mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 03:19:13 +03:00
0518c36f04
The 'direct' option in 'spacy download' is supposed to only download from our model releases repository. However, users were able to pass in a relative path, allowing download from arbitrary repositories. This meant that a service that sourced strings from user input and which used the direct option would allow users to install arbitrary packages.
1081 lines
40 KiB
Python
1081 lines
40 KiB
Python
import math
|
|
import os
|
|
from collections import Counter
|
|
from pathlib import Path
|
|
from typing import Any, Dict, List, Tuple
|
|
|
|
import pytest
|
|
import srsly
|
|
from click import NoSuchOption
|
|
from packaging.specifiers import SpecifierSet
|
|
from thinc.api import Config
|
|
|
|
import spacy
|
|
from spacy import about
|
|
from spacy.cli import download_module, info
|
|
from spacy.cli._util import parse_config_overrides, string_to_list, walk_directory
|
|
from spacy.cli.apply import apply
|
|
from spacy.cli.debug_data import (
|
|
_compile_gold,
|
|
_get_distribution,
|
|
_get_kl_divergence,
|
|
_get_labels_from_model,
|
|
_get_labels_from_spancat,
|
|
_get_span_characteristics,
|
|
_get_spans_length_freq_dist,
|
|
_print_span_characteristics,
|
|
)
|
|
from spacy.cli.download import get_compatibility, get_version
|
|
from spacy.cli.evaluate import render_parses
|
|
from spacy.cli.find_threshold import find_threshold
|
|
from spacy.cli.init_config import RECOMMENDATIONS, fill_config, init_config
|
|
from spacy.cli.init_pipeline import _init_labels
|
|
from spacy.cli.package import _is_permitted_package_name, get_third_party_dependencies
|
|
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 RecommendationSchema
|
|
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, iob_to_docs
|
|
from spacy.util import ENV_VARS, get_minor_version, load_config, load_model_from_config
|
|
|
|
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"]
|
|
|
|
|
|
@pytest.mark.issue(12566)
|
|
@pytest.mark.parametrize(
|
|
"factory,output_file",
|
|
[("deps", "parses.html"), ("ents", "entities.html"), ("spans", "spans.html")],
|
|
)
|
|
def test_issue12566(factory: str, output_file: str):
|
|
"""
|
|
Test if all displaCy types (ents, dep, spans) produce an HTML file
|
|
"""
|
|
with make_tempdir() as tmp_dir:
|
|
# Create sample spaCy file
|
|
doc_json = {
|
|
"ents": [
|
|
{"end": 54, "label": "nam_adj_country", "start": 44},
|
|
{"end": 83, "label": "nam_liv_person", "start": 69},
|
|
{"end": 100, "label": "nam_pro_title_book", "start": 86},
|
|
],
|
|
"spans": {
|
|
"sc": [
|
|
{"end": 54, "kb_id": "", "label": "nam_adj_country", "start": 44},
|
|
{"end": 83, "kb_id": "", "label": "nam_liv_person", "start": 69},
|
|
{
|
|
"end": 100,
|
|
"kb_id": "",
|
|
"label": "nam_pro_title_book",
|
|
"start": 86,
|
|
},
|
|
]
|
|
},
|
|
"text": "Niedawno czytał em nową książkę znakomitego szkockiego medioznawcy , "
|
|
"Briana McNaira - Cultural Chaos .",
|
|
"tokens": [
|
|
# fmt: off
|
|
{"id": 0, "start": 0, "end": 8, "tag": "ADV", "pos": "ADV", "morph": "Degree=Pos", "lemma": "niedawno", "dep": "advmod", "head": 1, },
|
|
{"id": 1, "start": 9, "end": 15, "tag": "PRAET", "pos": "VERB", "morph": "Animacy=Hum|Aspect=Imp|Gender=Masc|Mood=Ind|Number=Sing|Tense=Past|VerbForm=Fin|Voice=Act", "lemma": "czytać", "dep": "ROOT", "head": 1, },
|
|
{"id": 2, "start": 16, "end": 18, "tag": "AGLT", "pos": "NOUN", "morph": "Animacy=Inan|Case=Ins|Gender=Masc|Number=Sing", "lemma": "em", "dep": "iobj", "head": 1, },
