/usr/share/pyshared/mvpa/measures/noiseperturbation.py is in python-mvpa 0.4.7-2ubuntu1.
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#
# See COPYING file distributed along with the PyMVPA package for the
# copyright and license terms.
#
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"""This is a `FeaturewiseDatasetMeasure` that uses a scalar `DatasetMeasure` and
selective noise perturbation to compute a sensitivity map.
"""
__docformat__ = 'restructuredtext'
if __debug__:
from mvpa.base import debug
from mvpa.support.copy import deepcopy
import numpy as N
from mvpa.measures.base import FeaturewiseDatasetMeasure
class NoisePerturbationSensitivity(FeaturewiseDatasetMeasure):
"""This is a `FeaturewiseDatasetMeasure` that uses a scalar
`DatasetMeasure` and selective noise perturbation to compute a sensitivity
map.
First the scalar `DatasetMeasure` computed using the original dataset. Next
the data measure is computed multiple times each with a single feature in
the dataset perturbed by noise. The resulting difference in the
scalar `DatasetMeasure` is used as the sensitivity for the respective
perturbed feature. Large differences are treated as an indicator of a
feature having great impact on the scalar `DatasetMeasure`.
The computed sensitivity map might have positive and negative values!
"""
def __init__(self, datameasure,
noise=N.random.normal):
"""Cheap initialization.
:Parameters:
datameasure: `Datameasure` that is used to quantify the effect of
noise perturbation.
noise: Functor to generate noise. The noise generator has to return
an 1d array of n values when called the `size=n` keyword
argument. This is the default interface of the random number
generators in NumPy's `random` module.
"""
# init base classes first
FeaturewiseDatasetMeasure.__init__(self)
self.__datameasure = datameasure
self.__noise = noise
def _call(self, dataset):
"""Compute the sensitivity map.
Returns a 1d array of sensitivities for all features in `dataset`.
"""
# first cast to floating point dtype, because noise is most likely
# floating point as well and '+=' on int would not do the right thing
# XXX should we already deepcopy here to keep orig dtype?
if not N.issubdtype(dataset.samples.dtype, N.float):
dataset.setSamplesDType('float32')
if __debug__:
nfeatures = dataset.nfeatures
sens_map = []
# compute the datameasure on the original dataset
# this is used as a baseline
orig_measure = self.__datameasure(dataset)
# do for every _single_ feature in the dataset
for feature in xrange(dataset.nfeatures):
if __debug__:
debug('PSA', "Analyzing %i features: %i [%i%%]" \
% (nfeatures,
feature+1,
float(feature+1)/nfeatures*100,), cr=True)
# make a copy of the dataset to preserve data integrity
wdata = deepcopy(dataset)
# add noise to current feature
wdata.samples[:, feature] += self.__noise(size=wdata.nsamples)
# compute the datameasure on the perturbed dataset
perturbed_measure = self.__datameasure(wdata)
# difference from original datameasure is sensitivity
sens_map.append(perturbed_measure - orig_measure)
if __debug__:
debug('PSA', '')
return N.array(sens_map)
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