def test_precomputed(self, model, expected_score):
benchmark = DicarloRajalingham2018I2n()
probabilities = pd.read_pickle(
os.path.join(os.path.dirname(__file__), '..', 'test_metrics',
f'{model}-probabilities.pkl'))['data']
probabilities = BehavioralAssembly(probabilities)
candidate = PrecomputedProbabilities(probabilities)
score = benchmark(candidate)
assert score.raw.sel(aggregation='center') == approx(expected_score,
abs=.005)
assert score.sel(aggregation='center') == approx(expected_score /
np.sqrt(.479),
abs=.005)
def test_precomputed(self, model, expected_score):
benchmark = DicarloRajalingham2018I2n()
probabilities = Path(
__file__
).parent.parent / 'test_metrics' / f'{model}-probabilities.nc'
probabilities = BehavioralAssembly(xr.load_dataarray(probabilities))
candidate = PrecomputedProbabilities(probabilities)
score = benchmark(candidate)
assert score.raw.sel(aggregation='center') == approx(expected_score,
abs=.005)
assert score.sel(aggregation='center') == approx(expected_score /
np.sqrt(.479),
abs=.005)
def test_ceiling(self):
benchmark = DicarloRajalingham2018I2n()
ceiling = benchmark.ceiling
assert ceiling.sel(aggregation='center') == approx(.479, abs=.0064)