Multivariate Pattern Analysis in Python

# mvpa2.clfs.warehouse.SplitRFE

class `mvpa2.clfs.warehouse.``SplitRFE`(lrn, partitioner, fselector, errorfx=<function mean_mismatch_error>, analyzer_postproc=FxMapper(axis='samples', fx=<function max_of_abs at 0x7f6099a91de8>, attrfx=<function _uniquemerge2literal at 0x7f6099a20de8>), **kwargs)

  RFE with the nested cross-validation to estimate optimal number of features.

  Given a learner (classifier) with a sensitivity analyzer and a partitioner, during training SplitRFE first performs a cross-validation with RFE to later estimate optimal number of features which should survive in RFE. Optimal number is chosen as the mid-point among all minimums of the average errors across splits. After deducing optimal number of features, SplitRFE applies regular RFE again on the full training dataset stopping at the estimated optimal number of features.

  Notes

  Available conditional attributes:

  - `calling_time+`: None
  - `errors+`: History of errors
  - `history+`: Last step # when each feature was still present
  - `nfeatures+`: History of # of features left
  - `raw_results`: None
  - `sensitivities`: History of sensitivities (might consume too much memory
  - `trained_dataset`: None
  - `trained_nsamples+`: None
  - `trained_targets+`: None
  - `training_time+`: None

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

  Attributes

  `auto_train` | Whether the Learner performs automatic trainingwhen called untrained.
  `bestdetector` |
  `descr` | Description of the object if any
  `fmeasure` |
  `force_train` | Whether the Learner enforces training upon everycalled.
  `fselector` |
  `is_trained` | Whether the Learner is currently trained.
  `lrn` |
  `nfeatures_min` |
  `pass_attr` | Which attributes of the dataset or self.ca to pass into result dataset upon call
  `pmeasure` |
  `postproc` | Node to perform post-processing of results
  `slicearg` |
  `space` | Processing space name of this node
  `splitter` |
  `stopping_criterion` |
  `train_pmeasure` |
  `update_sensitivity` |

  Methods

  Parameters:

  lrn : Learner

  > Learner with a sensitivity analyzer which will be used both for the sensitivity analysis and transfer error estimation

  partitioner : Partitioner

  > Used to generate cross-validation partitions for cross-validation to deduce optimal number of features to maintain

  fselector : Functor

  > Given a sensitivity map it has to return the ids of those features that should be kept.

  errorfx : func, optional

  > Functor to use for estimation of cross-validation error

  analyzer_postproc : func, optional

  > Function to provide to the sensitivity analyzer as postproc

  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

  update_sensitivity : bool

  > If False the sensitivity map is only computed once and reused for each iteration. Otherwise the senstitivities are recomputed at each selection step.

  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.

  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

  `auto_train` | Whether the Learner performs automatic trainingwhen called untrained.
  `bestdetector` |
  `descr` | Description of the object if any
  `fmeasure` |
  `force_train` | Whether the Learner enforces training upon everycalled.
  `fselector` |
  `is_trained` | Whether the Learner is currently trained.
  `lrn` |
  `nfeatures_min` |
  `pass_attr` | Which attributes of the dataset or self.ca to pass into result dataset upon call
  `pmeasure` |
  `postproc` | Node to perform post-processing of results
  `slicearg` |
  `space` | Processing space name of this node
  `splitter` |
  `stopping_criterion` |
  `train_pmeasure` |
  `update_sensitivity` |

  Methods

  `lrn`