perf: optimize Greenwood Laplace statistic with Numba#128
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July 20, 2026 10:57
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That's cool! Further experimentation is needed. |
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С Numba создаётся новый массив или там уже всё оптимизировано? Иначе может получиться, что для образца ещё и сборщик мусора считается.
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Сортировку можно вынести отдельно и вызывать до замеров.
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| if rvs_array.ndim != 1: | ||
| raise ValueError("Sample must be one-dimensional.") |
| KolmogorovSmirnovLaplaceGofStatistic, | ||
| KuiperLaplaceGofStatistic, | ||
| WatsonLaplaceGofStatistic, | ||
| _greenwood_laplace_statistic, |
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Если метод выносится в паблик, лучше именовать его в CamelCase.
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Summary
Optimize the Greenwood Laplace goodness-of-fit statistic using Numba to reduce execution time and avoid unnecessary intermediate arrays
Part of #122
Quick changelog
What’s new?
The Greenwood Laplace statistic is now calculated in a Numba-compiled function
The implementation sorts the input sample, calculates the Laplace cumulative distribution function directly, and accumulates squared spacings in a single loop. This reduces Python overhead and avoids creating unnecessary intermediate arrays with np.concatenate and np.diff
The implementation now also validates that the input sample is one-dimensional and raises a clear ValueError for multidimensional input
Regression tests were added to compare the optimized implementation with the previous SciPy-based formula. The tests cover a single observation, unsorted samples, repeated values, different location and scale parameters, and integer NumPy arrays
A benchmark was added for a sample of 10,000 values and 1,000 executions. The Numba function is called once before measurement so that JIT compilation time is not included in the benchmark
Benchmark results: