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29906884c5
* Raise an error for textcat with <2 labels Raise an error if initializing a `textcat` component without at least two labels. * Add similar note to docs * Update positive_label description in API docs
547 lines
22 KiB
Python
547 lines
22 KiB
Python
import pytest
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import random
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import numpy.random
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from numpy.testing import assert_almost_equal
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from thinc.api import fix_random_seed
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from spacy import util
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from spacy.lang.en import English
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from spacy.language import Language
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from spacy.pipeline import TextCategorizer
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from spacy.tokens import Doc
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from spacy.pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
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from spacy.scorer import Scorer
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from spacy.training import Example
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from ..util import make_tempdir
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TRAIN_DATA_SINGLE_LABEL = [
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("I'm so happy.", {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}),
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("I'm so angry", {"cats": {"POSITIVE": 0.0, "NEGATIVE": 1.0}}),
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]
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TRAIN_DATA_MULTI_LABEL = [
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("I'm angry and confused", {"cats": {"ANGRY": 1.0, "CONFUSED": 1.0, "HAPPY": 0.0}}),
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("I'm confused but happy", {"cats": {"ANGRY": 0.0, "CONFUSED": 1.0, "HAPPY": 1.0}}),
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]
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def make_get_examples_single_label(nlp):
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train_examples = []
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for t in TRAIN_DATA_SINGLE_LABEL:
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train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
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def get_examples():
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return train_examples
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return get_examples
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def make_get_examples_multi_label(nlp):
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train_examples = []
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for t in TRAIN_DATA_MULTI_LABEL:
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train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
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def get_examples():
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return train_examples
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return get_examples
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@pytest.mark.skip(reason="Test is flakey when run with others")
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def test_simple_train():
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nlp = Language()
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textcat = nlp.add_pipe("textcat")
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textcat.add_label("answer")
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nlp.initialize()
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for i in range(5):
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for text, answer in [
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("aaaa", 1.0),
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("bbbb", 0),
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("aa", 1.0),
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("bbbbbbbbb", 0.0),
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("aaaaaa", 1),
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]:
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nlp.update((text, {"cats": {"answer": answer}}))
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doc = nlp("aaa")
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assert "answer" in doc.cats
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assert doc.cats["answer"] >= 0.5
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@pytest.mark.skip(reason="Test is flakey when run with others")
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def test_textcat_learns_multilabel():
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random.seed(5)
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numpy.random.seed(5)
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docs = []
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nlp = Language()
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letters = ["a", "b", "c"]
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for w1 in letters:
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for w2 in letters:
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cats = {letter: float(w2 == letter) for letter in letters}
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docs.append((Doc(nlp.vocab, words=["d"] * 3 + [w1, w2] + ["d"] * 3), cats))
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random.shuffle(docs)
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textcat = TextCategorizer(nlp.vocab, width=8)
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for letter in letters:
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textcat.add_label(letter)
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optimizer = textcat.initialize(lambda: [])
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for i in range(30):
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losses = {}
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examples = [Example.from_dict(doc, {"cats": cats}) for doc, cat in docs]
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textcat.update(examples, sgd=optimizer, losses=losses)
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random.shuffle(docs)
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for w1 in letters:
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for w2 in letters:
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doc = Doc(nlp.vocab, words=["d"] * 3 + [w1, w2] + ["d"] * 3)
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truth = {letter: w2 == letter for letter in letters}
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textcat(doc)
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for cat, score in doc.cats.items():
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if not truth[cat]:
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assert score < 0.5
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else:
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assert score > 0.5
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@pytest.mark.parametrize("name", ["textcat", "textcat_multilabel"])
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def test_label_types(name):
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nlp = Language()
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textcat = nlp.add_pipe(name)
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textcat.add_label("answer")
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with pytest.raises(ValueError):
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textcat.add_label(9)
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# textcat requires at least two labels
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if name == "textcat":
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with pytest.raises(ValueError):
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nlp.initialize()
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else:
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nlp.initialize()
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@pytest.mark.parametrize("name", ["textcat", "textcat_multilabel"])
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def test_no_label(name):
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nlp = Language()
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nlp.add_pipe(name)
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with pytest.raises(ValueError):
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nlp.initialize()
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@pytest.mark.parametrize(
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"name,get_examples",
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[
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("textcat", make_get_examples_single_label),
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("textcat_multilabel", make_get_examples_multi_label),
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],
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)
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def test_implicit_label(name, get_examples):
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nlp = Language()
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nlp.add_pipe(name)
