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log scale
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@ -217,13 +217,28 @@ def benchmark_json_dumps():
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param_group = ['impl', 'impl_version']
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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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"col": ["input"],
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"hue": ["impl"],
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"size": [],
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}
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import kwplot
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kwplot.autompl()
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facet = analysis.plot(xlabel, metric_key, group_labels)
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for ax in facet.axes.ravel():
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ax.set_xscale('log')
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ax.set_yscale('log')
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print('facet = {!r}'.format(facet))
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kwplot.show_if_requested()
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if __name__ == "__main__":
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"""
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CommandLine:
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python ~/code/ultrajson/tests/benchmark3.py
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python ~/code/ultrajson/tests/benchmark3.py --show
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"""
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benchmark_json_dumps()
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@ -240,9 +240,6 @@ class ResultAnalysis(ub.NiceRepr):
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self._description['num_results'] = len(self.results)
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def __nice__(self):
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# if len(self._description) == 0:
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# return 'unbuilt'
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# else:
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return ub.repr2(self._description, si=1, sv=1)
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@classmethod
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@ -405,8 +402,13 @@ class ResultAnalysis(ub.NiceRepr):
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def _objective_is_ascending(self, metric_key):
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"""
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Return True if we should minimize the objective (lower is better)
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Return False if we should maximize the objective (higher is better)
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Args:
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metric_key (str): the metric in question
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Returns:
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bool:
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True if we should minimize the objective (lower is better)
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False if we should maximize the objective (higher is better)
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"""
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objective = self.metric_objectives.get(metric_key, None)
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if objective is None:
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@ -578,6 +580,8 @@ class ResultAnalysis(ub.NiceRepr):
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comparable_groups2 = metric_group2.loc[comparable_indexes2, metric_key]
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# Does this need to have the values aligned?
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# I think that is the case giving my understanding of paired
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# t-tests, but the docs need a PR to make that more clear.
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ttest_rel_result = scipy.stats.ttest_rel(comparable_groups1, comparable_groups2)
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pair_statistics['n_common'] = len(common)
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pair_statistics['ttest_rel'] = ttest_rel_result
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@ -746,6 +750,7 @@ class ResultAnalysis(ub.NiceRepr):
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facet = sns.FacetGrid(data, col=col, sharex=False, sharey=False)
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facet.map_dataframe(sns.lineplot, x=xlabel, y=metric_key, marker="o", **plotkw)
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facet.add_legend()
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return facet
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class SkillTracker:
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