mirror of https://github.com/explosion/spaCy.git
973 lines
39 KiB
Python
973 lines
39 KiB
Python
import random
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import numpy.random
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import pytest
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from numpy.testing import assert_almost_equal
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from thinc.api import Config, compounding, fix_random_seed, get_current_ops
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from wasabi import msg
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import spacy
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from spacy import util
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from spacy.cli.evaluate import print_prf_per_type, print_textcats_auc_per_cat
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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.pipeline.textcat import (
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single_label_bow_config,
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single_label_cnn_config,
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single_label_default_config,
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)
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from spacy.pipeline.textcat_multilabel import (
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multi_label_bow_config,
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multi_label_cnn_config,
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multi_label_default_config,
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)
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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.tokens import Doc, DocBin
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from spacy.training import Example
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from spacy.training.initialize import init_nlp
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# Ensure that the architecture gets added to the registry.
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from ..tok2vec import build_lazy_init_tok2vec as _
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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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lazy_init_model_config = """
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[model]
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@architectures = "test.LazyInitTok2Vec.v1"
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width = 96
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"""
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LAZY_INIT_TOK2VEC_MODEL = Config().from_str(lazy_init_model_config)["model"]
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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.issue(3611)
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def test_issue3611():
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"""Test whether adding n-grams in the textcat works even when n > token length of some docs"""
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unique_classes = ["offensive", "inoffensive"]
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x_train = [
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"This is an offensive text",
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"This is the second offensive text",
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"inoff",
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]
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y_train = ["offensive", "offensive", "inoffensive"]
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nlp = spacy.blank("en")
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# preparing the data
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train_data = []
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for text, train_instance in zip(x_train, y_train):
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cat_dict = {label: label == train_instance for label in unique_classes}
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train_data.append(Example.from_dict(nlp.make_doc(text), {"cats": cat_dict}))
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# add a text categorizer component
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model = {
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"@architectures": "spacy.TextCatBOW.v1",
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"exclusive_classes": True,
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"ngram_size": 2,
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"no_output_layer": False,
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}
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textcat = nlp.add_pipe("textcat", config={"model": model}, last=True)
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for label in unique_classes:
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textcat.add_label(label)
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# training the network
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with nlp.select_pipes(enable="textcat"):
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optimizer = nlp.initialize()
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for i in range(3):
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losses = {}
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batches = util.minibatch(train_data, size=compounding(4.0, 32.0, 1.001))
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for batch in batches:
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nlp.update(examples=batch, sgd=optimizer, drop=0.1, losses=losses)
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@pytest.mark.issue(4030)
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def test_issue4030():
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"""Test whether textcat works fine with empty doc"""
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unique_classes = ["offensive", "inoffensive"]
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x_train = [
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"This is an offensive text",
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"This is the second offensive text",
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"inoff",
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]
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y_train = ["offensive", "offensive", "inoffensive"]
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nlp = spacy.blank("en")
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# preparing the data
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train_data = []
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for text, train_instance in zip(x_train, y_train):
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cat_dict = {label: label == train_instance for label in unique_classes}
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train_data.append(Example.from_dict(nlp.make_doc(text), {"cats": cat_dict}))
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# add a text categorizer component
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model = {
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"@architectures": "spacy.TextCatBOW.v1",
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"exclusive_classes": True,
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"ngram_size": 2,
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"no_output_layer": False,
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}
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textcat = nlp.add_pipe("textcat", config={"model": model}, last=True)
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for label in unique_classes:
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textcat.add_label(label)
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# training the network
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with nlp.select_pipes(enable="textcat"):
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optimizer = nlp.initialize()
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for i in range(3):
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losses = {}
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batches = util.minibatch(train_data, size=compounding(4.0, 32.0, 1.001))
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for batch in batches:
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nlp.update(examples=batch, sgd=optimizer, drop=0.1, losses=losses)
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# processing of an empty doc should result in 0.0 for all categories
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doc = nlp("")
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assert doc.cats["offensive"] == 0.0
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assert doc.cats["inoffensive"] == 0.0
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@pytest.mark.parametrize(
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"textcat_config",
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[
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single_label_default_config,
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single_label_bow_config,
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single_label_cnn_config,
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multi_label_default_config,
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multi_label_bow_config,
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multi_label_cnn_config,
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],
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)
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@pytest.mark.issue(5551)
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def test_issue5551(textcat_config):
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"""Test that after fixing the random seed, the results of the pipeline are truly identical"""
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component = "textcat"
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pipe_cfg = Config().from_str(textcat_config)
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results = []
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for i in range(3):
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fix_random_seed(0)
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nlp = English()
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text = "Once hot, form ping-pong-ball-sized balls of the mixture, each weighing roughly 25 g."
