mvpa2.measures.noiseperturbation.NoisePerturbationSensitivity¶
-
class
mvpa2.measures.noiseperturbation.
NoisePerturbationSensitivity
(datameasure, noise=<built-in method normal of mtrand.RandomState object>)¶ Sensitivity based on the effect of noise perturbation on a measure.
This is a
FeaturewiseMeasure
that uses a scalarMeasure
and selective noise perturbation to compute a sensitivity map.First the scalar
Measure
computed using the original dataset. Next the data measure is computed multiple times each with a single feature in the dataset perturbed by noise. The resulting difference in the scalarMeasure
is used as the sensitivity for the respective perturbed feature. Large differences are treated as an indicator of a feature having great impact on the scalarMeasure
.Notes
The computed sensitivity map might have positive and negative values!
Available conditional attributes:
calling_time+
: Nonenull_prob+
: Nonenull_t
: Noneraw_results
: Nonetrained_dataset
: Nonetrained_nsamples+
: Nonetrained_targets+
: Nonetraining_time+
: None
(Conditional attributes enabled by default suffixed with
+
)Attributes
auto_train
Whether the Learner performs automatic trainingwhen called untrained. descr
Description of the object if any force_train
Whether the Learner enforces training upon everycalled. 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 Parameters: datameasure :
Measure
Used to quantify the effect of noise perturbation.
noise: Callable
Used to generate noise. The noise generator has to return an 1d array of n values when called the
size=n
keyword argument. This is the default interface of the random number generators in NumPy’srandom
module.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
auto_train
Whether the Learner performs automatic trainingwhen called untrained. descr
Description of the object if any force_train
Whether the Learner enforces training upon everycalled. 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 -
is_trained
= True¶ Indicate that this measure is always trained.