def __init__(
self,
problem,
targets,
cmin,
cmax,
cstep=None,
crossval=5,
plotflag=False,
maxdepth=8,
searchlog="gridsearch_results.txt",
**params
):
""" Set up (log) grid search over the two RBF kernel parameters C and gamma.
@param problem: the LIBSVM svm_problem to be optimized, ie. the input and target data
@param targets: unfortunately, the targets used in the problem definition have to be given again here
@param cmin: lower left corner of the log2C/log2gamma window to search
@param cmax: upper right corner of the log2C/log2gamma window to search
@param cstep: step width for log2C and log2gamma (ignored for DOE search)
@param crossval: split dataset into this many parts for cross-validation
@param plotflag: if True, plot the error surface contour (regular) or search pattern (DOE)
@param maxdepth: maximum window bisection depth (DOE only)
@param searchlog: Save a list of coordinates and the achieved CV accuracy to this file
@param others: ...are passed through to the cross_validation method of LIBSVM
"""
self.nPars = len(cmin)
self.usermin = cmin
self.usermax = cmax
self.userstep = cstep
self.crossval = crossval
self.plotflag = plotflag
self.maxdepth = maxdepth # number of zoom-in steps (DOE search only!)
# set default parameters for training
self.params = params
if self.plotflag:
import pylab as p
p.ion()
p.figure(figsize=[12, 8])
assert isinstance(problem, svm_problem)
self.problem = problem
self.targets = targets
self.resfile = open(searchlog, "w")
# do the parameter searching
param = self.search()
if self.plotflag:
p.ioff()
p.show()
self.resfile.close()
svm_model.__init__(self, problem, param)
def __init__(self,
problem,
targets,
cmin,
cmax,
cstep=None,
crossval=5,
plotflag=False,
maxdepth=8,
searchlog='gridsearch_results.txt',
**params):
""" Set up (log) grid search over the two RBF kernel parameters C and gamma.
:arg problem: the LIBSVM svm_problem to be optimized, ie. the input and target data
:arg targets: unfortunately, the targets used in the problem definition have to be given again here
:arg cmin: lower left corner of the log2C/log2gamma window to search
:arg cmax: upper right corner of the log2C/log2gamma window to search
:key cstep: step width for log2C and log2gamma (ignored for DOE search)
:key crossval: split dataset into this many parts for cross-validation
:key plotflag: if True, plot the error surface contour (regular) or search pattern (DOE)
:key maxdepth: maximum window bisection depth (DOE only)
:key searchlog: Save a list of coordinates and the achieved CV accuracy to this file
:key others: ...are passed through to the cross_validation method of LIBSVM
"""
self.nPars = len(cmin)
self.usermin = cmin
self.usermax = cmax
self.userstep = cstep
self.crossval = crossval
self.plotflag = plotflag
self.maxdepth = maxdepth # number of zoom-in steps (DOE search only!)
# set default parameters for training
self.params = params
if self.plotflag:
import pylab as p
p.ion()
p.figure(figsize=[12, 8])
assert isinstance(problem, svm_problem)
self.problem = problem
self.targets = targets
self.resfile = open(searchlog, 'w')
# do the parameter searching
param = self.search()
if self.plotflag:
p.ioff()
p.show()
self.resfile.close()
svm_model.__init__(self, problem, param)