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Update documentation and light refactoring
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@yzhao062
yzhao062 authored and yuezhao@cs.toronto.edu committed Dec 10, 2018
1 parent 3edf743 commit e114781
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36 changes: 16 additions & 20 deletions pyod/models/lscp.py
View file
Expand Up @@ -146,7 +146,7 @@ def fit(self, X, y=None):
Returns
-------
None
self
"""

# check detector_list
Expand Down Expand Up @@ -265,15 +265,13 @@ def _get_decision_scores(self, X):
# and local train scores
pearson_corr_scores = np.zeros([self.n_clf, ])
for d in range(self.n_clf):
pearson_corr_scores[d,] = \
pearsonr(local_pseudo_ground_truth,
local_train_scores[:, d])[
0]
pearson_corr_scores[d,] = pearsonr(
local_pseudo_ground_truth, local_train_scores[:, d])[0]

# return best score
pred_scores_ens[i,] = np.mean(test_scores_norm[
i, self._get_competent_detectors(
pearson_corr_scores)])
pred_scores_ens[i,] = np.mean(
test_scores_norm[
i, self._get_competent_detectors(pearson_corr_scores)])

return pred_scores_ens

Expand Down Expand Up @@ -303,16 +301,14 @@ def _get_local_region(self, X_test_norm):
for _ in range(self.local_region_iterations):

# randomly generate feature subspaces
features = generate_bagging_indices(self.random_state,
bootstrap_features=False,
n_features=
self.X_train_norm_.shape[1],
min_features=int(
self.X_train_norm_.shape[
1] * self.local_min_features),
max_features=int(
self.X_train_norm_.shape[
1] * self.local_max_features))
features = generate_bagging_indices(
self.random_state,
bootstrap_features=False,
n_features=self.X_train_norm_.shape[1],
min_features=int(
self.X_train_norm_.shape[1] * self.local_min_features),
max_features=int(
self.X_train_norm_.shape[1] * self.local_max_features))

# build KDTree out of training subspace
tree = KDTree(self.X_train_norm_[:, features])
Expand All @@ -323,8 +319,8 @@ def _get_local_region(self, X_test_norm):

# add neighbors to local region list
for j in range(X_test_norm.shape[0]):
local_region_list[j] = local_region_list[j] + ind_arr[j,
:].tolist()
local_region_list[j] = local_region_list[j] + \
ind_arr[j, :].tolist()

# keep nearby points which occur at least local_region_threshold times
final_local_region_list = [[]] * X_test_norm.shape[0]
Expand Down
192 changes: 139 additions & 53 deletions pyod/models/pca.py
View file
Expand Up @@ -19,7 +19,6 @@


class PCA(BaseDetector):
# noinspection PyPep8
"""
Principal component analysis (PCA) can be used in detecting outliers. PCA
is a linear dimensionality reduction using Singular Value Decomposition
Expand All @@ -42,7 +41,10 @@ class PCA(BaseDetector):
Score(X) = Sum of weighted euclidean distance between each sample to the
hyperplane constructed by the selected eigenvectors
:param n_components: Number of principal components to keep.
Parameters
----------
n_components : int, float, None or string
Number of components to keep.
if n_components is not set all components are kept::
n_components == min(n_samples, n_features)
Expand All @@ -53,36 +55,34 @@ class PCA(BaseDetector):
of components such that the amount of variance that needs to be
explained is greater than the percentage specified by n_components
n_components cannot be equal to n_features for svd_solver == 'arpack'.
:type n_components: int, float, None or str
:param n_selected_components: Number of selected principal components
n_selected_components : int, optional (default=None)
Number of selected principal components
for calculating the outlier scores. It is not necessarily equal to
the total number of the principal components. If not set, use
all principal components.
:type n_selected_components: int, optional (default=None)
:param contamination: The amount of contamination of the data set,
i.e. the proportion of outliers in the data set. Used when fitting to
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.
:type contamination: float in (0., 0.5), optional (default=0.1)
:param copy: If False, data passed to fit are overwritten and running
copy : bool (default True)
If False, data passed to fit are overwritten and running
fit(X).transform(X) will not yield the expected results,
use fit_transform(X) instead.
:type copy: bool (default True)
:param whiten: When True (False by default) 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.
whiten : bool, optional (default False)
When True (False by default) 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.
Whitening will remove some information from the transformed signal
(the relative variance scales of the components) but can sometime
improve the predictive accuracy of the downstream estimators by
making their data respect some hard-wired assumptions.
:type whiten: bool, optional (default False)
:param svd_solver:
svd_solver : string {'auto', 'full', 'arpack', 'randomized'}
auto :
the solver is selected by a default policy based on `X.shape` and
`n_components`: if the input data is larger than 500x500 and the
Expand All @@ -99,66 +99,101 @@ class PCA(BaseDetector):
0 < n_components < X.shape[1]
randomized :
run randomized SVD by the method of Halko et al.
:type svd_solver: str {'auto', 'full', 'arpack', 'randomized'}
:param tol: Tolerance for singular values computed by
svd_solver == 'arpack'.
:type tol: float >= 0, optional (default .0)
.. versionadded:: 0.18.0
:param iterated_power: Number of iterations for the power method computed
by svd_solver == 'randomized'.
:type iterated_power: int >= 0, or 'auto', (default 'auto')
:param random_state: If int, random_state is the seed used by the random
number generator;
tol : float >= 0, optional (default .0)
Tolerance for singular values computed by svd_solver == 'arpack'.
.. versionadded:: 0.18.0
iterated_power : int >= 0, or 'auto', (default 'auto')
Number of iterations for the power method computed by
svd_solver == 'randomized'.
.. versionadded:: 0.18.0
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`. Used when ``svd_solver`` == 'arpack' or 'randomized'.
:type random_state: int, RandomState instance or None,
optional (default None)
:param weighted: If True, the eigenvalues are used in score computation.
.. versionadded:: 0.18.0
weighted : bool, optional (default=True)
If True, the eigenvalues are used in score computation.
The eigenvectors with samll eigenvalues comes with more importance
in outlier score calculation.
:type weighted: bool, optional (default=True)
:param standardization: If True, perform standardization first to convert
standardization : bool, optional (default=True)
If True, perform standardization first to convert
data to zero mean and unit variance.
See http://scikit-learn.org/stable/auto_examples/preprocessing/plot_scaling_importance.html
:type standardization: bool, optional (default=True)
:var components\_: Components with maximum variance.
:vartype components\_: array, shape (n_components, n_features)
Attributes
----------
components_ : array, shape (n_components, n_features)
Principal axes in feature space, representing the directions of
maximum variance in the data. The components are sorted by
``explained_variance_``.
explained_variance_ : array, shape (n_components,)
The amount of variance explained by each of the selected components.
Equal to n_components largest eigenvalues
of the covariance matrix of X.
.. versionadded:: 0.18
:var explained_variance_ratio\_: Percentage of variance explained by each
of the selected components. If k is not set then all components are
stored and the sum of explained variances is equal to 1.0.
:vartype explained_variance_ratio\_: array, shape (n_components,)
explained_variance_ratio_ : array, shape (n_components,)
Percentage of variance explained by each of the selected components.
:var singular_values\_: The singular values corresponding to each of the
selected components. The singular values are equal to the 2-norms of
the ``n_components`` variables in the lower-dimensional space.
:vartype singular_values\_: array, shape (n_components,)
If ``n_components`` is not set then all components are stored and the
sum of explained variances is equal to 1.0.
:var mean\_: Per-feature empirical mean, estimated from the training set.
:vartype mean\_: array, shape (n_features,)
singular_values_ : array, shape (n_components,)
The singular values corresponding to each of the selected components.
The singular values are equal to the 2-norms of the ``n_components``
variables in the lower-dimensional space.
:var decision_scores\_: The outlier scores of the training data.
mean_ : array, shape (n_features,)
Per-feature empirical mean, estimated from the training set.
Equal to `X.mean(axis=0)`.
n_components_ : int
The estimated number of components. When n_components is set
to 'mle' or a number between 0 and 1 (with svd_solver == 'full') this
number is estimated from input data. Otherwise it equals the parameter
n_components, or n_features if n_components is None.
noise_variance_ : float
The estimated noise covariance following the Probabilistic PCA model
from Tipping and Bishop 1999. See "Pattern Recognition and
Machine Learning" by C. Bishop, 12.2.1 p. 574 or
http://www.miketipping.com/papers/met-mppca.pdf. It is required to
computed the estimated data covariance and score samples.
Equal to the average of (min(n_features, n_samples) - n_components)
smallest eigenvalues of the covariance matrix of X.
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.
:vartype decision_scores\_: numpy array of shape (n_samples,)
scores. This value is available once the detector is fitted.
:var threshold\_: The threshold is based on ``contamination``. It is the
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.
:vartype threshold\_: float
:var labels\_: The binary labels of the training data. 0 stands for inliers
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_``.
:vartype labels\_: int, either 0 or 1
"""

def __init__(self, n_components=None, n_selected_components=None,
Expand Down Expand Up @@ -246,11 +281,34 @@ def decision_function(self, X):
cdist(X, self.selected_components_) / self.selected_w_components_,
axis=1).ravel()

# @property
# def components_(self):
# """Principal axes in feature space, representing the directions of
# maximum variance in the data. The components are sorted by
# ``explained_variance_``.
#
# Decorator for scikit-learn PCA attributes.
# """
# return self.detector_.components_

@property
def explained_variance_(self):
"""The amount of variance explained by each of the selected components.
Equal to n_components largest eigenvalues
of the covariance matrix of X.
Decorator for scikit-learn PCA attributes.
"""
return self.detector_.explained_variance_

@property
def explained_variance_ratio_(self):
"""Percentage of variance explained by each of the selected components.
If k is not set then all components are stored and the sum of explained
variances is equal to 1.0.
If ``n_components`` is not set then all components are stored and the
sum of explained variances is equal to 1.0.
Decorator for scikit-learn PCA attributes.
"""
return self.detector_.explained_variance_ratio_
Expand All @@ -260,13 +318,41 @@ def singular_values_(self):
"""The singular values corresponding to each of the selected
components. The singular values are equal to the 2-norms of the
``n_components`` variables in the lower-dimensional space.
Decorator for scikit-learn PCA attributes.
"""
return self.detector_.singular_values_

@property
def mean_(self):
"""Per-feature empirical mean, estimated from the training set.
Decorator for scikit-learn PCA attributes.
"""
return self.detector_.mean_

# @property
# def n_components_(self):
# """The estimated number of components. When n_components is set
# to 'mle' or a number between 0 and 1 (with svd_solver == 'full') this
# number is estimated from input data. Otherwise it equals the parameter
# n_components, or n_features if n_components is None.
#
# Decorator for scikit-learn PCA attributes.
# """
# return self.detector_.n_components_

@property
def noise_variance_(self):
""" The estimated noise covariance following the Probabilistic PCA model
from Tipping and Bishop 1999. See "Pattern Recognition and
Machine Learning" by C. Bishop, 12.2.1 p. 574 or
http://www.miketipping.com/papers/met-mppca.pdf. It is required to
computed the estimated data covariance and score samples.
Equal to the average of (min(n_features, n_samples) - n_components)
smallest eigenvalues of the covariance matrix of X.
Decorator for scikit-learn PCA attributes.
"""
return self.detector_.noise_variance_

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