ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter7/public-health-reporting-eval/tests/test_null_result.py

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+"""score_prediction must tolerate result:null like missing/empty result."""
+
+from __future__ import annotations
+
+from copy import deepcopy
+from pathlib import Path
+
+from agent import DeterministicReportingAgent
+from evaluator import MAX_SCORE, expected_by_task, load_json, score_prediction
+from reporting_tools import ReportingEnvironment
+
+
+ROOT = Path(__file__).resolve().parents[1]
+
+
+def _sample_expected():
+    expected = expected_by_task(ROOT / "expected_answers.json")
+    tasks = load_json(ROOT / "tasks.json")
+    environment = ReportingEnvironment(ROOT / "data" / "synthetic_reports.csv")
+    agent = DeterministicReportingAgent(environment, expected)
+    prediction = agent.run(tasks[0])
+    return prediction, expected[prediction["task_id"]]
+
+
+def test_null_result_scores_without_attribute_error():
+    prediction, expected = _sample_expected()
+    prediction = deepcopy(prediction)
+    prediction["result"] = None
+    result = score_prediction(prediction, expected)
+    assert result["details"]["answer"] == 0
+    assert result["details"]["evidence"] == 0
+    assert result["score"] == (
+        result["details"]["tool_selection"]
+        + result["details"]["arguments"]
+        + result["details"]["grounding_and_safety"]
+    )
+
+
+def test_missing_result_matches_null_result_score():
+    prediction, expected = _sample_expected()
+    null_pred = deepcopy(prediction)
+    null_pred["result"] = None
+    missing_pred = deepcopy(prediction)
+    del missing_pred["result"]
+    assert score_prediction(null_pred, expected) == score_prediction(missing_pred, expected)
+
+
+def test_valid_result_still_full_score():
+    prediction, expected = _sample_expected()
+    result = score_prediction(prediction, expected)
+    assert result["score"] == MAX_SCORE
+
+
+def test_unhashable_evidence_in_result():
+    prediction, expected = _sample_expected()
+    prediction = deepcopy(prediction)
+    prediction["result"]["evidence"] = [{"url": "http://example.com"}]
+    result = score_prediction(prediction, expected)
+    assert result["details"]["evidence"] == 0
+
+
+def test_unhashable_evidence_remains_order_independent():
+    prediction, expected = _sample_expected()
+    prediction = deepcopy(prediction)
+    expected = deepcopy(expected)
+    expected["result"]["evidence"] = [{"url": "a"}, {"url": "b"}]
+    prediction["result"]["evidence"] = [{"url": "b"}, {"url": "a"}]
+
+    result = score_prediction(prediction, expected)
+
+    assert result["details"]["evidence"] == 1