Source code for pyod.models.lof

# -*- coding: utf-8 -*-
"""Local Outlier Factor (LOF). Implemented on scikit-learn library.
# Author: Yue Zhao <>
# License: BSD 2 clause

from __future__ import division
from __future__ import print_function

from sklearn.neighbors import LocalOutlierFactor
from sklearn.utils.validation import check_is_fitted
from sklearn.utils.validation import check_array

from .base import BaseDetector
from ..utils.utility import invert_order
# noinspection PyProtectedMember
from ..utils.utility import _get_sklearn_version

[docs]class LOF(BaseDetector): """Wrapper of scikit-learn LOF Class with more functionalities. Unsupervised Outlier Detection using Local Outlier Factor (LOF). The anomaly score of each sample is called Local Outlier Factor. It measures the local deviation of density of a given sample with respect to its neighbors. It is local in that the anomaly score depends on how isolated the object is with respect to the surrounding neighborhood. More precisely, locality is given by k-nearest neighbors, whose distance is used to estimate the local density. By comparing the local density of a sample to the local densities of its neighbors, one can identify samples that have a substantially lower density than their neighbors. These are considered outliers. See :cite:`breunig2000lof` for details. Parameters ---------- n_neighbors : int, optional (default=20) Number of neighbors to use by default for `kneighbors` queries. If n_neighbors is larger than the number of samples provided, all samples will be used. algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional Algorithm used to compute the nearest neighbors: - 'ball_tree' will use BallTree - 'kd_tree' will use KDTree - 'brute' will use a brute-force search. - 'auto' will attempt to decide the most appropriate algorithm based on the values passed to :meth:`fit` method. Note: fitting on sparse input will override the setting of this parameter, using brute force. leaf_size : int, optional (default=30) Leaf size passed to `BallTree` or `KDTree`. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem. metric : string or callable, default 'minkowski' metric used for the distance computation. Any metric from scikit-learn or scipy.spatial.distance can be used. If 'precomputed', the training input X is expected to be a distance matrix. If metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays as input and return one value indicating the distance between them. This works for Scipy's metrics, but is less efficient than passing the metric name as a string. Valid values for metric are: - from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2', 'manhattan'] - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule'] See the documentation for scipy.spatial.distance for details on these metrics: p : integer, optional (default = 2) Parameter for the Minkowski metric from sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used. See metric_params : dict, optional (default = None) Additional keyword arguments for the metric function. contamination : float in (0., 0.5), optional (default=0.1) The amount of contamination of the data set, i.e. the proportion of outliers in the data set. When fitting this is used to define the threshold on the decision function. n_jobs : int, optional (default = 1) The number of parallel jobs to run for neighbors search. If ``-1``, then the number of jobs is set to the number of CPU cores. Affects only kneighbors and kneighbors_graph methods. novelty : bool (default=False) By default, LocalOutlierFactor is only meant to be used for outlier detection (novelty=False). Set novelty to True if you want to use LocalOutlierFactor for novelty detection. In this case be aware that that you should only use predict, decision_function and score_samples on new unseen data and not on the training set. Attributes ---------- n_neighbors_ : int The actual number of neighbors used for `kneighbors` queries. decision_scores_ : numpy array of shape (n_samples,) The outlier scores of the training data. The higher, the more abnormal. Outliers tend to have higher scores. This value is available once the detector is fitted. threshold_ : float The threshold is based on ``contamination``. It is the ``n_samples * contamination`` most abnormal samples in ``decision_scores_``. The threshold is calculated for generating binary outlier labels. labels_ : int, either 0 or 1 The binary labels of the training data. 0 stands for inliers and 1 for outliers/anomalies. It is generated by applying ``threshold_`` on ``decision_scores_``. """ def __init__(self, n_neighbors=20, algorithm='auto', leaf_size=30, metric='minkowski', p=2, metric_params=None, contamination=0.1, n_jobs=1, novelty=True): super(LOF, self).__init__(contamination=contamination) self.n_neighbors = n_neighbors self.algorithm = algorithm self.leaf_size = leaf_size self.metric = metric self.p = p self.metric_params = metric_params self.n_jobs = n_jobs self.novelty = novelty # noinspection PyIncorrectDocstring
[docs] def fit(self, X, y=None): """Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features) The input samples. y : Ignored Not used, present for API consistency by convention. Returns ------- self : object Fitted estimator. """ # validate inputs X and y (optional) X = check_array(X) self._set_n_classes(y) self.detector_ = LocalOutlierFactor(n_neighbors=self.n_neighbors, algorithm=self.algorithm, leaf_size=self.leaf_size, metric=self.metric, p=self.p, metric_params=self.metric_params, contamination=self.contamination, n_jobs=self.n_jobs, novelty=self.novelty), y=y) # Invert decision_scores_. Outliers comes with higher outlier scores self.decision_scores_ = invert_order( self.detector_.negative_outlier_factor_) self._process_decision_scores() return self
[docs] def decision_function(self, X): """Predict raw anomaly score of X using the fitted detector. The anomaly score of an input sample is computed based on different detector algorithms. For consistency, outliers are assigned with larger anomaly scores. Parameters ---------- X : numpy array of shape (n_samples, n_features) The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. Returns ------- anomaly_scores : numpy array of shape (n_samples,) The anomaly score of the input samples. """ check_is_fitted(self, ['decision_scores_', 'threshold_', 'labels_']) # Invert outlier scores. Outliers comes with higher outlier scores # noinspection PyProtectedMember try: return invert_order(self.detector_._score_samples(X)) except AttributeError: try: return invert_order(self.detector_._decision_function(X)) except AttributeError: return invert_order(self.detector_.score_samples(X))
@property def n_neighbors_(self): """The actual number of neighbors used for kneighbors queries. Decorator for scikit-learn LOF attributes. """ return self.detector_.n_neighbors_