Inheritance diagram of ProductFlattenMapper

class mvpa2.mappers.flatten.ProductFlattenMapper(factor_names, factor_values=None, **kwargs)

Reshaping mapper that flattens multidimensional arrays and preserves information for each dimension in feature attributes


This class’ name contains ‘product’ because it maps feature attributes in a cartesian-product way.

Available conditional attributes:

  • calling_time+: None
  • raw_results: None
  • trained_dataset: None
  • trained_nsamples+: None
  • trained_targets+: None
  • training_time+: None

(Conditional attributes enabled by default suffixed with +)



factor_names: iterable :

The names for each dimension. If the dataset to be flattened is shaped ns X nf1 x nf2 x ... x nfN, then factor_names should have a length of N. Furthermore when applied to a dataset ds, it should have each of the factor names factor_names[K] as an attribute and the value of this attribute should have nfK values. Applying this mapper to such a dataset yields a new dataset with size ns X (nf1 * nf2 * ... * nfN) with feature attributes nameK and nameKindices for each nameK in the factor names.

factor_values: iterable or None :

Optionally the factor values for each dimension. If not provided or set to None, then it will be inferred upon training on a dataset. Setting this parameter explicitly means this instance does not have to be trained.

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

shape : tuple

The shape of a single sample. If this argument is given the mapper is going to be fully configured and no training is necessary anymore.

maxdims : int or None

The maximum number of dimensions to flatten (starting with the first). If None, all axes will be flattened.

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.

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