[pymvpa] FeatureSelectionClassifier (in RFE) occasionally returns full features set
Vadim Axel
axel.vadim at gmail.com
Sun Apr 26 21:28:19 UTC 2009
Hi,
I see. The error rate was the best with the full set of features, so no
features were selected. However, then I don't understand how I achieve the
selection of fixed number of features using RFE. More specifically:
1. I would like to get 30 features, based on which I get the best
prediction. I don't care that with 31 features (or 3022) I will get a better
prediction. Isn't your graph Fig.1 in "Full Brain Classification: There Is No
“Face” Identification Area" paper was the result of such analysis? In
addition, attached the output which I get from classification of another
dataset, which resulted in desired 30 features. However, I am confused to
understand what I see there. If I read out it correctly, starting from step
1, one subset of 30 features was selected and classified all the time. What
about the other possible subsets? How RFE knows that it is the best one? It
just picked the best ranks from original 3022? I am not sure that it is very
optimal. If you have some working example of correct / optimal RFE usage, I
would very appreciate you sending me.
2. Unfortunately, even after reading Guyon 2002, I feel that I don't fully
understand RFE algorithm. Particularly, what is the size of the original
features subset, that algorithm starts with? Does it really start with full
features set, although for 1000 voxels it is an evident overfitting? The
solution with 3022 voxels, which I got, is not going to generalize well
(given that I have 480 trials only), what is a benefit from such a
solution? Any reference, which will clarify me all those issues are more
than welcomed.
Thank you for your assistance.
I really consider using PyMVPA, because I was impressed by robustness of
this software. However, although your doc is well written and organized, I
am still got stuck in some places.
Vadim
2009/4/26 Yaroslav Halchenko <debian at onerussian.com>
> actually I should have discovered the problem before asking you to
> upload the data...
>
> in your code you use
> N_FEATURES = 30
> ...
> feature_selector=FixedNElementTailSelector(N_FEATURES,
> tail='upper', mode='select'),
>
>
> so you aren't doing RFE per se ;) you just select 30 features right
> on first step of RFE.... then, those 30 features lead to higher
> generalization error than if you took all of them, therefore initial
> dataset with all features is taken as the result.
>
> to see that you had just to enable RFE debug target (or all RFE ones)
> with
>
> debug.active += ['RFE.*']
>
> to see what is happening:
>
> In [12]:## working on region in file /tmp/python-8102meB.py...
> [RFEC ] DBG: Step 0: nfeatures=3022
> [RFEC ] DBG: Step 0: nfeatures=3022 error=0.2125 best/stop=1/0
> [RFEC_] DBG: Sensitivity: [-0.00507313 0.00025722 0.00159871
> ..., -0.00212875 0.00078268
> -0.00027174], nfeatures_selected=30, selected_ids: [ 120 338 341 356
> 462 472 483 501 517 571 573 574 594 612 619
> 634 635 636 659 676 677 760 778 779 796 872 1109 1338 1545 1677]
> [RFEC ] DBG: Step 1: nfeatures=30
> [RFEC ] DBG: Step 1: nfeatures=30 error=0.2500 best/stop=0/0
> [RFEC_] DBG: Sensitivity: [ 0.09779742 0.16359045 0.02775154
> 0.09486282 -0.0804099 -0.04392221
> -0.06721182 0.09752928 0.03872871 0.08811431 0.14541801 0.13167303
> 0.13925132 0.03046704 0.04748648 0.09525846 -0.04226041 0.06917038
> 0.03207438 0.06333298 0.01423283 0.02703152 0.16574083 0.05634531
> 0.11383484 0.03402658 0.07105218 -0.02116503 0.24369252 0.20591227],
> nfeatures_selected=30, selected_ids: [ 0 1 2 3 4 5 6 7 8 9 10 11 12
> 13 14 15 16 17 18 19 20 21 22 23 24
