def modelselection_grid_search_liblinear_modular (traindat=traindat, label_traindat=label_traindat):
from modshogun import CrossValidation, CrossValidationResult
from modshogun import ContingencyTableEvaluation, ACCURACY
from modshogun import StratifiedCrossValidationSplitting
from modshogun import GridSearchModelSelection
from modshogun import ModelSelectionParameters, R_EXP
from modshogun import ParameterCombination
from modshogun import BinaryLabels
from modshogun import RealFeatures
from modshogun import LibLinear, L2R_L2LOSS_SVC
# build parameter tree to select C1 and C2
param_tree_root=ModelSelectionParameters()
c1=ModelSelectionParameters("C1");
param_tree_root.append_child(c1)
c1.build_values(-1.0, 0.0, R_EXP);
c2=ModelSelectionParameters("C2");
param_tree_root.append_child(c2);
c2.build_values(-1.0, 0.0, R_EXP);
# training data
features=RealFeatures(traindat)
labels=BinaryLabels(label_traindat)
# classifier
classifier=LibLinear(L2R_L2LOSS_SVC)
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
#classifier.print_modsel_params()
# splitting strategy for cross-validation
splitting_strategy=StratifiedCrossValidationSplitting(labels, 10)
# evaluation method
evaluation_criterium=ContingencyTableEvaluation(ACCURACY)
# cross-validation instance
cross_validation=CrossValidation(classifier, features, labels,
splitting_strategy, evaluation_criterium)
cross_validation.set_autolock(False)
# model selection instance
model_selection=GridSearchModelSelection(cross_validation, param_tree_root)
# perform model selection with selected methods
#print "performing model selection of"
#param_tree_root.print_tree()
best_parameters=model_selection.select_model()
# print best parameters
#print "best parameters:"
#best_parameters.print_tree()
# apply them and print result
best_parameters.apply_to_machine(classifier)
result=cross_validation.evaluate()
def modelselection_random_search_liblinear_modular (traindat=traindat, label_traindat=label_traindat):
from modshogun import CrossValidation, CrossValidationResult
from modshogun import ContingencyTableEvaluation, ACCURACY
from modshogun import StratifiedCrossValidationSplitting
from modshogun import RandomSearchModelSelection
from modshogun import ModelSelectionParameters, R_EXP
from modshogun import ParameterCombination
from modshogun import BinaryLabels
from modshogun import RealFeatures
from modshogun import LibLinear, L2R_L2LOSS_SVC
# 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);
# training data
features=RealFeatures(traindat)
labels=BinaryLabels(label_traindat)
# classifier
classifier=LibLinear(L2R_L2LOSS_SVC)
# print all parameter available for modelselection
# Dont worry if yours is not included but, write to the mailing list
#classifier.print_modsel_params()
# splitting strategy for cross-validation
splitting_strategy=StratifiedCrossValidationSplitting(labels, 10)
# evaluation method
evaluation_criterium=ContingencyTableEvaluation(ACCURACY)
# cross-validation instance
cross_validation=CrossValidation(classifier, features, labels,
splitting_strategy, evaluation_criterium)
cross_validation.set_autolock(False)
# model selection instance
model_selection=RandomSearchModelSelection(cross_validation, param_tree_root, 0.5)
# perform model selection with selected methods
#print "performing model selection of"
#param_tree_root.print_tree()
best_parameters=model_selection.select_model()
# print best parameters
#print "best parameters:"
#best_parameters.print_tree()
# apply them and print result
best_parameters.apply_to_machine(classifier)
result=cross_validation.evaluate()
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("log_width")
gaussian_kernel_width.build_values(-math.log(2.0), 0.0, 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 modshogun import ModelSelectionParameters, R_EXP, R_LINEAR
from modshogun import ParameterCombination
from modshogun import GaussianKernel, PolyKernel
import math
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("log_width")
gaussian_kernel_width.build_values(2.0 * math.log(2.0),
2.5 * math.log(2.0), R_LINEAR, 1.0)
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 modshogun import ModelSelectionParameters, R_EXP, R_LINEAR
from modshogun import ParameterCombination
from modshogun import GaussianKernel, PolyKernel
import math
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("log_width");
gaussian_kernel_width.build_values(2.0*math.log(2.0), 2.5*math.log(2.0), R_LINEAR, 1.0)
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 modshogun import ParameterCombination
from modshogun import ModelSelectionParameters, R_EXP, R_LINEAR
from modshogun import PowerKernel
from modshogun import GaussianKernel
from modshogun import DistantSegmentsKernel
from modshogun import MinkowskiMetric
import math
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('log_width')
param_gaussian_kernel_width.build_values(0.0, 0.5*math.log(2.0), R_LINEAR)
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()):
# params = ParameterCombination.obtain_from_generic(combinations.get_element(i))
# params.print_tree()
return
def run_demo():
LG.basicConfig(level=LG.INFO)
random.seed(572)
#1. create toy data
[x,y] = create_toy_data()
feat_train = RealFeatures(transpose(x));
