refactor: Output format of conformal predictions#221
refactor: Output format of conformal predictions#221baggiponte wants to merge 6 commits intomainfrom
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FBruzzesi
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FBruzzesi
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would like your opinion on the work so far
It looks good in general. My understanding from our private conversation was that you wanted to change the format from long to wide as well.
I left a couple of consideration in the codebase itself though, and I believe that hinting may require some more attention if you care about those.
Also I would like to hear your opinion on how you would test the conformal prediction code.
As behavioral testing I would like to see that the point prediction in regression is always between the lower and upper bounds, which it is not the case with the current implementation.
From the theoretical point of view, maybe one could also check that the actual value falls between the bounds (at least) x-percent of the times - this would be a flaky test though :)
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| # Make alpha base 100 | ||
| y_pred_quantiles = y_pred_quantiles.with_columns( | ||
| (pl.col("quantile") * 100).cast(pl.Int16) |
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If you have more than 2 decimal places, then the casting would just truncate the decimal values 🤔
Example:
import polars as pl
pl.DataFrame({"a": [0.111, 0.11]}).with_columns((pl.col("a")*100).cast(pl.Int16))
shape: (2, 1)
┌─────┐
│ a │
│ --- │
│ i16 │
╞═════╡
│ 11 │
│ 11 │
└─────┘
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That's correct; I am still to change that bit of the code fortunately. This column won't exist once I'm done ✔️
| return (0.1, 0.9) | ||
| elif len(alphas) != 2: | ||
| raise ValueError("alphas must be a list of length 2") | ||
| elif not all(0 < alpha < 1 for alpha in alphas): |
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Should we also check that the sequence is sorted?
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IIRC the function returns the alphas sorted, so that should be OK. Am I correct?
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@FBruzzesi I am starting to work on #135 ; would like your opinion on the work so far 😊 Also I would like to hear your opinion on how you would test the conformal prediction code.
(To keep track: this is preparatory work for #39)