ENH: add weighted quantile for inverted_cdf #24254
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lorentzenchr
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@seberg friendly ping. I know numpy 2.0 requires resources, but maybe you could have a quick look. The underlying issue is very old with a ton of discussions. (And I need help to fix the emscripten CI error.) |
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I am even here unsure whether this should be called weights or something more clear? For inverted_cdf I guess it doesn't matter (unless someone expects more than a single datapoint being mixed in the final step if its weight < 1, but I am not sure that can be argued to even make sense).
numpy/lib/function_base.py
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| # setup wgt to broadcast along axis | ||
| wgt = np.broadcast_to(wgt, (a.ndim-1)*(1,) + wgt.shape) | ||
| wgt = wgt.swapaxes(-1, axis) | ||
| return wgt |
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Interesting logic, but prior art in either case.
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Why interesting? I just shifted these lines of code out of average for reuse in quantile.
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The logic tries to add convenience for an N-D input with with axis and 1-D (or other input) and I find that very dubious guessing of user intent with relatively little gain (it isn't hard to make sure weights and data are properly broadcast/aligned) and potential for confusion.
(The potential for terrible bugs where you expect swapping of weights to happen but it doesn't because shapes happen to be identical seems possible but unlikely.)
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Again, that code is not new or from me. I just took these lines out of np.average for reuse in np.quantile.
numpy/lib/function_base.py
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| weights = np.moveaxis(weights, axis, destination=0) | ||
| index_array = np.argsort(arr, axis=axis, kind="stable") | ||
| # TODO: Which value to set to slices_having_nans. | ||
| slices_having_nans = None |
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@seberg Thank you very much for having a look! |
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Do I have to add tests for n-dim data and that |
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@seberg I put more effort into it to also test several |
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@ogrisel uncovered some shortcomings that I'm trying to solve, in particular the I'm stuck because of the following error: import numpy as np
np.take(np.arange(10), np.array([1, 4]), out=np.zeros(2, dtype=float))How can I write the content of some slice of an int64 array into a float64 array? |
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This is now ready for a - hopfully - final review. Test coverage is much extended and corner cases like nan handling are now fixed (and tested), no todo left. Edit: And I like the 100% 🟢 of the current CI. |
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@seberg Do you think this could get in numpy 2.0? That would be my hope. |
seberg
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56 - Needs Release Note.
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Thanks, @lorentzenchr and @ogrisel for reviewing. The new changes look good, and the new tests were really needed. I did have a look at the shape logic, and it seems really quite clean to me, only comments I added were so small, that there would be no point in following up. Since voiced anything about the added API here or on the mailing list. Putting this in. |
Partially solves #8935.
This PR adds support for
weightsinnp.quantilesimilar tonp.average(..., weights=w). As an uncontroversial start, onlymethod="inverted_cdf"is implemented for weights support.See #8935 (comment) for details.