mvpa2.measures.searchlight.BaseSearchlight¶
-
class
mvpa2.measures.searchlight.
BaseSearchlight
(queryengine, roi_ids=None, nproc=None, **kwargs)¶ Base class for searchlights.
The idea for a searchlight algorithm stems from a paper by Kriegeskorte et al. (2006).
Notes
Available conditional attributes:
calling_time+
: Time (in seconds) it took to call the nodenull_prob+
: Nonenull_t
: Noneraw_results
: Computed results before invoking postproc. Stored only if postproc is not None.roi_center_ids+
: Center ID for all generated ROIs.roi_feature_ids
: Feature IDs for all generated ROIs.roi_sizes
: Number of features in each ROI.trained_dataset
: The dataset it has been trained ontrained_nsamples+
: Number of samples it has been trained ontrained_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. 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 queryengine
roi_ids
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: queryengine : QueryEngine
Engine to use to discover the “neighborhood” of each feature. See
QueryEngine
.roi_ids : None or list(int) or str
List of query engine ids (e.g., feature ids, not coordinates, in case of
IndexQueryEngine
; andnode_indices
in case ofSurfaceQueryEngine
) that shall serve as ROI seeds (e.g., sphere centers). Alternatively, this can be the name of a feature attribute of the input dataset, whose non-zero values determine the feature ids (be careful to use it only withIndexQueryEngine
). By default all query engine ids will be used.nproc : None or int
How many processes to use for computation. Requires
pprocess
external module. If None – all available cores will be used.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
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. 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 queryengine
roi_ids
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 -
is_trained
= True¶ Indicate that this measure is always trained.
-
queryengine
¶
-
roi_ids
¶