Remove beam test

spacy/tests/parser/test_nn_beam.py

@@ -1,100 +0,0 @@
-import pytest
-import numpy
-from spacy.vocab import Vocab
-from spacy.language import Language
-from spacy.pipeline.defaults import default_parser
-from spacy.pipeline import DependencyParser
-from spacy.syntax.arc_eager import ArcEager
-from spacy.tokens import Doc
-from spacy.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 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, density=0.0)
-
-
-@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)
-    ]
-
-
-# All tests below are skipped after removing Beam stuff during the Example/GoldParse refactor
-@pytest.mark.skip
-def test_create_beam(beam):
-    pass
-
-
-@pytest.mark.skip
-def test_beam_advance(beam, scores):
-    beam.advance(scores)
-
-
-@pytest.mark.skip
-def test_beam_advance_too_few_scores(beam, scores):
-    with pytest.raises(IndexError):
-        beam.advance(scores[:-1])
-
-
-@pytest.mark.skip
-def test_beam_parse():
-    nlp = Language()
-    config = {"learn_tokens": False, "min_action_freq": 30, "beam_width":  1, "beam_update_prob": 1.0}
-    nlp.add_pipe(DependencyParser(nlp.vocab, default_parser(), **config), name="parser")
-    nlp.parser.add_label("nsubj")
-    nlp.parser.begin_training([], token_vector_width=8, hidden_width=8)
-    doc = nlp.make_doc("Australia is a country")
-    nlp.parser(doc, beam_width=2)