/usr/share/pyshared/mvpa/datasets/mapped.py is in python-mvpa 0.4.8-3.
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# See COPYING file distributed along with the PyMVPA package for the
# copyright and license terms.
#
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"""Mapped dataset"""
__docformat__ = 'restructuredtext'
import mvpa.support.copy as copy
from mvpa.datasets import Dataset
from mvpa.base.dochelpers import enhancedDocString
from mvpa.misc.exceptions import DatasetError
class MappedDataset(Dataset):
"""A `Dataset` which is created by applying a `Mapper` to the data.
Upon contruction `MappedDataset` uses a `Mapper` to transform the
samples from their original into the two-dimensional matrix
representation that is required by the `Dataset` class.
This class enhanced the `Dataset` interface with two additional
methods: `mapForward()` and `mapReverse()`. Both take arbitrary data
arrays (with matching shape) and transform them using the embedded
mapper from the original dataspace into a one- or two-dimensional
representation (for arrays corresponding to the shape of a single or
multiple samples respectively) or vice versa.
Most likely, this class will not be used directly, but rather
indirectly through one of its subclasses (e.g. `MaskedDataset`).
"""
def __init__(self, samples=None, mapper=None, dsattr=None, **kwargs):
"""
If `samples` and `mapper` arguments are not `None` the mapper is
used to forward-map the samples array and the result is passed
to the `Dataset` constructor.
:Parameters:
mapper: Instance of `Mapper`
This mapper will be embedded in the dataset and is used and
updated, by all subsequent mapping or feature selection
procedures.
**kwargs:
All other arguments are simply passed to and handled by
the constructor of `Dataset`.
"""
# there are basically two mode for the constructor:
# 1. internal mode - only data and dsattr dict
# 2. user mode - samples != None # and mapper != None
# see if dsattr is none, if so, set to empty dict
if dsattr is None:
dsattr = {}
# if a mapper was passed, store it in dsattr dict that gets passed
# to base Dataset
if not mapper is None:
# TODO: check mapper for compliance with dimensionality within _data
# may be only within __debug__
dsattr['mapper'] = mapper
# if the samples are passed to the special arg, use the mapper to
# transform them.
if not samples is None:
if not dsattr.has_key('mapper') or dsattr['mapper'] is None:
raise DatasetError, \
"Constructor of MappedDataset requires a mapper " \
"if unmapped samples are provided."
Dataset.__init__(self,
samples=mapper.forward(samples),
dsattr=dsattr,
**(kwargs))
else:
Dataset._checkCopyConstructorArgs(samples=samples,
dsattr=dsattr,
**kwargs)
Dataset.__init__(self, dsattr=dsattr, **(kwargs))
__doc__ = enhancedDocString('MappedDataset', locals(), Dataset)
def mapForward(self, data):
"""Map data from the original dataspace into featurespace.
"""
return self.mapper.forward(data)
def mapReverse(self, data):
"""Reverse map data from featurespace into the original dataspace.
"""
return self.mapper.reverse(data)
def mapSelfReverse(self):
"""Reverse samples from featurespace into the original dataspace.
"""
return self.mapper.reverse(self.samples)
def selectFeatures(self, ids, plain=False, sort=False):
"""Select features given their ids.
The methods behaves similar to Dataset.selectFeatures(), but
additionally takes care of adjusting the embedded mapper
appropriately.
:Parameters:
ids: sequence
Iterable container to select ids
plain: boolean
Flag whether to return MappedDataset (or just Dataset)
sort: boolean
Flag whether to sort Ids. Order matters and selectFeatures
assumes incremental order. If not such, in non-optimized
code selectFeatures would verify the order and sort
"""
# call base method to get selected feature subset
if plain:
sdata = Dataset(self._data, self._dsattr, check_data=False,
copy_samples=False, copy_data=False,
copy_dsattr=False)
return sdata.selectFeatures(ids=ids, sort=sort)
else:
sdata = Dataset.selectFeatures(self, ids=ids, sort=sort)
# since we have new DataSet we better have a new mapper
sdata._dsattr['mapper'] = copy.deepcopy(sdata._dsattr['mapper'])
if sort:
sdata._dsattr['mapper'].selectOut(sorted(ids))
else:
sdata._dsattr['mapper'].selectOut(ids)
return sdata
# read-only class properties
mapper = property(fget=lambda self: self._dsattr['mapper'])
samples_original = property(fget=mapSelfReverse,
doc="Return samples in the original shape")
# syntactic sugarings
O = property(fget=mapSelfReverse,
doc="Return samples in the original shape")
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