mirror of
https://github.com/explosion/spaCy.git
synced 2025-11-04 09:57:26 +03:00
e2b70df012
* Use isort with Black profile * isort all the things * Fix import cycles as a result of import sorting * Add DOCBIN_ALL_ATTRS type definition * Add isort to requirements * Remove isort from build dependencies check * Typo
334 lines
10 KiB
Python
334 lines
10 KiB
Python
import pickle
|
|
|
|
import hypothesis.strategies as st
|
|
import pytest
|
|
from hypothesis import given
|
|
|
|
from spacy import util
|
|
from spacy.lang.en import English
|
|
from spacy.language import Language
|
|
from spacy.pipeline._edit_tree_internals.edit_trees import EditTrees
|
|
from spacy.strings import StringStore
|
|
from spacy.training import Example
|
|
from spacy.util import make_tempdir
|
|
|
|
TRAIN_DATA = [
|
|
("She likes green eggs", {"lemmas": ["she", "like", "green", "egg"]}),
|
|
("Eat blue ham", {"lemmas": ["eat", "blue", "ham"]}),
|
|
]
|
|
|
|
PARTIAL_DATA = [
|
|
# partial annotation
|
|
("She likes green eggs", {"lemmas": ["", "like", "green", ""]}),
|
|
# misaligned partial annotation
|
|
(
|
|
"He hates green eggs",
|
|
{
|
|
"words": ["He", "hat", "es", "green", "eggs"],
|
|
"lemmas": ["", "hat", "e", "green", ""],
|
|
},
|
|
),
|
|
]
|
|
|
|
|
|
def test_initialize_examples():
|
|
nlp = Language()
|
|
lemmatizer = nlp.add_pipe("trainable_lemmatizer")
|
|
train_examples = []
|
|
for t in TRAIN_DATA:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
|
|
# you shouldn't really call this more than once, but for testing it should be fine
|
|
nlp.initialize(get_examples=lambda: train_examples)
|
|
with pytest.raises(TypeError):
|
|
nlp.initialize(get_examples=lambda: None)
|
|
with pytest.raises(TypeError):
|
|
nlp.initialize(get_examples=lambda: train_examples[0])
|
|
with pytest.raises(TypeError):
|
|
nlp.initialize(get_examples=lambda: [])
|
|
with pytest.raises(TypeError):
|
|
nlp.initialize(get_examples=train_examples)
|
|
|
|
|
|
def test_initialize_from_labels():
|
|
nlp = Language()
|
|
lemmatizer = nlp.add_pipe("trainable_lemmatizer")
|
|
lemmatizer.min_tree_freq = 1
|
|
train_examples = []
|
|
for t in TRAIN_DATA:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
|
|
nlp.initialize(get_examples=lambda: train_examples)
|
|
|
|
nlp2 = Language()
|
|
lemmatizer2 = nlp2.add_pipe("trainable_lemmatizer")
|
|
lemmatizer2.initialize(
|
|
# We want to check that the strings in replacement nodes are
|
|
# added to the string store. Avoid that they get added through
|
|
# the examples.
|
|
get_examples=lambda: train_examples[:1],
|
|
labels=lemmatizer.label_data,
|
|
)
|
|
assert lemmatizer2.tree2label == {1: 0, 3: 1, 4: 2, 6: 3}
|
|
assert lemmatizer2.label_data == {
|
|
"trees": [
|
|
{"orig": "S", "subst": "s"},
|
|
{
|
|
"prefix_len": 1,
|
|
"suffix_len": 0,
|
|
"prefix_tree": 0,
|
|
"suffix_tree": 4294967295,
|
|
},
|
|
{"orig": "s", "subst": ""},
|
|
{
|
|
"prefix_len": 0,
|
|
"suffix_len": 1,
|
|
"prefix_tree": 4294967295,
|
|
"suffix_tree": 2,
|
|
},
|
|
{
|
|
"prefix_len": 0,
|
|
"suffix_len": 0,
|
|
"prefix_tree": 4294967295,
|
|
"suffix_tree": 4294967295,
|
|
},
|
|
{"orig": "E", "subst": "e"},
|
|
{
|
|
"prefix_len": 1,
|
|
"suffix_len": 0,
|
|
"prefix_tree": 5,
|
|
"suffix_tree": 4294967295,
|
|
},
|
|
],
|
|
"labels": (1, 3, 4, 6),
|
|
}
|
|
|
|
|
|
@pytest.mark.parametrize("top_k", (1, 5, 30))
|
|
