Restore tests for beam parser

spacy/tests/parser/test_neural_parser.py

@@ -78,3 +78,16 @@ def test_predict_doc_beam(parser, tok2vec, model, doc):
     parser(doc, beam_width=32, beam_density=0.001)
     for word in doc:
         print(word.text, word.head, word.dep_)
+
+
+def test_update_doc_beam(parser, tok2vec, model, doc, gold):
+    parser.model = model
+    tokvecs, bp_tokvecs = tok2vec.begin_update([doc])
+    d_tokvecs = parser.update_beam(([doc], tokvecs), [gold])
+    assert d_tokvecs[0].shape == tokvecs[0].shape
+    def optimize(weights, gradient, key=None):
+        weights -= 0.001 * gradient
+    bp_tokvecs(d_tokvecs, sgd=optimize)
+    assert d_tokvecs[0].sum() == 0.
+
+

spacy/tests/parser/test_nn_beam.py

@@ -0,0 +1,87 @@
+from __future__ import unicode_literals
+import pytest
+import numpy
+from thinc.api import layerize
+
+from ...vocab import Vocab
+from ...syntax.arc_eager import ArcEager
+from ...tokens import Doc
+from ...gold import GoldParse
+from ...syntax._beam_utils import ParserBeam, update_beam
+from ...syntax.stateclass import StateClass
+
+
+@pytest.fixture
+def vocab():
+    return Vocab()
+
+@pytest.fixture
+def moves(vocab):
+    aeager = ArcEager(vocab.strings, {})
+    aeager.add_action(2, 'nsubj')
+    aeager.add_action(3, 'dobj')
+    aeager.add_action(2, 'aux')
+    return aeager
+
+
+@pytest.fixture
+def docs(vocab):
+    return [Doc(vocab, words=['Rats', 'bite', 'things'])]
+
+@pytest.fixture
+def states(docs):
+    return [StateClass(doc) for doc in docs]
+
+@pytest.fixture
+def tokvecs(docs, vector_size):
+    output = []
+    for doc in docs:
+        vec = numpy.random.uniform(-0.1, 0.1, (len(doc), vector_size))
+        output.append(numpy.asarray(vec))
+    return output
+
+
+@pytest.fixture
+def golds(docs):
+    return [GoldParse(doc) for doc in docs]
+
+
+@pytest.fixture
+def batch_size(docs):
+    return len(docs)
+
+
+@pytest.fixture
+def beam_width():
+    return 4
+
+
+@pytest.fixture
+def vector_size():
+    return 6
+
+
+@pytest.fixture
+def beam(moves, states, golds, beam_width):
+    return ParserBeam(moves, states, golds, width=beam_width)
+
+@pytest.fixture
+def scores(moves, batch_size, beam_width):
+    return [
+        numpy.asarray(
+            numpy.random.uniform(-0.1, 0.1, (batch_size, moves.n_moves)),
+            dtype='f')
+        for _ in range(batch_size)]
+
+
+def test_create_beam(beam):
+    pass
+
+
+def test_beam_advance(beam, scores):
+    beam.advance(scores)
+
+
+def test_beam_advance_too_few_scores(beam, scores):
+    with pytest.raises(IndexError):
+        beam.advance(scores[:-1])