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annots = {"cats": {"Labe1": 1.0, "Label2": 0.0, "Label3": 0.0}}
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pipe = nlp.add_pipe(component, config=pipe_cfg, last=True)
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for label in set(annots["cats"]):
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pipe.add_label(label)
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# Train
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nlp.initialize()
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doc = nlp.make_doc(text)
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nlp.update([Example.from_dict(doc, annots)])
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# Store the result of each iteration
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result = pipe.model.predict([doc])
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results.append(result[0])
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# All results should be the same because of the fixed seed
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assert len(results) == 3
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ops = get_current_ops()
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assert_almost_equal(ops.to_numpy(results[0]), ops.to_numpy(results[1]), decimal=5)
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assert_almost_equal(ops.to_numpy(results[0]), ops.to_numpy(results[2]), decimal=5)
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CONFIG_ISSUE_6908 = """
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[paths]
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train = "TRAIN_PLACEHOLDER"
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raw = null
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init_tok2vec = null
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vectors = null
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[system]
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seed = 0
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gpu_allocator = null
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[nlp]
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lang = "en"
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pipeline = ["textcat"]
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tokenizer = {"@tokenizers":"spacy.Tokenizer.v1"}
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disabled = []
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before_creation = null
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after_creation = null
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after_pipeline_creation = null
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batch_size = 1000
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[components]
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[components.textcat]
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factory = "TEXTCAT_PLACEHOLDER"
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[corpora]
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[corpora.train]
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@readers = "spacy.Corpus.v1"
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path = ${paths:train}
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[corpora.dev]
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@readers = "spacy.Corpus.v1"
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path = ${paths:train}
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[training]
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train_corpus = "corpora.train"
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dev_corpus = "corpora.dev"
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seed = ${system.seed}
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gpu_allocator = ${system.gpu_allocator}
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frozen_components = []
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before_to_disk = null
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[pretraining]
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[initialize]
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vectors = ${paths.vectors}
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init_tok2vec = ${paths.init_tok2vec}
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vocab_data = null
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lookups = null
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before_init = null
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after_init = null
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[initialize.components]
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[initialize.components.textcat]
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labels = ['label1', 'label2']
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[initialize.tokenizer]
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"""
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@pytest.mark.parametrize(
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"component_name",
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["textcat", "textcat_multilabel"],
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)
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@pytest.mark.issue(6908)
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def test_issue6908(component_name):
