from pyod.models.iforest import IForest
from dtaianomaly.anomaly_detection.BaseDetector import Supervision
from dtaianomaly.anomaly_detection.PyODAnomalyDetector import PyODAnomalyDetector
[docs]
class IsolationForest(PyODAnomalyDetector):
"""
Anomaly detector based on the Isolation Forest algorithm.
The isolation forest [Liu2008isolation]_ generates random binary trees to
split the data. If an instance requires fewer splits to isolate it from
the other data, it is nearer to the root of the tree, and consequently
receives a higher anomaly score.
Notes
-----
The isolation forest inherets from :py:class:`~dtaianomaly.anomaly_detection.PyodAnomalyDetector`.
Parameters
----------
window_size: int or str
The window size to use for extracting sliding windows from the time series. This
value will be passed to :py:meth:`~dtaianomaly.anomaly_detection.compute_window_size`.
stride: int, default=1
The stride, i.e., the step size for extracting sliding windows from the time series.
**kwargs
Arguments to be passed to the PyOD isolation forest.
Attributes
----------
window_size_: int
The effectively used window size for this anomaly detector
pyod_detector_ : IForest
An Isolation Forest detector of PyOD
Examples
--------
>>> from dtaianomaly.anomaly_detection import IsolationForest
>>> from dtaianomaly.data import demonstration_time_series
>>> x, y = demonstration_time_series()
>>> isolation_forest = IsolationForest(10).fit(x)
>>> isolation_forest.decision_function(x)
array([-0.02301142, -0.01266304, -0.00786237, ..., -0.04561172,
-0.0420979 , -0.04414417])
References
----------
.. [Liu2008isolation] F. T. Liu, K. M. Ting and Z. -H. Zhou, "Isolation Forest,"
2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy, 2008,
pp. 413-422, doi: `10.1109/ICDM.2008.17 <https://doi.org/10.1109/ICDM.2008.17>`_.
"""
def _initialize_detector(self, **kwargs) -> IForest:
return IForest(**kwargs)
def _supervision(self):
return Supervision.UNSUPERVISED