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Classes
OneClassSVM

OneClassSVM

Unsupervised Outlier Detection.

Estimate the support of a high-dimensional distribution.

The implementation is based on libsvm.

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new OneClassSVM(opts?: object): OneClassSVM;

Parameters

NameTypeDescription
opts?object-
opts.cache_size?numberSpecify the size of the kernel cache (in MB). Default Value 200
opts.coef0?numberIndependent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’. Default Value 0
opts.degree?numberDegree of the polynomial kernel function (‘poly’). Must be non-negative. Ignored by all other kernels. Default Value 3
opts.gamma?number | "auto" | "scale"Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’. Default Value 'scale'
opts.kernel?"sigmoid" | "precomputed" | "linear" | "poly" | "rbf"Specifies the kernel type to be used in the algorithm. If none is given, ‘rbf’ will be used. If a callable is given it is used to precompute the kernel matrix. Default Value 'rbf'
opts.max_iter?numberHard limit on iterations within solver, or -1 for no limit. Default Value -1
opts.nu?numberAn 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. Default Value 0.5
opts.shrinking?booleanWhether to use the shrinking heuristic. See the User Guide. Default Value true
opts.tol?numberTolerance for stopping criterion. Default Value 0.001
opts.verbose?booleanEnable verbose output. Note that this setting takes advantage of a per-process runtime setting in libsvm that, if enabled, may not work properly in a multithreaded context. Default Value false

Returns

OneClassSVM

Defined in: generated/svm/OneClassSVM.ts:27 (opens in a new tab)

Methods

decision_function()

Signed distance to the separating hyperplane.

Signed distance is positive for an inlier and negative for an outlier.

Signature

decision_function(opts: object): Promise<ArrayLike>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]The data matrix.

Returns

Promise<ArrayLike>

Defined in: generated/svm/OneClassSVM.ts:182 (opens in a new tab)

dispose()

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/svm/OneClassSVM.ts:163 (opens in a new tab)

fit()

Detect the soft boundary of the set of samples X.

Signature

fit(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeSet of samples, where n\_samples is the number of samples and n\_features is the number of features.
opts.sample_weight?ArrayLikePer-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.
opts.y?anyNot used, present for API consistency by convention.

Returns

Promise<any>

Defined in: generated/svm/OneClassSVM.ts:215 (opens in a new tab)

fit_predict()

Perform fit on X and returns labels for X.

Returns -1 for outliers and 1 for inliers.

Signature

fit_predict(opts: object): Promise<ArrayLike>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeThe input samples.
opts.y?anyNot used, present for API consistency by convention.

Returns

Promise<ArrayLike>

Defined in: generated/svm/OneClassSVM.ts:264 (opens in a new tab)

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Signature

get_metadata_routing(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.routing?anyA MetadataRequest encapsulating routing information.

Returns

Promise<any>

Defined in: generated/svm/OneClassSVM.ts:304 (opens in a new tab)

init()

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Signature

init(py: PythonBridge): Promise<void>;

Parameters

NameType
pyPythonBridge

Returns

Promise<void>

Defined in: generated/svm/OneClassSVM.ts:115 (opens in a new tab)

predict()

Perform classification on samples in X.

For a one-class model, +1 or -1 is returned.

Signature

predict(opts: object): Promise<ArrayLike>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeFor kernel=”precomputed”, the expected shape of X is (n_samples_test, n_samples_train).

Returns

Promise<ArrayLike>

Defined in: generated/svm/OneClassSVM.ts:341 (opens in a new tab)

score_samples()

Raw scoring function of the samples.

Signature

score_samples(opts: object): Promise<ArrayLike>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]The data matrix.

Returns

Promise<ArrayLike>

Defined in: generated/svm/OneClassSVM.ts:374 (opens in a new tab)

set_fit_request()

Request metadata passed to the fit method.

Note that this method is only relevant if enable\_metadata\_routing=True (see sklearn.set\_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

Signature

set_fit_request(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.sample_weight?string | booleanMetadata routing for sample\_weight parameter in fit.

Returns

Promise<any>

Defined in: generated/svm/OneClassSVM.ts:411 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/svm/OneClassSVM.ts:25 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/svm/OneClassSVM.ts:24 (opens in a new tab)

_py

PythonBridge

Defined in: generated/svm/OneClassSVM.ts:23 (opens in a new tab)

id

string

Defined in: generated/svm/OneClassSVM.ts:20 (opens in a new tab)

opts

any

Defined in: generated/svm/OneClassSVM.ts:21 (opens in a new tab)

Accessors

class_weight_

Multipliers of parameter C for each class. Computed based on the class\_weight parameter.

Signature

class_weight_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/svm/OneClassSVM.ts:444 (opens in a new tab)

dual_coef_

Coefficients of the support vectors in the decision function.

Signature

dual_coef_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/svm/OneClassSVM.ts:469 (opens in a new tab)

feature_names_in_

Names of features seen during fit. Defined only when X has feature names that are all strings.

Signature

feature_names_in_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/svm/OneClassSVM.ts:569 (opens in a new tab)

fit_status_

0 if correctly fitted, 1 otherwise (will raise warning)

Signature

fit_status_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/svm/OneClassSVM.ts:494 (opens in a new tab)

intercept_

Constant in the decision function.

Signature

intercept_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/svm/OneClassSVM.ts:519 (opens in a new tab)

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/svm/OneClassSVM.ts:544 (opens in a new tab)

n_iter_

Number of iterations run by the optimization routine to fit the model.

Signature

n_iter_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/svm/OneClassSVM.ts:594 (opens in a new tab)

offset_

Offset used to define the decision function from the raw scores. We have the relation: decision_function = score_samples - offset\_. The offset is the opposite of intercept\_ and is provided for consistency with other outlier detection algorithms.

Signature

offset_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/svm/OneClassSVM.ts:617 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/svm/OneClassSVM.ts:102 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/svm/OneClassSVM.ts:106 (opens in a new tab)

shape_fit_

Array dimensions of training vector X.

Signature

shape_fit_(): Promise<any[]>;

Returns

Promise<any[]>

Defined in: generated/svm/OneClassSVM.ts:640 (opens in a new tab)

support_

Indices of support vectors.

Signature

support_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/svm/OneClassSVM.ts:665 (opens in a new tab)

support_vectors_

Support vectors.

Signature

support_vectors_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/svm/OneClassSVM.ts:688 (opens in a new tab)