|
|
{"id": 3, "start": 19, "end": 23, "tag": "ADJ", "pos": "ADJ", "morph": "Case=Acc|Degree=Pos|Gender=Fem|Number=Sing", "lemma": "nowy", "dep": "amod", "head": 4, },
|
|
{"id": 4, "start": 24, "end": 31, "tag": "SUBST", "pos": "NOUN", "morph": "Case=Acc|Gender=Fem|Number=Sing", "lemma": "książka", "dep": "obj", "head": 1, },
|
|
{"id": 5, "start": 32, "end": 43, "tag": "ADJ", "pos": "ADJ", "morph": "Animacy=Nhum|Case=Gen|Degree=Pos|Gender=Masc|Number=Sing", "lemma": "znakomit", "dep": "acl", "head": 4, },
|
|
{"id": 6, "start": 44, "end": 54, "tag": "ADJ", "pos": "ADJ", "morph": "Animacy=Hum|Case=Gen|Degree=Pos|Gender=Masc|Number=Sing", "lemma": "szkockiy", "dep": "amod", "head": 7, },
|
|
{"id": 7, "start": 55, "end": 66, "tag": "SUBST", "pos": "NOUN", "morph": "Animacy=Hum|Case=Gen|Gender=Masc|Number=Sing", "lemma": "medioznawca", "dep": "iobj", "head": 5, },
|
|
{"id": 8, "start": 67, "end": 68, "tag": "INTERP", "pos": "PUNCT", "morph": "PunctType=Comm", "lemma": ",", "dep": "punct", "head": 9, },
|
|
{"id": 9, "start": 69, "end": 75, "tag": "SUBST", "pos": "PROPN", "morph": "Animacy=Hum|Case=Gen|Gender=Masc|Number=Sing", "lemma": "Brian", "dep": "nmod", "head": 4, },
|
|
{"id": 10, "start": 76, "end": 83, "tag": "SUBST", "pos": "PROPN", "morph": "Animacy=Hum|Case=Gen|Gender=Masc|Number=Sing", "lemma": "McNair", "dep": "flat", "head": 9, },
|
|
{"id": 11, "start": 84, "end": 85, "tag": "INTERP", "pos": "PUNCT", "morph": "PunctType=Dash", "lemma": "-", "dep": "punct", "head": 12, },
|
|
{"id": 12, "start": 86, "end": 94, "tag": "SUBST", "pos": "PROPN", "morph": "Animacy=Inan|Case=Nom|Gender=Masc|Number=Sing", "lemma": "Cultural", "dep": "conj", "head": 4, },
|
|
{"id": 13, "start": 95, "end": 100, "tag": "SUBST", "pos": "NOUN", "morph": "Animacy=Inan|Case=Nom|Gender=Masc|Number=Sing", "lemma": "Chaos", "dep": "flat", "head": 12, },
|
|
{"id": 14, "start": 101, "end": 102, "tag": "INTERP", "pos": "PUNCT", "morph": "PunctType=Peri", "lemma": ".", "dep": "punct", "head": 1, },
|
|
# fmt: on
|
|
],
|
|
}
|
|
|
|
# Create a .spacy file
|
|
nlp = spacy.blank("pl")
|
|
doc = Doc(nlp.vocab).from_json(doc_json)
|
|
|
|
# Run the evaluate command and check if the html files exist
|
|
render_parses(
|
|
docs=[doc], output_path=tmp_dir, model_name="", limit=1, **{factory: True}
|
|
)
|
|
|
|
assert (tmp_dir / output_file).is_file()
|
|
|
|
|
|
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"]
|
|
|
|
|
|
@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"],
|
|
["morphologizer", "spancat", "entity_linker"],
|
|
["spancat_singlelabel", "textcat_multilabel"],
|
|
],
|
|
)
|
|
@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]
|
|
|
|
|
|
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)
|
|
|
|
|
|
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.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
|
|
|
|
|
|
@pytest.mark.parametrize("component_name", ["spancat", "spancat_singlelabel"])
|
|
def test_debug_data_compile_gold_for_spans(component_name):
|
|
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], [component_name], 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_applycli_empty_dir():
|
|
with make_tempdir() as data_path:
|
|
output = data_path / "test.spacy"
|
|
apply(data_path, output, "blank:en", "text", 1, 1)
|
|
|
|
|
|
def test_applycli_docbin():
|
|
with make_tempdir() as data_path:
|
|
output = data_path / "testout.spacy"
|
|
nlp = spacy.blank("en")
|
|
doc = nlp("testing apply cli.")