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"""Test intializing textcat with labels in a list"""
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def create_data(out_file):
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nlp = spacy.blank("en")
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doc = nlp.make_doc("Some text")
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doc.cats = {"label1": 0, "label2": 1}
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out_data = DocBin(docs=[doc]).to_bytes()
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with out_file.open("wb") as file_:
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file_.write(out_data)
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with make_tempdir() as tmp_path:
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train_path = tmp_path / "train.spacy"
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create_data(train_path)
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config_str = CONFIG_ISSUE_6908.replace("TEXTCAT_PLACEHOLDER", component_name)
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config_str = config_str.replace("TRAIN_PLACEHOLDER", train_path.as_posix())
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config = util.load_config_from_str(config_str)
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init_nlp(config)
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@pytest.mark.issue(7019)
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def test_issue7019():
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scores = {"LABEL_A": 0.39829102, "LABEL_B": 0.938298329382, "LABEL_C": None}
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print_textcats_auc_per_cat(msg, scores)
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scores = {
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"LABEL_A": {"p": 0.3420302, "r": 0.3929020, "f": 0.49823928932},
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"LABEL_B": {"p": None, "r": None, "f": None},
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}
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print_prf_per_type(msg, scores, name="foo", type="bar")
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@pytest.mark.issue(9904)
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def test_issue9904():
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nlp = Language()
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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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nlp.initialize(get_examples)
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examples = get_examples()
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scores = textcat.predict([eg.predicted for eg in examples])
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loss = textcat.get_loss(examples, scores)[0]
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loss_double_bs = textcat.get_loss(examples * 2, scores.repeat(2, axis=0))[0]
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assert loss == pytest.approx(loss_double_bs)
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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(
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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_invalid_label_value(name, get_examples):
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nlp = Language()
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textcat = nlp.add_pipe(name)
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example_getter = get_examples(nlp)
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def invalid_examples():
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# make one example with an invalid score
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examples = example_getter()
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ref = examples[0].reference
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key = list(ref.cats.keys())[0]
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ref.cats[key] = 2.0
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return examples
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with pytest.raises(ValueError):
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nlp.initialize(get_examples=invalid_examples)
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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.slow
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@pytest.mark.parametrize(
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"name,textcat_config",
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[
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# BOW V1
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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 V1
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("textcat", {"@architectures": "spacy.TextCatEnsemble.v1", "exclusive_classes": False, "pretrained_vectors": None, "width": 64, "embed_size": 2000, "conv_depth": 2, "window_size": 1, "ngram_size": 1, "dropout": None}),
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("textcat_multilabel", {"@architectures": "spacy.TextCatEnsemble.v1", "exclusive_classes": False, "pretrained_vectors": None, "width": 64, "embed_size": 2000, "conv_depth": 2, "window_size": 1, "ngram_size": 1, "dropout": None}),
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# ENSEMBLE V2
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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
|
