def __init__(self,
pairwise_metric='correlation',
center_data=False,
square=False,
**kwargs):
"""Initialize
Parameters
----------
pairwise_metric : String. Distance metric to use for calculating
pairwise vector distances for dissimilarity matrix
(DSM). See scipy.spatial.distance.pdist for all
possible metrics. (Default ='correlation', i.e. one
minus Pearson correlation)
center_data : boolean. (Optional. Default = False) If True then center
each column of the data matrix by subtracing the column
mean from each element (by chunk if chunks_attr
specified). This is recommended especially when using
pairwise_metric = 'correlation'.
square : boolean. (Optional. Default = False) If True return
the square distance matrices, if False, returns the
flattened lower triangle.
Returns
-------
Dataset : Contains a column vector of length = n(n-1)/2 of pairwise
distances between all samples if square = False; square
dissimilarty matrix if square = True.
"""
Measure.__init__(self, **kwargs)
self.pairwise_metric = pairwise_metric
self.center_data = center_data
self.square = square
def __init__(self, xSs_behav, targ_comp, comparison_metric='pearson', chunks_attr='chunks', **kwargs):
"""Initialize
Parameters
----------
xSs_behav: Dictionary of behavioral value between subjects to be
correlated with intrasubject neural similarity (subjects are keys)
targ_comp: List of targets whose similarity is correlated with xSs_behav
chunks_attr : Chunks attribute to use for chunking dataset. Can be any
samples attribute specified in the dataset.sa dict.
(Default: 'chunks')
comparison_metric: Distance measure for behavioral to neural comparison.
'pearson' (default) or 'spearman'
center_data : boolean. (Optional. Default = False) If True then center
each column of the data matrix by subtracing the column
mean from each element (by chunk if chunks_attr
specified). This is recommended especially when using
pairwise_metric = 'correlation'.
Returns
-------
Dataset: Contains an array of the pairwise correlations between the
DSMs defined for each chunk of the dataset. Length of array
will be N(N-1)/2 for N chunks.
To Do:
Another metric for consistency metric could be the "Rv" coefficient... (ac)
"""
# init base classes first
Measure.__init__(self, **kwargs)
self.xSs_behav = xSs_behav
self.targ_comp = targ_comp
self.chunks_attr = chunks_attr
self.comparison_metric = comparison_metric
def __init__(self, dsmatrix, dset_metric, output_metric='spearman'):
Measure.__init__(self)
self.dsmatrix = dsmatrix
self.dset_metric = dset_metric
self.output_metric = output_metric
self.dset_dsm = []
def __init__(self, queryengine, roi_ids=None, nproc=None,
**kwargs):
"""
Parameters
----------
queryengine : QueryEngine
Engine to use to discover the "neighborhood" of each feature.
See :class:`~mvpa2.misc.neighborhood.QueryEngine`.
roi_ids : None or list(int) or str
List of feature ids (not coordinates) the shall serve as ROI seeds
(e.g. sphere centers). Alternatively, this can be the name of a
feature attribute of the input dataset, whose non-zero values
determine the feature ids. By default all features will be used.
nproc : None or int
How many processes to use for computation. Requires `pprocess`
external module. If None -- all available cores will be used.
**kwargs
In addition this class supports all keyword arguments of its
base-class :class:`~mvpa2.measures.base.Measure`.
"""
Measure.__init__(self, **kwargs)
if nproc is not None and nproc > 1 and not externals.exists('pprocess'):
raise RuntimeError("The 'pprocess' module is required for "
"multiprocess searchlights. Please either "
"install python-pprocess, or reduce `nproc` "
"to 1 (got nproc=%i) or set to default None"
% nproc)
self._queryengine = queryengine
if roi_ids is not None and not isinstance(roi_ids, str) \
and not len(roi_ids):
raise ValueError("Cannot run searchlight on an empty list of roi_ids")
self.__roi_ids = roi_ids
self.nproc = nproc
def __init__(self, dsmatrix, dset_metric, output_metric="spearman"):
Measure.__init__(self)
self.dsmatrix = dsmatrix
self.dset_metric = dset_metric
self.output_metric = output_metric
self.dset_dsm = []
def __init__(self, axis, fx, other_axis_prefix=None, **kwargs):
'''
Parameters
----------
axis: str or int
'samples' (or 0) or 'features' (or 1).
fx: callable
function to determine the winner. When called with a dataset ds,
it should return a vector with ds.nsamples values
(if axis=='features') or ds.nfeatures values (if axis=='samples').
other_axis_prefix: str
prefix used for feature or sample attributes set on the other axis.
