import os import math from collections import Counter from typing import Tuple, List, Dict, Any import pkg_resources import time import numpy import pytest import srsly from click import NoSuchOption from packaging.specifiers import SpecifierSet from thinc.api import Config, ConfigValidationError from spacy import about from spacy.cli import info from spacy.cli._util import is_subpath_of, load_project_config from spacy.cli._util import parse_config_overrides, string_to_list from spacy.cli._util import substitute_project_variables from spacy.cli._util import validate_project_commands from spacy.cli._util import upload_file, download_file from spacy.cli.debug_data import _compile_gold, _get_labels_from_model from spacy.cli.debug_data import _get_labels_from_spancat from spacy.cli.debug_data import _get_distribution, _get_kl_divergence from spacy.cli.debug_data import _get_span_characteristics from spacy.cli.debug_data import _print_span_characteristics from spacy.cli.debug_data import _get_spans_length_freq_dist from spacy.cli.download import get_compatibility, get_version from spacy.cli.init_config import RECOMMENDATIONS, init_config, fill_config from spacy.cli.package import get_third_party_dependencies from spacy.cli.package import _is_permitted_package_name from spacy.cli.project.remote_storage import RemoteStorage from spacy.cli.project.run import _check_requirements from spacy.cli.validate import get_model_pkgs from spacy.cli.find_threshold import find_threshold from spacy.lang.en import English from spacy.lang.nl import Dutch from spacy.language import Language from spacy.schemas import ProjectConfigSchema, RecommendationSchema, validate from spacy.tokens import Doc, DocBin from spacy.tokens.span import Span from spacy.training import Example, docs_to_json, offsets_to_biluo_tags from spacy.training.converters import conll_ner_to_docs, conllu_to_docs from spacy.training.converters import iob_to_docs from spacy.util import ENV_VARS, get_minor_version, load_model_from_config, load_config from ..cli.init_pipeline import _init_labels from .util import make_tempdir @pytest.mark.issue(4665) def test_cli_converters_conllu_empty_heads_ner(): """ conllu_to_docs should not raise an exception if the HEAD column contains an underscore """ input_data = """ 1 [ _ PUNCT -LRB- _ _ punct _ _ 2 This _ DET DT _ _ det _ _ 3 killing _ NOUN NN _ _ nsubj _ _ 4 of _ ADP IN _ _ case _ _ 5 a _ DET DT _ _ det _ _ 6 respected _ ADJ JJ _ _ amod _ _ 7 cleric _ NOUN NN _ _ nmod _ _ 8 will _ AUX MD _ _ aux _ _ 9 be _ AUX VB _ _ aux _ _ 10 causing _ VERB VBG _ _ root _ _ 11 us _ PRON PRP _ _ iobj _ _ 12 trouble _ NOUN NN _ _ dobj _ _ 13 for _ ADP IN _ _ case _ _ 14 years _ NOUN NNS _ _ nmod _ _ 15 to _ PART TO _ _ mark _ _ 16 come _ VERB VB _ _ acl _ _ 17 . _ PUNCT . _ _ punct _ _ 18 ] _ PUNCT -RRB- _ _ punct _ _ """ docs = list(conllu_to_docs(input_data)) # heads are all 0 assert not all([t.head.i for t in docs[0]]) # NER is unset assert not docs[0].has_annotation("ENT_IOB") @pytest.mark.issue(4924) def test_issue4924(): nlp = Language() example = Example.from_dict(nlp.make_doc(""), {}) nlp.evaluate([example]) @pytest.mark.issue(7055) def test_issue7055(): """Test that fill-config doesn't turn sourced components into factories.""" source_cfg = { "nlp": {"lang": "en", "pipeline": ["tok2vec", "tagger"]}, "components": { "tok2vec": {"factory": "tok2vec"}, "tagger": {"factory": "tagger"}, }, } source_nlp = English.from_config(source_cfg) with make_tempdir() as dir_path: # We need to create a loadable source pipeline source_path = dir_path / "test_model" source_nlp.to_disk(source_path) base_cfg = { "nlp": {"lang": "en", "pipeline": ["tok2vec", "tagger", "ner"]}, "components": { "tok2vec": {"source": str(source_path)}, "tagger": {"source": str(source_path)}, "ner": {"factory": "ner"}, }, } base_cfg = Config(base_cfg) base_path = dir_path / "base.cfg" base_cfg.to_disk(base_path) output_path = dir_path / "config.cfg" fill_config(output_path, base_path, silent=True) filled_cfg = load_config(output_path) assert filled_cfg["components"]["tok2vec"]["source"] == str(source_path) assert