def test_nan_forecasts_consistency(self):
rs = np.random.RandomState(123)
# make some forecasts entirely missing
self.forecasts[rs.rand(*self.obs.shape) > 0.5] = np.nan
assert_allclose(crps_ensemble(self.obs, self.forecasts), _crps_ensemble_vectorized(self.obs, self.forecasts))
# forecasts shaped like obs
forecasts = self.forecasts[:, 0]
assert_allclose(crps_ensemble(self.obs, forecasts), _crps_ensemble_vectorized(self.obs, forecasts))
def test_nan_forecasts_consistency(self):
rs = np.random.RandomState(123)
# make some forecasts entirely missing
self.forecasts[rs.rand(*self.obs.shape) > 0.5] = np.nan
assert_allclose(crps_ensemble(self.obs, self.forecasts),
_crps_ensemble_vectorized(self.obs, self.forecasts))
# forecasts shaped like obs
forecasts = self.forecasts[:, 0]
assert_allclose(crps_ensemble(self.obs, forecasts),
_crps_ensemble_vectorized(self.obs, forecasts))
def test_crps_toy_examples(self):
examples = [
(0, 0, 0.0),
(0, 1, 1.0),
(-1, 0, 1.0),
(0, [-1], 1.0),
(0, [0], 0.0),
(0, [1], 1.0),
(0, [0, 0], 0.0),
(0, [0, 1], 0.25),
(0, [1, 0], 0.25),
(0, [1, 1], 1.0),
(2, [0, 1], 1.25),
(0, [-1, 1], 0.5),
(0, [0, 0, 1], 1.0 / 9),
(1, [0, 0, 1], 4.0 / 9),
(0, [-1, 0, 0, 1], 1.0 / 8),
]
for x, ensemble, expected in examples:
self.assertAlmostEqual(crps_ensemble(x, ensemble), expected)
self.assertAlmostEqual(_crps_ensemble_vectorized(x, ensemble), expected)
def test_crps_toy_examples(self):
examples = [
(0, 0, 0.0),
(0, 1, 1.0),
(-1, 0, 1.0),
(0, [-1], 1.0),
(0, [0], 0.0),
(0, [1], 1.0),
(0, [0, 0], 0.0),
(0, [0, 1], 0.25),
(0, [1, 0], 0.25),
(0, [1, 1], 1.0),
(2, [0, 1], 1.25),
(0, [-1, 1], 0.5),
(0, [0, 0, 1], 1.0 / 9),
(1, [0, 0, 1], 4.0 / 9),
(0, [-1, 0, 0, 1], 1.0 / 8),
]
for x, ensemble, expected in examples:
self.assertAlmostEqual(crps_ensemble(x, ensemble), expected)
self.assertAlmostEqual(_crps_ensemble_vectorized(x, ensemble),
expected)
def test_nan_observations_consistency(self):
rs = np.random.RandomState(123)
self.obs[rs.rand(*self.obs.shape) > 0.5] = np.nan
assert_allclose(
crps_ensemble(self.obs, self.forecasts),
_crps_ensemble_vectorized(self.obs, self.forecasts))
def test_high_dimensional_consistency(self):
obs = np.random.randn(10, 20)
forecasts = np.random.randn(10, 20, 5)
assert_allclose(crps_ensemble(obs, forecasts),
_crps_ensemble_vectorized(obs, forecasts))
def test_nan_observations_consistency(self):
rs = np.random.RandomState(123)
self.obs[rs.rand(*self.obs.shape) > 0.5] = np.nan
assert_allclose(crps_ensemble(self.obs, self.forecasts),
_crps_ensemble_vectorized(self.obs, self.forecasts))
def test_high_dimensional_consistency(self):
obs = np.random.randn(10, 20)
forecasts = np.random.randn(10, 20, 5)
assert_allclose(crps_ensemble(obs, forecasts),
_crps_ensemble_vectorized(obs, forecasts))
