606 lines
20 KiB
Python
606 lines
20 KiB
Python
"""
|
||
GEO诊断服务单元测试
|
||
|
||
测试6大维度诊断逻辑、评分算法、推荐生成和服务类
|
||
"""
|
||
import pytest
|
||
from app.services.geo_diagnosis import (
|
||
GEODiagnosisService,
|
||
GEODiagnosisInput,
|
||
diagnose_content_extractability,
|
||
diagnose_entity_clarity,
|
||
diagnose_eeat_signals,
|
||
diagnose_schema_markup,
|
||
diagnose_topic_authority,
|
||
diagnose_citation_readiness,
|
||
generate_recommendations,
|
||
get_health_level,
|
||
get_health_level_label,
|
||
)
|
||
|
||
|
||
class TestContentExtractability:
|
||
"""内容可提取性诊断测试"""
|
||
|
||
def test_all_pass(self):
|
||
"""所有项都通过"""
|
||
result = diagnose_content_extractability(
|
||
has_direct_answer=True,
|
||
has_qa_headings=True,
|
||
has_structured_data=True,
|
||
has_internal_links=True,
|
||
has_freshness_info=True,
|
||
update_days_ago=10,
|
||
)
|
||
assert result.score == 20.0
|
||
assert result.max_score == 20.0
|
||
assert result.status == "pass"
|
||
assert result.percentage == 100.0
|
||
|
||
def test_all_fail(self):
|
||
"""所有项都失败"""
|
||
result = diagnose_content_extractability(
|
||
has_direct_answer=False,
|
||
has_qa_headings=False,
|
||
has_structured_data=False,
|
||
has_internal_links=False,
|
||
has_freshness_info=False,
|
||
)
|
||
assert result.score == 0.0
|
||
assert result.status == "warning"
|
||
|
||
def test_partial_pass(self):
|
||
"""部分通过"""
|
||
result = diagnose_content_extractability(
|
||
has_direct_answer=True,
|
||
has_qa_headings=True,
|
||
has_structured_data=False,
|
||
has_internal_links=False,
|
||
has_freshness_info=False,
|
||
)
|
||
assert result.score == 11.0 # 6 + 5
|
||
|
||
def test_freshness_recent(self):
|
||
"""内容新鲜度 - 近期更新"""
|
||
result = diagnose_content_extractability(
|
||
has_freshness_info=True,
|
||
update_days_ago=10,
|
||
)
|
||
freshness_item = [i for i in result.items if i.name == "内容新鲜度"][0]
|
||
assert freshness_item.score == 2.0
|
||
assert freshness_item.status == "pass"
|
||
|
||
def test_freshness_old(self):
|
||
"""内容新鲜度 - 过期更新"""
|
||
result = diagnose_content_extractability(
|
||
has_freshness_info=True,
|
||
update_days_ago=100,
|
||
)
|
||
freshness_item = [i for i in result.items if i.name == "内容新鲜度"][0]
|
||
assert freshness_item.score == 0.5
|
||
assert freshness_item.status == "warning"
|
||
|
||
|
||
class TestEntityClarity:
|
||
"""实体清晰度诊断测试"""
|
||
|
||
def test_all_pass(self):
|
||
"""所有项都通过"""
|
||
result = diagnose_entity_clarity(
|
||
has_brand_definition=True,
|
||
has_target_audience=True,
|
||
has_unique_value=True,
|
||
has_industry_classification=True,
|
||
)
|
||
assert result.score == 15.0
|
||
assert result.max_score == 15.0
|
||
assert result.status == "pass"
|
||
|
||
def test_all_fail(self):
|
||
"""所有项都失败"""
|
||
result = diagnose_entity_clarity(
|
||
has_brand_definition=False,
|
||
has_target_audience=False,
|
||
has_unique_value=False,
|
||
has_industry_classification=False,
|
||
)
|
||
assert result.score == 0.0
|
||
assert result.status == "warning"
|
||
|
||
def test_partial_pass(self):
|
||
"""部分通过"""
|
||
result = diagnose_entity_clarity(
|
||
has_brand_definition=True,
|
||
has_target_audience=True,
|
||
has_unique_value=False,
|
||
has_industry_classification=False,
|
||
)
|
||
assert result.score == 9.0 # 5 + 4
|
||
|
||
|
||
class TestEEATSignals:
|
||
"""E-E-A-T信号诊断测试"""
|
||
|
||
def test_all_pass(self):
|
||
"""所有项都通过"""
|
||
result = diagnose_eeat_signals(
|
||
has_author_bio=True,
|
||
