[MRG] Add benchmarking script for multilabel metrics #2643
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| def benchmark(metrics=[v for k, v in sorted(METRICS.items())], | ||
| formats=[v for k, v in sorted(FORMATS.items())], | ||
| samples=1000, classes=4, density=.2, | ||
| n_times=5): |
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Can you use tuple instead of list for function arguments?
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You're concerned that they're mutable?
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Yes, I think this is better to use immutable for default arguments.
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Except for the minor comments, +1 to merge. |
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@arjoly Okay, yes, it's quick-and-dirty code. I don't think that's a big deal for benchmarks, but I'll get some of the lint out of it. |
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Thanks ! |
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Your benchmark could be improved by adding dense c-layout and dense fortran-layout. |
Only if you want to see closely-overlapping curves... |
arjoly
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Dec 9, 2013
[MRG] Add benchmarking script for multilabel metrics
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merged ! Thanks for the bench !! |
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These are not very important metrics in the context of scikit-learn. Yet whenever metric implementations gets changed, people seem to be interested in how it affects execution time. This makes such reports easy to calculate.
This benchmarks metrics for different multilabel target formats, also giving us an idea of their relative performance. Benchmarks are otherwise parametrised by (number of samples, classes, average density of positive labels), one of which may be plotted against time.