'''
Measure.__init__(self, **kwargs)
if type(axis) is str:
str2num = dict(samples=0, features=1)
if not axis in str2num:
raise ValueError("Illegal axis: should be %s" %
' or '.join(str2num))
axis = str2num[axis]
elif not axis in (0, 1):
raise ValueError("Illegal axis: should be 0 or 1")
self.__axis = axis
self.__fx = fx
self.__other_axis_prefix = other_axis_prefix
def __init__(self, queryengine, roi_ids=None, nproc=None, **kwargs):
"""
Parameters
----------
queryengine : QueryEngine
Engine to use to discover the "neighborhood" of each feature.
See :class:`~mvpa2.misc.neighborhood.QueryEngine`.
roi_ids : None or list(int) or str
List of feature ids (not coordinates) the shall serve as ROI seeds
(e.g. sphere centers). Alternatively, this can be the name of a
feature attribute of the input dataset, whose non-zero values
determine the feature ids. By default all features will be used.
nproc : None or int
How many processes to use for computation. Requires `pprocess`
external module. If None -- all available cores will be used.
**kwargs
In addition this class supports all keyword arguments of its
base-class :class:`~mvpa2.measures.base.Measure`.
"""
Measure.__init__(self, **kwargs)
if nproc is not None and nproc > 1 and not externals.exists('pprocess'):
raise RuntimeError("The 'pprocess' module is required for "
"multiprocess searchlights. Please either "
"install python-pprocess, or reduce `nproc` "
"to 1 (got nproc=%i)" % nproc)
self._queryengine = queryengine
if roi_ids is not None and not isinstance(roi_ids, str) \
and not len(roi_ids):
raise ValueError, \
"Cannot run searchlight on an empty list of roi_ids"
self.__roi_ids = roi_ids
self.nproc = nproc
def __init__(self, pairwise_metric='correlation', center_data=False,
square=False, **kwargs):
"""Initialize
Parameters
----------
pairwise_metric : String. Distance metric to use for calculating
pairwise vector distances for dissimilarity matrix
(DSM). See scipy.spatial.distance.pdist for all
possible metrics. (Default ='correlation', i.e. one
minus Pearson correlation)
center_data : boolean. (Optional. Default = False) If True then center
each column of the data matrix by subtracing the column
mean from each element (by chunk if chunks_attr
specified). This is recommended especially when using
pairwise_metric = 'correlation'.
square : boolean. (Optional. Default = False) If True return
the square distance matrices, if False, returns the
flattened lower triangle.
Returns
-------
Dataset : Contains a column vector of length = n(n-1)/2 of pairwise
distances between all samples if square = False; square
dissimilarty matrix if square = True.
"""
Measure.__init__(self, **kwargs)
self.pairwise_metric = pairwise_metric
self.center_data = center_data
self.square = square
def __init__(self, xSs_behav, targ_comp, comparison_metric='pearson', chunks_attr='chunks', **kwargs):
"""Initialize
Parameters
----------
xSs_behav: Dictionary of behavioral value between subjects to be
correlated with intrasubject neural similarity (subjects are keys)
targ_comp: List of targets whose similarity is correlated with xSs_behav
chunks_attr : Chunks attribute to use for chunking dataset. Can be any
samples attribute specified in the dataset.sa dict.
(Default: 'chunks')
comparison_metric: Distance measure for behavioral to neural comparison.
'pearson' (default) or 'spearman'
center_data : boolean. (Optional. Default = False) If True then center
each column of the data matrix by subtracing the column
mean from each element (by chunk if chunks_attr
specified). This is recommended especially when using
pairwise_metric = 'correlation'.
Returns
-------
Dataset: Contains an array of the pairwise correlations between the
DSMs defined for each chunk of the dataset. Length of array
will be N(N-1)/2 for N chunks.
