def main():
print "starting debugging:"
SPLIT_POINTER = 1
from expenv import MultiSplitSet
# select dataset
multi_split_set = MultiSplitSet.get(384)
# flags
flags = {}
flags["normalize_cost"] = False
#flags["epsilon"] = 0.005
flags["kernel_cache"] = 200
flags["use_bias"] = False
# arts params
#flags["svm_type"] = "liblineardual"
flags["degree"] = 24
flags["local"] = False
flags["mem"] = "6G"
flags["maxNumThreads"] = 1
#create mock param object by freezable struct
param = Options()
#param.kernel = "GaussianKernel"
param.kernel = "PolyKernel"
param.sigma = 3.0
param.cost = 10.0
param.transform = 1.0
param.id = 666
param.flags = flags
param.taxonomy = multi_split_set.taxonomy.data
param.freeze()
data_train = multi_split_set.get_train_data(SPLIT_POINTER)
data_eval = multi_split_set.get_eval_data(SPLIT_POINTER)
# train
mymethod = Method(param)
mymethod.train(data_train)
print "training done"
assessment = mymethod.evaluate(data_eval)
print assessment
assessment.destroySelf()
def main():
print "starting debugging:"
SPLIT_POINTER = 1
from expenv import MultiSplitSet
# select dataset
multi_split_set = MultiSplitSet.get(384)
# flags
flags = {}
flags["normalize_cost"] = False
#flags["epsilon"] = 0.005
flags["kernel_cache"] = 200
flags["use_bias"] = False
# arts params
#flags["svm_type"] = "liblineardual"
flags["degree"] = 24
flags["local"] = False
flags["mem"] = "6G"
flags["maxNumThreads"] = 1
#create mock param object by freezable struct
param = Options()
#param.kernel = "GaussianKernel"
param.kernel = "PolyKernel"
param.sigma = 3.0
param.cost = 10.0
param.transform = 1.0
param.id = 666
param.flags = flags
param.taxonomy = multi_split_set.taxonomy.data
param.freeze()
data_train = multi_split_set.get_train_data(SPLIT_POINTER)
data_eval = multi_split_set.get_eval_data(SPLIT_POINTER)
# train
mymethod = Method(param)
mymethod.train(data_train)
print "training done"
assessment = mymethod.evaluate(data_eval)
print assessment
assessment.destroySelf()
def main():
print "starting debugging:"
SPLIT_POINTER = 1
from expenv import MultiSplitSet
from helper import Options
# select dataset
multi_split_set = MultiSplitSet.get(399)
#create mock param object by freezable struct
param = Options()
param.kernel = "WeightedDegreeRBFKernel" #"WeightedDegreeStringKernel"# #
param.wdk_degree = 1
param.cost = 1.0
param.transform = 1.0
param.sigma = 1.0
param.id = 666
param.base_similarity = 1
param.degree = 2
param.freeze()
data_train = multi_split_set.get_train_data(SPLIT_POINTER)
data_eval = multi_split_set.get_eval_data(SPLIT_POINTER)
# train
mymethod = Method(param)
mymethod.train(data_train)
print "training done"
assessment = mymethod.evaluate(data_eval)
print assessment
assessment.destroySelf()
def setUp(self):
import expenv
run = expenv.Run.get(13490)
self.instances = run.get_train_data()
self.test_data = run.get_eval_data()
self.param = run.method.param
flags = {}
flags["kernel_cache"] = 200
#create mock param object by freezable struct
param = Options()
param.kernel = "GaussianKernel"
param.sigma = 3.0
param.cost = 10.0
param.flags = flags
self.param = param
def setUp(self):
import expenv
run = expenv.Run.get(13490)
self.instances = run.get_train_data()
self.test_data = run.get_eval_data()
self.param = run.method.param
flags = {}
flags["kernel_cache"] = 200
#create mock param object by freezable struct
param = Options()
param.kernel = "GaussianKernel"
param.sigma = 3.0
param.cost = 10.0
param.flags = flags
self.param = param
def main():
print "starting debugging:"
SPLIT_POINTER = 1
from expenv import MultiSplitSet
from helper import Options
# select dataset
multi_split_set = MultiSplitSet.get(399)
#create mock param object by freezable struct
param = Options()
param.kernel = "WeightedDegreeRBFKernel" #"WeightedDegreeStringKernel"# #
param.wdk_degree = 1
param.cost = 1.0
param.transform = 1.0
param.sigma = 1.0
param.id = 666
param.base_similarity = 1
param.degree = 2
param.freeze()
data_train = multi_split_set.get_train_data(SPLIT_POINTER)
data_eval = multi_split_set.get_eval_data(SPLIT_POINTER)
# train
mymethod = Method(param)
mymethod.train(data_train)
print "training done"
assessment = mymethod.evaluate(data_eval)
print assessment
assessment.destroySelf()
