mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 12:18:04 +03:00
dd207a28be
* add keyword separator for update functions and drop unused "state" * few more Example tests and various small fixes * consistently return losses after update call * eliminate unused tensors field across pipe components * fix name * fix arg name
470 lines
16 KiB
Python
470 lines
16 KiB
Python
import pytest
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from spacy.pipeline import EntityRuler, EntityRecognizer, Pipe
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from spacy.pipeline.defaults import default_ner
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from spacy.matcher import PhraseMatcher, Matcher
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from spacy.tokens import Doc, Span, DocBin
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from spacy.gold import Example, Corpus
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from spacy.gold.converters import json2docs
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from spacy.vocab import Vocab
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from spacy.lang.en import English
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from spacy.util import minibatch, ensure_path, load_model
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from spacy.util import compile_prefix_regex, compile_suffix_regex, compile_infix_regex
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from spacy.tokenizer import Tokenizer
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from spacy.lang.el import Greek
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from spacy.language import Language
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import spacy
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from thinc.api import compounding
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from collections import defaultdict
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from ..util import make_tempdir
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def test_issue4002(en_vocab):
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"""Test that the PhraseMatcher can match on overwritten NORM attributes.
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"""
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matcher = PhraseMatcher(en_vocab, attr="NORM")
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pattern1 = Doc(en_vocab, words=["c", "d"])
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assert [t.norm_ for t in pattern1] == ["c", "d"]
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matcher.add("TEST", [pattern1])
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doc = Doc(en_vocab, words=["a", "b", "c", "d"])
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assert [t.norm_ for t in doc] == ["a", "b", "c", "d"]
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matches = matcher(doc)
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assert len(matches) == 1
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matcher = PhraseMatcher(en_vocab, attr="NORM")
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pattern2 = Doc(en_vocab, words=["1", "2"])
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pattern2[0].norm_ = "c"
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pattern2[1].norm_ = "d"
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assert [t.norm_ for t in pattern2] == ["c", "d"]
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matcher.add("TEST", [pattern2])
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matches = matcher(doc)
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assert len(matches) == 1
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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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textcat = nlp.create_pipe(
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"textcat",
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config={"exclusive_classes": True, "architecture": "bow", "ngram_size": 2},
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)
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for label in unique_classes:
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textcat.add_label(label)
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nlp.add_pipe(textcat, last=True)
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# training the network
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with nlp.select_pipes(enable="textcat"):
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optimizer = nlp.begin_training()
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for i in range(3):
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losses = {}
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batches = 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(
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examples=batch, sgd=optimizer, drop=0.1, losses=losses,
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)
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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.filterwarnings("ignore::UserWarning")
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def test_issue4042():
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"""Test that serialization of an EntityRuler before NER works fine."""
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nlp = English()
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# add ner pipe
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ner = nlp.create_pipe("ner")
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ner.add_label("SOME_LABEL")
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nlp.add_pipe(ner)
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nlp.begin_training()
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# Add entity ruler
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ruler = EntityRuler(nlp)
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patterns = [
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{"label": "MY_ORG", "pattern": "Apple"},
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{"label": "MY_GPE", "pattern": [{"lower": "san"}, {"lower": "francisco"}]},
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]
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ruler.add_patterns(patterns)
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nlp.add_pipe(ruler, before="ner") # works fine with "after"
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doc1 = nlp("What do you think about Apple ?")
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assert doc1.ents[0].label_ == "MY_ORG"
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with make_tempdir() as d:
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output_dir = ensure_path(d)
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if not output_dir.exists():
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output_dir.mkdir()
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nlp.to_disk(output_dir)
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nlp2 = load_model(output_dir)
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doc2 = nlp2("What do you think about Apple ?")
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assert doc2.ents[0].label_ == "MY_ORG"
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@pytest.mark.filterwarnings("ignore::UserWarning")
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def test_issue4042_bug2():
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"""
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Test that serialization of an NER works fine when new labels were added.
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This is the second bug of two bugs underlying the issue 4042.
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"""
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nlp1 = English()
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vocab = nlp1.vocab
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# add ner pipe
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ner1 = nlp1.create_pipe("ner")
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ner1.add_label("SOME_LABEL")
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nlp1.add_pipe(ner1)
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nlp1.begin_training()
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# add a new label to the doc
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doc1 = nlp1("What do you think about Apple ?")
