape #[code N*M]
-    |  where #[code N] is the length of the document and #[code M] is the number
-    |  of attribute IDs to export. The values will be 32-bit integers.
+    |  Export given token attributes to a numpy #[code ndarray].
+    |  If #[code attr_ids] is a sequence of #[code M] attributes,
+    |  the output array will  be of shape #[code (N, M)], where #[code N]
+    |  is the length of the #[code Doc] (in tokens). If #[code attr_ids] is
+    |  a single attribute, the output shape will be #[code (N,)]. You can
+    |  specify attributes by integer ID (e.g. #[code spacy.attrs.LEMMA])
+    |  or string name (e.g. 'LEMMA' or 'lemma'). The values will be 32-bit
+    |  integers.
 
 +aside-code("Example").
     from spacy import attrs
     doc = nlp(text)
     # All strings mapped to integers, for easy export to numpy
     np_array = doc.to_array([attrs.LOWER, attrs.POS,
                              attrs.ENT_TYPE, attrs.IS_ALPHA])
+    np_array = doc.to_array("POS")
 
 +table(["Name", "Type", "Description"])
     +row
         +cell #[code attr_ids]
-        +cell ints
-        +cell A list of attribute ID ints.
+        +cell int or string
+        +cell
+            | A list of attributes (int IDs or string names) or
+            | a single attribute (int ID or string name)
 
     +footrow
         +cell return
-        +cell #[code numpy.ndarray[ndim=2, dtype='int32']]
+        +cell
+            | #[code numpy.ndarray[ndim=2, dtype='int32']] or
+            | #[code numpy.ndarray[ndim=1, dtype='int32']]
         +cell
             |  The exported attributes as a 2D numpy array, with one row per
-            |  token and one column per attribute.
+            |  token and one column per attribute (when #[code attr_ids] is a
+            |  list), or as a 1D numpy array, with one item per attribute (when
+            |  #[code attr_ids] is a single value).
 
 +h(2, "count_by") Doc.count_by
     +tag method