1 - gpd.cdf(score) < threshold

examples/anomaly_detection_pytorch.py

@@ -90,6 +90,12 @@ def main(args):
  prediction_length=dataset.metadata.prediction_length,
  context_length=args.context_length,
  freq=dataset.metadata.freq,
+ num_feat_static_cat=len(dataset.metadata.feat_static_cat),
+ cardinality=[
+ int(cat_feat_info.cardinality)
+ for cat_feat_info in dataset.metadata.feat_static_cat
+ ],
+ embedding_dimension=[3],
  trainer_kwargs=dict(
  max_epochs=args.max_epochs,
  ),
@@ -108,6 +114,7 @@ def main(args):
  )
 
  anomalies = []
+ means = []
  model.eval()
  with torch.no_grad():
  for batch in tqdm(test_data_loader, desc="Processing batches"):
@@ -154,19 +161,20 @@ def main(args):
  )
  scaled_past_target = inputs["past_target"] / scale
  batch_anomalies = []
+ batch_means = []
  for i in tqdm(
  range(inputs["future_target"].shape[1]),
  desc="Processing prediction length",
  leave=False,
  ):
  target = inputs["future_target"][:, i : i + 1]
  score = -distr.log_prob(target)
-
+ batch_means.append(distr.mean)
  # only check if its an anomaly for scores greater than gpd.loc for each entry in the batch
  is_anomaly = torch.where(
  score < gpd.loc,
  False,
- gpd.cdf(score) < args.anomaly_threshold,
+ 1 - gpd.cdf(score) < args.anomaly_threshold,
  )
  batch_anomalies.append(is_anomaly)
 
@@ -193,14 +201,19 @@ def main(args):
  distr = model.output_distribution(params, scale=scale)
  # stack the batch_anomalies along the prediction length dimension
  anomalies.append(torch.stack(batch_anomalies, dim=1))
+ means.append(torch.stack(batch_means, dim=1))
  # concat the anomalies along the batch dimension
  anomalies = torch.cat(anomalies, dim=0).cpu().numpy()
+ means = torch.cat(means, dim=0).cpu().numpy()
 
- # save as csv
+ # save as pkl
  all_dates = []
  all_flags = []
  all_targets = []
- for i, (entry, flags) in enumerate(zip(dataset.test, anomalies)):
+ all_means = []
+ for i, (entry, flags, mean) in enumerate(
+ zip(dataset.test, anomalies, means)
+ ):
  start_date = entry["start"].to_timestamp()
  target = entry["target"]
  dates = pd.date_range(
@@ -212,13 +225,19 @@ def main(args):
  all_dates.append(date_index)
  all_flags.append(flags.flatten().astype(bool))
  all_targets.append(target_slice)
+ all_means.append(mean.flatten())
 
  # create a dataframe with the date_index and the flags
  anomaly_df = pd.DataFrame(
- {"date": all_dates, "is_anomaly": all_flags, "target": all_targets}
+ {
+ "date": all_dates,
+ "is_anomaly": all_flags,
+ "target": all_targets,
+ "mean": all_means,
+ }
  )
  anomaly_df.set_index("date", inplace=True)
- anomaly_df.to_csv(f"anomalies_{args.dataset}.csv")
+ anomaly_df.to_pickle(f"anomalies_{args.dataset}.pkl")
 
 
 if __name__ == "__main__":
@@ -246,7 +265,7 @@ def main(args):
  parser.add_argument(
  "--top_score_percentage",
  type=float,
- default=0.2,
+ default=0.1,
  help="Percentage of top scores to consider for GPD fitting",
  )
  parser.add_argument(