Merge branch 'develop' of https://github.com/explosion/spaCy into develop

spacy/_ml.py

@@ -311,7 +311,7 @@ def link_vectors_to_models(vocab):
 
 def Tok2Vec(width, embed_size, **kwargs):
     pretrained_dims = kwargs.get('pretrained_dims', 0)
-    cnn_maxout_pieces = kwargs.get('cnn_maxout_pieces', 3)
+    cnn_maxout_pieces = kwargs.get('cnn_maxout_pieces', 2)
     cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
     with Model.define_operators({'>>': chain, '|': concatenate, '**': clone, '+': add,
                                  '*': reapply}):

spacy/cli/train.py

@@ -68,6 +68,8 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=10, n_sents=0,
     if not isinstance(meta, dict):
         prints("Expected dict but got: {}".format(type(meta)),
                title="Not a valid meta.json format", exits=1)
+    meta.setdefault('lang', lang)
+    meta.setdefault('name', 'unnamed')
 
     pipeline = ['tagger', 'parser', 'ner']
     if no_tagger and 'tagger' in pipeline: pipeline.remove('tagger')
@@ -89,6 +91,8 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=10, n_sents=0,
 
     lang_class = util.get_lang_class(lang)
     nlp = lang_class()
+    meta['pipeline'] = pipeline
+    nlp.meta.update(meta)
     if vectors:
         util.load_model(vectors, vocab=nlp.vocab)
     for name in pipeline:

spacy/lemmatizer.py

@@ -24,6 +24,8 @@ class Lemmatizer(object):
             univ_pos = 'adj'
         elif univ_pos == PUNCT:
             univ_pos = 'punct'
+        else:
+            return set([string.lower()])
         # See Issue #435 for example of where this logic is requied.
         if self.is_base_form(univ_pos, morphology):
             return set([string.lower()])

spacy/morphology.pxd

@@ -35,6 +35,8 @@ cdef class Morphology:
     cdef RichTagC* rich_tags
     cdef PreshMapArray _cache
 
+    cdef int assign_untagged(self, TokenC* token) except -1
+
     cdef int assign_tag(self, TokenC* token, tag) except -1
 
     cdef int assign_tag_id(self, TokenC* token, int tag_id) except -1

spacy/morphology.pyx

@@ -42,7 +42,7 @@ cdef class Morphology:
         self.tag_names = tuple(sorted(tag_map.keys()))
         self.reverse_index = {}
 
-        self.rich_tags = <RichTagC*>self.mem.alloc(self.n_tags, sizeof(RichTagC))
+        self.rich_tags = <RichTagC*>self.mem.alloc(self.n_tags+1, sizeof(RichTagC))
         for i, (tag_str, attrs) in enumerate(sorted(tag_map.items())):
             self.tag_map[tag_str] = dict(attrs)
             attrs = _normalize_props(attrs)
@@ -52,6 +52,10 @@ cdef class Morphology:
             self.rich_tags[i].morph = 0
             self.rich_tags[i].pos = attrs[POS]
             self.reverse_index[self.rich_tags[i].name] = i
+        # Add a 'null' tag, which we can reference when assign morphology to
+        # untagged tokens.
+        self.rich_tags[self.n_tags].id = self.n_tags
+ 
         self._cache = PreshMapArray(self.n_tags)
         self.exc = {}
         if exc is not None:
@@ -62,6 +66,10 @@ cdef class Morphology:
         return (Morphology, (self.strings, self.tag_map, self.lemmatizer,
                              self.exc), None, None)
 
+    cdef int assign_untagged(self, TokenC* token) except -1:
+        '''Set morphological attributes on a token without a POS tag.'''
+        token.lemma = self.lemmatize(0, token.lex.orth, {})
+
     cdef int assign_tag(self, TokenC* token, tag) except -1:
         if isinstance(tag, basestring):
             tag = self.strings.add(tag)
@@ -72,7 +80,7 @@ cdef class Morphology:
             token.tag = tag
 
     cdef int assign_tag_id(self, TokenC* token, int tag_id) except -1:
-        if tag_id >= self.n_tags:
+        if tag_id > self.n_tags:
             raise ValueError("Unknown tag ID: %s" % tag_id)
         # TODO: It's pretty arbitrary to put this logic here. I guess the justification
         # is that this is where the specific word and the tag interact. Still,
@@ -151,8 +159,6 @@ cdef class Morphology:
         cdef unicode py_string = self.strings[orth]
         if self.lemmatizer is None:
             return self.strings.add(py_string.lower())
-        if univ_pos not in (NOUN, VERB, ADJ, PUNCT):
-            return self.strings.add(py_string.lower())
         cdef set lemma_strings
         cdef unicode lemma_string
         lemma_strings = self.lemmatizer(py_string, univ_pos, morphology)

