mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 04:08:09 +03:00
afc5714d32
* multi-label textcat component * formatting * fix comment * cleanup * fix from #6481 * random edit to push the tests * add explicit error when textcat is called with multi-label gold data * fix error nr * small fix
416 lines
16 KiB
Python
416 lines
16 KiB
Python
import pytest
|
|
import random
|
|
import numpy.random
|
|
from numpy.testing import assert_equal
|
|
from thinc.api import fix_random_seed
|
|
from spacy import util
|
|
from spacy.lang.en import English
|
|
from spacy.language import Language
|
|
from spacy.pipeline import TextCategorizer
|
|
from spacy.tokens import Doc
|
|
from spacy.pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
|
|
from spacy.scorer import Scorer
|
|
from spacy.training import Example
|
|
|
|
from ..util import make_tempdir
|
|
|
|
|
|
TRAIN_DATA_SINGLE_LABEL = [
|
|
("I'm so happy.", {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}),
|
|
("I'm so angry", {"cats": {"POSITIVE": 0.0, "NEGATIVE": 1.0}}),
|
|
]
|
|
|
|
TRAIN_DATA_MULTI_LABEL = [
|
|
("I'm angry and confused", {"cats": {"ANGRY": 1.0, "CONFUSED": 1.0, "HAPPY": 0.0}}),
|
|
("I'm confused but happy", {"cats": {"ANGRY": 0.0, "CONFUSED": 1.0, "HAPPY": 1.0}}),
|
|
]
|
|
|
|
|
|
def make_get_examples_single_label(nlp):
|
|
train_examples = []
|
|
for t in TRAIN_DATA_SINGLE_LABEL:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
|
|
|
|
def get_examples():
|
|
return train_examples
|
|
|
|
return get_examples
|
|
|
|
|
|
def make_get_examples_multi_label(nlp):
|
|
train_examples = []
|
|
for t in TRAIN_DATA_MULTI_LABEL:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
|
|
|
|
def get_examples():
|
|
return train_examples
|
|
|
|
return get_examples
|
|
|
|
|
|
@pytest.mark.skip(reason="Test is flakey when run with others")
|
|
def test_simple_train():
|
|
nlp = Language()
|
|
textcat = nlp.add_pipe("textcat")
|
|
textcat.add_label("answer")
|
|
nlp.initialize()
|
|
for i in range(5):
|
|
for text, answer in [
|
|
("aaaa", 1.0),
|
|
("bbbb", 0),
|
|
("aa", 1.0),
|
|
("bbbbbbbbb", 0.0),
|
|
("aaaaaa", 1),
|
|
]:
|
|
nlp.update((text, {"cats": {"answer": answer}}))
|
|
doc = nlp("aaa")
|
|
assert "answer" in doc.cats
|
|
assert doc.cats["answer"] >= 0.5
|
|
|
|
|
|
@pytest.mark.skip(reason="Test is flakey when run with others")
|
|
def test_textcat_learns_multilabel():
|
|
random.seed(5)
|
|
numpy.random.seed(5)
|
|
docs = []
|
|
nlp = Language()
|
|
letters = ["a", "b", "c"]
|
|
for w1 in letters:
|
|
for w2 in letters:
|
|
cats = {letter: float(w2 == letter) for letter in letters}
|
|
docs.append((Doc(nlp.vocab, words=["d"] * 3 + [w1, w2] + ["d"] * 3), cats))
|
|
random.shuffle(docs)
|
|
textcat = TextCategorizer(nlp.vocab, width=8)
|
|
for letter in letters:
|
|
textcat.add_label(letter)
|
|
optimizer = textcat.initialize(lambda: [])
|
|
for i in range(30):
|
|
losses = {}
|
|
examples = [Example.from_dict(doc, {"cats": cats}) for doc, cat in docs]
|
|
textcat.update(examples, sgd=optimizer, losses=losses)
|
|
random.shuffle(docs)
|
|
for w1 in letters:
|
|
for w2 in letters:
|
|
doc = Doc(nlp.vocab, words=["d"] * 3 + [w1, w2] + ["d"] * 3)
|
|
truth = {letter: w2 == letter for letter in letters}
|
|
