import gc import numpy import pytest from thinc.api import get_current_ops import spacy from spacy.lang.en import English from spacy.lang.en.syntax_iterators import noun_chunks from spacy.language import Language from spacy.pipeline import TrainablePipe from spacy.tokens import Doc from spacy.training import Example from spacy.util import SimpleFrozenList, get_arg_names, make_tempdir from spacy.vocab import Vocab @pytest.fixture def nlp(): return Language() @Language.component("new_pipe") def new_pipe(doc): return doc @Language.component("other_pipe") def other_pipe(doc): return doc @pytest.mark.issue(1506) def test_issue1506(): def string_generator(): for _ in range(10001): yield "It's sentence produced by that bug." for _ in range(10001): yield "I erase some hbdsaj lemmas." for _ in range(10001): yield "I erase lemmas." for _ in range(10001): yield "It's sentence produced by that bug." for _ in range(10001): yield "It's sentence produced by that bug." nlp = English() for i, d in enumerate(nlp.pipe(string_generator())): # We should run cleanup more than one time to actually cleanup data. # In first run — clean up only mark strings as «not hitted». if i == 10000 or i == 20000 or i == 30000: gc.collect() for t in d: str(t.lemma_) @pytest.mark.issue(1654) def test_issue1654(): nlp = Language(Vocab()) assert not nlp.pipeline @Language.component("component") def component(doc): return doc nlp.add_pipe("component", name="1") nlp.add_pipe("component", name="2", after="1") nlp.add_pipe("component", name="3", after="2") assert nlp.pipe_names == ["1", "2", "3"] nlp2 = Language(Vocab()) assert not nlp2.pipeline nlp2.add_pipe("component", name="3") nlp2.add_pipe("component", name="2", before="3") nlp2.add_pipe("component", name="1", before="2") assert nlp2.pipe_names == ["1", "2", "3"] @pytest.mark.issue(3880) def test_issue3880(): """Test that `nlp.pipe()` works when an empty string ends the batch. Fixed in v7.0.5 of Thinc. """ texts = ["hello", "world", "", ""] nlp = English() nlp.add_pipe("parser").add_label("dep") nlp.add_pipe("ner").add_label("PERSON") nlp.add_pipe("tagger").add_label("NN") nlp.initialize() for doc in nlp.pipe(texts): pass @pytest.mark.issue(5082) def test_issue5082(): # Ensure the 'merge_entities' pipeline does something sensible for the vectors of the merged tokens nlp = English() vocab = nlp.vocab array1 = numpy.asarray([0.1, 0.5, 0.8], dtype=numpy.float32) array2 = numpy.asarray([-0.2, -0.6, -0.9], dtype=numpy.float32) array3 = numpy.asarray([0.3, -0.1, 0.7], dtype=numpy.float32) array4 = numpy.asarray([0.5, 0, 0.3], dtype=numpy.float32) array34 = numpy.asarray([0.4, -0.05, 0.5], dtype=numpy.float32) vocab.set_vector("I", array1) vocab.set_vector("like", array2) vocab.set_vector("David", array3) vocab.set_vector("Bowie", array4) text = "I like David Bowie" patterns = [ {"label": "PERSON", "pattern": [{"LOWER": "david"}, {"LOWER": "bowie"}]} ] ruler = nlp.add_pipe("entity_ruler") ruler.add_patterns(patterns) parsed_vectors_1 = [t.vector for t in nlp(text)] assert len(parsed_vectors_1) == 4 ops = get_current_ops() numpy.testing.assert_array_equal(ops.to_numpy(parsed_vectors_1[0]), array1) numpy.testing.assert_array_equal(ops.to_numpy(parsed_vectors_1[1]), array2) numpy.testing.assert_array_equal(ops.to_numpy(parsed_vectors_1[2]), array3) numpy.testing.assert_array_equal(ops.to_numpy(parsed_vectors_1[3]), array4) nlp.add_pipe("merge_entities") parsed_vectors_2 = [t.vector for t in nlp(text)] assert len(parsed_vectors_2) == 3 numpy.testing.assert_array_equal(ops.to_numpy(parsed_vectors_2[0]), array1) numpy.testing.assert_array_equal(ops.to_numpy(parsed_vectors_2[1]), array2) numpy.testing.assert_array_equal(ops.to_numpy(parsed_vectors_2[2]), array34) @pytest.mark.issue(5458) def test_issue5458(): # Test that the noun chuncker does not