[scikit-learn] MLPClassifier on WIndows 10 is 4 times slower than that on macOS?

[Guillaume Lemaître]

I checked on 0.20.1 using scikit-learn shipped by Anaconda and both seem to
have the same default.

On Mon, 17 Dec 2018 at 16:01, Guillaume Lemaître <g.lemaitre58 at gmail.com>
wrote:

> could you provide the scikit-learn version in both case?
>
> Sent from my phone - sorry to be brief and potential misspell.
> *From:* kouichi.matsuda at gmail.com
> *Sent:* 17 December 2018 15:56
> *To:* scikit-learn at python.org
> *Reply to:* scikit-learn at python.org
> *Subject:* Re: [scikit-learn] MLPClassifier on WIndows 10 is 4 times
> slower than that on macOS?
>
> Thank you for your quick reply. It's very helpful.
> It's because of Anaconda: Its python stops the iteration soon as follows
> (w/ verbose=True).
> I am not sure why 'n_iter_no_change=10' is changed in Anaconda.
> Anaconda might modify the MLPClassifier implementation.
>
> > python learn.py (in pure Python+Scikit-Learn)
> ...
>
> Iteration 125, loss = 0.26152263
>
> Iteration 126, loss = 0.25705940
>
> Iteration 127, loss = 0.25957841
>
> Training loss did not improve more than tol=0.000100 for 10 consecutive
> epochs. Stopping.
> 0.8496
>
> > python learn.py (in Anaconda)
> ...
> Iteration 23, loss = 0.34410594
> Iteration 24, loss = 0.34663903
> Iteration 25, loss = 0.34376815
> Training loss did not improve more than tol=0.000100 for two consecutive
> epochs. Stopping.
> 0.852
>
> Thanks,
>
>
> ---
> 松田晃一 MATSUDA, Kouichi, Ph.D.
>
>
> 2018年12月16日(日) 0:50 Gael Varoquaux <gael.varoquaux at normalesup.org>:
>
>> I suspect that it is probably due to the linear-algebra libraries: your
>> scientific Python install on macOS is probably using optimized
>> linear-algebra (ie optimized numpy and scipy), but not your install on
>> Windows.
>>
>> I would recommend you to look at how you installed you Python
>> distribution on macOS and on Windows, as you likely have installed an
>> optimized one on one of the platforms and not on the other.
>>
>> Cheers,
>>
>> Gaël
>>
>> On Sat, Dec 15, 2018 at 09:02:06AM -0500, Kouichi Matsuda wrote:
>> > Hi Hi everyone,
>>
>> > I am writing a scikit-learn program to use MLPClassifier to learn
>> > Fashion-MNIST.
>> > The following is the program. It's very simple.
>> > When I ran it on Windows 10 (Core-i7-8565U, 1.8GHz, 16GB) note book, it
>> took
>> > about 4 minutes.
>> > However, when I ran it on MacBook(macOS), it took about 1 minutes.
>> > Does anyone help me to understand the reason why Windows 10 is so slow?
>> > Am I missing something?
>>
>> > Thanks,
>>
>> > import os import gzip import numpy as np #from https://github.com/
>> > zalandoresearch/fashion-mnist/blob/master/utils/mnist_reader.py def
>> load_mnist
>> > (path, kind='train'): labels_path = os.path.join(path,'%
>> s-labels-idx1-ubyte.gz'
>> > % kind) images_path = os.path.join(path,'%s-images-idx3-ubyte.gz' %
>> kind) with
>> > gzip.open(labels_path, 'rb') as lbpath: labels = np.frombuffer(
>> lbpath.read(),
>> > dtype=np.uint8, offset=8) with gzip.open(images_path, 'rb') as
>> imgpath: images
>> > = np.frombuffer(imgpath.read(), dtype=np.uint8, offset=16) images =
>> > images.reshape(len(labels), 784) return images, labels x_train,
>> y_train =
>> > load_mnist('data', kind='train') x_test, y_test = load_mnist('data',
>> kind=
>> > 't10k') from sklearn.neural_network import MLPClassifier import time
>> import
>> > datetime print(datetime.datetime.today()) start = time.time() mlp =
>> > MLPClassifier() mlp.fit(x_train, y_train) print((time.time() - start)/
>> 60)
>>
>>
>> > ---
>> > MATSUDA, Kouichi, Ph.D.
>>
>> > _______________________________________________
>> > scikit-learn mailing list
>> > scikit-learn at python.org
>> > https://mail.python.org/mailman/listinfo/scikit-learn
>>
>>
>> --
>>     Gael Varoquaux
>>     Senior Researcher, INRIA Parietal
>>     NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France
>>     Phone:  ++ 33-1-69-08-79-68 <+33169087968>
>>     http://gael-varoquaux.info
>> http://twitter.com/GaelVaroquaux
>> _______________________________________________
>> scikit-learn mailing list
>> scikit-learn at python.org
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>>
>

-- 
Guillaume Lemaitre
INRIA Saclay - Parietal team
Center for Data Science Paris-Saclay
https://glemaitre.github.io/
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