|
with pytest.raises(ValueError):
|
|
textcat.add_label("NEUTRAL")
|
|
|
|
|
|
# fmt: off
|
|
@pytest.mark.parametrize(
|
|
"name,textcat_config",
|
|
[
|
|
# BOW V3
|
|
("textcat", {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": True, "no_output_layer": False, "ngram_size": 3}),
|
|
("textcat", {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": True, "no_output_layer": True, "ngram_size": 3}),
|
|
("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": False, "no_output_layer": False, "ngram_size": 3}),
|
|
("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": False, "no_output_layer": True, "ngram_size": 3}),
|
|
# CNN
|
|
("textcat", {"@architectures": "spacy.TextCatReduce.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": True, "use_reduce_first": True, "use_reduce_last": True, "use_reduce_max": True, "use_reduce_mean": True}),
|
|
("textcat_multilabel", {"@architectures": "spacy.TextCatReduce.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": False, "use_reduce_first": True, "use_reduce_last": True, "use_reduce_max": True, "use_reduce_mean": True}),
|
|
],
|
|
)
|
|
# fmt: on
|
|
def test_resize(name, textcat_config):
|
|
"""The new textcat architectures are resizable"""
|
|
nlp = Language()
|
|
pipe_config = {"model": textcat_config}
|
|
textcat = nlp.add_pipe(name, config=pipe_config)
|
|
textcat.add_label("POSITIVE")
|
|
textcat.add_label("NEGATIVE")
|
|
assert textcat.model.maybe_get_dim("nO") in [2, None]
|
|
nlp.initialize()
|
|
assert textcat.model.maybe_get_dim("nO") in [2, None]
|
|
textcat.add_label("NEUTRAL")
|
|
assert textcat.model.maybe_get_dim("nO") in [3, None]
|
|
|
|
|
|
# fmt: off
|
|
@pytest.mark.parametrize(
|
|
"name,textcat_config",
|
|
[
|
|
# BOW v3
|
|
("textcat", {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": True, "no_output_layer": False, "ngram_size": 3}),
|
|
("textcat", {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": True, "no_output_layer": True, "ngram_size": 3}),
|
|
("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": False, "no_output_layer": False, "ngram_size": 3}),
|
|
("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": False, "no_output_layer": True, "ngram_size": 3}),
|
|
# REDUCE
|
|
("textcat", {"@architectures": "spacy.TextCatReduce.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": True, "use_reduce_first": True, "use_reduce_last": True, "use_reduce_max": True, "use_reduce_mean": True}),
|
|
("textcat_multilabel", {"@architectures": "spacy.TextCatReduce.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": False, "use_reduce_first": True, "use_reduce_last": True, "use_reduce_max": True, "use_reduce_mean": True}),
|
|
],
|
|
)
|
|
# fmt: on
|
|
def test_resize_same_results(name, textcat_config):
|
|
# Ensure that the resized textcat classifiers still produce the same results for old labels
|
|
fix_random_seed(0)
|
|
nlp = English()
|
|
pipe_config = {"model": textcat_config}
|
|
textcat = nlp.add_pipe(name, config=pipe_config)
|
|
|
|
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.maybe_get_dim("nO") in [2, None]
|
|
|
|
for i in range(5):
|
|
losses = {}
|
|
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
|
|
|
# test the trained model before resizing
|
|
test_text = "I am happy."
|
|
doc = nlp(test_text)
|
|
assert len(doc.cats) == 2
|
|
pos_pred = doc.cats["POSITIVE"]
|
|
neg_pred = doc.cats["NEGATIVE"]
|
|
|
|
# test the trained model again after resizing
|
|
textcat.add_label("NEUTRAL")
|
|
doc = nlp(test_text)
|
|
assert len(doc.cats) == 3
|
|
assert doc.cats["POSITIVE"] == pos_pred
|
|
assert doc.cats["NEGATIVE"] == neg_pred
|
|
assert doc.cats["NEUTRAL"] <= 1
|
|
|
|
for i in range(5):
|
|
losses = {}
|
|
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
|
|
|
# test the trained model again after training further with new label
|
|
doc = nlp(test_text)
|
|
assert len(doc.cats) == 3
|
|
assert doc.cats["POSITIVE"] != pos_pred
|
|
assert doc.cats["NEGATIVE"] != neg_pred
|
|
for cat in doc.cats:
|
|
assert doc.cats[cat] <= 1
|
|
|
|
|
|
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)
|
|
|
|
|
|
# fmt: off
|
|
@pytest.mark.parametrize(
|
|
"name,textcat_config",
|
|
[
|
|
# ENSEMBLE V2
|
|
("textcat_multilabel", {"@architectures": "spacy.TextCatEnsemble.v2", "tok2vec": LAZY_INIT_TOK2VEC_MODEL, "linear_model": {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": False, "ngram_size": 1, "no_output_layer": False}}),
|
|
("textcat", {"@architectures": "spacy.TextCatEnsemble.v2", "tok2vec": LAZY_INIT_TOK2VEC_MODEL, "linear_model": {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": True, "ngram_size": 5, "no_output_layer": False}}),
|
|
# PARAMETRIC ATTENTION V1
|
|
("textcat", {"@architectures": "spacy.TextCatParametricAttention.v1", "tok2vec": LAZY_INIT_TOK2VEC_MODEL, "exclusive_classes": True}),
|
|
("textcat_multilabel", {"@architectures": "spacy.TextCatParametricAttention.v1", "tok2vec": LAZY_INIT_TOK2VEC_MODEL, "exclusive_classes": False}),
|
|
# REDUCE
|
|
("textcat", {"@architectures": "spacy.TextCatReduce.v1", "tok2vec": LAZY_INIT_TOK2VEC_MODEL, "exclusive_classes": True, "use_reduce_first": True, "use_reduce_last": True, "use_reduce_max": True, "use_reduce_mean": True}),
|
|
("textcat_multilabel", {"@architectures": "spacy.TextCatReduce.v1", "tok2vec": LAZY_INIT_TOK2VEC_MODEL, "exclusive_classes": False, "use_reduce_first": True, "use_reduce_last": True, "use_reduce_max": True, "use_reduce_mean": True}),
|
|
],
|
|
)
|
|
# fmt: on
|
|
def test_tok2vec_lazy_init(name, textcat_config):
