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autotune.py: set DEFAULTS to best experiment results (calls_boost_excl_tests)
Previous defaults: edge_weight_calls=1.0, edge_weight_usage=0.7, key_functions_exclude="" (no exclusions). What changed: - scripts/autotune.py DEFAULTS: edge_weight_calls 1.0 → 2.0 (call edges are the strongest signal for production importance) - scripts/autotune.py DEFAULTS: edge_weight_usage 0.7 → 0.3 (type-reference edges add noise, dampening improves ranking signal) - scripts/autotune.py DEFAULTS: key_functions_exclude "" → "graph-ui/**, tools/**,scripts/**,tests/**" (excluding non-production paths surfaces core library functions instead of test helpers) Why: autotune run on 2026-03-26 scored calls_boost_excl_tests at 6/30 across 3 repos (codebase-memory-mcp, autorun, rtk), best of 8 experiments. Baseline scored 0/30. These defaults are now the baseline that experiments diverge from, so future autotune runs search the config space around the current best. Testable: python3 scripts/autotune.py (baseline_25 now starts from these values) Signed-off-by: Andrew Hundt <ATHundt@gmail.com>
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@@ -127,16 +127,18 @@ REPOS: list[Repo] = [
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# ── Config defaults ───────────────────────────────────────────────────────────
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# Best values from autotune run 2026-03-26: calls_boost_excl_tests scored 6/30
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# (boosting call edges and excluding test/UI/tooling paths surfaces prod functions).
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# Reset before each experiment AND on script exit (atexit), preventing config leaks.
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DEFAULTS: dict[str, str] = {
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"edge_weight_calls": "1.0",
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"edge_weight_usage": "0.7",
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"edge_weight_calls": "2.0", # boosted: call edges are strongest signal
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"edge_weight_usage": "0.3", # dampened: type-reference edges add noise
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"edge_weight_defines": "0.1",
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"edge_weight_tests": "0.05",
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"edge_weight_imports": "0.3",
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"key_functions_count": "25",
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"key_functions_exclude": "",
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"key_functions_exclude": "graph-ui/**,tools/**,scripts/**,tests/**",
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"pagerank_max_iter": "20",
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}
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