pysad.models.HalfSpaceTrees

class pysad.models.HalfSpaceTrees(feature_mins, feature_maxes, window_size=100, num_trees=25, max_depth=15, initial_window_X=None)[source]

Half-Space Trees method [BTTL11].

Parameters:

  • feature_mins (np.float64 array of shape (num_features,)) – Minimum boundary of the features.

  • feature_maxes (np.float64 array of shape (num_features,)) – Maximum boundary of the features.

  • window_size (int) – The size of the window (Default=100).

  • num_trees (int) – The number of treesint (Default=25).

  • max_depth (int) – Maximum depth of the trees (Default=15).

  • initial_window_X (np.float64 array of shape (num_initial_instances,num_features)) – The initial window to fit for initial calibration period. If not None, we simply apply fit to these instances (Default=None).

Methods

__init__(feature_mins, feature_maxes[, ...])

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)[source]

Fits the model to next instance.

Parameters:

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

  • 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)[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:

float