> 25 26 27 28 29]
> [RFEC ] DBG: Step 2: nfeatures=30
> [RFEC ] DBG: Step 2: nfeatures=30 error=0.2500 best/stop=0/0
> [RFEC_] DBG: Sensitivity: [ 0.09779742 0.16359045 0.02775154
> 0.09486282 -0.0804099 -0.04392221
> -0.06721182 0.09752928 0.03872871 0.08811431 0.14541801 0.13167303
> 0.13925132 0.03046704 0.04748648 0.09525846 -0.04226041 0.06917038
> 0.03207438 0.06333298 0.01423283 0.02703152 0.16574083 0.05634531
> 0.11383484 0.03402658 0.07105218 -0.02116503 0.24369252 0.20591227],
> nfeatures_selected=30, selected_ids: [ 0 1 2 3 4 5 6 7 8 9 10 11 12
> 13 14 15 16 17 18 19 20 21 22 23 24
> 25 26 27 28 29]
> [RFEC ] DBG: Step 3: nfeatures=30
> [RFEC ] DBG: Step 3: nfeatures=30 error=0.2500 best/stop=0/0
> [RFEC_] DBG: Sensitivity: [ 0.09779742 0.16359045 0.02775154
> 0.09486282 -0.0804099 -0.04392221
> -0.06721182 0.09752928 0.03872871 0.08811431 0.14541801 0.13167303
> 0.13925132 0.03046704 0.04748648 0.09525846 -0.04226041 0.06917038
> 0.03207438 0.06333298 0.01423283 0.02703152 0.16574083 0.05634531
> 0.11383484 0.03402658 0.07105218 -0.02116503 0.24369252 0.20591227],
> nfeatures_selected=30, selected_ids: [ 0 1 2 3 4 5 6 7 8 9 10 11 12
> 13 14 15 16 17 18 19 20 21 22 23 24
> 25 26 27 28 29]
>
> ....
>
> see original RFE definition on how to actually do RFE ;) or just try SMLR
> which might be more efficient, who knows ;)
>
>
> On Sat, 25 Apr 2009, Yaroslav Halchenko wrote:
>
> > at first I thought that I know what is the reason, but then I realized
> > that it shouldn't be... didn't test though. to expedite things would you
> > mind uploading your data + code to the address I will provide you in a
> > followup email? ;)
>
> > On Sat, 25 Apr 2009, Vadim Axel wrote:
>
> > > Hi,
> > > I implemented some simple RFE logic, similar to what was described
> > > here: [1]http://www.pymvpa.org/featsel.html
> > > At the end of the classification procedure, I verify the the
> features
> > > that were selected based on what was described here:
> > > [2]
> http://www.pymvpa.org/faq.html#how-do-i-know-which-features-were-fin
> > > ally-selected-by-a-classifier-doing-feature-selection
> > > Now the problem: sometimes the resulted number of selected features
> is
> > > the exact number, which is required (I use
> FixedNElementTailSelector),
> > > whereas in some other case, for completely unknown reason, I get
> full
> > > set of features. The issue is really weired, since for two sessions
> of
> > > a subject I get selected feature set, but for two other sessions of
> the
> > > same subject I get full feature set. I suspect, that the problem
> might
> > > be in updating the feature_ids variable and not with classification,
> > > because the classification error rate was pretty low.
> > > Attached my code. Is it any problem with it?
> > > I can also upload my dataset (~50 Mb zip). I didn't succeed to
> > > reproduce it with smaller amount of data.
> > > Thanks for your help,
> > > Vadim
>
> > > Ссылки
>
> > > 1. http://www.pymvpa.org/featsel.html
> > > 2.
> http://www.pymvpa.org/faq.html#how-do-i-know-which-features-were-finally-selected-by-a-classifier-doing-feature-selection
> --
> Yaroslav Halchenko
> Research Assistant, Psychology Department, Rutgers-Newark
> Student Ph.D. @ CS Dept. NJIT
> Office: (973) 353-1412 | FWD: 82823 | Fax: (973) 353-1171
> 101 Warren Str, Smith Hall, Rm 4-105, Newark NJ 07102
> 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/mailman/listinfo/pkg-exppsy-pymvpa
>
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C:\Python25\lib\site-packages\nifti\nifticlib.py:15: DeprecationWarning: This module has been renamed to 'nifti.clib'. This redirect will be removed with PyNIfTI 1.0.
