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 main():
print "starting debugging:"
SPLIT_POINTER = -1
from expenv import MultiSplitSet
from helper import Options
# select dataset
multi_split_set = MultiSplitSet.get(399)
#dataset_name = multi_split_set.description
flags = {}
flags["normalize_cost"] = False
flags["epsilon"] = 0.05
flags["cache_size"] = 7
#flags["solver_type"] = "ST_DIRECT" #ST_CPLEX #ST_GLPK) #ST_DIRECT) #ST_NEWTON)
flags["normalize_trace"] = True
flags["interleaved"] = True
#create mock param object by freezable struct
param = Options()
param.kernel = "WeightedDegreeStringKernel"
param.wdk_degree = 1
param.cost = 1
param.transform = 1 #2.0
param.taxonomy = multi_split_set.taxonomy
param.id = 666
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
from task_similarities import fetch_gammas
# select dataset
multi_split_set = MultiSplitSet.get(317)
#multi_split_set = MultiSplitSet.get(374)
#multi_split_set = MultiSplitSet.get(2)
dataset_name = multi_split_set.description
transform = 1.0
base = 1.0
similarity_matrix = fetch_gammas(transform, base, dataset_name)
#create mock taxonomy object by freezable struct
taxonomy = Options()
taxonomy.data = similarity_matrix
taxonomy.description = dataset_name
taxonomy.freeze()
#create mock param object by freezable struct
param = Options()
param.kernel = "WeightedDegreeStringKernel"
param.wdk_degree = 1
param.cost = 1.0
param.transform = 1.0
param.taxonomy = taxonomy
param.id = 666
param.freeze()
data_train = multi_split_set.get_train_data(SPLIT_POINTER)
data_eval = multi_split_set.get_eval_data(SPLIT_POINTER)
create_plot_inner(param, data_train, data_eval)
def main():
print "starting debugging:"
SPLIT_POINTER = 1
from expenv import MultiSplitSet
from helper import Options
# select dataset
multi_split_set = MultiSplitSet.get(379)
dataset_name = multi_split_set.description
print "dataset_name", dataset_name
#create mock taxonomy object by freezable struct
#taxonomy = Options()
#taxonomy.data = taxonomy_graph.data
#taxonomy.description = dataset_name
#taxonomy.freeze()
#create mock param object by freezable struct
param = Options()
param.kernel = "WeightedDegreeStringKernel"
param.wdk_degree = 1
param.cost = 1.0
param.transform = 2.0
param.taxonomy = multi_split_set.taxonomy
param.id = 666
param.freeze()
data_train = multi_split_set.get_train_data(SPLIT_POINTER)
data_eval = multi_split_set.get_eval_data(SPLIT_POINTER)
# train hierarchical xval
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(399)
#dataset_name = multi_split_set.description
flags = {}
flags["normalize_cost"] = False
flags["epsilon"] = 0.05
flags["cache_size"] = 7
#flags["solver_type"] = "ST_DIRECT" #ST_CPLEX #ST_GLPK) #ST_DIRECT) #ST_NEWTON)
flags["normalize_trace"] = True
flags["interleaved"] = True
#create mock param object by freezable struct
param = Options()
param.kernel = "WeightedDegreeStringKernel"
param.wdk_degree = 1
param.cost = 1
param.transform = 1 #2.0
param.taxonomy = multi_split_set.taxonomy
param.id = 666
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(379)
dataset_name = multi_split_set.description
print "dataset_name", dataset_name
#create mock taxonomy object by freezable struct
#taxonomy = Options()
#taxonomy.data = taxonomy_graph.data
#taxonomy.description = dataset_name
#taxonomy.freeze()
#create mock param object by freezable struct
param = Options()
param.kernel = "WeightedDegreeStringKernel"
param.wdk_degree = 1
param.cost = 1.0
param.transform = 2.0
param.taxonomy = multi_split_set.taxonomy
param.id = 666
param.freeze()
data_train = multi_split_set.get_train_data(SPLIT_POINTER)
data_eval = multi_split_set.get_eval_data(SPLIT_POINTER)
# train hierarchical xval
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(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 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(386)
