icrlearn.rarity.calculate_cb_loop

icrlearn.rarity.calculate_cb_loop(X, y, min_score=0.0, extent=3, n_neighbors=10, timing=False)

Calculate Class-Based Local Outlier Probability (CB-LoOP) rarity scores.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features)

The input samples. Internally, its dtype will be converted to dtype=np.float32. If a sparse matrix is provided, it will be converted into a sparse csc_matrix.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

The class labels of the input samples.

min_scorefloat, default=0.0

The minimum rarity score to assign to samples that are not rare. Samples with a rarity score smaller than min_score will be set to min_score.

extentint, default=3

The extent of the local neighborhood to consider. See PyNomaly.loop.LocalOutlierProbability() for more details.

n_neighborsint, default=10

The number of neighbors to consider.

timingbool, default=False

If True, prints the time taken for processing each class.

Returns:

np.ndarray of shape (n_samples,)

The rarity scores for each sample in X. If min_score is set to 0.0, the scores will be in the range [0, 1], where 0 indicates a common sample and 1 indicates a rare sample.