A ratio greater than 1.0 denotes a possible regression (marked with ❌), while a ratio less
than 1.0 denotes a possible improvement (marked with ✅). Only significant results - results
that indicate possible regressions or improvements - are shown below (thus, an empty table means that all
benchmark results remained invariant between builds).
| ID | time ratio | memory ratio |
|---|---|---|
["build tree","BallTree 3 × 100000, ls = 10"] |
1.12 (5%) ❌ | 0.50 (1%) ✅ |
["build tree","KDTree 3 × 100000, ls = 10"] |
1.32 (5%) ❌ | 1.00 (1%) |
["inrange","BallTree 3 × 100000, ls = 10, input_size = 1, r = 1.91e-01"] |
0.46 (5%) ✅ | 1.00 (1%) |
["inrange","BallTree 3 × 100000, ls = 10, input_size = 1000, r = 1.91e-01"] |
0.40 (5%) ✅ | 1.00 (1%) |
["inrange","KDTree 3 × 100000, ls = 10, input_size = 1, r = 1.91e-01"] |
0.75 (5%) ✅ | 1.00 (1%) |
["inrange","KDTree 3 × 100000, ls = 10, input_size = 1000, r = 1.91e-01"] |
0.73 (5%) ✅ | 1.00 (1%) |
["knn","BallTree 3 × 100000, ls = 10, input_size = 1, k = 10"] |
0.26 (5%) ✅ | 1.02 (1%) ❌ |
["knn","BallTree 3 × 100000, ls = 10, input_size = 1000, k = 10"] |
0.38 (5%) ✅ | 1.00 (1%) |
["knn","KDTree 3 × 100000, ls = 10, input_size = 1, k = 10"] |
1.00 (5%) | 1.07 (1%) ❌ |
["knn","KDTree 3 × 100000, ls = 10, input_size = 1000, k = 10"] |
0.81 (5%) ✅ | 1.08 (1%) ❌ |
Nice!