taxonomy = shogun_factory.create_taxonomy(mss.taxonomy.data)
# create tree normalizer
tree_normalizer = MultitaskKernelTreeNormalizer(data.task_vector_names, data.task_vector_names, taxonomy)
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
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 _train(self, train_data, param):
"""
training procedure using training examples and labels
@param train_data: Data relevant to SVM training
@type train_data: dict<str, list<instances> >
@param param: Parameters for the training procedure
@type param: ParameterSvm
"""
assert(param.base_similarity >= 1)
# merge data sets
data = PreparedMultitaskData(train_data, shuffle=False)
# create shogun data objects
base_wdk = shogun_factory.create_kernel(data.examples, param)
lab = shogun_factory.create_labels(data.labels)
# set normalizer
normalizer = MultitaskKernelNormalizer(data.task_vector_nums)
# load data
#f = file("/fml/ag-raetsch/home/cwidmer/Documents/phd/projects/multitask/data/mhc/MHC_Distanzen/MHC_Distanzen/ALL_PseudoSeq_BlosumEnc_pearson.txt")
f = file("/fml/ag-raetsch/home/cwidmer/Documents/phd/projects/multitask/data/mhc/MHC_Distanzen/MHC_Distanzen/All_PseudoSeq_Hamming.txt")
#f = file("/fml/ag-raetsch/home/cwidmer/Documents/phd/projects/multitask/data/mhc/MHC_Distanzen/MHC_Distanzen/ALL_PseudoSeq_BlosumEnc_euklid.txt")
#f = file("/fml/ag-raetsch/home/cwidmer/Documents/phd/projects/multitask/data/mhc/MHC_Distanzen/MHC_Distanzen/ALL_RAxML.txt")
num_lines = int(f.readline().strip())
task_distances = numpy.zeros((num_lines, num_lines))
name_to_id = {}
for (i, line) in enumerate(f):
tokens = line.strip().split("\t")
name = str(tokens[0])
name_to_id[name] = i
entry = numpy.array([v for (j,v) in enumerate(tokens) if j!=0])
assert len(entry)==num_lines, "len_entry %i, num_lines %i" % (len(entry), num_lines)
task_distances[i,:] = entry
# cut relevant submatrix
active_ids = [name_to_id[name] for name in data.get_task_names()]
tmp_distances = task_distances[active_ids, :]
tmp_distances = tmp_distances[:, active_ids]
print "distances ", tmp_distances.shape
# normalize distances
task_distances = task_distances / numpy.max(tmp_distances)
similarities = numpy.zeros((data.get_num_tasks(), data.get_num_tasks()))
# convert distance to similarity
for task_name_lhs in data.get_task_names():
for task_name_rhs in data.get_task_names():
# convert similarity with simple transformation
similarity = param.base_similarity - task_distances[name_to_id[task_name_lhs], name_to_id[task_name_rhs]]
normalizer.set_task_similarity(data.name_to_id(task_name_lhs), data.name_to_id(task_name_rhs), similarity)
# save for later
similarities[data.name_to_id(task_name_lhs),data.name_to_id(task_name_rhs)] = similarity
# set normalizer
base_wdk.set_normalizer(normalizer)
base_wdk.init_normalizer()
# set up svm
svm = SVMLight(param.cost, base_wdk, lab)
svm.set_linadd_enabled(False)
svm.set_batch_computation_enabled(False)
# normalize cost
norm_c_pos = param.cost / float(len([l for l in data.labels if l==1]))
norm_c_neg = param.cost / float(len([l for l in data.labels if l==-1]))
svm.set_C(norm_c_neg, norm_c_pos)
# start training
svm.train()
# save additional information
self.additional_information["svm objective"] = svm.get_objective()
self.additional_information["num sv"] = svm.get_num_support_vectors()
#self.additional_information["distances"] = distances
self.additional_information["similarities"] = similarities
# wrap up predictors
svms = {}
# use a reference to the same svm several times
for task_name in data.get_task_names():
task_num = data.name_to_id(task_name)
# save svm and task_num
svms[task_name] = (task_num, param, svm)
return svms
def _train(self, train_data, param):
"""
