mvpa2.clfs.warehouse.SplitClassifier

class mvpa2.clfs.warehouse.SplitClassifier(clf, partitioner=NFoldPartitioner(), splitter=Splitter(space='partitions'), **kwargs)

BoostedClassifier to work on splits of the data

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_estimates: Estimates obtained from each classifier
• raw_predictions: Predictions obtained from each classifier
• raw_results: None
• splits: Store the actual splits of the data. Can be memory expensive
• stats: Resultant confusion whenever classifier trained on 1 part and tested on 2nd part of each split
• 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 the instance

Parameters:

clf : Classifier

classifier based on which multiple classifiers are created for multiclass

splitter : Splitter

Splitter to use to split the dataset prior training

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

clfs : list of Classifier

list of classifier instances to use

combiner : PredictionsCombiner, optional

callable which takes care about combining multiple results into a single one. If default (‘auto’) chooses MaximalVote for classification and MeanPrediction for regression. If None is provided – no combination is done

propagate_ca : bool

either to propagate enabled ca into slave classifiers. It is in effect only when slaves get assigned - so if state is enabled not during construction, it would not necessarily propagate into slaves

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

get_sensitivity_analyzer(*args_, **kwargs_)

partitioner

Partitioner used by SplitClassifier

splitter

Splitter used by SplitClassifier