textcat(doc)
|
|
for cat, score in doc.cats.items():
|
|
if not truth[cat]:
|
|
assert score < 0.5
|
|
else:
|
|
assert score > 0.5
|
|
|
|
|
|
@pytest.mark.parametrize("name", ["textcat", "textcat_multilabel"])
|
|
def test_label_types(name):
|
|
nlp = Language()
|
|
textcat = nlp.add_pipe(name)
|
|
textcat.add_label("answer")
|
|
with pytest.raises(ValueError):
|
|
textcat.add_label(9)
|
|
|
|
|
|
@pytest.mark.parametrize("name", ["textcat", "textcat_multilabel"])
|
|
def test_no_label(name):
|
|
nlp = Language()
|
|
nlp.add_pipe(name)
|
|
with pytest.raises(ValueError):
|
|
nlp.initialize()
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"name,get_examples",
|
|
[
|
|
("textcat", make_get_examples_single_label),
|
|
("textcat_multilabel", make_get_examples_multi_label),
|
|
],
|
|
)
|
|
def test_implicit_label(name, get_examples):
|
|
nlp = Language()
|
|
nlp.add_pipe(name)
|
|
nlp.initialize(get_examples=get_examples(nlp))
|
|
|
|
|
|
@pytest.mark.parametrize("name", ["textcat", "textcat_multilabel"])
|
|
def test_no_resize(name):
|
|
nlp = Language()
|
|
textcat = nlp.add_pipe(name)
|
|
textcat.add_label("POSITIVE")
|
|
textcat.add_label("NEGATIVE")
|
|
nlp.initialize()
|
|
assert textcat.model.get_dim("nO") >= 2
|
|
# this throws an error because the textcat can't be resized after initialization
|
|
with pytest.raises(ValueError):
|
|
textcat.add_label("NEUTRAL")
|
|
|
|
|
|
def test_error_with_multi_labels():
|
|
nlp = Language()
|
|
textcat = nlp.add_pipe("textcat")
|
|
train_examples = []
|
|
for text, annotations in TRAIN_DATA_MULTI_LABEL:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
|
|
with pytest.raises(ValueError):
|
|
optimizer = nlp.initialize(get_examples=lambda: train_examples)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"name,get_examples, train_data",
|
|
[
|
|
("textcat", make_get_examples_single_label, TRAIN_DATA_SINGLE_LABEL),
|
|
("textcat_multilabel", make_get_examples_multi_label, TRAIN_DATA_MULTI_LABEL),
|
|
],
|
|
)
|
|
def test_initialize_examples(name, get_examples, train_data):
|
|
nlp = Language()
|
|
textcat = nlp.add_pipe(name)
|
|
for text, annotations in train_data:
|
|
for label, value in annotations.get("cats").items():
|
|
textcat.add_label(label)
|
|
# you shouldn't really call this more than once, but for testing it should be fine
|
|
nlp.initialize()
|
|
nlp.initialize(get_examples=get_examples(nlp))
|
|
with pytest.raises(TypeError):
|
|
nlp.initialize(get_examples=lambda: None)
|
|
with pytest.raises(TypeError):
|
|
nlp.initialize(get_examples=get_examples())
|
|
|
|
|
|
def test_overfitting_IO():
|
|
# Simple test to try and quickly overfit the single-label textcat component - ensuring the ML models work correctly
|
|
fix_random_seed(0)
|
|
nlp = English()
|
|
textcat = nlp.add_pipe("textcat")
|
|
|
|
train_examples = []
|
|
for text, annotations in TRAIN_DATA_SINGLE_LABEL:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
|
|
optimizer = nlp.initialize(get_examples=lambda: train_examples)
|
|
assert textcat.model.get_dim("nO") == 2
|
|
|
|
for i in range(50):
|
|
losses = {}
|
|
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
|
assert losses["textcat"] < 0.01
|
|
|
|
# test the trained model
|
|
test_text = "I am happy."
