pysad.models.IForestASD¶
- class pysad.models.IForestASD(initial_window_X=None, window_size=2048)[source]¶
An Anomaly Detection Approach Based on Isolation Forest Algorithm for Streaming Data using Sliding Window [BDF13]. Note that concept drift is not implemented since it is a part of the simulation. See Algorithm 2 in “An Anomaly Detection Approach Based on Isolation Forest Algorithm for Streaming Data using Sliding Window” paper. This method is unsupervised so it is not needed to give y as parameter.
- Parameters:
initial_window_X (np.float64 array of shape (num_initial_instances,num_features)) – The initial window to fit for initial calibration period. We simply apply fit to these instances (Default=None).
window_size (int) – The size of the reference window and its sliding (Default=2048).
Methods
__init__
([initial_window_X, window_size])- param model_cls:
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.
Removes the old model from the memory and instantiates a new one.
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_partial(X, y=None)¶
Fits the model to next instance.
- 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.
- reset_model()¶
Removes the old model from the memory and instantiates a new one.
- 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: