def ToliasCadena2017PLS():
loader = AssemblyLoader()
assembly_repetition = loader(average_repetition=False)
assembly = loader(average_repetition=True)
assembly.stimulus_set.name = assembly.stimulus_set_name
similarity_metric = CrossRegressedCorrelation(
regression=pls_regression(),
correlation=pearsonr_correlation(),
crossvalidation_kwargs={'stratification_coord': None})
identifier = f'tolias.Cadena2017-pls'
ceiler = InternalConsistency(split_coord='repetition_id')
def ceiling():
# This assembly has many stimuli that are only shown to a subset of the neurons.
# When the loader runs with `average_repetition=True`, it automatically drops nan cells,
# but for the `assembly_repetition`, it keeps all the rows.
# If we now use the usual ceiling approach, the two halves will end up with NaN values
# due to stimuli not being shown to neurons, which doesn't let us correlate.
# Instead, we here drop all NaN cells and their corresponding stimuli,
# which keeps only 43% of the original presentation rows, but lets us correlate again.
assembly_nonan, stimuli = loader.dropna(assembly_repetition, assembly_repetition.attrs['stimulus_set'])
return ceiler(assembly_nonan)
return NeuralBenchmark(identifier=identifier, version=1,
assembly=assembly, similarity_metric=similarity_metric, visual_degrees=VISUAL_DEGREES,
ceiling_func=ceiling)
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 DicarloSanghavi2020ITPLS():
return _DicarloSanghavi2020Region(
'IT',
identifier_metric_suffix='pls',
similarity_metric=CrossRegressedCorrelation(
regression=pls_regression(),
correlation=pearsonr_correlation(),
crossvalidation_kwargs=dict(stratification_coord='object_name')),
ceiler=InternalConsistency())
def MovshonFreemanZiemba2013V2PLS():
return _MovshonFreemanZiemba2013Region(
'V2',
identifier_metric_suffix='pls',
similarity_metric=CrossRegressedCorrelation(
regression=pls_regression(),
correlation=pearsonr_correlation(),
crossvalidation_kwargs=dict(stratification_coord='texture_type')),
ceiler=InternalConsistency())
def __init__(self, bold_shift=None):
assembly = load_Fedorenko2016()
self._target_assembly = assembly
self._regression = pls_regression(xarray_kwargs=dict(stimulus_coord='stimulus_id')) # word
self._correlation = pearsonr_correlation(xarray_kwargs=dict(correlation_coord='stimulus_id'))
self._metric = CrossRegressedCorrelation(
regression=self._regression, correlation=self._correlation,
# crossvalidation_kwargs=dict(split_coord='sentence_id', stratification_coord=None))
crossvalidation_kwargs=dict(split_coord='stimulus_id', stratification_coord='sentence_id', train_size=.8))
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', ['some_region'] * 25)},
dims=['presentation', 'neuroid'])
metric = CrossRegressedCorrelation(regression=pls_regression(), correlation=pearsonr_correlation())
score = metric(source=assembly, target=assembly)
assert score.sel(aggregation='center') == approx(1, abs=.00001)
def __init__(self, bold_shift=None):
assembly = load_Fedorenko2016()
self._target_assembly = assembly
# avg code
# packaging file, change the benchmark to include averaging.
self._regression = pls_regression(xarray_kwargs=dict(stimulus_coord='stimulus_id')) # word
self._correlation = pearsonr_correlation(xarray_kwargs=dict(correlation_coord='stimulus_id'))
self._metric = CrossRegressedCorrelation(
regression=self._regression, correlation=self._correlation,
crossvalidation_kwargs=dict(split_coord='stimulus_id', stratification_coord='stimulus_id'))
def _standard_benchmark(identifier, load_assembly, visual_degrees, number_of_trials, stratification_coord, bibtex):
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=stratification_coord))
ceiler = InternalConsistency()
return NeuralBenchmark(identifier=f"{identifier}-pls", version=1,
assembly=assembly, similarity_metric=similarity_metric,
visual_degrees=visual_degrees, number_of_trials=number_of_trials,
ceiling_func=lambda: ceiler(assembly_repetition),
parent=None,
bibtex=bibtex)
def _standard_benchmark(identifier, load_assembly, stratification_coord):
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=stratification_coord))
ceiler = InternalConsistency()
return NeuralBenchmark(
identifier=f"{identifier}-pls",
version=1,
assembly=assembly,
similarity_metric=similarity_metric,
ceiling_func=lambda: ceiler(assembly_repetition),
parent=None,
paper_link='http://www.jneurosci.org/content/35/39/13402.short')
def test_small(self):
values = (np.arange(30 * 25 * 5) +
np.random.standard_normal(30 * 25 * 5)).reshape((30, 25, 5))
assembly = NeuroidAssembly(
values,
coords={
'image_id': ('presentation', np.arange(30)),
'object_name': ('presentation', ['a', 'b', 'c'] * 10),
'neuroid_id': ('neuroid', np.arange(25)),
'region': ('neuroid', ['some_region'] * 25),
'time_bin_start': ('time_bin', list(range(5))),
'time_bin_end': ('time_bin', list(range(1, 6))),
},
dims=['presentation', 'neuroid', 'time_bin'])
regression = TemporalRegressionAcrossTime(pls_regression())
regression.fit(source=assembly, target=assembly)
prediction = regression.predict(source=assembly)
assert all(prediction['image_id'] == assembly['image_id'])
assert all(prediction['neuroid_id'] == assembly['neuroid_id'])
assert all(prediction['time_bin'] == assembly['time_bin'])
