Support strings 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).
+The SCA applies to any contribution that you make to any product or project
+managed by us (the **"project"**), and sets out the intellectual property rights
+you grant to us in the contributed materials. The term **"us"** shall mean
+[ExplosionAI UG (haftungsbeschränkt)](https://explosion.ai/legal). The term
+**"you"** shall mean the person or entity identified below.
+
+If you agree to be bound by these terms, fill in the information requested
+below and include the filled-in version with your first pull request, under the
+folder [`.github/contributors/`](/.github/contributors/). The name of the file
+should be your GitHub username, with the extension `.md`. For example, the user
+example_user would create the file `.github/contributors/example_user.md`.
+
+Read this agreement carefully before signing. These terms and conditions
+constitute a binding legal agreement.
+
+## Contributor Agreement
+
+1. The term "contribution" or "contributed materials" means any source code,
+object code, patch, tool, sample, graphic, specification, manual,
+documentation, or any other material posted or submitted by you to the project.
+
+2. With respect to any worldwide copyrights, or copyright applications and
+registrations, in your contribution:
+
+    * you hereby assign to us joint ownership, and to the extent that such
+    assignment is or becomes invalid, ineffective or unenforceable, you hereby
+    grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
+    royalty-free, unrestricted license to exercise all rights under those
+    copyrights. This includes, at our option, the right to sublicense these same
+    rights to third parties through multiple levels of sublicensees or other
+    licensing arrangements;
+
+    * you agree that each of us can do all things in relation to your
+    contribution as if each of us were the sole owners, and if one of us makes
+    a derivative work of your contribution, the one who makes the derivative
+    work (or has it made will be the sole owner of that derivative work;
+
+    * you agree that you will not assert any moral rights in your contribution
+    against us, our licensees or transferees;
+
+    * you agree that we may register a copyright in your contribution and
+    exercise all ownership rights associated with it; and
+
+    * you agree that neither of us has any duty to consult with, obtain the
+    consent of, pay or render an accounting to the other for any use or
+    distribution of your contribution.
+
+3. With respect to any patents you own, or that you can license without payment
+to any third party, you hereby grant to us a perpetual, irrevocable,
+non-exclusive, worldwide, no-charge, royalty-free license to:
+
+    * make, have made, use, sell, offer to sell, import, and otherwise transfer
+    your contribution in whole or in part, alone or in combination with or
+    included in any product, work or materials arising out of the project to
+    which your contribution was submitted, and
+
+    * at our option, to sublicense these same rights to third parties through
+    multiple levels of sublicensees or other licensing arrangements.
+
+4. Except as set out above, you keep all right, title, and interest in your
+contribution. The rights that you grant to us under these terms are effective
+on the date you first submitted a contribution to us, even if your submission
+took place before the date you sign these terms.
+
+5. You covenant, represent, warrant and agree that:
+
+    * Each contribution that you submit is and shall be an original work of
+    authorship and you can legally grant the rights set out in this SCA;
+
+    * to the best of your knowledge, each contribution will not violate any
+    third party's copyrights, trademarks, patents, or other intellectual
+    property rights; and
+
+    * each contribution shall be in compliance with U.S. export control laws and
+    other applicable export and import laws. You agree to notify us if you
+    become aware of any circumstance which would make any of the foregoing
+    representations inaccurate in any respect. We may publicly disclose your 
+    participation in the project, including the fact that you have signed the SCA.
+
+6. This SCA is governed by the laws of the State of California and applicable
+U.S. Federal law. Any choice of law rules will not apply.
+
+7. Please place an “x” on one of the applicable statement below. Please do NOT
+mark both statements:
+
+    * [x] I am signing on behalf of myself as an individual and no other person
+    or entity, including my employer, has or will have rights with respect to my
+    contributions.
+
+    * [ ] I am signing on behalf of my employer or a legal entity and I have the
+    actual authority to contractually bind that entity.
+
+## 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
@@ -486,22 +490,29 @@ 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=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
         # dict iteration.
-        if( type(py_attr_ids) is not list ):
-            py_attr_ids = [ py_attr_ids ]
-        cdef np.ndarray[attr_t, ndim=1] attr_ids = numpy.asarray(py_attr_ids, dtype=numpy.int32)
+        cdef np.ndarray[attr_t, ndim=1] attr_ids = numpy.asarray(py_attr_ids_input, 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):