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class
kenchi.outlier_detection.density_based.
LOF
(algorithm='auto', contamination='auto', leaf_size=30, metric='minkowski', novelty=False, n_jobs=1, n_neighbors=20, p=2, metric_params=None)[source]¶ Bases:
kenchi.outlier_detection.base.BaseOutlierDetector
Local Outlier Factor.
Parameters: - algorithm (str, default 'auto') – Tree algorithm to use. Valid algorithms are [‘kd_tree’|’ball_tree’|’auto’].
- contamination (float, default 'auto') – Proportion of outliers in the data set. Used to define the threshold.
- leaf_size (int, default 30) – Leaf size of the underlying tree.
- metric (str or callable, default 'minkowski') – Distance metric to use.
- novelty (bool, default False) – If True, you can use predict, decision_function and anomaly_score on new unseen data and not on the training data.
- n_jobs (int, default 1) – Number of jobs to run in parallel. If -1, then the number of jobs is set to the number of CPU cores.
- n_neighbors (int, default 20) – Number of neighbors.
- p (int, default 2) – Power parameter for the Minkowski metric.
- metric_params (dict, default None) – Additioal parameters passed to the requested metric.
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anomaly_score_
¶ array-like of shape (n_samples,) – Anomaly score for each training data.
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contamination_
¶ float – Actual proportion of outliers in the data set.
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threshold_
¶ float – Threshold.
References
[1] Breunig, M. M., Kriegel, H.-P., Ng, R. T., and Sander, J., “LOF: identifying density-based local outliers,” In Proceedings of SIGMOD, pp. 93-104, 2000. [2] Kriegel, H.-P., Kroger, P., Schubert, E., and Zimek, A., “Interpreting and unifying outlier scores,” In Proceedings of SDM, pp. 13-24, 2011. Examples
>>> import numpy as np >>> from kenchi.outlier_detection import LOF >>> X = np.array([ ... [0., 0.], [1., 1.], [2., 0.], [3., -1.], [4., 0.], ... [5., 1.], [6., 0.], [7., -1.], [8., 0.], [1000., 1.] ... ]) >>> det = LOF(n_neighbors=3) >>> det.fit_predict(X) array([ 1, 1, 1, 1, 1, 1, 1, 1, 1, -1])
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X_
¶ array-like of shape (n_samples, n_features) – Training data.
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n_neighbors_
¶ int – Actual number of neighbors used for
kneighbors
queries.
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negative_outlier_factor_
¶ array-like of shape (n_samples,) – Opposite LOF of the training samples.