def test_no_data(top_k):
|
|
# Test that the lemmatizer provides a nice error when there's no tagging data / labels
|
|
TEXTCAT_DATA = [
|
|
("I'm so happy.", {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}),
|
|
("I'm so angry", {"cats": {"POSITIVE": 0.0, "NEGATIVE": 1.0}}),
|
|
]
|
|
nlp = English()
|
|
nlp.add_pipe("trainable_lemmatizer", config={"top_k": top_k})
|
|
nlp.add_pipe("textcat")
|
|
|
|
train_examples = []
|
|
for t in TEXTCAT_DATA:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
|
|
|
|
with pytest.raises(ValueError):
|
|
nlp.initialize(get_examples=lambda: train_examples)
|
|
|
|
|
|
@pytest.mark.parametrize("top_k", (1, 5, 30))
|
|
def test_incomplete_data(top_k):
|
|
# Test that the lemmatizer works with incomplete information
|
|
nlp = English()
|
|
lemmatizer = nlp.add_pipe("trainable_lemmatizer", config={"top_k": top_k})
|
|
lemmatizer.min_tree_freq = 1
|
|
train_examples = []
|
|
for t in PARTIAL_DATA:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
|
|
optimizer = nlp.initialize(get_examples=lambda: train_examples)
|
|
for i in range(50):
|
|
losses = {}
|
|
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
|
assert losses["trainable_lemmatizer"] < 0.00001
|
|
|
|
# test the trained model
|
|
test_text = "She likes blue eggs"
|
|
doc = nlp(test_text)
|
|
assert doc[1].lemma_ == "like"
|
|
assert doc[2].lemma_ == "blue"
|
|
|
|
# Check that incomplete annotations are ignored.
|
|
scores, _ = lemmatizer.model([eg.predicted for eg in train_examples], is_train=True)
|
|
_, dX = lemmatizer.get_loss(train_examples, scores)
|
|
xp = lemmatizer.model.ops.xp
|
|
|
|
# Missing annotations.
|
|
assert xp.count_nonzero(dX[0][0]) == 0
|
|
assert xp.count_nonzero(dX[0][3]) == 0
|
|
assert xp.count_nonzero(dX[1][0]) == 0
|
|
assert xp.count_nonzero(dX[1][3]) == 0
|
|
|
|
# Misaligned annotations.
|
|
assert xp.count_nonzero(dX[1][1]) == 0
|
|
|
|
|
|
@pytest.mark.parametrize("top_k", (1, 5, 30))
|
|
def test_overfitting_IO(top_k):
|
|
nlp = English()
|
|
lemmatizer = nlp.add_pipe("trainable_lemmatizer", config={"top_k": top_k})
|
|
lemmatizer.min_tree_freq = 1
|
|
train_examples = []
|
|
for t in TRAIN_DATA:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
|
|
|
|
optimizer = nlp.initialize(get_examples=lambda: train_examples)
|
|
|
|
for i in range(50):
|
|
losses = {}
|
|
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
|
assert losses["trainable_lemmatizer"] < 0.00001
|
|
|
|
test_text = "She likes blue eggs"
|
|
doc = nlp(test_text)
|
|
assert doc[0].lemma_ == "she"
|
|
assert doc[1].lemma_ == "like"
|
|
assert doc[2].lemma_ == "blue"
|
|
assert doc[3].lemma_ == "egg"
|
|
|
|
# Check model after a {to,from}_disk roundtrip
|
|
with util.make_tempdir() as tmp_dir:
|
|
nlp.to_disk(tmp_dir)
|
|
nlp2 = util.load_model_from_path(tmp_dir)
|
|
doc2 = nlp2(test_text)
|
|
assert doc2[0].lemma_ == "she"
|
|
assert doc2[1].lemma_ == "like"
|
|
assert doc2[2].lemma_ == "blue"
|
|
assert doc2[3].lemma_ == "egg"
|
|
|
|
# Check model after a {to,from}_bytes roundtrip
|
|
nlp_bytes = nlp.to_bytes()
|
|
nlp3 = English()
|
|
nlp3.add_pipe("trainable_lemmatizer", config={"top_k": top_k})
|
|
nlp3.from_bytes(nlp_bytes)
|
|
doc3 = nlp3(test_text)
|
|
assert doc3[0].lemma_ == "she"
|
|
assert doc3[1].lemma_ == "like"
|
|
assert doc3[2].lemma_ == "blue"
|
|
assert doc3[3].lemma_ == "egg"
