mvpa2.clfs.warehouse.GPR¶

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

Gaussian Process Regression (GPR).

Notes

Available conditional attributes:

calling_time+: Time (in seconds) it took to call the node
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: Computed results before invoking postproc. Stored only if postproc is not None.
trained_dataset: The dataset it has been trained on
trained_nsamples+: Number of samples it has been trained on
trained_targets+: Set of unique targets (or any other space) it has been trained on (if present in the dataset trained on)
training_stats: Confusion matrix of learning performance
training_time+: Time (in seconds) it took to train the learner

(Conditional attributes enabled by default suffixed with +)

Attributes

`auto_train`	Whether the Learner performs automatic trainingwhen called untrained.
`descr`	Description of the object if any
`force_train`	Whether the Learner enforces training upon every call.
`kernel`
`pass_attr`	Which attributes of the dataset or self.ca to pass into result dataset upon call
`postproc`	Node to perform post-processing of results
`space`	Processing space name of this node
`trained`	Either classifier was already trained

Methods

`__call__`(ds)
`clone`()	Create full copy of the classifier.
`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.
`generate`(ds)	Yield processing results.
`get_postproc`()	Returns the post-processing node or None.
`get_sensitivity_analyzer`([flavor])	Returns a sensitivity analyzer for GPR.
`get_space`()	Query the processing space name of this node.
`is_trained`([dataset])	Either classifier was already trained.
`predict`(obj, data, \args, \\*kwargs)
`repredict`(obj, data, \args, \\*kwargs)
`reset`()
`retrain`(dataset, \\kwargs)	Helper to avoid check if data was changed actually changed
`set_hyperparameters`(hyperparameter)	Set hyperparameters’ values.
`set_postproc`(node)	Assigns a post-processing node
`set_space`(name)	Set the processing space name of this node.
`summary`()	Providing summary over the classifier
`train`(ds)	The default implementation calls `_pretrain()`, `_train()`, and finally `_posttrain()`.
`untrain`()	Reverts changes in the state of this node caused by previous training

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

Attributes

`auto_train`	Whether the Learner performs automatic trainingwhen called untrained.
`descr`	Description of the object if any
`force_train`	Whether the Learner enforces training upon every call.
`kernel`
`pass_attr`	Which attributes of the dataset or self.ca to pass into result dataset upon call
`postproc`	Node to perform post-processing of results
`space`	Processing space name of this node
`trained`	Either classifier was already trained

Methods

`__call__`(ds)
`clone`()	Create full copy of the classifier.
`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.
`generate`(ds)	Yield processing results.
`get_postproc`()	Returns the post-processing node or None.
`get_sensitivity_analyzer`([flavor])	Returns a sensitivity analyzer for GPR.
`get_space`()	Query the processing space name of this node.
`is_trained`([dataset])	Either classifier was already trained.
`predict`(obj, data, \args, \\*kwargs)
`repredict`(obj, data, \args, \\*kwargs)
`reset`()
`retrain`(dataset, \\kwargs)	Helper to avoid check if data was changed actually changed
`set_hyperparameters`(hyperparameter)	Set hyperparameters’ values.
`set_postproc`(node)	Assigns a post-processing node
`set_space`(name)	Set the processing space name of this node.
`summary`()	Providing summary over the classifier
`train`(ds)	The default implementation calls `_pretrain()`, `_train()`, and finally `_posttrain()`.
`untrain`()	Reverts changes in the state of this node caused by previous training

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.