[GODE]GODE Other Methods

from pyod.models.lof import LOF

LOF[@breunig2000lof]

LOF(
    n_neighbors=20,
    algorithm='auto',
    leaf_size=30,
    metric='minkowski',
    p=2,
    metric_params=None,
    contamination=0.1,
    n_jobs=1,
    novelty=True,
)

Parameter	Description	Default Value
n_neighbors	Number of neighbors to use by default for kneighbors queries. If n_neighbors is larger than the number of samples provided, all samples will be used.	20
algorithm	Algorithm used to compute the nearest neighbors. Options: ‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’. ‘auto’ attempts to decide the most appropriate algorithm based on the values passed to the fit method.	‘auto’
leaf_size	Leaf size passed to BallTree or KDTree. Affects the speed of construction and query, as well as the memory required to store the tree. Optimal value depends on the nature of the problem.	30
metric	Metric used for distance computation. Options include various distances from scikit-learn and scipy.spatial.distance. If ‘precomputed’, X is expected to be a distance matrix.	‘minkowski’
p	Parameter for the Minkowski metric. For p = 1, equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.	2
metric_params	Additional keyword arguments for the metric function.	None
contamination	The amount of contamination of the data set, i.e., the proportion of outliers. Used to define the threshold on the decision function.	0.1
n_jobs	The number of parallel jobs to run for neighbors search. If -1, the number of jobs is set to the number of CPU cores. Affects only kneighbors and kneighbors_graph methods.	1
novelty	Set to True if using LOF for novelty detection. Use predict, decision_function, and score_samples only on new unseen data, not on the training set.	False

from pyod.models.knn import KNN

kNN[@ramaswamy2000efficient]

KNN(
    contamination=0.1,
    n_neighbors=5,
    method='largest',
    radius=1.0,
    algorithm='auto',
    leaf_size=30,
    metric='minkowski',
    p=2,
    metric_params=None,
    n_jobs=1,
    **kwargs,
)

Parameter	Description	Default
contamination	Proportion of outliers in the data set, used to define the threshold on the decision function.	0.1
n_neighbors	Number of neighbors to use for k neighbors queries.	5
method	Method for kNN detection: ‘largest’, ‘mean’, or ‘median’.	‘largest’
radius	Range of parameter space for radius_neighbors queries.	1.0
algorithm	Algorithm to compute nearest neighbors: ‘auto’, ‘ball_tree’, ‘kd_tree’, or ‘brute’.	‘auto’
leaf_size	Leaf size passed to BallTree, affecting construction/query speed and memory.	30
metric	Metric for distance computation, from scikit-learn or scipy.spatial.distance.	‘minkowski’
p	Parameter for Minkowski metric, equivalent to manhattan_distance (l1) for p = 1 and euclidean_distance (l2) for p = 2.	2
metric_params	Additional keyword arguments for the metric function.	None
n_jobs	Number of parallel jobs for neighbors search. -1 uses CPU cores.	1

from pyod.models.cblof import CBLOF

CBLOF[@he2003discovering]

CBLOF(
    n_clusters=8,
    contamination=0.1,
    clustering_estimator=None,
    alpha=0.9,
    beta=5,
    use_weights=False,
    check_estimator=False,
    random_state=None,
    n_jobs=1,
)

Parameter	Description	Default
n_clusters	Number of clusters to form and centroids to generate.	8
contamination	Amount of contamination in the data set, proportion of outliers. Used to define threshold.	0.1
clustering_estimator	Base clustering algorithm for data clustering. Requires fit() and predict(). Default is KMeans.	None
alpha	Coefficient for deciding small and large clusters.	0.9
beta	Coefficient for deciding small and large clusters.	5
use_weights	Use cluster sizes as weights in outlier score calculation.	False
check_estimator	Check if base estimator is consistent with sklearn standard.	False
random_state	Seed for random number generator.	None

from pyod.models.ocsvm import OCSVM

OCSVM@manevitz2001one]

OCSVM(
    kernel='rbf',
    degree=3,
    gamma='auto',
    coef0=0.0,
    tol=0.001,
    nu=0.5,
    shrinking=True,
    cache_size=200,
    verbose=False,
    max_iter=-1,
    contamination=0.1,
)

