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removing redundant tests, cf #8106

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svlandeg 2022-01-20 16:48:47 +01:00
parent 394862b0f4
commit 68e3d464b6
2 changed files with 1 additions and 36 deletions
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35
spacy/tests/parser/test_ner.py
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@ -132,41 +132,6 @@ def test_negative_sample_key_is_in_config(vocab, entity_types):
assert tsys.cfg["neg_key"] == "non_entities"
@pytest.mark.filterwarnings("ignore::UserWarning")
def test_get_oracle_moves_negative_entities(tsys, doc, entity_annots):
entity_annots = [(s, e, "!" + label) for s, e, label in entity_annots]
example = Example.from_dict(doc, {"entities": entity_annots})
ex_dict = example.to_dict()
for i, tag in enumerate(ex_dict["doc_annotation"]["entities"]):
if tag == "L-!GPE":
ex_dict["doc_annotation"]["entities"][i] = "-"
example = Example.from_dict(doc, ex_dict)
act_classes = tsys.get_oracle_sequence(example)
names = [tsys.get_class_name(act) for act in act_classes]
assert names
def test_get_oracle_moves_negative_entities2(tsys, vocab):
doc = Doc(vocab, words=["A", "B", "C", "D"])
entity_annots = ["B-!PERSON", "L-!PERSON", "B-!PERSON", "L-!PERSON"]
example = Example.from_dict(doc, {"entities": entity_annots})
act_classes = tsys.get_oracle_sequence(example)
names = [tsys.get_class_name(act) for act in act_classes]
assert names
@pytest.mark.skip(reason="Maybe outdated? Unsure")
def test_get_oracle_moves_negative_O(tsys, vocab):
doc = Doc(vocab, words=["A", "B", "C", "D"])
entity_annots = ["O", "!O", "O", "!O"]
example = Example.from_dict(doc, {"entities": entity_annots})
act_classes = tsys.get_oracle_sequence(example)
names = [tsys.get_class_name(act) for act in act_classes]
assert names
# We can't easily represent this on a Doc object. Not sure what the best solution
# would be, but I don't think it's an important use case?
@pytest.mark.skip(reason="No longer supported")

2
spacy/tests/parser/test_parse.py
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@ -268,7 +268,7 @@ def test_overfitting_IO(pipe_name):
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
for dep in annotations.get("deps", []):
parser.add_label(dep)
#train_examples = train_examples[:1]
# train_examples = train_examples[:1]
optimizer = nlp.initialize()
# run overfitting
for i in range(200):