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assert len(ner1.labels) == 1
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assert "SOME_LABEL" in ner1.labels
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apple_ent = Span(doc1, 5, 6, label="MY_ORG")
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doc1.ents = list(doc1.ents) + [apple_ent]
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# reapply the NER - at this point it should resize itself
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ner1(doc1)
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assert len(ner1.labels) == 2
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assert "SOME_LABEL" in ner1.labels
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assert "MY_ORG" in ner1.labels
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with make_tempdir() as d:
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# assert IO goes fine
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output_dir = ensure_path(d)
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if not output_dir.exists():
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output_dir.mkdir()
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ner1.to_disk(output_dir)
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config = {
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"learn_tokens": False,
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"min_action_freq": 30,
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"beam_width": 1,
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"beam_update_prob": 1.0,
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}
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ner2 = EntityRecognizer(vocab, default_ner(), **config)
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ner2.from_disk(output_dir)
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assert len(ner2.labels) == 2
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def test_issue4054(en_vocab):
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"""Test that a new blank model can be made with a vocab from file,
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and that serialization does not drop the language at any point."""
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nlp1 = English()
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vocab1 = nlp1.vocab
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with make_tempdir() as d:
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vocab_dir = ensure_path(d / "vocab")
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if not vocab_dir.exists():
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vocab_dir.mkdir()
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vocab1.to_disk(vocab_dir)
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vocab2 = Vocab().from_disk(vocab_dir)
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print("lang", vocab2.lang)
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nlp2 = spacy.blank("en", vocab=vocab2)
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nlp_dir = ensure_path(d / "nlp")
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if not nlp_dir.exists():
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nlp_dir.mkdir()
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nlp2.to_disk(nlp_dir)
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nlp3 = load_model(nlp_dir)
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assert nlp3.lang == "en"
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def test_issue4120(en_vocab):
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"""Test that matches without a final {OP: ?} token are returned."""
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matcher = Matcher(en_vocab)
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matcher.add("TEST", [[{"ORTH": "a"}, {"OP": "?"}]])
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doc1 = Doc(en_vocab, words=["a"])
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assert len(matcher(doc1)) == 1 # works
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doc2 = Doc(en_vocab, words=["a", "b", "c"])
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assert len(matcher(doc2)) == 2 # fixed
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matcher = Matcher(en_vocab)
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matcher.add("TEST", [[{"ORTH": "a"}, {"OP": "?"}, {"ORTH": "b"}]])
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doc3 = Doc(en_vocab, words=["a", "b", "b", "c"])
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assert len(matcher(doc3)) == 2 # works
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matcher = Matcher(en_vocab)
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matcher.add("TEST", [[{"ORTH": "a"}, {"OP": "?"}, {"ORTH": "b", "OP": "?"}]])
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doc4 = Doc(en_vocab, words=["a", "b", "b", "c"])
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assert len(matcher(doc4)) == 3 # fixed
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def test_issue4133(en_vocab):
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nlp = English()
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vocab_bytes = nlp.vocab.to_bytes()
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words = ["Apple", "is", "looking", "at", "buying", "a", "startup"]
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pos = ["NOUN", "VERB", "ADP", "VERB", "PROPN", "NOUN", "ADP"]
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doc = Doc(en_vocab, words=words)
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for i, token in enumerate(doc):
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token.pos_ = pos[i]
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# usually this is already True when starting from proper models instead of blank English
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doc.is_tagged = True
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doc_bytes = doc.to_bytes()
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vocab = Vocab()
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vocab = vocab.from_bytes(vocab_bytes)
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doc = Doc(vocab).from_bytes(doc_bytes)
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actual = []
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for token in doc:
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actual.append(token.pos_)
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assert actual == pos
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def test_issue4190():
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def customize_tokenizer(nlp):
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prefix_re = compile_prefix_regex(nlp.Defaults.prefixes)
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suffix_re = compile_suffix_regex(nlp.Defaults.suffixes)
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infix_re = compile_infix_regex(nlp.Defaults.infixes)
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# Remove all exceptions where a single letter is followed by a period (e.g. 'h.')
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exceptions = {
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k: v
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for k, v in dict(nlp.Defaults.tokenizer_exceptions).items()
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if not (len(k) == 2 and k[1] == ".")