|
|
doc = nlp(test_text)
|
|
cats = doc.cats
|
|
assert cats["POSITIVE"] > 0.9
|
|
assert cats["POSITIVE"] + cats["NEGATIVE"] == pytest.approx(1.0, 0.001)
|
|
|
|
# Also test the results are still the same after IO
|
|
with make_tempdir() as tmp_dir:
|
|
nlp.to_disk(tmp_dir)
|
|
nlp2 = util.load_model_from_path(tmp_dir)
|
|
doc2 = nlp2(test_text)
|
|
cats2 = doc2.cats
|
|
assert cats2["POSITIVE"] > 0.9
|
|
assert cats2["POSITIVE"] + cats2["NEGATIVE"] == pytest.approx(1.0, 0.001)
|
|
|
|
# Test scoring
|
|
scores = nlp.evaluate(train_examples)
|
|
assert scores["cats_micro_f"] == 1.0
|
|
assert scores["cats_macro_f"] == 1.0
|
|
assert scores["cats_macro_auc"] == 1.0
|
|
assert scores["cats_score"] == 1.0
|
|
assert "cats_score_desc" in scores
|
|
|
|
# Make sure that running pipe twice, or comparing to call, always amounts to the same predictions
|
|
texts = ["Just a sentence.", "I like green eggs.", "I am happy.", "I eat ham."]
|
|
batch_cats_1 = [doc.cats for doc in nlp.pipe(texts)]
|
|
batch_cats_2 = [doc.cats for doc in nlp.pipe(texts)]
|
|
no_batch_cats = [doc.cats for doc in [nlp(text) for text in texts]]
|
|
assert_equal(batch_cats_1, batch_cats_2)
|
|
assert_equal(batch_cats_1, no_batch_cats)
|
|
|
|
|
|
def test_overfitting_IO_multi():
|
|
# Simple test to try and quickly overfit the multi-label textcat component - ensuring the ML models work correctly
|
|
fix_random_seed(0)
|
|
nlp = English()
|
|
# Set exclusive labels to False
|
|
config = {"model": {"linear_model": {"exclusive_classes": False}}}
|
|
textcat = nlp.add_pipe("textcat", config=config)
|
|
train_examples = []
|
|
for text, annotations in TRAIN_DATA_MULTI_LABEL:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
|
|
optimizer = nlp.initialize(get_examples=lambda: train_examples)
|
|
assert textcat.model.get_dim("nO") == 2
|
|
|
|
for i in range(50):
|
|
losses = {}
|
|
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
|
assert losses["textcat"] < 0.01
|
|
|
|
# test the trained model
|
|
test_text = "I am happy."
|
|
doc = nlp(test_text)
|
|
cats = doc.cats
|
|
assert cats["POSITIVE"] > 0.9
|
|
|
|
# Also test the results are still the same after IO
|
|
with make_tempdir() as tmp_dir:
|
|
nlp.to_disk(tmp_dir)
|
|
nlp2 = util.load_model_from_path(tmp_dir)
|
|
doc2 = nlp2(test_text)
|
|
cats2 = doc2.cats
|
|
assert cats2["POSITIVE"] > 0.9
|
|
|
|
# Test scoring
|
|
scores = nlp.evaluate(train_examples)
|
|
assert scores["cats_micro_f"] == 1.0
|
|
assert scores["cats_score"] == 1.0
|
|
assert "cats_score_desc" in scores
|
|
|
|
# Make sure that running pipe twice, or comparing to call, always amounts to the same predictions
|
|
texts = ["Just a sentence.", "I like green eggs.", "I am happy.", "I eat ham."]