|
|
# test empty DocBin case
|
|
docbin = DocBin()
|
|
docbin.to_disk(data_path / "testin.spacy")
|
|
apply(data_path, output, "blank:en", "text", 1, 1)
|
|
docbin.add(doc)
|
|
docbin.to_disk(data_path / "testin.spacy")
|
|
apply(data_path, output, "blank:en", "text", 1, 1)
|
|
|
|
|
|
def test_applycli_jsonl():
|
|
with make_tempdir() as data_path:
|
|
output = data_path / "testout.spacy"
|
|
data = [{"field": "Testing apply cli.", "key": 234}]
|
|
data2 = [{"field": "234"}]
|
|
srsly.write_jsonl(data_path / "test.jsonl", data)
|
|
apply(data_path, output, "blank:en", "field", 1, 1)
|
|
srsly.write_jsonl(data_path / "test2.jsonl", data2)
|
|
apply(data_path, output, "blank:en", "field", 1, 1)
|
|
|
|
|
|
def test_applycli_txt():
|
|
with make_tempdir() as data_path:
|
|
output = data_path / "testout.spacy"
|
|
with open(data_path / "test.foo", "w") as ftest:
|
|
ftest.write("Testing apply cli.")
|
|
apply(data_path, output, "blank:en", "text", 1, 1)
|
|
|
|
|
|
def test_applycli_mixed():
|
|
with make_tempdir() as data_path:
|
|
output = data_path / "testout.spacy"
|
|
text = "Testing apply cli"
|
|
nlp = spacy.blank("en")
|
|
doc = nlp(text)
|
|
jsonl_data = [{"text": text}]
|
|
srsly.write_jsonl(data_path / "test.jsonl", jsonl_data)
|
|
docbin = DocBin()
|
|
docbin.add(doc)
|
|
docbin.to_disk(data_path / "testin.spacy")
|
|
with open(data_path / "test.txt", "w") as ftest:
|
|
ftest.write(text)
|
|
apply(data_path, output, "blank:en", "text", 1, 1)
|
|
# Check whether it worked
|
|
result = list(DocBin().from_disk(output).get_docs(nlp.vocab))
|
|
assert len(result) == 3
|
|
for doc in result:
|
|
assert doc.text == text
|
|
|
|
|
|
def test_applycli_user_data():
|
|
Doc.set_extension("ext", default=0)
|
|
val = ("ext", 0)
|
|
with make_tempdir() as data_path:
|
|
output = data_path / "testout.spacy"
|
|
nlp = spacy.blank("en")
|
|
doc = nlp("testing apply cli.")
|
|
doc._.ext = val
|
|
docbin = DocBin(store_user_data=True)
|
|
docbin.add(doc)
|
|
docbin.to_disk(data_path / "testin.spacy")
|
|
apply(data_path, output, "blank:en", "", 1, 1)
|
|
result = list(DocBin().from_disk(output).get_docs(nlp.vocab))
|
|
assert result[0]._.ext == val
|
|
|
|
|
|
def test_cli_find_threshold(capsys):
|
|
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)
|
|
best_threshold, best_score, 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 best_score == max(res.values())
