Source code for pyod.models.sod

# -*- coding: utf-8 -*-
"""Subspace Outlier Detection (SOD)
# Author: Yahya Almardeny <>
# License: BSD 2 clause

import numba as nb
import numpy as np
from sklearn.neighbors import NearestNeighbors
from sklearn.utils import check_array

from .base import BaseDetector
from ..utils.utility import check_parameter

def _snn_imp(ind, ref_set_):
    """Internal function for fast snn calculation

    ind : int
        Indices return by kNN.

    ref_set_ : int, optional (default=10)
        specifies the number of shared nearest neighbors to create the
        reference set. Note that ref_set must be smaller than n_neighbors.

    n = ind.shape[0]
    _count = np.zeros(shape=(n, ref_set_), dtype=np.uint32)
    for i in nb.prange(n):
        temp = np.empty(n, dtype=np.uint32)
        test_element_set = set(ind[i])
        for j in nb.prange(n):
            temp[j] = len(set(ind[j]).intersection(test_element_set))
        temp[i] = np.iinfo(np.uint32).max
        _count[i] = np.argsort(temp)[::-1][1:ref_set_ + 1]

    return _count

[docs]class SOD(BaseDetector): """Subspace outlier detection (SOD) schema aims to detect outlier in varying subspaces of a high dimensional feature space. For each data object, SOD explores the axis-parallel subspace spanned by the data object's neighbors and determines how much the object deviates from the neighbors in this subspace. See :cite:`kriegel2009outlier` for details. Parameters ---------- n_neighbors : int, optional (default=20) Number of neighbors to use by default for k neighbors queries. ref_set: int, optional (default=10) specifies the number of shared nearest neighbors to create the reference set. Note that ref_set must be smaller than n_neighbors. alpha: float in (0., 1.), optional (default=0.8) specifies the lower limit for selecting subspace. 0.8 is set as default as suggested in the original paper. 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. Used when fitting to define the threshold on the decision function. Attributes ---------- 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, contamination=0.1, n_neighbors=20, ref_set=10, alpha=0.8): super(SOD, self).__init__(contamination=contamination) if isinstance(n_neighbors, int): check_parameter(n_neighbors, low=1, param_name='n_neighbors') else: raise ValueError( "n_neighbors should be int. Got %s" % type(n_neighbors)) if isinstance(ref_set, int): check_parameter(ref_set, low=1, high=n_neighbors, param_name='ref_set') else: raise ValueError("ref_set should be int. Got %s" % type(ref_set)) if isinstance(alpha, float): check_parameter(alpha, low=0.0, high=1.0, param_name='alpha') else: raise ValueError("alpha should be float. Got %s" % type(alpha)) self.n_neighbors_ = n_neighbors self.ref_set_ = ref_set self.alpha_ = alpha self.decision_scores_ = None
[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.decision_scores_ = self.decision_function(X) 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. """ return self._sod(X)
def _snn(self, X): """This function is called internally to calculate the shared nearest neighbors (SNN). SNN is reported to be more robust than k nearest neighbors. Returns ------- snn_indices : numpy array of shape (n_shared_nearest_neighbors,) The indices of top k shared nearest neighbors for each observation. """ knn = NearestNeighbors(n_neighbors=self.n_neighbors_) # Get the knn index ind = knn.kneighbors(return_distance=False) return _snn_imp(ind, self.ref_set_) def _sod(self, X): """This function is called internally to perform subspace outlier detection algorithm. Returns ------- anomaly_scores : numpy array of shape (n_samples,) The anomaly score of the input samples. """ ref_inds = self._snn(X) anomaly_scores = np.zeros(shape=(X.shape[0],)) for i in range(X.shape[0]): obs = X[i] ref = X[ref_inds[i,],] means = np.mean(ref, axis=0) # mean of each column # average squared distance of the reference to the mean var_total = np.sum(np.sum(np.square(ref - means))) / self.ref_set_ var_expect = self.alpha_ * var_total / X.shape[1] var_actual = np.var(ref, axis=0) # variance of each attribute var_inds = [1 if (j < var_expect) else 0 for j in var_actual] rel_dim = np.sum(var_inds) if rel_dim != 0: anomaly_scores[i] = np.sqrt(, np.square(obs - means)) / rel_dim) return anomaly_scores