mvpa2.measures.base.ProxyClassifierSensitivityAnalyzer¶

class mvpa2.measures.base.ProxyClassifierSensitivityAnalyzer(*args_, **kwargs_)¶

Set sensitivity analyzer output just to pass through

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