nipype.interfaces.afni.svm module

AFNI’s svm interfaces.

SVMTest

Link to code

Bases: AFNICommand

Wrapped executable: 3dsvm.

Temporally predictive modeling with the support vector machine SVM Test Only For complete details, see the 3dsvm Documentation.

Examples

>>> from nipype.interfaces import afni as afni
>>> svmTest = afni.SVMTest()
>>> svmTest.inputs.in_file= 'run2+orig'
>>> svmTest.inputs.model= 'run1+orig_model'
>>> svmTest.inputs.testlabels= 'run2_categories.1D'
>>> svmTest.inputs.out_file= 'pred2_model1'
>>> res = svmTest.run() # doctest: +SKIP
in_file : a pathlike object or string representing an existing file
A 3D or 3D+t AFNI brik dataset to be used for testing. Maps to a command-line argument: -testvol %s.
model : a unicode string
Modname is the basename for the brik containing the SVM model. Maps to a command-line argument: -model %s.
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
classout : a boolean
Flag to specify that pname files should be integer-valued, corresponding to class category decisions. Maps to a command-line argument: -classout.
environ : a dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’
Environment variables. (Nipype default value: {})
multiclass : a boolean
Specifies multiclass algorithm for classification. Maps to a command-line argument: -multiclass %s.
nodetrend : a boolean
Flag to specify that pname files should not be linearly detrended. Maps to a command-line argument: -nodetrend.
nopredcensord : a boolean
Flag to prevent writing predicted values for censored time-points. Maps to a command-line argument: -nopredcensord.
num_threads : an integer (int or long)
Set number of threads. (Nipype default value: 1)
options : a unicode string
Additional options for SVM-light. Maps to a command-line argument: %s.
out_file : a pathlike object or string representing a file
Filename for .1D prediction file(s). Maps to a command-line argument: -predictions %s.
outputtype : ‘NIFTI’ or ‘AFNI’ or ‘NIFTI_GZ’
AFNI output filetype.
testlabels : a pathlike object or string representing an existing file
true class category .1D labels for the test dataset. It is used to calculate the prediction accuracy performance. Maps to a command-line argument: -testlabels %s.
out_file : a pathlike object or string representing an existing file
Output file.

SVMTrain

Link to code

Bases: AFNICommand

Wrapped executable: 3dsvm.

Temporally predictive modeling with the support vector machine SVM Train Only For complete details, see the 3dsvm Documentation.

Examples

>>> from nipype.interfaces import afni as afni
>>> svmTrain = afni.SVMTrain()
>>> svmTrain.inputs.in_file = 'run1+orig'
>>> svmTrain.inputs.trainlabels = 'run1_categories.1D'
>>> svmTrain.inputs.ttype = 'regression'
>>> svmTrain.inputs.mask = 'mask.nii'
>>> svmTrain.inputs.model = 'model_run1'
>>> svmTrain.inputs.alphas = 'alphas_run1'
>>> res = svmTrain.run() # doctest: +SKIP
in_file : a pathlike object or string representing an existing file
A 3D+t AFNI brik dataset to be used for training. Maps to a command-line argument: -trainvol %s.
ttype : a unicode string
Tname: classification or regression. Maps to a command-line argument: -type %s.
alphas : a pathlike object or string representing a file
Output alphas file name. Maps to a command-line argument: -alpha %s.
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
censor : a pathlike object or string representing an existing file
.1D censor file that allows the user to ignore certain samples in the training data. Maps to a command-line argument: -censor %s.
environ : a dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’
Environment variables. (Nipype default value: {})
kernel : a unicode string
String specifying type of kernel function:linear, polynomial, rbf, sigmoid. Maps to a command-line argument: -kernel %s.
mask : a pathlike object or string representing an existing file
Byte-format brik file used to mask voxels in the analysis. Maps to a command-line argument: -mask %s (position: -1).
max_iterations : an integer (int or long)
Specify the maximum number of iterations for the optimization. Maps to a command-line argument: -max_iterations %d.
model : a pathlike object or string representing a file
Basename for the brik containing the SVM model. Maps to a command-line argument: -model %s.
nomodelmask : a boolean
Flag to enable the omission of a mask file. Maps to a command-line argument: -nomodelmask.
num_threads : an integer (int or long)
Set number of threads. (Nipype default value: 1)
options : a unicode string
Additional options for SVM-light. Maps to a command-line argument: %s.
out_file : a pathlike object or string representing a file
Output sum of weighted linear support vectors file name. Maps to a command-line argument: -bucket %s.
outputtype : ‘NIFTI’ or ‘AFNI’ or ‘NIFTI_GZ’
AFNI output filetype.
trainlabels : a pathlike object or string representing an existing file
.1D labels corresponding to the stimulus paradigm for the training data. Maps to a command-line argument: -trainlabels %s.
w_out : a boolean
Output sum of weighted linear support vectors. Maps to a command-line argument: -wout.
alphas : a pathlike object or string representing a file
Output alphas file name.
model : a pathlike object or string representing a file
Brik containing the SVM model file name.
out_file : a pathlike object or string representing a file
Sum of weighted linear support vectors file name.