spaCy/spacy/tests/pipeline/test_attributeruler.py

264 lines
8.2 KiB
Python

import pytest
import numpy
from spacy.training import Example
from spacy.lang.en import English
from spacy.pipeline import AttributeRuler
from spacy import util, registry
from spacy.tokens import Doc
from ..util import make_tempdir
@pytest.fixture
def nlp():
return English()
@pytest.fixture
def pattern_dicts():
return [
{
"patterns": [[{"ORTH": "a"}], [{"ORTH": "irrelevant"}]],
"attrs": {"LEMMA": "the", "MORPH": "Case=Nom|Number=Plur"},
},
# one pattern sets the lemma
{"patterns": [[{"ORTH": "test"}]], "attrs": {"LEMMA": "cat"}},
# another pattern sets the morphology
{
"patterns": [[{"ORTH": "test"}]],
"attrs": {"MORPH": "Case=Nom|Number=Sing"},
"index": 0,
},
]
@registry.misc("attribute_ruler_patterns")
def attribute_ruler_patterns():
return [
{
"patterns": [[{"ORTH": "a"}], [{"ORTH": "irrelevant"}]],
"attrs": {"LEMMA": "the", "MORPH": "Case=Nom|Number=Plur"},
},
# one pattern sets the lemma
{"patterns": [[{"ORTH": "test"}]], "attrs": {"LEMMA": "cat"}},
# another pattern sets the morphology
{
"patterns": [[{"ORTH": "test"}]],
"attrs": {"MORPH": "Case=Nom|Number=Sing"},
"index": 0,
},
]
@pytest.fixture
def tag_map():
return {
".": {"POS": "PUNCT", "PunctType": "peri"},
",": {"POS": "PUNCT", "PunctType": "comm"},
}
@pytest.fixture
def morph_rules():
return {"DT": {"the": {"POS": "DET", "LEMMA": "a", "Case": "Nom"}}}
def check_tag_map(ruler):
doc = Doc(
ruler.vocab,
words=["This", "is", "a", "test", "."],
tags=["DT", "VBZ", "DT", "NN", "."],
)
doc = ruler(doc)
for i in range(len(doc)):
if i == 4:
assert doc[i].pos_ == "PUNCT"
assert str(doc[i].morph) == "PunctType=peri"
else:
assert doc[i].pos_ == ""
assert str(doc[i].morph) == ""
def check_morph_rules(ruler):
doc = Doc(
ruler.vocab,
words=["This", "is", "the", "test", "."],
tags=["DT", "VBZ", "DT", "NN", "."],
)
doc = ruler(doc)
for i in range(len(doc)):
if i != 2:
assert doc[i].pos_ == ""
assert str(doc[i].morph) == ""
else:
assert doc[2].pos_ == "DET"
assert doc[2].lemma_ == "a"
assert str(doc[2].morph) == "Case=Nom"
def test_attributeruler_init(nlp, pattern_dicts):
a = nlp.add_pipe("attribute_ruler")
for p in pattern_dicts:
a.add(**p)
doc = nlp("This is a test.")
assert doc[2].lemma_ == "the"
assert str(doc[2].morph) == "Case=Nom|Number=Plur"
assert doc[3].lemma_ == "cat"
assert str(doc[3].morph) == "Case=Nom|Number=Sing"
assert doc.has_annotation("LEMMA")
assert doc.has_annotation("MORPH")
def test_attributeruler_init_patterns(nlp, pattern_dicts):
# initialize with patterns
ruler = nlp.add_pipe("attribute_ruler")
ruler.initialize(lambda: [], patterns=pattern_dicts)
doc = nlp("This is a test.")
assert doc[2].lemma_ == "the"
assert str(doc[2].morph) == "Case=Nom|Number=Plur"
assert doc[3].lemma_ == "cat"
assert str(doc[3].morph) == "Case=Nom|Number=Sing"
assert doc.has_annotation("LEMMA")
assert doc.has_annotation("MORPH")
nlp.remove_pipe("attribute_ruler")
# initialize with patterns from asset
nlp.config["initialize"]["components"]["attribute_ruler"] = {
"patterns": {"@misc": "attribute_ruler_patterns"}
}
nlp.add_pipe("attribute_ruler")
nlp.initialize()
doc = nlp("This is a test.")
assert doc[2].lemma_ == "the"
assert str(doc[2].morph) == "Case=Nom|Number=Plur"
assert doc[3].lemma_ == "cat"
assert str(doc[3].morph) == "Case=Nom|Number=Sing"
assert doc.has_annotation("LEMMA")
assert doc.has_annotation("MORPH")
def test_attributeruler_score(nlp, pattern_dicts):
# initialize with patterns
ruler = nlp.add_pipe("attribute_ruler")
ruler.initialize(lambda: [], patterns=pattern_dicts)
doc = nlp("This is a test.")
