Multivariate Pattern Analysis in Python

# mvpa2.measures.searchlight.Searchlight

class `mvpa2.measures.searchlight.``Searchlight`(datameasure, queryengine, add_center_fa=False, results_postproc_fx=None, results_backend='native', results_fx=None, tmp_prefix='tmpsl', nblocks=None, **kwargs)

  The implementation of a generic searchlight measure.

  The idea for a searchlight algorithm stems from a paper by Kriegeskorte et al. (2006). As a result it produces a map of measures given a `datameasure` instance of interest, which is ran at each spatial location.

  Notes

  Available conditional attributes:

  - `calling_time+`: None
  - `null_prob+`: None
  - `null_t`: None
  - `raw_results`: 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`: None
  - `trained_nsamples+`: None
  - `trained_targets+`: None
  - `training_time+`: None

  (Conditional attributes enabled by default suffixed with `+`)

  Attributes

  `add_center_fa` |
  `auto_train` | Whether the Learner performs automatic trainingwhen called untrained.
  `datameasure` |
  `descr` | Description of the object if any
  `force_train` | Whether the Learner enforces training upon everycalled.
  `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

  Parameters:

  datameasure : callable

  > Any object that takes a `Dataset` and returns some measure when called.

  add_center_fa : bool or str

  > If True or a string, each searchlight ROI dataset will have a boolean vector as a feature attribute that indicates the feature that is the seed (e.g. sphere center) for the respective ROI. If True, the attribute is named ‘roi_seed’, the provided string is used as the name otherwise.

  results_postproc_fx : callable

  > Called with all the results computed in a block for possible post-processing which needs to be done in parallel instead of serial aggregation in results_fx.

  results_backend : (‘native’, ‘hdf5’), optional

  > Specifies the way results are provided back from a processing block in case of nproc > 1. ‘native’ is pickling/unpickling of results by pprocess, while ‘hdf5’ would use h5save/h5load functionality. ‘hdf5’ might be more time and memory efficient in some cases.

  results_fx : callable, optional

  > Function to process/combine results of each searchlight block run. By default it would simply append them all into the list. It receives as keyword arguments sl, dataset, roi_ids, and results (iterable of lists). It is the one to take care of assigning roi_* ca’s

  tmp_prefix : str, optional

  > If specified – serves as a prefix for temporary files storage if results_backend == ‘hdf5’. Thus can specify the directory to use (trailing file path separator is not added automagically).

  nblocks : None or int

  > Into how many blocks to split the computation (could be larger than nproc). If None – nproc is 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

  queryengine : QueryEngine

  > Engine to use to discover the “neighborhood” of each feature. See `QueryEngine`.

  roi_ids : None or list(int) or str

  > List of feature ids (not coordinates) the 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. By default all features 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.

  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

  `add_center_fa` |
  `auto_train` | Whether the Learner performs automatic trainingwhen called untrained.
  `datameasure` |
  `descr` | Description of the object if any
  `force_train` | Whether the Learner enforces training upon everycalled.
  `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

  `add_center_fa`
  `datameasure`