Github user jkbradley commented on the issue:
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Merging with master
Thanks!
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Sounds good. I'll run fresh tests before merging to be safe though.
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Github user srowen commented on the issue:
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@jkbradley I'll merge if there are no other thoughts here. Seems OK to me
too
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Github user jkbradley commented on the issue:
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Thanks for the updates! This LGTM, except for deciding about negative
weights.
Responding to your comment above, negative weights are just as problematic
as 0 weights. See my comment
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My thought was that there's nothing in the computation that prevents it
(unlike 0 weights, which cause NaNs). Why not err on the side of maximum
flexibility to the end user? Just because there's no
Github user jkbradley commented on the issue:
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@zapletal-martin Pinging since you wrote the original PR: There's
discussion here about whether IsotonicRegression should support negative
weights. Is there a good reason to? I haven't seen
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@neggert I'll ask @mengxr about the negative weights since he oversaw the
original work here.
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LGTM
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Your changes sound fine. I don't have strong feelings one way or another,
although I think we should at least throw a warning if we're discarding points.
I do want to point out that disallowing
Github user viirya commented on the issue:
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SGTM. See if @jkbradley has more comment?
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Done. It was surprisingly tricky to test, since the exception is thrown in
the executor.
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Sorry, I've been on vacation. I'll make the requested changes in the next
day or two.
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Ping @neggert
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Github user srowen commented on the issue:
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@neggert I think this is looking good except for the question about zero
weights. If throwing an error is OK to you go for it. The test should be
restored, but be updated to expect an exception.
Github user viirya commented on the issue:
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@neggert I am fine for throwing an error.
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@viirya Better to remove them, or throw an error? Personally, I'd rather be
alerted that I'm passing invalid input, rather than have it "fixed" for me.
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Github user viirya commented on the issue:
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For the zero-weight values, can we do similar to scikit-learn to remove
zero-weight values, like
https://github.com/amueller/scikit-learn/commit/2415100f79293bbbf52c12c36d63a6cf602cf3c4
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@srowen Addressed your comments and fixed some style issues.
Some updated timings:
Alternating decreasing input
val x = (1 to length).toArray.map(_.toDouble)
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Alright, the PAV algorithm has been completely re-written to follow what's
outlined in "Minimizing Separable Convex Functions Subject to Simple Chain
Constraints". I've tested it with a bunch of
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Just add commits here.
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Github user neggert commented on the issue:
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Found another input that triggers non-polynomial time with the code in this
PR. I'm again borrowing from scikit-learn. I think this is the case they found
that led them to re-write their
Github user srowen commented on the issue:
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If it's correct and faster, I tend to favor merging this. The code looks OK
to my understanding and passes existing tests.
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Github user neggert commented on the issue:
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Looks like the new scikit-learn implementation suffers from a similar
problem to the one that the original Spark implementation had. I left them a
note pointing it out.
Right now I'm
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Given that the scikit-learn implementation I based this on has changed
(https://github.com/scikit-learn/scikit-learn/pull/7444) since I submitted the
PR, and @jkbradley has pointed out a reference
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Thanks for pinging about this---it looks important!
One request: Do you know if there is a resource with a clearer statement of
this precise algorithm for the L2 norm? That'd be nice to
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Anyone? @srowen?
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Could someone please take a look at this? @mengxr maybe? Looks like you
merged the initial implementation.
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