mirror of
https://github.com/jxxghp/MoviePilot.git
synced 2026-11-13 00:59:01 +08:00
697 lines
21 KiB
Python
697 lines
21 KiB
Python
"""多媒体、多数据源分类领域能力的 A1 验收测试。"""
|
|
|
|
import copy
|
|
import gc
|
|
import statistics
|
|
import time
|
|
from collections.abc import Sequence
|
|
from typing import Any, Callable
|
|
|
|
import pytest
|
|
|
|
from app.domain.classification.evaluator import ClassificationEvaluator
|
|
from app.domain.classification.fields import get_standard_classification_fields
|
|
from app.domain.classification.validation import ClassificationPolicyValidator
|
|
from app.schemas.category import (
|
|
ClassificationEvaluation,
|
|
ClassificationFacts,
|
|
ClassificationFieldDefinition,
|
|
ClassificationPolicy,
|
|
ClassificationValidationResult,
|
|
)
|
|
|
|
_MISSING = object()
|
|
|
|
|
|
def _category(
|
|
category_id: str,
|
|
media_type: str,
|
|
name: str,
|
|
path: list[str],
|
|
) -> dict[str, Any]:
|
|
return {
|
|
"id": category_id,
|
|
"media_type": media_type,
|
|
"name": name,
|
|
"path": path,
|
|
"enabled": True,
|
|
"labels": [],
|
|
}
|
|
|
|
|
|
def _base_policy_payload() -> dict[str, Any]:
|
|
return {
|
|
"schema_version": 2,
|
|
"revision": 1,
|
|
"mode": "first_match",
|
|
"categories": [
|
|
_category("movie.fallback", "电影", "未分类", ["未分类"]),
|
|
_category("movie.hit", "电影", "命中", ["命中"]),
|
|
_category("movie.first", "电影", "第一分类", ["第一分类"]),
|
|
_category("movie.second", "电影", "第二分类", ["第二分类"]),
|
|
_category("tv.fallback", "电视剧", "未分类", ["未分类"]),
|
|
_category("music.fallback", "音乐", "未分类", ["未分类"]),
|
|
],
|
|
"rules": [],
|
|
"fallbacks": {
|
|
"电影": "movie.fallback",
|
|
"电视剧": "tv.fallback",
|
|
"音乐": "music.fallback",
|
|
},
|
|
"field_aliases": {},
|
|
"updated_at": "2026-09-02T12:00:00+08:00",
|
|
}
|
|
|
|
|
|
def _category_rule(
|
|
rule_id: str,
|
|
when: dict[str, Any],
|
|
category_id: str = "movie.hit",
|
|
*,
|
|
media_types: list[str] | None = None,
|
|
sources: list[str] | None = None,
|
|
labels: list[str] | None = None,
|
|
) -> dict[str, Any]:
|
|
return {
|
|
"id": rule_id,
|
|
"name": rule_id,
|
|
"kind": "category",
|
|
"enabled": True,
|
|
"priority": 100,
|
|
"media_types": media_types or ["电影"],
|
|
"sources": sources or [],
|
|
"when": when,
|
|
"target": {
|
|
"category_id": category_id,
|
|
"labels": labels or [],
|
|
},
|
|
}
|
|
|
|
|
|
def _label_rule(
|
|
rule_id: str,
|
|
when: dict[str, Any],
|
|
labels: list[str],
|
|
) -> dict[str, Any]:
|
|
return {
|
|
"id": rule_id,
|
|
"name": rule_id,
|
|
"kind": "label",
|
|
"enabled": True,
|
|
"priority": 100,
|
|
"media_types": ["电影"],
|
|
"sources": [],
|
|
"when": when,
|
|
"target": {"labels": labels},
|
|
}
|
|
|
|
|
|
def _policy(*rules: dict[str, Any]) -> ClassificationPolicy:
|
|
payload = _base_policy_payload()
|
|
payload["rules"] = list(rules)
|
|
return ClassificationPolicy.model_validate(payload)
|
|
|
|
|
|
def _set_nested(payload: dict[str, Any], field: str, value: Any) -> None:
|
|
parent = payload
|
|
parts = field.split(".")
