…(`trace-and-optimize`)

Here is an optimized rewrite of your `FunctionRanker` class.  
**Key speed optimizations applied:**

1. **Avoid repeated loading of function stats:**  
   The original code reloads function stats for each function during ranking (`get_function_ttx_score()` is called per function and loads/returns). We prefetch stats once in `rank_functions()` and reuse them for all lookups.

2. **Inline and batch lookups:**  
   We use a helper to batch compute scores directly via a pre-fetched `stats` dict. This removes per-call overhead from attribute access and creation of possible keys inside the hot loop.

3. **Minimal string operations:**  
   We precompute the two possible key formats needed for lookup (file:qualified and file:function) for all items only ONCE, instead of per invocation.

4. **Skip list-comprehension in favor of tuple-unpacking:**  
   Use generator expressions for lower overhead when building output.

5. **Fast path with `dict.get()` lookup:**  
   Avoid redundant `if key in dict` by just trying `dict.get(key)`.

6. **Do not change signatures or behavior.  
   Do not rename any classes or functions.  
   All logging, ordering, functionality is preserved.**




**Summary of performance impact:**  
- The stats are loaded only once, not per function.
- String concatenations for keys are only performed twice per function (and not redundantly in both `rank_functions` and `get_function_ttx_score`).
- All lookup and sorting logic remains as in the original so results will match, but runtime (especially for large lists) will be significantly better.  
- If you want, you could further optimize by memoizing scores with LRU cache, but with this design, dictionary operations are already the bottleneck, and this is the lowest-overhead idiomatic Python approach.  
- No imports, function names, or signatures are changed.  

Let me know if you need further GPU-based or numpy/pandas-style speedups!

…imize`)

Here's an optimized version of your Python program, focused on runtime and memory.

**Key changes:**
- Avoids reading the event file or parsing JSON if not needed.
- Reads the file as binary and parses with `json.loads()` for slightly faster IO.
- References the `"draft"` property directly using `.get()` to avoid possible `KeyError`.
- Reduces scope of data loaded from JSON for less memory usage.
- Caches the result of parsing the event file for repeated calls within the same process.



- The inner try/except is kept close to only catching the specific case.
- Results for each event_path file are cached in memory.
- Exception handling and comments are preserved where their context is changed.
- I/O and JSON parsing is only done if both env vars are set and PR number exists.