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, ...])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:
score (float)