pysad.models.LocalOutlierProbability¶
- class pysad.models.LocalOutlierProbability(initial_X, num_neighbors=10, extent=3)[source]¶
The implementation of streaming Local Outlier Probabilities method [BKKrogerSZ09], which uses the implementation of PyNomaly library [BCon18].
- Parameters:
initial_X (np.float64 array of shape (num_instances, num_features)) – Initial training data to calibrate the model.
num_neighbors (int) – Number of neighbors (Default=10).
extent (int) – an integer value that controls the statistical extent, e.g. lambda times the standard deviation from the mean (optional, default 3)
n_neighbors (int) – the total number of neighbors to consider w.r.t. each sample (optional, default 10)
Methods
__init__
(initial_X[, num_neighbors, extent])fit
(X[, y])Fits the model to all instances in order.
fit_partial
(X[, y])Fits the model to next instance.
fit_score
(X[, y])This helper method applies fit_score_partial to all instances in order.
fit_score_partial
(X[, y])Applies fit_partial and score_partial to the next instance, respectively.
score
(X)Scores all instaces via score_partial iteratively.
Scores the anomalousness of the next instance.
- fit(X, y=None)¶
Fits the model to all instances in order.
- fit_score(X, y=None)¶
This helper method applies fit_score_partial to all instances in order.
- Parameters:
X (np.float64 array of shape (num_instances, num_features)) – The instances in order to fit.
y (np.int32 array of shape (num_instances, )) – The labels of the instances in order to fit (Optional for unsupervised models, default=None).
- Returns:
The anomalousness scores of the instances in order.
- Return type:
np.float64 array of shape (num_instances,)
- fit_score_partial(X, y=None)¶
Applies fit_partial and score_partial to the next instance, respectively.
- score(X)¶
Scores all instaces via score_partial iteratively.
- Parameters:
X (np.float64 array of shape (num_instances, num_features)) – The instances in order to score.
- Returns:
The anomalousness scores of the instances in order.
- Return type:
np.float64 array of shape (num_instances,)
- score_partial(X)[source]¶
Scores the anomalousness of the next instance.
- Parameters:
X (np.float64 array of shape (num_features,)) – The instance to score. Higher scores represent more anomalous instances whereas lower scores correspond to more normal instances.
- Returns:
The anomalousness score of the input instance.
- Return type: