On 05/28/2015 05:11 PM, Michael Eickenberg wrote: > > Code-wise, I would attack the problem as a function first. Write a > function that takes X and y (plus maybe some options) and gives back > L. You can put a skeleton of a sklearn estimator around it by calling > this function from fit. > Please keep your code either in a sklearn WIP PR or a public gist, so > it can be reviewed. Writing benchmarks can be framed as writing > examples, i.e. plot_* functions (maybe Andy or Olivier have a comment > on how benchmarks have been handled in the past?). > There is a "benchmark" folder, which is in a horrible shape. Basically there are three ways to do it: examples (with or without plot depending on the runtime), a script in the benchmark folder, or a gist. Often we just use a gist and the PR person posts the output. Not that great for reproducibility, though.
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