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nlp.initialize(get_examples=get_examples(nlp))
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# fmt: off
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@pytest.mark.parametrize(
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"name,textcat_config",
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[
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# BOW
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("textcat", {"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": True, "no_output_layer": False, "ngram_size": 3}),
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("textcat", {"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": True, "no_output_layer": True, "ngram_size": 3}),
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("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": False, "no_output_layer": False, "ngram_size": 3}),
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("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": False, "no_output_layer": True, "ngram_size": 3}),
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# ENSEMBLE
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("textcat", {"@architectures": "spacy.TextCatEnsemble.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "linear_model": {"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": True, "no_output_layer": False, "ngram_size": 3}}),
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("textcat", {"@architectures": "spacy.TextCatEnsemble.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "linear_model": {"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": True, "no_output_layer": True, "ngram_size": 3}}),
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("textcat_multilabel", {"@architectures": "spacy.TextCatEnsemble.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "linear_model": {"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": False, "no_output_layer": False, "ngram_size": 3}}),
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("textcat_multilabel", {"@architectures": "spacy.TextCatEnsemble.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "linear_model": {"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": False, "no_output_layer": True, "ngram_size": 3}}),
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# CNN
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("textcat", {"@architectures": "spacy.TextCatCNN.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": True}),
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("textcat_multilabel", {"@architectures": "spacy.TextCatCNN.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": False}),
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],
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)
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# fmt: on
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def test_no_resize(name, textcat_config):
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"""The old textcat architectures weren't resizable"""
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nlp = Language()
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pipe_config = {"model": textcat_config}
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textcat = nlp.add_pipe(name, config=pipe_config)
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textcat.add_label("POSITIVE")
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textcat.add_label("NEGATIVE")
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nlp.initialize()
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assert textcat.model.maybe_get_dim("nO") in [2, None]
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# this throws an error because the textcat can't be resized after initialization
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with pytest.raises(ValueError):
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textcat.add_label("NEUTRAL")
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# fmt: off
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@pytest.mark.parametrize(
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"name,textcat_config",
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[
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# BOW
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("textcat", {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": True, "no_output_layer": False, "ngram_size": 3}),
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("textcat", {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": True, "no_output_layer": True, "ngram_size": 3}),
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("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": False, "no_output_layer": False, "ngram_size": 3}),
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("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": False, "no_output_layer": True, "ngram_size": 3}),
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# CNN
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("textcat", {"@architectures": "spacy.TextCatCNN.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": True}),
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("textcat_multilabel", {"@architectures": "spacy.TextCatCNN.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": False}),
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],
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)
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# fmt: on
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def test_resize(name, textcat_config):
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"""The new textcat architectures are resizable"""
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nlp = Language()
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pipe_config = {"model": textcat_config}
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textcat = nlp.add_pipe(name, config=pipe_config)
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textcat.add_label("POSITIVE")
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textcat.add_label("NEGATIVE")
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assert textcat.model.maybe_get_dim("nO") in [2, None]
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nlp.initialize()
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assert textcat.model.maybe_get_dim("nO") in [2, None]
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textcat.add_label("NEUTRAL")
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assert textcat.model.maybe_get_dim("nO") in [3, None]
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# fmt: off
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@pytest.mark.parametrize(
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"name,textcat_config",
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[
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# BOW
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("textcat", {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": True, "no_output_layer": False, "ngram_size": 3}),
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("textcat", {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": True, "no_output_layer": True, "ngram_size": 3}),
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("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": False, "no_output_layer": False, "ngram_size": 3}),
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("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": False, "no_output_layer": True, "ngram_size": 3}),
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# CNN
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("textcat", {"@architectures": "spacy.TextCatCNN.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": True}),
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("textcat_multilabel", {"@architectures": "spacy.TextCatCNN.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": False}),
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],
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)
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# fmt: on
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def test_resize_same_results(name, textcat_config):
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# Ensure that the resized textcat classifiers still produce the same results for old labels
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fix_random_seed(0)
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nlp = English()
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pipe_config = {"model": textcat_config}
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textcat = nlp.add_pipe(name, config=pipe_config)
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train_examples = []
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for text, annotations in TRAIN_DATA_SINGLE_LABEL:
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train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
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optimizer = nlp.initialize(get_examples=lambda: train_examples)
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assert textcat.model.maybe_get_dim("nO") in [2, None]
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for i in range(5):
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losses = {}
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nlp.update(train_examples, sgd=optimizer, losses=losses)
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# test the trained model before resizing
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test_text = "I am happy."
