mvpa2.clfs.meta.MappedClassifier

Inheritance diagram of MappedClassifier

class mvpa2.clfs.meta.MappedClassifier(clf, mapper, **kwargs)

ProxyClassifier which uses some mapper prior training/testing.

MaskMapper can be used just a subset of features to train/classify. Having such classifier we can easily create a set of classifiers for BoostedClassifier, where each classifier operates on some set of features, e.g. set of best spheres from SearchLight, set of ROIs selected elsewhere. It would be different from simply applying whole mask over the dataset, since here initial decision is made by each classifier and then later on they vote for the final decision across the set of classifiers.

Notes

Available conditional attributes:

  • calling_time+: Time (in seconds) it took to call the node
  • estimates+: Internal classifier estimates the most recent predictions are based on
  • predicting_time+: Time (in seconds) which took classifier to predict
  • predictions+: Most recent set of predictions
  • raw_results: Computed results before invoking postproc. Stored only if postproc is not None.
  • trained_dataset: The dataset it has been trained on
  • trained_nsamples+: Number of samples it has been trained on
  • trained_targets+: Set of unique targets it has been trained on
  • training_stats: Confusion matrix of learning performance
  • training_time+: Time (in seconds) it took to train the learner

(Conditional attributes enabled by default suffixed with +)

Methods

clone() Create full copy of the classifier.
generate(ds) Yield processing results.
get_postproc() Returns the post-processing node or None.
get_sensitivity_analyzer(*args_, **kwargs_)
get_space() Query the processing space name of this node.
is_trained([dataset]) Either classifier was already trained.
predict(obj, data, *args, **kwargs)
repredict(obj, data, *args, **kwargs)
reset()
retrain(dataset, **kwargs) Helper to avoid check if data was changed actually changed Useful if just some aspects of classifier were changed since its previous training.
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.
summary()
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 instance

Parameters:

clf : Classifier

classifier based on which mask classifiers is created

mapper :

whatever Mapper comes handy

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

clone() Create full copy of the classifier.
generate(ds) Yield processing results.
get_postproc() Returns the post-processing node or None.
get_sensitivity_analyzer(*args_, **kwargs_)
get_space() Query the processing space name of this node.
is_trained([dataset]) Either classifier was already trained.
predict(obj, data, *args, **kwargs)
repredict(obj, data, *args, **kwargs)
reset()
retrain(dataset, **kwargs) Helper to avoid check if data was changed actually changed Useful if just some aspects of classifier were changed since its previous training.
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.
summary()
train(ds) The default implementation calls _pretrain(), _train(), and finally _posttrain().
untrain() Reverts changes in the state of this node caused by previous training
mapper

Used mapper

NeuroDebian

NITRC-listed