filled_cfg["components"]["tagger"]["source"] == str(source_path) assert filled_cfg["components"]["ner"]["factory"] == "ner" assert "model" in filled_cfg["components"]["ner"] def test_cli_info(): nlp = Dutch() nlp.add_pipe("textcat") with make_tempdir() as tmp_dir: nlp.to_disk(tmp_dir) raw_data = info(tmp_dir, exclude=[""]) assert raw_data["lang"] == "nl" assert raw_data["components"] == ["textcat"] def test_cli_converters_conllu_to_docs(): # from NorNE: https://github.com/ltgoslo/norne/blob/3d23274965f513f23aa48455b28b1878dad23c05/ud/nob/no_bokmaal-ud-dev.conllu lines = [ "1\tDommer\tdommer\tNOUN\t_\tDefinite=Ind|Gender=Masc|Number=Sing\t2\tappos\t_\tO", "2\tFinn\tFinn\tPROPN\t_\tGender=Masc\t4\tnsubj\t_\tB-PER", "3\tEilertsen\tEilertsen\tPROPN\t_\t_\t2\tname\t_\tI-PER", "4\tavstår\tavstå\tVERB\t_\tMood=Ind|Tense=Pres|VerbForm=Fin\t0\troot\t_\tO", ] input_data = "\n".join(lines) converted_docs = list(conllu_to_docs(input_data, n_sents=1)) assert len(converted_docs) == 1 converted = [docs_to_json(converted_docs)] assert converted[0]["id"] == 0 assert len(converted[0]["paragraphs"]) == 1 assert len(converted[0]["paragraphs"][0]["sentences"]) == 1 sent = converted[0]["paragraphs"][0]["sentences"][0] assert len(sent["tokens"]) == 4 tokens = sent["tokens"] assert [t["orth"] for t in tokens] == ["Dommer", "Finn", "Eilertsen", "avstår"] assert [t["tag"] for t in tokens] == ["NOUN", "PROPN", "PROPN", "VERB"] assert [t["head"] for t in tokens] == [1, 2, -1, 0] assert [t["dep"] for t in tokens] == ["appos", "nsubj", "name", "ROOT"] ent_offsets = [ (e[0], e[1], e[2]) for e in converted[0]["paragraphs"][0]["entities"] ] biluo_tags = offsets_to_biluo_tags(converted_docs[0], ent_offsets, missing="O") assert biluo_tags == ["O", "B-PER", "L-PER", "O"] @pytest.mark.parametrize( "lines", [ ( "1\tDommer\tdommer\tNOUN\t_\tDefinite=Ind|Gender=Masc|Number=Sing\t2\tappos\t_\tname=O", "2\tFinn\tFinn\tPROPN\t_\tGender=Masc\t4\tnsubj\t_\tSpaceAfter=No|name=B-PER", "3\tEilertsen\tEilertsen\tPROPN\t_\t_\t2\tname\t_\tname=I-PER", "4\tavstår\tavstå\tVERB\t_\tMood=Ind|Tense=Pres|VerbForm=Fin\t0\troot\t_\tSpaceAfter=No|name=O", "5\t.\t$.\tPUNCT\t_\t_\t4\tpunct\t_\tname=B-BAD", ), ( "1\tDommer\tdommer\tNOUN\t_\tDefinite=Ind|Gender=Masc|Number=Sing\t2\tappos\t_\t_", "2\tFinn\tFinn\tPROPN\t_\tGender=Masc\t4\tnsubj\t_\tSpaceAfter=No|NE=B-PER", "3\tEilertsen\tEilertsen\tPROPN\t_\t_\t2\tname\t_\tNE=L-PER", "4\tavstår\tavstå\tVERB\t_\tMood=Ind|Tense=Pres|VerbForm=Fin\t0\troot\t_\tSpaceAfter=No", "5\t.\t$.\tPUNCT\t_\t_\t4\tpunct\t_\tNE=B-BAD", ), ], ) def test_cli_converters_conllu_to_docs_name_ner_map(lines): input_data = "\n".join(lines) converted_docs = list( conllu_to_docs(input_data, n_sents=1, ner_map={"PER": "PERSON", "BAD": ""}) ) assert len(converted_docs) == 1 converted = [docs_to_json(converted_docs)] assert converted[0]["id"] == 0 assert len(converted[0]["paragraphs"]) == 1 assert converted[0]["paragraphs"][0]["raw"] == "Dommer FinnEilertsen avstår. " assert len(converted[0]["paragraphs"][0]["sentences"]) == 1 sent = converted[0]["paragraphs"][0]["sentences"][0] assert len(sent["tokens"]) == 5 tokens = sent["tokens"] assert [t["orth"] for t in tokens] == ["Dommer", "Finn", "Eilertsen", "avstår", "."] assert [t["tag"] for t in tokens] == ["NOUN", "PROPN", "PROPN", "VERB", "PUNCT"] assert [t["head"] for t in tokens] == [1, 2, -1, 0, -1] assert [t["dep"] for t in tokens] == ["appos", "nsubj", "name", "ROOT", "punct"] ent_offsets = [ (e[0], e[1], e[2]) for e in converted[0]["paragraphs"][0]["entities"] ] biluo_tags = offsets_to_biluo_tags(converted_docs[0], ent_offsets, missing="O") assert biluo_tags == ["O", "B-PERSON", "L-PERSON", "O", "O"] def test_cli_converters_conllu_to_docs_subtokens(): # https://raw.githubusercontent.com/ohenrik/nb_news_ud_sm/master/original_data/no-ud-dev-ner.conllu lines = [ "1\tDommer\tdommer\tNOUN\t_\tDefinite=Ind|Gender=Masc|Number=Sing\t2\tappos\t_\tname=O", "2-3\tFE\t_\t_\t_\t_\t_\t_\t_\t_", "2\tFinn\tFinn\tPROPN\t_\tGender=Masc\t4\tnsubj\t_\tname=B-PER", "3\tEilertsen\tEilertsen\tX\t_\tGender=Fem|Tense=past\t2\tname\t_\tname=I-PER", "4\tavstår\tavstå\tVERB\t_\tMood=Ind|Tense=Pres|VerbForm=Fin\t0\troot\t_\tSpaceAfter=No|name=O", "5\t.