labels = RegressionLabels(y);
n_dimensions = 1
#2. location of unispaced predictions
X = SP.linspace(0,10,10)[:,SP.newaxis]
#new interface with likelihood parametres being decoupled from the covaraince function
likelihood = GaussianLikelihood()
covar_parms = SP.log([2])
hyperparams = {'covar':covar_parms,'lik':SP.log([1])}
#construct covariance function
SECF = GaussianKernel(feat_train, feat_train,2)
covar = SECF
zmean = ZeroMean();
inf = ExactInferenceMethod(SECF, feat_train, zmean, labels, likelihood);
gp = GaussianProcessRegression(inf, feat_train, labels);
root=ModelSelectionParameters();
c1=ModelSelectionParameters("inference_method", inf);
root.append_child(c1);
c2 = ModelSelectionParameters("scale");
c1.append_child(c2);
c2.build_values(0.01, 4.0, R_LINEAR);
c3 = ModelSelectionParameters("likelihood_model", likelihood);
c1.append_child(c3);
c4=ModelSelectionParameters("sigma");
c3.append_child(c4);
c4.build_values(0.001, 4.0, R_LINEAR);
c5 =ModelSelectionParameters("kernel", SECF);
c1.append_child(c5);
c6 =ModelSelectionParameters("width");
c5.append_child(c6);
c6.build_values(0.001, 4.0, R_LINEAR);
crit = GradientCriterion();
grad=GradientEvaluation(gp, feat_train, labels,
crit);
grad.set_function(inf);
gp.print_modsel_params();
root.print_tree();
grad_search=GradientModelSelection(
root, grad);
grad.set_autolock(0);
best_combination=grad_search.select_model(1);
gp.set_return_type(GaussianProcessRegression.GP_RETURN_COV);
St = gp.apply_regression(feat_train);
St = St.get_labels();
gp.set_return_type(GaussianProcessRegression.GP_RETURN_MEANS);
M = gp.apply_regression();
M = M.get_labels();
#create plots
plot_sausage(transpose(x),transpose(M),transpose(SP.sqrt(St)));
plot_training_data(x,y);
PL.show();
def modelselection_parameter_tree_modular(dummy):
from modshogun import ParameterCombination
from modshogun import ModelSelectionParameters, R_EXP, R_LINEAR
from modshogun import PowerKernel
from modshogun import GaussianKernel
from modshogun import DistantSegmentsKernel
from modshogun 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_grid_search_libsvr_modular (fm_train=traindat,fm_test=testdat,label_train=label_traindat,\
width=2.1,C=1,epsilon=1e-5,tube_epsilon=1e-2):
from modshogun import CrossValidation, CrossValidationResult
from modshogun import MeanSquaredError
from modshogun import CrossValidationSplitting
from modshogun import RegressionLabels
from modshogun import RealFeatures
from modshogun import GaussianKernel
from modshogun import LibSVR
from modshogun import GridSearchModelSelection
from modshogun import ModelSelectionParameters, R_EXP
from modshogun import ParameterCombination
# training data
features_train = RealFeatures(traindat)
labels = RegressionLabels(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 = RegressionLabels(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 (set larger to get better estimates, at least two
# for confidence intervals)
cross_validation.set_num_runs(2)
# (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(-1.0, 0.0, R_EXP)
c2 = ModelSelectionParameters("C2")
param_tree_root.append_child(c2)
c2.build_values(-1.0, 0.0, R_EXP)
# model selection instance
model_selection = GridSearchModelSelection(cross_validation,
param_tree_root)
# 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 = False
# 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()
def run_demo():
LG.basicConfig(level=LG.INFO)
random.seed(572)
#1. create toy data
[x, y] = create_toy_data()
feat_train = RealFeatures(transpose(x))
labels = RegressionLabels(y)
n_dimensions = 1
#2. location of unispaced predictions
X = SP.linspace(0, 10, 10)[:, SP.newaxis]
#new interface with likelihood parametres being decoupled from the covaraince function
likelihood = GaussianLikelihood()
covar_parms = SP.log([2])
hyperparams = {'covar': covar_parms, 'lik': SP.log([1])}
#construct covariance function
SECF = GaussianKernel(feat_train, feat_train, 2)
covar = SECF
zmean = ZeroMean()
inf = ExactInferenceMethod(SECF, feat_train, zmean, labels, likelihood)
gp = GaussianProcessRegression(inf, feat_train, labels)
root = ModelSelectionParameters()
c1 = ModelSelectionParameters("inference_method", inf)
root.append_child(c1)
c2 = ModelSelectionParameters("scale")
c1.append_child(c2)
c2.build_values(0.01, 4.0, R_LINEAR)
c3 = ModelSelectionParameters("likelihood_model", likelihood)
c1.append_child(c3)
c4 = ModelSelectionParameters("sigma")
c3.append_child(c4)
c4.build_values(0.001, 4.0, R_LINEAR)
c5 = ModelSelectionParameters("kernel", SECF)
c1.append_child(c5)
c6 = ModelSelectionParameters("width")
c5.append_child(c6)
c6.build_values(0.001, 4.0, R_LINEAR)
crit = GradientCriterion()
grad = GradientEvaluation(gp, feat_train, labels, crit)
grad.set_function(inf)
gp.print_modsel_params()
root.print_tree()
grad_search = GradientModelSelection(root, grad)
grad.set_autolock(0)
best_combination = grad_search.select_model(1)
gp.set_return_type(GaussianProcessRegression.GP_RETURN_COV)
St = gp.apply_regression(feat_train)
St = St.get_labels()
gp.set_return_type(GaussianProcessRegression.GP_RETURN_MEANS)
M = gp.apply_regression()
M = M.get_labels()
#create plots
plot_sausage(transpose(x), transpose(M), transpose(SP.sqrt(St)))
plot_training_data(x, y)
PL.show()