To Do:
Another metric for consistency metric could be the "Rv" coefficient... (ac)
"""
# init base classes first
Measure.__init__(self, **kwargs)
self.xSs_behav = xSs_behav
self.targ_comp = targ_comp
self.chunks_attr = chunks_attr
self.comparison_metric = comparison_metric
def __init__(self, axis, fx, other_axis_prefix=None, **kwargs):
'''
Parameters
----------
axis: str or int
'samples' (or 0) or 'features' (or 1).
fx: callable
function to determine the winner. When called with a dataset ds,
it should return a vector with ds.nsamples values
(if axis=='features') or ds.nfeatures values (if axis=='samples').
other_axis_prefix: str
prefix used for feature or sample attributes set on the other axis.
'''
Measure.__init__(self, **kwargs)
if type(axis) is str:
str2num = dict(samples=0, features=1)
if not axis in str2num:
raise ValueError("Illegal axis: should be %s" %
' or '.join(str2num))
axis = str2num[axis]
elif not axis in (0, 1):
raise ValueError("Illegal axis: should be 0 or 1")
self.__axis = axis
self.__fx = fx
self.__other_axis_prefix = other_axis_prefix
def __init__(self, dset_metric, nsubjs, compare_ave, k, **kwargs):
Measure.__init__(self, **kwargs)
self.dset_metric = dset_metric
self.dset_dsm = []
self.nsubjs = nsubjs
self.compare_ave = compare_ave
self.k = k
def __init__(self, dset_metric, nsubjs, compare_ave, k, **kwargs):
Measure.__init__(self, **kwargs)
self.dset_metric = dset_metric
self.dset_dsm = []
self.nsubjs = nsubjs
self.compare_ave = compare_ave
self.k = k
def __init__(self, model, n_ev, mat, nuisance, scan_onsets, min_voxels=10):
Measure.__init__(self)
self.model = model
self.n_ev = n_ev
self.n = (n_ev**2 - n_ev) // 2
self.mat = mat
self.nuisance = nuisance
self.scan_onsets = scan_onsets
self.min_voxels = min_voxels
def __init__(self, **kwargs):
"""
Returns
-------
Dataset
If square is False, contains a column vector of length = n(n-1)/2 of
pairwise distances between all samples. A sample attribute ``pairs``
identifies the indices of input samples for each individual pair.
If square is True, the dataset contains a square dissimilarty matrix
and the entire sample attributes collection of the input dataset.
"""
Measure.__init__(self, **kwargs)
def __init__(self, targs_comps, sample_covariable, pairwise_metric='correlation',
comparison_metric='pearson', center_data = False,
corrcoef_only = False, **kwargs):
"""
Initialize
Parameters
----------
dataset : Dataset with N samples such that corresponding dissimilarity
matrix has N*(N-1)/2 unique pairwise distances
targs_comps: Dict of trial by trial targets (keys) and their comparison targets
(values) - ***this measure assumes other omitted first***
sample_covariable: Name of the variable (sample attribute) with a value for each sample.
The distance of each sample with the comparison_sample will be
correlated with this variable.
pairwise_metric : To be used by pdist to calculate dataset DSM
Default: 'correlation',
see scipy.spatial.distance.pdist for other metric options.
comparison_metric : To be used for comparing dataset dsm with target dsm
Default: 'pearson'. Options: 'pearson' or 'spearman'
center_data : Center data by subtracting mean column values from
columns prior to calculating dataset dsm.
Default: False
corrcoef_only : If true, return only the correlation coefficient
(rho), otherwise return rho and probability, p.
Default: False
Returns
-------
Dataset : Dataset contains the correlation coefficient (rho) only or
rho plus p, when corrcoef_only is set to false.
-------
TO DO: Should this be done as repeated measures ANCOVA instead?
Does not currently handle rho comparison of samples, or rho
corr with covariable
Should use mean_group_sample in wrapper function to get comparison_sample
Maybe have omit inside this method?
"""
# init base classes first
Measure.__init__(self, **kwargs)
if comparison_metric not in ['spearman','pearson']:
raise Exception("comparison_metric %s is not in "
"['spearman','pearson']" % comparison_metric)
self.targs_comps = targs_comps
self.sample_covariable = sample_covariable
#if comparison_metric == 'spearman':
# self.target_dsm = rankdata(target_dsm)
self.pairwise_metric = pairwise_metric
self.comparison_metric = comparison_metric
self.center_data = center_data
self.corrcoef_only = corrcoef_only
def __init__(self, targs_comps, sample_covariable, pairwise_metric='correlation',
comparison_metric='pearson', center_data = False,
corrcoef_only = False, **kwargs):
"""
Initialize
Parameters
----------
dataset : Dataset with N samples such that corresponding dissimilarity
matrix has N*(N-1)/2 unique pairwise distances
targs_comps: Dict of trial by trial targets (keys) and their comparison targets
(values) - ***this measure assumes other omitted first***
sample_covariable: Name of the variable (sample attribute) with a value for each sample.
The distance of each sample with the comparison_sample will be
correlated with this variable.
pairwise_metric : To be used by pdist to calculate dataset DSM
Default: 'correlation',
see scipy.spatial.distance.pdist for other metric options.
comparison_metric : To be used for comparing dataset dsm with target dsm
Default: 'pearson'. Options: 'pearson' or 'spearman'
center_data : Center data by subtracting mean column values from
columns prior to calculating dataset dsm.
Default: False
corrcoef_only : If true, return only the correlation coefficient
(rho), otherwise return rho and probability, p.
Default: False
Returns
-------
Dataset : Dataset contains the correlation coefficient (rho) only or
rho plus p, when corrcoef_only is set to false.
-------
TO DO: Should this be done as repeated measures ANCOVA instead?
Does not currently handle rho comparison of samples, or rho
corr with covariable
Should use mean_group_sample in wrapper function to get comparison_sample
Maybe have omit inside this method?
"""
# init base classes first
Measure.__init__(self, **kwargs)
if comparison_metric not in ['spearman','pearson']:
raise Exception("comparison_metric %s is not in "
"['spearman','pearson']" % comparison_metric)
self.targs_comps = targs_comps
self.sample_covariable = sample_covariable
#if comparison_metric == 'spearman':
# self.target_dsm = rankdata(target_dsm)
self.pairwise_metric = pairwise_metric
self.comparison_metric = comparison_metric
self.center_data = center_data
self.corrcoef_only = corrcoef_only
def __init__(self, **kwargs):
"""
Returns
-------
Dataset
Contains the pairwise correlations between the DSMs
computed from each chunk of the input dataset. If square is False,
this is a column vector of length N(N-1)/2 for N chunks. If square
is True, this is a square matrix of size NxN for N chunks.
"""
# TODO: Another metric for consistency metric could be the "Rv"
# coefficient... (ac)
# init base classes first
Measure.__init__(self, **kwargs)
def __init__(self, target_dsm, control_dsms = None, resid = False,
pairwise_metric='correlation', comparison_metric='pearson',
center_data = False, corrcoef_only = False, **kwargs):
"""
Initialize
Parameters
----------
dataset : Dataset with N samples such that corresponding dissimilarity
matrix has N*(N-1)/2 unique pairwise distances
target_dsm : numpy array, length N*(N-1)/2. Target dissimilarity matrix
this is the predictor who's results get mapped back
control_dsms: list of numpy arrays, length N*(N-1)/2. DMs to be controlled for
Default: 'None'
controlled for when getting results of target_dsm back
resid: Set to True to return residuals to searchlight center for
smoothing estimation, default to False
pairwise_metric : To be used by pdist to calculate dataset DSM
Default: 'correlation',
see scipy.spatial.distance.pdist for other metric options.
comparison_metric : To be used for comparing dataset dsm with target dsm
Default: 'pearson'. Options: 'pearson' or 'spearman'
center_data : Center data by subtracting mean column values from
columns prior to calculating dataset dsm.
Default: False
corrcoef_only : If true, return only the correlation coefficient
(rho), otherwise return rho and probability, p.
Default: False
Returns
-------
Dataset : Dataset contains the correlation coefficient (rho) only or
rho plus p, when corrcoef_only is set to false.
"""
# init base classes first
Measure.__init__(self, **kwargs)
if comparison_metric not in ['spearman','pearson']:
raise Exception("comparison_metric %s is not in "
"['spearman','pearson']" % comparison_metric)
self.target_dsm = target_dsm
if comparison_metric == 'spearman':
self.target_dsm = rankdata(target_dsm)
self.pairwise_metric = pairwise_metric
self.comparison_metric = comparison_metric
self.center_data = center_data
self.corrcoef_only = corrcoef_only
self.control_dsms = control_dsms
if comparison_metric == 'spearman' and control_dsms != None:
self.control_dsms = [rankdata(dm) for dm in control_dsms]
self.resid = resid
def __init__(self, target_dsm, control_dsms = None, resid = False,
pairwise_metric='correlation', comparison_metric='pearson',
center_data = False, corrcoef_only = False, **kwargs):
"""
Initialize
Parameters
----------
dataset : Dataset with N samples such that corresponding dissimilarity
matrix has N*(N-1)/2 unique pairwise distances
target_dsm : numpy array, length N*(N-1)/2. Target dissimilarity matrix
this is the predictor who's results get mapped back
control_dsms: list of numpy arrays, length N*(N-1)/2. DMs to be controlled for
Default: 'None'
controlled for when getting results of target_dsm back
resid: Set to True to return residuals to searchlight center for
smoothing estimation, default to False
pairwise_metric : To be used by pdist to calculate dataset DSM
Default: 'correlation',
see scipy.spatial.distance.pdist for other metric options.
comparison_metric : To be used for comparing dataset dsm with target dsm
Default: 'pearson'. Options: 'pearson' or 'spearman'
center_data : Center data by subtracting mean column values from
columns prior to calculating dataset dsm.
Default: False
corrcoef_only : If true, return only the correlation coefficient
(rho), otherwise return rho and probability, p.
Default: False
Returns
-------
Dataset : Dataset contains the correlation coefficient (rho) only or
rho plus p, when corrcoef_only is set to false.
"""
# init base classes first
Measure.__init__(self, **kwargs)
if comparison_metric not in ['spearman','pearson']:
raise Exception("comparison_metric %s is not in "
"['spearman','pearson']" % comparison_metric)
self.target_dsm = target_dsm
if comparison_metric == 'spearman':
self.target_dsm = rankdata(target_dsm)
self.pairwise_metric = pairwise_metric
self.comparison_metric = comparison_metric
self.center_data = center_data
self.corrcoef_only = corrcoef_only
self.control_dsms = control_dsms
if comparison_metric == 'spearman' and control_dsms != None:
self.control_dsms = [rankdata(dm) for dm in control_dsms]
self.resid = resid
def __init__(self, vols, item_comp, stat, contrasts, n_perm=1000,
min_voxels=10):
Measure.__init__(self)
# get indices of items to compare
if stat == 'vector':
perm_type = 'item_type'
elif stat == 'vectri':
perm_type = 'triad'
else:
raise ValueError(f'Unknown stat: {stat}')
train_ind, test_ind = rsa.prep_triad_vector(vols, item_comp, n_perm,
perm_type=perm_type)
self.train_ind = train_ind
self.test_ind = test_ind
self.stat = stat
self.contrasts = contrasts
self.min_voxels = min_voxels
def __init__(self, target_dsm, **kwargs):
"""
Parameters
----------
target_dsm : array (length N*(N-1)/2)
Target dissimilarity matrix
Returns
-------
Dataset
If ``corrcoef_only`` is True, contains one feature: the correlation
coefficient (rho); or otherwise two-fetaures: rho plus p.
"""
# init base classes first
Measure.__init__(self, **kwargs)
self.target_dsm = target_dsm
if self.params.comparison_metric == 'spearman':
self.target_dsm = rankdata(target_dsm)
def __init__(self, target_dsm, partial_dsm = None, pairwise_metric='correlation',
comparison_metric='pearson', center_data = False,
corrcoef_only = False, **kwargs):
"""
Initialize
Parameters
----------
dataset : Dataset with N samples such that corresponding dissimilarity
matrix has N*(N-1)/2 unique pairwise distances
target_dsm : numpy array, length N*(N-1)/2. Target dissimilarity matrix
partial_dsm: numpy array, length N*(N-1)/2. DSM to be partialled out
Default: 'None'; assumes use of pcorr.py
pairwise_metric : To be used by pdist to calculate dataset DSM
Default: 'correlation',
see scipy.spatial.distance.pdist for other metric options.
comparison_metric : To be used for comparing dataset dsm with target dsm
Default: 'pearson'. Options: 'pearson' or 'spearman'
center_data : Center data by subtracting mean column values from
columns prior to calculating dataset dsm.
Default: False
corrcoef_only : If true, return only the correlation coefficient
(rho), otherwise return rho and probability, p.
Default: False
Returns
-------
Dataset : Dataset contains the correlation coefficient (rho) only or
rho plus p, when corrcoef_only is set to false.
"""
# init base classes first
Measure.__init__(self, **kwargs)
if comparison_metric not in ['spearman','pearson']:
raise Exception("comparison_metric %s is not in "
"['spearman','pearson']" % comparison_metric)
self.target_dsm = target_dsm
if comparison_metric == 'spearman':
self.target_dsm = rankdata(target_dsm)
self.pairwise_metric = pairwise_metric
self.comparison_metric = comparison_metric
self.center_data = center_data
self.corrcoef_only = corrcoef_only
self.partial_dsm = partial_dsm
if comparison_metric == 'spearman' and partial_dsm != None:
self.partial_dsm = rankdata(partial_dsm)
def __init__(self,
target_dsm,
pairwise_metric='correlation',
comparison_metric='pearson',
center_data=False,
corrcoef_only=False,
**kwargs):
"""
Initialize
Parameters
----------
dataset : Dataset with N samples such that corresponding dissimilarity
matrix has N*(N-1)/2 unique pairwise distances
target_dsm : numpy array, length N*(N-1)/2. Target dissimilarity matrix
pairwise_metric : To be used by pdist to calculate dataset DSM
Default: 'correlation',
see scipy.spatial.distance.pdist for other metric options.
comparison_metric : To be used for comparing dataset dsm with target dsm
Default: 'pearson'. Options: 'pearson' or 'spearman'
center_data : Center data by subtracting mean column values from
columns prior to calculating dataset dsm.
Default: False
corrcoef_only : If true, return only the correlation coefficient
(rho), otherwise return rho and probability, p.
Default: False
Returns
-------
Dataset : Dataset contains the correlation coefficient (rho) only or
rho plus p, when corrcoef_only is set to false.
"""
# init base classes first
Measure.__init__(self, **kwargs)
if comparison_metric not in ['spearman', 'pearson']:
raise Exception("comparison_metric %s is not in "
"['spearman','pearson']" % comparison_metric)
self.target_dsm = target_dsm
if comparison_metric == 'spearman':
self.target_dsm = rankdata(target_dsm)
self.pairwise_metric = pairwise_metric
self.comparison_metric = comparison_metric
self.center_data = center_data
self.corrcoef_only = corrcoef_only
def __init__(self, pairs_dsm, pairwise_metric='correlation',
comparison_metric='pearson', center_data = False,
corrcoef_only = False, **kwargs):
"""
Initialize
Parameters
----------
dataset : Dataset with N samples such that corresponding dissimilarity
matrix has N*(N-1)/2 unique pairwise distances
pairs_dsm : Dictionary of target pairs separated by '-' (keys) and
corresponding predicted model dissimilarity values (values)
pairwise_metric : To be used by pdist to calculate dataset DSM
Default: 'correlation',
see scipy.spatial.distance.pdist for other metric options.
comparison_metric : To be used for comparing dataset dsm with target dsm
Default: 'pearson'. Options: 'pearson' or 'spearman'
center_data : Center data by subtracting mean column values from
columns prior to calculating dataset dsm.
Default: False
corrcoef_only : If true, return only the correlation coefficient
(rho), otherwise return rho and probability, p.
Default: False
Returns
-------
Dataset : Dataset contains the correlation coefficient (rho) only or
rho plus p, when corrcoef_only is set to false.
-------
TO DO: Add partial correlation and multiple regression RSA
"""
# init base classes first
Measure.__init__(self, **kwargs)
if comparison_metric not in ['spearman','pearson','euclidean']:
raise Exception("comparison_metric %s is not in "
"['spearman','pearson','euclidean']" % comparison_metric)
self.pairs_dsm = pairs_dsm
self.pairwise_metric = pairwise_metric
self.comparison_metric = comparison_metric
self.center_data = center_data
self.corrcoef_only = corrcoef_only
self.pairs = [i.split('-') for i in self.pairs_dsm.keys()]
def __init__(self, pairs_dsm, pairwise_metric='correlation',
comparison_metric='pearson', center_data = False,
corrcoef_only = False, **kwargs):
"""
Initialize
Parameters
----------
dataset : Dataset with N samples such that corresponding dissimilarity
matrix has N*(N-1)/2 unique pairwise distances
pairs_dsm : Dictionary of target pairs separated by '-' (keys) and
corresponding predicted model dissimilarity values (values)
pairwise_metric : To be used by pdist to calculate dataset DSM
Default: 'correlation',
see scipy.spatial.distance.pdist for other metric options.
comparison_metric : To be used for comparing dataset dsm with target dsm
Default: 'pearson'. Options: 'pearson' or 'spearman'
center_data : Center data by subtracting mean column values from
columns prior to calculating dataset dsm.
Default: False
corrcoef_only : If true, return only the correlation coefficient
(rho), otherwise return rho and probability, p.
Default: False
Returns
-------
Dataset : Dataset contains the correlation coefficient (rho) only or
rho plus p, when corrcoef_only is set to false.
-------
TO DO: Add partial correlation and multiple regression RSA
"""
# init base classes first
Measure.__init__(self, **kwargs)
if comparison_metric not in ['spearman','pearson','euclidean']:
raise Exception("comparison_metric %s is not in "
"['spearman','pearson','euclidean']" % comparison_metric)
self.pairs_dsm = pairs_dsm
self.pairwise_metric = pairwise_metric
self.comparison_metric = comparison_metric
self.center_data = center_data
self.corrcoef_only = corrcoef_only
self.pairs = [i.split('-') for i in self.pairs_dsm.keys()]
def __init__(self,
chunks_attr='chunks',
pairwise_metric='correlation',
consistency_metric='pearson',
center_data=False,
**kwargs):
"""Initialize
Parameters
----------
chunks_attr : Chunks attribute to use for chunking dataset. Can be any
samples attribute specified in the dataset.sa dict.
(Default: 'chunks')
pairwise_metric : Distance metric to use for calculating dissimilarity
matrices from the set of samples in each chunk specified.
See spatial.distance.pdist for all possible metrics.
(Default = 'correlation', i.e. one minus Pearson correlation)
consistency_metric: Correlation measure to use for the correlation
between dissimilarity matrices. Options are
'pearson' (default) or 'spearman'
center_data : boolean. (Optional. Default = False) If True then center
each column of the data matrix by subtracing the column
mean from each element (by chunk if chunks_attr
specified). This is recommended especially when using
pairwise_metric = 'correlation'.
Returns
-------
Dataset: Contains an array of the pairwise correlations between the
DSMs defined for each chunk of the dataset. Length of array
will be N(N-1)/2 for N chunks.
To Do:
Another metric for consistency metric could be the "Rv" coefficient... (ac)
"""
# init base classes first
Measure.__init__(self, **kwargs)
self.pairwise_metric = pairwise_metric
self.consistency_metric = consistency_metric
self.chunks_attr = chunks_attr
self.center_data = center_data
def __init__(self, chunks_attr='chunks', pairwise_metric='correlation',
consistency_metric='pearson', center_data=False, **kwargs):
"""Initialize
Parameters
----------
chunks_attr : Chunks attribute to use for chunking dataset. Can be any
samples attribute specified in the dataset.sa dict.
(Default: 'chunks')
pairwise_metric : Distance metric to use for calculating dissimilarity
matrices from the set of samples in each chunk specified.
See spatial.distance.pdist for all possible metrics.
(Default = 'correlation', i.e. one minus Pearson correlation)
consistency_metric: Correlation measure to use for the correlation
between dissimilarity matrices. Options are
'pearson' (default) or 'spearman'
center_data : boolean. (Optional. Default = False) If True then center
each column of the data matrix by subtracing the column
mean from each element (by chunk if chunks_attr
specified). This is recommended especially when using
pairwise_metric = 'correlation'.
Returns
-------
Dataset: Contains an array of the pairwise correlations between the
DSMs defined for each chunk of the dataset. Length of array
will be N(N-1)/2 for N chunks.
To Do:
Another metric for consistency metric could be the "Rv" coefficient... (ac)
"""
# init base classes first
Measure.__init__(self, **kwargs)
self.pairwise_metric = pairwise_metric
self.consistency_metric = consistency_metric
self.chunks_attr = chunks_attr
self.center_data = center_data
def __init__(self, pairs, pairwise_metric='correlation', **kwargs):
"""
Initialize
Parameters
----------
dataset : Dataset with N samples such that corresponding dissimilarity
matrix has N*(N-1)/2 unique pairwise distances
Make sure is in alphabetical order!
pairs : list of lists (pairs) of target names
pairwise_metric : To be used by pdist to calculate dataset DSM
Default: 'correlation',
see scipy.spatial.distance.pdist for other metric options.
Returns
-------
Dataset : Dataset contains the sim value
-------
"""
# init base classes first
Measure.__init__(self, **kwargs)
self.pairs = pairs
self.pairwise_metric = pairwise_metric
def __init__(self, target_dsm, control_dsms = None, **kwargs):
"""
Parameters
----------
target_dsm : array (length N*(N-1)/2)
Target dissimilarity matrix
control_dsms : list of arrays (each length N*(N-1)/2)
Dissimilarity matrices to control for in multiple regression; flexible number allowed
*Optional. Returns r/rho coefficients for target_dsm, controlling for these dsms
Returns
-------
Dataset
If ``corrcoef_only`` is True, contains one feature: the correlation
coefficient (rho); or otherwise two-fetaures: rho plus p.
"""
# init base classes first
Measure.__init__(self, **kwargs)
self.target_dsm = target_dsm
self.control_dsms = control_dsms
if self.params.comparison_metric == 'spearman':
self.target_dsm = rankdata(target_dsm)
if control_dsms != None: self.control_dsms = [rankdata(dm) for dm in control_dsms]
def __init__(self, pairs, pairwise_metric='correlation', **kwargs):
"""
Initialize
Parameters
----------
dataset : Dataset with N samples such that corresponding dissimilarity
matrix has N*(N-1)/2 unique pairwise distances
Make sure is in alphabetical order!
pairs : list of lists (pairs) of target names
pairwise_metric : To be used by pdist to calculate dataset DSM
Default: 'correlation',
see scipy.spatial.distance.pdist for other metric options.
Returns
-------
Dataset : Dataset contains the sim value
-------
"""
# init base classes first
Measure.__init__(self, **kwargs)
self.pairs = pairs
self.pairwise_metric = pairwise_metric
def __init__(self):
Measure.__init__(self, auto_train=True)
def __init__(self, **kwargs):
Measure.__init__(self, **kwargs)
def __init__(self):
Measure.__init__(self, auto_train=True)
def __init__(self, **kwargs):
Measure.__init__(self, **kwargs)
def __init__(self, model_rdms, n_perm, min_voxels=10):
Measure.__init__(self)
self.perm = prsa.init_pRSA(n_perm, model_rdms)
self.min_voxels = min_voxels
self.n_model = len(model_rdms)
def __init__(self, vols, n_perm=1000):
Measure.__init__(self)
self.vols = vols
self.cond_ind = rsa.prep_triad_delta(vols, n_perm)
def __init__(self, dtype, **kwargs):
Measure.__init__(self, **kwargs)
self.dtype = dtype
def __init__(self, dtype, **kwargs):
Measure.__init__(self, **kwargs)
self.dtype = dtype
def __init__(self, model='regression', cthresh=0.10):
Measure.__init__(self)
self.cthresh = cthresh
self.model = model
def __init__(self, **kwargs):
Measure.__init__(self, **kwargs)
self._train_ds = None
def __init__(self, **kwargs):
Measure.__init__(self, **kwargs)
self._train_ds = None
def __init__(self, metric='pearson', space='targets', **kwargs):
Measure.__init__(self, **kwargs)
self.metric = metric
self.space = space
def __init__(self, model='regression', cthresh=0.10):
Measure.__init__(self)
self.cthresh = cthresh
self.model = model
def __init__(self, metric='pearson', space='targets', **kwargs):
Measure.__init__(self, **kwargs)
self.metric = metric
self.space = space