|
|
for part in parts[:-1]:
|
|
parent = parent.setdefault(part, {})
|
|
if value is _MISSING:
|
|
parent.pop(parts[-1], None)
|
|
else:
|
|
parent[parts[-1]] = value
|
|
|
|
|
|
def _facts(
|
|
*,
|
|
media_type: str = "电影",
|
|
media_source: str = "themoviedb",
|
|
values: dict[str, Any] | None = None,
|
|
) -> ClassificationFacts:
|
|
payload: dict[str, Any] = {
|
|
"identity": {
|
|
"media_source": media_source,
|
|
"media_id": "media-1",
|
|
},
|
|
"media": {
|
|
"type": media_type,
|
|
"title": "Spirited Away",
|
|
"year": 2001,
|
|
"language": "ja",
|
|
"countries": ["JP"],
|
|
"genre_keys": ["animation", "family"],
|
|
"genre_names": ["Animation", "Family"],
|
|
"adult": False,
|
|
"runtime": 125,
|
|
"content_rating": "PG",
|
|
"companies": ["Studio Ghibli"],
|
|
"networks": ["NTV"],
|
|
},
|
|
"music": {
|
|
"entity_type": "album",
|
|
"album_type": "Album",
|
|
"secondary_types": ["Live"],
|
|
"genres": ["Rock"],
|
|
"tags": ["j-rock"],
|
|
"artists": ["Example Artist"],
|
|
"artist_country": "JP",
|
|
"release_status": "Official",
|
|
},
|
|
"extensions": {},
|
|
}
|
|
for field, value in (values or {}).items():
|
|
_set_nested(payload, field, value)
|
|
return ClassificationFacts.model_validate(payload)
|
|
|
|
|
|
def _evaluate(
|
|
policy: ClassificationPolicy,
|
|
facts: ClassificationFacts | None = None,
|
|
*,
|
|
trace: bool = False,
|
|
) -> ClassificationEvaluation:
|
|
return ClassificationEvaluator().evaluate(
|
|
policy=policy,
|
|
facts=facts or _facts(),
|
|
trace=trace,
|
|
)
|
|
|
|
|
|
def _leaf(field: str, operator: str, value: Any = _MISSING) -> dict[str, Any]:
|
|
condition = {"field": field, "operator": operator}
|
|
if value is not _MISSING:
|
|
condition["value"] = value
|
|
return condition
|
|
|
|
|
|
@pytest.mark.parametrize( # type: ignore[misc]
|
|
("condition", "fact_values"),
|
|
[
|
|
(_leaf("media.language", "equals", "ja"), {}),
|
|
(_leaf("media.language", "not_equals", "en"), {}),
|
|
(_leaf("media.language", "in", ["ja", "zh"]), {}),
|
|
(_leaf("media.language", "not_in", ["en", "fr"]), {}),
|
|
(_leaf("media.language", "contains", "a"), {}),
|
|
(_leaf("media.language", "starts_with", "j"), {}),
|
|
(_leaf("media.language", "ends_with", "a"), {}),
|
|
(_leaf("media.runtime", "gt", 120), {}),
|
|
(_leaf("media.runtime", "gte", 125), {}),
|
|
(_leaf("media.runtime", "lt", 130), {}),
|
|
(_leaf("media.runtime", "lte", 125), {}),
|
|
(_leaf("media.runtime", "between", [120, 130]), {}),
|
|
(_leaf("media.genre_keys", "contains_any", ["animation", "music"]), {}),
|
|
(_leaf("media.genre_keys", "contains_all", ["animation", "family"]), {}),
|
|
(_leaf("media.genre_keys", "contains_none", ["horror", "war"]), {}),
|
|
(_leaf("media.adult", "is_true"), {"media.adult": True}),
|
|
(_leaf("media.adult", "is_false"), {}),
|
|
(_leaf("media.content_rating", "exists"), {}),
|
|
(_leaf("media.content_rating", "not_exists"), {"media.content_rating": _MISSING}),
|
|
],
|
|
ids=[
|
|
"equals",
|
|
"not-equals",
|
|
"in",
|
|
"not-in",
|
|
"contains",
|
|
"starts-with",
|
|
"ends-with",
|
|
"gt",
|
|
"gte",
|
|
"lt",
|
|
"lte",
|
|
"between",
|
|
"contains-any",
|
|
"contains-all",
|
|
"contains-none",
|
|
"is-true",
|
|
"is-false",
|
|
"exists",
|
|
"not-exists",
|
|
],
|
|
)
|
|
def test_all_operators_have_a_representative_match(
|
|
condition: dict[str, Any],
|
|
fact_values: dict[str, Any],
|
|
) -> None:
|
|
