Multivariate Pattern Analysis in Python

### Navigation

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

# mvpa2.featsel.base.FeatureSelection

class `mvpa2.featsel.base.``FeatureSelection`(filler=0, **kwargs)

  Mapper to select a subset of features.

  Depending on the actual slicing two FeatureSelections can be merged in a number of ways: incremental selection (+=), union (&=) and intersection (|=). Were the former assumes that two feature selections are applied subsequently, and the latter two assume that both slicings operate on the set of input features.

  Notes

  Available conditional attributes:

  - `calling_time+`: Time (in seconds) it took to call the node
  - `raw_results`: Computed results before invoking postproc. Stored only if postproc is not None.
  - `training_time+`: Time (in seconds) it took to train the learner

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

  Examples

      >>> from mvpa2.datasets import *
      >>> ds = Dataset([[1,2,3,4,5]])
      >>> fs0 = StaticFeatureSelection([0,1,2,3])
      >>> fs0(ds).samples
      array([[1, 2, 3, 4]])

  Merge two incremental selections: the resulting mapper performs a selection that is equivalent to first applying one slicing and subsequently the next slicing. In this scenario the slicing argument of the second mapper is relative to the output feature space of the first mapper.

      >>> fs1 = StaticFeatureSelection([0,2])
      >>> fs0 += fs1
      >>> fs0(ds).samples
      array([[1, 3]])

  Methods

  `forward`(data) | Map data from input to output space.
  `forward1`(data) | Wrapper method to map single samples.
  `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`() |
  `reverse`(data) | Reverse-map data from output back into input space.
  `reverse1`(data) |
  `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:

  filler : optional

  > Value to fill empty entries upon reverse operation

  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

  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

  `forward`(data) | Map data from input to output space.
  `forward1`(data) | Wrapper method to map single samples.
  `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`() |
  `reverse`(data) | Reverse-map data from output back into input space.
  `reverse1`(data) |
  `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

  `reverse1`(data)

### Navigation

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