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Vishesh Mangla
Is it like replacing every hidden node with a rnn cell?
Samesh Lakhotia
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
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
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
on median data(10000*100), with MAE criteria, achieved 20 times speed up :)
Adrin Jalali
still haven't checked the code in depth. But it's definitely interesting @yupbank . What do you think @NicolasHug ?
Peng Yu
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
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
sure.. that would be nice,
Nicolas Hug
@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
lol, you are right, actually with max_depth=10, i only get 5 times faster.
Peng Yu
@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
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
Peng Yu
but i really like the fact that, switching to GPU is so trivial …
Matthew Bowling
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
Do you have imbalanced classes?
Samesh Lakhotia
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.
Roman Yurchak
@sameshl See https://github.com/pypa/pip/issues/5247#issuecomment-381550610 probably best to reinstall in a new virtual environment.
Andreas Mueller
@thomasjpfan you like puzzles, right? scikit-learn/scikit-learn#14704

I can't seem to get to make the virtual environment with sphinxgallery

conda create -n sklearndev numpy scipy matplotlib pytest sphinx cython ipykernel sphinxgallery


conda create -n sklearndev numpy scipy matplotlib pytest sphinx cython ipykernel sphinx-gallery

never mind! the solution is in the other gitter chat!
Bin Wang

Hi team, I am new to Cpython but really wants to play with the internals of sklearn. I want to test out some of the cdef classes in the pyx file but looks like the methods are inaccessible within Python. Any thought?

For example:

from sklearn.tree import _utils
ph = _utils.PriorityHeap(100)

And I cannot find call methods like pop, push.
Usually how does the workflow look like if I want to play with the internals of sklearn within Jupyter notebook.

hello everyone. I'm really new to Machine learning in general and i have been working with some sklearn Regressors. I need some help :). My question is how do i know if the RMSE i have is minimum enough for good predictions. To what do i compare this RMSE to?
I was able to create a model by curve fitting a set of data that has 5 variables using GaussianProcessRegressor. The problem is I am unable to export/load this model into an older version of python (version 2.5.2). Is there a way to dump the equation/formula into mathematical terms in relations to these 5 variables so that I can use this prediction on the older python? Thanks
Adrin Jalali
@enoch-sun We don't really support those Python versions anymore. You can try and figure it out with some other persisting models such as ONNX or PMML, but you'll be mostly on your own
Thomas J Fan
@biwa7636 The PriorityHeap functions pop and push are cdef, which means they are not available in python.
Jesse Leigh Patsolic
Is there a scikit-learn preferred way to store a vector using Cython? I've seen libcpp.vector, array.array and numpy used in the code base. @NicolasHug @amueller
Nicolas Hug
The way we do it now is to allocate numpy arrays (in python or in cython), and then use a memory view for pure cython parts. You can take a look at how we do it in e.g. ensemble/_hist_gradient_boosting
Hi, does apply in df.apply(fun) iterate over each columns in 'df' data-frame and pass them to 'fun' function as a series?
Bin Wang
@thomasjpfan, you are right, however, I also tried to execute the above code too using %%cython magic also from sklearn.tree cimport _utils but still did not work. Was it supposed to be like that?
# requires numpy headers
from sklearn.tree._utils cimport Stack
s = Stack(10)
>>> AttributeError: 'sklearn.tree._utils.Stack' object has no attribute 'top'
I found the source code so well written, fascinating and really want to be able to get the development environment up and running.
Bin Wang
Weird, the above code will work if I replace s = Stack(10) with cdef Stack s = Stack(10), I believe this must have something to do with static type declaration.
Jesse Leigh Patsolic
Does anyone know why the base estimator for ExtraTreesClassifier is ExtraTreeClassifier, instead of DecisionTreeClassifier with splitter='random'? I am working on adding a new type of tree. @NicolasHug @amueller
Nicolas Hug
No idea. It doesn't make much sense for ExtraTreeClassifier to allow for a splitter that isn't 'random' IMO.
Would you want to submit a PR to deprecate the parameter?

Hi All, I`m getting the following error while executing the python setup.py install
error: Command "cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MT -IC:\Users\Moti\Anaconda3\envs\motidevs\lib\site-packages\numpy\core\include /EHsc /Tpsklearn\svm\src\libsvm\libsvm_template.cpp /Fobuild\temp.win-amd64-3.7\sklearn\svm\src\libsvm\libsvm_template.obj" failed with exit status 127

Do you have any idea? Thanks!

Any scikit devs who can shed some light on why calibration_curve is only for binary estimators?
Anjali Singh
how can i start committing to the open source
Adrin Jalali
@Anj-ali you can start by going through our contributing guides: https://scikit-learn.org/dev/developers/contributing.html#contributing
Anjali Singh
thank you Sir, surely i will do that
Olivier Grisel
Heads up: if you use conda and upgrade your env, you might get a crash when using n_jobs>=2. This is caused by an updated version of intel-openmp in the default channel of conda. I reported the issue upstream as ContinuumIO/anaconda-issues#11294 and the problem is tracked in this PR on the scikit-learn side: scikit-learn/scikit-learn#15020
The error message is OMP: Error #13: Assertion failure at z_Linux_util.cpp(2361) reported by the dying worker process.
Which in turns causes loky to raise: TerminatedWorkerError: A worker process managed by the executor was unexpectedly terminated. This could be caused by a segmentation fault while calling the function or by an excessive memory usage causing the Operating System to kill the worker. The exit codes of the workers are {SIGABRT(-6)}.
Samesh Lakhotia
If someone is free to review, please take a look at scikit-learn/scikit-learn#14993 and scikit-learn/scikit-learn#15045.
Andreas Mueller
hm is there a pandas gitter? Or is @jorisvandenbossche around lol? For a pandas dtype, how do I get the closest numpy dtype to cast to?
Joris Van den Bossche
there is pandas gitter actually (pydata/pandas)
I don't think there is a typical way to do it
If I remember correctly, there is an issue about it