Multivariate Pattern Analysis in Python

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# mvpa2.measures.noiseperturbation.NoisePerturbationSensitivity

class `mvpa2.measures.noiseperturbation.``NoisePerturbationSensitivity`(datameasure, noise=<built-in method normal of mtrand.RandomState object at 0x557f1d40>)

  Sensitivity based on the effect of noise perturbation on a measure.

  This is a `FeaturewiseMeasure` that uses a scalar `Measure` 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 scalar `Measure` 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 scalar `Measure`.

  Notes

  The computed sensitivity map might have positive and negative values!

  Available conditional attributes:

  - `calling_time+`: Time (in seconds) it took to call the node
  - `null_prob+`: None
  - `null_t`: None
  - `raw_results`: Computed results before invoking postproc. Stored only if postproc is not None.
  - `training_time+`: Time (in seconds) it took to train the learner

  (Conditional attributes enabled by default suffixed with `+`)

  Methods

  `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 to `None` to disable postprocessing.
  `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’s `random` 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

  Methods

  `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 to `None` to disable postprocessing.
  `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.

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