warn("This module has been renamed to 'nifti.clib'. This redirect will be removed with PyNIfTI 1.0.", DeprecationWarning)
[RFEC ] DBG: Step 0: nfeatures=3022
..*
optimization finished, #iter = 256
nu = 0.579831
obj = -0.010802, rho = -0.360430
nSV = 98, nBSV = 37
Total nSV = 98
.*
optimization finished, #iter = 202
nu = 0.436455
obj = -0.008881, rho = 0.005519
nSV = 83, nBSV = 26
Total nSV = 83
.*
optimization finished, #iter = 233
nu = 0.565363
obj = -0.010187, rho = -0.162915
nSV = 94, nBSV = 42
Total nSV = 94
..*
optimization finished, #iter = 255
nu = 0.561779
obj = -0.012090, rho = -0.285537
nSV = 99, nBSV = 33
Total nSV = 99
C:\Python25\lib\site-packages\mvpa\clfs\libsvmc\_svm.py:460: DeprecationWarning: PyArray_FromDims: use PyArray_SimpleNew.
self.getTotalNSV())
C:\Python25\lib\site-packages\mvpa\clfs\libsvmc\_svm.py:460: DeprecationWarning: PyArray_FromDimsAndDataAndDescr: use PyArray_NewFromDescr.
self.getTotalNSV())
C:\Python25\lib\site-packages\mvpa\clfs\libsvmc\_svm.py:425: DeprecationWarning: PyArray_FromDims: use PyArray_SimpleNew.
self.prob.maxlen)
C:\Python25\lib\site-packages\mvpa\clfs\libsvmc\_svm.py:425: DeprecationWarning: PyArray_FromDimsAndDataAndDescr: use PyArray_NewFromDescr.
self.prob.maxlen)
..*
optimization finished, #iter = 256
nu = 0.579831
obj = -0.010802, rho = -0.360430
nSV = 98, nBSV = 37
Total nSV = 98
.*
optimization finished, #iter = 202
nu = 0.436455
obj = -0.008881, rho = 0.005519
nSV = 83, nBSV = 26
Total nSV = 83
.*
optimization finished, #iter = 233
nu = 0.565363
obj = -0.010187, rho = -0.162915
nSV = 94, nBSV = 42
Total nSV = 94
..*
optimization finished, #iter = 255
nu = 0.561779
obj = -0.012090, rho = -0.285537
nSV = 99, nBSV = 33
Total nSV = 99
[RFEC ] DBG: Step 0: nfeatures=3022 error=0.3063 best/stop=1/0
[RFEC_] DBG: Sensitivity: [-0.00231245 -0.00127887 -0.00061646 ..., 0.00026221 -0.00208419
-0.00022344], nfeatures_selected=30, selected_ids: [ 225 244 263 280 281 410 428 448 449 469 520 530 552 655 656
676 677 697 706 898 963 978 979 994 995 1175 2643 2703 2719 2729]
[RFEC ] DBG: Step 1: nfeatures=30
.*
optimization finished, #iter = 142
nu = 0.486532
obj = -0.794295, rho = 0.401320
nSV = 65, nBSV = 52
Total nSV = 65
*
optimization finished, #iter = 103
nu = 0.573362
obj = -1.071013, rho = 0.627662
nSV = 74, nBSV = 64
Total nSV = 74
...*
optimization finished, #iter = 396
nu = 0.555847
obj = -0.970954, rho = 0.545131
nSV = 75, nBSV = 58
Total nSV = 75
*
optimization finished, #iter = 104
nu = 0.496242
obj = -0.987939, rho = 0.657235
nSV = 64, nBSV = 54
Total nSV = 64
.*
optimization finished, #iter = 142
nu = 0.486532
obj = -0.794295, rho = 0.401320
nSV = 65, nBSV = 52
Total nSV = 65
*
optimization finished, #iter = 103
nu = 0.573362
obj = -1.071013, rho = 0.627662
nSV = 74, nBSV = 64
Total nSV = 74
...*
optimization finished, #iter = 396
nu = 0.555847
obj = -0.970954, rho = 0.545131
nSV = 75, nBSV = 58
Total nSV = 75
*
optimization finished, #iter = 104
nu = 0.496242
obj = -0.987939, rho = 0.657235
nSV = 64, nBSV = 54
Total nSV = 64
[RFEC ] DBG: Step 1: nfeatures=30 error=0.2500 best/stop=1/0