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doc = nlp(test_text)
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assert len(doc.cats) == 2
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pos_pred = doc.cats["POSITIVE"]
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neg_pred = doc.cats["NEGATIVE"]
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# test the trained model again after resizing
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textcat.add_label("NEUTRAL")
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doc = nlp(test_text)
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assert len(doc.cats) == 3
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assert doc.cats["POSITIVE"] == pos_pred
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assert doc.cats["NEGATIVE"] == neg_pred
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assert doc.cats["NEUTRAL"] <= 1
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for i in range(5):
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losses = {}
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nlp.update(train_examples, sgd=optimizer, losses=losses)
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# test the trained model again after training further with new label
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doc = nlp(test_text)
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assert len(doc.cats) == 3
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assert doc.cats["POSITIVE"] != pos_pred
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assert doc.cats["NEGATIVE"] != neg_pred
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for cat in doc.cats:
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assert doc.cats[cat] <= 1
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def test_error_with_multi_labels():
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nlp = Language()
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nlp.add_pipe("textcat")
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train_examples = []
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for text, annotations in TRAIN_DATA_MULTI_LABEL:
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train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
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with pytest.raises(ValueError):
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nlp.initialize(get_examples=lambda: train_examples)
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@pytest.mark.parametrize(
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"name,get_examples, train_data",
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[
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("textcat", make_get_examples_single_label, TRAIN_DATA_SINGLE_LABEL),
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("textcat_multilabel", make_get_examples_multi_label, TRAIN_DATA_MULTI_LABEL),
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],
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)
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def test_initialize_examples(name, get_examples, train_data):
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nlp = Language()
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textcat = nlp.add_pipe(name)
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for text, annotations in train_data:
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for label, value in annotations.get("cats").items():
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textcat.add_label(label)
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# you shouldn't really call this more than once, but for testing it should be fine
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nlp.initialize()
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nlp.initialize(get_examples=get_examples(nlp))
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with pytest.raises(TypeError):
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nlp.initialize(get_examples=lambda: None)
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with pytest.raises(TypeError):
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nlp.initialize(get_examples=get_examples())
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def test_overfitting_IO():
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# Simple test to try and quickly overfit the single-label textcat component - ensuring the ML models work correctly
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fix_random_seed(0)
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nlp = English()
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textcat = nlp.add_pipe("textcat")
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train_examples = []
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for text, annotations in TRAIN_DATA_SINGLE_LABEL:
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train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
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optimizer = nlp.initialize(get_examples=lambda: train_examples)
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assert textcat.model.get_dim("nO") == 2
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for i in range(50):
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losses = {}
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nlp.update(train_examples, sgd=optimizer, losses=losses)
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assert losses["textcat"] < 0.01
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# test the trained model
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test_text = "I am happy."
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doc = nlp(test_text)
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cats = doc.cats
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assert cats["POSITIVE"] > 0.9
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assert cats["POSITIVE"] + cats["NEGATIVE"] == pytest.approx(1.0, 0.001)
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# Also test the results are still the same after IO
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with make_tempdir() as tmp_dir:
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nlp.to_disk(tmp_dir)
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nlp2 = util.load_model_from_path(tmp_dir)
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doc2 = nlp2(test_text)
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cats2 = doc2.cats
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assert cats2["POSITIVE"] > 0.9
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assert cats2["POSITIVE"] + cats2["NEGATIVE"] == pytest.approx(1.0, 0.001)
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# Test scoring
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scores = nlp.evaluate(train_examples)
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assert scores["cats_micro_f"] == 1.0
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assert scores["cats_macro_f"] == 1.0
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assert scores["cats_macro_auc"] == 1.0
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assert scores["cats_score"] == 1.0
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assert "cats_score_desc" in scores
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# Make sure that running pipe twice, or comparing to call, always amounts to the same predictions
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texts = ["Just a sentence.", "I like green eggs.", "I am happy.", "I eat ham."]