|
|
# Check that we can properly initialize and use a textcat model using
|
|
# a lazily-initialized tok2vec.
|
|
nlp = English()
|
|
pipe_config = {"model": textcat_config}
|
|
textcat = nlp.add_pipe(name, config=pipe_config)
|
|
textcat.add_label("POSITIVE")
|
|
textcat.add_label("NEGATIVE")
|
|
nlp.initialize()
|
|
nlp.pipe(["This is a test."])
|
|
|
|
|
|
@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]]
|
|
for cats_1, cats_2 in zip(batch_cats_1, batch_cats_2):
|
|
for cat in cats_1:
|
|
assert_almost_equal(cats_1[cat], cats_2[cat], decimal=5)
|
|
for cats_1, cats_2 in zip(batch_cats_1, no_batch_cats):
|
|
for cat in cats_1:
|
|
assert_almost_equal(cats_1[cat], cats_2[cat], decimal=5)
|
|
|
|
|
|
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]]
|
|
for cats_1, cats_2 in zip(batch_deps_1, batch_deps_2):
|
|
for cat in cats_1:
|
|
assert_almost_equal(cats_1[cat], cats_2[cat], decimal=5)
|
|
for cats_1, cats_2 in zip(batch_deps_1, no_batch_deps):
|
|
for cat in cats_1:
|
|
assert_almost_equal(cats_1[cat], cats_2[cat], decimal=5)
|
|
|
|
|
|
# fmt: off
|
|
@pytest.mark.slow
|
|
@pytest.mark.parametrize(
|
|
"name,train_data,textcat_config",
|
|
[
|
|
# BOW V1
|
|
("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}),
|
|
# ENSEMBLE V1
|
|
("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatEnsemble.v1", "exclusive_classes": False, "pretrained_vectors": None, "width": 64, "embed_size": 2000, "conv_depth": 2, "window_size": 1, "ngram_size": 1, "dropout": None}),
|
|
("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatEnsemble.v1", "exclusive_classes": False, "pretrained_vectors": None, "width": 64, "embed_size": 2000, "conv_depth": 2, "window_size": 1, "ngram_size": 1, "dropout": None}),
|
|
# CNN V1
|
|
("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}),
|
|
# BOW V2
|
|
("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": False, "ngram_size": 1, "no_output_layer": False}),
|
|
("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": True, "ngram_size": 4, "no_output_layer": False}),
|
|
("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": False, "ngram_size": 3, "no_output_layer": True}),
|
|
("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": True, "ngram_size": 2, "no_output_layer": True}),
|
|
# BOW V3
|
|
("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": False, "ngram_size": 1, "no_output_layer": False}),
|
|
("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": True, "ngram_size": 4, "no_output_layer": False}),
|
|
("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": False, "ngram_size": 3, "no_output_layer": True}),
|
|
("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": True, "ngram_size": 2, "no_output_layer": True}),
|
|
# ENSEMBLE V2
|
|
("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatEnsemble.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "linear_model": {"@architectures": "spacy.TextCatBOW.v3", "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.v3", "exclusive_classes": True, "ngram_size": 5, "no_output_layer": False}}),
|
|
# CNN V2 (legacy)
|
|
("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatCNN.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": True}),
|
|
("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatCNN.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": False}),
|
|
# PARAMETRIC ATTENTION V1
|
|
("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatParametricAttention.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": True}),
|
|
("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatParametricAttention.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": False}),
|
|
# REDUCE V1
|
|
("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatReduce.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": True, "use_reduce_first": True, "use_reduce_last": True, "use_reduce_max": True, "use_reduce_mean": True}),
