I've been working on some bias mitigation metrics and methods and
that usecase
changes the data as well as up/down sampling as a transformer. Almost
all those
methods also need sample properties for the observations to work. I'm
trying to
make them "sklearn compatible", but for now it's pretty hacky. So I'd
be happy if
we discuss the union of what Joel and Andy suggest.
Cheers,
Adrin.
On Thu, Feb 14, 2019, 11:47 Guillaume Lemaître
<g.lemaitr...@gmail.com <mailto:g.lemaitr...@gmail.com> wrote:
I am really interested in the union of the list given by Andy and
Joel.
I'll like to have some discussions related to the "impute"
module. Compare to the other topics, it is not a high priority
discussion thought.
On Thu, 14 Feb 2019 at 05:31, Joel Nothman
<joel.noth...@gmail.com <mailto:joel.noth...@gmail.com>> wrote:
Convergence in logistic regression
(https://github.com/scikit-learn/scikit-learn/issues/11536) is
indeed one problem (and it presents a general issue of what
max_iter means when you have several solvers, or how good
defaults are selected). But I was sure we had problems with
non-determinism on some platforms... but now can't find.
> my students have basically no way to figure out what
features the coefficients in their linear model correspond
to, that seems a bit more important to me.
Yes, I agree... Assuming coefficients are helpful, rather
than using permutation-based measures of importance, for
instance.
I generally think a review of distances might be a good thing
at some point, given the confusing triplication across
sklearn.neighbors, sklearn.metrics.pairwise, scipy.spatial...
and that minkowski,p=2 is not implemented the same as euclidean.
On Thu, 14 Feb 2019 at 12:56, Andreas Mueller
<t3k...@gmail.com <mailto:t3k...@gmail.com>> wrote:
Do you have a reference for the logistic regression
stability? Is it convergence warnings?
Happy to discuss the other two issues, though I feel they
seem easier than most of what's on my list.
I have no idea what's going on with OPTICS tbh, and I'll
leave it up to you and the others to decide whether
that's something we should discuss.
I can try to read up and weigh in but that might not be
the most effective way to do it.
the sample props is something I left out because I
personally don't feel it's a priority compared to all the
other things;
my students have basically no way to figure out what
features the coefficients in their linear model
correspond to, that seems a bit more important to me.
We can put it on the discussion list again, but I'm not
super enthusiastic about it.
How should we prioritize things?
On 2/13/19 8:08 PM, Joel Nothman wrote:
Yes, I was thinking the same. I think there are some
other core issues to solve, such as:
* euclidean_distances numerical issues
* commitment to ARM testing and debugging
* logistic regression stability
We should also nut out OPTICS issues or remove it from
0.21. I'm still keen on trying to work out sample props
(supporting weighted scoring at least), but perhaps I'm
being persuaded this will never be a top-priority
requirement, and the solutions add much complexity.
On Thu, 14 Feb 2019 at 07:39, Andreas Mueller
<t3k...@gmail.com <mailto:t3k...@gmail.com>> wrote:
Hey all.
Should we collect some discussion points for the sprint?
There's an unusual amount of core-devs present and I
think we should seize the opportunity.
Maybe we should create a page in the wiki or add it
to the sprint page?
Things that are high on my list of priorities are:
* slicing pipelines
* add get_feature_names to pipelines
* freezing estimator
* faster multi-metric scoring
* fit_transform doing something other than
fit.transform
* imbalance-learn interface / subsampling in pipelines
* Specifying search spaces and valid hyper
parameters
(https://github.com/scikit-learn/scikit-learn/issues/13031).
* allowing EstimatorCV-style speed-up in GridSearches
* storing pandas column names and using them as
feature names
Trying to discuss all of these might be too much,
but maybe we can figure out a subset and make sure
we have sleps to discuss?
Most of these issues are on the roadmap, issue 13031
is reladed to #18 but not directly on the roadmap.
Thanks,
Andy
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--
Guillaume Lemaitre
INRIA Saclay - Parietal team
Center for Data Science Paris-Saclay
https://glemaitre.github.io/
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