# -*- coding: utf-8 -*-
"""Outlier detection based on Sampling (SP)
"""
# Author: Akira Tamamori <tamamori5917@gmail.com>
# License: BSD 2 clause
from __future__ import division, print_function
import numpy as np
from sklearn.utils import check_array, check_random_state
from sklearn.utils.validation import check_is_fitted
from .base import BaseDetector
from ..utils.utility import _get_sklearn_version
sklearn_version = _get_sklearn_version()
if sklearn_version[:3] >= '1.3':
from sklearn.metrics import DistanceMetric
else:
from sklearn.neighbors import DistanceMetric
[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