\t$.\tPUNCT\t_\t_\t4\tpunct\t_\tname=O", ] input_data = "\n".join(lines) converted_docs = list( conllu_to_docs( input_data, n_sents=1, merge_subtokens=True, append_morphology=True ) ) assert len(converted_docs) == 1 converted = [docs_to_json(converted_docs)] assert converted[0]["id"] == 0 assert len(converted[0]["paragraphs"]) == 1 assert converted[0]["paragraphs"][0]["raw"] == "Dommer FE avstår. " assert len(converted[0]["paragraphs"][0]["sentences"]) == 1 sent = converted[0]["paragraphs"][0]["sentences"][0] assert len(sent["tokens"]) == 4 tokens = sent["tokens"] assert [t["orth"] for t in tokens] == ["Dommer", "FE", "avstår", "."] assert [t["tag"] for t in tokens] == [ "NOUN__Definite=Ind|Gender=Masc|Number=Sing", "PROPN_X__Gender=Fem,Masc|Tense=past", "VERB__Mood=Ind|Tense=Pres|VerbForm=Fin", "PUNCT", ] assert [t["pos"] for t in tokens] == ["NOUN", "PROPN", "VERB", "PUNCT"] assert [t["morph"] for t in tokens] == [ "Definite=Ind|Gender=Masc|Number=Sing", "Gender=Fem,Masc|Tense=past", "Mood=Ind|Tense=Pres|VerbForm=Fin", "", ] assert [t["lemma"] for t in tokens] == ["dommer", "Finn Eilertsen", "avstå", "$."] assert [t["head"] for t in tokens] == [1, 1, 0, -1] assert [t["dep"] for t in tokens] == ["appos", "nsubj", "ROOT", "punct"] ent_offsets = [ (e[0], e[1], e[2]) for e in converted[0]["paragraphs"][0]["entities"] ] biluo_tags = offsets_to_biluo_tags(converted_docs[0], ent_offsets, missing="O") assert biluo_tags == ["O", "U-PER", "O", "O"] def test_cli_converters_iob_to_docs(): lines = [ "I|O like|O London|I-GPE and|O New|B-GPE York|I-GPE City|I-GPE .|O", "I|O like|O London|B-GPE and|O New|B-GPE York|I-GPE City|I-GPE .|O", "I|PRP|O like|VBP|O London|NNP|I-GPE and|CC|O New|NNP|B-GPE York|NNP|I-GPE City|NNP|I-GPE .|.|O", "I|PRP|O like|VBP|O London|NNP|B-GPE and|CC|O New|NNP|B-GPE York|NNP|I-GPE City|NNP|I-GPE .|.|O", ] input_data = "\n".join(lines) converted_docs = list(iob_to_docs(input_data, n_sents=10)) assert len(converted_docs) == 1 converted = docs_to_json(converted_docs) assert converted["id"] == 0 assert len(converted["paragraphs"]) == 1 assert len(converted["paragraphs"][0]["sentences"]) == 4 for i in range(0, 4): sent = converted["paragraphs"][0]["sentences"][i] assert len(sent["tokens"]) == 8 tokens = sent["tokens"] expected = ["I", "like", "London", "and", "New", "York", "City", "."] assert [t["orth"] for t in tokens] == expected assert len(converted_docs[0].ents) == 8 for ent in converted_docs[0].ents: assert ent.text in ["New York City", "London"] def test_cli_converters_conll_ner_to_docs(): lines = [ "-DOCSTART- -X- O O", "", "I\tO", "like\tO", "London\tB-GPE", "and\tO", "New\tB-GPE", "York\tI-GPE", "City\tI-GPE", ".\tO", "", "I O", "like O", "London B-GPE", "and O", "New B-GPE", "York I-GPE", "City I-GPE", ". O", "", "I PRP O", "like VBP O", "London NNP B-GPE", "and CC O", "New NNP B-GPE", "York NNP I-GPE", "City NNP I-GPE", ". . O", "", "I PRP _ O", "like VBP _ O", "London NNP _ B-GPE", "and CC _ O", "New NNP _ B-GPE", "York NNP _ I-GPE", "City NNP _ I-GPE", ". . _ O", "", "I\tPRP\t_\tO", "like\tVBP\t_\tO", "London\tNNP\t_\tB-GPE", "and\tCC\t_\tO", "New\tNNP\t_\tB-GPE", "York\tNNP\t_\tI-GPE", "City\tNNP\t_\tI-GPE", ".\t.\t_\tO", ] input_data = "\n".join(lines) converted_docs = list(conll_ner_to_docs(input_data, n_sents=10)) assert len(converted_docs) == 1 converted = docs_to_json(converted_docs) assert converted["id"] == 0 assert len(converted["paragraphs"]) == 1 assert len(converted["paragraphs"][0]["sentences"]) == 5 for i in range(0, 5): sent = converted["paragraphs"][0]["sentences"][i] assert len(sent["tokens"]) == 8 tokens = sent["tokens"] # fmt: off assert [t["orth"] for t in tokens] == ["I", "like", "London", "and", "New", "York", "City", "."] # fmt: on assert len(converted_docs[0].ents) == 10 for ent in converted_docs[0].ents: assert ent.text in ["New York City", "London"] def test_project_config_validation_full(): config = { "vars": {"some_var": 