#dataset_name = multi_split_set.description
# create mock param object by freezable struct
param = Options()
param.kernel = "WeightedDegreeStringKernel"#"PolyKernel"
param.wdk_degree = 1
param.cost = 100
param.transform = 2 #2.0
param.taxonomy = multi_split_set.taxonomy
param.id = 666
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(386)
#dataset_name = multi_split_set.description
# create mock param object by freezable struct
param = Options()
param.kernel = "WeightedDegreeStringKernel"#"PolyKernel"
param.wdk_degree = 1
param.cost = 1
param.transform = 2 #2.0
param.taxonomy = multi_split_set.taxonomy
param.id = 666
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 test_data():
##################################################################
# select MSS
##################################################################
mss = expenv.MultiSplitSet.get(379)
##################################################################
# data
##################################################################
# fetch data
instance_set = mss.get_train_data(-1)
# prepare data
data = PreparedMultitaskData(instance_set, shuffle=True)
# set parameters
param = Options()
param.kernel = "WeightedDegreeStringKernel"
param.wdk_degree = 4
param.cost = 1.0
param.transform = 1.0
param.id = 666
param.freeze()
##################################################################
# taxonomy
##################################################################
taxonomy = shogun_factory.create_taxonomy(mss.taxonomy.data)
support = numpy.linspace(0, 100, 4)
distances = [[0, 1, 2, 2], [1, 0, 2, 2], [2, 2, 0, 1], [2, 2, 1, 0]]
# create tree normalizer
tree_normalizer = MultitaskKernelPlifNormalizer(support, data.task_vector_names)
task_names = data.get_task_names()
FACTOR = 1.0
# init gamma matrix
gammas = numpy.zeros((data.get_num_tasks(), data.get_num_tasks()))
for t1_name in task_names:
for t2_name in task_names:
similarity = taxonomy.compute_node_similarity(taxonomy.get_id(t1_name), taxonomy.get_id(t2_name))
gammas[data.name_to_id(t1_name), data.name_to_id(t2_name)] = similarity
helper.save("/tmp/gammas", gammas)
gammas = gammas * FACTOR
cost = param.cost * numpy.sqrt(FACTOR)
print gammas
##########
# regular normalizer
normalizer = MultitaskKernelNormalizer(data.task_vector_nums)
for t1_name in task_names:
for t2_name in task_names:
similarity = gammas[data.name_to_id(t1_name), data.name_to_id(t2_name)]
normalizer.set_task_similarity(data.name_to_id(t1_name), data.name_to_id(t2_name), similarity)
##################################################################
# Train SVMs
##################################################################
# create shogun objects
wdk_tree = shogun_factory.create_kernel(data.examples, param)
lab = shogun_factory.create_labels(data.labels)
wdk_tree.set_normalizer(tree_normalizer)
wdk_tree.init_normalizer()
print "--->",wdk_tree.get_normalizer().get_name()
svm_tree = SVMLight(cost, wdk_tree, lab)
svm_tree.set_linadd_enabled(False)
svm_tree.set_batch_computation_enabled(False)
svm_tree.train()
del wdk_tree
del tree_normalizer
print "finished training tree-norm SVM:", svm_tree.get_objective()
wdk = shogun_factory.create_kernel(data.examples, param)
wdk.set_normalizer(normalizer)
wdk.init_normalizer()
print "--->",wdk.get_normalizer().get_name()
svm = SVMLight(cost, wdk, lab)
svm.set_linadd_enabled(False)
svm.set_batch_computation_enabled(False)
svm.train()
print "finished training manually set SVM:", svm.get_objective()
alphas_tree = svm_tree.get_alphas()
alphas = svm.get_alphas()
assert(len(alphas_tree)==len(alphas))
for i in xrange(len(alphas)):
assert(abs(alphas_tree[i] - alphas[i]) < 0.0001)
print "success: all alphas are the same"
mss = expenv.MultiSplitSet.get(379)
##################################################################