training procedure using training examples and labels
@param train_data: Data relevant to SVM training
@type train_data: dict<str, list<instances> >
@param param: Parameters for the training procedure
@type param: ParameterSvm
"""
# merge data sets
data = PreparedMultitaskData(train_data, shuffle=False)
# create shogun data objects
base_wdk = shogun_factory.create_kernel(data.examples, param)
lab = shogun_factory.create_labels(data.labels)
# support
support = numpy.linspace(0, 1, 5)
# set normalizer
normalizer = MultitaskKernelPlifNormalizer(support,
data.task_vector_nums)
# fetch taxonomy from parameter object
taxonomy = param.taxonomy.data
taxonomy.plot()
import os
os.system("evince demo.png &")
# compute distances
distances = numpy.zeros((data.get_num_tasks(), data.get_num_tasks()))
for (i, task_name_lhs) in enumerate(data.get_task_names()):
for (j, task_name_rhs) in enumerate(data.get_task_names()):
distances[i, j] = task_similarities.compute_hop_distance(
taxonomy, task_name_lhs, task_name_rhs)
# normalize distances
distances = distances / numpy.max(distances)
# set distances
for (i, task_name_lhs) in enumerate(data.get_task_names()):
for (j, task_name_rhs) in enumerate(data.get_task_names()):
normalizer.set_task_distance(i, j, distances[i, j])
# assign normalizer
base_wdk.set_normalizer(normalizer)
base_wdk.init_normalizer()
svm = None
debug_weights = {}
num_subk = base_wdk.get_num_subkernels()
print "num subkernels:", num_subk
#print "subkernel weights:", base_wdk.get_subkernel_weights()
debug_weights["before"] = [
normalizer.get_beta(i) for i in range(num_subk)
]
print "using MKL:", (param.transform >= 1.0)
if param.transform >= 1.0:
num_threads = 4
svm = MKLClassification()
svm.set_mkl_norm(param.transform)
#svm.set_solver_type(ST_CPLEX) #GLPK) #DIRECT) #NEWTON)#ST_CPLEX)
svm.set_C(param.cost, param.cost)
svm.set_kernel(base_wdk)
svm.set_labels(lab)
svm.parallel.set_num_threads(num_threads)
svm.set_linadd_enabled(False)
svm.set_batch_computation_enabled(False)
svm.train()
#print "subkernel weights (after):", base_wdk.get_subkernel_weights()
else:
# create SVM (disable unsupported optimizations)
svm = SVMLight(param.cost, base_wdk, lab)
svm.set_linadd_enabled(False)
svm.set_batch_computation_enabled(False)
svm.train()
print "svm objective:", svm.get_objective()
debug_weights["after"] = [
normalizer.get_beta(i) for i in range(num_subk)
]
# debugging output
print "debug weights (before/after):"
print debug_weights["before"]
print debug_weights["after"]
print ""
# wrap up predictors
svms = {}
# use a reference to the same svm several times
for task_name in train_data.keys():
svms[task_name] = (svm, data.name_to_id(task_name))
return svms
def _train(self, train_data, param):
"""
training procedure using training examples and labels
@param train_data: Data relevant to SVM training
@type train_data: dict<str, list<instances> >
@param param: Parameters for the training procedure
@type param: ParameterSvm
"""
# merge data sets
data = PreparedMultitaskData(train_data, shuffle=False)
# create shogun data objects
base_wdk = shogun_factory.create_kernel(data.examples, param)
lab = shogun_factory.create_labels(data.labels)
# support
support = numpy.linspace(0, 1, 5)
# set normalizer
normalizer = MultitaskKernelPlifNormalizer(support, data.task_vector_nums)
# fetch taxonomy from parameter object
taxonomy = param.taxonomy.data
taxonomy.plot()
import os
os.system("evince demo.png &")
# compute distances
distances = numpy.zeros((data.get_num_tasks(), data.get_num_tasks()))
for (i,task_name_lhs) in enumerate(data.get_task_names()):
for (j, task_name_rhs) in enumerate(data.get_task_names()):
distances[i,j] = task_similarities.compute_hop_distance(taxonomy, task_name_lhs, task_name_rhs)
