mvpa2.clfs.meta.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