kenchi/outlier_detection/reconstruction_based.py
import numpy as np
from sklearn.decomposition import PCA as _PCA
from sklearn.utils.validation import check_is_fitted
from .base import BaseOutlierDetector
__all__ = ['PCA']
class PCA(BaseOutlierDetector):
"""Outlier detector using Principal Component Analysis (PCA).
Parameters
----------
contamination : float, default 0.1
Proportion of outliers in the data set. Used to define the threshold.
iterated_power : int, default 'auto'
Number of iterations for the power method computed by svd_solver ==
'randomized'.
n_components : int, float, or string, default None
Number of components to keep.
random_state : int or RandomState instance, default None
Seed of the pseudo random number generator.
svd_solver : string, default 'auto'
SVD solver to use. Valid solvers are
['auto'|'full'|'arpack'|'randomized'].
tol : float, default 0.0
Tolerance to declare convergence for singular values computed by
svd_solver == 'arpack'.
whiten : bool, default False
If True, the ``components_`` vectors are multiplied by the square root
of n_samples and then divided by the singular values to ensure
uncorrelated outputs with unit component-wise variances.
Attributes
----------
anomaly_score_ : array-like of shape (n_samples,)
Anomaly score for each training data.
contamination_ : float
Actual proportion of outliers in the data set.
threshold_ : float
Threshold.
Examples
--------
>>> import numpy as np
>>> from kenchi.outlier_detection import PCA
>>> X = np.array([
... [0., 0.], [1., 1.], [2., 0.], [3., -1.], [4., 0.],
... [5., 1.], [6., 0.], [7., -1.], [8., 0.], [1000., 1.]
... ])
>>> det = PCA()
>>> det.fit_predict(X)
array([ 1, 1, 1, 1, 1, 1, 1, 1, 1, -1])
"""
@property
def components_(self):
"""array-like of shape (n_components, n_features): Principal axes in
feature space, representing the directions of maximum variance in the
data.
"""
return self.estimator_.components_
@property
def explained_variance_(self):
"""array-like of shape (n_components,): Amount of variance explained by
each of the selected components.
"""
return self.estimator_.explained_variance_
@property
def explained_variance_ratio_(self):
"""array-like of shape (n_components,): Percentage of variance
explained by each of the selected components.
"""
return self.estimator_.explained_variance_ratio_
@property
def mean_(self):
"""array-like of shape (n_features,): Per-feature empirical mean,
estimated from the training set.
"""
return self.estimator_.mean_
@property
def noise_variance_(self):
"""float: Estimated noise covariance following the Probabilistic PCA
model from Tipping and Bishop 1999.
"""
return self.estimator_.noise_variance_
@property
def n_components_(self):
"""int: Estimated number of components.
"""
return self.estimator_.n_components_
@property
def singular_values_(self):
"""array-like of shape (n_components,): Singular values corresponding
to each of the selected components.
"""
return self.estimator_.singular_values_
def __init__(
self, contamination=0.1, iterated_power='auto', n_components=None,
random_state=None, svd_solver='auto', tol=0., whiten=False
):
self.contamination = contamination
self.iterated_power = iterated_power
self.n_components = n_components
self.random_state = random_state
self.svd_solver = svd_solver
self.tol = tol
self.whiten = whiten
def _check_is_fitted(self):
super()._check_is_fitted()
check_is_fitted(
self, [
'components_', 'explained_variance_',
'explained_variance_ratio_', 'mean_',
'noise_variance_', 'n_components_',
'singular_values_'
]
)
def _fit(self, X):
self.estimator_ = _PCA(
iterated_power = self.iterated_power,
n_components = self.n_components,
random_state = self.random_state,
svd_solver = self.svd_solver,
tol = self.tol,
whiten = self.whiten
).fit(X)
return self
def _anomaly_score(self, X):
return np.sum((X - self._reconstruct(X)) ** 2, axis=1)
def _reconstruct(self, X):
"""Apply dimensionality reduction to the given data, and transform the
data back to its original space.
"""
return self.estimator_.inverse_transform(self.estimator_.transform(X))