Multivariate Pattern Analysis in Python

### Navigation

- index
- modules |
- modules |
- PyMVPA Home |
- Sitemap »

# mvpa2.measures.gnbsearchlight.SimpleStatBaseSearchlight

class `mvpa2.measures.gnbsearchlight.``SimpleStatBaseSearchlight`(generator, qe, errorfx=<function mean_mismatch_error at 0xce999cc>, indexsum=None, reuse_neighbors=False, **kwargs)

  Base class for clf searchlights based on basic univar. statistics

  Used for GNB and M1NN Searchlights

  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

  Initialize the base class for “naive” searchlight classifiers

  Parameters:

  generator : `Generator`

  > Some `Generator` to prepare partitions for cross-validation.

  qe : `QueryEngine`

  > Query engine which would provide neighborhood information

  errorfx : func, optional

  > Functor that computes a scalar error value from the vectors of desired and predicted values (e.g. subclass of `ErrorFunction`).

  indexsum : (‘sparse’, ‘fancy’), optional

  > What use to compute sums over arbitrary columns. ‘fancy’ corresponds to regular fancy indexing over columns, whenever in ‘sparse’, product of sparse matrices is used (usually faster, so is default if `scipy` is available).

  reuse_neighbors : bool, optional

  > Compute neighbors information only once, thus allowing for efficient reuse on subsequent calls where dataset’s feature attributes remain the same (e.g. during permutation testing)

  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.

  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

  `errorfx`
  `generator`
  `indexsum`
  `reuse_neighbors`

### Navigation

- index
- modules |
- modules |
- PyMVPA Home |
- Sitemap »