spacy/syntax/nn_parser.pyx

@@ -239,13 +239,13 @@ cdef class Parser:
     """
     @classmethod
     def Model(cls, nr_class, **cfg):
-        depth = util.env_opt('parser_hidden_depth', cfg.get('hidden_depth', 0))
-        token_vector_width = util.env_opt('token_vector_width', cfg.get('token_vector_width', 128))
-        hidden_width = util.env_opt('hidden_width', cfg.get('hidden_width', 128))
-        parser_maxout_pieces = util.env_opt('parser_maxout_pieces', cfg.get('maxout_pieces', 3))
+        depth = util.env_opt('parser_hidden_depth', cfg.get('hidden_depth', 1))
+        token_vector_width = util.env_opt('token_vector_width', cfg.get('token_vector_width', 64))
+        hidden_width = util.env_opt('hidden_width', cfg.get('hidden_width', 64))
+        parser_maxout_pieces = util.env_opt('parser_maxout_pieces', cfg.get('maxout_pieces', 2))
         embed_size = util.env_opt('embed_size', cfg.get('embed_size', 7000))
-        hist_size = util.env_opt('history_feats', cfg.get('hist_size', 0))
-        hist_width = util.env_opt('history_width', cfg.get('hist_width', 0))
+        hist_size = util.env_opt('history_feats', cfg.get('hist_size', 4))
+        hist_width = util.env_opt('history_width', cfg.get('hist_width', 16))
         if hist_size >= 1 and depth == 0:
             raise ValueError("Inconsistent hyper-params: "
                 "history_feats >= 1 but parser_hidden_depth==0")
@@ -800,11 +800,20 @@ cdef class Parser:
         if self.model not in (True, False, None) and resized:
             # Weights are stored in (nr_out, nr_in) format, so we're basically
             # just adding rows here.
-            smaller = self.model[-1]._layers[-1]
-            larger = Affine(self.moves.n_moves, smaller.nI)
-            copy_array(larger.W[:smaller.nO], smaller.W)
-            copy_array(larger.b[:smaller.nO], smaller.b)
-            self.model[-1]._layers[-1] = larger
+            if self.model[-1].is_noop:
+                smaller = self.model[1]
+                dims = dict(self.model[1]._dims)
+                dims['nO'] = self.moves.n_moves
+                larger = self.model[1].__class__(**dims)
+                copy_array(larger.W[:, :smaller.nO], smaller.W)
+                copy_array(larger.b[:smaller.nO], smaller.b)
+                self.model = (self.model[0], larger, self.model[2])
+            else:
+                smaller = self.model[-1]._layers[-1]
+                larger = Affine(self.moves.n_moves, smaller.nI)
+                copy_array(larger.W[:smaller.nO], smaller.W)
+                copy_array(larger.b[:smaller.nO], smaller.b)
+                self.model[-1]._layers[-1] = larger
 
     def begin_training(self, gold_tuples, pipeline=None, **cfg):
         if 'model' in cfg:

spacy/tests/doc/test_creation.py

@@ -0,0 +1,37 @@
+'''Test Doc sets up tokens correctly.'''
+from __future__ import unicode_literals
+import pytest
+
+from ...vocab import Vocab
+from ...tokens.doc import Doc
+from ...lemmatizerlookup import Lemmatizer
+
+
+@pytest.fixture
+def lemmatizer():
+    return Lemmatizer({'dogs': 'dog', 'boxen': 'box', 'mice': 'mouse'})
+
+
+@pytest.fixture
+def vocab(lemmatizer):
+    return Vocab(lemmatizer=lemmatizer)
+
+
+def test_empty_doc(vocab):
+    doc = Doc(vocab)
+    assert len(doc) == 0
+
+
+def test_single_word(vocab):
+    doc = Doc(vocab, words=['a'])
+    assert doc.text == 'a '
+    doc = Doc(vocab, words=['a'], spaces=[False])
+    assert doc.text == 'a'
+
+
+def test_lookup_lemmatization(vocab):
+    doc = Doc(vocab, words=['dogs', 'dogses'])
+    assert doc[0].text == 'dogs'
+    assert doc[0].lemma_ == 'dog'
+    assert doc[1].text == 'dogses'
+    assert doc[1].lemma_ == 'dogses'

spacy/tests/parser/test_add_label.py

@@ -22,14 +22,14 @@ def vocab():
 @pytest.fixture
 def parser(vocab):
     parser = NeuralDependencyParser(vocab)
-    parser.cfg['token_vector_width'] = 4
-    parser.cfg['hidden_width'] = 6
+    parser.cfg['token_vector_width'] = 8
+    parser.cfg['hidden_width'] = 30
     parser.cfg['hist_size'] = 0
     parser.add_label('left')
     parser.begin_training([], **parser.cfg)
     sgd = Adam(NumpyOps(), 0.001)
 
-    for i in range(30):
+    for i in range(10):
         losses = {}
         doc = Doc(vocab, words=['a', 'b', 'c', 'd'])
         gold = GoldParse(doc, heads=[1, 1, 3, 3],
@@ -37,6 +37,8 @@ def parser(vocab):
         parser.update([doc], [gold], sgd=sgd, losses=losses)
     return parser
 
+def test_init_parser(parser):
+    pass
 
 def test_add_label(parser):
     doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])

spacy/tests/parser/test_preset_sbd.py

@@ -64,7 +64,7 @@ def test_sents_1_3(parser):
     doc[1].sent_start = True
     doc[3].sent_start = True
     doc = parser(doc)
-    assert len(list(doc.sents)) == 4
+    assert len(list(doc.sents)) >= 3
     doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
     doc[1].sent_start = True
     doc[2].sent_start = False

spacy/tokens/doc.pyx

@@ -512,6 +512,8 @@ cdef class Doc:
         assert t.lex.orth != 0
         t.spacy = has_space
         self.length += 1
+        # Set morphological attributes, e.g. by lemma, if possible
+        self.vocab.morphology.assign_untagged(t)
         self._py_tokens.append(None)
         return t.idx + t.lex.length + t.spacy