def create_param_tree():
root=ModelSelectionParameters()
c1=ModelSelectionParameters("C1")
root.append_child(c1)
c1.build_values(-1.0, 1.0, R_EXP)
c2=ModelSelectionParameters("C2")
root.append_child(c2)
c2.build_values(-1.0, 1.0, R_EXP)
gaussian_kernel=GaussianKernel()
# print all parameter available for modelselection
# Dont worry if yours is not included, simply write to the mailing list
gaussian_kernel.print_modsel_params()
param_gaussian_kernel=ModelSelectionParameters("kernel", gaussian_kernel)
gaussian_kernel_width=ModelSelectionParameters("width")
gaussian_kernel_width.build_values(-1.0, 1.0, R_EXP, 1.0, 2.0)
param_gaussian_kernel.append_child(gaussian_kernel_width)
root.append_child(param_gaussian_kernel)
power_kernel=PowerKernel()
# print all parameter available for modelselection
# Dont worry if yours is not included, simply write to the mailing list
power_kernel.print_modsel_params()
param_power_kernel=ModelSelectionParameters("kernel", power_kernel)
root.append_child(param_power_kernel)
param_power_kernel_degree=ModelSelectionParameters("degree")
param_power_kernel_degree.build_values(1.0, 2.0, R_LINEAR)
param_power_kernel.append_child(param_power_kernel_degree)
metric=MinkowskiMetric(10)
# print all parameter available for modelselection
# Dont worry if yours is not included, simply write to the mailing list
metric.print_modsel_params()
param_power_kernel_metric1=ModelSelectionParameters("distance", metric)
param_power_kernel.append_child(param_power_kernel_metric1)
param_power_kernel_metric1_k=ModelSelectionParameters("k")
param_power_kernel_metric1_k.build_values(1.0, 2.0, R_LINEAR)
param_power_kernel_metric1.append_child(param_power_kernel_metric1_k)
return root
def create_param_tree():
from shogun.ModelSelection import ModelSelectionParameters, R_EXP, R_LINEAR
from shogun.ModelSelection import ParameterCombination
from shogun.Kernel import GaussianKernel, PolyKernel
root=ModelSelectionParameters()
tau=ModelSelectionParameters("tau")
root.append_child(tau)
# also R_LINEAR/R_LOG is available as type
min=-1
max=1
type=R_EXP
step=1.5
base=2
tau.build_values(min, max, type, step, base)
# gaussian kernel with width
gaussian_kernel=GaussianKernel()
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
gaussian_kernel.print_modsel_params()
param_gaussian_kernel=ModelSelectionParameters("kernel", gaussian_kernel)
gaussian_kernel_width=ModelSelectionParameters("width");
gaussian_kernel_width.build_values(5.0, 8.0, R_EXP, 1.0, 2.0)
param_gaussian_kernel.append_child(gaussian_kernel_width)
root.append_child(param_gaussian_kernel)
# polynomial kernel with degree
poly_kernel=PolyKernel()
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
poly_kernel.print_modsel_params()
param_poly_kernel=ModelSelectionParameters("kernel", poly_kernel)
root.append_child(param_poly_kernel)
# note that integers are used here
param_poly_kernel_degree=ModelSelectionParameters("degree")
param_poly_kernel_degree.build_values(1, 2, R_LINEAR)
param_poly_kernel.append_child(param_poly_kernel_degree)
return root
def create_param_tree():
from shogun.ModelSelection import ModelSelectionParameters, R_EXP, R_LINEAR
from shogun.ModelSelection import ParameterCombination
from shogun.Kernel import GaussianKernel, PolyKernel
root = ModelSelectionParameters()
tau = ModelSelectionParameters("tau")
root.append_child(tau)
# also R_LINEAR/R_LOG is available as type
min = -1
max = 1
type = R_EXP
step = 1.5
base = 2
tau.build_values(min, max, type, step, base)
# gaussian kernel with width
gaussian_kernel = GaussianKernel()
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
gaussian_kernel.print_modsel_params()
param_gaussian_kernel = ModelSelectionParameters("kernel", gaussian_kernel)
gaussian_kernel_width = ModelSelectionParameters("width")
gaussian_kernel_width.build_values(5.0, 8.0, R_EXP, 1.0, 2.0)
param_gaussian_kernel.append_child(gaussian_kernel_width)
root.append_child(param_gaussian_kernel)
# polynomial kernel with degree
poly_kernel = PolyKernel()
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
poly_kernel.print_modsel_params()
param_poly_kernel = ModelSelectionParameters("kernel", poly_kernel)
root.append_child(param_poly_kernel)
