# coding: utf8 from __future__ import unicode_literals import pytest from spacy.language import Language @pytest.fixture def nlp(): return Language() def new_pipe(doc): return 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): nlp.add_pipe(lambda doc: doc, 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", ["my_component"]) 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,not_callable", [("my_component", lambda doc: doc, {})] ) def test_replace_pipe(nlp, name, replacement, not_callable): 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, not_callable) nlp.replace_pipe(name, replacement) assert nlp.get_pipe(name) != new_pipe assert nlp.get_pipe(name) == replacement @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.disable_pipes(name) assert not nlp.has_pipe(name) disabled.restore() @pytest.mark.parametrize("name", ["my_component"]) def test_disable_pipes_context(nlp, name): nlp.add_pipe(new_pipe, name=name) assert nlp.has_pipe(name) with nlp.disable_pipes(name): assert not nlp.has_pipe(name) assert nlp.has_pipe(name) def test_disable_pipes_list_arg(nlp): for name in ["c1", "c2", "c3"]: nlp.add_pipe(new_pipe, name=name) assert nlp.has_pipe(name) with nlp.disable_pipes(["c1", "c2"]): assert not nlp.has_pipe("c1") assert not nlp.has_pipe("c2") assert nlp.has_pipe("c3") @pytest.mark.parametrize("n_pipes", [100]) def test_add_lots_of_pipes(nlp, n_pipes): for i in range(n_pipes): nlp.add_pipe(lambda doc: doc, name="pipe_%d" % i) assert len(nlp.pipe_names) == n_pipes @pytest.mark.parametrize("component", ["ner", {"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(): pipe = nlp.create_pipe(name) for label in labels: pipe.add_label(label) assert len(pipe.labels) == len(labels) nlp.add_pipe(pipe) assert len(nlp.pipe_labels) == len(input_labels) for name, labels in nlp.pipe_labels.items(): assert sorted(input_labels[name]) == sorted(labels)