Multivariate Pattern Analysis in Python

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# mvpa2.measures.searchlight.Searchlight

class `mvpa2.measures.searchlight.``Searchlight`(datameasure, queryengine, add_center_fa=False, 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+`: 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.
  - `roi_feature_ids`: Feature IDs for all generated ROIs.
  - `roi_sizes`: Number of features in each ROI.
  - `training_time+`: Time (in seconds) it took to train the learner

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

  Methods

  `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 to `None` to disable postprocessing.
  `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:

  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_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.

  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

  `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 to `None` to disable postprocessing.
  `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

  `add_center_fa`
  `datameasure`

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