author_credentials_complete=1.0,
|
||
has_certifications=True,
|
||
certification_count=5,
|
||
has_data_sources=True,
|
||
authoritative_source_ratio=1.0,
|
||
has_expert_endorsements=True,
|
||
endorsement_count=5,
|
||
)
|
||
assert result.score == 20.0
|
||
assert result.max_score == 20.0
|
||
assert result.status == "pass"
|
||
|
||
def test_all_fail(self):
|
||
"""所有项都失败"""
|
||
result = diagnose_eeat_signals(
|
||
has_author_bio=False,
|
||
has_certifications=False,
|
||
has_data_sources=False,
|
||
has_expert_endorsements=False,
|
||
)
|
||
assert result.score == 0.0
|
||
assert result.status == "warning"
|
||
|
||
def test_author_partial(self):
|
||
"""作者资质部分完成"""
|
||
result = diagnose_eeat_signals(
|
||
has_author_bio=True,
|
||
author_credentials_complete=0.5,
|
||
)
|
||
author_item = [i for i in result.items if i.name == "作者资质"][0]
|
||
assert author_item.score == 3.0 # 0.5 * 6.0
|
||
assert author_item.status == "warning"
|
||
|
||
def test_certification_tiers(self):
|
||
"""认证数量分级测试"""
|
||
# 5个以上
|
||
result = diagnose_eeat_signals(has_certifications=True, certification_count=5)
|
||
cert_item = [i for i in result.items if i.name == "专业认证"][0]
|
||
assert cert_item.score == 5.0
|
||
|
||
# 3-4个
|
||
result = diagnose_eeat_signals(has_certifications=True, certification_count=3)
|
||
cert_item = [i for i in result.items if i.name == "专业认证"][0]
|
||
assert cert_item.score == 4.0
|
||
|
||
# 1-2个
|
||
result = diagnose_eeat_signals(has_certifications=True, certification_count=1)
|
||
cert_item = [i for i in result.items if i.name == "专业认证"][0]
|
||
assert cert_item.score == 2.5
|
||
|
||
|
||
class TestSchemaMarkup:
|
||
"""Schema标记诊断测试"""
|
||
|
||
def test_all_pass(self):
|
||
"""所有项都通过"""
|
||
result = diagnose_schema_markup(
|
||
has_organization=True,
|
||
has_product=True,
|
||
has_article=True,
|
||
has_faq=True,
|
||
has_howto=True,
|
||
has_breadcrumb=True,
|
||
)
|
||
assert result.score == 15.0
|
||
assert result.max_score == 15.0
|
||
assert result.status == "pass"
|
||
|
||
def test_all_fail(self):
|
||
"""所有项都失败"""
|
||
result = diagnose_schema_markup(
|
||
has_organization=False,
|
||
has_product=False,
|
||
has_article=False,
|
||
has_faq=False,
|
||
has_howto=False,
|
||
has_breadcrumb=False,
|
||
)
|
||
assert result.score == 0.0
|
||
assert result.status == "warning"
|
||
|
||
def test_p0_only(self):
|
||
"""仅P0必须项"""
|
||
result = diagnose_schema_markup(
|
||
has_organization=True,
|
||
has_product=True,
|
||
has_article=True,
|
||
)
|
||
assert result.score == 10.0 # 4 + 3 + 3
|
||
assert result.status == "pass"
|
||
|
||
def test_schema_count(self):
|
||
"""Schema计数"""
|
||
result = diagnose_schema_markup(
|
||
has_organization=True,
|
||
has_product=True,
|
||
)
|
||
assert result.detail["schema_count"] == 2
|
||
|
||
|
||
class TestTopicAuthority:
|
||
"""主题权威诊断测试"""
|
||
|
||
def test_all_pass(self):
|
||
"""所有项都通过"""
|
||
result = diagnose_topic_authority(
|
||
content_depth_score=1.0,
|
||
topic_coverage_ratio=1.0,
|
||
entity_consistency_score=1.0,
|
||
cluster_completeness=1.0,
|
||
)
|
||
assert result.score == 15.0
|
||
assert result.max_score == 15.0
|
||
assert result.status == "pass"
|
||
|
||
def test_all_fail(self):
|
||
"""所有项都失败"""
|
||
result = diagnose_topic_authority(
|
||
content_depth_score=0.0,
|
||
topic_coverage_ratio=0.0,
|
||
entity_consistency_score=0.0,
|
||
cluster_completeness=0.0,
|
||
)
|
||
assert result.score == 0.0
|
||
