alibi.confidence.trustscore module¶

class
alibi.confidence.trustscore.
TrustScore
(k_filter=10, alpha=0.0, filter_type=None, leaf_size=40, metric='euclidean', dist_filter_type='point')[source]¶ Bases:
object

__init__
(k_filter=10, alpha=0.0, filter_type=None, leaf_size=40, metric='euclidean', dist_filter_type='point')[source]¶ Initialize trust scores.
 Parameters
k_filter (
int
) – Number of neighbors used during either kNN distance or probability filtering.alpha (
float
) – Fraction of instances to filter out to reduce impact of outliers.filter_type (
Optional
[str
]) – Filter method; either ‘distance_knn’ or ‘probability_knn’leaf_size (
int
) – Number of points at which to switch to bruteforce. Affects speed and memory required to build trees. Memory to store the tree scales with n_samples / leaf_size.metric (
str
) – Distance metric used for the tree. See sklearn’s DistanceMetric class for a list of available metrics.dist_filter_type (
str
) – Use either the distance to the knearest point (dist_filter_type = ‘point’) or the average distance from the first to the knearest point in the data (dist_filter_type = ‘mean’).
 Return type
None

filter_by_distance_knn
(X)[source]¶ Filter out instances with low kNN density. Calculate distance to knearest point in the data for each instance and remove instances above a cutoff distance.
 Parameters
X (
ndarray
) – Data Return type
ndarray
 Returns
Filtered data.

filter_by_probability_knn
(X, Y)[source]¶ Filter out instances with high label disagreement amongst its k nearest neighbors.
 Parameters
X (
ndarray
) – DataY (
ndarray
) – Predicted class labels
 Return type
Tuple
[ndarray
,ndarray
] Returns
Filtered data and labels.

score
(X, Y, k=2, dist_type='point')[source]¶ Calculate trust scores = ratio of distance to closest class other than the predicted class to distance to predicted class.
 Parameters
X (
ndarray
) – Instances to calculate trust score for.Y (
ndarray
) – Either prediction probabilities for each class or the predicted class.k (
int
) – Number of nearest neighbors used for distance calculation.dist_type (
str
) – Use either the distance to the knearest point (dist_type = ‘point’) or the average distance from the first to the knearest point in the data (dist_type = ‘mean’).
 Return type
Tuple
[ndarray
,ndarray
] Returns
Batch with trust scores and the closest not predicted class.