|
|
|
|
# Check model after a pickle roundtrip.
|
|
nlp_bytes = pickle.dumps(nlp)
|
|
nlp4 = pickle.loads(nlp_bytes)
|
|
doc4 = nlp4(test_text)
|
|
assert doc4[0].lemma_ == "she"
|
|
assert doc4[1].lemma_ == "like"
|
|
assert doc4[2].lemma_ == "blue"
|
|
assert doc4[3].lemma_ == "egg"
|
|
|
|
|
|
def test_lemmatizer_requires_labels():
|
|
nlp = English()
|
|
nlp.add_pipe("trainable_lemmatizer")
|
|
with pytest.raises(ValueError):
|
|
nlp.initialize()
|
|
|
|
|
|
def test_lemmatizer_label_data():
|
|
nlp = English()
|
|
lemmatizer = nlp.add_pipe("trainable_lemmatizer")
|
|
lemmatizer.min_tree_freq = 1
|
|
train_examples = []
|
|
for t in TRAIN_DATA:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
|
|
|
|
nlp.initialize(get_examples=lambda: train_examples)
|
|
|
|
nlp2 = English()
|
|
lemmatizer2 = nlp2.add_pipe("trainable_lemmatizer")
|
|
lemmatizer2.initialize(
|
|
get_examples=lambda: train_examples, labels=lemmatizer.label_data
|
|
)
|
|
|
|
# Verify that the labels and trees are the same.
|
|
assert lemmatizer.labels == lemmatizer2.labels
|
|
assert lemmatizer.trees.to_bytes() == lemmatizer2.trees.to_bytes()
|
|
|
|
|
|
def test_dutch():
|
|
strings = StringStore()
|
|
trees = EditTrees(strings)
|
|
tree = trees.add("deelt", "delen")
|
|
assert trees.tree_to_str(tree) == "(m 0 3 () (m 0 2 (s '' 'l') (s 'lt' 'n')))"
|
|
|
|
tree = trees.add("gedeeld", "delen")
|
|
assert (
|
|
trees.tree_to_str(tree) == "(m 2 3 (s 'ge' '') (m 0 2 (s '' 'l') (s 'ld' 'n')))"
|
|
)
|
|
|
|
|
|
def test_from_to_bytes():
|
|
strings = StringStore()
|
|
trees = EditTrees(strings)
|
|
trees.add("deelt", "delen")
|
|
trees.add("gedeeld", "delen")
|
|
|
|
b = trees.to_bytes()
|
|
|
|
trees2 = EditTrees(strings)
|
|
trees2.from_bytes(b)
|
|
|
|
# Verify that the nodes did not change.
|
|
assert len(trees) == len(trees2)
|
|
for i in range(len(trees)):
|
|
assert trees.tree_to_str(i) == trees2.tree_to_str(i)
|
|
|
|
# Reinserting the same trees should not add new nodes.
|
|
trees2.add("deelt", "delen")
|
|
trees2.add("gedeeld", "delen")
|
|
assert len(trees) == len(trees2)
|
|
|
|
|
|
def test_from_to_disk():
|
|
strings = StringStore()
|
|
trees = EditTrees(strings)
|
|
trees.add("deelt", "delen")
|
|
trees.add("gedeeld", "delen")
|
|
|
|
trees2 = EditTrees(strings)
|
|
with make_tempdir() as temp_dir:
|
|
trees_file = temp_dir / "edit_trees.bin"
|
|
trees.to_disk(trees_file)
|
|
trees2 = trees2.from_disk(trees_file)
|
|
|
|
# Verify that the nodes did not change.
|
|
assert len(trees) == len(trees2)
|
|
for i in range(len(trees)):
|
|
assert trees.tree_to_str(i) == trees2.tree_to_str(i)
|
|
|
|
# Reinserting the same trees should not add new nodes.
|
|
trees2.add("deelt", "delen")
|
|
trees2.add("gedeeld", "delen")
|
|
assert len(trees) == len(trees2)
|
|
|
|
|
|
@given(st.text(), st.text())
|
|
def test_roundtrip(form, lemma):
|
|
strings = StringStore()
|
|
trees = EditTrees(strings)
|
|
tree = trees.add(form, lemma)
|
|
assert trees.apply(tree, form) == lemma
|
|
|
|
|
|
@given(st.text(alphabet="ab"), st.text(alphabet="ab"))
|
|
def test_roundtrip_small_alphabet(form, lemma):
|
|
# Test with small alphabets to have more overlap.
|
|
strings = StringStore()
|
|
trees = EditTrees(strings)
|
|
tree = trees.add(form, lemma)
|
|
assert trees.apply(tree, form) == lemma
|
|
|
|
|
|
def test_unapplicable_trees():
|
|
strings = StringStore()
|
|
trees = EditTrees(strings)
|
|
tree3 = trees.add("deelt", "delen")
|
|
|
|
# Replacement fails.
|
|
assert trees.apply(tree3, "deeld") == None
|
|
|
|
# Suffix + prefix are too large.
|
|
assert trees.apply(tree3, "de") == None
|
|
|
|
|
|
def test_empty_strings():
|
|
strings = StringStore()
|
|
trees = EditTrees(strings)
|
|
no_change = trees.add("xyz", "xyz")
|
|
empty = trees.add("", "")
|
|
assert no_change == empty
|