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Classes
SGDOneClassSVM

SGDOneClassSVM

Solves linear One-Class SVM using Stochastic Gradient Descent.

This implementation is meant to be used with a kernel approximation technique (e.g. sklearn.kernel\_approximation.Nystroem) to obtain results similar to sklearn.svm.OneClassSVM which uses a Gaussian kernel by default.

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new SGDOneClassSVM(opts?: object): SGDOneClassSVM;

Parameters

NameTypeDescription
opts?object-
opts.average?number | booleanWhen set to true, computes the averaged SGD weights and stores the result in the coef\_ attribute. If set to an int greater than 1, averaging will begin once the total number of samples seen reaches average. So average=10 will begin averaging after seeing 10 samples. Default Value false
opts.eta0?numberThe initial learning rate for the ‘constant’, ‘invscaling’ or ‘adaptive’ schedules. The default value is 0.0 as eta0 is not used by the default schedule ‘optimal’. Default Value 0
opts.fit_intercept?booleanWhether the intercept should be estimated or not. Defaults to true. Default Value true
opts.learning_rate?"constant" | "optimal" | "invscaling" | "adaptive"The learning rate schedule to use with fit. (If using partial\_fit, learning rate must be controlled directly). Default Value 'optimal'
opts.max_iter?numberThe maximum number of passes over the training data (aka epochs). It only impacts the behavior in the fit method, and not the partial\_fit. Defaults to 1000. Default Value 1000
opts.nu?numberThe nu parameter of the One Class SVM: 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. Default Value 0.5
opts.power_t?numberThe exponent for inverse scaling learning rate [default 0.5]. Default Value 0.5
opts.random_state?numberThe seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If undefined, the random number generator is the RandomState instance used by np.random.
opts.shuffle?booleanWhether or not the training data should be shuffled after each epoch. Defaults to true. Default Value true
opts.tol?numberThe stopping criterion. If it is not undefined, the iterations will stop when (loss > previous_loss - tol). Defaults to 1e-3. Default Value 0.001
opts.verbose?numberThe verbosity level. Default Value 0
opts.warm_start?booleanWhen set to true, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See the Glossary. Repeatedly calling fit or partial_fit when warm_start is true can result in a different solution than when calling fit a single time because of the way the data is shuffled. If a dynamic learning rate is used, the learning rate is adapted depending on the number of samples already seen. Calling fit resets this counter, while partial\_fit will result in increasing the existing counter. Default Value false

Returns

SGDOneClassSVM

Defined in: generated/linear_model/SGDOneClassSVM.ts:25 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/linear_model/SGDOneClassSVM.ts:23 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/linear_model/SGDOneClassSVM.ts:22 (opens in a new tab)

_py

PythonBridge

Defined in: generated/linear_model/SGDOneClassSVM.ts:21 (opens in a new tab)

id

string

Defined in: generated/linear_model/SGDOneClassSVM.ts:18 (opens in a new tab)

opts

any

Defined in: generated/linear_model/SGDOneClassSVM.ts:19 (opens in a new tab)

Accessors

coef_

Weights assigned to the features.

Signature

coef_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/linear_model/SGDOneClassSVM.ts:501 (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/linear_model/SGDOneClassSVM.ts:644 (opens in a new tab)

loss_function_

Signature

loss_function_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/linear_model/SGDOneClassSVM.ts:594 (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/linear_model/SGDOneClassSVM.ts:619 (opens in a new tab)

n_iter_

The actual number of iterations to reach the stopping criterion.

Signature

n_iter_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/linear_model/SGDOneClassSVM.ts:549 (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.

Signature

offset_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/SGDOneClassSVM.ts:524 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/linear_model/SGDOneClassSVM.ts:114 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/linear_model/SGDOneClassSVM.ts:118 (opens in a new tab)

t_

Number of weight updates performed during training. Same as (n\_iter\_ \* n\_samples + 1).

Signature

t_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/linear_model/SGDOneClassSVM.ts:574 (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?anyTesting data.

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/SGDOneClassSVM.ts:198 (opens in a new tab)

densify()

Convert coefficient matrix to dense array format.

Converts the coef\_ member (back) to a numpy.ndarray. This is the default format of coef\_ and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a no-op.

Signature

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

Parameters

NameType
optsobject

Returns

Promise<any>

Defined in: generated/linear_model/SGDOneClassSVM.ts:235 (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/linear_model/SGDOneClassSVM.ts:179 (opens in a new tab)

fit()

Fit linear One-Class SVM with Stochastic Gradient Descent.

This solves an equivalent optimization problem of the One-Class SVM primal optimization problem and returns a weight vector w and an offset rho such that the decision function is given by <w, x> - rho.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?anyTraining data.
opts.coef_init?anyThe initial coefficients to warm-start the optimization.
opts.offset_init?anyThe initial offset to warm-start the optimization.
opts.sample_weight?ArrayLikeWeights applied to individual samples. If not provided, uniform weights are assumed. These weights will be multiplied with class_weight (passed through the constructor) if class_weight is specified.
opts.y?anyNot used, present for API consistency by convention.

Returns

Promise<any>

Defined in: generated/linear_model/SGDOneClassSVM.ts:263 (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/linear_model/SGDOneClassSVM.ts:322 (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/linear_model/SGDOneClassSVM.ts:127 (opens in a new tab)

partial_fit()

Fit linear One-Class SVM with Stochastic Gradient Descent.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?anySubset of the training data.
opts.sample_weight?ArrayLikeWeights applied to individual samples. If not provided, uniform weights are assumed.
opts.y?anyNot used, present for API consistency by convention.

Returns

Promise<any>

Defined in: generated/linear_model/SGDOneClassSVM.ts:360 (opens in a new tab)

predict()

Return labels (1 inlier, -1 outlier) of the samples.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?anyTesting data.

Returns

Promise<any>

Defined in: generated/linear_model/SGDOneClassSVM.ts:405 (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?anyTesting data.

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/SGDOneClassSVM.ts:438 (opens in a new tab)

sparsify()

Convert coefficient matrix to sparse format.

Converts the coef\_ member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation.

The intercept\_ member is not converted.

Signature

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

Parameters

NameType
optsobject

Returns

Promise<any>

Defined in: generated/linear_model/SGDOneClassSVM.ts:475 (opens in a new tab)