20}, "directories": ["assets", "configs", "corpus", "scripts", "training"], "assets": [ { "dest": "x", "extra": True, "url": "https://example.com", "checksum": "63373dd656daa1fd3043ce166a59474c", }, { "dest": "y", "git": { "repo": "https://github.com/example/repo", "branch": "develop", "path": "y", }, }, { "dest": "z", "extra": False, "url": "https://example.com", "checksum": "63373dd656daa1fd3043ce166a59474c", }, ], "commands": [ { "name": "train", "help": "Train a model", "script": ["python -m spacy train config.cfg -o training"], "deps": ["config.cfg", "corpus/training.spcy"], "outputs": ["training/model-best"], }, {"name": "test", "script": ["pytest", "custom.py"], "no_skip": True}, ], "workflows": {"all": ["train", "test"], "train": ["train"]}, } errors = validate(ProjectConfigSchema, config) assert not errors @pytest.mark.parametrize( "config", [ {"commands": [{"name": "a"}, {"name": "a"}]}, {"commands": [{"name": "a"}], "workflows": {"a": []}}, {"commands": [{"name": "a"}], "workflows": {"b": ["c"]}}, ], ) def test_project_config_validation1(config): with pytest.raises(SystemExit): validate_project_commands(config) @pytest.mark.parametrize( "config,n_errors", [ ({"commands": {"a": []}}, 1), ({"commands": [{"help": "..."}]}, 1), ({"commands": [{"name": "a", "extra": "b"}]}, 1), ({"commands": [{"extra": "b"}]}, 2), ({"commands": [{"name": "a", "deps": [123]}]}, 1), ], ) def test_project_config_validation2(config, n_errors): errors = validate(ProjectConfigSchema, config) assert len(errors) == n_errors @pytest.mark.parametrize( "int_value", [10, pytest.param("10", marks=pytest.mark.xfail)], ) def test_project_config_interpolation(int_value): variables = {"a": int_value, "b": {"c": "foo", "d": True}} commands = [ {"name": "x", "script": ["hello ${vars.a} ${vars.b.c}"]}, {"name": "y", "script": ["${vars.b.c} ${vars.b.d}"]}, ] project = {"commands": commands, "vars": variables} with make_tempdir() as d: srsly.write_yaml(d / "project.yml", project) cfg = load_project_config(d) assert type(cfg) == dict assert type(cfg["commands"]) == list assert cfg["commands"][0]["script"][0] == "hello 10 foo" assert cfg["commands"][1]["script"][0] == "foo true" commands = [{"name": "x", "script": ["hello ${vars.a} ${vars.b.e}"]}] project = {"commands": commands, "vars": variables} with pytest.raises(ConfigValidationError): substitute_project_variables(project) @pytest.mark.parametrize( "greeting", [342, "everyone", "tout le monde", pytest.param("42", marks=pytest.mark.xfail)], ) def test_project_config_interpolation_override(greeting): variables = {"a": "world"} commands = [ {"name": "x", "script": ["hello ${vars.a}"]}, ] overrides = {"vars.a": greeting} project = {"commands": commands, "vars": variables} with make_tempdir() as d: srsly.write_yaml(d / "project.yml", project) cfg = load_project_config(d, overrides=overrides) assert type(cfg) == dict assert type(cfg["commands"]) == list assert cfg["commands"][0]["script"][0] == f"hello {greeting}" def test_project_config_interpolation_env(): variables = {"a": 10} env_var = "SPACY_TEST_FOO" env_vars = {"foo": env_var} commands = [{"name": "x", "script": ["hello ${vars.a} ${env.foo}"]}] project = {"commands": commands, "vars": variables, "env": env_vars} with make_tempdir() as d: srsly.write_yaml(d / "project.yml", project) cfg = load_project_config(d) assert cfg["commands"][0]["script"][0] == "hello 10 " os.environ[env_var] = "123" with make_tempdir() as d: srsly.write_yaml(d / "project.yml", project) cfg = load_project_config(d) assert cfg["commands"][0]["script"][0] == "hello 10 123" @pytest.mark.parametrize( "args,expected", [ # fmt: off (["--x.foo", "10"], {"x.foo": 10}), (["--x.foo=10"], {"x.foo": 10}), (["--x.foo", "bar"], {"x.foo": "bar"}), (["--x.foo=bar"], {"x.foo": "bar"}), (["--x.foo", "--x.bar", "baz"], {"x.foo": True, "x.bar": "baz"}), (["--x.foo", "--x.bar=baz"], {"x.foo": True, "x.bar": "baz"}), (["--x.foo", "10.1", "--x.bar", "--x.baz", "false"], {"x.foo": 10.1, "x.bar": True, "x.baz": False}), (["--x.foo", "10.1", "--x.bar", "--x.baz=false"], {"x.foo": 