Merge pull request #1401 from explosion/feature/add-parser-action

💫 Allow labels to be added to pre-trained parser and NER modes

spacy/_ml.py

@@ -743,5 +743,3 @@ def concatenate_lists(*layers, **kwargs): # pragma: no cover
         return ys, concatenate_lists_bwd
     model = wrap(concatenate_lists_fwd, concat)
     return model
-
-

spacy/syntax/nn_parser.pyx

@@ -51,7 +51,7 @@ from .._ml import Tok2Vec, doc2feats, rebatch, fine_tune
 from .._ml import Residual, drop_layer, flatten
 from .._ml import link_vectors_to_models
 from .._ml import HistoryFeatures
-from ..compat import json_dumps
+from ..compat import json_dumps, copy_array
 
 from . import _parse_features
 from ._parse_features cimport CONTEXT_SIZE
@@ -781,12 +781,22 @@ cdef class Parser:
             self.moves.finalize_doc(doc)
 
     def add_label(self, label):
+        resized = False
         for action in self.moves.action_types:
             added = self.moves.add_action(action, label)
             if added:
                 # Important that the labels be stored as a list! We need the
                 # order, or the model goes out of synch
                 self.cfg.setdefault('extra_labels', []).append(label)
+                resized = True
+        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
 
     def begin_training(self, gold_tuples, pipeline=None, **cfg):
         if 'model' in cfg:

spacy/tests/parser/test_add_label.py

@@ -0,0 +1,68 @@
+'''Test the ability to add a label to a (potentially trained) parsing model.'''
+from __future__ import unicode_literals
+import pytest
+import numpy.random
+from thinc.neural.optimizers import Adam
+from thinc.neural.ops import NumpyOps
+
+from ...attrs import NORM
+from ...gold import GoldParse
+from ...vocab import Vocab
+from ...tokens import Doc
+from ...pipeline import NeuralDependencyParser
+
+numpy.random.seed(0)
+
+
+@pytest.fixture
+def vocab():
+    return Vocab(lex_attr_getters={NORM: lambda s: s})
+
+
+@pytest.fixture
+def parser(vocab):
+    parser = NeuralDependencyParser(vocab)
+    parser.cfg['token_vector_width'] = 4
+    parser.cfg['hidden_width'] = 6
+    parser.cfg['hist_size'] = 0
+    parser.add_label('left')
+    parser.begin_training([], **parser.cfg)
+    sgd = Adam(NumpyOps(), 0.001)
+
+    for i in range(30):
+        losses = {}
+        doc = Doc(vocab, words=['a', 'b', 'c', 'd'])
+        gold = GoldParse(doc, heads=[1, 1, 3, 3],
+                deps=['left', 'ROOT', 'left', 'ROOT'])
+        parser.update([doc], [gold], sgd=sgd, losses=losses)
+    return parser
+
+
+def test_add_label(parser):
+    doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
+    doc = parser(doc)
+    assert doc[0].head.i == 1
+    assert doc[0].dep_ == 'left'
+    assert doc[1].head.i == 1
+    assert doc[2].head.i == 3
+    assert doc[2].head.i == 3
+    parser.add_label('right')
+    doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
+    doc = parser(doc)
+    assert doc[0].head.i == 1
+    assert doc[0].dep_ == 'left'
+    assert doc[1].head.i == 1
+    assert doc[2].head.i == 3
+    assert doc[2].head.i == 3
+    sgd = Adam(NumpyOps(), 0.001)
+    for i in range(10):
+        losses = {}
+        doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
+        gold = GoldParse(doc, heads=[1, 1, 3, 3],
+                deps=['right', 'ROOT', 'left', 'ROOT'])
+        parser.update([doc], [gold], sgd=sgd, losses=losses)
+    doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
+    doc = parser(doc)
+    assert doc[0].dep_ == 'right'
+    assert doc[2].dep_ == 'left'
+