def _call(self, ds_):
"""Extract weights from GPR
.. note:
Input dataset is not actually used. New dataset is
constructed from what is known to the classifier
"""
clf = self.clf
# normalize data:
clf._train_labels = (clf._train_labels - clf._train_labels.mean()) \
/ clf._train_labels.std()
# clf._train_fv = (clf._train_fv-clf._train_fv.mean(0)) \
# /clf._train_fv.std(0)
ds = dataset_wizard(samples=clf._train_fv,
targets=clf._train_labels)
clf.ca.enable("log_marginal_likelihood")
ms = ModelSelector(clf, ds)
# Note that some kernels does not have gradient yet!
# XXX Make it initialize to clf's current hyperparameter values
# or may be add ability to specify starting points in the constructor
sigma_noise_initial = 1.0e-5
sigma_f_initial = 1.0
length_scale_initial = np.ones(ds.nfeatures) * 1.0e4
# length_scale_initial = np.random.rand(ds.nfeatures)*1.0e4
hyp_initial_guess = np.hstack(
[sigma_noise_initial, sigma_f_initial, length_scale_initial])
fixedHypers = array([0] * hyp_initial_guess.size, dtype=bool)
fixedHypers = None
problem = ms.max_log_marginal_likelihood(
hyp_initial_guess=hyp_initial_guess,
optimization_algorithm="scipy_lbfgsb",
ftol=1.0e-3,
fixedHypers=fixedHypers,
use_gradient=True,
logscale=True)
if __debug__ and 'GPR_WEIGHTS' in debug.active:
problem.iprint = 1
lml = ms.solve()
weights = 1.0 / ms.hyperparameters_best[
2:] # weight = 1/length_scale
if __debug__:
debug(
"GPR", "%s, train: shape %s, labels %s, min:max %g:%g, "
"sigma_noise %g, sigma_f %g" %
(clf, clf._train_fv.shape, np.unique(clf._train_labels),
clf._train_fv.min(), clf._train_fv.max(),
ms.hyperparameters_best[0], ms.hyperparameters_best[1]))
return weights
def _call(self, ds_):
"""Extract weights from GPR
.. note:
Input dataset is not actually used. New dataset is
constructed from what is known to the classifier
"""
clf = self.clf
# normalize data:
clf._train_labels = (clf._train_labels - clf._train_labels.mean()) \
/ clf._train_labels.std()
# clf._train_fv = (clf._train_fv-clf._train_fv.mean(0)) \
# /clf._train_fv.std(0)
ds = dataset_wizard(samples=clf._train_fv, targets=clf._train_labels)
clf.ca.enable("log_marginal_likelihood")
ms = ModelSelector(clf, ds)
# Note that some kernels does not have gradient yet!
# XXX Make it initialize to clf's current hyperparameter values
# or may be add ability to specify starting points in the constructor
sigma_noise_initial = 1.0e-5
sigma_f_initial = 1.0
length_scale_initial = np.ones(ds.nfeatures)*1.0e4
# length_scale_initial = np.random.rand(ds.nfeatures)*1.0e4
hyp_initial_guess = np.hstack([sigma_noise_initial,
sigma_f_initial,
length_scale_initial])
fixedHypers = array([0]*hyp_initial_guess.size, dtype=bool)
fixedHypers = None
problem = ms.max_log_marginal_likelihood(
hyp_initial_guess=hyp_initial_guess,
optimization_algorithm="scipy_lbfgsb",
ftol=1.0e-3, fixedHypers=fixedHypers,
use_gradient=True, logscale=True)
if __debug__ and 'GPR_WEIGHTS' in debug.active:
problem.iprint = 1
lml = ms.solve()
weights = 1.0/ms.hyperparameters_best[2:] # weight = 1/length_scale
if __debug__:
debug("GPR",
"%s, train: shape %s, labels %s, min:max %g:%g, "
"sigma_noise %g, sigma_f %g" %
(clf, clf._train_fv.shape, np.unique(clf._train_labels),
clf._train_fv.min(), clf._train_fv.max(),
ms.hyperparameters_best[0], ms.hyperparameters_best[1]))
return weights
def _call(self, dataset):
"""Extract weights from GPR
"""
clf = self.clf
# normalize data:
clf._train_labels = (clf._train_labels - clf._train_labels.mean()) \
/ clf._train_labels.std()
# clf._train_fv = (clf._train_fv-clf._train_fv.mean(0)) \
# /clf._train_fv.std(0)
dataset = Dataset(samples=clf._train_fv, labels=clf._train_labels)
clf.states.enable("log_marginal_likelihood")
ms = ModelSelector(clf, dataset)
# Note that some kernels does not have gradient yet!
sigma_noise_initial = 1.0e-5
sigma_f_initial = 1.0
length_scale_initial = N.ones(dataset.nfeatures)*1.0e4
# length_scale_initial = N.random.rand(dataset.nfeatures)*1.0e4
hyp_initial_guess = N.hstack([sigma_noise_initial,
sigma_f_initial,
length_scale_initial])
fixedHypers = N.array([0]*hyp_initial_guess.size, dtype=bool)
fixedHypers = None
problem = ms.max_log_marginal_likelihood(
hyp_initial_guess=hyp_initial_guess,
optimization_algorithm="scipy_lbfgsb",
ftol=1.0e-3, fixedHypers=fixedHypers,
use_gradient=True, logscale=True)
if __debug__ and 'GPR_WEIGHTS' in debug.active:
problem.iprint = 1
lml = ms.solve()
weights = 1.0/ms.hyperparameters_best[2:] # weight = 1/length_scale
if __debug__:
debug("GPR",
"%s, train: shape %s, labels %s, min:max %g:%g, "
"sigma_noise %g, sigma_f %g" %
(clf, clf._train_fv.shape, N.unique(clf._train_labels),
clf._train_fv.min(), clf._train_fv.max(),
ms.hyperparameters_best[0], ms.hyperparameters_best[1]))
return weights
