Multivariate Pattern Analysis in Python

### Navigation

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

# mvpa2.featsel.base.IterativeFeatureSelection

class `mvpa2.featsel.base.``IterativeFeatureSelection`(fmeasure, pmeasure, splitter, fselector, stopping_criterion=<mvpa2.featsel.helpers.NBackHistoryStopCrit object at 0xb28418c>, bestdetector=<mvpa2.featsel.helpers.BestDetector object at 0xb284fcc>, train_pmeasure=True, **kwargs)

  Notes

  Available conditional attributes:

  - `calling_time+`: Time (in seconds) it took to call the node
  - `errors+`: History of errors
  - `nfeatures+`: History of # of features left
  - `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 `+`)

  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:

  fmeasure : Measure

  > Computed for each candidate feature selection. The measure has to compute a scalar value.

  pmeasure : Measure

  > Compute against a test dataset for each incremental feature set.

  splitter: Splitter :

  > This splitter instance has to generate at least one dataset split when called with the input dataset that is used to compute the per-feature criterion for feature selection.

  bestdetector : Functor

  > Given a list of error values it has to return a boolean that signals whether the latest error value is the total minimum.

  stopping_criterion : Functor

  > Given a list of error values it has to return whether the criterion is fulfilled.

  fselector : Functor

  train_clf : bool

  > Flag whether the classifier in `transfer_error` should be trained before computing the error. In general this is required, but if the `sensitivity_analyzer` and `transfer_error` share and make use of the same classifier it can be switched off to save CPU cycles. Default `None` checks if sensitivity_analyzer is based on a classifier and doesn’t train if so.

  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

  filler : optional

  > Value to fill empty entries upon reverse operation

  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

  `bestdetector`
  `fmeasure`
  `fselector`
  `pmeasure`
  `splitter`
  `stopping_criterion`
  `train_pmeasure`

### Navigation

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