Source code for pyod.models.mcd

# -*- coding: utf-8 -*-
"""Outlier Detection with Minimum Covariance Determinant (MCD)
"""
# Author: Yue Zhao <zhaoy@cmu.edu>
# License: BSD 2 clause

from __future__ import division
from __future__ import print_function

from sklearn.covariance import MinCovDet
from sklearn.utils.validation import check_is_fitted
from sklearn.utils.validation import check_array

from .base import BaseDetector

__all__ = ['MCD']


[docs]class MCD(BaseDetector): """Detecting outliers in a Gaussian distributed dataset using Minimum Covariance Determinant (MCD): robust estimator of covariance. The Minimum Covariance Determinant covariance estimator is to be applied on Gaussian-distributed data, but could still be relevant on data drawn from a unimodal, symmetric distribution. It is not meant to be used with multi-modal data (the algorithm used to fit a MinCovDet object is likely to fail in such a case). One should consider projection pursuit methods to deal with multi-modal datasets. First fit a minimum covariance determinant model and then compute the Mahalanobis distance as the outlier degree of the data See :cite:`rousseeuw1999fast,hardin2004outlier` for details. Parameters ---------- 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. store_precision : bool Specify if the estimated precision is stored. assume_centered : bool If True, the support of the robust location and the covariance estimates is computed, and a covariance estimate is recomputed from it, without centering the data. Useful to work with data whose mean is significantly equal to zero but is not exactly zero. If False, the robust location and covariance are directly computed with the FastMCD algorithm without additional treatment. support_fraction : float, 0 < support_fraction < 1 The proportion of points to be included in the support of the raw MCD estimate. Default is None, which implies that the minimum value of support_fraction will be used within the algorithm: [n_sample + n_features + 1] / 2 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 ---------- raw_location_ : array-like, shape (n_features,) The raw robust estimated location before correction and re-weighting. raw_covariance_ : array-like, shape (n_features, n_features) The raw robust estimated covariance before correction and re-weighting. raw_support_ : array-like, shape (n_samples,) A mask of the observations that have been used to compute the raw robust estimates of location and shape, before correction and re-weighting. location_ : array-like, shape (n_features,) Estimated robust location covariance_ : array-like, shape (n_features, n_features) Estimated robust covariance matrix precision_ : array-like, shape (n_features, n_features) Estimated pseudo inverse matrix. (stored only if store_precision is True) support_ : array-like, shape (n_samples,) A mask of the observations that have been used to compute the robust estimates of location and shape. 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. Mahalanobis distances of the training set (on which `:meth:`fit` is called) observations. 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, store_precision=True, assume_centered=False, support_fraction=None, random_state=None): super(MCD, self).__init__(contamination=contamination) self.store_precision = store_precision self.assume_centered = assume_centered self.support_fraction = support_fraction self.random_state = random_state # 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_ = MinCovDet(store_precision=self.store_precision, assume_centered=self.assume_centered, support_fraction=self.support_fraction, random_state=self.random_state) self.detector_.fit(X=X, y=y) # Use mahalanabis distance as the outlier score self.decision_scores_ = self.detector_.dist_ 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_']) X = check_array(X) # Computer mahalanobis distance of the samples return self.detector_.mahalanobis(X)
@property def raw_location_(self): """The raw robust estimated location before correction and re-weighting. Decorator for scikit-learn MinCovDet attributes. """ return self.detector_.raw_location_ @property def raw_covariance_(self): """The raw robust estimated location before correction and re-weighting. Decorator for scikit-learn MinCovDet attributes. """ return self.detector_.raw_covariance_ @property def raw_support_(self): """A mask of the observations that have been used to compute the raw robust estimates of location and shape, before correction and re-weighting. Decorator for scikit-learn MinCovDet attributes. """ return self.detector_.raw_support_ @property def location_(self): """Estimated robust location. Decorator for scikit-learn MinCovDet attributes. """ return self.detector_.location_ @property def covariance_(self): """Estimated robust covariance matrix. Decorator for scikit-learn MinCovDet attributes. """ return self.detector_.covariance_ @property def precision_(self): """ Estimated pseudo inverse matrix. (stored only if store_precision is True) Decorator for scikit-learn MinCovDet attributes. """ return self.detector_.precision_ @property def support_(self): """A mask of the observations that have been used to compute the robust estimates of location and shape. Decorator for scikit-learn MinCovDet attributes. """ return self.detector_.support_