|
|
batch_cats_1 = [doc.cats for doc in nlp.pipe(texts)]
|
|
batch_cats_2 = [doc.cats for doc in nlp.pipe(texts)]
|
|
no_batch_cats = [doc.cats for doc in [nlp(text) for text in texts]]
|
|
assert_equal(batch_cats_1, batch_cats_2)
|
|
assert_equal(batch_cats_1, no_batch_cats)
|
|
|
|
|
|
def test_overfitting_IO_multi():
|
|
# Simple test to try and quickly overfit the multi-label textcat component - ensuring the ML models work correctly
|
|
fix_random_seed(0)
|
|
nlp = English()
|
|
textcat = nlp.add_pipe("textcat_multilabel")
|
|
|
|
train_examples = []
|
|
for text, annotations in TRAIN_DATA_MULTI_LABEL:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
|
|
optimizer = nlp.initialize(get_examples=lambda: train_examples)
|
|
assert textcat.model.get_dim("nO") == 3
|
|
|
|
for i in range(100):
|
|
losses = {}
|
|
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
|
assert losses["textcat_multilabel"] < 0.01
|
|
|
|
# test the trained model
|
|
test_text = "I am confused but happy."
|
|
doc = nlp(test_text)
|
|
cats = doc.cats
|
|
assert cats["HAPPY"] > 0.9
|
|
assert cats["CONFUSED"] > 0.9
|
|
|
|
# Also test the results are still the same after IO
|
|
with make_tempdir() as tmp_dir:
|
|
nlp.to_disk(tmp_dir)
|
|
nlp2 = util.load_model_from_path(tmp_dir)
|
|
doc2 = nlp2(test_text)
|
|
cats2 = doc2.cats
|
|
assert cats2["HAPPY"] > 0.9
|
|
assert cats2["CONFUSED"] > 0.9
|
|
|
|
# Test scoring
|
|
scores = nlp.evaluate(train_examples)
|
|
assert scores["cats_micro_f"] == 1.0
|
|
assert scores["cats_macro_f"] == 1.0
|
|
assert "cats_score_desc" in scores
|
|
|
|
# Make sure that running pipe twice, or comparing to call, always amounts to the same predictions
|
|
texts = ["Just a sentence.", "I like green eggs.", "I am happy.", "I eat ham."]
|
|
batch_deps_1 = [doc.cats for doc in nlp.pipe(texts)]
|
|
batch_deps_2 = [doc.cats for doc in nlp.pipe(texts)]
|
|
no_batch_deps = [doc.cats for doc in [nlp(text) for text in texts]]
|
|
assert_equal(batch_deps_1, batch_deps_2)
|
|
assert_equal(batch_deps_1, no_batch_deps)
|
|
|
|
|
|
# fmt: off
|
|
@pytest.mark.parametrize(
|
|
"name,train_data,textcat_config",
|
|
[
|
|
("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": False, "ngram_size": 1, "no_output_layer": False}),
|
|
("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": True, "ngram_size": 4, "no_output_layer": False}),
|
|
("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": False, "ngram_size": 3, "no_output_layer": True}),
|
|
("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": True, "ngram_size": 2, "no_output_layer": True}),
|
|
("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatEnsemble.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "linear_model": {"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": False, "ngram_size": 1, "no_output_layer": False}}),
|
|
("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatEnsemble.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "linear_model": {"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": True, "ngram_size": 5, "no_output_layer": False}}),
|
|
("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatCNN.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": True}),
|
|