# normalize distances
distances = distances / numpy.max(distances)
# set distances
for (i,task_name_lhs) in enumerate(data.get_task_names()):
for (j, task_name_rhs) in enumerate(data.get_task_names()):
normalizer.set_task_distance(i, j, distances[i,j])
# assign normalizer
base_wdk.set_normalizer(normalizer)
base_wdk.init_normalizer()
svm = None
debug_weights = {}
num_subk = base_wdk.get_num_subkernels()
print "num subkernels:", num_subk
#print "subkernel weights:", base_wdk.get_subkernel_weights()
debug_weights["before"] = [normalizer.get_beta(i) for i in range(num_subk)]
print "using MKL:", (param.transform >= 1.0)
if param.transform >= 1.0:
num_threads = 4
svm = MKLClassification()
svm.set_mkl_norm(param.transform)
#svm.set_solver_type(ST_CPLEX) #GLPK) #DIRECT) #NEWTON)#ST_CPLEX)
svm.set_C(param.cost, param.cost)
svm.set_kernel(base_wdk)
svm.set_labels(lab)
svm.parallel.set_num_threads(num_threads)
svm.set_linadd_enabled(False)
svm.set_batch_computation_enabled(False)
svm.train()
#print "subkernel weights (after):", base_wdk.get_subkernel_weights()
else:
# create SVM (disable unsupported optimizations)
svm = SVMLight(param.cost, base_wdk, lab)
svm.set_linadd_enabled(False)
svm.set_batch_computation_enabled(False)
svm.train()
print "svm objective:", svm.get_objective()
debug_weights["after"] = [normalizer.get_beta(i) for i in range(num_subk)]
# debugging output
print "debug weights (before/after):"
print debug_weights["before"]
print debug_weights["after"]
print ""
# wrap up predictors
svms = {}
# use a reference to the same svm several times
for task_name in train_data.keys():
svms[task_name] = (svm, data.name_to_id(task_name))
return svms
def _train(self, train_data, param):
"""
training procedure using training examples and labels
@param train_data: Data relevant to SVM training
@type train_data: dict<str, list<instances> >
@param param: Parameters for the training procedure
@type param: ParameterSvm
"""
assert (param.base_similarity >= 1)
# merge data sets
data = PreparedMultitaskData(train_data, shuffle=False)
# create shogun data objects
base_wdk = shogun_factory.create_kernel(data.examples, param)
lab = shogun_factory.create_labels(data.labels)
# set normalizer
normalizer = MultitaskKernelNormalizer(data.task_vector_nums)
# load data
#f = file("/fml/ag-raetsch/home/cwidmer/Documents/phd/projects/multitask/data/mhc/MHC_Distanzen/MHC_Distanzen/ALL_PseudoSeq_BlosumEnc_pearson.txt")
f = file(
"/fml/ag-raetsch/home/cwidmer/Documents/phd/projects/multitask/data/mhc/MHC_Distanzen/MHC_Distanzen/All_PseudoSeq_Hamming.txt"
)
#f = file("/fml/ag-raetsch/home/cwidmer/Documents/phd/projects/multitask/data/mhc/MHC_Distanzen/MHC_Distanzen/ALL_PseudoSeq_BlosumEnc_euklid.txt")
#f = file("/fml/ag-raetsch/home/cwidmer/Documents/phd/projects/multitask/data/mhc/MHC_Distanzen/MHC_Distanzen/ALL_RAxML.txt")
num_lines = int(f.readline().strip())
task_distances = numpy.zeros((num_lines, num_lines))
name_to_id = {}
for (i, line) in enumerate(f):
tokens = line.strip().split("\t")
name = str(tokens[0])
name_to_id[name] = i
entry = numpy.array([v for (j, v) in enumerate(tokens) if j != 0])
assert len(entry) == num_lines, "len_entry %i, num_lines %i" % (
len(entry), num_lines)
task_distances[i, :] = entry
# cut relevant submatrix
active_ids = [name_to_id[name] for name in data.get_task_names()]
tmp_distances = task_distances[active_ids, :]
tmp_distances = tmp_distances[:, active_ids]
print "distances ", tmp_distances.shape
# normalize distances
task_distances = task_distances / numpy.max(tmp_distances)
similarities = numpy.zeros(
(data.get_num_tasks(), data.get_num_tasks()))
# convert distance to similarity
for task_name_lhs in data.get_task_names():
for task_name_rhs in data.get_task_names():
# convert similarity with simple transformation
similarity = param.base_similarity - task_distances[
name_to_id[task_name_lhs], name_to_id[task_name_rhs]]
normalizer.set_task_similarity(data.name_to_id(task_name_lhs),
data.name_to_id(task_name_rhs),
similarity)
# save for later
similarities[data.name_to_id(task_name_lhs),
data.name_to_id(task_name_rhs)] = similarity
# set normalizer
base_wdk.set_normalizer(normalizer)
base_wdk.init_normalizer()
# set up svm
svm = SVMLight(param.cost, base_wdk, lab)
svm.set_linadd_enabled(False)
svm.set_batch_computation_enabled(False)
# normalize cost
norm_c_pos = param.cost / float(len([l
for l in data.labels if l == 1]))
norm_c_neg = param.cost / float(
len([l for l in data.labels if l == -1]))
svm.set_C(norm_c_neg, norm_c_pos)
# start training
svm.train()
# save additional information
self.additional_information["svm objective"] = svm.get_objective()
self.additional_information["num sv"] = svm.get_num_support_vectors()
#self.additional_information["distances"] = distances
self.additional_information["similarities"] = similarities
# wrap up predictors
svms = {}
# use a reference to the same svm several times
for task_name in data.get_task_names():
task_num = data.name_to_id(task_name)
# save svm and task_num
svms[task_name] = (task_num, param, svm)
return svms
def _train(self, train_data, param):
"""
training procedure using training examples and labels
@param train_data: Data relevant to SVM training
@type train_data: dict<str, list<instances> >
@param param: Parameters for the training procedure
@type param: ParameterSvm
"""
# merge data sets
data = PreparedMultitaskData(train_data, shuffle=False)
# create shogun data objects
base_wdk = shogun_factory.create_kernel(data.examples, param)
lab = shogun_factory.create_labels(data.labels)
# set normalizer
normalizer = MultitaskKernelNormalizer(data.task_vector_nums)
########################################################
print "creating a kernel for each node:"
########################################################
# init seq handler
task_kernel = SequencesHandlerRbf(1, param.base_similarity,
data.get_task_names(),
param.flags["wdk_rbf_on"])
similarities = numpy.zeros(
(data.get_num_tasks(), data.get_num_tasks()))
# convert distance to similarity
for task_name_lhs in data.get_task_names():
for task_name_rhs in data.get_task_names():
# convert similarity with simple transformation
similarity = task_kernel.get_similarity(
task_name_lhs, task_name_rhs)
print similarity
print "similarity (%s,%s)=%f" % (task_name_lhs, task_name_rhs,
similarity)
normalizer.set_task_similarity(data.name_to_id(task_name_lhs),
data.name_to_id(task_name_rhs),
similarity)
# save for later
similarities[data.name_to_id(task_name_lhs),
data.name_to_id(task_name_rhs)] = similarity
# set normalizer
base_wdk.set_normalizer(normalizer)
base_wdk.init_normalizer()
# set up svm
svm = SVMLight(param.cost, base_wdk, lab)
svm.set_linadd_enabled(False)
svm.set_batch_computation_enabled(False)
# normalize cost
norm_c_pos = param.cost / float(len([l
for l in data.labels if l == 1]))
norm_c_neg = param.cost / float(
len([l for l in data.labels if l == -1]))
svm.set_C(norm_c_neg, norm_c_pos)
# start training
svm.train()
# save additional information
self.additional_information["svm objective"] = svm.get_objective()
self.additional_information["num sv"] = svm.get_num_support_vectors()
#self.additional_information["distances"] = distances
self.additional_information["similarities"] = similarities
# wrap up predictors
svms = {}
# use a reference to the same svm several times
for task_name in data.get_task_names():
task_num = data.name_to_id(task_name)
# save svm and task_num
svms[task_name] = (task_num, param, svm)
return svms
def _train(self, train_data, param):
"""
training procedure using training examples and labels
@param train_data: Data relevant to SVM training
@type train_data: dict<str, list<instances> >
@param param: Parameters for the training procedure
@type param: ParameterSvm
"""
# merge data sets
data = PreparedMultitaskData(train_data, shuffle=False)
# create shogun data objects
base_wdk = shogun_factory.create_kernel(data.examples, param)
lab = shogun_factory.create_labels(data.labels)
# set normalizer
normalizer = MultitaskKernelNormalizer(data.task_vector_nums)
########################################################
print "creating a kernel for each node:"
########################################################
# init seq handler
task_kernel = SequencesHandlerRbf(1, param.base_similarity, data.get_task_names(), param.flags["wdk_rbf_on"])
similarities = numpy.zeros((data.get_num_tasks(), data.get_num_tasks()))
# convert distance to similarity
for task_name_lhs in data.get_task_names():
for task_name_rhs in data.get_task_names():
# convert similarity with simple transformation
similarity = task_kernel.get_similarity(task_name_lhs, task_name_rhs)
print similarity
print "similarity (%s,%s)=%f" % (task_name_lhs, task_name_rhs, similarity)
normalizer.set_task_similarity(data.name_to_id(task_name_lhs), data.name_to_id(task_name_rhs), similarity)
# save for later
similarities[data.name_to_id(task_name_lhs),data.name_to_id(task_name_rhs)] = similarity
# set normalizer
base_wdk.set_normalizer(normalizer)
base_wdk.init_normalizer()
# set up svm
svm = SVMLight(param.cost, base_wdk, lab)
svm.set_linadd_enabled(False)
svm.set_batch_computation_enabled(False)
# normalize cost
norm_c_pos = param.cost / float(len([l for l in data.labels if l==1]))
norm_c_neg = param.cost / float(len([l for l in data.labels if l==-1]))
svm.set_C(norm_c_neg, norm_c_pos)
# start training
svm.train()
# save additional information
self.additional_information["svm objective"] = svm.get_objective()
self.additional_information["num sv"] = svm.get_num_support_vectors()
#self.additional_information["distances"] = distances
self.additional_information["similarities"] = similarities
# wrap up predictors
svms = {}
# use a reference to the same svm several times
for task_name in data.get_task_names():
task_num = data.name_to_id(task_name)
# save svm and task_num
svms[task_name] = (task_num, param, svm)
return svms
def _train(self, train_data, param):
"""
training procedure using training examples and labels
@param train_data: Data relevant to SVM training
@type train_data: dict<str, list<instances> >
@param param: Parameters for the training procedure
@type param: ParameterSvm
"""
assert(param.base_similarity >= 1)
# merge data sets
data = PreparedMultitaskData(train_data, shuffle=False)
# create shogun data objects
base_wdk = shogun_factory.create_kernel(data.examples, param)
lab = shogun_factory.create_labels(data.labels)
# create normalizer
normalizer = MultitaskKernelNormalizer(data.task_vector_nums)
# load hard-coded task-similarity
task_similarity = helper.load("/fml/ag-raetsch/home/cwidmer/svn/projects/alt_splice_code/src/task_sim_tis.bz2")
# set similarity
similarities = numpy.zeros((data.get_num_tasks(), data.get_num_tasks()))
for (i, task_name_lhs) in enumerate(data.get_task_names()):
#max_value_row = max(task_similarity.get_row(task_name_lhs))
max_value_row = 1.0
for (j, task_name_rhs) in enumerate(data.get_task_names()):
similarity = task_similarity.get_value(task_name_lhs, task_name_rhs) / max_value_row
normalizer.set_task_similarity(i, j, similarity)
similarities[i,j] = similarity
pprint.pprint similarities
# set normalizer
#print "WARNING MTK disabled!!!!!!!!!!!!!!!!!!!!!"