10.1, "x.bar": True, "x.baz": False}) # fmt: on ], ) def test_parse_config_overrides(args, expected): assert parse_config_overrides(args) == expected @pytest.mark.parametrize("args", [["--foo"], ["--x.foo", "bar", "--baz"]]) def test_parse_config_overrides_invalid(args): with pytest.raises(NoSuchOption): parse_config_overrides(args) @pytest.mark.parametrize("args", [["--x.foo", "bar", "baz"], ["x.foo"]]) def test_parse_config_overrides_invalid_2(args): with pytest.raises(SystemExit): parse_config_overrides(args) def test_parse_cli_overrides(): overrides = "--x.foo bar --x.bar=12 --x.baz false --y.foo=hello" os.environ[ENV_VARS.CONFIG_OVERRIDES] = overrides result = parse_config_overrides([]) assert len(result) == 4 assert result["x.foo"] == "bar" assert result["x.bar"] == 12 assert result["x.baz"] is False assert result["y.foo"] == "hello" os.environ[ENV_VARS.CONFIG_OVERRIDES] = "--x" assert parse_config_overrides([], env_var=None) == {} with pytest.raises(SystemExit): parse_config_overrides([]) os.environ[ENV_VARS.CONFIG_OVERRIDES] = "hello world" with pytest.raises(SystemExit): parse_config_overrides([]) del os.environ[ENV_VARS.CONFIG_OVERRIDES] @pytest.mark.parametrize("lang", ["en", "nl"]) @pytest.mark.parametrize( "pipeline", [["tagger", "parser", "ner"], [], ["ner", "textcat", "sentencizer"]] ) @pytest.mark.parametrize("optimize", ["efficiency", "accuracy"]) @pytest.mark.parametrize("pretraining", [True, False]) def test_init_config(lang, pipeline, optimize, pretraining): # TODO: add more tests and also check for GPU with transformers config = init_config( lang=lang, pipeline=pipeline, optimize=optimize, pretraining=pretraining, gpu=False, ) assert isinstance(config, Config) if pretraining: config["paths"]["raw_text"] = "my_data.jsonl" load_model_from_config(config, auto_fill=True) def test_model_recommendations(): for lang, data in RECOMMENDATIONS.items(): assert RecommendationSchema(**data) @pytest.mark.parametrize( "value", [ # fmt: off "parser,textcat,tagger", " parser, textcat ,tagger ", 'parser,textcat,tagger', ' parser, textcat ,tagger ', ' "parser"," textcat " ,"tagger "', " 'parser',' textcat ' ,'tagger '", '[parser,textcat,tagger]', '["parser","textcat","tagger"]', '[" parser" ,"textcat ", " tagger " ]', "[parser,textcat,tagger]", "[ parser, textcat , tagger]", "['parser','textcat','tagger']", "[' parser' , 'textcat', ' tagger ' ]", # fmt: on ], ) def test_string_to_list(value): assert string_to_list(value, intify=False) == ["parser", "textcat", "tagger"] @pytest.mark.parametrize( "value", [ # fmt: off "1,2,3", '[1,2,3]', '["1","2","3"]', '[" 1" ,"2 ", " 3 " ]', "[' 1' , '2', ' 3 ' ]", # fmt: on ], ) def test_string_to_list_intify(value): assert string_to_list(value, intify=False) == ["1", "2", "3"] assert string_to_list(value, intify=True) == [1, 2, 3] @pytest.mark.skip(reason="Temporarily skip for dev version") def test_download_compatibility(): spec = SpecifierSet("==" + about.__version__) spec.prereleases = False if about.__version__ in spec: model_name = "en_core_web_sm" compatibility = get_compatibility() version = get_version(model_name, compatibility) assert get_minor_version(about.__version__) == get_minor_version(version) @pytest.mark.skip(reason="Temporarily skip for dev version") def test_validate_compatibility_table(): spec = SpecifierSet("==" + about.__version__) spec.prereleases = False if about.__version__ in spec: model_pkgs, compat = get_model_pkgs() spacy_version = get_minor_version(about.