[RFEC_] DBG: Sensitivity: [ 0.01863664 0.14741445 -0.00095756 0.16798785 -0.07272826 -0.09315195
0.11723761 0.04790919 0.31369055 0.07274535 0.120612 0.14175582
0.09146975 -0.07351106 -0.05344982 0.21333059 -0.15477025 0.03495759
0.02807971 0.02073587 -0.1046485 0.1972759 -0.09555095 0.1939292
-0.11374669 -0.0573067 -0.00413817 0.07428317 0.00193021 0.10250109], nfeatures_selected=30, selected_ids: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
25 26 27 28 29]
[RFEC ] DBG: Step 2: nfeatures=30
.*
optimization finished, #iter = 142
nu = 0.486532
obj = -0.794295, rho = 0.401320
nSV = 65, nBSV = 52
Total nSV = 65
*
optimization finished, #iter = 103
nu = 0.573362
obj = -1.071013, rho = 0.627662
nSV = 74, nBSV = 64
Total nSV = 74
...*
optimization finished, #iter = 396
nu = 0.555847
obj = -0.970954, rho = 0.545131
nSV = 75, nBSV = 58
Total nSV = 75
*
optimization finished, #iter = 104
nu = 0.496242
obj = -0.987939, rho = 0.657235
nSV = 64, nBSV = 54
Total nSV = 64
.*
optimization finished, #iter = 142
nu = 0.486532
obj = -0.794295, rho = 0.401320
nSV = 65, nBSV = 52
Total nSV = 65
*
optimization finished, #iter = 103
nu = 0.573362
obj = -1.071013, rho = 0.627662
nSV = 74, nBSV = 64
Total nSV = 74
...*
optimization finished, #iter = 396
nu = 0.555847
obj = -0.970954, rho = 0.545131
nSV = 75, nBSV = 58
Total nSV = 75
*
optimization finished, #iter = 104
nu = 0.496242
obj = -0.987939, rho = 0.657235
nSV = 64, nBSV = 54
Total nSV = 64
[RFEC ] DBG: Step 2: nfeatures=30 error=0.2500 best/stop=0/0
[RFEC_] DBG: Sensitivity: [ 0.01863664 0.14741445 -0.00095756 0.16798785 -0.07272826 -0.09315195
0.11723761 0.04790919 0.31369055 0.07274535 0.120612 0.14175582
0.09146975 -0.07351106 -0.05344982 0.21333059 -0.15477025 0.03495759
0.02807971 0.02073587 -0.1046485 0.1972759 -0.09555095 0.1939292
-0.11374669 -0.0573067 -0.00413817 0.07428317 0.00193021 0.10250109], nfeatures_selected=30, selected_ids: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
25 26 27 28 29]
[RFEC ] DBG: Step 3: nfeatures=30
.*
optimization finished, #iter = 142
nu = 0.486532
obj = -0.794295, rho = 0.401320
nSV = 65, nBSV = 52
Total nSV = 65
*
optimization finished, #iter = 103
nu = 0.573362
obj = -1.071013, rho = 0.627662
nSV = 74, nBSV = 64
Total nSV = 74
...*
optimization finished, #iter = 396
nu = 0.555847
obj = -0.970954, rho = 0.545131
nSV = 75, nBSV = 58
Total nSV = 75
*
optimization finished, #iter = 104
nu = 0.496242
obj = -0.987939, rho = 0.657235
nSV = 64, nBSV = 54
Total nSV = 64
.*
optimization finished, #iter = 142
nu = 0.486532
obj = -0.794295, rho = 0.401320
nSV = 65, nBSV = 52
Total nSV = 65
*
optimization finished, #iter = 103
nu = 0.573362
obj = -1.071013, rho = 0.627662
nSV = 74, nBSV = 64
Total nSV = 74
...*
optimization finished, #iter = 396
nu = 0.555847
obj = -0.970954, rho = 0.545131
nSV = 75, nBSV = 58
Total nSV = 75
*
optimization finished, #iter = 104
nu = 0.496242
obj = -0.987939, rho = 0.657235
nSV = 64, nBSV = 54
Total nSV = 64
[RFEC ] DBG: Step 3: nfeatures=30 error=0.2500 best/stop=0/0
