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 it has been 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 +)
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 :
the standard deviation of the gaussian noise. (Default: 0.001)
lm :
The regularization term lambda. Increase this when the kernel matrix is not positive definite. If None, some regularization will be provided upon necessity. (Default: None)
retrainable :
Either to enable retraining for ‘retrainable’ classifier. (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.
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
- 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.
