Improve performance and readability of recall and precision measurements

[tlwillke]

Summary

This PR optimizes AccuracyMetrics to improve the performance and readability of recall and precision measurements. The primary focus is shifting from $O(N^2)$ list-scanning to $O(N)$ Set-based lookups and reducing object allocation overhead by interacting directly with primitive NodeScore arrays.

Key changes

Logic cleanup and "dead code" removal

• Unused Signature Removal: Removed the redundant topKCorrect(List, List, ...) signature, consolidating logic into a single private method that operates directly on SearchResult.
• Branch Elimination: Removed the unreachable if (gtView.size() > retrieved.size()) branch. Due to existing guard clauses (kGT <= kRetrieved and kRetrieved <= retrieved.size()), this condition was mathematically impossible.
• Parity Preservation: Preserved specific, argument-focused exception messaging to ensure clear feedback for invalid k parameters.

Performance optimizations

• Algorithmic Shift: Replaced $O(N)$ List.contains() calls with $O(1)$ HashSet.contains(). This moves the core intersection logic from quadratic to linear time complexity.
• AP Nested Scan Removal: In averagePrecisionAtK, replaced the $O(i)$ subList(0, i).contains(p) duplicate check with a HashSet lookup. This transforms the AP calculation from an $O(K^2)$ operation to $O(K)$.
• Allocation Reduction: Eliminated intermediate boxing and ArrayList creation in the SearchResult path. The logic now iterates directly over the NodeScore[] array, significantly reducing GC pressure during large benchmark runs.
• Loop Efficiency: Replaced Stream API calls with manual for loops in high-frequency paths to avoid the object overhead and "setup tax" of the Stream API.
• HashSet Pre-Sizing: Sets are now initialized with explicit capacities ($K / 0.75$) to prevent expensive internal rehashing.

Test coverage

• Functional Validation: Verified Recall, AP, and MAP calculations against hand-calculated results to ensure mathematical parity with standard IR definitions.
• Duplicate Handling: Confirmed that duplicate IDs in search results are handled via the "seen" Set, preventing artificial score inflation while maintaining $O(1)$ lookup speed.
• Exception Parity: Explicitly verified that IllegalArgumentException strings remain 1:1 identical to the original implementation to prevent breaking downstream error-parsing.

Performance results

Benchmarks were conducted using 10,000 queries on the gecko-100k dataset (768 dimensions).

Metric	Original Time	Optimized Time	Delta
recall@10	~8ms	~same	-
recall@100	~58ms	~25ms	~57% reduction
AP@100 / MAP@100	~62ms	~26ms	~58% reduction

While the benefit is negligible for $K=10$, the optimization becomes critical as $K$ increases. The reduction in AP measurement time is particularly significant as it eliminates the $O(K^2)$ complexity previously caused by nested sublist scans.

Notes / limitations

• Autoboxing: While this PR eliminates intermediate List allocations, it still utilizes HashSet<Integer>. Further gains could be achieved using primitive-specific collections (like IntHashSet) if necessary in the future.

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