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Kristiyan Katsarov
@katsar0v
I have no mkl (from conda or pip)
Andreas Mueller
@amueller
pip has no mkl ;)
(so far)
Kristiyan Katsarov
@katsar0v
Andreas Mueller
@amueller
@katsar0v I don't think that helps given that numpy and scipy will not be linked against it
Kristiyan Katsarov
@katsar0v
this saved my life @amueller
Andreas Mueller
@amueller
well in your script n and m are way too small to show anything useful
Kristiyan Katsarov
@katsar0v
it reduced my validation curve
from 500s to 15 seconds
@amueller this is a life saver
Andreas Mueller
@amueller
what did?
these envs
Andreas Mueller
@amueller
ah
well stackoverflow saved your live
*life
Kristiyan Katsarov
@katsar0v
It's good for performance tweaks
Samesh Lakhotia
@sameshl
How should I build the docs for harversine_distances in my local repo? I ran python setup.py install but still I can't find it under doc/modules/
Guillaume Lemaitre
@glemaitre
The documentation is another command line
cd doc
make html
should work all OS I think
then it will create a _build/html folder and you can search for the index.html
Loïc Estève
@lesteve
@sameshl note this part of the contributing scikit-learn doc: https://scikit-learn.org/stable/developers/contributing.html#documentation
If you see ways the contributing doc can be improved while you face this "setup" issues, let us know or/and open PRs to improve the contributing docs!
Samesh Lakhotia
@sameshl
@lesteve Sure. Thanks for the help.
As a beginner contributor to this organisation, the arrangements of the docs did feel a bit tough to navigate. I will put my thoughts about it more concisely and then open a issue and PR for the same
Adrin Jalali
@adrinjalali
We're working on improving our contributing docs @sameshl, there's some discussion under #14582
Samesh Lakhotia
@sameshl
Thats great. Would love to contribute on scikit-learn/scikit-learn#14582
I am working on scikit-learn/scikit-learn#14575. So I found the corresponding example under sklearn/metrics/pairwise.py. My question is, are the examples run in the doc building process and output is generated or I am supposed to manually write the output of the example in the docstring of a function?
Adrin Jalali
@adrinjalali
you should write the output in the example. The doc build will run the code and check if the generated output is the same as the one you put there. See https://docs.python.org/3.5/library/doctest.html for more info
Samesh Lakhotia
@sameshl
Thanks @adrinjalali !
Vishesh Mangla
@XtremeGood
Does anyone here knows a good source to learn rnn structure ?
Is it like replacing every hidden node with a rnn cell?
Samesh Lakhotia
@sameshl
I am working on scikit-learn/scikit-learn#14131 . So, I thought that I could append a note in the docstring of KDTree regarding the issue. But I looked into sklearn/neighbors/kd_tree.pyx and it looks like KDTree is inheriting its docstring from BinaryTree. So can someone tell me an elegant way to append my note docstring to the inherited docstring of KDTree or if I could do something else to solve this issue.
Manish Aradwad
@ManishAradwad
Currently working on #14081.
I am supposed to create a pitfalls section which includes practices not to be followed by users. Quite confused about how should I approach it, should I create a whole new section in documentation.html or is there another way to do this??
Thanks for the help!!!
Peng Yu
@yupbank
Hey channel, i’ve being working on vectorizing regression tree with Numpy, and i have achieved some speed up against the cython version of sklearn. in case anyone is interested, here is the link https://github.com/yupbank/np_decision_tree#regression-with-mae
Peng Yu
@yupbank
on median data(10000*100), with MAE criteria, achieved 20 times speed up :)
Adrin Jalali
@adrinjalali
still haven't checked the code in depth. But it's definitely interesting @yupbank . What do you think @NicolasHug ?
Peng Yu
@yupbank
i haven’t clean the code yet, and also working on a blog post explainning what i did, and add some CI to it. But i would love to have some extra inputs before i proceed, e.g. reviews.
Adrin Jalali
@adrinjalali
I don't think it'd be easy, but I'd love to see if it actually passes our tree tests, and if it doesn't why not and which tests. Feel free to ping me when you write the blog post.
Peng Yu
@yupbank
sure.. that would be nice,
Nicolas Hug
@NicolasHug
@yupbank pretty cool stuff! I took a quick glance at the tree grower and the greedy_split function and it looks good as far as I can tell. I wouldn't advertise benchmarks with only max_depth=1 though ;)
Please definitely ping us when you write the blog post!!
Peng Yu
@yupbank
lol, you are right, actually with max_depth=10, i only get 5 times faster.
Peng Yu
@yupbank
@NicolasHug @adrinjalali hey.. i have a draft version here.. comments are very welcome :) https://yupbank.github.io/learning/2019/08/08/faster-regression-tree.html
Peng Yu
@yupbank
omg omg omg, For L2 loss, if i replace import numpy as np with import cupy as np, i get another 10x Speed up for 1 split, but i would lost the edge when i have too many depth.. i need to refactor my code…
Adrin Jalali
@adrinjalali
+1
Peng Yu
@yupbank
but i really like the fact that, switching to GPU is so trivial …
Matthew Bowling
@Ryuhphino_twitter
Have a question maybe someone can answer. Trying to use a simple model on a set of data. About a couple thousand rows and only a dozen features, most are binary. I'm training on Logistic Regression, and found my model overfits. So when I try to tune my hyperparameters, my accuracy remains entirely unchanged. Has anyone seen this before or know why this is happening?
Guillaume Lemaitre
@glemaitre
Do you have imbalanced classes?
Samesh Lakhotia
@sameshl
I want to rebuild the 'scikit-learn' project. I tried running pip install --editable . as stated in the docs https://scikit-learn.org/stable/developers/advanced_installation.html#building-from-source but I am getting this error. Can someone help me out.
ERROR: Cannot uninstall 'scikit-learn'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.