[pymvpa] probabilities from GNB

[Yaroslav Halchenko]

Let's continue at https://github.com/PyMVPA/PyMVPA/issues/581

I will have a look later today

Thanks!

On April 29, 2018 3:52:15 AM EDT, Richard Dinga <dinga92 at gmail.com> wrote:
>Thanks, this seems to be the solution according to documentation, but
>it
>doesn't work for my data. Here is an example using tutorial data:
>
>from mvpa2.suite import *
>datapath = '/usr/share/data/pymvpa2-tutorial/'
>haxby = load_tutorial_data(datapath,
>        roi='vt',
>        add_fa={'vt_thr_glm': os.path.join(datapath, 'haxby2001',
>                                           'sub001', 'masks',
>                                                 'orig', 'vt.nii.gz')})
>poly_detrend(haxby, polyord=1, chunks_attr='chunks')
>haxby = haxby[np.array([l in ['rest', 'house', 'face']
>                           for l in haxby.targets], dtype='bool')]
>zscore(haxby, chunks_attr='chunks', param_est=('targets', ['rest']),
>       dtype='float32')
>haxby = haxby[haxby.sa.targets != 'rest']
>haxby = remove_invariant_features(haxby)
>
>clf = GNB(enable_ca='estimates', logprob=True, normalize=True)
>cv = CrossValidation(clf, NFoldPartitioner(attr='chunks'),
>postproc=None)
>cv_results = cv(haxby)
>print clf.ca.estimates
>
>[[          inf           inf]
> [-234.34792494    0.        ]
> [          inf           inf]
> ...,
> [          inf           inf]
> [          inf           inf]
> [          inf           inf]]
>
>However it works with test data:
>
>from mvpa2.testing.datasets import *
>ds_test = datasets['uni2medium']
>cv_results = cv(ds_test)
>print np.round(np.exp(clf.ca.estimates), 3)
>
>[[ 0.956  0.044]
> [ 1.     0.   ]
> [ 1.     0.   ]
> ...,
> [ 0.     1.   ]
> [ 0.168  0.832]
> [ 0.001  0.999]]
>
>
>On Thu, Apr 26, 2018 at 6:11 PM, Yaroslav Halchenko
><debian at onerussian.com>
>wrote:
>
>>
>> On Thu, 26 Apr 2018, Richard Dinga wrote:
>>
>> > Hi,
>> > I am trying to get a probability prediction for each sample from
>> > cross-validation. I used .ca.stats.sets to get those, however, for
>GNB
>> > these all look like this:
>>
>> > print cvte.ca.stats.sets[0][2]
>> > [[-1009.22758728 -1079.77409491]
>> >  [ -795.59690176 -1038.32481958]
>> >  [ -875.73917377 -1189.377741  ]
>> >  ...,
>> >  [-1483.49338276  -856.61441132]
>> >  [-1308.29372328  -815.90664933]
>> >  [-1169.79999768  -737.54291075]]
>>
>> > I thought these are log probabilities, but after exponentiation,
>they are
>> > all 0, although based on accuracy and AUC the classifier works
>fine.
>>
>> > Any idea how to fix this or is this as good as it can get? My
>ultimate
>> goal
>> > is to get GNB probabilities from GNB searchlight. Trying the same
>thing
>> > using SMLR seems to produce valid probabilities (in a sane range
>and rows
>> > sums to 1).
>>
>> > Best regards,
>> > Richard
>>
>>
>> Try using setting normalize=True for your GNB.  Here is from GNB? in
>> ipython,
>> check other parameters which might be relevant:
>>
>> normalize : bool, optional
>>   Normalize (log)prob by P(data). Requires probabilities thus for
>>   `logprob` case would require exponentiation of 'logprob's, thus
>>   disabled by default since does not impact classification output.
>>   Constraints: value must be convertible to type bool. [Default:
>>   False]
>>
>>
>>
>> --
>> Yaroslav O. Halchenko
>> Center for Open Neuroscience     http://centerforopenneuroscience.org
>> 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
>>
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-- 
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