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# -*- coding: utf-8 -*- | |
""" | |
Python script for outlier detection based on Sampling | |
Copyright (C) 2022 by Akira TAMAMORI | |
Permission is hereby granted, free of charge, to any person obtaining | |
a copy of this software and associated documentation files (the | |
"Software"), to deal in the Software without restriction, including | |
without limitation the rights to use, copy, modify, merge, publish, | |
distribute, sublicense, and/or sell copies of the Software, and to | |
permit persons to whom the Software is furnished to do so, subject to | |
the following conditions: | |
The above copyright notice and this permission notice shall be | |
included in all copies or substantial portions of the Software. | |
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, | |
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF | |
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND | |
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE | |
LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION | |
OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION | |
WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. | |
""" | |
from __future__ import division, print_function | |
import numpy as np | |
from pyod.models.base import BaseDetector | |
from sklearn.neighbors import DistanceMetric | |
from sklearn.utils import check_array, check_random_state | |
from sklearn.utils.validation import check_is_fitted | |
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. | |
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 | |
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 | |
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 |
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