Thanks so much! All the best, Arman
On Fri, Jan 31, 2014 at 9:22 PM, Yaroslav Halchenko <[email protected]>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 > [email protected] > http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa >
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