("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatCNN.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": False}),
|
|
],
|
|
)
|
|
# fmt: on
|
|
def test_textcat_configs(name, train_data, textcat_config):
|
|
pipe_config = {"model": textcat_config}
|
|
nlp = English()
|
|
textcat = nlp.add_pipe(name, config=pipe_config)
|
|
train_examples = []
|
|
for text, annotations in train_data:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
|
|
for label, value in annotations.get("cats").items():
|
|
textcat.add_label(label)
|
|
optimizer = nlp.initialize()
|
|
for i in range(5):
|
|
losses = {}
|
|
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
|
|
|
|
|
def test_positive_class():
|
|
nlp = English()
|
|
textcat = nlp.add_pipe("textcat")
|
|
get_examples = make_get_examples_single_label(nlp)
|
|
textcat.initialize(get_examples, labels=["POS", "NEG"], positive_label="POS")
|
|
assert textcat.labels == ("POS", "NEG")
|
|
assert textcat.cfg["positive_label"] == "POS"
|
|
|
|
textcat_multilabel = nlp.add_pipe("textcat_multilabel")
|
|
get_examples = make_get_examples_multi_label(nlp)
|
|
with pytest.raises(TypeError):
|
|
textcat_multilabel.initialize(get_examples, labels=["POS", "NEG"], positive_label="POS")
|
|
textcat_multilabel.initialize(get_examples, labels=["FICTION", "DRAMA"])
|
|
assert textcat_multilabel.labels == ("FICTION", "DRAMA")
|
|
assert "positive_label" not in textcat_multilabel.cfg
|
|
|
|
|
|
def test_positive_class_not_present():
|
|
nlp = English()
|
|
textcat = nlp.add_pipe("textcat")
|
|
get_examples = make_get_examples_single_label(nlp)
|
|
with pytest.raises(ValueError):
|
|
textcat.initialize(get_examples, labels=["SOME", "THING"], positive_label="POS")
|
|
|
|
|
|
def test_positive_class_not_binary():
|
|
nlp = English()
|
|
textcat = nlp.add_pipe("textcat")
|
|
get_examples = make_get_examples_multi_label(nlp)
|
|
with pytest.raises(ValueError):
|
|
textcat.initialize(get_examples, labels=["SOME", "THING", "POS"], positive_label="POS")
|
|
|
|
|
|
def test_textcat_evaluation():
|
|
train_examples = []
|
|
nlp = English()
|
|
ref1 = nlp("one")
|
|
ref1.cats = {"winter": 1.0, "summer": 1.0, "spring": 1.0, "autumn": 1.0}
|
|
pred1 = nlp("one")
|
|
pred1.cats = {"winter": 1.0, "summer": 0.0, "spring": 1.0, "autumn": 1.0}
|
|
train_examples.append(Example(pred1, ref1))
|
|
|
|
ref2 = nlp("two")
|
|
ref2.cats = {"winter": 0.0, "summer": 0.0, "spring": 1.0, "autumn": 1.0}
|
|
pred2 = nlp("two")
|
|
pred2.cats = {"winter": 1.0, "summer": 0.0, "spring": 0.0, "autumn": 1.0}
|
|
train_examples.append(Example(pred2, ref2))
|
|
|
|
scores = Scorer().score_cats(
|
|
train_examples, "cats", labels=["winter", "summer", "spring", "autumn"]
|
|
)
|
|
assert scores["cats_f_per_type"]["winter"]["p"] == 1 / 2
|
|
assert scores["cats_f_per_type"]["winter"]["r"] == 1 / 1
|
|
assert scores["cats_f_per_type"]["summer"]["p"] == 0
|
|
assert scores["cats_f_per_type"]["summer"]["r"] == 0 / 1
|
|
assert scores["cats_f_per_type"]["spring"]["p"] == 1 / 1
|
|
assert scores["cats_f_per_type"]["spring"]["r"] == 1 / 2
|
|
assert scores["cats_f_per_type"]["autumn"]["p"] == 2 / 2
|
|
assert scores["cats_f_per_type"]["autumn"]["r"] == 2 / 2
|
|
|
|
assert scores["cats_micro_p"] == 4 / 5
|
|
assert scores["cats_micro_r"] == 4 / 6
|