def __init__(self, num_permutations=200, num_bootstraps=100, **kwargs):
# init base classes first
FeaturewiseDatasetMeasure.__init__(self, **kwargs)
# save the args for the analysis
self.num_permutations = num_permutations
self.num_bootstraps = num_bootstraps
def __init__(self, design, voi="pe", **kwargs):
"""
Parameters
----------
design : array (nsamples x nregressors)
GLM design matrix.
voi : {'pe', 'zstat'}
Variable of interest that should be reported as feature-wise
measure. 'beta' are the parameter estimates and 'zstat' returns
standardized parameter estimates.
"""
FeaturewiseDatasetMeasure.__init__(self, **kwargs)
# store the design matrix as a such (no copying if already array)
self._design = np.asmatrix(design)
# what should be computed ('variable of interest')
if not voi in ["pe", "zstat"]:
raise ValueError, "Unknown variable of interest '%s'" % str(voi)
self._voi = voi
# will store the precomputed Moore-Penrose pseudo-inverse of the
# design matrix (lazy calculation)
self._inv_design = None
# also store the inverse of the inner product for beta variance
# estimation
self._inv_ip = None
def __init__(self, design, voi='pe', **kwargs):
"""
Parameters
----------
design : array (nsamples x nregressors)
GLM design matrix.
voi : {'pe', 'zstat'}
Variable of interest that should be reported as feature-wise
measure. 'beta' are the parameter estimates and 'zstat' returns
standardized parameter estimates.
"""
FeaturewiseDatasetMeasure.__init__(self, **kwargs)
# store the design matrix as a such (no copying if already array)
self._design = np.asmatrix(design)
# what should be computed ('variable of interest')
if not voi in ['pe', 'zstat']:
raise ValueError, \
"Unknown variable of interest '%s'" % str(voi)
self._voi = voi
# will store the precomputed Moore-Penrose pseudo-inverse of the
# design matrix (lazy calculation)
self._inv_design = None
# also store the inverse of the inner product for beta variance
# estimation
self._inv_ip = None
def __init__(self, sensana,
splitter=NoneSplitter,
combiner=FirstAxisMean,
**kwargs):
"""Cheap initialization.
:Parameters:
sensana : FeaturewiseDatasetMeasure
that shall be run on the `Dataset` splits.
splitter : Splitter
used to split the `Dataset`. By convention the first dataset
in the tuple returned by the splitter on each iteration is used
to compute the sensitivity map.
combiner
This functor will be called on an array of sensitivity maps
and the result will be returned by __call__(). The result of
a combiner must be an 1d ndarray.
"""
# init base classes first
FeaturewiseDatasetMeasure.__init__(self, **kwargs)
self.__sensana = sensana
"""Sensitivity analyzer used to compute the sensitivity maps.
"""
self.__splitter = splitter
"""Splitter instance used to split the datasets."""
self.__combiner = combiner
"""Function to combine sensitivities to serve a result of
def __init__(self, num_permutations=200, num_bootstraps=100, **kwargs):
raise NotImplemented, 'PLS was not yet implemented fully'
# init base classes first
FeaturewiseDatasetMeasure.__init__(self, **kwargs)
# save the args for the analysis
self.num_permutations = num_permutations
self.num_bootstraps = num_bootstraps
def __init__(self, targets_attr='targets', **kwargs):
"""
Parameters
----------
targets_attr : str
What samples attribute to use as targets (labels).
"""
self._targets_attr = targets_attr
FeaturewiseDatasetMeasure.__init__(self, **kwargs)
def __init__(self, num_permutations=200, num_bootstraps=100, **kwargs):
raise NotImplemented, 'PLS was not yet implemented fully'
# init base classes first
FeaturewiseDatasetMeasure.__init__(self, **kwargs)
# save the args for the analysis
self.num_permutations = num_permutations
self.num_bootstraps = num_bootstraps
def __init__(self, targets_attr='targets', **kwargs):
"""
Parameters
----------
targets_attr : str
What samples attribute to use as targets (labels).
"""
self._targets_attr = targets_attr
FeaturewiseDatasetMeasure.__init__(self, **kwargs)
def __init__(self, attr="labels", **kwargs):
"""Initialize
:Parameters:
attr : basestring
Attribute to correlate across chunks.
"""
# init base classes first
FeaturewiseDatasetMeasure.__init__(self, **kwargs)
self.__attr = attr
def __init__(self, attr='targets', **kwargs):
"""Initialize
Parameters
----------
attr : str
Attribute to correlate across chunks.
"""
# init base classes first
FeaturewiseDatasetMeasure.__init__(self, **kwargs)
self.__attr = attr
def __init__(self, threshold=1.0e-2, kernel_width=1.0,
w_guess=None, **kwargs):
"""Constructor of the IRELIEF class.
"""
# init base classes first
FeaturewiseDatasetMeasure.__init__(self, **kwargs)
# Threshold in W changes (stopping criterion for irelief).
self.threshold = threshold
self.w_guess = w_guess
self.w = None
self.kernel_width = kernel_width
def __init__(self, val=0, combiner=SecondAxisSumOfAbs, **kwargs):
"""Initialize
:Parameters:
val : float
Real-valued number for the null-hypothesis.
combiner : Functor
The combiner is only applied if the computed featurewise dataset
measure is more than one-dimensional. This is different from a
`transformer`, which is always applied. By default, the sum of
absolute values along the second axis is computed.
"""
FeaturewiseDatasetMeasure.__init__(self,combiner,**kwargs)
self.__val = val
def __init__(self, pvalue=False, attr='labels', **kwargs):
"""Initialize
:Parameters:
pvalue : bool
Either to report p-value of pearsons correlation coefficient
instead of pure correlation coefficient
attr : basestring
What attribut to correlate with
"""
# init base classes first
FeaturewiseDatasetMeasure.__init__(self, **kwargs)
self.__pvalue = int(pvalue)
self.__attr = attr
def __init__(self,
threshold=1.0e-2,
kernel_width=1.0,
w_guess=None,
**kwargs):
"""Constructor of the IRELIEF class.
"""
# init base classes first
FeaturewiseDatasetMeasure.__init__(self, **kwargs)
# Threshold in W changes (stopping criterion for irelief).
self.threshold = threshold
self.w_guess = w_guess
self.w = None
self.kernel_width = kernel_width
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 __init__(self, datameasure, noise=np.random.normal):
"""
Parameters
----------
datameasure : `DatasetMeasure`
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
FeaturewiseDatasetMeasure.__init__(self)
self.__datameasure = datameasure
self.__noise = noise
def __init__(self, mult=1, **kwargs):
FeaturewiseDatasetMeasure.__init__(self, **kwargs)
self.__mult = mult
def __init__(self, combine = lambda x: np.median(x,axis=1), **kwargs):
"""Initialize
"""
FeaturewiseDatasetMeasure.__init__(self,**kwargs)
self._combine = combine
def __init__(self, mult=1, **kwargs):
FeaturewiseDatasetMeasure.__init__(self, **kwargs)
self.__mult = mult
