pysad.models.xStream

class pysad.models.xStream(num_components=100, n_chains=100, depth=25, window_size=25)[source]

The xStream model for row-streaming data [BMLA18]. It first projects the data via streamhash projection. It then fits half space chains by reference windowing. It scores the instances using the window fitted to the reference window.

Parameters
  • n_components (int) – The number of components for streamhash projection (Default=100).

  • n_chains (int) – The number of half-space chains (Default=100).

  • depth (int) – The maximum depth for the chains (Default=25).

  • window_size (int) – The size (and the sliding length) of the reference window (Default=25).

Methods

__init__([num_components, n_chains, depth, …])

Initialize self.

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.float 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.float 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.float array of shape (num_instances, num_features)) – The instances in order to fit.

  • y (np.int 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.float 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.float 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.float 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.float array of shape (num_instances,)

score_partial(X)[source]

Scores the anomalousness of the next instance.

Parameters

X (np.float 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

score (float)