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mvpa2.datasets.formats.to_lightsvm_format

mvpa2.datasets.formats.to_lightsvm_format(dataset, out, targets_attr='targets', domain=None, am=None)

Export dataset into LightSVM format

Parameters :

dataset : Dataset

out :

Anything understanding .write(string), such as File

targets_attr : string, optional

Name of the samples attribute to be output

domain : {None, ‘regression’, ‘binary’, ‘multiclass’}, optional

What domain dataset belongs to. If None, it would be deduced depending on the datatype (‘regression’ if float, classification in case of int or string, with ‘binary’/’multiclass’ depending on the number of unique targets)

am : AttributeMap or None, optional

Which mapping to use for storing the non-conformant targets. If None was provided, new one would be automagically generated depending on the given/deduced domain.

Returns :

am :

LightSVM format is an ASCII representation with a single sample per :

each line:: :

output featureIndex:featureValue ... featureIndex:featureValue

where ``output`` is specific for a given domain: :

regression :

float number

binary :

integer labels from {-1, 1}

multiclass :

integer labels from {1..ds.targets_attr.nunique}