base_wdk.set_normalizer(normalizer)
base_wdk.init_normalizer()
# set up svm
param.flags["svm_type"] = "svmlight" #fix svm type
svm = shogun_factory.create_svm(param, base_wdk, lab)
# make sure these parameters are set correctly
#print "WARNING MTK WONT WORK WITH THESE SETTINGS!!!!!!!!!!!!!!!!!!!!!"
svm.set_linadd_enabled(False)
svm.set_batch_computation_enabled(False)
assert svm.get_linadd_enabled() == False, "linadd should be disabled"
assert svm.get_batch_computation_enabled == False, "batch compute should be disabled"
# start training
svm.train()
# save additional information
self.additional_information["svm objective"] = svm.get_objective()
self.additional_information["num sv"] = svm.get_num_support_vectors()
#self.additional_information["distances"] = distances
self.additional_information["similarities"] = similarities
# wrap up predictors
svms = {}
# use a reference to the same svm several times
for task_name in data.get_task_names():
task_num = data.name_to_id(task_name)
# save svm and task_num
svms[task_name] = (task_num, svm)
return svms
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"
# taxonomy
##################################################################
taxonomy = shogun_factory.create_taxonomy(mss.taxonomy.data)
# create tree normalizer
tree_normalizer = MultitaskKernelTreeNormalizer(data.task_vector_names,
data.task_vector_names,
taxonomy)
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
def _train(self, train_data, param):
"""
training procedure using training examples and labels
@param train_data: Data relevant to SVM training
@type train_data: dict<str, list<instances> >
@param param: Parameters for the training procedure
@type param: ParameterSvm
"""
# merge data sets
data = PreparedMultitaskData(train_data, shuffle=False)
# create shogun data objects
base_wdk = shogun_factory.create_empty_kernel(param)
lab = shogun_factory.create_labels(data.labels)
combined_kernel = CombinedKernel()
combined_kernel.io.set_loglevel(shogun.Kernel.MSG_INFO)
base_features = shogun_factory.create_features(data.examples)
combined_features = CombinedFeatures()
# set normalizer
normalizer = MultitaskKernelNormalizer(data.task_vector_nums)
# load data
#f = file("/fml/ag-raetsch/home/cwidmer/Documents/phd/projects/multitask/data/mhc/MHC_Distanzen/MHC_Distanzen/ALL_PseudoSeq_BlosumEnc_pearson.txt")
f = file(
"/fml/ag-raetsch/home/cwidmer/Documents/phd/projects/multitask/data/mhc/MHC_Distanzen/MHC_Distanzen/All_PseudoSeq_Hamming.txt"
)
#f = file("/fml/ag-raetsch/home/cwidmer/Documents/phd/projects/multitask/data/mhc/MHC_Distanzen/MHC_Distanzen/ALL_PseudoSeq_BlosumEnc_euklid.txt")
#f = file("/fml/ag-raetsch/home/cwidmer/Documents/phd/projects/multitask/data/mhc/MHC_Distanzen/MHC_Distanzen/ALL_RAxML.txt")
num_lines = int(f.readline().strip())
task_distances = numpy.zeros((num_lines, num_lines))
name_to_id = {}
for (i, line) in enumerate(f):
tokens = line.strip().split("\t")
name = str(tokens[0])
name_to_id[name] = i
entry = numpy.array([v for (j, v) in enumerate(tokens) if j != 0])
assert len(entry) == num_lines, "len_entry %i, num_lines %i" % (
len(entry), num_lines)
task_distances[i, :] = entry
# cut relevant submatrix
active_ids = [name_to_id[name] for name in data.get_task_names()]
