fit returns GPD without validation

examples/anomaly_detection_pytorch.py

@@ -37,7 +37,7 @@ def fit_gpd(data, num_iterations=100, learning_rate=0.001):
  learning_rate (float): Learning rate for the optimizer
 
  Returns:
- GeneralizedPareto: Fitted GPD(loc, scale, concentration) distribution
+ GeneralizedPareto: Fitted GPD(loc, scale, concentration) distribution without any validation
  """
  batch_size, _ = data.shape
 
@@ -75,7 +75,10 @@ def closure():
  optimizer.step(closure)
 
  return GeneralizedPareto(
- loc.detach(), scale.detach(), concentration.detach()
+ loc.detach(),
+ scale.detach(),
+ concentration.detach(),
+ validate_args=False,
  )
 
 
@@ -152,12 +155,11 @@ def main(args):
  leave=False,
  ):
  score = -distr.log_prob(scaled_future_target[:, i : i + 1])
- # check if the score are less than gpd.loc? for each entry in the batch
- is_anomaly = score < gpd.loc
- # mask out the score where is_anomaly is True
- score = torch.where(is_anomaly, gpd.loc + 1, score)
+ # only check if its an anomaly for scores greater than gpd.loc for each entry in the batch
  is_anomaly = torch.where(
- is_anomaly, False, gpd.cdf(score) < args.anomaly_threshold
+ score < gpd.loc,
+ False,
+ gpd.cdf(score) < args.anomaly_threshold,
  )
  batch_anomalies.append(is_anomaly)