[RFEC_] DBG: Sensitivity: [ 0.01863664 0.14741445 -0.00095756 0.16798785 -0.07272826 -0.09315195
0.11723761 0.04790919 0.31369055 0.07274535 0.120612 0.14175582
0.09146975 -0.07351106 -0.05344982 0.21333059 -0.15477025 0.03495759
0.02807971 0.02073587 -0.1046485 0.1972759 -0.09555095 0.1939292
-0.11374669 -0.0573067 -0.00413817 0.07428317 0.00193021 0.10250109], nfeatures_selected=30, selected_ids: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
25 26 27 28 29]
[RFEC ] DBG: Step 4: nfeatures=30
.*
optimization finished, #iter = 142
nu = 0.486532
obj = -0.794295, rho = 0.401320
nSV = 65, nBSV = 52
Total nSV = 65
*
optimization finished, #iter = 103
nu = 0.573362
obj = -1.071013, rho = 0.627662
nSV = 74, nBSV = 64
Total nSV = 74
...*
optimization finished, #iter = 396
nu = 0.555847
obj = -0.970954, rho = 0.545131
nSV = 75, nBSV = 58
Total nSV = 75
*
optimization finished, #iter = 104
nu = 0.496242
obj = -0.987939, rho = 0.657235
nSV = 64, nBSV = 54
Total nSV = 64
.*
optimization finished, #iter = 142
nu = 0.486532
obj = -0.794295, rho = 0.401320
nSV = 65, nBSV = 52
Total nSV = 65
*
optimization finished, #iter = 103
nu = 0.573362
obj = -1.071013, rho = 0.627662
nSV = 74, nBSV = 64
Total nSV = 74
...*
optimization finished, #iter = 396
nu = 0.555847
obj = -0.970954, rho = 0.545131
nSV = 75, nBSV = 58
Total nSV = 75
*
optimization finished, #iter = 104
nu = 0.496242
obj = -0.987939, rho = 0.657235
nSV = 64, nBSV = 54
Total nSV = 64
[RFEC ] DBG: Step 4: nfeatures=30 error=0.2500 best/stop=0/0
[RFEC_] DBG: Sensitivity: [ 0.01863664 0.14741445 -0.00095756 0.16798785 -0.07272826 -0.09315195
0.11723761 0.04790919 0.31369055 0.07274535 0.120612 0.14175582
0.09146975 -0.07351106 -0.05344982 0.21333059 -0.15477025 0.03495759
0.02807971 0.02073587 -0.1046485 0.1972759 -0.09555095 0.1939292
-0.11374669 -0.0573067 -0.00413817 0.07428317 0.00193021 0.10250109], nfeatures_selected=30, selected_ids: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
25 26 27 28 29]
[RFEC ] DBG: Step 5: nfeatures=30
.*
optimization finished, #iter = 142
nu = 0.486532
obj = -0.794295, rho = 0.401320
nSV = 65, nBSV = 52
Total nSV = 65
*
optimization finished, #iter = 103
nu = 0.573362
obj = -1.071013, rho = 0.627662
nSV = 74, nBSV = 64
Total nSV = 74
...*
optimization finished, #iter = 396
nu = 0.555847
obj = -0.970954, rho = 0.545131
nSV = 75, nBSV = 58
Total nSV = 75
*
optimization finished, #iter = 104
nu = 0.496242
obj = -0.987939, rho = 0.657235
nSV = 64, nBSV = 54
Total nSV = 64
.*
optimization finished, #iter = 142
nu = 0.486532
obj = -0.794295, rho = 0.401320
nSV = 65, nBSV = 52
Total nSV = 65
*
optimization finished, #iter = 103
nu = 0.573362
obj = -1.071013, rho = 0.627662
nSV = 74, nBSV = 64
Total nSV = 74
...*
optimization finished, #iter = 396
nu = 0.555847
obj = -0.970954, rho = 0.545131
nSV = 75, nBSV = 58
Total nSV = 75
*
optimization finished, #iter = 104
nu = 0.496242
obj = -0.987939, rho = 0.657235
nSV = 64, nBSV = 54
Total nSV = 64
[RFEC ] DBG: Step 5: nfeatures=30 error=0.2500 best/stop=0/0
[RFEC_] DBG: Sensitivity: [ 0.01863664 0.14741445 -0.00095756 0.16798785 -0.07272826 -0.09315195