tmp_distances = task_distances[active_ids, :]
tmp_distances = tmp_distances[:, active_ids]
print "distances ", tmp_distances.shape
# normalize distances
task_distances = task_distances / numpy.max(tmp_distances)
similarities = numpy.zeros(
(data.get_num_tasks(), data.get_num_tasks()))
# convert distance to similarity
for task_name_lhs in data.get_task_names():
for task_name_rhs in data.get_task_names():
# convert similarity with simple transformation
similarity = param.base_similarity - task_distances[
name_to_id[task_name_lhs], name_to_id[task_name_rhs]]
normalizer.set_task_similarity(data.name_to_id(task_name_lhs),
data.name_to_id(task_name_rhs),
similarity)
# save for later
similarities[data.name_to_id(task_name_lhs),
data.name_to_id(task_name_rhs)] = similarity
# set normalizer
base_wdk.set_normalizer(normalizer)
#base_wdk.init_normalizer()
combined_features.append_feature_obj(base_features)
combined_kernel.append_kernel(base_wdk)
##################################################
# intra-domain blocks
intra_block_vec = PairiiVec()
for task_id in data.get_task_ids():
intra_block_vec.push_back(Pairii(task_id, task_id))
# create mask-based normalizer
normalizer = MultitaskKernelMaskPairNormalizer(data.task_vector_nums,
intra_block_vec)
kernel = shogun_factory.create_empty_kernel(param)
kernel.set_normalizer(normalizer)
# append current kernel to CombinedKernel
combined_kernel.append_kernel(kernel)
# append features
combined_features.append_feature_obj(base_features)
# set mixing factor (used if MKL is OFF)
assert (param.base_similarity <= 1)
assert (param.base_similarity >= 0)
combined_kernel.set_subkernel_weights(
[param.base_similarity, 1 - param.base_similarity])
combined_kernel.init(combined_features, combined_features)
svm = None
print "using MKL:", (param.transform >= 1.0)
if param.transform >= 1.0:
svm = MKLClassification()
svm.set_mkl_norm(param.transform)
#svm.set_solver_type(ST_CPLEX) #ST_GLPK) #DIRECT) #NEWTON)#ST_CPLEX) #auto
svm.set_C(param.cost, param.cost)
svm.set_kernel(combined_kernel)
svm.set_labels(lab)
else:
# create SVM (disable unsupported optimizations)
combined_kernel.set_cache_size(500)
svm = SVMLight(param.cost, combined_kernel, lab)
# set up SVM
num_threads = 8
svm.io.enable_progress()
svm.io.set_loglevel(shogun.Classifier.MSG_DEBUG)
svm.parallel.set_num_threads(num_threads)
svm.set_linadd_enabled(False)
svm.set_batch_computation_enabled(False)
print "WARNING: custom epsilon set"
svm.set_epsilon(0.05)
# normalize cost
norm_c_pos = param.cost / float(len([l
for l in data.labels if l == 1]))
norm_c_neg = param.cost / float(
len([l for l in data.labels if l == -1]))
svm.set_C(norm_c_neg, norm_c_pos)
# start training
svm.train()
# save additional information
self.additional_information["svm objective"] = svm.get_objective()
self.additional_information["num sv"] = svm.get_num_support_vectors()
self.additional_information["similarities"] = similarities
self.additional_information[
"post_weights"] = combined_kernel.get_subkernel_weights()
# wrap up predictors
svms = {}
# use a reference to the same svm several times
for task_name in data.get_task_names():
task_num = data.name_to_id(task_name)
# save svm and task_num
svms[task_name] = (task_num, combined_kernel, svm)
return svms