generate overlapping spans # fmt: off words = ["In", "an", "era", "where", "markets", "have", "brought", "prosperity", "and", "empowerment", "."] vocab = Vocab(strings=words) deps = ["ROOT", "det", "pobj", "advmod", "nsubj", "aux", "relcl", "dobj", "cc", "conj", "punct"] pos = ["ADP", "DET", "NOUN", "ADV", "NOUN", "AUX", "VERB", "NOUN", "CCONJ", "NOUN", "PUNCT"] heads = [0, 2, 0, 9, 6, 6, 2, 6, 7, 7, 0] # fmt: on en_doc = Doc(vocab, words=words, pos=pos, heads=heads, deps=deps) en_doc.noun_chunks_iterator = noun_chunks # if there are overlapping spans, this will fail with an E102 error "Can't merge non-disjoint spans" nlp = English() merge_nps = nlp.create_pipe("merge_noun_chunks") merge_nps(en_doc) def test_multiple_predictions(): class DummyPipe(TrainablePipe): def __init__(self): self.model = "dummy_model" def predict(self, docs): return ([1, 2, 3], [4, 5, 6]) def set_annotations(self, docs, scores): return docs nlp = Language() doc = nlp.make_doc("foo") dummy_pipe = DummyPipe() dummy_pipe(doc) def test_add_pipe_no_name(nlp): nlp.add_pipe("new_pipe") assert "new_pipe" in nlp.pipe_names def test_add_pipe_duplicate_name(nlp): nlp.add_pipe("new_pipe", name="duplicate_name") with pytest.raises(ValueError): nlp.add_pipe("new_pipe", name="duplicate_name") @pytest.mark.parametrize("name", ["parser"]) def test_add_pipe_first(nlp, name): nlp.add_pipe("new_pipe", name=name, first=True) assert nlp.pipeline[0][0] == name @pytest.mark.parametrize("name1,name2", [("parser", "lambda_pipe")]) def test_add_pipe_last(nlp, name1, name2): Language.component("new_pipe2", func=lambda doc: doc) nlp.add_pipe("new_pipe2", name=name2) nlp.add_pipe("new_pipe", name=name1, last=True) assert nlp.pipeline[0][0] != name1 assert nlp.pipeline[-1][0] == name1 def test_cant_add_pipe_first_and_last(nlp): with pytest.raises(ValueError): nlp.add_pipe("new_pipe", first=True, last=True) @pytest.mark.parametrize("name", ["test_get_pipe"]) def test_get_pipe(nlp, name): with pytest.raises(KeyError): nlp.get_pipe(name) nlp.add_pipe("new_pipe", name=name) assert nlp.get_pipe(name) == new_pipe @pytest.mark.parametrize( "name,replacement,invalid_replacement", [("test_replace_pipe", "other_pipe", lambda doc: doc)], ) def test_replace_pipe(nlp, name, replacement, invalid_replacement): with pytest.raises(ValueError): nlp.replace_pipe(name, new_pipe) nlp.add_pipe("new_pipe", name=name) with pytest.raises(ValueError): nlp.replace_pipe(name, invalid_replacement) nlp.replace_pipe(name, replacement) assert nlp.get_pipe(name) == nlp.create_pipe(replacement) def test_replace_last_pipe(nlp): nlp.add_pipe("sentencizer") nlp.add_pipe("ner") assert nlp.pipe_names == ["sentencizer", "ner"] nlp.replace_pipe("ner", "ner") assert nlp.pipe_names == ["sentencizer", "ner"] def test_replace_pipe_config(nlp): nlp.add_pipe("entity_linker") nlp.add_pipe("sentencizer") assert nlp.get_pipe("entity_linker").incl_prior is True nlp.replace_pipe("entity_linker", "entity_linker", config={"incl_prior": False}) assert nlp.get_pipe("entity_linker").incl_prior is False @pytest.mark.parametrize("old_name,new_name", [("old_pipe", "new_pipe")]) def test_rename_pipe(nlp, old_name, new_name): with pytest.raises(ValueError): nlp.rename_pipe(old_name, new_name) nlp.add_pipe("new_pipe", name=old_name) nlp.rename_pipe(old_name, new_name) assert nlp.pipeline[0][0] == new_name @pytest.mark.parametrize("name", ["my_component"]) def test_remove_pipe(nlp, name): with pytest.raises(ValueError): nlp.remove_pipe(name) nlp.add_pipe("new_pipe", name=name) assert len(nlp.pipeline) == 1 removed_name, removed_component = nlp.remove_pipe(name) assert not len(nlp.pipeline) assert removed_name == name assert removed_component == new_pipe @pytest.mark.parametrize("name", ["my_component"]) def test_disable_pipes_method(nlp, name): nlp.add_pipe("new_pipe", name=name) assert