mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 20:28:20 +03:00
483dddc9bc
* Add custom MatchPatternError * Improve validators and add validation option to Matcher * Adjust formatting * Never validate in Matcher within PhraseMatcher If we do decide to make validate default to True, the PhraseMatcher's Matcher shouldn't ever validate. Here, we create the patterns automatically anyways (and it's currently unclear whether the validation has performance impacts at a very large scale).
51 lines
1.5 KiB
Python
51 lines
1.5 KiB
Python
# coding: utf-8
|
|
from __future__ import unicode_literals
|
|
|
|
from spacy.util import get_json_validator, validate_json, validate_schema
|
|
from spacy.cli._schemas import META_SCHEMA, TRAINING_SCHEMA
|
|
from spacy.matcher._schemas import TOKEN_PATTERN_SCHEMA
|
|
import pytest
|
|
|
|
|
|
@pytest.fixture(scope="session")
|
|
def training_schema_validator():
|
|
return get_json_validator(TRAINING_SCHEMA)
|
|
|
|
|
|
def test_validate_schema():
|
|
validate_schema({"type": "object"})
|
|
with pytest.raises(Exception):
|
|
validate_schema({"type": lambda x: x})
|
|
|
|
|
|
@pytest.mark.parametrize("schema", [TRAINING_SCHEMA, META_SCHEMA, TOKEN_PATTERN_SCHEMA])
|
|
def test_schemas(schema):
|
|
validate_schema(schema)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"data",
|
|
[
|
|
{"text": "Hello world"},
|
|
{"text": "Hello", "ents": [{"start": 0, "end": 5, "label": "TEST"}]},
|
|
],
|
|
)
|
|
def test_json_schema_training_valid(data, training_schema_validator):
|
|
errors = validate_json([data], training_schema_validator)
|
|
assert not errors
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"data,n_errors",
|
|
[
|
|
({"spans": []}, 1),
|
|
({"text": "Hello", "ents": [{"start": "0", "end": "5", "label": "TEST"}]}, 2),
|
|
({"text": "Hello", "ents": [{"start": 0, "end": 5}]}, 1),
|
|
({"text": "Hello", "ents": [{"start": 0, "end": 5, "label": "test"}]}, 1),
|
|
({"text": "spaCy", "tokens": [{"pos": "PROPN"}]}, 2),
|
|
],
|
|
)
|
|
def test_json_schema_training_invalid(data, n_errors, training_schema_validator):
|
|
errors = validate_json([data], training_schema_validator)
|
|
assert len(errors) == n_errors
|