def DicarloMajaj2015ITRDM():
return _DicarloMajaj2015Region(
'IT',
identifier_metric_suffix='rdm',
similarity_metric=RDMCrossValidated(crossvalidation_kwargs=dict(
stratification_coord='object_name')),
ceiler=RDMConsistency())
def MovshonFreemanZiemba2013V2RDM():
return _MovshonFreemanZiemba2013Region(
'V2',
identifier_metric_suffix='rdm',
similarity_metric=RDMCrossValidated(crossvalidation_kwargs=dict(
stratification_coord='texture_type')),
ceiler=RDMConsistency())
def __init__(self, *args, **kwargs):
super(Blank2014fROIRDM, self).__init__(*args, **kwargs)
self._metric = RDMCrossValidated(
comparison_coord='stimulus_id',
crossvalidation_kwargs=dict(split_coord='stimulus_id', stratification_coord=None, splits=5,
kfold=True, test_size=None))
self._ceiler.extrapolation_dimension = 'subject_UID'
self._cross = CartesianProduct(dividers=['subject_UID'])
def __init__(self, **kwargs):
metric = RDMCrossValidated(comparison_coord='stimulus_id',
crossvalidation_kwargs=dict(
split_coord='stimulus_id',
stratification_coord=None,
splits=5,
kfold=True,
test_size=None))
super(PereiraRDM, self).__init__(metric=metric, **kwargs)
def __init__(self, identifier):
metric = RDMCrossValidated(
comparison_coord='stimulus_id',
crossvalidation_kwargs=dict(split_coord='stimulus_id', stratification_coord='sentence_id',
# doesn't work because train_size is deemed too small.
# even though `train` is not needed, CrossValidation still splits it that way
splits=5, kfold=True, test_size=None))
super(Fedorenko2016V3RDM, self).__init__(identifier=identifier, metric=metric)
self._ceiler.extrapolation_dimension = 'subject_UID'
self._cross = CartesianProduct(dividers=['subject_UID'])
def __init__(self, identifier):
metric = RDMCrossValidated(
comparison_coord='stimulus_id',
crossvalidation_kwargs=dict(
split_coord='stimulus_id',
stratification_coord='sentence_id',
# doesn't work because train_size is deemed too small.
# even though `train` is not needed, CrossValidation still splits it that way
splits=5,
kfold=True,
test_size=None))
super(Fedorenko2016V3RDM, self).__init__(identifier=identifier,
metric=metric)
def test_small(self):
assembly = NeuroidAssembly(
(np.arange(30 * 25) + np.random.standard_normal(30 * 25)).reshape(
(30, 25)),
coords={
'image_id': ('presentation', np.arange(30)),
'object_name': ('presentation', ['a', 'b', 'c'] * 10),
'neuroid_id': ('neuroid', np.arange(25)),
'region': ('neuroid', [None] * 25)
},
dims=['presentation', 'neuroid'])
metric = RDMCrossValidated()
score = metric(assembly1=assembly, assembly2=assembly)
assert score.sel(aggregation='center') == approx(1)
def AruCichy2019RDM():
assembly = LazyLoad(lambda: load_assembly(average_repetitions=False))
similarity_metric = RDMCrossValidated(crossvalidation_kwargs=dict(
stratification_coord=None))
ceiler = RDMConsistency()
ceiling = Score([1, np.nan],
coords={'aggregation': ['center', 'error']},
dims=['aggregation'])
return RDMBenchmark(identifier=f'aru.Cichy2019-rdm',
version=1,
assembly=assembly,
similarity_metric=similarity_metric,
visual_degrees=VISUAL_DEGREES,
ceiling_func=lambda: ceiling,
parent='Kamila',
number_of_trials=1,
region='IT',
time_bins=[(62.5, 156.2)])
def AruKuzovkin2018PLS():
assembly_repetition = LazyLoad(
lambda: load_assembly(average_repetitions=False))
assembly = LazyLoad(lambda: load_assembly(average_repetitions=True))
# similarity_metric = CrossRegressedCorrelation(
# regression=pls_regression(),
# correlation=pearsonr_correlation(),
# crossvalidation_kwargs=dict(stratification_coord=None)
# )
similarity_metric = RDMCrossValidated(crossvalidation_kwargs=dict(
stratification_coord=None))
# sub-select the entire IT region
# x = assembly['region']
# idx = x.data == 20
# sub-select only important it electrodes for IT: 1094
# idx = np.zeros((len(assembly.neuroid_id)), dtype=bool)
# idx[1094] = True
# idx[847] = True
#idx = get_IT_MNI()
idx = it_idx
new_assembly = assembly[:, idx, :]
# Can replace the "name" of the brain region
new_assembly = type(new_assembly)(
new_assembly.values,
coords={
coord: (dims, values if coord != 'region' else ['IT'] *
len(new_assembly['region']))
for coord, dims, values in walk_coords(new_assembly)
},
dims=new_assembly.dims)
new_assembly.attrs = assembly.attrs
new_assembly = new_assembly.where(new_assembly['time_bin_start'] < 350,
drop=True)
new_assembly = new_assembly.where(new_assembly['time_bin_start'] > 250,
drop=True)
new_assembly = new_assembly.mean('time_bin')
new_assembly = new_assembly.expand_dims('time_bin')
new_assembly.coords['time_bin'] = pd.MultiIndex.from_tuples(
[(62.5, 156.2)], names=['time_bin_start', 'time_bin_end'])
new_assembly = new_assembly.squeeze('time_bin')
new_assembly.attrs = assembly.attrs
ceiling = Score([1, np.nan],
coords={'aggregation': ['center', 'error']},
dims=['aggregation'])
return NeuralBenchmark(identifier=f'aru.Kuzovkin2018-pls',
version=1,
assembly=new_assembly,
similarity_metric=similarity_metric,
visual_degrees=VISUAL_DEGREES,
ceiling_func=lambda: ceiling,
parent='Kamila',
number_of_trials=1)