nlp.has_pipe(name) disabled = nlp.select_pipes(disable=name) assert not nlp.has_pipe(name) disabled.restore() @pytest.mark.parametrize("name", ["my_component"]) def test_enable_pipes_method(nlp, name): nlp.add_pipe("new_pipe", name=name) assert nlp.has_pipe(name) disabled = nlp.select_pipes(enable=[]) assert not nlp.has_pipe(name) disabled.restore() @pytest.mark.parametrize("name", ["my_component"]) def test_disable_pipes_context(nlp, name): """Test that an enabled component stays enabled after running the context manager.""" nlp.add_pipe("new_pipe", name=name) assert nlp.has_pipe(name) with nlp.select_pipes(disable=name): assert not nlp.has_pipe(name) assert nlp.has_pipe(name) @pytest.mark.parametrize("name", ["my_component"]) def test_disable_pipes_context_restore(nlp, name): """Test that a disabled component stays disabled after running the context manager.""" nlp.add_pipe("new_pipe", name=name) assert nlp.has_pipe(name) nlp.disable_pipe(name) assert not nlp.has_pipe(name) with nlp.select_pipes(disable=name): assert not nlp.has_pipe(name) assert not nlp.has_pipe(name) def test_select_pipes_list_arg(nlp): for name in ["c1", "c2", "c3"]: nlp.add_pipe("new_pipe", name=name) assert nlp.has_pipe(name) with nlp.select_pipes(disable=["c1", "c2"]): assert not nlp.has_pipe("c1") assert not nlp.has_pipe("c2") assert nlp.has_pipe("c3") with nlp.select_pipes(enable="c3"): assert not nlp.has_pipe("c1") assert not nlp.has_pipe("c2") assert nlp.has_pipe("c3") with nlp.select_pipes(enable=["c1", "c2"], disable="c3"): assert nlp.has_pipe("c1") assert nlp.has_pipe("c2") assert not nlp.has_pipe("c3") with nlp.select_pipes(enable=[]): assert not nlp.has_pipe("c1") assert not nlp.has_pipe("c2") assert not nlp.has_pipe("c3") with nlp.select_pipes(enable=["c1", "c2", "c3"], disable=[]): assert nlp.has_pipe("c1") assert nlp.has_pipe("c2") assert nlp.has_pipe("c3") with nlp.select_pipes(disable=["c1", "c2", "c3"], enable=[]): assert not nlp.has_pipe("c1") assert not nlp.has_pipe("c2") assert not nlp.has_pipe("c3") def test_select_pipes_errors(nlp): for name in ["c1", "c2", "c3"]: nlp.add_pipe("new_pipe", name=name) assert nlp.has_pipe(name) with pytest.raises(ValueError): nlp.select_pipes() with pytest.raises(ValueError): nlp.select_pipes(enable=["c1", "c2"], disable=["c1"]) with pytest.raises(ValueError): nlp.select_pipes(enable=["c1", "c2"], disable=[]) with pytest.raises(ValueError): nlp.select_pipes(enable=[], disable=["c3"]) disabled = nlp.select_pipes(disable=["c2"]) nlp.remove_pipe("c2") with pytest.raises(ValueError): disabled.restore() @pytest.mark.parametrize("n_pipes", [100]) def test_add_lots_of_pipes(nlp, n_pipes): Language.component("n_pipes", func=lambda doc: doc) for i in range(n_pipes): nlp.add_pipe("n_pipes", name=f"pipe_{i}") assert len(nlp.pipe_names) == n_pipes @pytest.mark.parametrize("component", [lambda doc: doc, {"hello": "world"}]) def test_raise_for_invalid_components(nlp, component): with pytest.raises(ValueError): nlp.add_pipe(component) @pytest.mark.parametrize("component", ["ner", "tagger", "parser", "textcat"]) def test_pipe_base_class_add_label(nlp, component): label = "TEST" pipe = nlp.create_pipe(component) pipe.add_label(label) if component == "tagger": # Tagger always has the default coarse-grained label scheme assert label in pipe.labels else: assert pipe.labels == (label,) def test_pipe_labels(nlp): input_labels = { "ner": ["PERSON", "ORG", "GPE"], "textcat": ["POSITIVE", "NEGATIVE"], } for name, labels in input_labels.items(): nlp.add_pipe(name) pipe = nlp.get_pipe(name) for label in labels: pipe.add_label(label) assert len(pipe.labels) == len(labels) assert len(nlp.pipe_labels) == len(input_labels) for name, labels in nlp.pipe_labels.items(): assert sorted(input_labels[name]) == sorted(labels) def