mvpa2.clfs.meta.RegressionAsClassifierSensitivityAnalyzer

Inheritance diagram of RegressionAsClassifierSensitivityAnalyzer
class mvpa2.clfs.meta.RegressionAsClassifierSensitivityAnalyzer(*args_, **kwargs_)

Set sensitivity analyzer output to have proper labels

Notes

Available conditional attributes:

  • calling_time+: Time (in seconds) it took to call the node
  • clf_sensitivities: Stores sensitivities of the proxied classifier
  • null_prob+: None
  • null_t: None
  • 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 (or any other space) it has been trained on (if present in the dataset trained on)
  • training_time+: Time (in seconds) it took to train the learner

(Conditional attributes enabled by default suffixed with +)

Attributes

analyzer
auto_train Whether the Learner performs automatic trainingwhen called untrained.
clf
descr Description of the object if any
feature_ids Return feature_ids used by the underlying classifier
force_train Whether the Learner enforces training upon every call.
is_trained
null_dist Return Null Distribution estimator
pass_attr Which attributes of the dataset or self.ca to pass into result dataset upon call
postproc Node to perform post-processing of results
space Processing space name of this node

Methods

__call__(ds)
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()
set_postproc(node) Assigns a post-processing node
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

Initialize instance of ProxyClassifierSensitivityAnalyzer

Parameters:

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

Attributes

analyzer
auto_train Whether the Learner performs automatic trainingwhen called untrained.
clf
descr Description of the object if any
feature_ids Return feature_ids used by the underlying classifier
force_train Whether the Learner enforces training upon every call.
is_trained
null_dist Return Null Distribution estimator
pass_attr Which attributes of the dataset or self.ca to pass into result dataset upon call
postproc Node to perform post-processing of results
space Processing space name of this node

Methods

__call__(ds)
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()
set_postproc(node) Assigns a post-processing node
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