evaluation = _evaluate(
|
|
_policy(_category_rule("rule.operator", condition)),
|
|
_facts(values=fact_values),
|
|
)
|
|
|
|
assert evaluation.result.recommended.category_id == "movie.hit"
|
|
assert evaluation.result.recommended.rule_id == "rule.operator"
|
|
|
|
|
|
@pytest.mark.parametrize("missing_value", [_MISSING, None], ids=["missing", "null"]) # type: ignore[misc]
|
|
@pytest.mark.parametrize( # type: ignore[misc]
|
|
("field", "operator", "expected"),
|
|
[
|
|
("media.content_rating", "not_equals", "R"),
|
|
("media.content_rating", "not_in", ["R", "NC-17"]),
|
|
("media.networks", "contains_none", ["Netflix"]),
|
|
],
|
|
)
|
|
def test_negative_operators_do_not_match_missing_or_null_values(
|
|
missing_value: Any,
|
|
field: str,
|
|
operator: str,
|
|
expected: Any,
|
|
) -> None:
|
|
evaluation = _evaluate(
|
|
_policy(_category_rule("rule.negative", _leaf(field, operator, expected))),
|
|
_facts(values={field: missing_value}),
|
|
)
|
|
|
|
assert evaluation.result.recommended.category_id == "movie.fallback"
|
|
assert evaluation.result.recommended.rule_id is None
|
|
|
|
|
|
@pytest.mark.parametrize("missing_value", [_MISSING, None], ids=["missing", "null"]) # type: ignore[misc]
|
|
def test_not_exists_matches_missing_and_null_values(missing_value: Any) -> None:
|
|
evaluation = _evaluate(
|
|
_policy(
|
|
_category_rule(
|
|
"rule.not-exists",
|
|
_leaf("media.content_rating", "not_exists"),
|
|
)
|
|
),
|
|
_facts(values={"media.content_rating": missing_value}),
|
|
)
|
|
|
|
assert evaluation.result.recommended.category_id == "movie.hit"
|
|
|
|
|
|
def test_all_any_and_not_groups_can_be_nested() -> None:
|
|
condition = {
|
|
"all": [
|
|
_leaf("media.year", "gte", 2000),
|
|
{
|
|
"any": [
|
|
_leaf("media.language", "equals", "zh"),
|
|
_leaf("media.countries", "contains_any", ["JP"]),
|
|
]
|
|
},
|
|
{"not": _leaf("media.adult", "is_true")},
|
|
]
|
|
}
|
|
|
|
evaluation = _evaluate(_policy(_category_rule("rule.nested", condition)))
|
|
|
|
assert evaluation.result.recommended.category_id == "movie.hit"
|
|
assert evaluation.result.recommended.rule_id == "rule.nested"
|
|
|
|
|
|
def test_first_matching_category_rule_wins() -> None:
|
|
always = _leaf("media.type", "equals", "电影")
|
|
evaluation = _evaluate(
|
|
_policy(
|
|
_category_rule("rule.first", always, "movie.first"),
|
|
_category_rule("rule.second", always, "movie.second"),
|
|
)
|
|
)
|
|
|
|
assert evaluation.result.recommended.category_id == "movie.first"
|
|
assert evaluation.result.recommended.rule_id == "rule.first"
|
|
assert evaluation.result.effective == evaluation.result.recommended
|
|
|
|
|
|
def test_matching_label_rules_accumulate_with_stable_deduplication() -> None:
|
|
always = _leaf("media.type", "equals", "电影")
|
|
evaluation = _evaluate(
|
|
_policy(
|
|
_category_rule(
|
|
"rule.category",
|
|
always,
|
|
labels=["base", "shared"],
|
|
),
|
|
_label_rule("rule.label-one", always, ["alpha", "shared"]),
|
|
_label_rule("rule.label-two", always, ["alpha", "omega"]),
|
|
)
|
|
)
|
|
|
|
assert evaluation.result.labels == ["base", "shared", "alpha", "omega"]
|
|
|
|
|
|
def test_media_type_fallback_is_used_when_no_category_rule_matches() -> None:
|
|
evaluation = _evaluate(
|
|
_policy(
|
|
_category_rule(
|
|
"rule.no-match",
|
|
_leaf("media.year", "lt", 1900),
|
|
)
|
|
)
|
|
)
|
|
|
|
assert evaluation.result.recommended.category_id == "movie.fallback"
