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117 lines
3.2 KiB
Python
117 lines
3.2 KiB
Python
"""
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The analysis of the measurements
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"""
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import scriptconfig as scfg
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import ubelt as ub
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class AnalysisConfig(scfg.Config):
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default = {
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"cache_dir": scfg.Value(
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None,
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help=ub.paragraph(
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"""
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Location for benchmark cache.
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Defaults to $XDG_CACHE/ujson/benchmark_results/
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"""
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),
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),
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}
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def normalize(self):
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dpath = self["cache_dir"]
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if dpath is None:
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dpath = ub.Path.appdir("ujson/benchmark_results")
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dpath = ub.Path(dpath)
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self["cache_dir"] = dpath
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def analyze_results(result_fpaths):
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import json
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from json_benchmarks import benchmarker
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from json_benchmarks.benchmarker import util_stats
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results = []
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for fpath in ub.ProgIter(result_fpaths, desc="load results"):
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data = json.loads(fpath.read_text())
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for row in data["rows"]:
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result = benchmarker.BenchmarkerResult.load(fpath)
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results.extend(result.to_result_list())
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RECORD_ALL = 0
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metric_key = "time" if RECORD_ALL else "mean_time"
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# results = benchmark.result.to_result_list()
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analysis = benchmarker.result_analysis.ResultAnalysis(
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results,
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metrics=[metric_key],
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params=["impl", "impl_version"],
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metric_objectives={
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"min_time": "min",
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"mean_time": "min",
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"time": "min",
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},
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)
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analysis.analysis()
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table = analysis.table
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stats_table = util_stats.aggregate_stats(
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table, suffix="_time", group_keys=["name", "impl_version"]
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)
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single_size = stats_table[
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(stats_table["size"] == 256) | stats_table["size"].isnull()
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]
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# single_size_combo = aggregate_stats(single_size, None)
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single_size_combo = util_stats.aggregate_stats(
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single_size, suffix="_time", group_keys=["name", "impl_version"]
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)
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param_group = ["impl", "impl_version"]
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single_size_combo["calls/sec"] = 1 / single_size_combo["mean_time"]
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# _single_size_combo = single_size_combo.copy()
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time_piv = single_size_combo.pivot(["input", "func"], param_group, "mean_time")
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hz_piv = 1 / time_piv
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# hzstr_piv = (1 / time_piv).applymap(lambda x: f"{x:,.02f}")
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print("Table for size=256")
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# print(hzstr_piv.to_markdown())
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print(hz_piv.to_markdown(floatfmt=",.02f"))
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print("")
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print("Above metrics are in call/sec, larger is better.")
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speedup_piv = hz_piv / hz_piv["json"].values
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print(speedup_piv.to_markdown(floatfmt=",.02g"))
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analysis.abalate(param_group)
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# benchmark_analysis(rows, xlabel, group_labels, basis, RECORD_ALL)
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xlabel = "size"
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# Set these to empty lists if they are not used
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group_labels = {
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"fig": ["input"],
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"col": ["func"],
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# "fig": [],
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# "col": ["func" "input"],
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"hue": ["impl", "impl_version"],
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"size": [],
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}
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import kwplot
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kwplot.autosns()
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self = analysis # NOQA
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data = stats_table
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plots = analysis.plot(
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xlabel,
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metric_key,
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group_labels,
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xscale="log",
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yscale="log",
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data=data,
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)
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plots
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kwplot.show_if_requested()
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