0.11723761 0.04790919 0.31369055 0.07274535 0.120612 0.14175582
0.09146975 -0.07351106 -0.05344982 0.21333059 -0.15477025 0.03495759
0.02807971 0.02073587 -0.1046485 0.1972759 -0.09555095 0.1939292
-0.11374669 -0.0573067 -0.00413817 0.07428317 0.00193021 0.10250109], nfeatures_selected=30, selected_ids: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
25 26 27 28 29]
[RFEC ] DBG: Step 6: nfeatures=30
.*
optimization finished, #iter = 142
nu = 0.486532
obj = -0.794295, rho = 0.401320
nSV = 65, nBSV = 52
Total nSV = 65
*
optimization finished, #iter = 103
nu = 0.573362
obj = -1.071013, rho = 0.627662
nSV = 74, nBSV = 64
Total nSV = 74
...*
optimization finished, #iter = 396
nu = 0.555847
obj = -0.970954, rho = 0.545131
nSV = 75, nBSV = 58
Total nSV = 75
*
optimization finished, #iter = 104
nu = 0.496242
obj = -0.987939, rho = 0.657235
nSV = 64, nBSV = 54
Total nSV = 64
.*
optimization finished, #iter = 142
nu = 0.486532
obj = -0.794295, rho = 0.401320
nSV = 65, nBSV = 52
Total nSV = 65
*
optimization finished, #iter = 103
nu = 0.573362
obj = -1.071013, rho = 0.627662
nSV = 74, nBSV = 64
Total nSV = 74
...*
optimization finished, #iter = 396
nu = 0.555847
obj = -0.970954, rho = 0.545131
nSV = 75, nBSV = 58
Total nSV = 75
*
optimization finished, #iter = 104
nu = 0.496242
obj = -0.987939, rho = 0.657235
nSV = 64, nBSV = 54
Total nSV = 64
[RFEC ] DBG: Step 6: nfeatures=30 error=0.2500 best/stop=0/0
[RFEC_] DBG: Sensitivity: [ 0.01863664 0.14741445 -0.00095756 0.16798785 -0.07272826 -0.09315195
0.11723761 0.04790919 0.31369055 0.07274535 0.120612 0.14175582
0.09146975 -0.07351106 -0.05344982 0.21333059 -0.15477025 0.03495759
0.02807971 0.02073587 -0.1046485 0.1972759 -0.09555095 0.1939292
-0.11374669 -0.0573067 -0.00413817 0.07428317 0.00193021 0.10250109], nfeatures_selected=30, selected_ids: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
25 26 27 28 29]
[RFEC ] DBG: Step 7: nfeatures=30
.*
optimization finished, #iter = 142
nu = 0.486532
obj = -0.794295, rho = 0.401320
nSV = 65, nBSV = 52
Total nSV = 65
*
optimization finished, #iter = 103
nu = 0.573362
obj = -1.071013, rho = 0.627662
nSV = 74, nBSV = 64
Total nSV = 74
...*
optimization finished, #iter = 396
nu = 0.555847
obj = -0.970954, rho = 0.545131
nSV = 75, nBSV = 58
Total nSV = 75
*
optimization finished, #iter = 104
nu = 0.496242
obj = -0.987939, rho = 0.657235
nSV = 64, nBSV = 54
Total nSV = 64
.*
optimization finished, #iter = 142
nu = 0.486532
obj = -0.794295, rho = 0.401320
nSV = 65, nBSV = 52
Total nSV = 65
*
optimization finished, #iter = 103
nu = 0.573362
obj = -1.071013, rho = 0.627662
nSV = 74, nBSV = 64
Total nSV = 74
...*
optimization finished, #iter = 396
nu = 0.555847
obj = -0.970954, rho = 0.545131
nSV = 75, nBSV = 58
Total nSV = 75
*
optimization finished, #iter = 104
nu = 0.496242
obj = -0.987939, rho = 0.657235
nSV = 64, nBSV = 54
Total nSV = 64
[RFEC ] DBG: Step 7: nfeatures=30 error=0.2500 best/stop=0/0
[RFEC_] DBG: Sensitivity: [ 0.01863664 0.14741445 -0.00095756 0.16798785 -0.07272826 -0.09315195
