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

[Kouichi Matsuda]

 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
>     http://gael-varoquaux.info            http://twitter.com/GaelVaroquaux
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