Source code for pyod.models.hbos

# -*- coding: utf-8 -*-
"""Histogram-based Outlier Detection (HBOS)
"""
# Author: Yue Zhao <zhaoy@cmu.edu>
# License: BSD 2 clause

from __future__ import division
from __future__ import print_function

import numpy as np
from numba import njit
from sklearn.utils import check_array
from sklearn.utils.validation import check_is_fitted

from ..utils.utility import check_parameter
from ..utils.utility import invert_order

from .base import BaseDetector


[docs]class HBOS(BaseDetector): """Histogram- based outlier detection (HBOS) is an efficient unsupervised method. It assumes the feature independence and calculates the degree of outlyingness by building histograms. See :cite:`goldstein2012histogram` for details. Parameters ---------- n_bins : int, optional (default=10) The number of bins. alpha : float in (0, 1), optional (default=0.1) The regularizer for preventing overflow. tol : float in (0, 1), optional (default=0.5) The parameter to decide the flexibility while dealing the samples falling outside the bins. 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 ---------- bin_edges_ : numpy array of shape (n_bins + 1, n_features ) The edges of the bins. hist_ : numpy array of shape (n_bins, n_features) The density of each histogram. 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_bins=10, alpha=0.1, tol=0.5, contamination=0.1): super(HBOS, self).__init__(contamination=contamination) self.n_bins = n_bins self.alpha = alpha self.tol = tol check_parameter(alpha, 0, 1, param_name='alpha') check_parameter(tol, 0, 1, param_name='tol')
[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) n_samples, n_features = X.shape[0], X.shape[1] self.hist_ = np.zeros([self.n_bins, n_features]) self.bin_edges_ = np.zeros([self.n_bins + 1, n_features]) # build the histograms for all dimensions for i in range(n_features): self.hist_[:, i], self.bin_edges_[:, i] = \ np.histogram(X[:, i], bins=self.n_bins, density=True) # the sum of (width * height) should equal to 1 assert (np.isclose(1, np.sum( self.hist_[:, i] * np.diff(self.bin_edges_[:, i])), atol=0.1)) # outlier_scores = self._calculate_outlier_scores(X) outlier_scores = _calculate_outlier_scores(X, self.bin_edges_, self.hist_, self.n_bins, self.alpha, self.tol) # invert decision_scores_. Outliers comes with higher outlier scores self.decision_scores_ = invert_order(np.sum(outlier_scores, axis=1)) 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, ['hist_', 'bin_edges_']) X = check_array(X) # outlier_scores = self._calculate_outlier_scores(X) outlier_scores = _calculate_outlier_scores(X, self.bin_edges_, self.hist_, self.n_bins, self.alpha, self.tol) return invert_order(np.sum(outlier_scores, axis=1))
@njit def _calculate_outlier_scores(X, bin_edges, hist, n_bins, alpha, tol): # pragma: no cover """The internal function to calculate the outlier scores based on the bins and histograms constructed with the training data. The program is optimized through numba. It is excluded from coverage test for eliminating the redundancy. Parameters ---------- X : numpy array of shape (n_samples, n_features) The input samples. bin_edges : numpy array of shape (n_bins + 1, n_features ) The edges of the bins. hist : numpy array of shape (n_bins, n_features) The density of each histogram. n_bins : int, optional (default=10) The number of bins. alpha : float in (0, 1), optional (default=0.1) The regularizer for preventing overflow. tol : float in (0, 1), optional (default=0.1) The parameter to decide the flexibility while dealing the samples falling outside the bins. Returns ------- outlier_scores : numpy array of shape (n_samples, n_features) Outlier scores on all features (dimensions). """ n_samples, n_features = X.shape[0], X.shape[1] outlier_scores = np.zeros(shape=(n_samples, n_features)) for i in range(n_features): # Find the indices of the bins to which each value belongs. # See documentation for np.digitize since it is tricky # >>> x = np.array([0.2, 6.4, 3.0, 1.6, -1, 100, 10]) # >>> bins = np.array([0.0, 1.0, 2.5, 4.0, 10.0]) # >>> np.digitize(x, bins, right=True) # array([1, 4, 3, 2, 0, 5, 4], dtype=int64) bin_inds = np.digitize(X[:, i], bin_edges[:, i], right=True) # Calculate the outlying scores on dimension i # Add a regularizer for preventing overflow out_score_i = np.log2(hist[:, i] + alpha) for j in range(n_samples): # If the sample does not belong to any bins # bin_ind == 0 (fall outside since it is too small) if bin_inds[j] == 0: dist = bin_edges[0, i] - X[j, i] bin_width = bin_edges[1, i] - bin_edges[0, i] # If it is only slightly lower than the smallest bin edge # assign it to bin 1 if dist <= bin_width * tol: outlier_scores[j, i] = out_score_i[0] else: outlier_scores[j, i] = np.min(out_score_i) # If the sample does not belong to any bins # bin_ind == n_bins+1 (fall outside since it is too large) elif bin_inds[j] == n_bins + 1: dist = X[j, i] - bin_edges[-1, i] bin_width = bin_edges[-1, i] - bin_edges[-2, i] # If it is only slightly larger than the largest bin edge # assign it to the last bin if dist <= bin_width * tol: outlier_scores[j, i] = out_score_i[n_bins - 1] else: outlier_scores[j, i] = np.min(out_score_i) else: outlier_scores[j, i] = out_score_i[bin_inds[j] - 1] return outlier_scores