mvpa2.measures.base.TransferMeasure

class mvpa2.measures.base.TransferMeasure(measure, splitter, **kwargs)

Train and run a measure on two different parts of a dataset.

Upon calling a TransferMeasure instance with a dataset the input dataset is passed to a Splitter to will generate dataset subsets. The first generated dataset is used to train an arbitray embedded `Measure. Once trained, the measure is then called with the second generated dataset and the result is returned.

Notes

Available conditional attributes:

• calling_time+: None
• null_prob+: None
• null_t: None
• raw_results: None
• stats: Optional summary statistics about the transfer performance
• trained_dataset: None
• trained_nsamples+: None
• trained_targets+: None
• training_stats: Summary statistics about the training status of the learner
• training_time+: None

(Conditional attributes enabled by default suffixed with +)

Methods

Parameters:

measure: Measure :

This measure instance is trained on the first dataset and called with the second.

splitter: Splitter :

This splitter instance has to generate at least two dataset splits when called with the input dataset. The first split is used to train the measure, the second split is used to run the trained measure.

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

null_dist : instance of distribution estimator

The estimated distribution is used to assign a probability for a certain value of the computed measure.

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

is_trained = True

Indicate that this measure is always trained.

measure

splitter