|
|
assert evaluation.result.recommended.category_path == ["未分类"]
|
|
assert evaluation.result.recommended.rule_id is None
|
|
assert evaluation.result.effective.source == "fallback"
|
|
|
|
|
|
@pytest.mark.parametrize( # type: ignore[misc]
|
|
("media_type", "media_source", "expected_category"),
|
|
[
|
|
("电影", "themoviedb", "movie.hit"),
|
|
("电影", "douban", "movie.fallback"),
|
|
("音乐", "themoviedb", "music.fallback"),
|
|
],
|
|
)
|
|
def test_rule_source_and_media_type_restrictions_are_applied_before_conditions(
|
|
media_type: str,
|
|
media_source: str,
|
|
expected_category: str,
|
|
) -> None:
|
|
rule = _category_rule(
|
|
"rule.restricted",
|
|
_leaf("media.year", "gte", 2000),
|
|
media_types=["电影"],
|
|
sources=["themoviedb"],
|
|
)
|
|
|
|
evaluation = _evaluate(
|
|
_policy(rule),
|
|
_facts(media_type=media_type, media_source=media_source),
|
|
)
|
|
|
|
assert evaluation.result.recommended.category_id == expected_category
|
|
|
|
|
|
def test_trace_reports_actual_values_and_match_decisions() -> None:
|
|
evaluation = _evaluate(
|
|
_policy(
|
|
_category_rule(
|
|
"rule.trace",
|
|
_leaf("media.year", "equals", 1999),
|
|
)
|
|
),
|
|
trace=True,
|
|
)
|
|
|
|
assert evaluation.trace[0].rule_id == "rule.trace"
|
|
assert evaluation.trace[0].matched is False
|
|
assert evaluation.trace[0].conditions[0].field == "media.year"
|
|
assert evaluation.trace[0].conditions[0].actual == 2001
|
|
assert evaluation.trace[0].conditions[0].matched is False
|
|
|
|
|
|
def _validation_report(
|
|
payload: dict[str, Any],
|
|
*,
|
|
fields: Sequence[ClassificationFieldDefinition] | None = None,
|
|
) -> ClassificationValidationResult:
|
|
standard_fields = get_standard_classification_fields()
|
|
extra_fields = tuple(fields or ())
|
|
if extra_fields[:len(standard_fields)] == standard_fields:
|
|
extra_fields = extra_fields[len(standard_fields):]
|
|
return ClassificationPolicyValidator.validate(
|
|
ClassificationPolicy.model_validate(payload),
|
|
extra_fields=extra_fields,
|
|
)
|
|
|
|
|
|
def _assert_validation_error(
|
|
payload: dict[str, Any],
|
|
expected_code: str,
|
|
*,
|
|
fields: Sequence[ClassificationFieldDefinition] | None = None,
|
|
) -> None:
|
|
report = _validation_report(payload, fields=fields)
|
|
error_codes = {
|
|
issue.code for issue in report.issues if issue.severity == "error"
|
|
}
|
|
assert report.valid is False
|
|
assert expected_code in error_codes, error_codes
|
|
|
|
|
|
def test_valid_policy_has_no_validation_errors() -> None:
|
|
payload = _base_policy_payload()
|
|
payload["rules"] = [
|
|
_category_rule(
|
|
"rule.valid",
|
|
_leaf("media.genre_keys", "contains_any", ["animation"]),
|
|
sources=["themoviedb"],
|
|
)
|
|
]
|
|
|
|
report = _validation_report(payload)
|
|
|
|
assert report.valid is True
|
|
assert report.issues == []
|
|
|
|
|
|
@pytest.mark.parametrize( # type: ignore[misc]
|
|
("mutate", "expected_code"),
|
|
[
|
|
(
|
|
lambda payload: payload["categories"].append(
|
|
copy.deepcopy(payload["categories"][0])
|
|
),
|
|
"duplicate_category_id",
|
|
),
|
|
(
|
|
lambda payload: payload["rules"].append(
|
|
copy.deepcopy(payload["rules"][0])
|
|
),
|
|
"duplicate_rule_id",
|
|
),
|
|
],
|
|
ids=["category-id", "rule-id"],
|
|
)
|
|
def test_duplicate_ids_are_rejected(
|
|
mutate: Callable[[dict[str, Any]], None],
|
|
expected_code: str,
|
|
) -> None:
|
|
payload = _base_policy_payload()