test_add_pipe_before_after(): """Test that before/after works with strings and ints.""" nlp = Language() nlp.add_pipe("ner") with pytest.raises(ValueError): nlp.add_pipe("textcat", before="parser") nlp.add_pipe("textcat", before="ner") assert nlp.pipe_names == ["textcat", "ner"] with pytest.raises(ValueError): nlp.add_pipe("parser", before=3) with pytest.raises(ValueError): nlp.add_pipe("parser", after=3) nlp.add_pipe("parser", after=0) assert nlp.pipe_names == ["textcat", "parser", "ner"] nlp.add_pipe("tagger", before=2) assert nlp.pipe_names == ["textcat", "parser", "tagger", "ner"] with pytest.raises(ValueError): nlp.add_pipe("entity_ruler", after=1, first=True) with pytest.raises(ValueError): nlp.add_pipe("entity_ruler", before="ner", after=2) with pytest.raises(ValueError): nlp.add_pipe("entity_ruler", before=True) with pytest.raises(ValueError): nlp.add_pipe("entity_ruler", first=False) def test_disable_enable_pipes(): name = "test_disable_enable_pipes" results = {} def make_component(name): results[name] = "" def component(doc): nonlocal results results[name] = doc.text return doc return component c1 = Language.component(f"{name}1", func=make_component(f"{name}1")) c2 = Language.component(f"{name}2", func=make_component(f"{name}2")) nlp = Language() nlp.add_pipe(f"{name}1") nlp.add_pipe(f"{name}2") assert results[f"{name}1"] == "" assert results[f"{name}2"] == "" assert nlp.pipeline == [(f"{name}1", c1), (f"{name}2", c2)] assert nlp.pipe_names == [f"{name}1", f"{name}2"] nlp.disable_pipe(f"{name}1") assert nlp.disabled == [f"{name}1"] assert nlp.component_names == [f"{name}1", f"{name}2"] assert nlp.pipe_names == [f"{name}2"] assert nlp.config["nlp"]["disabled"] == [f"{name}1"] nlp("hello") assert results[f"{name}1"] == "" # didn't run assert results[f"{name}2"] == "hello" # ran nlp.enable_pipe(f"{name}1") assert nlp.disabled == [] assert nlp.pipe_names == [f"{name}1", f"{name}2"] assert nlp.config["nlp"]["disabled"] == [] nlp("world") assert results[f"{name}1"] == "world" assert results[f"{name}2"] == "world" nlp.disable_pipe(f"{name}2") nlp.remove_pipe(f"{name}2") assert nlp.components == [(f"{name}1", c1)] assert nlp.pipeline == [(f"{name}1", c1)] assert nlp.component_names == [f"{name}1"] assert nlp.pipe_names == [f"{name}1"] assert nlp.disabled == [] assert nlp.config["nlp"]["disabled"] == [] nlp.rename_pipe(f"{name}1", name) assert nlp.components == [(name, c1)] assert nlp.component_names == [name] nlp("!") assert results[f"{name}1"] == "!" assert results[f"{name}2"] == "world" with pytest.raises(ValueError): nlp.disable_pipe(f"{name}2") nlp.disable_pipe(name) assert nlp.component_names == [name] assert nlp.pipe_names == [] assert nlp.config["nlp"]["disabled"] == [name] nlp("?") assert results[f"{name}1"] == "!" def test_pipe_methods_frozen(): """Test that spaCy raises custom error messages if "frozen" properties are accessed. We still want to use a list here to not break backwards compatibility, but users should see an error if they're trying to append to nlp.pipeline etc.""" nlp = Language() ner = nlp.add_pipe("ner") assert nlp.pipe_names == ["ner"] for prop in [ nlp.pipeline, nlp.pipe_names, nlp.components, nlp.component_names, nlp.disabled, nlp.factory_names, ]: assert isinstance(prop, list) assert isinstance(prop, SimpleFrozenList) with pytest.raises(NotImplementedError): nlp.pipeline.append(("ner2", ner)) with pytest.raises(NotImplementedError): nlp.pipe_names.pop() with pytest.raises(NotImplementedError): nlp.components.sort() with pytest.raises(NotImplementedError): nlp.component_names.clear() @pytest.mark.parametrize( "pipe", ["tagger", "parser", "ner", "textcat", "morphologizer"] ) def test_pipe_label_data_exports_labels(pipe): nlp = Language() pipe = nlp.add_pipe(pipe) # Make sure pipe has pipe labels assert getattr(pipe, "label_data", None) is