def __init__(self):
ceiling = Score([1, np.nan],
coords={'aggregation': ['center', 'error']},
dims=['aggregation'])
assembly_repetition = get_assembly()
assert len(np.unique(assembly_repetition['region'])) == 1
assert hasattr(assembly_repetition, 'repetition')
self.region = 'IT'
self.assembly = average_repetition(assembly_repetition)
self._assembly = self.assembly
self.timebins = timebins_from_assembly(self.assembly)
self._similarity_metric = CrossRegressedCorrelation(
regression=pls_regression(),
correlation=pearsonr_correlation(),
crossvalidation_kwargs=dict(
stratification_coord=Split.Defaults.stratification_coord
if hasattr(self.assembly, Split.Defaults.stratification_coord
) else None))
identifier = f'{assembly_repetition.name}-layer_selection'
ceiler = InternalConsistency()
super(_MockBenchmark,
self).__init__(identifier=identifier,
ceiling_func=lambda: ceiler(assembly_repetition),
version='1.0')
def load_assembly(average_repetitions):
assembly = brainscore.get_assembly(name='aru.Kuzovkin2018')
if average_repetitions:
assembly = average_repetition(assembly)
# assembly.reset_index('time', drop=True, inplace=True)
return assembly
def load_assembly(average_repetitions):
assembly = brainscore.get_assembly(name='aru.Cichy2019')
# fix the off by 1 error with the stimulus set
# assembly = assembly['image_id'] + 1
if average_repetitions:
assembly = average_repetition(assembly)
return assembly
def load_assembly(average_repetitions, region, access='private'):
assembly = brainscore.get_assembly(name=f'dicarlo.Majaj2015.{access}')
assembly = assembly.sel(region=region)
assembly['region'] = 'neuroid', [region] * len(assembly['neuroid'])
assembly = assembly.squeeze("time_bin")
assembly.load()
assembly = assembly.transpose('presentation', 'neuroid')
if average_repetitions:
assembly = average_repetition(assembly)
return assembly
def load_assembly(average_repetitions, region):
assembly = brainscore.get_assembly(name=f'dicarlo.Rajalingham2020')
assembly = assembly.sel(region=region)
assembly['region'] = 'neuroid', [region] * len(assembly['neuroid'])
assembly.load()
assembly = assembly.squeeze('time_bin')
assert NUMBER_OF_TRIALS == len(np.unique(assembly.coords['repetition']))
if average_repetitions:
assembly = average_repetition(assembly)
return assembly
def load_assembly(average_repetitions, region):
assembly = brainscore.get_assembly(name=f'dicarlo.Sanghavi2020')
assembly = assembly.sel(region=region)
assembly['region'] = 'neuroid', [region] * len(assembly['neuroid'])
assembly.load()
assembly = assembly.sel(time_bin_id=0) # 70-170ms
assembly = assembly.squeeze('time_bin')
assert NUMBER_OF_TRIALS == len(np.unique(assembly.coords['repetition']))
assert VISUAL_DEGREES == assembly.attrs['image_size_degree']
if average_repetitions:
assembly = average_repetition(assembly)
return assembly
def load_assembly(average_repetitions, region, access='private'):
assembly = brainscore.get_assembly(f'movshon.FreemanZiemba2013.{access}')
assembly = assembly.sel(region=region)
assembly = assembly.stack(
neuroid=['neuroid_id']) # work around xarray multiindex issues
assembly['region'] = 'neuroid', [region] * len(assembly['neuroid'])
assembly.load()
time_window = (50, 200)
assembly = assembly.sel(time_bin=[(t, t + 1) for t in range(*time_window)])
assembly = assembly.mean(dim='time_bin', keep_attrs=True)
assembly = assembly.expand_dims('time_bin_start').expand_dims(
'time_bin_end')
assembly['time_bin_start'], assembly['time_bin_end'] = [time_window[0]], [
time_window[1]
]
assembly = assembly.stack(time_bin=['time_bin_start', 'time_bin_end'])
assembly = assembly.squeeze('time_bin')
assembly = assembly.transpose('presentation', 'neuroid')
if average_repetitions:
assembly = average_repetition(assembly)
return assembly