mvpa2.clfs.gpr.GPR

class mvpa2.clfs.gpr.GPR(kernel=None, **kwargs)

Gaussian Process Regression (GPR).

Notes

Available conditional attributes:

• calling_time+: None
• estimates+: Internal classifier estimates the most recent predictions are based on
• log_marginal_likelihood: Log Marginal Likelihood
• log_marginal_likelihood_gradient: Log Marginal Likelihood Gradient
• predicted_variances: Variance per each predicted value
• predicting_time+: Time (in seconds) which took classifier to predict
• predictions+: Most recent set of predictions
• raw_results: None
• trained_dataset: None
• trained_nsamples+: None
• trained_targets+: None
• training_stats: Confusion matrix of learning performance
• training_time+: None

(Conditional attributes enabled by default suffixed with +)

Methods

compute_gradient_log_marginal_likelihood()	Compute gradient of the log marginal likelihood.
compute_gradient_log_marginal_likelihood_logscale()	Compute gradient of the log marginal likelihood when hyperparameters are in logscale.
compute_log_marginal_likelihood()	Compute log marginal likelihood using self.train_fv and self.targets.
get_sensitivity_analyzer([flavor])	Returns a sensitivity analyzer for GPR.
set_hyperparameters(hyperparameter)	Set hyperparameters’ values.

Initialize a GPR regression analysis.

Parameters:

kernel : Kernel

a kernel object defining the covariance between instances. (Defaults to SquaredExponentialKernel if None in arguments)

sigma_noise : float, optional

the standard deviation of the gaussian noise. Constraints: value must be convertible to type ‘float’, and value must be in range [1e-10, inf]. [Default: 0.001]

lm : float or None, optional

The regularization term lambda. Increase this when the kernel matrix is not positive definite. If None, some regularization will be provided upon necessity. Constraints: (value must be convertible to type ‘float’, and value must be in range [0.0, inf]), or value must be None. [Default: None]

retrainable : bool, optional

Either to enable retraining for ‘retrainable’ classifier. Constraints: value must be convertible to type bool. [Default: False]

enable_ca : None or list of str

Names of the conditional attributes which should be enabled in addition to the default ones

disable_ca : None or list of str

Names of the conditional attributes which should be disabled

auto_train : bool

Flag whether the learner will automatically train itself on the input dataset when called untrained.

force_train : bool

Flag whether the learner will enforce training on the input dataset upon every call.

space : str, optional

Name of the ‘processing space’. The actual meaning of this argument heavily depends on the sub-class implementation. In general, this is a trigger that tells the node to compute and store information about the input data that is “interesting” in the context of the corresponding processing in the output dataset.

pass_attr : str, list of str|tuple, optional

Additional attributes to pass on to an output dataset. Attributes can be taken from all three attribute collections of an input dataset (sa, fa, a – see Dataset.get_attr()), or from the collection of conditional attributes (ca) of a node instance. Corresponding collection name prefixes should be used to identify attributes, e.g. ‘ca.null_prob’ for the conditional attribute ‘null_prob’, or ‘fa.stats’ for the feature attribute stats. In addition to a plain attribute identifier it is possible to use a tuple to trigger more complex operations. The first tuple element is the attribute identifier, as described before. The second element is the name of the target attribute collection (sa, fa, or a). The third element is the axis number of a multidimensional array that shall be swapped with the current first axis. The fourth element is a new name that shall be used for an attribute in the output dataset. Example: (‘ca.null_prob’, ‘fa’, 1, ‘pvalues’) will take the conditional attribute ‘null_prob’ and store it as a feature attribute ‘pvalues’, while swapping the first and second axes. Simplified instructions can be given by leaving out consecutive tuple elements starting from the end.

postproc : Node instance, optional

Node to perform post-processing of results. This node is applied in __call__() to perform a final processing step on the to be result dataset. If None, nothing is done.

descr : str

Description of the instance

Methods

compute_gradient_log_marginal_likelihood()	Compute gradient of the log marginal likelihood.
compute_gradient_log_marginal_likelihood_logscale()	Compute gradient of the log marginal likelihood when hyperparameters are in logscale.
compute_log_marginal_likelihood()	Compute log marginal likelihood using self.train_fv and self.targets.
get_sensitivity_analyzer([flavor])	Returns a sensitivity analyzer for GPR.
set_hyperparameters(hyperparameter)	Set hyperparameters’ values.

compute_gradient_log_marginal_likelihood()

Compute gradient of the log marginal likelihood. This version use a more compact formula provided by Williams and Rasmussen book.

compute_gradient_log_marginal_likelihood_logscale()

Compute gradient of the log marginal likelihood when hyperparameters are in logscale. This version use a more compact formula provided by Williams and Rasmussen book.

compute_log_marginal_likelihood()

Compute log marginal likelihood using self.train_fv and self.targets.

get_sensitivity_analyzer(flavor='auto', **kwargs)

Returns a sensitivity analyzer for GPR.

Parameters:

flavor : str

What sensitivity to provide. Valid values are ‘linear’, ‘model_select’, ‘auto’. In case of ‘auto’ selects ‘linear’ for linear kernel and ‘model_select’ for the rest. ‘linear’ corresponds to GPRLinearWeights and ‘model_select’ to GRPWeights

kernel

set_hyperparameters(hyperparameter)

Set hyperparameters’ values.

Note that ‘hyperparameter’ is a sequence so the order of its values is important. First value must be sigma_noise, then other kernel’s hyperparameters values follow in the exact order the kernel expect them to be.