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}
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new_tokenizer = Tokenizer(
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nlp.vocab,
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exceptions,
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prefix_search=prefix_re.search,
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suffix_search=suffix_re.search,
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infix_finditer=infix_re.finditer,
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token_match=nlp.tokenizer.token_match,
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)
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nlp.tokenizer = new_tokenizer
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test_string = "Test c."
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# Load default language
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nlp_1 = English()
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doc_1a = nlp_1(test_string)
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result_1a = [token.text for token in doc_1a] # noqa: F841
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# Modify tokenizer
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customize_tokenizer(nlp_1)
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doc_1b = nlp_1(test_string)
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result_1b = [token.text for token in doc_1b]
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# Save and Reload
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with make_tempdir() as model_dir:
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nlp_1.to_disk(model_dir)
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nlp_2 = load_model(model_dir)
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# This should be the modified tokenizer
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doc_2 = nlp_2(test_string)
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result_2 = [token.text for token in doc_2]
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assert result_1b == result_2
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@pytest.mark.filterwarnings("ignore::UserWarning")
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def test_issue4267():
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""" Test that running an entity_ruler after ner gives consistent results"""
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nlp = English()
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ner = nlp.create_pipe("ner")
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ner.add_label("PEOPLE")
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nlp.add_pipe(ner)
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nlp.begin_training()
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assert "ner" in nlp.pipe_names
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# assert that we have correct IOB annotations
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doc1 = nlp("hi")
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assert doc1.is_nered
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for token in doc1:
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assert token.ent_iob == 2
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# add entity ruler and run again
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ruler = EntityRuler(nlp)
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patterns = [{"label": "SOFTWARE", "pattern": "spacy"}]
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ruler.add_patterns(patterns)
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nlp.add_pipe(ruler)
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assert "entity_ruler" in nlp.pipe_names
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assert "ner" in nlp.pipe_names
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# assert that we still have correct IOB annotations
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doc2 = nlp("hi")
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assert doc2.is_nered
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for token in doc2:
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assert token.ent_iob == 2
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def test_issue4272():
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"""Test that lookup table can be accessed from Token.lemma if no POS tags
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are available."""
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nlp = Greek()
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doc = nlp("Χθες")
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assert doc[0].lemma_
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def test_multiple_predictions():
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class DummyPipe(Pipe):
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def __init__(self):
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self.model = "dummy_model"
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def predict(self, docs):
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return ([1, 2, 3], [4, 5, 6])
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def set_annotations(self, docs, scores):
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return docs
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nlp = Language()
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doc = nlp.make_doc("foo")
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dummy_pipe = DummyPipe()
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dummy_pipe(doc)
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@pytest.mark.skip(reason="removed Beam stuff during the Example/GoldParse refactor")
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def test_issue4313():
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""" This should not crash or exit with some strange error code """
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beam_width = 16
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beam_density = 0.0001
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nlp = English()
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config = {
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"learn_tokens": False,
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"min_action_freq": 30,
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"beam_width": 1,
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"beam_update_prob": 1.0,
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}
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ner = EntityRecognizer(nlp.vocab, default_ner(), **config)
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ner.add_label("SOME_LABEL")
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ner.begin_training([])
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nlp.add_pipe(ner)
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# add a new label to the doc
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doc = nlp("What do you think about Apple ?")
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assert len(ner.labels) == 1
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assert "SOME_LABEL" in ner.labels
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apple_ent = Span(doc, 5, 6, label="MY_ORG")
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doc.ents = list(doc.ents) + [apple_ent]
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# ensure the beam_parse still works with the new label
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docs = [doc]
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beams = nlp.entity.beam_parse(
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docs, beam_width=beam_width, beam_density=beam_density
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)
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for doc, beam in zip(docs, beams):
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entity_scores = defaultdict(float)
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for score, ents in nlp.entity.moves.get_beam_parses(beam):
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for start, end, label in ents:
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entity_scores[(start, end, label)] += score
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@pytest.mark.filterwarnings("ignore::UserWarning")
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def test_issue4348():
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"""Test that training the tagger with empty data, doesn't throw errors"""
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nlp = English()
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example = Example.from_dict(nlp.make_doc(""), {"tags": []})
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TRAIN_DATA = [example, example]
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tagger = nlp.create_pipe("tagger")
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nlp.add_pipe(tagger)
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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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batches = 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(batch, sgd=optimizer, losses=losses)
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def test_issue4367():
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"""Test that docbin init goes well"""
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DocBin()
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DocBin(attrs=["LEMMA"])
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DocBin(attrs=["LEMMA", "ENT_IOB", "ENT_TYPE"])
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def test_issue4373():
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"""Test that PhraseMatcher.vocab can be accessed (like Matcher.vocab)."""