0.11723761 0.04790919 0.31369055 0.07274535 0.120612 0.14175582
0.09146975 -0.07351106 -0.05344982 0.21333059 -0.15477025 0.03495759
0.02807971 0.02073587 -0.1046485 0.1972759 -0.09555095 0.1939292
-0.11374669 -0.0573067 -0.00413817 0.07428317 0.00193021 0.10250109], nfeatures_selected=30, selected_ids: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
25 26 27 28 29]
[RFEC ] DBG: Step 8: nfeatures=30
.*
optimization finished, #iter = 142
nu = 0.486532
obj = -0.794295, rho = 0.401320
nSV = 65, nBSV = 52
Total nSV = 65
*
optimization finished, #iter = 103
nu = 0.573362
obj = -1.071013, rho = 0.627662
nSV = 74, nBSV = 64
Total nSV = 74
...*
optimization finished, #iter = 396
nu = 0.555847
obj = -0.970954, rho = 0.545131
nSV = 75, nBSV = 58
Total nSV = 75
*
optimization finished, #iter = 104
nu = 0.496242
obj = -0.987939, rho = 0.657235
nSV = 64, nBSV = 54
Total nSV = 64
.*
optimization finished, #iter = 142
nu = 0.486532
obj = -0.794295, rho = 0.401320
nSV = 65, nBSV = 52
Total nSV = 65
*
optimization finished, #iter = 103
nu = 0.573362
obj = -1.071013, rho = 0.627662
nSV = 74, nBSV = 64
Total nSV = 74
...*
optimization finished, #iter = 396
nu = 0.555847
obj = -0.970954, rho = 0.545131
nSV = 75, nBSV = 58
Total nSV = 75
*
optimization finished, #iter = 104
nu = 0.496242
obj = -0.987939, rho = 0.657235
nSV = 64, nBSV = 54
Total nSV = 64
[RFEC ] DBG: Step 8: nfeatures=30 error=0.2500 best/stop=0/0
[RFEC_] DBG: Sensitivity: [ 0.01863664 0.14741445 -0.00095756 0.16798785 -0.07272826 -0.09315195
0.11723761 0.04790919 0.31369055 0.07274535 0.120612 0.14175582
0.09146975 -0.07351106 -0.05344982 0.21333059 -0.15477025 0.03495759
0.02807971 0.02073587 -0.1046485 0.1972759 -0.09555095 0.1939292
-0.11374669 -0.0573067 -0.00413817 0.07428317 0.00193021 0.10250109], nfeatures_selected=30, selected_ids: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
25 26 27 28 29]
[RFEC ] DBG: Step 9: nfeatures=30
.*
optimization finished, #iter = 142
nu = 0.486532
obj = -0.794295, rho = 0.401320
nSV = 65, nBSV = 52
Total nSV = 65
*
optimization finished, #iter = 103
nu = 0.573362
obj = -1.071013, rho = 0.627662
nSV = 74, nBSV = 64
Total nSV = 74
...*
optimization finished, #iter = 396
nu = 0.555847
obj = -0.970954, rho = 0.545131
nSV = 75, nBSV = 58
Total nSV = 75
*
optimization finished, #iter = 104
nu = 0.496242
obj = -0.987939, rho = 0.657235
nSV = 64, nBSV = 54
Total nSV = 64
.*
optimization finished, #iter = 142
nu = 0.486532
obj = -0.794295, rho = 0.401320
nSV = 65, nBSV = 52
Total nSV = 65
*
optimization finished, #iter = 103
nu = 0.573362
obj = -1.071013, rho = 0.627662
nSV = 74, nBSV = 64
Total nSV = 74
...*
optimization finished, #iter = 396
nu = 0.555847
obj = -0.970954, rho = 0.545131
nSV = 75, nBSV = 58
Total nSV = 75
*
optimization finished, #iter = 104
nu = 0.496242
obj = -0.987939, rho = 0.657235
nSV = 64, nBSV = 54
Total nSV = 64
[RFEC ] DBG: Step 9: nfeatures=30 error=0.2500 best/stop=0/0
[RFEC_] DBG: Sensitivity: [ 0.01863664 0.14741445 -0.00095756 0.16798785 -0.07272826 -0.09315195
0.11723761 0.04790919 0.31369055 0.07274535 0.120612 0.14175582
