alibi_detect.od.isolationforest

Constants

logger

logger: logging.Logger = <Logger alibi_detect.od.isolationforest (WARNING)>

Instances of the Logger class represent a single logging channel. A "logging channel" indicates an area of an application. Exactly how an "area" is defined is up to the application developer. Since an application can have any number of areas, logging channels are identified by a unique string. Application areas can be nested (e.g. an area of "input processing" might include sub-areas "read CSV files", "read XLS files" and "read Gnumeric files"). To cater for this natural nesting, channel names are organized into a namespace hierarchy where levels are separated by periods, much like the Java or Python package namespace. So in the instance given above, channel names might be "input" for the upper level, and "input.csv", "input.xls" and "input.gnu" for the sub-levels. There is no arbitrary limit to the depth of nesting.

IForest

Inherits from: BaseDetector, FitMixin, ThresholdMixin, ABC

Constructor

IForest(self, threshold: float = None, n_estimators: int = 100, max_samples: Union[str, int, float] = 'auto', max_features: Union[int, float] = 1.0, bootstrap: bool = False, n_jobs: int = 1, data_type: str = 'tabular') -> None

Name	Type	Default	Description
threshold	Optional[float]	None	Threshold used for outlier score to determine outliers.
n_estimators	int	100	Number of base estimators in the ensemble.
max_samples	Union[str, int, float]	'auto'	Number of samples to draw from the training data to train each base estimator. If int, draw 'max_samples' samples. If float, draw 'max_samples * number of features' samples. If 'auto', max_samples = min(256, number of samples)
max_features	Union[int, float]	1.0	Number of features to draw from the training data to train each base estimator. If int, draw 'max_features' features. If float, draw 'max_features * number of features' features.
bootstrap	bool	False	Whether to fit individual trees on random subsets of the training data, sampled with replacement.
n_jobs	int	1	Number of jobs to run in parallel for 'fit' and 'predict'.
data_type	str	'tabular'	Optionally specify the data type (tabular, image or time-series). Added to metadata.

Methods

fit

fit(X: numpy.ndarray, sample_weight: Optional[numpy.ndarray] = None) -> None

Fit isolation forest.

Name	Type	Default	Description
X	numpy.ndarray		Training batch.
sample_weight	Optional[numpy.ndarray]	None	Sample weights.

Returns

• Type: None

infer_threshold

infer_threshold(X: numpy.ndarray, threshold_perc: float = 95.0) -> None

Update threshold by a value inferred from the percentage of instances considered to be

outliers in a sample of the dataset.

Name	Type	Default	Description
X	numpy.ndarray		Batch of instances.
threshold_perc	float	95.0	Percentage of X considered to be normal based on the outlier score.

Returns

• Type: None

predict

predict(X: numpy.ndarray, return_instance_score: bool = True) -> Dict[Dict[str, str], Dict[numpy.ndarray, numpy.ndarray]]

Compute outlier scores and transform into outlier predictions.

Name	Type	Default	Description
X	numpy.ndarray		Batch of instances.
return_instance_score	bool	True	Whether to return instance level outlier scores.

Returns

• Type: Dict[Dict[str, str], Dict[numpy.ndarray, numpy.ndarray]]

score

score(X: numpy.ndarray) -> numpy.ndarray

Compute outlier scores.

Name	Type	Default	Description
X	numpy.ndarray		Batch of instances to analyze.

Returns

• Type: numpy.ndarray