kplam
51dc8dc4f6
- FastAPI + Qdrant + Redis + Ollama 技术栈 - L1→L2→L3→chunk 四层分层检索（dense + sparse RRF 融合） - 文档三级总结与 2.5 级回退 - query 解析路由与分类 - /admin 管理页面
95 lines
3.2 KiB
Python
95 lines
3.2 KiB
Python
"""评测指标纯函数单元测试（不依赖 Qdrant / Ollama）"""
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import pytest
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from scripts.eval.metrics import (
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aggregate,
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entity_recall,
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precision_at_k,
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pruning_loss,
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recall_at_k,
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routing_f1,
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)
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class TestEntityRecall:
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def test_all_entities_kept(self) -> None:
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source = "使用 Qdrant 存储 1536 维向量，模型 bge-m3，见《部署手册》第三条。"
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summary = "基于 Qdrant 与 bge-m3 的 1536 维向量检索，详见《部署手册》第三条。"
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assert entity_recall(source, summary) == pytest.approx(1.0)
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def test_partial_entities_kept(self) -> None:
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source = "支持 text-embedding-3-small 和 bge-m3 两个模型。"
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summary = "支持 bge-m3 模型。"
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score = entity_recall(source, summary)
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assert 0.0 < score < 1.0
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def test_no_entities_returns_full_score(self) -> None:
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assert entity_recall("这是一段没有任何实体的中文普通句子。", "很短") == 1.0
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def test_empty_summary_drops_all(self) -> None:
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source = "版本 v1.2.0 修复了 3 个问题。"
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assert entity_recall(source, "") == pytest.approx(0.0)
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class TestRoutingF1:
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def test_perfect_routing(self) -> None:
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golden = [{"d1"}, {"d2"}]
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routed = [{"d1"}, {"d2"}]
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result = routing_f1(golden, routed)
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assert result == {"precision": 1.0, "recall": 1.0, "f1": 1.0}
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def test_micro_average(self) -> None:
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# query1: tp=1 fp=1；query2: tp=0 fn=1 → micro p=1/2 r=1/2 f1=1/2
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golden = [{"d1"}, {"d2"}]
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routed = [{"d1", "d3"}, set()]
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result = routing_f1(golden, routed)
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assert result["precision"] == pytest.approx(0.5)
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assert result["recall"] == pytest.approx(0.5)
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assert result["f1"] == pytest.approx(0.5)
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def test_empty_routed(self) -> None:
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result = routing_f1([{"d1"}], [set()])
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assert result["precision"] == 0.0
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assert result["recall"] == 0.0
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class TestPruningLoss:
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def test_mixed_cases(self) -> None:
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assert pruning_loss([False, True, False, False]) == pytest.approx(0.25)
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def test_empty(self) -> None:
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assert pruning_loss([]) == 0.0
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class TestPrecisionRecallAtK:
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def test_precision_at_k(self) -> None:
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hits = ["d1", "d2", "d3", "d4", "d5"]
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assert precision_at_k(hits, {"d1", "d3"}, 5) == pytest.approx(0.4)
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def test_precision_at_k_empty_hits(self) -> None:
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assert precision_at_k([], {"d1"}, 5) == 0.0
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def test_recall_at_k_dedup(self) -> None:
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hits = ["d1", "d1", "d2"]
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assert recall_at_k(hits, {"d1", "d2", "d3"}, 3) == pytest.approx(2 / 3)
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def test_recall_at_k_empty_golden(self) -> None:
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assert recall_at_k(["d1"], set(), 5) == 0.0
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class TestAggregate:
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def test_mean_per_key(self) -> None:
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records = [{"a": 1.0, "b": 0.5}, {"a": 0.0, "b": 1.0}]
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result = aggregate(records)
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assert result["a"] == pytest.approx(0.5)
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assert result["b"] == pytest.approx(0.75)
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def test_partial_keys(self) -> None:
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result = aggregate([{"a": 1.0}, {"a": 0.0, "b": 1.0}])
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assert result["a"] == pytest.approx(0.5)
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assert result["b"] == pytest.approx(1.0)
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def test_empty(self) -> None:
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assert aggregate([]) == {}
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