# note that integers are used here
param_poly_kernel_degree = ModelSelectionParameters("degree")
param_poly_kernel_degree.build_values(1, 2, R_LINEAR)
param_poly_kernel.append_child(param_poly_kernel_degree)
return root
def modelselection_parameter_tree_modular(dummy):
from shogun.ModelSelection import ParameterCombination
from shogun.ModelSelection import ModelSelectionParameters, R_EXP, R_LINEAR
from shogun.ModelSelection import DynamicParameterCombinationArray
from shogun.Kernel import PowerKernel
from shogun.Kernel import GaussianKernel
from shogun.Kernel import DistantSegmentsKernel
from shogun.Distance import MinkowskiMetric
root = ModelSelectionParameters()
combinations = root.get_combinations()
combinations.get_num_elements()
c = ModelSelectionParameters('C')
root.append_child(c)
c.build_values(1, 11, R_EXP)
power_kernel = PowerKernel()
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
power_kernel.print_modsel_params()
param_power_kernel = ModelSelectionParameters('kernel', power_kernel)
root.append_child(param_power_kernel)
param_power_kernel_degree = ModelSelectionParameters('degree')
param_power_kernel_degree.build_values(1, 1, R_EXP)
param_power_kernel.append_child(param_power_kernel_degree)
metric1 = MinkowskiMetric(10)
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
metric1.print_modsel_params()
param_power_kernel_metric1 = ModelSelectionParameters('distance', metric1)
param_power_kernel.append_child(param_power_kernel_metric1)
param_power_kernel_metric1_k = ModelSelectionParameters('k')
param_power_kernel_metric1_k.build_values(1, 12, R_LINEAR)
param_power_kernel_metric1.append_child(param_power_kernel_metric1_k)
gaussian_kernel = GaussianKernel()
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
gaussian_kernel.print_modsel_params()
param_gaussian_kernel = ModelSelectionParameters('kernel', gaussian_kernel)
root.append_child(param_gaussian_kernel)
param_gaussian_kernel_width = ModelSelectionParameters('width')
param_gaussian_kernel_width.build_values(1, 2, R_EXP)
param_gaussian_kernel.append_child(param_gaussian_kernel_width)
ds_kernel = DistantSegmentsKernel()
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
ds_kernel.print_modsel_params()
param_ds_kernel = ModelSelectionParameters('kernel', ds_kernel)
root.append_child(param_ds_kernel)
param_ds_kernel_delta = ModelSelectionParameters('delta')
param_ds_kernel_delta.build_values(1, 2, R_EXP)
param_ds_kernel.append_child(param_ds_kernel_delta)
param_ds_kernel_theta = ModelSelectionParameters('theta')
param_ds_kernel_theta.build_values(1, 2, R_EXP)
param_ds_kernel.append_child(param_ds_kernel_theta)
# root.print_tree()
combinations = root.get_combinations()
# for i in range(combinations.get_num_elements()):
# combinations.get_element(i).print_tree()
return
def modelselection_parameter_tree_modular(dummy):
from shogun.ModelSelection import ParameterCombination
from shogun.ModelSelection import ModelSelectionParameters, R_EXP, R_LINEAR
from shogun.ModelSelection import DynamicParameterCombinationArray
from shogun.Kernel import PowerKernel
from shogun.Kernel import GaussianKernel
from shogun.Kernel import DistantSegmentsKernel
from shogun.Distance import MinkowskiMetric
root=ModelSelectionParameters()
combinations=root.get_combinations()
combinations.get_num_elements()
c=ModelSelectionParameters('C');
root.append_child(c)
c.build_values(1, 11, R_EXP)
power_kernel=PowerKernel()
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
power_kernel.print_modsel_params()
param_power_kernel=ModelSelectionParameters('kernel', power_kernel)
root.append_child(param_power_kernel)
param_power_kernel_degree=ModelSelectionParameters('degree')
param_power_kernel_degree.build_values(1, 1, R_EXP)
param_power_kernel.append_child(param_power_kernel_degree)
metric1=MinkowskiMetric(10)
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
metric1.print_modsel_params()
param_power_kernel_metric1=ModelSelectionParameters('distance', metric1)
param_power_kernel.append_child(param_power_kernel_metric1)
param_power_kernel_metric1_k=ModelSelectionParameters('k')
param_power_kernel_metric1_k.build_values(1, 12, R_LINEAR)
