route optimizer agent with dailygrubs ai assign

evals/assignment_eval.py

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+"""
+Offline eval for the DispatchAgent assignment-failure decision
+(core.llm.decide_assignment_failure).
+
+Same scoring as the stall eval (see evals/_harness.py): acceptable-rate is the
+headline, exact-rate secondary. The cases use the real gatherer keys
+(zone_id, failures_today, nearest_miler_within_km, has_coordinates) so the eval
+reflects what DispatchAgent actually sends.
+
+Usage:
+    export ANTHROPIC_API_KEY=...
+    python -m evals.assignment_eval --runs 3
+    python -m evals.assignment_eval --dry-run
+    python -m evals.assignment_eval --model claude-haiku-4-5 --min-pass-rate 0.9
+"""
+from pathlib import Path
+
+import core.llm as llm
+from core.llm import build_assignment_failure_context, decide_assignment_failure
+from evals._harness import case_now, run_cli
+
+CASES_PATH = Path(__file__).with_name("assignment_cases.jsonl")
+
+
+def to_context(case):
+    return build_assignment_failure_context(case.get("facts", {}), now=case_now(case))
+
+
+if __name__ == "__main__":
+    run_cli(CASES_PATH, to_context, decide_assignment_failure, llm)