Parameter	Description	Default Value
kernel	Specifies the kernel type to be used in the algorithm. Options: ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’, or a callable. If none is given, ‘rbf’ will be used.	‘rbf’
nu	An upper bound on the fraction of training errors and a lower bound of the fraction of support vectors. Should be in the interval (0, 1]. By default, 0.5 will be taken.	0.5
degree	Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.	3
gamma	Kernel coefficient for ‘rbf’, ‘poly’, and ‘sigmoid’. If gamma is ‘auto’, then 1/n_features will be used instead.	‘auto’
coef0	Independent term in the kernel function. Only significant in ‘poly’ and ‘sigmoid’.	0.0
tol	Tolerance for stopping criterion.	None
shrinking	Whether to use the shrinking heuristic.	True
cache_size	Specify the size of the kernel cache (in MB).	200
verbose	Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in libsvm.	False
max_iter	Hard limit on iterations within the solver, or -1 for no limit.	-1
contamination	The amount of contamination of the data set, i.e., the proportion of outliers. Used when fitting to define the threshold on the decision function.	0.1

from pyod.models.mcd import MCD

MCD[@hardin2004outlier]

MCD(
    contamination=0.1,
    store_precision=True,
    assume_centered=False,
    support_fraction=None,
    random_state=None,
)

Parameter	Description	Default
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.	0.1
store_precision	bool Specify if the estimated precision is stored.	True
assume_centered	bool If True, the support of the robust location and the covariance estimates is computed, and a covariance estimate is recomputed from it, without centering the data. Useful to work with data whose mean is significantly equal to zero but is not exactly zero. If False, the robust location and covariance are directly computed with the FastMCD algorithm without additional treatment.	False
support_fraction	float, 0 < support_fraction < 1 The proportion of points to be included in the support of the raw MCD estimate. Default is None, which implies that the minimum value of support_fraction will be used within the algorithm: [n_sample + n_features + 1] / 2	None
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.	None

from pyod.models.feature_bagging import FeatureBagging

FeatureBagging[@lazarevic2005feature]

FeatureBagging(
    base_estimator=None,
    n_estimators=10,
    contamination=0.1,
    max_features=1.0,
    bootstrap_features=False,
    check_detector=True,
    check_estimator=False,
    n_jobs=1,
    random_state=None,
    combination='average',
    verbose=0,
    estimator_params=None,
)

Parameter	Description	Default
base_estimator	The base estimator to fit on random subsets of the dataset. If None, base estimator is LOF detector.	None
n_estimators	The number of base estimators in the ensemble.	10
contamination	Amount of contamination in the data set, proportion of outliers. Used to define threshold.	0.1
max_features	Number of features to draw from X to train each base estimator.	1.0
bootstrap_features	Whether features are drawn with replacement.	False
check_detector	If True, check if base estimator is consistent with pyod standard.	True
check_estimator	If True, check if base estimator is consistent with sklearn standard. Deprecated in pyod 0.6.9. Replaced by check_detector.	False
n_jobs	Number of jobs to run in parallel for both fit and predict.	1
random_state	Seed used by random number generator.	None
combination	Method of combination: ‘average’ for average scores, ‘max’ for maximum scores.	‘average’
verbose	Controls the verbosity of the building process.	0
estimator_params	List of attributes to use as parameters when instantiating a new base estimator.	None

from pyod.models.abod import ABOD

ABOD[@kriegel2008angle]

ABOD(contamination=0.1, n_neighbors=5, method='fast')

Parameter	Description	Default
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.	0.1
n_neighbors	int, optional (default=10) Number of neighbors to use by default for k neighbors queries.	10
method	str, optional (default=‘fast’) Method for ABOD: ‘fast’ for fast ABOD using n_neighbors only, ‘default’ for original ABOD using all training points (could be slower).	‘fast’

from pyod.models.iforest import IForest

IForest[@liu2008isolation]

IForest(
    n_estimators=100,
    max_samples='auto',
    contamination=0.1,
    max_features=1.0,
    bootstrap=False,
    n_jobs=1,
    behaviour='old',
    random_state=None,
    verbose=0,
)