assert result.status == "warning"
|
||
|
||
def test_partial_scores(self):
|
||
"""部分分数"""
|
||
result = diagnose_topic_authority(
|
||
content_depth_score=0.8,
|
||
topic_coverage_ratio=0.5,
|
||
entity_consistency_score=0.7,
|
||
cluster_completeness=0.4,
|
||
)
|
||
# 0.8*5 + 0.5*4 + 0.7*3 + 0.4*3 = 4 + 2 + 2.1 + 1.2 = 9.3
|
||
assert result.score == pytest.approx(9.3, rel=0.01)
|
||
|
||
|
||
class TestCitationReadiness:
|
||
"""引用就绪度诊断测试"""
|
||
|
||
def test_all_pass(self):
|
||
"""所有项都通过"""
|
||
result = diagnose_citation_readiness(
|
||
answer_ownership_rate=0.6,
|
||
citation_accuracy=1.0,
|
||
ai_sov=0.35,
|
||
competitor_gap=0.0,
|
||
)
|
||
assert result.score == 15.0
|
||
assert result.max_score == 15.0
|
||
assert result.status == "pass"
|
||
|
||
def test_all_fail(self):
|
||
"""所有项都失败"""
|
||
result = diagnose_citation_readiness(
|
||
answer_ownership_rate=0.0,
|
||
citation_accuracy=0.0,
|
||
ai_sov=0.0,
|
||
competitor_gap=0.6,
|
||
)
|
||
assert result.score == 0.0
|
||
assert result.status == "warning"
|
||
|
||
def test_aor_tiers(self):
|
||
"""AOR分级测试"""
|
||
# >= 50%
|
||
result = diagnose_citation_readiness(answer_ownership_rate=0.5)
|
||
aor_item = [i for i in result.items if i.name == "引用频率 (AOR)"][0]
|
||
assert aor_item.score == 5.0
|
||
|
||
# 30-49%
|
||
result = diagnose_citation_readiness(answer_ownership_rate=0.3)
|
||
aor_item = [i for i in result.items if i.name == "引用频率 (AOR)"][0]
|
||
assert aor_item.score == 3.5
|
||
|
||
# 10-29%
|
||
result = diagnose_citation_readiness(answer_ownership_rate=0.1)
|
||
aor_item = [i for i in result.items if i.name == "引用频率 (AOR)"][0]
|
||
assert aor_item.score == 2.0
|
||
|
||
def test_competitor_gap_tiers(self):
|
||
"""竞品差距分级测试"""
|
||
# <= 10pp
|
||
result = diagnose_citation_readiness(competitor_gap=0.05)
|
||
gap_item = [i for i in result.items if i.name == "竞品对比"][0]
|
||
assert gap_item.score == 3.0
|
||
|
||
# 10-20pp
|
||
result = diagnose_citation_readiness(competitor_gap=0.15)
|
||
gap_item = [i for i in result.items if i.name == "竞品对比"][0]
|
||
assert gap_item.score == 2.0
|
||
|
||
|
||
class TestRecommendations:
|
||
"""推荐生成测试"""
|
||
|
||
def test_generate_from_fail_items(self):
|
||
"""从fail项生成P0推荐"""
|
||
dimensions = [
|
||
diagnose_content_extractability(
|
||
has_direct_answer=False,
|
||
has_qa_headings=False,
|
||
),
|
||
]
|
||
recommendations = generate_recommendations(dimensions)
|
||
assert len(recommendations) >= 2
|
||
# 检查有P0推荐（不一定是全部）
|
||
p0_recs = [r for r in recommendations if r.priority == "P0"]
|
||
assert len(p0_recs) >= 2
|
||
|
||
def test_generate_from_warning_items(self):
|
||
"""从warning项生成P1推荐"""
|
||
dimensions = [
|
||
diagnose_content_extractability(
|
||
has_direct_answer=True,
|
||
has_qa_headings=True,
|
||
has_structured_data=True,
|
||
has_internal_links=False,
|
||
has_freshness_info=True,
|
||
update_days_ago=50,
|
||
),
|
||
]
|
||
recommendations = generate_recommendations(dimensions)
|
||
p1_recs = [r for r in recommendations if r.priority == "P1"]
|
||
assert len(p1_recs) >= 1
|
||
|
||
def test_priority_ordering(self):
|
||
"""推荐按优先级排序"""
|
||
dimensions = [
|
||
diagnose_content_extractability(
|
||
has_direct_answer=False,
|
||
has_qa_headings=False,
|
||
has_structured_data=True,
|
||
has_internal_links=False,
|
||
has_freshness_info=True,
|
||
update_days_ago=50,
|
||
),
|
||
]
|
||
recommendations = generate_recommendations(dimensions)
|
||
priorities = [r.priority for r in recommendations]
