[HN Gopher] PiML: Python Interpretable Machine Learning Toolbox
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PiML: Python Interpretable Machine Learning Toolbox
Author : skadamat
Score : 70 points
Date : 2024-11-05 15:25 UTC (7 hours ago)
(HTM) web link (github.com)
(TXT) w3m dump (github.com)
| setopt wrote:
| PiML is also a common abbreviation for Physics-informed Machine
| Learning.
| jszymborski wrote:
| TIL. I've only ever heard the term PINN (physics-informed
| neural networks) before.
| pragma_x wrote:
| I misread that as "impenetrable" machine learning and thought:
| "Well, it's about time someone admitted it."
|
| Ironically, the low-code examples do a good job of making this
| space a little more approachable.
| frakt0x90 wrote:
| Very Happy to see FIGS on the list. I almost got a chance to use
| it on a recent project but the csutomer in the end decided they
| valued accuracy over interpretability. I wonder if the recent
| KANs fit into this? They're more interpretable than other NN
| architectures. Also, Berkeley has had a few articles on
| interpretable methods (including FIGS) over the years but idk how
| they compare to what you've already implemented:
|
| https://bair.berkeley.edu/blog/2022/02/02/imodels/
|
| https://bair.berkeley.edu/blog/2020/04/23/decisions/
| 3abiton wrote:
| QRD on FIGS for the curious ones?
| frakt0x90 wrote:
| This explains it better than I could:
|
| https://bair.berkeley.edu/blog/2022/06/30/figs/
| bravura wrote:
| Pretty cool. So it greedily constructs an ensemble of trees
| from scratch. At each step, it takes the one decision that
| reduces the loss the most. That's a nice approximation for
| the most compact tree ensemble.
| abhgh wrote:
| I have used a successor [1] of FIGS and my experience was that
| although the theory is elegant, their benchmarking was
| incomplete.This is something I would look out for if you are
| planning to use it on a real-world problem. Again, this is not
| to be negative about the paper, but there _is_ a gap between
| the theory and practice.
|
| I had raised this as an issue [2] on their repo, but after a
| few exchanges didn't receive any response. If you look at the
| last comment on that issue thread, you will see that Random
| Forest with proper hyperparam. search produces competitive
| results.
|
| [1] https://proceedings.mlr.press/v162/agarwal22b.html
|
| [2] https://github.com/csinva/imodels/issues/129
| melenaboija wrote:
| Is the source code available?
| zwaps wrote:
| Am i blind? Where is the code?
| dagw wrote:
| Looks like they haven't actually published the source code,
| despite both GitHub and PyPI claiming the project is apache
| licensed. If you install the package all you get in the wheel
| is precompiled cython libraries and the absolute minimum python
| code needed to import them.
| mrks_hy wrote:
| Given the broader state of the Python ecosystem, that is a
| red flag for me. Why would you add an OSS license but
| withhold the sourcecode?
|
| The only reason I can think of is to "trick" people into
| downloading and betting that no-one actually checks the
| source, then hiding something in the .so files that are
| shipped as wheels. Tread carefully.
| Labo333 wrote:
| Indeed, this is a major blocker. I am very wary of
| installing it and would never ship this to production.
| maaaaattttt wrote:
| That's a name that won't go unnoticed by the german speaking
| colleagues.
| haustlauf wrote:
| As well as the Dutch ones.
| j7c6 wrote:
| I just pitched "Product Information Management Machine
| Learning" to my team lead ;) Which, in my opinion, is largely
| underestimated in that field of software... Maybe we should
| consider a different name though
| starbugs wrote:
| Please explain for international audiences :)
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