0.09146975 -0.07351106 -0.05344982 0.21333059 -0.15477025 0.03495759
0.02807971 0.02073587 -0.1046485 0.1972759 -0.09555095 0.1939292
-0.11374669 -0.0573067 -0.00413817 0.07428317 0.00193021 0.10250109], nfeatures_selected=30, selected_ids: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
25 26 27 28 29]
[RFEC ] DBG: Step 10: nfeatures=30
.*
optimization finished, #iter = 142
nu = 0.486532
obj = -0.794295, rho = 0.401320
nSV = 65, nBSV = 52
Total nSV = 65
*
optimization finished, #iter = 103
nu = 0.573362
obj = -1.071013, rho = 0.627662
nSV = 74, nBSV = 64
Total nSV = 74
...*
optimization finished, #iter = 396
nu = 0.555847
obj = -0.970954, rho = 0.545131
nSV = 75, nBSV = 58
Total nSV = 75
*
optimization finished, #iter = 104
nu = 0.496242
obj = -0.987939, rho = 0.657235
nSV = 64, nBSV = 54
Total nSV = 64
.*
optimization finished, #iter = 142
nu = 0.486532
obj = -0.794295, rho = 0.401320
nSV = 65, nBSV = 52
Total nSV = 65
*
optimization finished, #iter = 103
nu = 0.573362
obj = -1.071013, rho = 0.627662
nSV = 74, nBSV = 64
Total nSV = 74
...*
optimization finished, #iter = 396
nu = 0.555847
obj = -0.970954, rho = 0.545131
nSV = 75, nBSV = 58
Total nSV = 75
*
optimization finished, #iter = 104
nu = 0.496242
obj = -0.987939, rho = 0.657235
nSV = 64, nBSV = 54
Total nSV = 64
[RFEC ] DBG: Step 10: nfeatures=30 error=0.2500 best/stop=0/0
[RFEC_] DBG: Sensitivity: [ 0.01863664 0.14741445 -0.00095756 0.16798785 -0.07272826 -0.09315195
0.11723761 0.04790919 0.31369055 0.07274535 0.120612 0.14175582
0.09146975 -0.07351106 -0.05344982 0.21333059 -0.15477025 0.03495759
0.02807971 0.02073587 -0.1046485 0.1972759 -0.09555095 0.1939292
-0.11374669 -0.0573067 -0.00413817 0.07428317 0.00193021 0.10250109], nfeatures_selected=30, selected_ids: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
25 26 27 28 29]
[RFEC ] DBG: Step 11: nfeatures=30
.*
optimization finished, #iter = 142
nu = 0.486532
obj = -0.794295, rho = 0.401320
nSV = 65, nBSV = 52
Total nSV = 65
*
optimization finished, #iter = 103
nu = 0.573362
obj = -1.071013, rho = 0.627662
nSV = 74, nBSV = 64
Total nSV = 74
...*
optimization finished, #iter = 396
nu = 0.555847
obj = -0.970954, rho = 0.545131
nSV = 75, nBSV = 58
Total nSV = 75
*
optimization finished, #iter = 104
nu = 0.496242
obj = -0.987939, rho = 0.657235
nSV = 64, nBSV = 54
Total nSV = 64
.*
optimization finished, #iter = 142
nu = 0.486532
obj = -0.794295, rho = 0.401320
nSV = 65, nBSV = 52
Total nSV = 65
*
optimization finished, #iter = 103
nu = 0.573362
obj = -1.071013, rho = 0.627662
nSV = 74, nBSV = 64
Total nSV = 74
...*
optimization finished, #iter = 396
nu = 0.555847
obj = -0.970954, rho = 0.545131
nSV = 75, nBSV = 58
Total nSV = 75
*
optimization finished, #iter = 104
nu = 0.496242
obj = -0.987939, rho = 0.657235
nSV = 64, nBSV = 54
Total nSV = 64
[RFEC ] DBG: Step 11: nfeatures=30 error=0.2500 best/stop=0/1
.*
optimization finished, #iter = 166
nu = 0.512442
obj = -1.267167, rho = 0.538102
nSV = 90, nBSV = 77
Total nSV = 90
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