[pymvpa] Individual measures for subjects

[Arman Eshaghi]

Thanks so much!

All the best,
Arman


On Fri, Jan 31, 2014 at 9:22 PM, Yaroslav Halchenko
<debian at onerussian.com>wrote:

>
> On Fri, 31 Jan 2014, Arman Eshaghi wrote:
>
> >    MyData is structural MRI data coming from fmri_dataset function.
> There are
> >    two chunks, and similar to clf.predictions (in tutorial), I'm
> wondering
> >    whether I can get each predicted label, because I want to compare AUC
> in
>
> so each sample is a subject. ok
> cvte.stats.sets would have sets of original targets and their
> predictions per each cross-validation split.
>
> also if you set your errorfx=None I guess you would also get raw
> predictions (and possibly original targets) in your  results... yeap:
>
> In [2]: cv = CrossValidation(kNN(), HalfPartitioner(attr='chunks'),
> errorfx=None, enable_ca=['stats'])
>
> In [3]: from mvpa2.testing.datasets import datasets as tdatasets
>
> In [4]: results = cv(tdatasets['uni2small'])
>
> In [5]: results
> Out[5]: <Dataset: 24x1@|S2, <sa: cvfolds,targets>>
>
> In [6]: print results.targets, results.samples
> ['L0' 'L0' 'L0' 'L0' 'L0' 'L0' 'L1' 'L1' 'L1' 'L1' 'L1' 'L1' 'L0' 'L0' 'L0'
>  'L0' 'L0' 'L0' 'L1' 'L1' 'L1' 'L1' 'L1' 'L1'] [['L0']
>  ['L0']
>  ['L0']
>  ['L0']
>  ['L0']
>  ['L0']
>  ['L1']
>  ['L1']
>  ['L1']
>  ['L1']
>  ['L1']
>  ['L1']
>  ['L0']
>  ['L0']
>  ['L0']
>  ['L0']
>  ['L0']
>  ['L0']
>  ['L0']
>  ['L1']
>  ['L1']
>  ['L1']
>  ['L1']
>  ['L1']]
>
> *In [8]: print cv.ca.stats.sets
> [(array(['L0', 'L0', 'L0', 'L0', 'L0', 'L0', 'L1', 'L1', 'L1', 'L1', 'L1',
>        'L1'],
>       dtype='|S2'), array(['L0', 'L0', 'L0', 'L0', 'L0', 'L0', 'L1', 'L1',
> 'L1', 'L1', 'L1',
>        'L1'],
>       dtype='|S2'), [{'L0': 1.0, 'L1': 1.0}, {'L0': 1.0, 'L1': 1.0},
> {'L0': 2.0, 'L1': 0.0}, {'L0': 2.0, 'L1': 0.0}, {'L0': 2.0, 'L1': 0.0},
> {'L0': 2.0, 'L1': 0.0}, {'L0': 0.0, 'L1': 2.0}, {'L0': 0.0, 'L1': 2.0},
> {'L0': 0.0, 'L1': 2.0}, {'L0': 0.0, 'L1': 2.0}, {'L0': 0.0, 'L1': 2.0},
> {'L0': 0.0, 'L1': 2.0}]), (array(['L0', 'L0', 'L0', 'L0', 'L0', 'L0', 'L1',
> 'L1', 'L1', 'L1', 'L1',
>        'L1'],
>       dtype='|S2'), array(['L0', 'L0', 'L0', 'L0', 'L0', 'L0', 'L0', 'L1',
> 'L1', 'L1', 'L1',
>        'L1'],
>       dtype='|S2'), [{'L0': 2.0, 'L1': 0.0}, {'L0': 2.0, 'L1': 0.0},
> {'L0': 2.0, 'L1': 0.0}, {'L0': 2.0, 'L1': 0.0}, {'L0': 2.0, 'L1': 0.0},
> {'L0': 2.0, 'L1': 0.0}, {'L0': 2.0, 'L1': 0.0}, {'L0': 0.0, 'L1': 2.0},
> {'L0': 0.0, 'L1': 2.0}, {'L0': 0.0, 'L1': 2.0}, {'L0': 0.0, 'L1': 2.0},
> {'L0': 0.0, 'L1': 2.0}])]
>
> and here are some snippets for you for AUC (you need a classifier which
> would provide estimates, not just final decisions):
>
> *In [10]: print cv.ca.stats.stats['AUC']
> [nan, nan]
>
> *In [11]: cv = CrossValidation(SMLR(enable_ca=['estimates']),
> HalfPartitioner(attr='chunks'), errorfx=None, enable_ca=['stats'])
>
> In [12]: results = cv(tdatasets['uni2small'])
>
> In [13]: print cv.ca.stats.stats['AUC']
> [1.0, 1.0]
>
> In [14]: tdatasets['uni2small'].samples +=
> np.random.normal(size=tdatasets['uni2small'].shape)*0.5
>
> In [15]: results = cv(tdatasets['uni2small'])
>
> In [16]: print cv.ca.stats.stats['AUC']
> [0.81944444444444442, 0.81944444444444442]
>
> *In [17]: results = cv(tdatasets['uni4small'])
>
> In [18]: print cv.ca.stats.stats['AUC']
> [1.0, 1.0, 1.0, 1.0]
>
> *In [19]: tdatasets['uni4small'].samples +=
> np.random.normal(size=tdatasets['uni4small'].shape)*0.5
>
> In [20]: results = cv(tdatasets['uni4small'])
>
> In [21]: print cv.ca.stats.stats['AUC']
> [0.64814814814814814, 0.68518518518518512, 0.76388888888888884,
> 0.55092592592592593]
>
>
> --
> Yaroslav O. Halchenko, Ph.D.
> http://neuro.debian.net http://www.pymvpa.org http://www.fail2ban.org
> Senior Research Associate,     Psychological and Brain Sciences Dept.
> Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
> Phone: +1 (603) 646-9834                       Fax: +1 (603) 646-1419
> WWW:   http://www.linkedin.com/in/yarik
>
> _______________________________________________
> Pkg-ExpPsy-PyMVPA mailing list
> Pkg-ExpPsy-PyMVPA at lists.alioth.debian.org
> http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.alioth.debian.org/pipermail/pkg-exppsy-pymvpa/attachments/20140131/e9c2759b/attachment-0001.html>