pysad.models.StandardAbsoluteDeviation

class pysad.models.StandardAbsoluteDeviation(substracted_statistic='mean', absolute=True)

The model that assigns the deviation from the mean (or median) and divides with the standard deviation. This model is based on the 3-Sigma rule described in [BHVK17].

substracted_statistic (str): The statistic to be substracted for scoring. It is either “mean” or “median”. (Default=”mean”). absolute (bool): Whether to output score’s absolute value. (Default=True).

Methods

__init__([substracted_statistic, absolute])
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.

Parameters:

• X (np.float64 array of shape (1,)) – The instance to fit. Note that this model is univariate.

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

Returns:

Returns the 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 (1,)) – 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