Parameter	Description	Default Value
n_estimators	The number of base estimators in the ensemble.	100
max_samples	The number of samples to draw from X to train each base estimator. If int, then draw max_samples samples. If float, draw max_samples * X.shape[0] samples. If “auto”, then max_samples=min(256, n_samples).	“auto”
contamination	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.	0.1
max_features	The number of features to draw from X to train each base estimator. If int, then draw max_features features. If float, draw max_features * X.shape[1] features.	1.0
bootstrap	If True, individual trees are fit on random subsets of the training data sampled with replacement. If False, sampling without replacement is performed.	False
n_jobs	The number of jobs to run in parallel for both fit and predict. If -1, then the number of jobs is set to the number of cores.	1
behaviour	Behaviour of the decision_function. Options: ‘old’ or ‘new’. ‘old’ is deprecated in sklearn 0.20 and will not be possible in 0.22. ‘new’ becomes dependent on the contamination parameter, with 0 being the natural threshold.	‘old’
random_state	Seed used by the random number generator. If int, random_state is the seed. If RandomState instance, random_state is the random number generator. If None, the random number generator is the RandomState instance used by np.random.	None
verbose	Controls the verbosity of the tree building process.	0

from pyod.models.hbos import HBOS

HBOS[@goldstein2012histogram]

HBOS(n_bins=10, alpha=0.1, tol=0.5, contamination=0.1)

Parameter	Description	Default
n_bins	int or string, optional (default=10) The number of bins. “auto” uses the birge-rozenblac method for automatic selection of the optimal number of bins for each feature.	10
alpha	float in (0, 1), optional (default=0.1) The regularizer for preventing overflow.	0.1
tol	float in (0, 1), optional (default=0.5) The parameter to decide the flexibility while dealing the samples falling outside the bins.	0.5
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.	0.1

from pyod.models.sos import SOS

SOS[@janssens2012stochastic]

SOS(contamination=0.1, perplexity=4.5, metric='euclidean', eps=1e-05)

Parameter	Description	Default
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.	0.1
perplexity	float, optional (default=4.5) A smooth measure of the effective number of neighbors. Perplexity is similar to parameter k in kNN algorithm (number of nearest neighbors). Perplexity range: 1 to n-1, where n is number of samples.	4.5
metric	str, default ‘euclidean’ Metric used for distance computation. Can use any metric from scipy.spatial.distance. Valid values: ‘euclidean’, [‘braycurtis’, ‘canberra’, ‘chebyshev’, …]. See scipy.spatial.distance documentation for details.	‘euclidean’
eps	float, optional (default=1e-5) Tolerance threshold for floating point errors.	1e-5

from pyod.models.so_gaal import SO_GAAL

SO_GAAL[@liu2019generative]

SO_GAAL(
    stop_epochs=20,
    lr_d=0.01,
    lr_g=0.0001,
    momentum=0.9,
    contamination=0.1,
)

Parameter	Description	Default
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.	0.1
stop_epochs	int, optional (default=20) The number of epochs of training. Total epochs equals three times stop_epochs.	20
lr_d	float, optional (default=0.01) The learn rate of the discriminator.	0.01
lr_g	float, optional (default=0.0001) The learn rate of the generator.	0.0001
momentum	float, optional (default=0.9) The momentum parameter for SGD.	0.9

from pyod.models.mo_gaal import MO_GAAL

MO_GAAL[@liu2019generative]

MO_GAAL(
    k=10,
    stop_epochs=20,
    lr_d=0.01,
    lr_g=0.0001,
    momentum=0.9,
    contamination=0.1,
)

Parameter	Description	Default
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.	0.1
k	int, optional (default=10) The number of sub generators.	10
stop_epochs	int, optional (default=20) The number of epochs of training. Total epochs equals three times stop_epochs.	20
lr_d	float, optional (default=0.01) The learn rate of the discriminator.	0.01
lr_g	float, optional (default=0.0001) The learn rate of the generator.	0.0001
momentum	float, optional (default=0.9) The momentum parameter for SGD.	0.9

from pyod.models.lscp import LSCP

LSCP[@zhao2019lscp]

LSCP(
    detector_list,
    local_region_size=30,
    local_max_features=1.0,
    n_bins=10,
    random_state=None,
    contamination=0.1,
)

Parameter	Description	Default
detector_list	List, length must be greater than 1 Base unsupervised outlier detectors from PyOD. Requires fit and decision_function methods.	-
local_region_size	int, optional (default=30) Number of training points to consider in each iteration of local region generation process (30 by default).	30
local_max_features	float in (0.5, 1.), optional (default=1.0) Maximum proportion of number of features to consider when defining local region (1.0 by default).	1.0
n_bins	int, optional (default=10) Number of bins to use when selecting the local region.	10
random_state	RandomState, optional (default=None) A random number generator instance to define the state of the random permutations generator.	None
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 (0.1 by default).	0.1