def test_basic(self):
dataset = data_generators.linear1d_gaussian_noise()
k = GeneralizedLinearKernel()
clf = GPR(k)
clf.train(dataset)
y = clf.predict(dataset.samples)
assert_array_equal(y.shape, dataset.targets.shape)
def _test_gpr_model_selection(self): # pragma: no cover
"""Smoke test for running model selection while getting GPRWeights
TODO: DISABLED because setting of hyperparameters was not adopted for 0.6 (yet)
"""
if not externals.exists('openopt'):
return
amap = AttributeMap() # we would need to pass numbers into the GPR
dataset = datasets['uni2small'].copy(
) #data_generators.linear1d_gaussian_noise()
dataset.targets = amap.to_numeric(dataset.targets).astype(float)
k = GeneralizedLinearKernel()
clf = GPR(k, enable_ca=['log_marginal_likelihood'])
sa = clf.get_sensitivity_analyzer() # should be regular weights
sa_ms = clf.get_sensitivity_analyzer(
flavor='model_select') # with model selection
def prints():
print clf.ca.log_marginal_likelihood, clf.kernel.Sigma_p, clf.kernel.sigma_0
sa(dataset)
lml = clf.ca.log_marginal_likelihood
sa_ms(dataset)
lml_ms = clf.ca.log_marginal_likelihood
self.assertTrue(lml_ms > lml)
def test_basic(self):
dataset = data_generators.linear1d_gaussian_noise()
k = GeneralizedLinearKernel()
clf = GPR(k)
clf.train(dataset)
y = clf.predict(dataset.samples)
assert_array_equal(y.shape, dataset.targets.shape)
def test_basic(self):
skip_if_no_external('scipy') # needed by GPR code
dataset = data_generators.linear1d_gaussian_noise()
k = GeneralizedLinearKernel()
clf = GPR(k)
clf.train(dataset)
y = clf.predict(dataset.samples)
assert_array_equal(y.shape, dataset.targets.shape)
def test_basic(self):
skip_if_no_external('scipy') # needed by GPR code
dataset = data_generators.linear1d_gaussian_noise()
k = GeneralizedLinearKernel()
clf = GPR(k)
clf.train(dataset)
y = clf.predict(dataset.samples)
assert_array_equal(y.shape, dataset.targets.shape)
def _test_gpr_model_selection(self): # pragma: no cover
"""Smoke test for running model selection while getting GPRWeights
TODO: DISABLED because setting of hyperparameters was not adopted for 0.6 (yet)
"""
if not externals.exists('openopt'):
return
amap = AttributeMap() # we would need to pass numbers into the GPR
dataset = datasets['uni2small'].copy() #data_generators.linear1d_gaussian_noise()
dataset.targets = amap.to_numeric(dataset.targets).astype(float)
k = GeneralizedLinearKernel()
clf = GPR(k, enable_ca=['log_marginal_likelihood'])
sa = clf.get_sensitivity_analyzer() # should be regular weights
sa_ms = clf.get_sensitivity_analyzer(flavor='model_select') # with model selection
def prints():
print clf.ca.log_marginal_likelihood, clf.kernel.Sigma_p, clf.kernel.sigma_0
sa(dataset)
lml = clf.ca.log_marginal_likelihood
sa_ms(dataset)
lml_ms = clf.ca.log_marginal_likelihood
self.assertTrue(lml_ms > lml)
clfswh += GNB(descr="GNB()")
clfswh += GNB(common_variance=True, descr="GNB(common_variance=True)")
clfswh += GNB(prior='uniform', descr="GNB(prior='uniform')")
clfswh += \
FeatureSelectionClassifier(
GNB(),
SensitivityBasedFeatureSelection(
OneWayAnova(),
FractionTailSelector(0.05, mode='select', tail='upper')),
descr="GNB on 5%(ANOVA)")
# GPR
if externals.exists('scipy'):
from mvpa2.clfs.gpr import GPR
regrswh += GPR(kernel=LinearKernel(), descr="GPR(kernel='linear')")
regrswh += GPR(kernel=SquaredExponentialKernel(),
descr="GPR(kernel='sqexp')")
# Add wrapped GPR as a classifier
gprcb = RegressionAsClassifier(GPR(kernel=GeneralizedLinearKernel()),
descr="GPRC(kernel='linear')")
# lets remove multiclass label from it
gprcb.__tags__.pop(gprcb.__tags__.index('multiclass'))
clfswh += gprcb
# and create a proper multiclass one
clfswh += MulticlassClassifier(RegressionAsClassifier(
GPR(kernel=GeneralizedLinearKernel())),
descr="GPRCM(kernel='linear')")
#ds.fa['parcel'] = msk_data
ds_all.append(ds)
verbose(2, "subject %i of %i loaded" % (index, nsubs))
fds = vstack(ds_all) #stack datasets
#classifier algorithm
if clf_type is 'SVM':
clf = LinearCSVMC(tube_epsilon=0.01)
elif clf_type is 'SVM-rbf':
clf = RbfCSVMC(tube_epsilon=0.01)
elif clf_type is 'ridgeReg':
clf = RidgeReg()
elif clf_type is 'gpr':
clf = GPR()
# #feature selection
# fsel = SensitivityBasedFeatureSelection(
# OneWayAnova(),
# FractionTailSelector(0.05, mode='select', tail='upper'))
# fclf = FeatureSelectionClassifier(clf, fsel)
#cross-validation algorithm
if cv_type is 'split_half':
cv = CrossValidation(clf,
HalfPartitioner(count=2,
selection_strategy='random', attr='subject'),
errorfx=mean_match_accuracy)
elif cv_type is 'LOSO':
cv = CrossValidation(clf,
clfswh += GNB(descr="GNB()")
clfswh += GNB(common_variance=True, descr="GNB(common_variance=True)")
clfswh += GNB(prior='uniform', descr="GNB(prior='uniform')")
clfswh += \
FeatureSelectionClassifier(
GNB(),
SensitivityBasedFeatureSelection(
OneWayAnova(),
FractionTailSelector(0.05, mode='select', tail='upper')),
descr="GNB on 5%(ANOVA)")
# GPR
if externals.exists('scipy'):
from mvpa2.clfs.gpr import GPR
regrswh += GPR(kernel=LinearKernel(), descr="GPR(kernel='linear')")
regrswh += GPR(kernel=SquaredExponentialKernel(),
descr="GPR(kernel='sqexp')")
# Add wrapped GPR as a classifier
gprcb = RegressionAsClassifier(
GPR(kernel=GeneralizedLinearKernel()), descr="GPRC(kernel='linear')")
# lets remove multiclass label from it
gprcb.__tags__.pop(gprcb.__tags__.index('multiclass'))
clfswh += gprcb
# and create a proper multiclass one
clfswh += MulticlassClassifier(
RegressionAsClassifier(
GPR(kernel=GeneralizedLinearKernel())),
descr="GPRCM(kernel='linear')")