|
||
assert priorities == sorted(priorities)
|
||
|
||
def test_empty_dimensions(self):
|
||
"""空维度列表"""
|
||
recommendations = generate_recommendations([])
|
||
assert len(recommendations) == 0
|
||
|
||
|
||
class TestHealthLevel:
|
||
"""健康等级测试"""
|
||
|
||
def test_excellent(self):
|
||
assert get_health_level(85) == "excellent"
|
||
assert get_health_level(80) == "excellent"
|
||
|
||
def test_good(self):
|
||
assert get_health_level(70) == "good"
|
||
assert get_health_level(60) == "good"
|
||
|
||
def test_pass(self):
|
||
assert get_health_level(50) == "pass"
|
||
assert get_health_level(40) == "pass"
|
||
|
||
def test_danger(self):
|
||
assert get_health_level(30) == "danger"
|
||
assert get_health_level(0) == "danger"
|
||
|
||
def test_labels(self):
|
||
assert get_health_level_label("excellent") == "优秀"
|
||
assert get_health_level_label("good") == "良好"
|
||
assert get_health_level_label("pass") == "及格"
|
||
assert get_health_level_label("danger") == "危险"
|
||
|
||
|
||
class TestGEODiagnosisService:
|
||
"""GEO诊断服务类测试"""
|
||
|
||
@pytest.fixture
|
||
def service(self):
|
||
return GEODiagnosisService()
|
||
|
||
def test_full_diagnosis_all_pass(self, service):
|
||
"""完整诊断 - 所有项通过"""
|
||
input_data = GEODiagnosisInput(
|
||
# 内容可提取性
|
||
has_direct_answer=True,
|
||
has_qa_headings=True,
|
||
has_structured_data=True,
|
||
has_internal_links=True,
|
||
has_freshness_info=True,
|
||
update_days_ago=10,
|
||
# 实体清晰度
|
||
has_brand_definition=True,
|
||
has_target_audience=True,
|
||
has_unique_value=True,
|
||
has_industry_classification=True,
|
||
# E-E-A-T
|
||
has_author_bio=True,
|
||
author_credentials_complete=1.0,
|
||
has_certifications=True,
|
||
certification_count=5,
|
||
has_data_sources=True,
|
||
authoritative_source_ratio=1.0,
|
||
has_expert_endorsements=True,
|
||
endorsement_count=5,
|
||
# Schema
|
||
has_organization=True,
|
||
has_product=True,
|
||
has_article=True,
|
||
has_faq=True,
|
||
has_howto=True,
|
||
has_breadcrumb=True,
|
||
# 主题权威
|
||
content_depth_score=1.0,
|
||
topic_coverage_ratio=1.0,
|
||
entity_consistency_score=1.0,
|
||
cluster_completeness=1.0,
|
||
# 引用就绪度
|
||
answer_ownership_rate=0.6,
|
||
citation_accuracy=1.0,
|
||
ai_sov=0.35,
|
||
competitor_gap=0.0,
|
||
)
|
||
result = service.diagnose(input_data)
|
||
|
||
assert result.overall_score == 100.0
|
||
assert result.health_level == "excellent"
|
||
assert len(result.dimensions) == 6
|
||
assert len(result.recommendations) == 0
|
||
|
||
def test_full_diagnosis_all_fail(self, service):
|
||
"""完整诊断 - 所有项失败"""
|
||
input_data = GEODiagnosisInput()
|
||
result = service.diagnose(input_data)
|
||
|
||
# 由于有些项在默认情况下会得少量分数，总分不一定是0
|
||
assert result.overall_score < 10.0
|
||
assert result.health_level == "danger"
|
||
assert len(result.dimensions) == 6
|
||
assert len(result.recommendations) > 0
|
||
|
||
def test_diagnose_from_dict(self, service):
|
||
"""从字典执行诊断"""
|
||
data = {
|
||
"has_direct_answer": True,
|
||
"has_brand_definition": True,
|
||
"has_author_bio": True,
|
||
"author_credentials_complete": 0.8,
|
||
"has_organization": True,
|
||
"content_depth_score": 0.8,
|
||
"answer_ownership_rate": 0.5,
|
||
}
|
||
result = service.diagnose_from_dict(data)
|
||
|
||
assert result.overall_score > 0
|
||
assert len(result.dimensions) == 6
|
||
|
||
def test_result_to_dict(self, service):
|
||
"""结果转字典"""
|
||
input_data = GEODiagnosisInput(
|
||
has_direct_answer=True,
|
