mvpa2.clfs.meta.CombinedClassifier

Inheritance diagram of CombinedClassifier

class mvpa2.clfs.meta.CombinedClassifier(clfs=None, combiner=None, **kwargs)

BoostedClassifier which combines predictions using some PredictionsCombiner functor.

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_estimates: Estimates obtained from each classifier
  • raw_predictions: Predictions obtained from each classifier
  • 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(**kwargs) Return an appropriate SensitivityAnalyzer
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
set_postproc(node) Assigns a post-processing node
set_space(name) Set the processing space name of this node.
summary() Provide summary for the CombinedClassifier.
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 :

clfs : list of Classifier

list of classifier instances to use

combiner : PredictionsCombiner

callable which takes care about combining multiple results into a single one (e.g. maximal vote for classification, MeanPrediction for regression))

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

propagate_ca : bool

either to propagate enabled ca into slave classifiers. It is in effect only when slaves get assigned - so if state is enabled not during construction, it would not necessarily propagate into slaves

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

NB: `combiner` might need to operate not on ‘predictions’ descrete :

labels but rather on raw ‘class’ estimates classifiers estimate (which is pretty much what is stored under estimates

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(**kwargs) Return an appropriate SensitivityAnalyzer
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
set_postproc(node) Assigns a post-processing node
set_space(name) Set the processing space name of this node.
summary() Provide summary for the CombinedClassifier.
train(ds) The default implementation calls _pretrain(), _train(), and finally _posttrain().
untrain() Reverts changes in the state of this node caused by previous training
combiner
summary()

Provide summary for the CombinedClassifier.

NeuroDebian

NITRC-listed