def __init__(self, dsmatrix, dset_metric, output_metric='spearman'): DatasetMeasure.__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:`~mvpa.misc.neighborhood.QueryEngine`. roi_ids : None or list of int List of feature ids (not coordinates) the shall serve as sphere centers. 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:`~mvpa.measures.base.DatasetMeasure`. """ DatasetMeasure.__init__(self, **(kwargs)) if 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._qe = queryengine if roi_ids is not None 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, datameasure, radius=1.0, center_ids=None, **kwargs): """ :Parameters: datameasure: callable Any object that takes a :class:`~mvpa.datasets.base.Dataset` and returns some measure when called. radius: float All features within the radius around the center will be part of a sphere. Provided dataset should have a metric assigned (for NiftiDataset, voxel size is used to provide such a metric, hence radius should be specified in mm). center_ids: list(int) List of feature ids (not coordinates) the shall serve as sphere centers. By default all features will be used. **kwargs In additions this class supports all keyword arguments of its base-class :class:`~mvpa.measures.base.DatasetMeasure`. .. note:: If `Searchlight` is used as `SensitivityAnalyzer` one has to make sure that the specified scalar `DatasetMeasure` returns large (absolute) values for high sensitivities and small (absolute) values for low sensitivities. Especially when using error functions usually low values imply high performance and therefore high sensitivity. This would in turn result in sensitivity maps that have low (absolute) values indicating high sensitivites and this conflicts with the intended behavior of a `SensitivityAnalyzer`. """ DatasetMeasure.__init__(self, **(kwargs)) self.__datameasure = datameasure self.__radius = radius self.__center_ids = center_ids
def __init__(self, transerror, splitter=None, combiner='mean', expose_testdataset=False, harvest_attribs=None, copy_attribs='copy', **kwargs): """ :Parameters: transerror: TransferError instance Provides the classifier used for cross-validation. splitter: Splitter | None Used to split the dataset for cross-validation folds. By convention the first dataset in the tuple returned by the splitter is used to train the provided classifier. If the first element is 'None' no training is performed. The second dataset is used to generate predictions with the (trained) classifier. If `None` (default) an instance of :class:`~mvpa.datasets.splitters.NoneSplitter` is used. combiner: Functor | 'mean' Used to aggregate the error values of all cross-validation folds. If 'mean' (default) the grand mean of the transfer errors is computed. expose_testdataset: bool In the proper pipeline, classifier must not know anything about testing data, but in some cases it might lead only to marginal harm, thus migth wanted to be enabled (provide testdataset for RFE to determine stopping point). harvest_attribs: list of basestr What attributes of call to store and return within harvested state variable copy_attribs: None | basestr Force copying values of attributes on harvesting **kwargs: All additional arguments are passed to the :class:`~mvpa.measures.base.DatasetMeasure` base class. """ DatasetMeasure.__init__(self, **kwargs) Harvestable.__init__(self, harvest_attribs, copy_attribs) if splitter is None: self.__splitter = NoneSplitter() else: self.__splitter = splitter if combiner == 'mean': self.__combiner = GrandMean else: self.__combiner = combiner self.__transerror = transerror self.__expose_testdataset = expose_testdataset
def __init__(self, transerror, splitter=None, expose_testdataset=False, harvest_attribs=None, copy_attribs='copy', samples_idattr='origids', **kwargs): """ Parameters ---------- transerror : TransferError instance Provides the classifier used for cross-validation. splitter : Splitter or None Used to split the dataset for cross-validation folds. By convention the first dataset in the tuple returned by the splitter is used to train the provided classifier. If the first element is 'None' no training is performed. The second dataset is used to generate predictions with the (trained) classifier. If `None` (default) an instance of :class:`~mvpa.datasets.splitters.NoneSplitter` is used. expose_testdataset : bool, optional In the proper pipeline, classifier must not know anything about testing data, but in some cases it might lead only to marginal harm, thus migth wanted to be enabled (provide testdataset for RFE to determine stopping point). harvest_attribs : list of str What attributes of call to store and return within harvested conditional attribute copy_attribs : None or str, optional Force copying values of attributes on harvesting samples_idattr : str, optional What samples attribute to use to identify and store samples_errors conditional attribute **kwargs All additional arguments are passed to the :class:`~mvpa.measures.base.DatasetMeasure` base class. """ DatasetMeasure.__init__(self, **kwargs) Harvestable.__init__(self, harvest_attribs, copy_attribs) if splitter is None: self.__splitter = NoneSplitter() else: self.__splitter = splitter self.__transerror = transerror self.__expose_testdataset = expose_testdataset self.__samples_idattr = samples_idattr