||
has_brand_definition=True,
|
||
)
|
||
result = service.diagnose(input_data)
|
||
result_dict = result.to_dict()
|
||
|
||
assert "overall_score" in result_dict
|
||
assert "health_level" in result_dict
|
||
assert "health_level_label" in result_dict
|
||
assert "dimensions" in result_dict
|
||
assert "recommendations" in result_dict
|
||
assert len(result_dict["dimensions"]) == 6
|
||
|
||
def test_score_boundaries(self, service):
|
||
"""评分边界测试"""
|
||
# 最低分
|
||
result = service.diagnose(GEODiagnosisInput())
|
||
assert result.overall_score >= 0.0
|
||
|
||
# 最高分
|
||
input_data = GEODiagnosisInput(
|
||
has_direct_answer=True,
|
||
has_qa_headings=True,
|
||
has_structured_data=True,
|
||
has_internal_links=True,
|
||
has_freshness_info=True,
|
||
update_days_ago=10,
|
||
has_brand_definition=True,
|
||
has_target_audience=True,
|
||
has_unique_value=True,
|
||
has_industry_classification=True,
|
||
has_author_bio=True,
|
||
author_credentials_complete=1.0,
|
||
has_certifications=True,
|
||
certification_count=5,
|
||
has_data_sources=True,
|
||
authoritative_source_ratio=1.0,
|
||
has_expert_endorsements=True,
|
||
endorsement_count=5,
|
||
has_organization=True,
|
||
has_product=True,
|
||
has_article=True,
|
||
has_faq=True,
|
||
has_howto=True,
|
||
has_breadcrumb=True,
|
||
content_depth_score=1.0,
|
||
topic_coverage_ratio=1.0,
|
||
entity_consistency_score=1.0,
|
||
cluster_completeness=1.0,
|
||
answer_ownership_rate=0.6,
|
||
citation_accuracy=0.95,
|
||
ai_sov=0.35,
|
||
competitor_gap=0.05,
|
||
)
|
||
result = service.diagnose(input_data)
|
||
assert result.overall_score <= 100.0
|
||
|
||
def test_health_levels(self, service):
|
||
"""健康等级测试"""
|
||
# excellent (>= 80)
|
||
input_data = GEODiagnosisInput(
|
||
has_direct_answer=True,
|
||
has_qa_headings=True,
|
||
has_structured_data=True,
|
||
has_internal_links=True,
|
||
has_freshness_info=True,
|
||
update_days_ago=10,
|
||
has_brand_definition=True,
|
||
has_target_audience=True,
|
||
has_unique_value=True,
|
||
has_industry_classification=True,
|
||
has_author_bio=True,
|
||
author_credentials_complete=0.9,
|
||
has_certifications=True,
|
||
certification_count=4,
|
||
has_data_sources=True,
|
||
authoritative_source_ratio=0.9,
|
||
has_expert_endorsements=True,
|
||
endorsement_count=4,
|
||
has_organization=True,
|
||
has_product=True,
|
||
has_article=True,
|
||
has_faq=True,
|
||
has_howto=True,
|
||
has_breadcrumb=True,
|
||
content_depth_score=0.9,
|
||
topic_coverage_ratio=0.9,
|
||
entity_consistency_score=0.9,
|
||
cluster_completeness=0.8,
|
||
answer_ownership_rate=0.6,
|
||
citation_accuracy=0.95,
|
||
ai_sov=0.35,
|
||
competitor_gap=0.05,
|
||
)
|
||
result = service.diagnose(input_data)
|
||
assert result.health_level == "excellent"
|
||
|
||
def test_dimension_scores_sum(self, service):
|
||
"""维度分数求和验证"""
|
||
input_data = GEODiagnosisInput(
|
||
has_direct_answer=True,
|
||
has_brand_definition=True,
|
||
)
|
||
result = service.diagnose(input_data)
|
||
|
||
# 各维度分数求和应等于总分
|
||
total = sum(dim.score for dim in result.dimensions)
|
||
assert result.overall_score == pytest.approx(total, rel=0.01)
|
||
|
||
def test_recommendations_generated(self, service):
|
||
"""推荐生成验证"""
|
||
input_data = GEODiagnosisInput()
|
||
result = service.diagnose(input_data)
|
||
|
||
# 所有项都失败时应该有推荐
|
||
assert len(result.recommendations) > 0
|
||
assert all(r.priority in ["P0", "P1", "P2"] for r in result.recommendations)
|