# data
##################################################################
# fetch data
instance_set = mss.get_train_data(-1)
# prepare data
data = PreparedMultitaskData(instance_set, shuffle=True)
# set parameters
param = Options()
param.kernel = "WeightedDegreeStringKernel"
param.wdk_degree = 4
param.cost = 1.0
param.transform = 1.0
param.id = 666
param.freeze()
##################################################################
# taxonomy
##################################################################
taxonomy = shogun_factory.create_taxonomy(mss.taxonomy.data)
# create tree normalizer
tree_normalizer = MultitaskKernelTreeNormalizer(data.task_vector_names,
data.task_vector_names,
taxonomy)
def test_data():
##################################################################
# select MSS
##################################################################
mss = expenv.MultiSplitSet.get(379)
##################################################################
# data
##################################################################
# fetch data
instance_set = mss.get_train_data(-1)
# prepare data
data = PreparedMultitaskData(instance_set, shuffle=True)
# set parameters
param = Options()
param.kernel = "WeightedDegreeStringKernel"
param.wdk_degree = 4
param.cost = 1.0
param.transform = 1.0
param.id = 666
param.freeze()
##################################################################
# taxonomy
##################################################################
taxonomy = shogun_factory.create_taxonomy(mss.taxonomy.data)
support = numpy.linspace(0, 100, 4)
distances = [[0, 1, 2, 2], [1, 0, 2, 2], [2, 2, 0, 1], [2, 2, 1, 0]]
# create tree normalizer
tree_normalizer = MultitaskKernelPlifNormalizer(support,
data.task_vector_names)
task_names = data.get_task_names()
FACTOR = 1.0
# init gamma matrix
gammas = numpy.zeros((data.get_num_tasks(), data.get_num_tasks()))
for t1_name in task_names:
for t2_name in task_names:
similarity = taxonomy.compute_node_similarity(
taxonomy.get_id(t1_name), taxonomy.get_id(t2_name))
gammas[data.name_to_id(t1_name),
data.name_to_id(t2_name)] = similarity
helper.save("/tmp/gammas", gammas)
gammas = gammas * FACTOR
cost = param.cost * numpy.sqrt(FACTOR)
print gammas
##########
# regular normalizer
normalizer = MultitaskKernelNormalizer(data.task_vector_nums)
for t1_name in task_names:
for t2_name in task_names:
similarity = gammas[data.name_to_id(t1_name),
data.name_to_id(t2_name)]
normalizer.set_task_similarity(data.name_to_id(t1_name),
data.name_to_id(t2_name),
similarity)
##################################################################
# Train SVMs
##################################################################
# create shogun objects
wdk_tree = shogun_factory.create_kernel(data.examples, param)
lab = shogun_factory.create_labels(data.labels)
wdk_tree.set_normalizer(tree_normalizer)
wdk_tree.init_normalizer()
print "--->", wdk_tree.get_normalizer().get_name()
svm_tree = SVMLight(cost, wdk_tree, lab)
svm_tree.set_linadd_enabled(False)
svm_tree.set_batch_computation_enabled(False)
svm_tree.train()
del wdk_tree
del tree_normalizer
print "finished training tree-norm SVM:", svm_tree.get_objective()
wdk = shogun_factory.create_kernel(data.examples, param)
wdk.set_normalizer(normalizer)
wdk.init_normalizer()
print "--->", wdk.get_normalizer().get_name()
svm = SVMLight(cost, wdk, lab)
svm.set_linadd_enabled(False)
svm.set_batch_computation_enabled(False)
svm.train()
print "finished training manually set SVM:", svm.get_objective()
alphas_tree = svm_tree.get_alphas()
alphas = svm.get_alphas()
assert (len(alphas_tree) == len(alphas))
for i in xrange(len(alphas)):
assert (abs(alphas_tree[i] - alphas[i]) < 0.0001)
print "success: all alphas are the same"
def __init__(self, degree, sigma, active_set, wdk_rbf_on):
'''
