Multivariate Pattern Analysis in Python

# mvpa2.measures.rsa.Measure

class `mvpa2.measures.rsa.``Measure`(null_dist=None, **kwargs)

  A measure computed from a `Dataset`

  All dataset measures support arbitrary transformation of the measure after it has been computed. Transformation are done by processing the measure with a functor that is specified via the `transformer` keyword argument of the constructor. Upon request, the raw measure (before transformations are applied) is stored in the `raw_results` conditional attribute.

  Additionally all dataset measures support the estimation of the probabilit(y,ies) of a measure under some distribution. Typically this will be the NULL distribution (no signal), that can be estimated with permutation tests. If a distribution estimator instance is passed to the `null_dist` keyword argument of the constructor the respective probabilities are automatically computed and stored in the `null_prob` conditional attribute.

  Notes

  For developers: All subclasses shall get all necessary parameters via their constructor, so it is possible to get the same type of measure for multiple datasets by passing them to the __call__() method successively.

  Available conditional attributes:

  - `calling_time+`: None
  - `null_prob+`: None
  - `null_t`: None
  - `raw_results`: None
  - `trained_dataset`: None
  - `trained_nsamples+`: None
  - `trained_targets+`: None
  - `training_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.
  `is_trained` | Whether the Learner is currently 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

  Parameters:

  null_dist : instance of distribution estimator

  > The estimated distribution is used to assign a probability for a certain value of the computed measure.

  auto_train : bool

  > Flag whether the learner will automatically train itself on the input dataset when called untrained.

  force_train : bool

  > Flag whether the learner will enforce training on the input dataset upon every call.

  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

  space : str, optional

  > Name of the ‘processing space’. The actual meaning of this argument heavily depends on the sub-class implementation. In general, this is a trigger that tells the node to compute and store information about the input data that is “interesting” in the context of the corresponding processing in the output dataset.

  pass_attr : str, list of str|tuple, optional

  > Additional attributes to pass on to an output dataset. Attributes can be taken from all three attribute collections of an input dataset (sa, fa, a – see `Dataset.get_attr()`), or from the collection of conditional attributes (ca) of a node instance. Corresponding collection name prefixes should be used to identify attributes, e.g. ‘ca.null_prob’ for the conditional attribute ‘null_prob’, or ‘fa.stats’ for the feature attribute stats. In addition to a plain attribute identifier it is possible to use a tuple to trigger more complex operations. The first tuple element is the attribute identifier, as described before. The second element is the name of the target attribute collection (sa, fa, or a). The third element is the axis number of a multidimensional array that shall be swapped with the current first axis. The fourth element is a new name that shall be used for an attribute in the output dataset. Example: (‘ca.null_prob’, ‘fa’, 1, ‘pvalues’) will take the conditional attribute ‘null_prob’ and store it as a feature attribute ‘pvalues’, while swapping the first and second axes. Simplified instructions can be given by leaving out consecutive tuple elements starting from the end.

  postproc : Node instance, optional

  > Node to perform post-processing of results. This node is applied in `__call__()` to perform a final processing step on the to be result dataset. If None, nothing is done.

  descr : str

  > Description of the instance

  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.
  `is_trained` | Whether the Learner is currently 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

  `null_dist`

    Return Null Distribution estimator

  `null_prob` = None

    Stores the probability of a measure under the NULL hypothesis

  `null_t` = None

    Stores the t-score corresponding to null_prob under assumption of Normal distribution