spaCy/spacy/tests/pipeline/test_textcat.py

421 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()
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):
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()
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
def test_textcat_threshold():
# Ensure the scorer can be called with a different threshold
nlp = English()
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))
nlp.initialize(get_examples=lambda: train_examples)
# score the model (it's not actually trained but that doesn't matter)
scores = nlp.evaluate(train_examples)
assert 0 <= scores["cats_score"] <= 1
scores = nlp.evaluate(train_examples, scorer_cfg={"threshold": 1.0})
assert scores["cats_f_per_type"]["POSITIVE"]["r"] == 0
scores = nlp.evaluate(train_examples, scorer_cfg={"threshold": 0})
macro_f = scores["cats_score"]
assert scores["cats_f_per_type"]["POSITIVE"]["r"] == 1.0
scores = nlp.evaluate(train_examples, scorer_cfg={"threshold": 0, "positive_label": "POSITIVE"})
pos_f = scores["cats_score"]
assert scores["cats_f_per_type"]["POSITIVE"]["r"] == 1.0
assert pos_f > macro_f
def test_textcat_multi_threshold():
# Ensure the scorer can be called with a different threshold
nlp = English()
nlp.add_pipe("textcat_multilabel")
train_examples = []
for text, annotations in TRAIN_DATA_SINGLE_LABEL:
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
nlp.initialize(get_examples=lambda: train_examples)
# score the model (it's not actually trained but that doesn't matter)
scores = nlp.evaluate(train_examples)
assert 0 <= scores["cats_score"] <= 1
scores = nlp.evaluate(train_examples, scorer_cfg={"threshold": 1.0})
assert scores["cats_f_per_type"]["POSITIVE"]["r"] == 0
scores = nlp.evaluate(train_examples, scorer_cfg={"threshold": 0})
assert scores["cats_f_per_type"]["POSITIVE"]["r"] == 1.0