loads data into handler
'''
self.active_set = active_set
fn = "/fml/ag-raetsch/home/cwidmer/Documents/phd/projects/multitask/data/mhc/MHCsequenzen/pseudo.txt"
tmp_key = ""
tmp_idx = 0
self.seqs = []
self.keys = []
self.name_to_id = {}
# parse file
for line in file(fn):
if line.startswith(">"):
tmp_key = line.strip()[1:]
else:
if active_set.count(tmp_key) > 0:
assert self.keys.count(tmp_key) == 0, "key %s is already contained in self.keys" % (tmp_key)
self.seqs.append(line.strip())
self.keys.append(tmp_key)
self.name_to_id[tmp_key] = tmp_idx
tmp_idx += 1
assert len(self.seqs) == tmp_idx, "incorrect number of sequences %i != %i" % (len(self.seqs), tmp_idx)
assert len(self.keys) == tmp_idx, "incorrect number of keys %i != %i" % (len(self.keys), tmp_idx)
# setup kernel
param = Options()
if wdk_rbf_on:
param.kernel = "WeightedDegreeRBFKernel"
else:
param.kernel = "WeightedDegreeStringKernel"
param.wdk_degree = degree
param.transform = sigma
self.kernel = shogun_factory.create_kernel(self.seqs, param)
#######################
# compute kernel
#######################
num_tasks = len(self.seqs)
self.similarity = numpy.zeros((num_tasks, num_tasks))
for i in xrange(num_tasks):
for j in xrange(num_tasks):
self.similarity[i,j] = self.kernel.kernel(i, j)
# normalize kernel
my_min = numpy.min(self.similarity)
my_max = numpy.max(self.similarity)
my_diff = my_max - my_min
# scale to interval [0,1]
#self.similarity = (self.similarity - my_min) / my_diff
self.similarity = (self.similarity) / my_max
print self.similarity
def __init__(self, degree, sigma, active_set, wdk_rbf_on):
'''
loads data into handler
'''
self.active_set = active_set
fn = "/fml/ag-raetsch/home/cwidmer/Documents/phd/projects/multitask/data/mhc/MHCsequenzen/pseudo.txt"
tmp_key = ""
tmp_idx = 0
self.seqs = []
self.keys = []
self.name_to_id = {}
# parse file
for line in file(fn):
if line.startswith(">"):
tmp_key = line.strip()[1:]
else:
if active_set.count(tmp_key) > 0:
assert self.keys.count(
tmp_key
) == 0, "key %s is already contained in self.keys" % (
tmp_key)
self.seqs.append(line.strip())
self.keys.append(tmp_key)
self.name_to_id[tmp_key] = tmp_idx
tmp_idx += 1
assert len(
self.seqs
) == tmp_idx, "incorrect number of sequences %i != %i" % (
len(self.seqs), tmp_idx)
assert len(
self.keys
) == tmp_idx, "incorrect number of keys %i != %i" % (len(
self.keys), tmp_idx)
# setup kernel
param = Options()
if wdk_rbf_on:
param.kernel = "WeightedDegreeRBFKernel"
else:
param.kernel = "WeightedDegreeStringKernel"
param.wdk_degree = degree
param.transform = sigma
self.kernel = shogun_factory.create_kernel(self.seqs, param)
#######################
# compute kernel
#######################
num_tasks = len(self.seqs)
self.similarity = numpy.zeros((num_tasks, num_tasks))
for i in xrange(num_tasks):
for j in xrange(num_tasks):
self.similarity[i, j] = self.kernel.kernel(i, j)
# normalize kernel
my_min = numpy.min(self.similarity)
my_max = numpy.max(self.similarity)
my_diff = my_max - my_min
# scale to interval [0,1]
#self.similarity = (self.similarity - my_min) / my_diff
self.similarity = (self.similarity) / my_max
print self.similarity
################################################################## # data ################################################################## # fetch data instance_set = mss.get_train_data(-1) # prepare data data = PreparedMultitaskData(instance_set, shuffle=True) # set parameters param = Options() param.kernel = "WeightedDegreeStringKernel" param.wdk_degree = 4 param.cost = 1.0 param.transform = 1.0 param.id = 666 param.freeze() ################################################################## # taxonomy ################################################################## taxonomy = shogun_factory.create_taxonomy(mss.taxonomy.data)
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)