svm tools added

Author: stymy

nipype/interfaces/afni/svm.py

@@ -0,0 +1,140 @@
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft = python sts = 4 ts = 4 sw = 4 et:
+"""Afni svm interfaces
+
+    Change directory to provide relative paths for doctests
+    >>> import os
+    >>> filepath = os.path.dirname( os.path.realpath( __file__ ) )
+    >>> datadir = os.path.realpath(os.path.join(filepath, '../../testing/data'))
+    >>> os.chdir(datadir)
+"""
+import warnings
+
+import os
+import re
+
+from ..base import (Directory, TraitedSpec,
+                    traits, isdefined, File, InputMultiPath, Undefined)
+from ...utils.filemanip import (load_json, save_json, split_filename)
+from nipype.utils.filemanip import fname_presuffix
+from .base import AFNICommand, AFNICommandInputSpec,\
+    AFNICommandOutputSpec
+from nipype.interfaces.base import CommandLineInputSpec, CommandLine,\
+    OutputMultiPath
+
+warn = warnings.warn
+warnings.filterwarnings('always', category=UserWarning)
+    
+class SVMTrainInputSpec(AFNICommandInputSpec):
+    #training options
+    ttype = traits.Str(desc='tname: classification or regression', 
+                argstr='-type %s',
+                mandatory=True)
+    in_file = File(desc='A 3D+t AFNI brik dataset to be used for training.',
+                argstr='-trainvol %s',
+                mandatory=True,
+                exists=True,
+                copyfile=False)
+    out_file = File(name_template="%s_vectors", 
+                desc='output sum of weighted linear support vectors file name',
+                argstr='-bucket %s', 
+                name_source="in_file")
+    model = traits.Str(name_template="%s_model",
+                desc='modname is the basename for the brik containing the SVM model',
+                argstr='-model %s')    
+    alphas = traits.Str(name_template="%s_alphas", 
+                desc='output alphas file name',
+                argstr='-alpha %s')
+    mask = File(desc='byte-format brik file used to mask voxels in the analysis',
+                argstr='-mask %s',
+                position=-1,
+                exists=True,
+                copyfile=False)
+    nomodelmask = traits.Bool(desc='Flag to enable the omission of a mask file',
+                argstr='-nomodelmask')
+    trainlabels = File(desc='.1D labels corresponding to the stimulus paradigm for the training data.',
+                argstr='-trainlabels %s',
+                exists=True)
+    censor = File(desc='.1D censor file that allows the user to ignore certain samples in the training data.',
+                argstr='-censor %s',
+                exists=True)   
+    kernel = traits.Str(desc='string specifying type of kernel function:linear, polynomial, rbf, sigmoid',
+                argstr='-kernel %s')
+    max_iterations = traits.Int(desc='Specify the maximum number of iterations for the optimization.',
+                argstr='-max_iterations %d')
+    w_out = traits.Bool(desc='output sum of weighted linear support vectors',
+                argstr='-wout')
+    options = traits.Str(desc='additional options for SVM-light', argstr='%s')
+                
+class SVMTrain(AFNICommand):
+    """Temporally predictive modeling with the support vector machine
+    SVM Train Only
+    For complete details, see the `3dsvm Documentation.
+    <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dsvm.html>`_
+
+    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+orig'
+    >>> svmTrain.inputs.model= 'model_run1'
+    >>> svmTrain.inputs.alphas= 'alphas_run1'
+    >>> res = svmTrain.run() # doctest: +SKIP
+
+    """
+
+    _cmd = '3dsvm'
+    input_spec = SVMTrainInputSpec
+    output_spec = AFNICommandOutputSpec
+
+class SVMTestInputSpec(AFNICommandInputSpec):
+    #testing options
+    model = traits.Str(desc='modname is the basename for the brik containing the SVM model',
+                argstr='-model %s',
+                exists=True,
+                mandatory=True)
+    in_file = File(desc='A 3D or 3D+t AFNI brik dataset to be used for testing.',
+                argstr='-testvol %s',
+                exists=True,
+                mandatory=True)
+    out_file = File(name_template="%s_predictions",
+                desc='filename for .1D prediction file(s).',
+                argstr='-predictions %s')
+    testlabels = File(desc='*true* class category .1D labels for the test dataset. It is used to calculate the prediction accuracy performance',
+                exists=True,
+                argstr='-testlabels %s')
+    classout = traits.Bool(desc='Flag to specify that pname files should be integer-valued, corresponding to class category decisions.',
+                argstr='-classout')
+    nopredcensord = traits.Bool(desc='Flag to prevent writing predicted values for censored time-points',
+                argstr='-nopredcensord')
+    nodetrend = traits.Bool(desc='Flag to specify that pname files should not be linearly detrended',
+                argstr='-nodetrend')
+    multiclass = traits.Bool(desc='Specifies multiclass algorithm for classification',
+                argstr='-multiclass %s')
+    options = traits.Str(desc='additional options for SVM-light', argstr='%s')
+    
+class SVMTest(AFNICommand):
+    """Temporally predictive modeling with the support vector machine
+    SVM Test Only
+    For complete details, see the `3dsvm Documentation.
+    <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dsvm.html>`_
+
+    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
+
+    """
+    _cmd = '3dsvm'
+    input_spec = SVMTestInputSpec
+    output_spec = AFNICommandOutputSpec