/usr/share/pyshared/mvpa2/measures/noiseperturbation.py is in python-mvpa2 2.1.0-1.
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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 `FeaturewiseMeasure` that uses a scalar `Measure` and
selective noise perturbation to compute a sensitivity map.
"""
__docformat__ = 'restructuredtext'
if __debug__:
from mvpa2.base import debug
from mvpa2.support.copy import deepcopy
import numpy as np
from mvpa2.measures.base import FeaturewiseMeasure
from mvpa2.datasets.base import Dataset
class NoisePerturbationSensitivity(FeaturewiseMeasure):
"""Sensitivity based on the effect of noise perturbation on a measure.
This is a `FeaturewiseMeasure` that uses a scalar `Measure`
and selective noise perturbation to compute a sensitivity map.
First the scalar `Measure` 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 `Measure` 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 `Measure`.
Notes
-----
The computed sensitivity map might have positive and negative values!
"""
is_trained = True
"""Indicate that this measure is always trained."""
def __init__(self, datameasure,
noise=np.random.normal):
"""
Parameters
----------
datameasure : `Measure`
Used to quantify the effect of noise perturbation.
noise: Callable
Used 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
FeaturewiseMeasure.__init__(self)
self.__datameasure = datameasure
self.__noise = noise
def _call(self, 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
if not np.issubdtype(dataset.samples.dtype, np.float):
ds = dataset.copy(deep=False)
ds.samples = dataset.samples.astype('float32')
dataset = ds
if __debug__:
nfeatures = dataset.nfeatures
# using a list here, to be able to handle output of unknown
# dimensionality
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)
# store current feature to restore it later on
current_feature = dataset.samples[:, feature].copy()
# add noise to current feature
dataset.samples[:, feature] += self.__noise(size=len(dataset))
# compute the datameasure on the perturbed dataset
perturbed_measure = self.__datameasure(dataset)
# restore the current feature
dataset.samples[:, feature] = current_feature
# difference from original datameasure is sensitivity
sens_map.append(perturbed_measure.samples - orig_measure.samples)
if __debug__:
debug('PSA', '')
# turn into an array and get rid of unnecessary axes -- ideally yielding
# 2D array
sens_map = np.array(sens_map).squeeze()
# swap first to axis: we have nfeatures on first but want it as second
# in a dataset
sens_map = np.swapaxes(sens_map, 0, 1)
return Dataset(sens_map)
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