On Mon, Oct 06, 2014 at 05:35:12PM -0400, Alan G Isaac wrote:
> On 10/6/2014 Gael Varoquaux wrote:
> > Parallel computing has problems with lambda functions.
> Can you elaborate on that please?
Lambdas don't pickle. Parallel computing needs pickling (or it needs to
do hacks).
Gaël
-
On 10/6/2014 Gael Varoquaux wrote:
> Parallel computing has problems with lambda functions.
Can you elaborate on that please?
I'm aware of the currying problem:
http://stackoverflow.com/questions/11371009/parallel-mapping-functions-in-ipython-w-multiple-parameters
But that is not a problem with la
This is because multiprocessing in python cannot handle functions that
cannot be pickled. I've learnt this the tough way.
You can maybe have a look at this,
http://matthewrocklin.com/blog/work/2013/12/05/Parallelism-and-Serialization/
On Mon, Oct 6, 2014 at 10:48 PM, Gael Varoquaux <
gael.varoqu.
On Mon, Oct 06, 2014 at 07:50:42PM +0100, Dominic Steinitz wrote:
> > Try not using a lambda function, but a fully-feldged function. Parallel
> > computing has problems with lambda functions.
> Do you mean parallel computing generally or in Python?
In Python.
Gaël
--
On Mon, Oct 06, 2014 at 06:27:21PM +, Pagliari, Roberto wrote:
> I’d like to use multiprocessing module to run different tasks at the same time
> (each of which may run grid search).
> Are there any known issues when using this module with gridSearchCV (and njobs
> >1 ), or anything I should
On Mon, Oct 06, 2014 at 04:56:22PM -0300, Nicolas Emiliani wrote:
> This will print out:
>
> >>> array([[ 0., 1., 68.]])
> but ... how do I know which position in that array belongs to which class ?
> The classifier has a classes_ attribute which is also a list
> >>> clf.classes_
>
Hi!
I am using a scikit-learn DecissionTreeClassifier on a 3 class dataset.
After I fit the classifier I access all leaf nodes on the tree_ attribute
in order to get the amount of instances that end up in a given node for
each class.
clf = tree.DecisionTreeClassifier(max_depth=5)
clf.fit(
Dominic Steinitz
domi...@steinitz.org
http://idontgetoutmuch.wordpress.com
>
> Message: 2
> Date: Mon, 6 Oct 2014 15:39:43 +0200
> From: Gael Varoquaux
> Subject: Re: [Scikit-learn-general] cross_val_score crashes python
> every time
> To: scikit-learn-general@lists.sourceforge.net
> Mess
Hi Matthieu,
Which dataset are you referring to?
Thanks
From: Mathieu Blondel [mailto:math...@mblondel.org]
Sent: Saturday, October 04, 2014 10:13 AM
To: scikit-learn-general
Subject: Re: [Scikit-learn-general] error when using linear SVM with AdaBoost
On Sat, Oct 4, 2014 at 1:09 AM, Andy
ma
I'd like to use multiprocessing module to run different tasks at the same time
(each of which may run grid search).
Are there any known issues when using this module with gridSearchCV (and njobs
>1 ), or anything I should consider when doing this?
Thank you,
Hi Zoraida.
I am not expert in R glms but I think the glm call just does logistic
regression.
For the binary case, this is the same as
sklearn.linear_model.LogisticRegression.
Just a wild guess: Did you use clf.decision function results as input to
roc_auc_score?
If you use clf.predict results
thanks Olivier -- much appreciated - this will de-militarize my
conversations in English a lot.
2014-10-06 16:36 GMT+02:00 Olivier Grisel :
> 2014-10-06 15:27 GMT+02:00 Peter Prettenhofer <
> peter.prettenho...@gmail.com>:
> >
> > Both scikit-learn and R (glmnet) should be thoroughly documented.
2014-10-06 15:27 GMT+02:00 Peter Prettenhofer :
>
> Both scikit-learn and R (glmnet) should be thoroughly documented. ML tools
> have come a long way and are very robust and usable these days but they are
> not completely fire-and-forget**.
>
> ** sorry for the military term but I lack a good alter
> I'm trying to use cross_val_score inside a lambda function to take full
> advantage of my processors -
Try not using a lambda function, but a fully-feldged function. Parallel
computing has problems with lambda functions.
Gaël
Hi Zoraida,
can you provide a code snippet (e.g. upload it to gist.github.com) that
illustrates the problem -- especially how you evaluate the goodness of the
predictions (both R and scikit-learn)?
Its pretty difficult to argue about the issue without seeing what you
actually do. The difference be
Hi all,
I know the subject is ugly but I don¹t really know how to call it.
I am newbie with all this machine learning techniques and what I do most
of the time is to follow a ³try and error² approach. I now this method has
some inconvenients but for now
is what I am able to do.
I am working with
You can use the `lasso_path` function to get the full path:
http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.lasso_path.html#sklearn.linear_model.lasso_path
Here is an example:
http://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_coordinate_descent_path
Hi Michael,
thanks for your answer, it helped a lot already.
On 06.10.2014 11:06, Michael Eickenberg wrote:
> may prefer using e.g. LassoLarsCV.
I gave it a shot and indeed the coefs are available, and it worked
really fine on my well designed test problems. However, with my real use
case it p
Hi Fabien,
welcome to the list!
If you are interested in the exact locations of the kinks in the
coefficient path, you may prefer using e.g. LassoLarsCV. It works on your
size of problem (iff "highly collinear" doesn't mean "basically equal") and
has the attributed "coef_path_" (
https://github.c
There might be a problem with running multiprocessing (that is used
internally by cross_val_score with n_jobs=-1) in concurrent Python
threads.
BTW, why do you use threads in the first place?
--
Olivier
--
Slashdot TV.
Folks,
this is my first message on this newsgroup, so first: Hi!
I have two questions, I hope they are not too trivial:
1. Access to coefficients in LassoCV
I use LassoCV to find the optimal alpha for my problem. For analysis
purposes I'd like to get access to the paths coefficients, more or le
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