__version__) current_compat = compat.get(spacy_version, {}) assert len(current_compat) > 0 assert "en_core_web_sm" in current_compat @pytest.mark.parametrize("component_name", ["ner", "textcat", "spancat", "tagger"]) def test_init_labels(component_name): nlp = Dutch() component = nlp.add_pipe(component_name) for label in ["T1", "T2", "T3", "T4"]: component.add_label(label) assert len(nlp.get_pipe(component_name).labels) == 4 with make_tempdir() as tmp_dir: _init_labels(nlp, tmp_dir) config = init_config( lang="nl", pipeline=[component_name], optimize="efficiency", gpu=False, ) config["initialize"]["components"][component_name] = { "labels": { "@readers": "spacy.read_labels.v1", "path": f"{tmp_dir}/{component_name}.json", } } nlp2 = load_model_from_config(config, auto_fill=True) assert len(nlp2.get_pipe(component_name).labels) == 0 nlp2.initialize() assert len(nlp2.get_pipe(component_name).labels) == 4 def test_get_third_party_dependencies(): # We can't easily test the detection of third-party packages here, but we # can at least make sure that the function and its importlib magic runs. nlp = Dutch() # Test with component factory based on Cython module nlp.add_pipe("tagger") assert get_third_party_dependencies(nlp.config) == [] # Test with legacy function nlp = Dutch() nlp.add_pipe( "textcat", config={ "model": { # Do not update from legacy architecture spacy.TextCatBOW.v1 "@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": True, "ngram_size": 1, "no_output_layer": False, } }, ) assert get_third_party_dependencies(nlp.config) == [] # Test with lang-specific factory @Dutch.factory("third_party_test") def test_factory(nlp, name): return lambda x: x nlp.add_pipe("third_party_test") # Before #9674 this would throw an exception get_third_party_dependencies(nlp.config) @pytest.mark.parametrize( "parent,child,expected", [ ("/tmp", "/tmp", True), ("/tmp", "/", False), ("/tmp", "/tmp/subdir", True), ("/tmp", "/tmpdir", False), ("/tmp", "/tmp/subdir/..", True), ("/tmp", "/tmp/..", False), ], ) def test_is_subpath_of(parent, child, expected): assert is_subpath_of(parent, child) == expected @pytest.mark.slow @pytest.mark.parametrize( "factory_name,pipe_name", [ ("ner", "ner"), ("ner", "my_ner"), ("spancat", "spancat"), ("spancat", "my_spancat"), ], ) def test_get_labels_from_model(factory_name, pipe_name): labels = ("A", "B") nlp = English() pipe = nlp.add_pipe(factory_name, name=pipe_name) for label in labels: pipe.add_label(label) nlp.initialize() assert nlp.get_pipe(pipe_name).labels == labels if factory_name == "spancat": assert _get_labels_from_spancat(nlp)[pipe.key] == set(labels) else: assert _get_labels_from_model(nlp, factory_name) == set(labels) def test_permitted_package_names(): # https://www.python.org/dev/peps/pep-0426/#name assert _is_permitted_package_name("Meine_Bäume") == False assert _is_permitted_package_name("_package") == False assert _is_permitted_package_name("package_") == False assert _is_permitted_package_name(".package") == False assert _is_permitted_package_name("package.") == False assert _is_permitted_package_name("-package") == False assert _is_permitted_package_name("package-") == False def test_debug_data_compile_gold(): nlp = English() pred = Doc(nlp.vocab, words=["Token", ".", "New", "York", "City"]) ref = Doc( nlp.vocab, words=["Token", ".", "New York City"], sent_starts=[True, False, True], ents=["O", "O", "B-ENT"], ) eg = Example(pred, ref) data = _compile_gold([eg], ["ner"], nlp, True) assert data["boundary_cross_ents"] == 0 pred = Doc(nlp.vocab, words=["Token", ".", "New", "York", "City"]) ref = Doc( nlp.vocab, words=["Token", ".", "New York City"], sent_starts=[True, False, True], ents=["O", "B-ENT", "I-ENT"], ) eg = Example(pred, ref) data = _compile_gold([eg], ["ner"], nlp, True) assert data["boundary_cross_ents"] == 1 def test_debug_data_compile_gold_for_spans(): nlp = English() spans_key = "sc" pred = Doc(nlp.vocab, words=["Welcome", "to", "the", "Bank", "of", "China", "."]) pred.spans[spans_key] = [Span(pred, 3, 6, "ORG"), Span(pred, 5, 6, "GPE")] ref = Doc(nlp.vocab, words=["Welcome", "to", "the", "Bank", "of", "China", "."]) ref.spans[spans_key] = [Span(ref, 3, 6, "ORG"), Span(ref, 5, 6, "GPE")] eg = Example(pred, ref) data = _compile_gold([eg], ["spancat"], nlp, True) assert data["spancat"][spans_key] == Counter({"ORG": 1, "GPE": 1}) assert data["spans_length"][spans_key] == {"ORG": [3], "GPE": [1]} assert data["spans_per_type"][spans_key] == { "ORG": [Span(ref, 3, 6, "ORG")], "GPE": [Span(ref, 5, 6, "GPE")], } assert data["sb_per_type"][spans_key] == { "ORG": {"start": [ref[2:3]], "end": [ref[6:7]]}, "GPE": {"start": [ref[4:5]], "end": [ref[6:7]]}, } def