param_power_kernel_metric1.append_child(param_power_kernel_metric1_k)
gaussian_kernel=GaussianKernel()
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
gaussian_kernel.print_modsel_params()
param_gaussian_kernel=ModelSelectionParameters('kernel', gaussian_kernel)
root.append_child(param_gaussian_kernel)
param_gaussian_kernel_width=ModelSelectionParameters('width')
param_gaussian_kernel_width.build_values(1, 2, R_EXP)
param_gaussian_kernel.append_child(param_gaussian_kernel_width)
ds_kernel=DistantSegmentsKernel()
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
ds_kernel.print_modsel_params()
param_ds_kernel=ModelSelectionParameters('kernel', ds_kernel)
root.append_child(param_ds_kernel)
param_ds_kernel_delta=ModelSelectionParameters('delta')
param_ds_kernel_delta.build_values(1, 2, R_EXP)
param_ds_kernel.append_child(param_ds_kernel_delta)
param_ds_kernel_theta=ModelSelectionParameters('theta')
param_ds_kernel_theta.build_values(1, 2, R_EXP)
param_ds_kernel.append_child(param_ds_kernel_theta)
root.print_tree()
combinations=root.get_combinations()
for i in range(combinations.get_num_elements()):
combinations.get_element(i).print_tree()
return
def evaluation_cross_validation_classification(fm_train=traindat,fm_test=testdat,label_train=label_traindat,\
width=2.1,C=1,epsilon=1e-5,tube_epsilon=1e-2):
from shogun.Evaluation import CrossValidation, CrossValidationResult
from shogun.Evaluation import MeanSquaredError
from shogun.Evaluation import CrossValidationSplitting
from shogun.Features import Labels
from shogun.Features import RealFeatures
from shogun.Kernel import GaussianKernel
from shogun.Regression import LibSVR
from shogun.ModelSelection import GridSearchModelSelection
from shogun.ModelSelection import ModelSelectionParameters, R_EXP
from shogun.ModelSelection import ParameterCombination
# training data
features_train=RealFeatures(traindat)
labels=Labels(label_traindat)
# kernel
kernel=GaussianKernel(features_train, features_train, width)
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
kernel.print_modsel_params()
labels=Labels(label_train)
# predictor
predictor=LibSVR(C, tube_epsilon, kernel, labels)
predictor.set_epsilon(epsilon)
# splitting strategy for 5 fold cross-validation (for classification its better
# to use "StratifiedCrossValidation", but the standard
# "StratifiedCrossValidationSplitting" is also available
splitting_strategy=CrossValidationSplitting(labels, 5)
# evaluation method
evaluation_criterium=MeanSquaredError()
# cross-validation instance
cross_validation=CrossValidation(predictor, features_train, labels,
splitting_strategy, evaluation_criterium)
# (optional) repeat x-val 10 times
cross_validation.set_num_runs(10)
# (optional) request 95% confidence intervals for results (not actually needed
# for this toy example)
cross_validation.set_conf_int_alpha(0.05)
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
predictor.print_modsel_params()
# build parameter tree to select C1 and C2
param_tree_root=ModelSelectionParameters()
c1=ModelSelectionParameters("C1");
param_tree_root.append_child(c1)
c1.build_values(-2.0, 2.0, R_EXP);
c2=ModelSelectionParameters("C2");
param_tree_root.append_child(c2);
c2.build_values(-2.0, 2.0, R_EXP);
# model selection instance
model_selection=GridSearchModelSelection(param_tree_root,
cross_validation)
# perform model selection with selected methods
#print "performing model selection of"
print "parameter tree"
param_tree_root.print_tree()
print "starting model selection"
# print the current parameter combination, if no parameter nothing is printed
print_state=True
# lock data before since model selection will not change the kernel matrix
# (use with care) This avoids that the kernel matrix is recomputed in every
# iteration of the model search
predictor.data_lock(labels, features_train)
best_parameters=model_selection.select_model(print_state)
# print best parameters
print "best parameters:"
best_parameters.print_tree()
# apply them and print result
best_parameters.apply_to_machine(predictor)
result=cross_validation.evaluate()
print "mean:", result.mean
if result.has_conf_int:
print "[", result.conf_int_low, ",", result.conf_int_up, "] with alpha=", result.conf_int_alpha
