mvpa2.base.learner.CombinedLearner

class mvpa2.base.learner.CombinedLearner(learners, combine_axis, a=None, **kwargs)

Notes

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 +)

Methods

train(ds)
untrain()

Parameters:

learners : list of Learner

combine_axis : [‘h’, ‘v’]

a: {‘unique’,’drop_nonunique’,’uniques’,’all’} or True or False or None (default: None) :

Indicates which dataset attributes from datasets are stored in merged_dataset. If an int k, then the dataset attributes from datasets[k] are taken. If ‘unique’ then it is assumed that any attribute common to more than one dataset in datasets is unique; if not an exception is raised. If ‘drop_nonunique’ then as ‘unique’, except that exceptions are not raised. If ‘uniques’ then, for each attribute, any unique value across the datasets is stored in a tuple in merged_datasets. If ‘all’ then each attribute present in any dataset across datasets is stored as a tuple in merged_datasets; missing values are replaced by None. If None (the default) then no attributes are stored in merged_dataset. True is equivalent to ‘drop_nonunique’. False is equivalent to None.

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

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

nodes: list :

Node instances.

mappers : list

Methods

train(ds)
untrain()