mvpa2.measures.searchlight.Measure¶

class mvpa2.measures.searchlight.Measure(null_dist=None, **kwargs)¶

A measure computed from a Dataset

All dataset measures support arbitrary transformation of the measure after it has been computed. Transformation are done by processing the measure with a functor that is specified via the transformer keyword argument of the constructor. Upon request, the raw measure (before transformations are applied) is stored in the raw_results conditional attribute.

Additionally all dataset measures support the estimation of the probabilit(y,ies) of a measure under some distribution. Typically this will be the NULL distribution (no signal), that can be estimated with permutation tests. If a distribution estimator instance is passed to the null_dist keyword argument of the constructor the respective probabilities are automatically computed and stored in the null_prob conditional attribute.

Notes

For developers: All subclasses shall get all necessary parameters via their constructor, so it is possible to get the same type of measure for multiple datasets by passing them to the __call__() method successively.

Available conditional attributes:

calling_time+: Time (in seconds) it took to call the node
null_prob+: None
null_t: None
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_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.
`is_trained`	Whether the Learner is currently trained.
`null_dist`	Return Null Distribution estimator
`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

Methods

`__call__`(ds)
`generate`(ds)	Yield processing results.
`get_postproc`()	Returns the post-processing node or None.
`get_space`()	Query the processing space name of this node.
`reset`()
`set_postproc`(node)	Assigns a post-processing node
`set_space`(name)	Set the processing space name of this node.
`train`(ds)	The default implementation calls `_pretrain()`, `_train()`, and finally `_posttrain()`.
`untrain`()	Reverts changes in the state of this node caused by previous training

Parameters:

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.

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

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.
`is_trained`	Whether the Learner is currently trained.
`null_dist`	Return Null Distribution estimator
`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

Methods

`__call__`(ds)
`generate`(ds)	Yield processing results.
`get_postproc`()	Returns the post-processing node or None.
`get_space`()	Query the processing space name of this node.
`reset`()
`set_postproc`(node)	Assigns a post-processing node
`set_space`(name)	Set the processing space name of this node.
`train`(ds)	The default implementation calls `_pretrain()`, `_train()`, and finally `_posttrain()`.
`untrain`()	Reverts changes in the state of this node caused by previous training

null_dist¶: Return Null Distribution estimator

null_prob = None¶: Stores the probability of a measure under the NULL hypothesis

null_t = None¶: Stores the t-score corresponding to null_prob under assumption of Normal distribution