import itertools import pytest from spacy.gold import GoldParse from spacy.language import Language from spacy.tokens import Doc, Span from spacy.vocab import Vocab from .util import add_vecs_to_vocab, assert_docs_equal @pytest.fixture def nlp(): nlp = Language(Vocab()) textcat = nlp.create_pipe("textcat") for label in ("POSITIVE", "NEGATIVE"): textcat.add_label(label) nlp.add_pipe(textcat) nlp.begin_training() return nlp def test_language_update(nlp): text = "hello world" annots = {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}} wrongkeyannots = {"LABEL": True} doc = Doc(nlp.vocab, words=text.split(" ")) gold = GoldParse(doc, **annots) # Update with doc and gold objects nlp.update((doc, gold)) # Update with text and dict nlp.update((text, annots)) # Update with doc object and dict nlp.update((doc, annots)) # Update with text and gold object nlp.update((text, gold)) # Update with empty doc and gold object nlp.update((None, gold)) # Update badly with pytest.raises(ValueError): nlp.update((doc, None)) with pytest.raises(TypeError): nlp.update((text, wrongkeyannots)) def test_language_evaluate(nlp): text = "hello world" annots = {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}} doc = Doc(nlp.vocab, words=text.split(" ")) gold = GoldParse(doc, **annots) # Evaluate with doc and gold objects nlp.evaluate([(doc, gold)]) # Evaluate with text and dict nlp.evaluate([(text, annots)]) # Evaluate with doc object and dict nlp.evaluate([(doc, annots)]) # Evaluate with text and gold object nlp.evaluate([(text, gold)]) # Evaluate badly with pytest.raises(Exception): nlp.evaluate([text, gold]) def test_evaluate_no_pipe(nlp): """Test that docs are processed correctly within Language.pipe if the component doesn't expose a .pipe method.""" def pipe(doc): return doc text = "hello world" annots = {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}} nlp = Language(Vocab()) nlp.add_pipe(pipe) nlp.evaluate([(text, annots)]) def vector_modification_pipe(doc): doc.vector += 1 return doc def userdata_pipe(doc): doc.user_data["foo"] = "bar" return doc def ner_pipe(doc): span = Span(doc, 0, 1, label="FIRST") doc.ents += (span,) return doc @pytest.fixture def sample_vectors(): return [ ("spacy", [-0.1, -0.2, -0.3]), ("world", [-0.2, -0.3, -0.4]), ("pipe", [0.7, 0.8, 0.9]), ] @pytest.fixture def nlp2(nlp, sample_vectors): add_vecs_to_vocab(nlp.vocab, sample_vectors) nlp.add_pipe(vector_modification_pipe) nlp.add_pipe(ner_pipe) nlp.add_pipe(userdata_pipe) return nlp @pytest.fixture def texts(): data = [ "Hello world.", "This is spacy.", "You can use multiprocessing with pipe method.", "Please try!", ] return data @pytest.mark.parametrize("n_process", [1, 2]) def test_language_pipe(nlp2, n_process, texts): texts = texts * 10 expecteds = [nlp2(text) for text in texts] docs = nlp2.pipe(texts, n_process=n_process, batch_size=2) for doc, expected_doc in zip(docs, expecteds): assert_docs_equal(doc, expected_doc) @pytest.mark.parametrize("n_process", [1, 2]) def test_language_pipe_stream(nlp2, n_process, texts): # check if nlp.pipe can handle infinite length iterator properly. stream_texts = itertools.cycle(texts) texts0, texts1 = itertools.tee(stream_texts) expecteds = (nlp2(text) for text in texts0) docs = nlp2.pipe(texts1, n_process=n_process, batch_size=2) n_fetch = 20 for doc, expected_doc in itertools.islice(zip(docs, expecteds), n_fetch): assert_docs_equal(doc, expected_doc)