mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-27 10:26:35 +03:00
860 lines
30 KiB
Python
860 lines
30 KiB
Python
import os
|
|
import math
|
|
from random import sample
|
|
from typing import Counter
|
|
|
|
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.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.validate import get_model_pkgs
|
|
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
|
|
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]
|