[pymvpa] Feature selection for multiple classifiers

Hi all, I have a question in regards of feature selection if more than one classifier is involved, because there are more than two classes. If I understand it correctly, in multi-class problem PyMVPA will train a classifier for every possible pair of classes and result is decided by vote. So if

Re: [pymvpa] Searchlight statistical inference

This is achieved through a 'searchlight' command from command line interface (http://www.pymvpa.org/generated/cmd_searchlight.html), which is a different thing as 'sphere_searchlight' you are calling from python. In the code it is done in dosl.sh script. You can see that preprocessing and cv setup

Re: [pymvpa] Altering the weights of classes in binary SVM classifier

My sample is not balanced (there happens to have been 22 responders and 13 non-responders) and is not particularly large. I would like, if possible, to use all the data and adjust the classifier to the unbalanced set rather than selecting a subset of the responders. You don't have to

Re: [pymvpa] Searchlight statistical inference

Hello, I cannot help you with the inference on subject level, however we do have nice example for the group level inference. It is what we used in pandora data paper http://f1000research.com/articles/4-174/v1 (review pending) to produce figure 4 and table3. Code for the replication of the analysis

Re: [pymvpa] Searchlight statistical inference

Thank you so much Richard! This was super helpful! One last question, do you know if the averaging can be done using the command line without sparse ROI's? Maybe by using --scatter-rois 0? or is it the default regardless to the input of scatter-rois? I am sorry, but I don't know. The feature

[pymvpa] within chunk permutation only for training set

Hello, I was playing with a permutation schemes. There is one case I don't know how to do If I want to shuffle targets within chunks, I would use limit='chunks' in my permutator, If I want to shuffle targets only in test set, I would use limit={'partitions': 1}, How can I shuffle targets within

Re: [pymvpa] Labels from permutation testing

If you don't want to do the fancy dance, you can do just simply: permutator = AttributePermutator(attr='targets') permuted_dataset = naive_permutation(original_dataset) print Here are your new labels: , permuted_dataset.sa.targets then you don't need to put permutator into your CV, just use

Re: [pymvpa] Problems interpreting GroupClusterThresholding results

> Hi, > > Oh, I had skimmed over the page and had not noticed the algorithm was > expecting accuracy maps, so I was passing it error maps (thus lower is > better), hence my confusion. > I'm performing searchlight support vector regression, not classification, > so my error goes from 0 to 2

Re: [pymvpa] Problems interpreting GroupClusterThresholding results

Hi, I am pretty sure, that they are added to a cluster if they are higher than a threshold. You are looking for voxels with the accuracy significantly higher than a chance. Best, Richard ___ Pkg-ExpPsy-PyMVPA mailing list

Re: [pymvpa] Combinatorial MVPA

Hi Bill, You might take a look at Relief algorithm (also implemented in PyMVPA), that is less hacky approach to your feature weighting problem. BW, Richard ___ Pkg-ExpPsy-PyMVPA mailing list Pkg-ExpPsy-PyMVPA@lists.alioth.debian.org

Re: [pymvpa] Combinatorial MVPA

Bill Broderick wrote: > However, to determine which timecourse is contributing the most to the > classifiers performance, > see which timecourses or which combination > of time courses caused the greatest drop in performance when removed. I wrote: > You might take a look at Relief algorithm

Re: [pymvpa] Crossvalidation and permutation scheme on one run only

ich I assume you mean by RSA) may also be > suitable, depending on your hypotheses. > > Jo > > On May 30, 2016 4:31:50 PM Richard Dinga <ding...@gmail.com> wrote: > >> > Do you have to do within-subjects classification, or could you >> cross-validate

Re: [pymvpa] kNN and sensitivity map

You cannot use sensitivity banalysis with KNN because kNN is not using or producing any feature weights. On Tue, May 31, 2016 at 5:39 PM, marco tettamanti wrote: > Dear all, > since kNN performs best on a particular dataset, I am trying to obtain a > sensitivity map based

Re: [pymvpa] searchlight for data with different runs with different masks

> On Sat, Jan 16, 2016 at 5:40 AM, Kaustubh Patil wrote: > BTW another way to handle imbbalanced data (and perhaps easier to implement and test) could be assign weights in libvsm. This has to be done for each partition separately, any ideas on how this can be done? >

Re: [pymvpa] Searchlight training and testing with different sets of data

You can just do the same search light crossvalidation as is showed in the tutorial or example scripts, but combine your two datasets to one and make data set A be partition 1 and dataset B partition 2 and don't use postproc=mean_sample() argument. On Fri, Jan 22, 2016 at 7:15 PM, Alyson Saenz

Re: [pymvpa] Visualization of the sensitivity map

I might be wrong, but it sounds like you have invariant features in your data. U can get a better mask or just remove them with remove_invariant_features() On Sat, Jan 23, 2016 at 5:37 PM, Maria Hakonen wrote: > > Hi, > > Many thanks for your answers! > I would like to

Re: [pymvpa] Visualization of the sensitivity map

; 80%, 85% and 90%) and viewed the results. > > -Maria > > 2016-01-23 20:31 GMT+02:00 Richard Dinga <ding...@gmail.com>: > >> I might be wrong, but it sounds like you have invariant features in your >> data. U can get a better mask or just remove them with >&g

Re: [pymvpa] GNBSearchlight below/above chance accuracy ... again

Hi, I am sorry about your bellow chance accuracy, it's always very annoying. Do you also have bellow chance accuracy with other than classifiers than GNB? So is it just speed that is your concern? you can try M1NNSearchlight, that should be also efficiently implemented, but i think the results

Re: [pymvpa] ENET error

I guess you have invariant features in your dataset, therefore you will get problems when trying to divide by 0. There is a function to remove them. On Fri, Sep 16, 2016 at 8:01 PM, Liang, Guangsheng wrote: > Hello PyMVPA community, > > > > I am currently working on a

Re: [pymvpa] GroupClusterThreshold data / attributes

gt; When replying, please edit your Subject line so it is more specific >> than "Re: Contents of Pkg-ExpPsy-PyMVPA digest..." >> >> >> Today's Topics: >> >> 1. Re: GroupClusterThreshold data / attributes (Richard Dinga) >> >> >> -

Re: [pymvpa] AttributePermutator: Permute within chunks (& subjects) but only training labels

Hi, Does this answer your question? http://www.pymvpa.org/tutorial_significance.html#avoiding-the-trap-or-advanced-magic-101 On Fri, May 19, 2017 at 8:18 PM, Michael Bannert wrote: > Dear all, > > I would like to use permutation testing for spatially aligned >

Re: [pymvpa] permutation test for unbalanced data

> well -- all the disbalanced cases are "tricky" to say the least ;) Imbalanced cases are fine, just use AUC or some other measure that is not thresholding the results, and all your problems will go away. On Wed, Oct 18, 2017 at 5:21 AM, Anna Manelis wrote: > Thanks for

Re: [pymvpa] parallelizing for cross-subjects NFold cross-validation?

Yes, you can do it, each permutation is independent, doesn't matter if they are computed in series or parallel. Unless you specify the same RNG seed for your splits, there shouldn't be any problem. BW, Richard On Sat, Dec 9, 2017 at 3:33 AM, Regina Lapate wrote: > Hello all: