def __repr__(self, prefixes=[]): return super(FxMapper, self).__repr__( prefixes=prefixes + _repr_attrs(self, ["axis", "fx", "uattrs"]) + _repr_attrs(self, ["fxargs"], default=()) + _repr_attrs(self, ["attrfx"], default="merge") )
def __repr__(self, prefixes=[]): return super(CrossValidation, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['learner', 'splitter']) + _repr_attrs(self, ['errorfx'], default=mean_mismatch_error) + _repr_attrs(self, ['space'], default='sa.cvfolds') )
def __repr__(self, prefixes=[]): return super(CrossValidation, self).__repr__( prefixes=prefixes + _repr_attrs(self, ["learner", "splitter"]) + _repr_attrs(self, ["errorfx"], default=mean_mismatch_error) + _repr_attrs(self, ["space"], default="sa.cvfolds") )
def __repr__(self, prefixes=[]): return super(SplitRFE, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['lrn', 'partitioner']) + _repr_attrs(self, ['errorfx'], default=mean_mismatch_error) + _repr_attrs(self, ['analyzer_postproc'], default=maxofabs_sample()) )
def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] return super(SurfaceRingQueryEngine, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['inner_radius']) + _repr_attrs(self, ['include_center']))
def __repr__(self, prefixes=[]): return super(FxMapper, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['axis', 'fx', 'uattrs']) + _repr_attrs(self, ['fxargs'], default=()) + _repr_attrs(self, ['attrfx'], default='merge') )
def __repr__(self, prefixes=[]): return super(SurfaceVerticesQueryEngine, self).__repr__( prefixes=prefixes + _repr_attrs(self, ["voxsel"]) + _repr_attrs(self, ["space"], default="voxel_indices") + _repr_attrs(self, ["add_fa"], []) )
def __repr__(self, prefixes=[]): return super(Searchlight, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['datameasure']) + _repr_attrs(self, ['add_center_fa'], default=False) + _repr_attrs(self, ['results_backend'], default='native') )
def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] return super(ZScoreMapper, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['params', 'param_est', 'chunks_attr']) + _repr_attrs(self, ['dtype'], default='float64'))
def __repr__(self, prefixes=[]): return super(GNBSearchlight, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['gnb', 'generator']) + _repr_attrs(self, ['errorfx'], default=mean_mismatch_error) + _repr_attrs(self, ['indexsum']) )
def __repr__(self, prefixes=[], exclude=[]): return super(RepeatedMeasure, self).__repr__( prefixes=prefixes + _repr_attrs(self, [x for x in ['node', 'generator', 'callback'] if not x in exclude]) + _repr_attrs(self, ['concat_as'], default='samples') )
def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] return super(SplitRFE, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['lrn', 'partitioner']) + _repr_attrs(self, ['errorfx'], default=mean_mismatch_error) + _repr_attrs(self, ['fmeasure_postproc'], default=None) + _repr_attrs(self, ['nproc'], default=1))
def __repr__(self, prefixes=[]): # Here we are jumping over Node's __repr__ since # it would enforce placing space return super(Node, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['count']) + _repr_attrs(self, ['selection_strategy'], default='equidistant') + _repr_attrs(self, ['attr'], default='chunks') + _repr_attrs(self, ['space'], default='partitions'))
def __repr__(self, prefixes=[]): # Here we are jumping over Node's __repr__ since # it would enforce placing space return super(ExcludeTargetsCombinationsPartitioner, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['k', 'targets_attr']) + _repr_attrs(self, ['partitions_attr'], default='partitions') + _repr_attrs(self, ['partitions_keep'], default=2) + _repr_attrs(self, ['partition_assign'], default=3))
def __repr__(self, prefixes=[]): return super(SimpleStatBaseSearchlight, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['generator']) + _repr_attrs(self, ['errorfx'], default=mean_mismatch_error) + _repr_attrs(self, ['indexsum']) + _repr_attrs(self, ['reuse_neighbors'], default=False) )
def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] return super(CombinedFeaturewiseMeasure, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['analyzers']) + _repr_attrs(self, ['sa_attr'], default='combinations') )
def __repr__(self, prefixes=[]): return super(SurfaceQueryEngine, self).__repr__( prefixes=prefixes + _repr_attrs(self, ["surface"]) + _repr_attrs(self, ["radius"]) + _repr_attrs(self, ["distance_metric"], default="dijkstra") + _repr_attrs(self, ["fa_node_key"], default="node_indices") )
def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] return super(SurfaceQueryEngine, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['surface']) + _repr_attrs(self, ['radius']) + _repr_attrs(self, ['distance_metric'], default='dijkstra') + _repr_attrs(self, ['fa_node_key'], default='node_indices'))
def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] return super(FxMapper, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['axis', 'fx', 'uattrs']) + _repr_attrs(self, ['fxargs'], default=()) + _repr_attrs(self, ['attrfx'], default='merge') + _repr_attrs(self, ['order'], default='uattrs'))
def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] return super(Sensitivity, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['clf']) + _repr_attrs(self, ['force_train'], default=True) )
def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] return super(SurfaceVerticesQueryEngine, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['voxsel']) + _repr_attrs(self, ['space'], default='voxel_indices') + _repr_attrs(self, ['add_fa'], []))
def __repr__(self, prefixes=[]): """String representation of a `ProxyMeasure` """ return super(ProxyMeasure, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['measure']) + _repr_attrs(self, ['skip_train'], default=False) )
def __repr__(self, prefixes=None): """String representation of a `ProxyMeasure` """ if prefixes is None: prefixes = [] return super( ProxyMeasure, self).__repr__(prefixes=prefixes + _repr_attrs(self, ['measure']) + _repr_attrs(self, ['skip_train'], default=False))
def __repr__(self, prefixes=[]): return super(Searchlight, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['datameasure']) + _repr_attrs(self, ['add_center_fa'], default=False) + _repr_attrs(self, ['results_postproc_fx']) + _repr_attrs(self, ['results_backend'], default='native') + _repr_attrs(self, ['results_fx', 'nblocks']) )
def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] return super(SurfaceQueryEngine, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['hemi']) + _repr_attrs(self, ['radius']) + _repr_attrs(self, ['fa_node_key'], default='node_indices'))
def __repr__(self, prefixes=[]): return super(CrossValidation, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['learner', 'splitter']) + _repr_attrs(self, ['errorfx'], default=mean_mismatch_error) + _repr_attrs(self, ['space'], default='sa.cvfolds'), # Since it is the constructor which generates and passes # node=TransferMeasure, it must not be present in __repr__ of CV # TODO: clear up hierarchy exclude=('node', ))
def __repr__(self, prefixes=[]): return super(SurfaceQueryEngine, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['surface']) + _repr_attrs(self, ['radius']) + _repr_attrs(self, ['distance_metric'], default='dijkstra') + _repr_attrs(self, ['fa_node_key'], default='node_indices'))
def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] return super( BinaryFxFeaturewiseMeasure, self).__repr__(prefixes=prefixes + _repr_attrs(self, ['fx']) + _repr_attrs(self, ['space'], default='targets') + _repr_attrs(self, ['uni'], default=True) + _repr_attrs(self, ['numeric'], default=False))
def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] return super(FxMapper, self).__repr__( prefixes=prefixes + _repr_attrs(self, ["axis", "fx", "uattrs"]) + _repr_attrs(self, ["fxargs"], default=()) + _repr_attrs(self, ["attrfx"], default="merge") + _repr_attrs(self, ["order"], default="uattrs") )
def __repr__(self, prefixes=None, exclude=None): if prefixes is None: prefixes = [] if exclude is None: exclude = [] return super(RepeatedMeasure, self).__repr__( prefixes=prefixes + _repr_attrs(self, [ x for x in ['node', 'generator', 'callback'] if not x in exclude ]) + _repr_attrs(self, ['concat_as'], default='samples'))
def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] return super(BinaryFxFeaturewiseMeasure, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['fx']) + _repr_attrs(self, ['space'], default='targets') + _repr_attrs(self, ['uni'], default=True) + _repr_attrs(self, ['numeric'], default=False) )
def __repr__(self, prefixes=[]): # Here we are jumping over Node's __repr__ since # it would enforce placing space return super(ExcludeTargetsCombinationsPartitioner, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['k', 'targets_attr']) + _repr_attrs(self, ['partitions_attr'], default='partitions') + _repr_attrs(self, ['partitions_keep'], default=2) + _repr_attrs(self, ['partition_assign'], default=3) )
def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] return super(SplitRFE, self).__repr__( prefixes=prefixes + _repr_attrs(self, ["lrn", "partitioner"]) + _repr_attrs(self, ["errorfx"], default=mean_mismatch_error) + _repr_attrs(self, ["fmeasure_postproc"], default=None) + _repr_attrs(self, ["nproc"], default=1) )
def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] return super(SplitRFE, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['lrn', 'partitioner']) + _repr_attrs(self, ['errorfx'], default=mean_mismatch_error) + _repr_attrs(self, ['fmeasure_postproc'], default=None) + _repr_attrs(self, ['nproc'], default=1) )
def __repr__(self, prefixes=[]): # Here we are jumping over Node's __repr__ since # it would enforce placing space return super(Node, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['count']) + _repr_attrs(self, ['selection_strategy'], default='equidistant') + _repr_attrs(self, ['attr'], default='chunks') + _repr_attrs(self, ['space'], default='partitions') )
def __repr__(self, prefixes=[]): return super(CrossValidation, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['learner', 'splitter']) + _repr_attrs(self, ['errorfx'], default=mean_mismatch_error) + _repr_attrs(self, ['space'], default='sa.cvfolds'), # Since it is the constructor which generates and passes # node=TransferMeasure, it must not be present in __repr__ of CV # TODO: clear up hierarchy exclude=('node',) )
def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] return super(Searchlight, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['datameasure']) + _repr_attrs(self, ['add_center_fa'], default=False) + _repr_attrs(self, ['results_postproc_fx']) + _repr_attrs(self, ['results_backend'], default='native') + _repr_attrs(self, ['results_fx', 'nblocks']) )
def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] return super(ProductFlattenMapper, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['factor_names', 'factor_values']))
def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] return super( OddEvenPartitioner, self).__repr__(prefixes=prefixes + _repr_attrs(self, ['usevalues'], default=False))
def __repr__(self, prefixes=[]): """String representation of a `Measure` Includes only arguments which differ from default ones """ return super(Measure, self).__repr__(prefixes=prefixes + _repr_attrs(self, ['null_dist']))
def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] return super(TransferMeasure, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['measure', 'splitter']) )
def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] return super(M1NNSearchlight, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['knn']) )
def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] return super(StaticMeasure, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['measure', 'bias']) )
def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] prefixes_ = prefixes + _repr_attrs( self, ['fallback_euclidean_distance'], default=False) return super(SurfaceVoxelsQueryEngine, self).__repr__(prefixes=prefixes_)
def __repr__(self): """String representation of `SKLLearnerWrapper` """ prefixes = [repr(self._skl_learner)] if self.__tags__ != ['skl']: prefixes += ['tags=%r' % [t for t in self.__tags__ if t != 'skl']] prefixes += _repr_attrs(self, ['enforce_dim']) return Classifier.__repr__(self, prefixes=prefixes)
def __repr__(self, prefixes=[]): """String representation of a `Measure` Includes only arguments which differ from default ones """ return super(BaseSearchlight, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['queryengine', 'roi_ids', 'nproc']))
def __repr__(self, prefixes=[]): return super( AttributePermutator, self).__repr__(prefixes=prefixes + _repr_attrs(self, ['attr']) + _repr_attrs(self, ['count'], default=1) + _repr_attrs(self, ['limit']) + _repr_attrs(self, ['assure'], default=False) + _repr_attrs(self, ['strategy'], default='simple') + _repr_attrs(self, ['rng'], default=np.random))
def __repr__(self, prefixes=None, fullname=False): """String definition of the object of ClassWithCollections object Parameters ---------- prefixes : list of str What other prefixes to prepend to list of arguments fullname : bool Either to include full name of the module """ prefixes = prefixes or [] prefixes = prefixes[:] # copy list # filter using __init__doc__exclude__ for f in getattr(self, '__init__doc__exclude__', []): prefixes = [x for x in prefixes if not x.startswith(f+'=')] id_str = "" module_str = "" if __debug__: if 'MODULE_IN_REPR' in debug.active: fullname = True if 'ID_IN_REPR' in debug.active: id_str = _strid(self) if fullname: modulename = '%s' % self.__class__.__module__ if modulename != "__main__": module_str = "%s." % modulename # Collections' attributes collections = self._collections # we want them in this particular order for col in _COLLECTIONS_ORDER: collection = collections.get(col, None) if collection is None: continue prefixes += collection._cls_repr() # Description if present prefixes += _repr_attrs(self, ['descr']) out = "%s%s(%s)%s" % (module_str, self.__class__.__name__, ', '.join(prefixes), id_str) # To possibly debug mass repr/str-fication # print str(self), ' REPR: ', out return out
def __repr__(self, prefixes=[]): return super(FlattenMapper, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['shape', 'maxdims']))
def __repr__(self, prefixes=[]): return super(ProductFlattenMapper, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['factor_name_values']))
def __repr__(self, prefixes=[]): return super(ChainNode, self).__repr__(prefixes=prefixes + _repr_attrs(self, ['nodes']))
def __repr__(self, prefixes=[]): return super(Node, self).__repr__(prefixes=prefixes + _repr_attrs(self, ['space', 'postproc']))
def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] return super(CustomPartitioner, self).__repr__(prefixes=prefixes + _repr_attrs(self, ['splitrule']))
def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] return super(FactorialPartitioner, self).__repr__( prefixes=prefixes + _repr_attrs(self, ['partitioner'], default=1))
def __repr__(self, prefixes=None): #pylint: disable-msg=W0102 if prefixes is None: prefixes = [] return super(NFoldPartitioner, self).__repr__(prefixes=prefixes + _repr_attrs(self, ['cvtype'], default=1))