test_frequency_distribution_is_correct(): nlp = English() docs = [ Doc(nlp.vocab, words=["Bank", "of", "China"]), Doc(nlp.vocab, words=["China"]), ] expected = Counter({"china": 0.5, "bank": 0.25, "of": 0.25}) freq_distribution = _get_distribution(docs, normalize=True) assert freq_distribution == expected def test_kl_divergence_computation_is_correct(): p = Counter({"a": 0.5, "b": 0.25}) q = Counter({"a": 0.25, "b": 0.50, "c": 0.15, "d": 0.10}) result = _get_kl_divergence(p, q) expected = 0.1733 assert math.isclose(result, expected, rel_tol=1e-3) def test_get_span_characteristics_return_value(): nlp = English() spans_key = "sc" pred = Doc(nlp.vocab, words=["Welcome", "to", "the", "Bank", "of", "China", "."]) pred.spans[spans_key] = [Span(pred, 3, 6, "ORG"), Span(pred, 5, 6, "GPE")] ref = Doc(nlp.vocab, words=["Welcome", "to", "the", "Bank", "of", "China", "."]) ref.spans[spans_key] = [Span(ref, 3, 6, "ORG"), Span(ref, 5, 6, "GPE")] eg = Example(pred, ref) examples = [eg] data = _compile_gold(examples, ["spancat"], nlp, True) span_characteristics = _get_span_characteristics( examples=examples, compiled_gold=data, spans_key=spans_key ) assert {"sd", "bd", "lengths"}.issubset(span_characteristics.keys()) assert span_characteristics["min_length"] == 1 assert span_characteristics["max_length"] == 3 def test_ensure_print_span_characteristics_wont_fail(): """Test if interface between two methods aren't destroyed if refactored""" nlp = English() spans_key = "sc" pred = Doc(nlp.vocab, words=["Welcome", "to", "the", "Bank", "of", "China", "."]) pred.spans[spans_key] = [Span(pred, 3, 6, "ORG"), Span(pred, 5, 6, "GPE")] ref = Doc(nlp.vocab, words=["Welcome", "to", "the", "Bank", "of", "China", "."]) ref.spans[spans_key] = [Span(ref, 3, 6, "ORG"), Span(ref, 5, 6, "GPE")] eg = Example(pred, ref) examples = [eg] data = _compile_gold(examples, ["spancat"], nlp, True) span_characteristics = _get_span_characteristics( examples=examples, compiled_gold=data, spans_key=spans_key ) _print_span_characteristics(span_characteristics) @pytest.mark.parametrize("threshold", [70, 80, 85, 90, 95]) def test_span_length_freq_dist_threshold_must_be_correct(threshold): sample_span_lengths = { "span_type_1": [1, 4, 4, 5], "span_type_2": [5, 3, 3, 2], "span_type_3": [3, 1, 3, 3], } span_freqs = _get_spans_length_freq_dist(sample_span_lengths, threshold) assert sum(span_freqs.values()) >= threshold def test_span_length_freq_dist_output_must_be_correct(): sample_span_lengths = { "span_type_1": [1, 4, 4, 5], "span_type_2": [5, 3, 3, 2], "span_type_3": [3, 1, 3, 3], } threshold = 90 span_freqs = _get_spans_length_freq_dist(sample_span_lengths, threshold) assert sum(span_freqs.values()) >= threshold assert list(span_freqs.keys()) == [3, 1, 4, 5, 2] def test_local_remote_storage(): with make_tempdir() as d: filename = "a.txt" content_hashes = ("aaaa", "cccc", "bbbb") for i, content_hash in enumerate(content_hashes): # make sure that each subsequent file has a later timestamp if i > 0: time.sleep(1) content = f"{content_hash} content" loc_file = d / "root" / filename if not loc_file.parent.exists(): loc_file.parent.mkdir(parents=True) with loc_file.open(mode="w") as file_: file_.write(content) # push first version to remote storage remote = RemoteStorage(d / "root", str(d / "remote")) remote.push(filename, "aaaa", content_hash) # retrieve with full hashes loc_file.unlink() remote.pull(filename, command_hash="aaaa", content_hash=content_hash) with loc_file.open(mode="r") as file_: assert file_.read() == content # retrieve with command hash loc_file.unlink() remote.pull(filename, command_hash="aaaa") with loc_file.open(mode="r") as file_: assert file_.read() == content # retrieve with content hash loc_file.unlink() remote.pull(filename, content_hash=content_hash) with loc_file.open(mode="r") as file_: assert file_.read() == content # retrieve with no hashes loc_file.unlink() remote.pull(filename) with loc_file.open(mode="r") as file_: assert file_.read() == content def test_local_remote_storage_pull_missing(): # pulling from a non-existent remote pulls nothing