scikit-learn/scikit-learn

scikit-learn: machine learning in Python. Please feel free to ask specific questions about scikit-learn. Please try to keep the discussion focused on scikit-learn usage and immediately related open source projects from the Python ecosystem.

Roman Yurchak

@rthy:matrix.org

[m]

Yes, you can pass CFLAGS env variable https://stackoverflow.com/a/10867041/1791279

Harsh Kumar

@HarshVardhanKumar

thanks @rthy:matrix.org

Reshama Shaikh

@reshamas

Hello, I am wondering why this PR (scikit-learn/scikit-learn#18758) doesn't show up at the top here:
https://github.com/scikit-learn/scikit-learn/pulls

Is it because I had submitted it a long time ago, but my recent changes are considered updates?

Guillaume Lemaitre

@glemaitre

I think that PRs are ordered by PR numbers by default in github

Reshama Shaikh

@reshamas

Oh, wow, that's interesting. Not what I would have expected.

Guillaume Lemaitre

@glemaitre

However, you have the option of "Sort by recently updated". This would actually be a good default while reviewing :)

Reshama Shaikh

@reshamas

Yes, that's what I was looking for (and expecting as a default). Thanks!

Roman Yurchak

@rthy:matrix.org

[m]

The "Refined GitHub" browser extension makes it the default among other improvements.

Harsh Kumar

@HarshVardhanKumar

I see that the binary_tree.pxi passes the value num_samples in the _recursive_build() procedure as an argument. The num_samples is calculated using self.data_arr.shape[0] whereas _recursive_build() expects an ITYPE_t argument. ITYPE_t is defined as np.intp_t which I assume is only 32 bit signed integer. So how is the binary tree built in cases when n_samples is greater than this value - let's say 10million data points?

In default python, this would've been handled by increasing the data size to long long implicitly. Does cython take care of it? I don't see any methods to take care of such scenario in the scikit-learn implementation code.

Harsh Kumar

@HarshVardhanKumar

Except for that idx_end - idx_start < 2 will be true in this case (due to signed integer overflow?) and the node 0 will be made a leaf node. But this is an unexpected behaviour, right?

Roman Yurchak

@rthy:matrix.org

[m]

@HarshVardhanKumar: maximum value for int32 is ~2e9 not 2e6. So probably no one has tried using it with more than 2 billion samples. Not sure it's really an issue for the near future.

+1 to check for that overflow though.

Harsh Kumar

@HarshVardhanKumar

@rthy:matrix.org thanks for pointing it out. A silly mistake on my part.

Reshama Shaikh

@reshamas

Hello, I ran pytest sklearn and see the following. Is this ok, or is there something wrong with my build:

SKIPPED [16] sklearn/utils/tests/test_validation.py:1374: could not import 'pandas': No module named 'pandas'
==== 355 failed, 19625 passed, 1443 skipped, 117 xfailed, 37 xpassed, 3371 warnings in 2380.84s (0:39:40) ====
(sklearn-dev)

Reshama Shaikh

@reshamas

OK, it works now. Thanks Thomas Fan.

Yashasvi Misra

@yashasvimisra2798

Hello, I was working on this issue scikit-learn/scikit-learn#20435

However I was not able to find the file to contribute the documentation into, can someone please help me with that

Adrin Jalali

@adrinjalali

@yashasvimisra2798 the documentation is generated from the docstrings in the .py files where those classes are implemented. You should look for classes which inherit that class, and find the relevant part of the docstring there.

Yashasvi Misra

@yashasvimisra2798

Thanks, @adrinjalali I will look into it.

bmoroz82

@bmoroz82:matrix.org

[m]

Does Scikit support regression parameters with multidimensional data structure, e.g., 3-dimensional point data? I would like to perform a regression to predict the position of a 3-dimensional point (x,y,z) using other known 3-dimensional points while weighting by inverse-distance. I have a small sample of dependents Y comprised (xi, yi, zi) and a complete set of independents X1, X2, X3, ... each comprised of (xi, yi, zi). I would like to test a simple model Y = X1 + X2 + ...

Guillaume Lemaitre

@glemaitre

you probably want to look at the following: https://scikit-learn.org/stable/modules/multiclass.html#multioutput-regression

bmoroz82

@bmoroz82:matrix.org

[m]

🙏 appreciated

tejaswivg

@tejaswivg

Can I use a confusion matrix to see the accuracy of SVR(support vector regression) ?

or is it only for classification ?

Guillaume Lemaitre

@glemaitre

confusion matrix and derived metrics are only for classification

look at the regression metrics instead

Markus Löning

@mloning

Hi all, how do you manage your changelog? Any useful tools or files that you could point me too?

