"""
This function is adapted from [pyod] by [yzhao062]
Original source: [https://github.com/yzhao062/pyod]
"""
from __future__ import division
from __future__ import print_function
from warnings import warn
import numpy as np
from sklearn.neighbors import BallTree
from sklearn.neighbors import NearestNeighbors
from sklearn.utils.validation import check_is_fitted
from sklearn.utils.validation import check_is_fitted
from sklearn.utils import check_array
import math
import sys
import os
BASE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../../'))
if BASE_DIR not in sys.path:
sys.path.append(BASE_DIR)
from src.models.base import BaseDetector
from src.models.feature import Window
from src.utils.utility import zscore
class KNN(BaseDetector):
# noinspection PyPep8
"""kNN class for outlier detection.
For an observation, its distance to its kth nearest neighbor could be
viewed as the outlying score. It could be viewed as a way to measure
the density. See :cite:`ramaswamy2000efficient,angiulli2002fast` for
details.
Three kNN detectors are supported:
largest: use the distance to the kth neighbor as the outlier score
mean: use the average of all k neighbors as the outlier score
median: use the median of the distance to k neighbors as the outlier score
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.
n_neighbors : int, optional (default = 10)
Number of neighbors to use by default for k neighbors queries.
method : str, optional (default='largest')
{'largest', 'mean', 'median'}
- 'largest': use the distance to the kth neighbor as the outlier score
- 'mean': use the average of all k neighbors as the outlier score
- 'median': use the median of the distance to k neighbors as the
outlier score
radius : float, optional (default = 1.0)
Range of parameter space to use by default for `radius_neighbors`
queries.
algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
Algorithm used to compute the nearest neighbors:
- 'ball_tree' will use BallTree
- 'kd_tree' will use KDTree
- 'brute' will use a brute-force search.
- 'auto' will attempt to decide the most appropriate algorithm
based on the values passed to :meth:`fit` method.
Note: fitting on sparse input will override the setting of
this parameter, using brute force.
.. deprecated:: 0.74
``algorithm`` is deprecated in PyOD 0.7.4 and will not be
possible in 0.7.6. It has to use BallTree for consistency.
leaf_size : int, optional (default = 30)
Leaf size passed to BallTree. This can affect the
speed of the construction and query, as well as the memory
required to store the tree. The optimal value depends on the
nature of the problem.
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', '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.
p : integer, optional (default = 2)
Parameter for the Minkowski metric from
sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is
equivalent to using manhattan_distance (l1), and euclidean_distance
(l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
See http://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.pairwise_distances
metric_params : dict, optional (default = None)
Additional keyword arguments for the metric function.
n_jobs : int, optional (default = 1)
The number of parallel jobs to run for neighbors search.
If ``-1``, then the number of jobs is set to the number of CPU cores.
Affects only kneighbors and kneighbors_graph methods.
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, slidingWindow=100, sub=True, contamination=0.1, n_neighbors=10, method='largest',
radius=1.0, algorithm='auto', leaf_size=30,
metric='minkowski', p=2, metric_params=None, n_jobs=1, normalize=True,
**kwargs):
self.slidingWindow = slidingWindow
self.sub = sub
self.n_neighbors = n_neighbors
self.method = method
self.radius = radius
self.algorithm = algorithm
self.leaf_size = leaf_size
self.metric = metric
self.p = p
self.metric_params = metric_params
self.normalize = normalize
self.n_jobs = n_jobs
if self.algorithm != 'auto' and self.algorithm != 'ball_tree':
warn('algorithm parameter is deprecated and will be removed '
'in version 0.7.6. By default, ball_tree will be used.',
FutureWarning)
self.neigh_ = NearestNeighbors(n_neighbors=self.n_neighbors,
radius=self.radius,
algorithm=self.algorithm,
leaf_size=self.leaf_size,
metric=self.metric,
p=self.p,
metric_params=self.metric_params,
n_jobs=self.n_jobs,
**kwargs)
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.
"""
n_samples, n_features = X.shape
# Converting time series data into matrix format
X = Window(window = self.slidingWindow).convert(X)
if self.normalize: X = zscore(X, axis=1, ddof=1)
# validate inputs X and y (optional)
X = check_array(X)
self.neigh_.fit(X)
if self.neigh_._tree is not None:
self.tree_ = self.neigh_._tree
else:
if self.metric_params is not None:
self.tree_ = BallTree(X, leaf_size=self.leaf_size,
metric=self.metric,
**self.metric_params)
else:
self.tree_ = BallTree(X, leaf_size=self.leaf_size,
metric=self.metric)
dist_arr, _ = self.neigh_.kneighbors(n_neighbors=self.n_neighbors,
return_distance=True)
self.decision_scores_ = self._get_dist_by_method(dist_arr)
# padded decision_scores_
if self.decision_scores_.shape[0] < n_samples:
self.decision_scores_ = np.array([self.decision_scores_[0]]*math.ceil((self.slidingWindow-1)/2) +
list(self.decision_scores_) + [self.decision_scores_[-1]]*((self.slidingWindow-1)//2))
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 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.
"""
print("inside decision Function")
# check_is_fitted(self, ['tree_', 'decision_scores_',
# 'threshold_', 'labels_'])
n_samples = X.shape[0]
X = check_array(X)
X = Window(window = self.slidingWindow).convert(X)
# initialize the output score
pred_scores = np.zeros([X.shape[0], 1])
for i in range(X.shape[0]):
x_i = X[i, :]
x_i = np.asarray(x_i).reshape(1, x_i.shape[0])
# get the distance of the current point
dist_arr, _ = self.tree_.query(x_i, k=self.n_neighbors)
dist = self._get_dist_by_method(dist_arr)
pred_score_i = dist[-1]
# record the current item
pred_scores[i, :] = pred_score_i
pred_scores = pred_scores.ravel()
if pred_scores.shape[0] < n_samples:
padded_decision_scores_ = np.array([pred_scores[0]]*math.ceil((self.slidingWindow-1)/2) +
list(pred_scores) + [pred_scores[-1]]*((self.slidingWindow-1)//2))
return padded_decision_scores_
def _get_dist_by_method(self, dist_arr):
"""Internal function to decide how to process passed in distance array
Parameters
----------
dist_arr : numpy array of shape (n_samples, n_neighbors)
Distance matrix.
Returns
-------
dist : numpy array of shape (n_samples,)
The outlier scores by distance.
"""
if self.method == 'largest':
return dist_arr[:, -1]
elif self.method == 'mean':
return np.mean(dist_arr, axis=1)
elif self.method == 'median':
return np.median(dist_arr, axis=1)