[Autoloop: perf-comparison] - #461
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… pair Run: https://github.com/githubnext/tsb/actions/runs/32766310388 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
…chmark pair Benchmarks remaining WASM rolling/expanding statistics functions: rollingMinF64Accelerated, rollingMaxF64Accelerated, rollingVarF64Accelerated, rollingStdF64Accelerated, rollingMedianF64Accelerated, and their expanding variants — not covered by bench_wasm_agg_ops which only benchmarked sum/mean. Python counterpart uses pandas Series.rolling()/expanding() with same ops. Run: https://github.com/githubnext/tsb/actions/runs/32820317584 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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…benchmark pair Run: https://github.com/githubnext/tsb/actions/runs/32887698741 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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…rk pair Run: https://github.com/githubnext/tsb/actions/runs/32941495823 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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Benchmarks MultiIndex.toList() (tsb) vs pd.MultiIndex.tolist() (pandas) on a 100k-pair MultiIndex. This brings the total benchmark pairs from 825 to 826. Run: https://github.com/githubnext/tsb/actions/runs/33033035649 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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…enchmark pair Run: https://github.com/githubnext/tsb/actions/runs/33094086022 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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Run: https://github.com/githubnext/tsb/actions/runs/33139413271 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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…mark pair Run: https://github.com/githubnext/tsb/actions/runs/33192323250 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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…hmark pair Benchmarks addPrefixSeries / addSuffixSeries / setAxisSeries / setAxisDataFrame / seriesToFrame against pandas Series.add_prefix / add_suffix / set_axis / DataFrame.set_axis / Series.to_frame. These 5 exported functions from src/stats/rename_ops.ts had no dedicated benchmark pair. Dataset: 100k-element Series and DataFrame with string index labels. Run: https://github.com/githubnext/tsb/actions/runs/33225322029 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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…benchmark pair Benchmarks rollingSumF64Accelerated, rollingMeanF64Accelerated, expandingSumF64Accelerated, expandingMeanF64Accelerated from src/wasm/accelerated.ts against pandas Series.rolling(50).sum/mean and Series.expanding().sum/mean on 100k-element float64 arrays. Run: https://github.com/githubnext/tsb/actions/runs/33253694015 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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… benchmark pair Benchmarks DatetimeIndex.min(), max(), at(), toArray(), toTimestamps() methods on a regular DatetimeIndex (TZDatetimeIndex already covered by bench_tz_datetime_index_extra). Run: https://github.com/githubnext/tsb/actions/runs/33285207528 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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…benchmark pair Run: https://github.com/githubnext/tsb/actions/runs/33297998405 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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…enchmark pair Run: https://github.com/githubnext/tsb/actions/runs/33329400368 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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Run: https://github.com/githubnext/tsb/actions/runs/33366886818 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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…oefficientOfVariation Run: https://github.com/githubnext/tsb/actions/runs/33427668624 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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…tation benchmark Benchmarks removeCategories, renameCategories, setCategories, reorderCategories, asOrdered, and asUnordered — the mutation methods on CategoricalAccessor not covered by bench_cat_accessor.ts. Run: https://github.com/githubnext/tsb/actions/runs/33480036252 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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Benchmarked sumF64Accelerated/meanF64Accelerated/minF64Accelerated/ maxF64Accelerated/varF64Accelerated/stdF64Accelerated/medianF64Accelerated — the WASM-backed full-array scalar reductions (distinct from the already benchmarked rolling/expanding variants). Mirrors numpy.sum/mean/min/max/ var(ddof=1)/std(ddof=1)/median on a 100k-element float64 dataset. Run: https://github.com/githubnext/tsb/actions/runs/34003457424
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Warning The Iteration 499: Added Metric: 839 TS / 839 Python benchmark pairs (previous best 838; iteration 498's Notes:
Run: https://github.com/githubnext/tsb/actions/runs/34053149353 Warning Firewall blocked 1 domainThe following domain was blocked by the firewall during workflow execution:
To allow these domains, add them to the network:
allowed:
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🤖 This PR is maintained by Autoloop. Each accepted iteration adds a commit to this branch.
Goal
Systematically benchmark every tsb function against its pandas equivalent, building a performance comparison dataset displayed on the playground site.
Program Issue
#221
Current Best Metric
821 benchmarked function pairs (target: open-ended)
Latest Iteration (482)
Added
bench_wasm_agg_ops— benchmarks the WASM-accelerated aggregate operations:sumF64Accelerated,meanF64Accelerated,minF64Accelerated,maxF64Accelerated,varF64Accelerated,stdF64Accelerated,medianF64AcceleratedplusrollingSumF64Accelerated,rollingMeanF64Accelerated,expandingSumF64Accelerated,expandingMeanF64Acceleratednp.sum,np.mean,np.min,np.max,np.var,np.std,np.medianplusSeries.rolling().sum/mean(),Series.expanding().sum/mean()This covers the WASM aggregate dispatch wrappers that were not previously benchmarked (only searchsorted/argsort/natsort WASM functions had benchmarks).
Metric: 821 → 822 benchmarked pairs (+1)