|
|
assert res[1.0] == 0.0
|
|
|
|
# Test with spancat.
|
|
nlp, _ = init_nlp((("spancat", {}),))
|
|
with make_tempdir() as nlp_dir:
|
|
nlp.to_disk(nlp_dir)
|
|
best_threshold, best_score, 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 best_score == max(res.values())
|
|
assert res[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,
|
|
)
|
|
|
|
|
|
def test_walk_directory():
|
|
with make_tempdir() as d:
|
|
files = [
|
|
"data1.iob",
|
|
"data2.iob",
|
|
"data3.json",
|
|
"data4.conll",
|
|
"data5.conll",
|
|
"data6.conll",
|
|
"data7.txt",
|
|
]
|
|
|
|
for f in files:
|
|
Path(d / f).touch()
|
|
|
|
assert (len(walk_directory(d))) == 7
|
|
assert (len(walk_directory(d, suffix=None))) == 7
|
|
assert (len(walk_directory(d, suffix="json"))) == 1
|
|
assert (len(walk_directory(d, suffix="iob"))) == 2
|
|
assert (len(walk_directory(d, suffix="conll"))) == 3
|
|
assert (len(walk_directory(d, suffix="pdf"))) == 0
|
|
|
|
|
|
def test_debug_data_trainable_lemmatizer_basic():
|
|
examples = [
|
|
("She likes green eggs", {"lemmas": ["she", "like", "green", "egg"]}),
|
|
("Eat blue ham", {"lemmas": ["eat", "blue", "ham"]}),
|
|
]
|
|
nlp = Language()
|
|
train_examples = []
|
|
for t in examples:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
|
|
|
|
data = _compile_gold(train_examples, ["trainable_lemmatizer"], nlp, True)
|
|
# ref test_edit_tree_lemmatizer::test_initialize_from_labels
|
|
# this results in 4 trees
|
|
assert len(data["lemmatizer_trees"]) == 4
|
|
|
|
|
|
def test_debug_data_trainable_lemmatizer_partial():
|
|
partial_examples = [
|
|
# partial annotation
|
|
("She likes green eggs", {"lemmas": ["", "like", "green", ""]}),
|
|
# misaligned partial annotation
|
|
(
|
|
"He hates green eggs",
|
|
{
|
|
"words": ["He", "hat", "es", "green", "eggs"],
|
|
"lemmas": ["", "hat", "e", "green", ""],
|
|
},
|
|
),
|
|
]
|
|
nlp = Language()
|
|
train_examples = []
|
|
for t in partial_examples:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
|
|
|
|
data = _compile_gold(train_examples, ["trainable_lemmatizer"], nlp, True)
|
|
assert data["partial_lemma_annotations"] == 2
|
|
|
|
|
|
def test_debug_data_trainable_lemmatizer_low_cardinality():
|
|
low_cardinality_examples = [
|
|
("She likes green eggs", {"lemmas": ["no", "no", "no", "no"]}),
|
|
("Eat blue ham", {"lemmas": ["no", "no", "no"]}),
|
|
]
|
|
nlp = Language()
|
|
train_examples = []
|
|
for t in low_cardinality_examples:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
|
|
|
|
data = _compile_gold(train_examples, ["trainable_lemmatizer"], nlp, True)
|
|
assert data["n_low_cardinality_lemmas"] == 2
|
|
|
|
|
|
def test_debug_data_trainable_lemmatizer_not_annotated():
|
|
unannotated_examples = [
|
|
("She likes green eggs", {}),
|
|
("Eat blue ham", {}),
|
|
]
|
|
nlp = Language()
|
|
train_examples = []
|
|
for t in unannotated_examples:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
|
|
|
|
data = _compile_gold(train_examples, ["trainable_lemmatizer"], nlp, True)
|
|
assert data["no_lemma_annotations"] == 2
|
|
|
|
|
|
def test_project_api_imports():
|
|
from spacy.cli import project_run
|
|
from spacy.cli.project.run import project_run # noqa: F401, F811
|
|
|
|
|
|
def test_download_rejects_relative_urls(monkeypatch):
|
|
"""Test that we can't tell spacy download to get an arbitrary model by using a
|
|
relative path in the filename"""
|
|
|
|
monkeypatch.setattr(download_module, "run_command", lambda cmd: None)
|
|
|
|
# Check that normal download works
|
|
download_module.download("en_core_web_sm-3.7.1", direct=True)
|
|
with pytest.raises(SystemExit):
|
|
download_module.download("../en_core_web_sm-3.7.1", direct=True)
|