alibi_detect.od.isolationforest module
- class alibi_detect.od.isolationforest.IForest(threshold=None, n_estimators=100, max_samples='auto', max_features=1.0, bootstrap=False, n_jobs=1, data_type='tabular')[source]
Bases:
BaseDetector
,FitMixin
,ThresholdMixin
- __init__(threshold=None, n_estimators=100, max_samples='auto', max_features=1.0, bootstrap=False, n_jobs=1, data_type='tabular')[source]
Outlier detector for tabular data using isolation forests.
- Parameters:
threshold (
Optional
[float
]) – Threshold used for outlier score to determine outliers.n_estimators (
int
) – Number of base estimators in the ensemble.max_samples (
Union
[str
,int
,float
]) – 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
]) – 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
) – Whether to fit individual trees on random subsets of the training data, sampled with replacement.n_jobs (
int
) – Number of jobs to run in parallel for ‘fit’ and ‘predict’.data_type (
str
) – Optionally specify the data type (tabular, image or time-series). Added to metadata.
- infer_threshold(X, threshold_perc=95.0)[source]
Update threshold by a value inferred from the percentage of instances considered to be outliers in a sample of the dataset.
- predict(X, return_instance_score=True)[source]
Compute outlier scores and transform into outlier predictions.
- Parameters:
X (
ndarray
) – Batch of instances.return_instance_score (
bool
) – Whether to return instance level outlier scores.
- Return type:
- Returns:
Dictionary containing
'meta'
and'data'
dictionaries. –'meta'
has the model’s metadata.'data'
contains the outlier predictions and instance level outlier scores.