Source code for pyod.models.sampling

# -*- coding: utf-8 -*-
"""Outlier detection based on Sampling (SP)
# Author: Akira Tamamori <>
# License: BSD 2 clause

from __future__ import division, print_function

import numpy as np
from sklearn.neighbors import DistanceMetric
from sklearn.utils import check_array, check_random_state
from sklearn.utils.validation import check_is_fitted

from .base import BaseDetector

[docs]class Sampling(BaseDetector): """Sampling class for outlier detection. Sugiyama, M., Borgwardt, K. M.: Rapid Distance-Based Outlier Detection via Sampling, Advances in Neural Information Processing Systems (NIPS 2013), 467-475, 2013. See :cite:`sugiyama2013rapid` 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. subset_size : float in (0., 1.0) or int (0, n_samples), optional (default=20) The size of subset of the data set. Sampling subset from the data set is performed only once. metric : string or callable, default 'minkowski' metric to use for distance computation. Any metric from scikit-learn or scipy.spatial.distance can be used. 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. Distance matrices are not supported. 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. metric_params : dict, optional (default = None) Additional keyword arguments for the metric 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 ---------- 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, subset_size=20, metric="minkowski", metric_params=None, random_state=None, ): super().__init__(contamination=contamination) self.subset_size = subset_size self.metric = metric self.metric_params = metric_params self.random_state = check_random_state(random_state) self.dist = None self.subset = None 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) n_samples, _ = X.shape if (isinstance(self.subset_size, int) is True) and ( not 0 < self.subset_size <= n_samples): raise ValueError( "subset_size=%r must be between 0 and n_samples=%r." % (self.subset_size, n_samples) ) if isinstance(self.subset_size, float) is True: if 0.0 < self.subset_size <= 1.0: self.subset_size = int(self.subset_size * n_samples) else: raise ValueError("subset_size=%r must be between 0.0 and 1.0") random_indices = self.random_state.choice( n_samples, size=self.subset_size, replace=False, ) self.subset = X[random_indices, :] if self.metric_params is None: self.dist = DistanceMetric.get_metric(self.metric) else: self.dist = DistanceMetric.get_metric(self.metric, **self.metric_params) pair_dist = self.dist.pairwise(X, self.subset) anomaly_scores = np.min(pair_dist, axis=1) self.decision_scores_ = anomaly_scores 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 test 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_"]) X = check_array(X) pair_dist = self.dist.pairwise(X, self.subset) anomaly_scores = np.min(pair_dist, axis=1) return anomaly_scores