not None # Make sure pipe can be initialized with labels initialize = getattr(pipe, "initialize", None) assert initialize is not None assert "labels" in get_arg_names(initialize) @pytest.mark.parametrize("pipe", ["senter", "entity_linker"]) def test_pipe_label_data_no_labels(pipe): nlp = Language() pipe = nlp.add_pipe(pipe) assert getattr(pipe, "label_data", None) is None initialize = getattr(pipe, "initialize", None) if initialize is not None: assert "labels" not in get_arg_names(initialize) def test_warning_pipe_begin_training(): with pytest.warns(UserWarning, match="begin_training"): class IncompatPipe(TrainablePipe): def __init__(self): ... def begin_training(*args, **kwargs): ... def test_pipe_methods_initialize(): """Test that the [initialize] config reflects the components correctly.""" nlp = Language() nlp.add_pipe("tagger") assert "tagger" not in nlp.config["initialize"]["components"] nlp.config["initialize"]["components"]["tagger"] = {"labels": ["hello"]} assert nlp.config["initialize"]["components"]["tagger"] == {"labels": ["hello"]} nlp.remove_pipe("tagger") assert "tagger" not in nlp.config["initialize"]["components"] nlp.add_pipe("tagger") assert "tagger" not in nlp.config["initialize"]["components"] nlp.config["initialize"]["components"]["tagger"] = {"labels": ["hello"]} nlp.rename_pipe("tagger", "my_tagger") assert "tagger" not in nlp.config["initialize"]["components"] assert nlp.config["initialize"]["components"]["my_tagger"] == {"labels": ["hello"]} nlp.config["initialize"]["components"]["test"] = {"foo": "bar"} nlp.add_pipe("ner", name="test") assert "test" in nlp.config["initialize"]["components"] nlp.remove_pipe("test") assert "test" not in nlp.config["initialize"]["components"] def test_update_with_annotates(): name = "test_with_annotates" results = {} def make_component(name): results[name] = "" def component(doc): nonlocal results results[name] += doc.text return doc return component Language.component(f"{name}1", func=make_component(f"{name}1")) Language.component(f"{name}2", func=make_component(f"{name}2")) components = set([f"{name}1", f"{name}2"]) nlp = English() texts = ["a", "bb", "ccc"] examples = [] for text in texts: examples.append(Example(nlp.make_doc(text), nlp.make_doc(text))) for components_to_annotate in [ [], [f"{name}1"], [f"{name}1", f"{name}2"], [f"{name}2", f"{name}1"], ]: for key in results: results[key] = "" nlp = English(vocab=nlp.vocab) nlp.add_pipe(f"{name}1") nlp.add_pipe(f"{name}2") nlp.update(examples, annotates=components_to_annotate) for component in components_to_annotate: assert results[component] == "".join(eg.predicted.text for eg in examples) for component in components - set(components_to_annotate): assert results[component] == "" def test_load_disable_enable() -> None: """ Tests spacy.load() with dis-/enabling components. """ base_nlp = English() for pipe in ("sentencizer", "tagger", "parser"): base_nlp.add_pipe(pipe) with make_tempdir() as tmp_dir: base_nlp.to_disk(tmp_dir) to_disable = ["parser", "tagger"] to_enable = ["tagger", "parser"] # Setting only `disable`. nlp = spacy.load(tmp_dir, disable=to_disable) assert all([comp_name in nlp.disabled for comp_name in to_disable]) # Setting only `enable`. nlp = spacy.load(tmp_dir, enable=to_enable) assert all( [ (comp_name in nlp.disabled) is (comp_name not in to_enable) for comp_name in nlp.component_names ] ) # Testing consistent enable/disable combination. nlp = spacy.load( tmp_dir, enable=to_enable, disable=[ comp_name for comp_name in nlp.component_names if comp_name not in to_enable ], ) assert all( [ (comp_name in nlp.disabled) is (comp_name not in to_enable) for comp_name in nlp.component_names ] ) # Inconsistent enable/disable combination. with pytest.raises(ValueError): spacy.load(tmp_dir, enable=to_enable, disable=["parser"])