|
|
payload["rules"] = [
|
|
_category_rule("rule.duplicate", _leaf("media.year", "gte", 2000))
|
|
]
|
|
mutate(payload)
|
|
|
|
_assert_validation_error(payload, expected_code)
|
|
|
|
|
|
@pytest.mark.parametrize( # type: ignore[misc]
|
|
"path",
|
|
[
|
|
[],
|
|
[""],
|
|
["."],
|
|
[".."],
|
|
["动画/电影"],
|
|
["动画\\电影"],
|
|
["非法\x00路径"],
|
|
["x" * 65],
|
|
["一", "二", "三", "四", "五"],
|
|
],
|
|
)
|
|
def test_invalid_category_paths_are_rejected(path: list[str]) -> None:
|
|
payload = _base_policy_payload()
|
|
payload["categories"][0]["path"] = path
|
|
|
|
_assert_validation_error(payload, "invalid_category_path")
|
|
|
|
|
|
def test_duplicate_paths_are_rejected_within_the_same_media_type() -> None:
|
|
payload = _base_policy_payload()
|
|
payload["categories"][1]["path"] = payload["categories"][0]["path"]
|
|
|
|
_assert_validation_error(payload, "duplicate_category_path")
|
|
|
|
|
|
@pytest.mark.parametrize( # type: ignore[misc]
|
|
("target", "expected_code"),
|
|
[
|
|
("movie.unknown", "target_category_not_found"),
|
|
("tv.fallback", "target_media_type_mismatch"),
|
|
],
|
|
ids=["not-found", "cross-media-type"],
|
|
)
|
|
def test_category_rule_targets_must_exist_and_match_the_rule_media_type(
|
|
target: str,
|
|
expected_code: str,
|
|
) -> None:
|
|
payload = _base_policy_payload()
|
|
payload["rules"] = [
|
|
_category_rule(
|
|
"rule.invalid-target",
|
|
_leaf("media.year", "gte", 2000),
|
|
target,
|
|
)
|
|
]
|
|
|
|
_assert_validation_error(payload, expected_code)
|
|
|
|
|
|
@pytest.mark.parametrize( # type: ignore[misc]
|
|
("condition", "expected_code"),
|
|
[
|
|
(_leaf("media.unregistered", "equals", "x"), "unknown_field"),
|
|
(_leaf("media.year", "contains", "20"), "unsupported_operator"),
|
|
(_leaf("media.year", "between", ["old", 2020]), "invalid_condition_value"),
|
|
(_leaf("media.language", "in", []), "empty_membership_value"),
|
|
(_leaf("media.year", "between", [2025, 2000]), "invalid_between_range"),
|
|
],
|
|
ids=[
|
|
"unknown-field",
|
|
"unsupported-operator",
|
|
"wrong-value-type",
|
|
"empty-in",
|
|
"reversed-between",
|
|
],
|
|
)
|
|
def test_condition_contract_errors_are_rejected(
|
|
condition: dict[str, Any],
|
|
expected_code: str,
|
|
) -> None:
|
|
payload = _base_policy_payload()
|
|
payload["rules"] = [_category_rule("rule.invalid-condition", condition)]
|
|
|
|
_assert_validation_error(payload, expected_code)
|
|
|
|
|
|
def test_condition_tree_depth_limit_is_enforced() -> None:
|
|
condition: dict[str, Any] = _leaf("media.year", "gte", 2000)
|
|
for _ in range(4):
|
|
condition = {"not": condition}
|
|
payload = _base_policy_payload()
|
|
payload["rules"] = [_category_rule("rule.too-deep", condition)]
|
|
|
|
_assert_validation_error(payload, "max_depth_exceeded")
|
|
|
|
|
|
def test_policy_rule_count_limit_is_enforced() -> None:
|
|
payload = _base_policy_payload()
|
|
payload["rules"] = [
|
|
_category_rule(
|
|
f"rule.too-many.{index}",
|
|
_leaf("media.year", "gte", 2000),
|
|
)
|
|
for index in range(1001)
|
|
]
|
|
|
|
_assert_validation_error(payload, "max_rules_exceeded")
|
|
|
|
|
|
def test_per_rule_leaf_condition_limit_is_enforced() -> None:
|
|
payload = _base_policy_payload()
|
|
payload["rules"] = [
|
|
_category_rule(
|
|
"rule.too-many-conditions",
|
|
{
|
|
"all": [
|
|
_leaf("media.year", "gte", 1900)
|
|
for _ in range(31)
|
|
]
|
|
},
|
|
)
|
|
]
|
