def test_crps_empirical(num_samples, event_shape):
truth = torch.randn(event_shape)
pred = truth + 0.1 * torch.randn((num_samples, ) + event_shape)
actual = crps_empirical(pred, truth)
assert actual.shape == truth.shape
expected = (pred - truth).abs().mean(
0) - 0.5 * (pred - pred.unsqueeze(1)).abs().mean([0, 1])
assert_close(actual, expected)
def eval_crps(pred, truth):
"""
Evaluate continuous ranked probability score, averaged over all data
elements.
**References**
[1] Tilmann Gneiting, Adrian E. Raftery (2007)
`Strictly Proper Scoring Rules, Prediction, and Estimation`
https://www.stat.washington.edu/raftery/Research/PDF/Gneiting2007jasa.pdf
:param torch.Tensor pred: Forecasted samples.
:param torch.Tensor truth: Ground truth.
:rtype: float
"""
return crps_empirical(pred, truth).mean().cpu().item()
def eval_one(args, result):
logging.debug('evaluating')
pred = result['forecast']
truth = result['truth']
t, n, n = truth.shape
assert pred.shape == (args.num_samples, t, n, n)
# Evaluate point estimate using Mean Absolute Error.
mae = float((pred.median(dim=0).values - truth).abs().mean())
# Evaluate uncertainty using negative Continuous Ranked Probability Score.
crps = float(crps_empirical(pred, truth).mean())
result = {'MAE': mae, 'CRPS': crps, 'ELBO': result['log_prob']}
logging.info(result)
return result
def eval_crps(pred, truth):
"""
Like pyro.contrib.forecast.eval_crps but does not average over batch dimensions.
"""
logger.info("Evaluating CRPS...")
return crps_empirical(pred, truth).reshape(truth.shape[:-2] + (-1,)).mean(-1)
