mirror of
https://github.com/explosion/spaCy.git
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3216a33149
* hook up positive_label in textcat * unit tests * documentation * formatting * tests * fix typo * move verify_config to after begin_training * revert accidential commit
227 lines
8.2 KiB
Python
227 lines
8.2 KiB
Python
import pytest
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import random
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import numpy.random
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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 ..util import make_tempdir
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from ...cli.train import verify_textcat_config
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from ...training import Example
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TRAIN_DATA = [
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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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@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.begin_training()
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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.begin_training(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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def test_label_types():
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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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with pytest.raises(ValueError):
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textcat.add_label(9)
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def test_no_label():
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nlp = Language()
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nlp.add_pipe("textcat")
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with pytest.raises(ValueError):
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nlp.begin_training()
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def test_implicit_label():
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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 t in TRAIN_DATA:
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train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
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nlp.begin_training(get_examples=lambda: train_examples)
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def test_no_resize():
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nlp = Language()
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textcat = nlp.add_pipe("textcat")
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textcat.add_label("POSITIVE")
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textcat.add_label("NEGATIVE")
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nlp.begin_training()
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assert textcat.model.get_dim("nO") == 2
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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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def test_begin_training_examples():
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nlp = Language()
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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:
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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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# you shouldn't really call this more than once, but for testing it should be fine
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nlp.begin_training()
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nlp.begin_training(get_examples=lambda: train_examples)
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with pytest.raises(TypeError):
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nlp.begin_training(get_examples=lambda: None)
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with pytest.raises(ValueError):
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nlp.begin_training(get_examples=train_examples)
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def test_overfitting_IO():
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# Simple test to try and quickly overfit the 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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# Set exclusive labels
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textcat = nlp.add_pipe(
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"textcat",
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config={"model": {"exclusive_classes": True}, "positive_label": "POSITIVE"},
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)
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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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optimizer = nlp.begin_training(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_score"] == 1.0
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assert "cats_score_desc" in scores
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# fmt: off
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@pytest.mark.parametrize(
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"textcat_config",
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[
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{"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": False, "ngram_size": 1, "no_output_layer": False},
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{"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": True, "ngram_size": 4, "no_output_layer": False},
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{"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": False, "ngram_size": 3, "no_output_layer": True},
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{"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": True, "ngram_size": 2, "no_output_layer": True},
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{"@architectures": "spacy.TextCatEnsemble.v1", "exclusive_classes": False, "ngram_size": 1, "pretrained_vectors": False, "width": 64, "conv_depth": 2, "embed_size": 2000, "window_size": 2, "dropout": None},
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{"@architectures": "spacy.TextCatEnsemble.v1", "exclusive_classes": True, "ngram_size": 5, "pretrained_vectors": False, "width": 128, "conv_depth": 2, "embed_size": 2000, "window_size": 1, "dropout": None},
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{"@architectures": "spacy.TextCatEnsemble.v1", "exclusive_classes": True, "ngram_size": 2, "pretrained_vectors": False, "width": 32, "conv_depth": 3, "embed_size": 500, "window_size": 3, "dropout": None},
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{"@architectures": "spacy.TextCatCNN.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": True},
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{"@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_textcat_configs(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("textcat", 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.begin_training()
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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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def test_positive_class():
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nlp = English()
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pipe_config = {"positive_label": "POS", "labels": ["POS", "NEG"]}
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textcat = nlp.add_pipe("textcat", config=pipe_config)
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assert textcat.labels == ("POS", "NEG")
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verify_textcat_config(nlp, pipe_config)
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def test_positive_class_not_present():
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nlp = English()
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pipe_config = {"positive_label": "POS", "labels": ["SOME", "THING"]}
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textcat = nlp.add_pipe("textcat", config=pipe_config)
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assert textcat.labels == ("SOME", "THING")
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with pytest.raises(ValueError):
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verify_textcat_config(nlp, pipe_config)
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def test_positive_class_not_binary():
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nlp = English()
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pipe_config = {"positive_label": "POS", "labels": ["SOME", "THING", "POS"]}
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textcat = nlp.add_pipe("textcat", config=pipe_config)
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assert textcat.labels == ("SOME", "THING", "POS")
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with pytest.raises(ValueError):
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verify_textcat_config(nlp, pipe_config)
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