gracefully with make_tempdir() as d: filename = "a.txt" remote = RemoteStorage(d / "root", str(d / "remote")) assert remote.pull(filename, command_hash="aaaa") is None assert remote.pull(filename) is None def test_cli_find_threshold(capsys): thresholds = numpy.linspace(0, 1, 10) def make_examples(nlp: Language) -> List[Example]: docs: List[Example] = [] for t in [ ( "I am angry and confused in the Bank of America.", { "cats": {"ANGRY": 1.0, "CONFUSED": 1.0, "HAPPY": 0.0}, "spans": {"sc": [(31, 46, "ORG")]}, }, ), ( "I am confused but happy in New York.", { "cats": {"ANGRY": 0.0, "CONFUSED": 1.0, "HAPPY": 1.0}, "spans": {"sc": [(27, 35, "GPE")]}, }, ), ]: doc = nlp.make_doc(t[0]) docs.append(Example.from_dict(doc, t[1])) return docs def init_nlp( components: Tuple[Tuple[str, Dict[str, Any]], ...] = () ) -> Tuple[Language, List[Example]]: new_nlp = English() new_nlp.add_pipe( # type: ignore factory_name="textcat_multilabel", name="tc_multi", config={"threshold": 0.9}, ) # Append additional components to pipeline. for cfn, comp_config in components: new_nlp.add_pipe(cfn, config=comp_config) new_examples = make_examples(new_nlp) new_nlp.initialize(get_examples=lambda: new_examples) for i in range(5): new_nlp.update(new_examples) return new_nlp, new_examples with make_tempdir() as docs_dir: # Check whether find_threshold() identifies lowest threshold above 0 as (first) ideal threshold, as this matches # the current model behavior with the examples above. This can break once the model behavior changes and serves # mostly as a smoke test. nlp, examples = init_nlp() DocBin(docs=[example.reference for example in examples]).to_disk( docs_dir / "docs.spacy" ) with make_tempdir() as nlp_dir: nlp.to_disk(nlp_dir) res = find_threshold( model=nlp_dir, data_path=docs_dir / "docs.spacy", pipe_name="tc_multi", threshold_key="threshold", scores_key="cats_macro_f", silent=True, ) assert res[0] != thresholds[0] assert thresholds[0] < res[0] < thresholds[9] assert res[1] == 1.0 assert res[2][1.0] == 0.0 # Test with spancat. nlp, _ = init_nlp((("spancat", {}),)) with make_tempdir() as nlp_dir: nlp.to_disk(nlp_dir) res = find_threshold( model=nlp_dir, data_path=docs_dir / "docs.spacy", pipe_name="spancat", threshold_key="threshold", scores_key="spans_sc_f", silent=True, ) assert res[0] != thresholds[0] assert thresholds[0] < res[0] < thresholds[8] assert res[1] >= 0.6 assert res[2][1.0] == 0.0 # Having multiple textcat_multilabel components should work, since the name has to be specified. nlp, _ = init_nlp((("textcat_multilabel", {}),)) with make_tempdir() as nlp_dir: nlp.to_disk(nlp_dir) assert find_threshold( model=nlp_dir, data_path=docs_dir / "docs.spacy", pipe_name="tc_multi", threshold_key="threshold", scores_key="cats_macro_f", silent=True, ) # Specifying the name of an non-existing pipe should fail. nlp, _ = init_nlp() with make_tempdir() as nlp_dir: nlp.to_disk(nlp_dir) with pytest.raises(AttributeError): find_threshold( model=nlp_dir, data_path=docs_dir / "docs.spacy", pipe_name="_", threshold_key="threshold", scores_key="cats_macro_f", silent=True, ) @pytest.mark.parametrize( "reqs,output", [ [ """ spacy # comment thinc""", (False, False), ], [ """# comment --some-flag spacy""", (False, False), ], [ """# comment --some-flag spacy; python_version >= '3.6'""", (False, False), ], [ """# comment spacyunknowndoesnotexist12345""", (True, False), ], ], ) def test_project_check_requirements(reqs, output): # excessive guard against unlikely package name try: pkg_resources.require("spacyunknowndoesnotexist12345") except pkg_resources.DistributionNotFound: assert output == _check_requirements([req.strip() for req in reqs.split("\n")]) def test_upload_download_local_file(): with make_tempdir() as d1, make_tempdir() as d2: filename = "f.txt" content = "content" local_file = d1 / filename remote_file = d2 / filename with local_file.open(mode="w") as file_: file_.write(content) upload_file(local_file, remote_file) local_file.unlink() download_file(remote_file, local_file) with local_file.open(mode="r") as file_: assert file_.read() == content