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making conformance score more robust #16

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making conformance score more robust #16

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22 changes: 18 additions & 4 deletions src/signature_mahalanobis_knn/sig_mahal_knn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -181,6 +181,7 @@ def conformance(
signatures_test: np.ndarray | None = None,
n_neighbors: int = 20,
return_indices: bool = False,
take_median: bool = False,
) -> np.ndarray:
"""
Compute the conformance scores for the data points either passed in
Expand All @@ -202,9 +203,14 @@ def conformance(
signatures_test : np.ndarray | None, optional
Signatures of the data points, by default None.
Two dimensional array of shape (n_samples, sig_dim).
n_neighbors : int, optional
return_indices : bool, optional
Whether to return the indices of the nearest neighbors,
by default False.
take_median : bool, optional
Whether we should take the median distance of the k nearest neighbours.
By default, the pipeline takes the minimum distance. (min of k nearest neighbours = closest neighbour)
Setting take_median to true allows for a more robust measure.

Returns
-------
Expand Down Expand Up @@ -274,8 +280,16 @@ def conformance(
candidate_distances[denominator < self.mahal_distance.zero_thres] = 0
candidate_distances[rho > self.mahal_distance.subspace_thres] = np.inf

# compute the minimum of the candidate distances for each data point
if return_indices:
return np.min(candidate_distances, axis=-1), train_indices

if take_median:
# compute the median of the k nearest neighbour distances for each data point for robustness
if return_indices:
return np.median(candidate_distances, axis=-1), train_indices
return np.median(candidate_distances, axis=-1)

else:
# compute the minimum of the candidate distances for each data point = 1-nearest neighbour distance (the vanilla implementation)
if return_indices:
return np.min(candidate_distances, axis=-1), train_indices

return np.min(candidate_distances, axis=-1)
return np.min(candidate_distances, axis=-1)