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matcher = Matcher(Vocab())
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assert isinstance(matcher.vocab, Vocab)
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matcher = PhraseMatcher(Vocab())
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assert isinstance(matcher.vocab, Vocab)
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def test_issue4402():
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json_data = {
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"id": 0,
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"paragraphs": [
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{
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"raw": "How should I cook bacon in an oven?\nI've heard of people cooking bacon in an oven.",
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"sentences": [
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{
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"tokens": [
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{"id": 0, "orth": "How", "ner": "O"},
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{"id": 1, "orth": "should", "ner": "O"},
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{"id": 2, "orth": "I", "ner": "O"},
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{"id": 3, "orth": "cook", "ner": "O"},
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{"id": 4, "orth": "bacon", "ner": "O"},
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{"id": 5, "orth": "in", "ner": "O"},
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{"id": 6, "orth": "an", "ner": "O"},
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{"id": 7, "orth": "oven", "ner": "O"},
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{"id": 8, "orth": "?", "ner": "O"},
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],
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"brackets": [],
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},
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{
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"tokens": [
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{"id": 9, "orth": "\n", "ner": "O"},
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{"id": 10, "orth": "I", "ner": "O"},
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{"id": 11, "orth": "'ve", "ner": "O"},
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{"id": 12, "orth": "heard", "ner": "O"},
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{"id": 13, "orth": "of", "ner": "O"},
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{"id": 14, "orth": "people", "ner": "O"},
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{"id": 15, "orth": "cooking", "ner": "O"},
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{"id": 16, "orth": "bacon", "ner": "O"},
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{"id": 17, "orth": "in", "ner": "O"},
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{"id": 18, "orth": "an", "ner": "O"},
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{"id": 19, "orth": "oven", "ner": "O"},
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{"id": 20, "orth": ".", "ner": "O"},
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],
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"brackets": [],
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},
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],
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"cats": [
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{"label": "baking", "value": 1.0},
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{"label": "not_baking", "value": 0.0},
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],
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},
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{
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"raw": "What is the difference between white and brown eggs?\n",
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"sentences": [
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{
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"tokens": [
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{"id": 0, "orth": "What", "ner": "O"},
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{"id": 1, "orth": "is", "ner": "O"},
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{"id": 2, "orth": "the", "ner": "O"},
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{"id": 3, "orth": "difference", "ner": "O"},
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{"id": 4, "orth": "between", "ner": "O"},
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{"id": 5, "orth": "white", "ner": "O"},
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{"id": 6, "orth": "and", "ner": "O"},
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{"id": 7, "orth": "brown", "ner": "O"},
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{"id": 8, "orth": "eggs", "ner": "O"},
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{"id": 9, "orth": "?", "ner": "O"},
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],
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"brackets": [],
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},
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{"tokens": [{"id": 10, "orth": "\n", "ner": "O"}], "brackets": []},
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],
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"cats": [
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{"label": "baking", "value": 0.0},
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{"label": "not_baking", "value": 1.0},
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],
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},
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],
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}
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nlp = English()
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attrs = ["ORTH", "SENT_START", "ENT_IOB", "ENT_TYPE"]
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with make_tempdir() as tmpdir:
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output_file = tmpdir / "test4402.spacy"
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docs = json2docs([json_data])
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data = DocBin(docs=docs, attrs=attrs).to_bytes()
|
|
with output_file.open("wb") as file_:
|
|
file_.write(data)
|
|
corpus = Corpus(train_loc=str(output_file), dev_loc=str(output_file))
|
|
|
|
train_data = list(corpus.train_dataset(nlp))
|
|
assert len(train_data) == 2
|
|
|
|
split_train_data = []
|
|
for eg in train_data:
|
|
split_train_data.extend(eg.split_sents())
|
|
assert len(split_train_data) == 4
|