Nicolas Hug

@NicolasHug

@mloning most PRs come with a changelog entry, e.g. https://github.com/scikit-learn/scikit-learn/pull/20727/files
Then we have scripts to process / check it which are described in https://scikit-learn.org/stable/developers/maintainer.html (we call the change log a "What's new")

Markus Löning

@mloning

@NicolasHug thanks - that's very helpful!

Harsh Chauhan

@RAVANv2

A very good evening everyone!!
I am trying to setup scikit-learn project into my development env but encountered with an error when I try to run "pip install --verbose --no-build-isolation --editable ."

Can someone help me to fix this error?

Adrin Jalali

@adrinjalali

@RAVANv2 can only help if you tell us what the issue is :)

Harsh Chauhan

@RAVANv2

Hi @adrinjalali,
Actually, I solve it on my own :)

Harsh Chauhan

@RAVANv2

Hi all, I made one PR on issue #20754 but it failing the linting job of black and showing the error "##[error]Bash exited with code '1'". Can anyone tell me where I am doing wrong? I am totally a newbie in open-source but I m really enjoying :)

Adrin Jalali

@adrinjalali

You can see how to fix that here: https://scikit-learn.org/dev/developers/contributing.html#pull-request-checklist You need to run black on your code

Kiran Hipparagi

@KiranHipparagi

Hello Everyone, I am a newbie to open source with basic knowledge about scikit-learn and want to contribute to this repository. Can anyone tell me how should I start? I have basic knowledge about generating my first PR and solving good first issues. Now I want to fix bugs and do some code contributions but I am unable to understand the issues. Can anyone guide me?

Andreas Mueller

@amueller

Hi @KiranHipparagi , thanks for wanting to contribute! Have you read the contributor's guide? I would suggest you look for issues tagged "good first issue". Many of those are multi-part issues where you can pick just a part to work on

see https://scikit-learn.org/dev/developers/contributing.html and https://github.com/scikit-learn/scikit-learn/labels/good%20first%20issue for good first issues

this one might be good to get started: scikit-learn/scikit-learn#20308

Aura09c

@Aura09c

Hello i am starting out this one project and some of the parts are really complicated . And i am happy to learn during the complicated parts. But i feel i need more people. If anyone is interested in working together and learning together feel free to dm me

my discord is Aura#5549

Reshama Shaikh

@reshamas

Deleted this message from the /dev/ channel. Copying and pasting here:

I am Bhavya Bhardwaj (https://github.com/Bhavya1705). I am a student of Electronics and Communication at Amrita Vishwa Vidyapeetham, India. My thanks to you and the team for sklearn. I have been try to make some contributions to the scikit-learn library - scikit-learn/scikit-learn#5516. I have made the code, and the necessary changes to the init file and test files, in addition to the _classification file. This is the links to my commits - scikit-learn/scikit-learn#20861, as you will see, there are many mistakes, that I have made, Any help that you can render to me would be much appreciated and would be a wonderful learning experience.
Thank You

Bhavya Bhardwaj

@Bhavya1705

@reshamas Thank You, I have managed to solve the issue.

adriente

@adriente

Hi. I am trying to develop my own Estimator based on TransformerMixin and BaseEstimator. To make sure I am doing things right I have added a test to my project :

import MyEstimator
from sklearn.utils.estimator_checks import check_estimator
def test () : 
    me = MyEstimator(**params)
    check_estimator(me)

If I run the test, I get the following error message :

AssertionError: The error message should contain one of the following patterns:
               0 feature\(s\) \(shape=\(\d*, 0\)\) while a minimum of \d* is required.

I don't understand how I am supposed to take care of that. I am even more surprise because my fit_transorm method uses self._validate_data at the beginning. I would expect that function to take care of case like these. Could someone help me with that issue ?

Roman Yurchak

@rthy:matrix.org

[m]

@adriente: Could you please open a Github issue with full traceback and tag me (@rth) in?

Freddy Boulton

@freddyaboulton

Hello! I opened this feature request a week ago. Just bumping it here in case it got lost: scikit-learn/scikit-learn#20890

Guillaume Lemaitre

@glemaitre

@freddyaboulton I can assure you it is not lost :) I saw it but I did not look at it yet because we are kind of working on releasing 1.0. Once the release done, you might get some attention from core-devs

1 reply

Loki The Great

@makingglitches

Morning all

I was kind of curious . I’ve been dragging my feet using torch and I was wondering does this lib offer anything over torch ? Maybe this is better suited for the low level scientist trying to learn theory ? Or is it just an alternative ?

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