Jeffrey Sperling
8eda5fad5c
Add an optional local evaluator that compares a baseline revision against a candidate checkout, computes deterministic stability metrics, and can call Gemini for judged ranking metrics when configured. The harness isolates child runs with a temporary HOME and a node-free PATH so historical revisions cannot trigger Bird browser-cookie auth during evaluation. Validation: uv run python -m unittest and local smoke/full deterministic eval runs.
82 lines
2.7 KiB
Python
82 lines
2.7 KiB
Python
"""Tests for the local search-quality evaluation harness."""
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import sys
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import unittest
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from pathlib import Path
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from unittest.mock import patch
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sys.path.insert(0, str(Path(__file__).parent.parent / "scripts"))
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import evaluate_search_quality as evalsq
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class TestMetrics(unittest.TestCase):
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def test_jaccard(self):
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self.assertAlmostEqual(evalsq.jaccard({"a", "b"}, {"b", "c"}), 1 / 3)
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def test_retention(self):
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self.assertAlmostEqual(evalsq.retention({"a", "b"}, {"b", "c"}), 0.5)
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def test_precision_at_k(self):
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ranking = [
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{"key": "a", "source": "reddit"},
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{"key": "b", "source": "x"},
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{"key": "c", "source": "youtube"},
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]
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judgments = {"a": 3, "b": 1, "c": 2}
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self.assertAlmostEqual(evalsq.precision_at_k(ranking, judgments, 2), 0.5)
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def test_ndcg_at_k(self):
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ranking = [
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{"key": "a", "source": "reddit"},
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{"key": "b", "source": "x"},
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{"key": "c", "source": "youtube"},
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]
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judgments = {"a": 3, "b": 0, "c": 2}
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self.assertGreater(evalsq.ndcg_at_k(ranking, judgments, 3), 0.8)
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def test_source_coverage_recall_uses_union_pool(self):
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judged_pool = [
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{"key": "a", "source": "reddit"},
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{"key": "b", "source": "x"},
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{"key": "c", "source": "youtube"},
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]
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ranking = [
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{"key": "a", "source": "reddit"},
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{"key": "b", "source": "x"},
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]
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judgments = {"a": 3, "b": 0, "c": 2}
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self.assertAlmostEqual(evalsq.source_coverage_recall(ranking, judged_pool, judgments), 0.5)
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class TestRankedItems(unittest.TestCase):
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def test_build_ranked_items_sorts_by_score(self):
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report = {
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"reddit": [{"id": "R1", "title": "Low", "url": "r1", "score": 20}],
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"x": [{"id": "X1", "text": "High", "url": "x1", "score": 90}],
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"youtube": [],
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"tiktok": [],
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"instagram": [],
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"hackernews": [],
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"bluesky": [],
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"truthsocial": [],
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"polymarket": [],
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"websearch": [],
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}
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ranked = evalsq.build_ranked_items(report, per_source_limit=5)
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self.assertEqual(ranked[0]["key"], "x1")
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class TestPathWithoutNode(unittest.TestCase):
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def test_removes_node_entries(self):
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path = "/usr/bin:/tmp/node-bin:/opt/homebrew/bin"
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def fake_exists(path_obj):
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return str(path_obj).endswith("/tmp/node-bin/node")
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with patch.object(evalsq.Path, "exists", fake_exists):
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filtered = evalsq.path_without_node(path)
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self.assertEqual(filtered, "/usr/bin:/opt/homebrew/bin")
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if __name__ == "__main__":
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unittest.main()
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