Filter out data points with low kNN density. Link to blog: https://towardsdatascience.com/how-can-i-trust-you-fb433a06256c?source=friends_link&sk=0af208dc53be2a326d2407577184686b

filter_by_density.py

def filter_by_distance_knn(self, X: np.ndarray) -> np.ndarray:
  kdtree = KDTree(X, leaf_size=self.leaf_size, metric=self.metric)
  knn_r = kdtree.query(
          X, k=self.k_filter + 1
          )[0]  # distances from 0 to k-nearest points
  if self.dist_filter_type == 'point':
      knn_r = knn_r[:, -1]
  elif self.dist_filter_type == 'mean':
      knn_r = np.mean(
              knn_r[:, 1:], axis=1
              )  # exclude distance of instance to itself
  cutoff_r = np.percentile(
          knn_r, (1 - self.alpha) * 100
          )  # cutoff distance
  X_keep = X[np.where(knn_r <= cutoff_r)[0], :]  # define instances to keep
  return X_keep