mvpa2.clfs.lars.Classifier

class mvpa2.clfs.lars.Classifier(space=None, **kwargs)

Abstract classifier class to be inherited by all classifiers

Notes

Available conditional attributes:

• calling_time+: None
• estimates+: Internal classifier estimates the most recent predictions are based on
• 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

Initialize instance of Classifier

Parameters:

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

clone()

Create full copy of the classifier.

It might require classifier to be untrained first due to present SWIG bindings.

TODO: think about proper re-implementation, without enrollment of deepcopy

get_sensitivity_analyzer(**kwargs)

Factory method to return an appropriate sensitivity analyzer for the respective classifier.

is_trained(dataset=None)

Either classifier was already trained.

MUST BE USED WITH CARE IF EVER

predict(obj, data, *args, **kwargs)

repredict(obj, data, *args, **kwargs)

retrain(dataset, **kwargs)

Helper to avoid check if data was changed actually changed

Useful if just some aspects of classifier were changed since its previous training. For instance if dataset wasn’t changed but only classifier parameters, then kernel matrix does not have to be computed.

Words of caution: classifier must be previously trained, results always should first be compared to the results on not ‘retrainable’ classifier (without calling retrain). Some additional checks are enabled if debug id ‘CHECK_RETRAIN’ is enabled, to guard against obvious mistakes.

Parameters:

kwargs :

that is what _changedData gets updated with. So, smth like (params=['C'], targets=True) if parameter C and targets got changed

summary()

Providing summary over the classifier

trained

Either classifier was already trained