pysad.models.ExactStorm

class pysad.models.ExactStorm(window_size=10000, max_radius=0.1)

The Exact-STORM method [BAF07]. This method assigns anomaly score that is the mean of distances to the instances in window of length window_size with distnaces less than max_radius. Note that the decision making in [BAF07] is not implemented.

Parameters:

• window_size – int (Default=10000) The number of instances in the window to score.

• max_radius – float (Default=0.1) Maximum radius for the near instance selection.

Methods

__init__([window_size, max_radius])
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.
score_partial(X)	Scores the anomalousness of the next instance.

fit(X, y=None)

Fits the model to all instances in order.

Parameters:

• X (np.float64 array of shape (num_instances, num_features)) – The instances in order to fit.

• y (int) – The labels of the instances in order to fit (Optional for unsupervised models, default=None).

Returns:

Fitted model.

Return type:

object

fit_partial(X, y=None)

Fits the model to next instance. Simply, adds the instance to the window.

Parameters:

• X (np.float64 array of shape (num_features,)) – The instance to fit.

• y (int) – Ignored since the model is unsupervised (Default=None).

Returns:

self.

Return type:

object

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.

Parameters:

• X (np.float64 array of shape (num_features,)) – The instance to fit and score.

• y (int) – The label of the instance (Optional for unsupervised models, default=None).

Returns:

The anomalousness score of the input instance.

Return type:

float

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)

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:

float