The class is useful to combine several Datasets with different origin and
type and bind them together. Such a combined dataset can then by used to
e.g. pass it to a classifier.
MetaDataset does not permanently duplicate data stored in the dataset it
contains. The combined samples matrix is build on demand and samples
attribute access is redirected to the first dataset in the container.
Currently operations other than samples or feature selection are not fully
supported, e.g. passing a MetaDataset to detrend() will initially result in
a detrended MetaDataset, but the combined and detrended samples matrix will
be lost after the next call to selectSamples() or selectFeatures(), which
freshly pulls samples from all datasets in the container.
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rebuildSamples(self)
Update the combined samples matrix from all underlying datasets. |
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__getattr__(self,
name)
Implemented to redirect access to underlying datasets. |
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selectFeatures(self,
ids,
sort=True)
Do feature selection on all underlying datasets at once. |
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applyMapper(self,
*args,
**kwargs)
Apply a mapper on all underlying datasets. |
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selectSamples(self,
*args,
**kwargs)
Select samples from all underlying datasets at once. |
source code
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permuteLabels(self,
*args,
**kwargs)
Toggle label permutation. |
source code
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getRandomSamples(self,
nperlabel)
Return a MetaDataset with a random subset of samples. |
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getNSamples(self)
Currently available number of samples. |
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getNFeatures(self)
Number of features per sample. |
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setSamplesDType(self,
dtype)
Set the data type of the samples array. |
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Inherited from object:
__delattr__,
__format__,
__getattribute__,
__hash__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__,
__subclasshook__
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