|
|
("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatReduce.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": False, "use_reduce_first": True, "use_reduce_last": True, "use_reduce_max": True, "use_reduce_mean": True}),
|
|
],
|
|
)
|
|
# 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()
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|
textcat = nlp.add_pipe("textcat")
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|
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")
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|
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
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"multi_label,spring_p",
|
|
[(True, 1 / 1), (False, 1 / 2)],
|
|
)
|
|
def test_textcat_eval_missing(multi_label: bool, spring_p: float):
|
|
"""
|
|
multi-label: the missing 'spring' in gold_doc_2 doesn't incur a penalty
|
|
exclusive labels: the missing 'spring' in gold_doc_2 is interpreted as 0.0"""
|
|
train_examples = []
|
|
nlp = English()
|
|
|
|
ref1 = nlp("one")
|
|
ref1.cats = {"winter": 0.0, "summer": 0.0, "autumn": 0.0, "spring": 1.0}
|
|
pred1 = nlp("one")
|
|
pred1.cats = {"winter": 0.0, "summer": 0.0, "autumn": 0.0, "spring": 1.0}
|
|
train_examples.append(Example(ref1, pred1))
|
|
|
|
ref2 = nlp("two")
|
|
# reference 'spring' is missing, pred 'spring' is 1
|
|
ref2.cats = {"winter": 0.0, "summer": 0.0, "autumn": 1.0}
|
|
pred2 = nlp("two")
|
|
pred2.cats = {"winter": 0.0, "summer": 0.0, "autumn": 0.0, "spring": 1.0}
|
|
train_examples.append(Example(pred2, ref2))
|
|
|
|
scores = Scorer().score_cats(
|
|
train_examples,
|
|
"cats",
|
|
labels=["winter", "summer", "spring", "autumn"],
|
|
multi_label=multi_label,
|
|
)
|
|
assert scores["cats_f_per_type"]["spring"]["p"] == spring_p
|
|
assert scores["cats_f_per_type"]["spring"]["r"] == 1 / 1
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"multi_label,expected_loss",
|
|
[(True, 0), (False, 0.125)],
|
|
)
|
|
def test_textcat_loss(multi_label: bool, expected_loss: float):
|
|
"""
|
|
multi-label: the missing 'spring' in gold_doc_2 doesn't incur an increase in loss
|
|
exclusive labels: the missing 'spring' in gold_doc_2 is interpreted as 0.0 and adds to the loss"""
|
|
train_examples = []
|
|
nlp = English()
|
|
|
|
doc1 = nlp("one")
|
|
cats1 = {"winter": 0.0, "summer": 0.0, "autumn": 0.0, "spring": 1.0}
|
|
train_examples.append(Example.from_dict(doc1, {"cats": cats1}))
|
|
|
|
doc2 = nlp("two")
|
|
cats2 = {"winter": 0.0, "summer": 0.0, "autumn": 1.0}
|
|
train_examples.append(Example.from_dict(doc2, {"cats": cats2}))
|
|
|
|
if multi_label:
|
|
textcat = nlp.add_pipe("textcat_multilabel")
|
|
else:
|
|
textcat = nlp.add_pipe("textcat")
|
|
assert isinstance(textcat, TextCategorizer)
|
|
textcat.initialize(lambda: train_examples)
|
|
scores = textcat.model.ops.asarray(
|
|
[[0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 1.0, 1.0]], dtype="f" # type: ignore
|
|
)
|
|
loss, d_scores = textcat.get_loss(train_examples, scores)
|
|
assert loss == expected_loss
|
|
|
|
|
|
def test_textcat_multilabel_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})
|
|
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
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"component_name,scorer",
|
|
[
|
|
("textcat", "spacy.textcat_scorer.v1"),
|
|
("textcat_multilabel", "spacy.textcat_multilabel_scorer.v1"),
|
|
],
|
|
)
|
|
def test_textcat_legacy_scorers(component_name, scorer):
|
|
"""Check that legacy scorers are registered and produce the expected score
|
|
keys."""
|
|
nlp = English()
|
|
nlp.add_pipe(component_name, config={"scorer": {"@scorers": scorer}})
|
|
|
|
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
|