assert doc[2].lemma_ == "the"
assert str(doc[2].morph) == "Case=Nom|Number=Plur"
assert doc[3].lemma_ == "cat"
assert str(doc[3].morph) == "Case=Nom|Number=Sing"
doc = nlp.make_doc("This is a test.")
dev_examples = [Example.from_dict(doc, {"lemmas": ["this", "is", "a", "cat", "."]})]
scores = nlp.evaluate(dev_examples)
# "cat" is the only correct lemma
assert scores["lemma_acc"] == pytest.approx(0.2)
# the empty morphs are correct
assert scores["morph_acc"] == pytest.approx(0.6)
def test_attributeruler_rule_order(nlp):
a = AttributeRuler(nlp.vocab)
patterns = [
{"patterns": [[{"TAG": "VBZ"}]], "attrs": {"POS": "VERB"}},
{"patterns": [[{"TAG": "VBZ"}]], "attrs": {"POS": "NOUN"}},
]
a.add_patterns(patterns)
doc = Doc(
nlp.vocab,
words=["This", "is", "a", "test", "."],
tags=["DT", "VBZ", "DT", "NN", "."],
)
doc = a(doc)
assert doc[1].pos_ == "NOUN"
def test_attributeruler_tag_map(nlp, tag_map):
ruler = AttributeRuler(nlp.vocab)
ruler.load_from_tag_map(tag_map)
check_tag_map(ruler)
def test_attributeruler_tag_map_initialize(nlp, tag_map):
ruler = nlp.add_pipe("attribute_ruler")
ruler.initialize(lambda: [], tag_map=tag_map)
check_tag_map(ruler)
def test_attributeruler_morph_rules(nlp, morph_rules):
ruler = AttributeRuler(nlp.vocab)
ruler.load_from_morph_rules(morph_rules)
check_morph_rules(ruler)
def test_attributeruler_morph_rules_initialize(nlp, morph_rules):
ruler = nlp.add_pipe("attribute_ruler")
ruler.initialize(lambda: [], morph_rules=morph_rules)
check_morph_rules(ruler)
def test_attributeruler_indices(nlp):
a = nlp.add_pipe("attribute_ruler")
a.add(
[[{"ORTH": "a"}, {"ORTH": "test"}]],
{"LEMMA": "the", "MORPH": "Case=Nom|Number=Plur"},
index=0,
)
a.add(
[[{"ORTH": "This"}, {"ORTH": "is"}]],
{"LEMMA": "was", "MORPH": "Case=Nom|Number=Sing"},
index=1,
)
a.add([[{"ORTH": "a"}, {"ORTH": "test"}]], {"LEMMA": "cat"}, index=-1)
text = "This is a test."
doc = nlp(text)
for i in range(len(doc)):
if i == 1:
assert doc[i].lemma_ == "was"
assert str(doc[i].morph) == "Case=Nom|Number=Sing"
elif i == 2:
assert doc[i].lemma_ == "the"
assert str(doc[i].morph) == "Case=Nom|Number=Plur"
elif i == 3:
assert doc[i].lemma_ == "cat"
else:
assert str(doc[i].morph) == ""
# raises an error when trying to modify a token outside of the match
a.add([[{"ORTH": "a"}, {"ORTH": "test"}]], {"LEMMA": "cat"}, index=2)
with pytest.raises(ValueError):
doc = nlp(text)
# raises an error when trying to modify a token outside of the match
a.add([[{"ORTH": "a"}, {"ORTH": "test"}]], {"LEMMA": "cat"}, index=10)
with pytest.raises(ValueError):
doc = nlp(text)
def test_attributeruler_patterns_prop(nlp, pattern_dicts):
a = nlp.add_pipe("attribute_ruler")
a.add_patterns(pattern_dicts)
for p1, p2 in zip(pattern_dicts, a.patterns):
assert p1["patterns"] == p2["patterns"]
assert p1["attrs"] == p2["attrs"]
if p1.get("index"):
assert p1["index"] == p2["index"]
def test_attributeruler_serialize(nlp, pattern_dicts):
a = nlp.add_pipe("attribute_ruler")
a.add_patterns(pattern_dicts)
text = "This is a test."
attrs = ["ORTH", "LEMMA", "MORPH"]
doc = nlp(text)
# bytes roundtrip
a_reloaded = AttributeRuler(nlp.vocab).from_bytes(a.to_bytes())
assert a.to_bytes() == a_reloaded.to_bytes()
doc1 = a_reloaded(nlp.make_doc(text))
numpy.array_equal(doc.to_array(attrs), doc1.to_array(attrs))
assert a.patterns == a_reloaded.patterns
# disk roundtrip
with make_tempdir() as tmp_dir:
nlp.to_disk(tmp_dir)
nlp2 = util.load_model_from_path(tmp_dir)
doc2 = nlp2(text)
assert nlp2.get_pipe("attribute_ruler").to_bytes() == a.to_bytes()
assert numpy.array_equal(doc.to_array(attrs), doc2.to_array(attrs))
assert a.patterns == nlp2.get_pipe("attribute_ruler").patterns