def check_C_testset(mss_id): import pylab import expenv import numpy from helper import Options from method_hierarchy_svm_new import Method #from method_augmented_svm_new import Method #costs = 10000 #[float(c) for c in numpy.exp(numpy.linspace(numpy.log(10), numpy.log(20000), 6))] costs = [float(c) for c in numpy.exp(numpy.linspace(numpy.log(0.4), numpy.log(10), 6))] print costs mss = expenv.MultiSplitSet.get(mss_id) train = mss.get_train_data(-1) test = mss.get_eval_data(-1) au_roc = [] au_prc = [] for cost in costs: #create mock param object by freezable struct param = Options() param.kernel = "WeightedDegreeStringKernel" param.wdk_degree = 10 param.transform = cost param.base_similarity = 1.0 param.taxonomy = mss.taxonomy param.id = 666 #param.cost = cost param.cost = 10000 param.freeze() # train mymethod = Method(param) mymethod.train(train) assessment = mymethod.evaluate(test) au_roc.append(assessment.auROC) au_prc.append(assessment.auPRC) print assessment assessment.destroySelf() pylab.title("auROC") pylab.semilogx(costs, au_roc, "-o") pylab.show() pylab.figure() pylab.title("auPRC") pylab.semilogx(costs, au_prc, "-o") pylab.show() return (costs, au_roc, au_prc)
def main(): print "starting debugging:" SPLIT_POINTER = -1 from expenv import MultiSplitSet from helper import Options # select dataset #multi_split_set = MultiSplitSet.get(387) #multi_split_set = MultiSplitSet.get(407) multi_split_set = MultiSplitSet.get(399) #dataset_name = multi_split_set.description # create mock param object by freezable struct param = Options() param.kernel = "WeightedDegreeRBFKernel" #"WeightedDegreeStringKernel"#"PolyKernel" param.wdk_degree = 2 param.cost = 1.0 param.transform = 0.2 param.base_similarity = 1.0 param.taxonomy = multi_split_set.taxonomy param.id = 666 flags= {} #flags["boosting"] = "ones" #flags["boosting"] = "L1" flags["boosting"] = "L2" #flags["boosting"] = "L2_reg" flags["signum"] = False flags["normalize_cost"] = True flags["all_positions"] = False flags["wdk_rbf_on"] = False param.flags = flags 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) 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(387) #multi_split_set = MultiSplitSet.get(407) multi_split_set = MultiSplitSet.get(399) #dataset_name = multi_split_set.description # create mock param object by freezable struct param = Options() param.kernel = "WeightedDegreeStringKernel"#"PolyKernel" param.wdk_degree = 2 param.cost = 1.0 param.transform = 0.2 param.base_similarity = 1 param.taxonomy = multi_split_set.taxonomy param.id = 666 flags= {} #flags["boosting"] = "ones" flags["boosting"] = "L1" #flags["boosting"] = "L2" #flags["boosting"] = "L2_reg" flags["signum"] = False flags["normalize_cost"] = True flags["all_positions"] = False flags["wdk_rbf_on"] = False param.flags = flags 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) assessment = mymethod.evaluate(data_eval) print assessment assessment.destroySelf()
def training_for_sigma(sigma): print "starting debugging:" from expenv import MultiSplitSet # select dataset multi_split_set = MultiSplitSet.get(393) SPLIT_POINTER = 1 #create mock param object by freezable struct param = Options() param.kernel = "WeightedDegreeStringKernel" #"WeightedDegreeRBFKernel" # # param.wdk_degree = 2 param.cost = 1.0 param.transform = 1.0 param.id = 666 param.base_similarity = sigma param.degree = 2 param.flags = {} param.flags["wdk_rbf_on"] = False 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() return assessment.auROC
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 check_C_testset(mss_id): import pylab import expenv import numpy from helper import Options from method_hierarchy_svm_new import Method #from method_augmented_svm_new import Method #costs = 10000 #[float(c) for c in numpy.exp(numpy.linspace(numpy.log(10), numpy.log(20000), 6))] costs = [ float(c) for c in numpy.exp(numpy.linspace(numpy.log(0.4), numpy.log(10), 6)) ] print costs mss = expenv.MultiSplitSet.get(mss_id) train = mss.get_train_data(-1) test = mss.get_eval_data(-1) au_roc = [] au_prc = [] for cost in costs: #create mock param object by freezable struct param = Options() param.kernel = "WeightedDegreeStringKernel" param.wdk_degree = 10 param.transform = cost param.base_similarity = 1.0 param.taxonomy = mss.taxonomy param.id = 666 #param.cost = cost param.cost = 10000 param.freeze() # train mymethod = Method(param) mymethod.train(train) assessment = mymethod.evaluate(test) au_roc.append(assessment.auROC) au_prc.append(assessment.auPRC) print assessment assessment.destroySelf() pylab.title("auROC") pylab.semilogx(costs, au_roc, "-o") pylab.show() pylab.figure() pylab.title("auPRC") pylab.semilogx(costs, au_prc, "-o") pylab.show() return (costs, au_roc, au_prc)