Source code for pyod.models.inne

# -*- coding: utf-8 -*-
"""Isolation-based anomaly detection using nearest-neighbor ensembles.
Part of the codes are adapted from
# Author: Xin Han <>
# License: BSD 2 clause

from __future__ import division
from __future__ import print_function

import numbers
from warnings import warn

import numpy as np
from sklearn.metrics import euclidean_distances
from sklearn.utils import check_array
from sklearn.utils.validation import check_is_fitted, check_random_state

from .base import BaseDetector
from ..utils.utility import MAX_INT, invert_order

MIN_FLOAT = np.finfo(float).eps

[docs]class INNE(BaseDetector): """ Isolation-based anomaly detection using nearest-neighbor ensembles. The INNE algorithm uses the nearest neighbour ensemble to isolate anomalies. It partitions the data space into regions using a subsample and determines an isolation score for each region. As each region adapts to local distribution, the calculated isolation score is a local measure that is relative to the local neighbourhood, enabling it to detect both global and local anomalies. INNE has linear time complexity to efficiently handle large and high-dimensional datasets with complex distributions. See :cite:`bandaragoda2018isolation` for details. Parameters ---------- n_estimators : int, default=200 The number of base estimators in the ensemble. max_samples : int or float, optional (default="auto") The number of samples to draw from X to train each base estimator. - If int, then draw `max_samples` samples. - If float, then draw `max_samples` * X.shape[0]` samples. - If "auto", then `max_samples=min(8, n_samples)`. 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. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Attributes ---------- max_samples_ : integer The actual number of samples 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_estimators=200, max_samples="auto", contamination=0.1, random_state=None): self.n_estimators = n_estimators self.max_samples = max_samples self.random_state = random_state self.contamination = contamination
[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) # Check data X = check_array(X, accept_sparse=False) self._set_n_classes(y) n_samples = X.shape[0] if isinstance(self.max_samples, str): if self.max_samples == "auto": max_samples = min(8, n_samples) else: raise ValueError( "max_samples (%s) is not supported." 'Valid choices are: "auto", int or' "float" % self.max_samples ) elif isinstance(self.max_samples, numbers.Integral): if self.max_samples > n_samples: warn( "max_samples (%s) is greater than the " "total number of samples (%s). max_samples " "will be set to n_samples for estimation." % (self.max_samples, n_samples) ) max_samples = n_samples else: max_samples = self.max_samples else: # float if not 0.0 < self.max_samples <= 1.0: raise ValueError( "max_samples must be in (0, 1], got %r" % self.max_samples ) max_samples = int(self.max_samples * X.shape[0]) self.max_samples_ = max_samples self._fit(X) self.decision_scores_ = invert_order(self._score_samples(X)) self._process_decision_scores() return self
def _fit(self, X): """ Build n_estimators sets of hyperspheres. Parameters ---------- X : numpy array of shape (n_samples, n_features) The training input samples. Returns ------- self : object """ n_samples, n_features = X.shape self._centroids = np.empty( [self.n_estimators, self.max_samples_, n_features]) self._ratio = np.empty([self.n_estimators, self.max_samples_]) self._centroids_radius = np.empty( [self.n_estimators, self.max_samples_]) random_state = check_random_state(self.random_state) self._seeds = random_state.randint(MAX_INT, size=self.n_estimators) for i in range(self.n_estimators): rnd = check_random_state(self._seeds[i]) # randomly selected subsamples of size max_samples_ as centroids. center_index = rnd.choice( n_samples, self.max_samples_, replace=False) self._centroids[i] = X[center_index] center_dist = euclidean_distances( self._centroids[i], self._centroids[i], squared=True) np.fill_diagonal(center_dist, np.inf) # radius of each hypersphere is the Nearest Neighbors # distance of centroid. self._centroids_radius[i] = np.amin(center_dist, axis=1) # Nearest Neighbors of centroids cnn_index = np.argmin(center_dist, axis=1) cnn_radius = self._centroids_radius[i][cnn_index] self._ratio[i] = 1 - (cnn_radius + MIN_FLOAT) / \ (self._centroids_radius[i] + MIN_FLOAT) 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. 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 return invert_order(self._score_samples(X))
def _score_samples(self, X): """ Opposite of the anomaly score defined in the original paper. The anomaly score of an input sample is computed as the mean anomaly score over all set of hyperspheres. Parameters ---------- X : array-like of shape (n_samples, n_features) The input samples. Returns ------- scores : ndarray of shape (n_samples,) The anomaly score of the input samples. The lower, the more abnormal. """ # check data X = check_array(X, accept_sparse=False) isolation_scores = np.ones([self.n_estimators, X.shape[0]]) # each test instance is evaluated against n_estimators sets of # hyperspheres for i in range(self.n_estimators): x_dists = euclidean_distances(X, self._centroids[i], squared=True) # find instances that are covered by at least one hypersphere. cover_radius = np.where( x_dists <= self._centroids_radius[i], self._centroids_radius[i], np.nan) x_covered = np.where(~np.isnan(cover_radius).all(axis=1)) # the centroid of the hypersphere covering x and having the # smallest radius cnn_x = np.nanargmin(cover_radius[x_covered], axis=1) isolation_scores[i][x_covered] = self._ratio[i][cnn_x] # the isolation scores are averaged to produce the anomaly score scores = np.mean(isolation_scores, axis=0) return -scores