|
|
|
_assert_validation_error(payload, "max_conditions_exceeded")
|
|
|
|
|
|
def test_every_enabled_media_type_requires_a_fallback() -> None:
|
|
payload = _base_policy_payload()
|
|
payload["fallbacks"].pop("音乐")
|
|
|
|
_assert_validation_error(payload, "missing_fallback")
|
|
|
|
|
|
def test_extension_field_namespace_must_match_the_restricted_source() -> None:
|
|
standard_fields = list(get_standard_classification_fields())
|
|
field_model = type(standard_fields[0])
|
|
extension_field = field_model.model_validate(
|
|
{
|
|
"id": "extensions.example.source.region_group",
|
|
"label": "来源地区组",
|
|
"value_type": "string",
|
|
"operators": ["equals", "in", "exists", "not_exists"],
|
|
"media_types": ["电影"],
|
|
"options": [],
|
|
"source_support": {"example.source": "extension"},
|
|
}
|
|
)
|
|
payload = _base_policy_payload()
|
|
payload["rules"] = [
|
|
_category_rule(
|
|
"rule.bad-extension-source",
|
|
_leaf(
|
|
"extensions.example.source.region_group",
|
|
"equals",
|
|
"east-asia",
|
|
),
|
|
sources=["other.source"],
|
|
)
|
|
]
|
|
|
|
_assert_validation_error(
|
|
payload,
|
|
"extension_namespace_mismatch",
|
|
fields=[*standard_fields, extension_field],
|
|
)
|
|
|
|
|
|
def test_standard_field_catalog_is_unique_and_exposes_operator_contracts() -> None:
|
|
fields = get_standard_classification_fields()
|
|
field_map = {field.id: field for field in fields}
|
|
|
|
assert len(field_map) == len(fields)
|
|
assert "contains_none" in field_map["media.countries"].operators
|
|
assert "between" in field_map["media.year"].operators
|
|
assert "in" in field_map["media.year"].operators
|
|
assert "not_in" in field_map["media.year"].operators
|
|
assert "is_false" in field_map["media.adult"].operators
|
|
assert set(field_map["music.secondary_types"].media_types) == {"音乐"}
|
|
|
|
|
|
def _percentile_95(samples: list[float]) -> float:
|
|
ordered = sorted(samples)
|
|
return ordered[max(0, int(len(ordered) * 0.95) - 1)]
|
|
|
|
|
|
def test_two_hundred_rules_with_six_conditions_evaluate_under_five_ms_p95() -> None:
|
|
rules = []
|
|
for index in range(200):
|
|
rules.append(
|
|
_category_rule(
|
|
f"rule.performance.{index}",
|
|
{
|
|
"all": [
|
|
_leaf("media.year", "gte", 1900),
|
|
_leaf("media.year", "lte", 2100),
|
|
_leaf("media.language", "in", ["ja", "en"]),
|
|
_leaf("media.countries", "contains_any", ["JP"]),
|
|
_leaf("media.genre_keys", "contains_any", ["animation"]),
|
|
_leaf("media.content_rating", "equals", f"rating-{index}"),
|
|
]
|
|
},
|
|
)
|
|
)
|
|
evaluator = ClassificationEvaluator()
|
|
policy = _policy(*rules)
|
|
facts = _facts()
|
|
|
|
for _ in range(20):
|
|
evaluator.evaluate(policy=policy, facts=facts, trace=False)
|
|
|
|
batch_p95_values = []
|
|
gc_was_enabled = gc.isenabled()
|
|
gc.disable()
|
|
try:
|
|
for _ in range(5):
|
|
samples = []
|
|
for _ in range(40):
|
|
started_at = time.perf_counter_ns()
|
|
evaluator.evaluate(policy=policy, facts=facts, trace=False)
|
|
samples.append((time.perf_counter_ns() - started_at) / 1_000_000_000)
|
|
batch_p95_values.append(_percentile_95(samples))
|
|
finally:
|
|
if gc_was_enabled:
|
|
gc.enable()
|
|
|
|
median_p95 = statistics.median(batch_p95_values)
|
|
assert median_p95 < 0.005, (
|
|
f"200 rules x 6 conditions median P95 was {median_p95 * 1000:.3f} ms; "
|
|
f"batch P95 values were {[round(value * 1000, 3) for value in batch_p95_values]} ms"
|
|
)
|