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batch_cats_1 = [doc.cats for doc in nlp.pipe(texts)]
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batch_cats_2 = [doc.cats for doc in nlp.pipe(texts)]
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no_batch_cats = [doc.cats for doc in [nlp(text) for text in texts]]
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for cats_1, cats_2 in zip(batch_cats_1, batch_cats_2):
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for cat in cats_1:
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assert_almost_equal(cats_1[cat], cats_2[cat], decimal=5)
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for cats_1, cats_2 in zip(batch_cats_1, no_batch_cats):
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for cat in cats_1:
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assert_almost_equal(cats_1[cat], cats_2[cat], decimal=5)
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def test_overfitting_IO_multi():
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# Simple test to try and quickly overfit the multi-label textcat component - ensuring the ML models work correctly
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fix_random_seed(0)
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nlp = English()
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textcat = nlp.add_pipe("textcat_multilabel")
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train_examples = []
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for text, annotations in TRAIN_DATA_MULTI_LABEL:
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train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
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optimizer = nlp.initialize(get_examples=lambda: train_examples)
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assert textcat.model.get_dim("nO") == 3
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for i in range(100):
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losses = {}
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nlp.update(train_examples, sgd=optimizer, losses=losses)
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assert losses["textcat_multilabel"] < 0.01
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# test the trained model
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test_text = "I am confused but happy."
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doc = nlp(test_text)
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cats = doc.cats
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assert cats["HAPPY"] > 0.9
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assert cats["CONFUSED"] > 0.9
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# Also test the results are still the same after IO
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with make_tempdir() as tmp_dir:
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nlp.to_disk(tmp_dir)
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nlp2 = util.load_model_from_path(tmp_dir)
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doc2 = nlp2(test_text)
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cats2 = doc2.cats
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assert cats2["HAPPY"] > 0.9
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assert cats2["CONFUSED"] > 0.9
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# Test scoring
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scores = nlp.evaluate(train_examples)
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assert scores["cats_micro_f"] == 1.0
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assert scores["cats_macro_f"] == 1.0
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assert "cats_score_desc" in scores
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# Make sure that running pipe twice, or comparing to call, always amounts to the same predictions
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texts = ["Just a sentence.", "I like green eggs.", "I am happy.", "I eat ham."]
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batch_deps_1 = [doc.cats for doc in nlp.pipe(texts)]
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batch_deps_2 = [doc.cats for doc in nlp.pipe(texts)]
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no_batch_deps = [doc.cats for doc in [nlp(text) for text in texts]]
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for cats_1, cats_2 in zip(batch_deps_1, batch_deps_2):
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for cat in cats_1:
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assert_almost_equal(cats_1[cat], cats_2[cat], decimal=5)
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for cats_1, cats_2 in zip(batch_deps_1, no_batch_deps):
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for cat in cats_1:
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assert_almost_equal(cats_1[cat], cats_2[cat], decimal=5)
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# fmt: off
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@pytest.mark.parametrize(
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"name,train_data,textcat_config",
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[
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("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": False, "ngram_size": 1, "no_output_layer": False}),
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("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": True, "ngram_size": 4, "no_output_layer": False}),
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("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": False, "ngram_size": 3, "no_output_layer": True}),
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("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": True, "ngram_size": 2, "no_output_layer": True}),
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("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatEnsemble.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "linear_model": {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": False, "ngram_size": 1, "no_output_layer": False}}),
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("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatEnsemble.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "linear_model": {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": True, "ngram_size": 5, "no_output_layer": False}}),
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("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatCNN.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": True}),
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("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatCNN.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": False}),
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],
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)
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# fmt: on
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def test_textcat_configs(name, train_data, textcat_config):
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pipe_config = {"model": textcat_config}
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nlp = English()
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textcat = nlp.add_pipe(name, config=pipe_config)
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train_examples = []
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for text, annotations in train_data:
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train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
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for label, value in annotations.get("cats").items():
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textcat.add_label(label)
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optimizer = nlp.initialize()
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for i in range(5):
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losses = {}
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nlp.update(train_examples, sgd=optimizer, losses=losses)
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|
|
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def test_positive_class():
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nlp = English()
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textcat = nlp.add_pipe("textcat")
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get_examples = make_get_examples_single_label(nlp)
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textcat.initialize(get_examples, labels=["POS", "NEG"], positive_label="POS")
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assert textcat.labels == ("POS", "NEG")
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assert textcat.cfg["positive_label"] == "POS"
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|
|
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textcat_multilabel = nlp.add_pipe("textcat_multilabel")
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get_examples = make_get_examples_multi_label(nlp)
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|
with pytest.raises(TypeError):
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|
textcat_multilabel.initialize(
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get_examples, labels=["POS", "NEG"], positive_label="POS"
|
|
)
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textcat_multilabel.initialize(get_examples, labels=["FICTION", "DRAMA"])
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assert textcat_multilabel.labels == ("FICTION", "DRAMA")
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assert "positive_label" not in textcat_multilabel.cfg
|
|
|
|
|
|
def test_positive_class_not_present():
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|
nlp = English()
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|
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
|